flightline_process_S2A_L8_09132020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import os\n",
    "import xarray as xr\n",
    "import rioxarray as rxr\n",
    "import rasterio as rio\n",
    "# import regionmask\n",
    "from fiona.crs import from_epsg \n",
    "from shapely.geometry import Point, Polygon\n",
    "import h5py as h5\n",
    "from glob import glob\n",
    "import json\n",
    "import dask as da\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from rasterio.warp import reproject, Resampling\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_extent_from_neon_h5(fi, nid='CPER'):\n",
    "    \n",
    "    ds = h5.File(fi, 'r')\n",
    "    map_info = str(ds['CPER']['Reflectance']['Metadata']['Coordinate_System']['Map_Info'][()])\n",
    "    epsg = int(ds['CPER']['Reflectance']['Metadata']['Coordinate_System']['EPSG Code'][()])\n",
    "    data_shape = ds['CPER']['Reflectance']['Reflectance_Data'].shape\n",
    "    \n",
    "    # produce the affine transform components\n",
    "    parts = [p.strip() for p in map_info.split(',')]\n",
    "    aff = [float(p) for p in parts[1:7]]\n",
    "    \n",
    "    px, py, ul_x, ul_y = aff[:4]\n",
    "    py = -py\n",
    "    \n",
    "    num_rows, num_cols, num_bands = data_shape\n",
    "    \n",
    "    # produce the points for the extent polygon\n",
    "    ul = Point(ul_x, ul_y)\n",
    "    ur = Point((ul_x +  num_cols*px), ul_y)\n",
    "    ll = Point(ul_x, (ul_y + num_rows*py))\n",
    "    lr = Point((ul_x + num_cols*px), (ul_y + num_rows*py))\n",
    "\n",
    "    extent_poly = Polygon((ul, ur, lr, ll, ul))\n",
    "    \n",
    "    return extent_poly, epsg\n",
    "\n",
    "# make function for reading h5 tiles\n",
    "def read_h5_file(fi, nid='CPER'):\n",
    "    # Read H5 file\n",
    "    f = h5.File(fi, \"r\")\n",
    "    \n",
    "    # spectral\n",
    "    wavelength = f[nid]['Reflectance']['Metadata']['Spectral_Data']['Wavelength'][:]\n",
    "    fwhm = f[nid]['Reflectance']['Metadata']['Spectral_Data']['FWHM'][:]\n",
    "\n",
    "    # CRS\n",
    "    crs_str = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Coordinate_System_String'][()]\n",
    "    crs_epsg = f[nid]['Reflectance']['Metadata']['Coordinate_System']['EPSG Code'][()]\n",
    "    crs_mapinfo = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Map_Info'][()]\n",
    "    crs_proj4 = f[nid]['Reflectance']['Metadata']['Coordinate_System']['Proj4'][()]\n",
    "\n",
    "    #arr = f[nid]['Radiance']['Radiance_Data'][:]\n",
    "    arr = da.array.from_array(f[nid]['Reflectance']['Reflectance_Data'], chunks=(256,256,256))\n",
    "    arr /= float(f[nid]['Reflectance']['Reflectance_Data'].attrs['Scale_Factor'])\n",
    "    \n",
    "    mapinfo_list = [a.strip() for a in str(crs_mapinfo).split(',')]\n",
    "    mapinfo = [float(a) for a in mapinfo_list[1:7]]\n",
    "    mapinfo\n",
    "    pix_size = mapinfo[0]\n",
    "    x = np.arange(mapinfo[2], mapinfo[2] + pix_size*arr.shape[1], pix_size)\n",
    "    y = np.arange(mapinfo[3], mapinfo[3] - pix_size* arr.shape[0], -pix_size)\n",
    "\n",
    "    xr_cube = xr.DataArray(arr, {'y': y, 'x': x, 'bands': wavelength}, dims=['y', 'x', 'bands'])\n",
    "    xr_cube_ma = xr_cube.where(xr_cube != -0.9999) # scaled nodata\n",
    "    \n",
    "    # add in CRS assignemnt!!!!!!!!!!!!\n",
    "    xr_cube_ma = xr_cube_ma.rio.write_crs(int(crs_epsg))\n",
    "    \n",
    "    return xr_cube_ma\n",
    "\n",
    "def resample_h5_spectral(neon_file, weights_file):\n",
    "    \n",
    "    nis_ds = read_h5_file(neon_file)\n",
    "    \n",
    "    \n",
    "    with open(weights_file, 'r') as fb:\n",
    "        weights = json.load(fb)\n",
    "    \n",
    "    sample_bands = []\n",
    "    for k in weights.keys():\n",
    "        b_weights, b_ids = weights[k]\n",
    "        sample_band = (nis_ds.isel(bands=b_ids)*np.array(b_weights)).sum(axis=-1)/ np.array(b_weights).sum()\n",
    "        sample_bands.append(sample_band)\n",
    "            \n",
    "    \n",
    "    sat_ds = xr.concat(sample_bands, dim=list(weights.keys())).rename({'concat_dim':'sat_band'}) # 'sat_band' will be new at dim=0\n",
    "    \n",
    "    return sat_ds\n",
    "\n",
    "def check_valid_pixel_overlap(neon_file, geometry, weights_file):\n",
    "    \n",
    "    nis_ds = resample_h5_spectral(neon_file, weights_file)\n",
    "    #print(nis_ds.spatial_ref)\n",
    "    \n",
    "    try:\n",
    "        sample_clip = nis_ds.rio.clip([geometry])\n",
    "        sample_band = sample_clip.sat_band.values[0]\n",
    "        sample_im_clip = sample_clip.sel(sat_band=sample_band)\n",
    "        \n",
    "        # count pixels vs total\n",
    "        total_pixels = sample_im_clip.size\n",
    "        gt0 = (sample_im_clip > 0.0).sum()\n",
    "        valid_frac = gt0.values/total_pixels\n",
    "        \n",
    "        print(f'valid fraction: {valid_frac}')\n",
    "        if valid_frac > 0.99:\n",
    "            return valid_frac\n",
    "        else:\n",
    "            print('valid pixels are not above 99% of AOI, returning False')\n",
    "            return valid_frac\n",
    "    except Exception as e:\n",
    "        \n",
    "        print(e)\n",
    "        print('not in bounds...')\n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLYGON ((523493.000 4525151.000, 524339.000 4...</td>\n",
       "      <td>NEON_D10_CPER_DP1_20200913_170936_reflectance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            geometry  \\\n",
       "0  POLYGON ((523493.000 4525151.000, 524339.000 4...   \n",
       "\n",
       "                                        filename  \n",
       "0  NEON_D10_CPER_DP1_20200913_170936_reflectance  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#geodataframe for file extents\n",
    "h5_files = glob('../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020/*.h5')\n",
    "polys = [gen_extent_from_neon_h5(fi)[0] for fi in h5_files]\n",
    "desc = [os.path.basename(fi).split('.')[0] for fi in h5_files]\n",
    "epsg = gen_extent_from_neon_h5(h5_files[0])[1]\n",
    "\n",
    "fl_gdf = gpd.GeoDataFrame({'geometry': polys, 'filename':desc}, crs=from_epsg(epsg))\n",
    "fl_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;truediv-92e26948f9d07f7a9af4a5900cebcf63&#x27; (y: 11831, x: 846, bands: 426)&gt;\n",
       "dask.array&lt;where, shape=(11831, 846, 426), dtype=float64, chunksize=(256, 256, 256), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * y            (y) float64 4.525e+06 4.525e+06 ... 4.513e+06 4.513e+06\n",
       "  * x            (x) float64 5.235e+05 5.235e+05 ... 5.243e+05 5.243e+05\n",
       "  * bands        (bands) float64 381.6 386.6 391.6 ... 2.506e+03 2.511e+03\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'truediv-92e26948f9d07f7a9af4a5900cebcf63' (y: 11831, x: 846, bands: 426)>\n",
       "dask.array<where, shape=(11831, 846, 426), dtype=float64, chunksize=(256, 256, 256), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y            (y) float64 4.525e+06 4.525e+06 ... 4.513e+06 4.513e+06\n",
       "  * x            (x) float64 5.235e+05 5.235e+05 ... 5.243e+05 5.243e+05\n",
       "  * bands        (bands) float64 381.6 386.6 391.6 ... 2.506e+03 2.511e+03\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = read_h5_file(r'../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020\\NEON_D10_CPER_DP1_20200913_170936_reflectance.h5')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(nan)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[0,0,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "l8 = '../../LS8_20200913_lat40lon10423_r32p33_vmsk_mclds_topshad_rad_srefdem_stdsref.tif'\n",
    "l8_ds = rxr.open_rasterio(l8)\n",
    "l8_ds = l8_ds/1000 # arcsi scale factor\n",
    "l8_weights_file = './spectral_response/NIS_weights_L8.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try the resample_match technique for landsat\n",
    "s2 = '../../OutputsSen2_10m_09132020/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_rad_srefdem.tif'\n",
    "s2 = '../../OutputsSen2_10m_09132020_AOTL8/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_rad_srefdem.tif' # same AOT as L8\n",
    "s2 = '../../OutputsSen2_10m_09132020_AOTL8_STDSREF/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_topshad_rad_srefdem_stdsref.tif' # same AOT as L8 and standardized surface reflectance\n",
    "\n",
    "s2 = '../../SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_topshad_rad_srefdem_stdsref.tif'\n",
    "\n",
    "# s2 = '../../OutputsSen2_10m_09132020_STDSREF/SEN2_20200913_lat41lon10435_T13TEF_ORB141_utm13n_vmsk_topshad_rad_srefdem_stdsref.tif' # standardized SR, DOS method for AOT\n",
    "# s2 = r'C:\\projects\\arcsi\\S2A_MSIL2A_20200913T174941_N9999_R141_T13TEF_20210301T150241.SAFE\\GRANULE\\L2A_T13TEF_A027308_20200913T175722\\IMG_DATA\\s2_stack.vrt' # sen2cor\n",
    "\n",
    "s2_ds = rxr.open_rasterio(s2)\n",
    "s2_ds = s2_ds/1000 # arcsi scale factor\n",
    "s2_weights_file = './spectral_response/NIS_weights_S2A.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loop over the geometries and do the groupby method to produce STD and MEAN per satellite pixel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020\\NEON_D10_CPER_DP1_20200913_170936_reflectance.h5\n"
     ]
    }
   ],
   "source": [
    "# lists to store results\n",
    "res_L8_means, res_S2_means, res_L8_stds, res_S2_stds =  [],[],[],[]\n",
    "L8_arrs, S2_arrs = [],[]\n",
    "\n",
    "num_fl = 2\n",
    "for  geom, fi in fl_gdf[['geometry', 'filename']].values[:num_fl]:\n",
    "    \n",
    "    fi = os.path.join('../../NEON_spectrometer-orthorectified-surface-directional-reflectance---flightline-09132020', fi + '.h5')\n",
    "    desc=fi\n",
    "    print(desc)\n",
    "    \n",
    "    # spectral resample to each satellite\n",
    "    #neon_ds_L8 = resample_h5_spectral(fi, l8_weights_file)\n",
    "    #neon_ds_S2 = resample_h5_spectral(fi, s2_weights_file)\n",
    "    \n",
    "    # clip to current AOI\n",
    "    #neon_ds_L8 = neon_ds_L8.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "    #neon_ds_S2 = neon_ds_S2.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "    \n",
    "    # clip landsat and sentinel datasets, add to list\n",
    "    l8_ds_clip = l8_ds.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "    s2_ds_clip = s2_ds.rio.clip([geom], all_touched=True, from_disk=True)\n",
    "    L8_arrs.append(l8_ds_clip)\n",
    "    S2_arrs.append(s2_ds_clip)\n",
    "    \n",
    "    # group by for mean\n",
    "    target_res = 30\n",
    "    reduced_S2_mean = (\n",
    "        s2_ds_clip\n",
    "        .groupby(((s2_ds_clip.x//target_res) + 0.5) * target_res)\n",
    "        .mean(dim='x')\n",
    "        .groupby(((s2_ds_clip.y//target_res) + 0.5) * target_res)\n",
    "        .mean(dim='y'))\n",
    "\n",
    "#     target_res = 10\n",
    "#     reduced_S2_mean = (\n",
    "#         neon_ds_S2\n",
    "#         .groupby(((neon_ds_S2.x//target_res) + 0.5) * target_res)\n",
    "#         .mean(dim='x')\n",
    "#         .groupby(((neon_ds_S2.y//target_res) + 0.5) * target_res)\n",
    "#         .mean(dim='y'))\n",
    "    \n",
    "    # group by for STD\n",
    "    target_res = 30\n",
    "    reduced_S2_std = (\n",
    "        s2_ds_clip\n",
    "        .groupby(((s2_ds_clip.x//target_res) + 0.5) * target_res)\n",
    "        .std(dim='x')\n",
    "        .groupby(((s2_ds_clip.y//target_res) + 0.5) * target_res)\n",
    "        .std(dim='y'))\n",
    "\n",
    "#     target_res = 10\n",
    "#     reduced_S2_std = (\n",
    "#         neon_ds_S2\n",
    "#         .groupby(((neon_ds_S2.x//target_res) + 0.5) * target_res)\n",
    "#         .std(dim='x')\n",
    "#         .groupby(((neon_ds_S2.y//target_res) + 0.5) * target_res)\n",
    "#         .std(dim='y'))\n",
    "    \n",
    "    # append to lists\n",
    "#     res_L8_means.append(reduced_L8_mean)\n",
    "    res_S2_means.append(reduced_S2_mean)\n",
    "#     res_L8_stds.append(reduced_L8_std)\n",
    "    res_S2_stds.append(reduced_S2_std)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NEON_D10_CPER_DP1_20200913_170936_reflectance\n"
     ]
    },
    {
     "data": {
      "image/png": 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SRX261HEcx3HuQbVIk6QtiIOyU2y3dnJnXsW1XzhHu9Gfvb/nTjYG9ObvN2+PyJ15GOsWBuxdB5yPnRDEKiZY4+QaUSKck53MVQxPtmoDaLQtlNobLFqCOHo7Li1MREsCCTq1XtgbyGTYPfUgO47jOI5zqeQIEuSu7YwtcEMQNV0QkrVXtMJa+bcK5JplUOKc46gbotmO2a4Nq/Nw7IQgZuPkbIkyuW9TpuMS4mCPTykz0r6XoThKJKZeI8sy1+I53FaAdbWJsJVGevuwkHxh6DiO4zj3RIOl0mow0Qut0EX1EU4masPQnKnmukAou8Sz4C677+vb/IY1mggPiYvHdcC52A1BXFPozopTbSdqHbzrVkqKJnyjWgAHtNYH2/qYxTJrXUm2E14DU3V5W3GNjuM4juPcH+2sz9Z2fdv9XYpTRL1nW4FLJ/u1ugGsXXGiCLM0ulJVDqNOu8lgzlaTfZtXiK88OyGIc9ds1UDsJMVO/JCU3BUxPDA5RUxm2iVxJnd2sdSIRkn1IgJF6FZ5QzyHUQnD9lomysd1HMdxHOdu1F3baLu+kwVruZ/XgK16D687vPa9/ZADhPVmwIcUraBBNhI4QlIo7lbbEMSuA87HTghixE7I7qT0/NaHs5YLRDYiGtGZCffMT1gSxNOMpGhCufgZqsC4FybxXIM/dItDdY7jOI7j3Ae1ym9IWjIIytzQaMP1MrYkuipsNYj1IZztIc6zym8Z1qs6oYV5tB3ks6FdztVjJwTx1OcTQXPbEqkns/ZY700vdKc6xTm/7QQuyXRxrZZaN9ZjZRLIwNQqkbfaSC8kj8JxHMdxnLuiEdLeTMyWuZ9UBGvqa0iHzRrlztypqqPExCykw74pLhXBhHV3MhMPWw3pch1wHnZCEEuGtGxBGqEMz+XeqsO5/FfoTnSyXgnJLoZquzZttQikpfUYU/qI4qDmYjG01SYKcRb4cdEorcfJcRzHcZxNtIPxAOKpoKIbLlK1iiu5tU1okLsPxs92kSXDuA9xVaxckam/uO4am53rNmzXXAech50QxLmHoy/IHLwYWNzSaVo0BSEv7ISOp+XYaKvIcZZoB0ypdXlhAjpmGA6tAtydWMvF1H9ct1e8ZcJxHMdxrgRa2iL7O8WHuNzj09J2frtTnexVcwf9kW0n51428gVQ2vEZlre0OEwUJ6uZ1cPkY+z2D1eenRDEkmDv84HlW0p3ovRHzWpFj8sAXGpN85KVUFZ0Wv4LaRAL8DhVDl+2sm88sYtFo6BRkKmHWMl9MEuXLV4DvlXiOI7jOPegWKkO14R42toncw/jfmA4ULrT0ibR2z0/jDWIy+6v4769npdsNimMNYegFMaWQlq0AfuacbCtHmLXAQ+P1y8dx3Ecx3GcnWYnKsTaK6unle5EIFvTO8C4L8VeBbpjW8FZX7H1A9W+IrDHhwOZ7FMkQzwIhEGnRLq6KuxWyupmXYFuZ1mo+MrQcRzHce5JtHbG5VvZ7ud7NUkOwlqJawvjEIXhGtNzKi2pLneyOYdUd4iTbrpVYWFfaVF2pLfUQ+w64OHZCUHc9an0CaudwKXhPa5aVHN3qizuZE6eic23GCbP4pDsmP4os7oRbCJ10U68uFa0JOBJVvbeqIN22/s789Zi8RzHcRzn8UNGWF8zEVuFqxTHiXHP8gPqV503yovWQyzJtIPkMqAvJeBLS4EttQS7jOmGHAWN2ymOuQ54eHZCEI8pkA4yuY90J9pOTLFVW+5KUt3K3COkDMPNxXOW4mGcZxHPZSWYO0GXzaYtx9Kgv9LtRTc7juM4jnNvFFbPZPo7ge64CdeaVRDzZoiGDdnPwrowpwpZF0cKtaAO0VJEmyKcy+s7seeqS5VzpdkJQSzrgB4kTt4bOHxxFqesraVBA6yvhWnFWMVwnRbNUdDeVn+pL3HPqSXYIYIU2zWN1o6xTXyrxHEcx3HuQxLyQeLkBWH/l61wBaXQVaq+3alOCbOSzwR0YR0R1ao1d+05DSZ606JZsPYnmXEpW02sdR3w8OzMUF3/cs9wTTl5zk7OunKz+Ga7GMJoMc5xsG2TelIDk2PE1AdkdoP2XG7/Srb2icXtbPnmW+gbchzHcRzn/kiG/vWO8TCT9q2oVTMFqvjNvZCWdnxcWxFMVE0tBTaykWtqnZbU2m6lk8gGGJcy2a/mxbb+Sudh2YkKMRkWbwrDUwntikFweVzqqq6GbUiNWwTVzW2OmkwX1201KAAK3WmeVoFjJ3Yh0HqQLhpFSLuzvnEcx3Gcd4dCPBFyF6ZwDWhiWEVIC52KXCqlv7ik2UJLsbPvN51Va5Jti25uhTLdQgux64DzsROCOIyQ9oFcLobeHo9rC9OQbGEdkuzYHLGzXGftFSWYI5QEm1DjHO0p0kI2KsVgYlgyW8Ob6R3HcRzn7miE1bOpJM7GUiADyv28ukSE0WKctWgFe7z2RjC1W2porlP1udSLVZSx95EyZCdbuj+7Dnh4dkIQp304fa8t6cZrOg3KdSf2fO5br7BkRXvrCxZtWyIIG8baKlKqxbO0m5ntWlyZsXfe0mSp4ziO4zj3IYAk2w0eD5VhZffs/rbd87UD1CzUmEUzW4tlG8Y/W4TVDhghlNbKqjHSwt6vxjw7V5udEMRVsPZvRBt4O7QH1iuhO2WaMJVRLbIZs1PJC1p7erZBu1oSrid46q2BPoxMF0wq8c7TinNLf6I30zuO4zjOPciWS4Cal/DqmVIIUyGsaG2TpdWxFsRquyTYLjG5uUxRhu7MTcKEc9UCObasgzBcfHHMdcD52Jk1i4zCeD0jI6QDJR2Y+fb6OtMWyCR4tRp1t59FrZeoulJMFeXZhOlE3TbJ27kIHMdxHMd5MHqQyDdGc4jI9jXuV2epclDxGK6D95KazRp5ZrnWtcE8DU1IWyuFTHZrNd7ZudrsRIVYMujhyOHNU47lGvG2nfXrm4okKas/m5Bb3NGpwiu5bIVQTvhS9c3Rtl3qcF3uZGOyVENtt1BbTW7nryT5nozjOI7j3B0F6TKxy4xR4aSWgiPdUbVaszS7qRW3VILn7ZNKeV5apXg+bxTGYsEqVhjLUabHLhbXAedhJwTxLmALV78QHMdxHGcXcR1wPnZDEGeQOx1H6wPCjTX5Rnn4tSWLN5o9mqxhdUOmbZHaGwSt6jut/GLbSqkJNZMVS7ZVpjXjb/UvdRzHcRznLkgGeW3BeC0hfUb3rKyrR5HcQ1xZVddKvkztk9VlCtjoGbY3tXu/Fo9iG6hrLZXmMiFbs2B1Hp6dkGsaYflq5PRDibyK9IeWvTwK5E4ZrgvdHTuhl29ay4RGpgsEZtsjQcyarYNuUDRKM+eexUBCuWC22ELszfSO4ziOcw8CLN4IDM+vSOtA6O3mHUbzV0tLs2Nl2LRPnSOptVNItufTogzOJTUHKmn34iqKt6UFXAc8PDtRW5cM8RS613ukU8YhMg4RgtLfKY4QC+vzGQ6FtFe8iLN5EaaZF2HuhW5V3CSWUmKcLdkuR5l6hTTKVsWw4ziO4zj3RxLw0pJ+fyAf9eSjnnhqbhDrm80pCsqMUJTJdrUWucJYxPAsgCMtyutq9lcpFOdONnuMnSvLTlSIAYbrysFLwuqL1qxuWy7j4lYgjHDytNIdy1TZjacwHLaEmUoYlTAoqxuBkEw0a4RhX4gDbbBOQURLgMd2/j5Vb6Z3HMdxnHuhYsWv7lgYhois7J4Z1mbHWn2Hw9Bs1jRaylxtfwxDa52Mgz0eSqBH7rA0vOIupUHKYzI9dqF/n+uAc7ETgrheBMMhDL90CPumcmWU6STXUCzSdLZF0lqBNmzVwthSbcJYtkz6stVCWyXaa7ZXJs6+VeI4juM4d0UypKVy8MvCcGMJT9tNe7y1ZHFLGA6x+aEibkVpUcyzanC7v5eiWSgxz9oioKE4TWlJx5Xt3J9dBzw8O7GUkMwkdG/8i0B/c0V/c8XqhZHu2FJq8qKFbeQOutPSYN+Vr2BCN5S+4ZCUOOi0hSIK41IYl0J3mumPsyfTOI7jOM4VQRJ0pxbKEdZCvxzplyO5t+fSUktanVWCY9EB1V5NpfYMSzlGy85x6y3WSAnmKu2aa0uu9UTlq89OSLYwwvJ1sVaIa7DcG1juDcggnDxvF8C4X45N0B9rS6apg3FlRZj2ZLpYqK4T9UIpgnrcC+QoLO5k6x/aApZQEx7p1ztBRL5aRH5WRD4tIt98l+e/XETeEpEfL19//FH/7Y7jOI7zIEShuwOrp8pMUArkFEiHmZMXlHSQp0rvVGgtHsTzFkoVmyfKvRBXSrfSqXI8L9DmviXVyRY2iy9CB7wTLfCk6ICdaJkA6E6K6FU4ObYeYq6PjGOPZKW/HTZO9hq6MV/V1ahmsIG6xe3M6kZoE6ez9or6VRPtnkREJAJ/EfhK4EXgEyLyA6r6qTOH/l+q+ju3/gEdx3Ecp1Dv7WGEtA/DkU3MC5D2MpLFArii9QWftVedKBoxdyZ6pbRZ1N9Rj800J4onlSdJB+yGIBaLXYayLXJsf7acBjTaUjB3Sl5IsUeRKca5DsXlyNQsr9F6hMb9diFotJMfWh/RuB+22EJ8Kc30XwZ8WlV/HkBEvgf4GuDsheA4juM4l4qU1kmtA3DDrOIVQIZazBJEdZotCkHIs9urjHXIXsidRTSbCxXFWaIcF5k8iGtOwQX/ha4DzsFOtEzUobq4thO2e72je70jnAb0INlw3QJkbGEaca2b1d36X6pGOCr37BGuPUa5eBk/xjwrIp+cfX3jmec/AHx29vOL5bGz/Jsi8k9F5IdE5Isv7NM6juM4zj2wVgdAoL8jLF6LLF6LLF+NoJAOs93/B536f6dBufJV7/1pYaI59aYZtIR31Ar0FNZRq8lPrhZ4YnTAla4Qi8hngNtAAkZV/aiIPA18L/BFwGeAr1XVNx74Zlq8hnvYf9lWhcN1QDryQulOylSowuK2vq1dog7O5a45SNT2iTAAI1M1OPcl1GO9vaS6C4psfFVVP3qf5+/WIH22Jv5PgC9U1Tsi8tuBvwV85FF9QMdxHOfJ5pFpgdrWWHIGuuPiN9xB/1Yg7duAXK34qlixbP7a6i4VBmU4KPfc0CzXptmj8po6X7StHuILim6+nxZ4YnTA41Ah/i2q+iWz/xnfDPyYqn4E+LHyswMklUf69Q54EfjQ7OcPAp+bH6Cqt1T1Tvn+B4FeRJ59VH+z4ziOsxO4FngHPGod8A60wBOjAx4HQXyWrwG+s3z/ncDvetALqk9gGEA7nSxRwhriibB8PdDdFkJqvUWpF0ubq5RWiTiYB/HcgSIt2zZMzTwPQ3nsyU6n+QTwERH5sIgsgI8BPzA/QETeK2IGjCLyZdg599rWP6njOI7zJPGutcD8/gxl13hh9/3+jrB8NdCdlrbI+pLcBuuqtZpkpTvVaehOZ5kFuSt9ytFmkOqwfu6fWN+1J0YHXOmWCewU+1ERUeAvq+rHgRdU9SUAVX1JRJ5/p+8kCRZvWjQz2AnbnQj9nTJNmtq2RkhqkY0FqQN1oZhtI5ZeU/qCVMrgHYC0Nor6uy4aRd6xVdoj+52qo4j8IeBHgAh8u6r+lIj8gfL8twG/G/jPRGQEToCPqeqTa73hOI7jPGoeiRYQbcK2P24tjbmD/k45JjUr1doMUFNp63toEMY9MeFc2yuKhpiHdpBnrRJbaZlwHXAerrog/g2q+rlyov89EfmZd/rC0vT9jQD9tacY92Fx20y2q0iNJ8V3+LYyHkhLmhFa5GJBBcY9C+YwZ4kimDOE2nRfj1f7HWEopt5PMGX74wfPPPZts++/FfjWbX8ux3Ec54nh0WiB609x8oIST6Ts+NoxFtShZqFWPYe19QVDqxrXwftxL9CtlHFPplRbs1plo6tWEsQMubty+u+R8aTogCstiFX1c+Xfl0Xk+zF7j8+LyPvKivB9wMv3eO3HgY8DHLzwIU17cPuZTHcU6I7LQQHCadnaGJVYMsrrNKloq/rWdoqQrCqsIpN4zl3bggHoTtRsW7JuLZgDIHs0nuM4jvOE8ai0wP77PqQIrJ7JLF8P5KJRp13irBsBHGD3+7OPhaG2T5bnRtAS0jEN4WFJd3lhhTHZUvuk64CH58r+lxORQxG5XgvN7dMAACAASURBVL8H/h3gJ7HelK8vh3098Lcf9F5a+4cDaFCGG/YlidbjO7NWmb9uuhC0rgxlSqSR1CZKzaDbvoZrxcXiULYWzHFZSXWO4ziOc1E8Si0AEE+FeGr38dpDDLYrPKHNRm3KGgibGiGW6nCu4R1ZrWA2K4JpNLGclmwlnOOykuqeFK5yhfgF4PtLH3YHfLeq/rCIfAL4PhH5BuAXgd/zoDeqK7MwCnEtDIe68XgVvpMli9TIGdrWh5Qq8iqzPoxT31Bc20WzvmntFFCa58VWmsPhE9tI7ziO4zgXzSPTAhowu7UTIfdKvFPsVnPpHWZWBKtzQbOBufq8Bmuh1NgEch2uiyudQjjmIvidGTc5l8mVFcQl9eTX3eXx14CveDfvZVVb5eAl4fgFJa7t8bC24bkqjKW4RmjXGuqn58q/w0EgDsq4lKm+rgLxVCfxbH3GiiTZSiM9lGZ6v+Icx3GcJ4hHqQUQ6E5hPCitjtVNQmeV4HLvrzvBKliK3bj5PrkXwhrGfSkuVjq93/S+pTos6e5mvY8a1wHn48oK4keJgp3AHeSlsnzTlGwNz6gV4jDaoNy0Ouxnk6UjBBVEdGquT0v7tzuBxS1LrIG2gqwTrY7jOI7jXDIK474NuMWVFcXAqroard0hqKJjGa6Xdj+vhNES7GphTAOkPSFHIccyg1R3nyNNbDtXnp0QxKJ24q+esuzxMJssRZlSZlLfUuigTZMCxNF6hBa3s100CVY3ZfIhDmNLrqvDdttIpplzQQk1juM4jvP4o5D2TPyGWZJsTabVCFmF7lTJgaklIoztfq5FLyxuZXIX6U5L73A5ViOkmUUbQFpsDt5fJK4DHp7dEMQjVvFdKDIKwzV7vDu2KVENxTYFJYylHaL0DU0ncbVkCyBqzcWxOFRorkEedmh5mhy2tzJUheTTpY7jOI5zf7IJ4/Gw9hDb/dzs1pp7xGSlWlsnACKoCnkhpL7NH03ieWivU4ooltl7XSCuA86H/5dzHMdxHMdxdpqdqBBriVWOJ0JewOp5a+xd3IqMe+ZBGNdlVajShutmFd60sEqyTadaiEcYlbEr32erCINtr9TV5PYWa0LeStu+4ziO4zyGCOReCaPdt6sW0BDpo80D2QNMsczVbWLDfCpBWoa2a5zNazgXrVFnhzQyxTpvZ7fYdcB52IkKsQqkpTJe0+JHrGhQ7nxBntocakxzGHXjBE7LMjxXtkzGPQvcqENz3anSrezn/kjpj7Ql22QfqnMcx3Gcq4C1NAiKFchkbV/D9Uzaq4P0Yu0NM2G7IWaleRdTYpxFbcgujJvH6jzG+ckNqnti2IkKMZTqcBG2i9dNBQ83MrmHtG8xztR45vT2yq4GrHeoWq1FWqgHVhWuhtz1YpG0vcE6xXuHHMdxHOd+9LeE8dDimyvz0I0w6DR3BG12qOUU1AyCzHg9ludaQEcYlbSwF2tng/c1AfeicR1wPnZDEAss3hJWzyrdHZmt4GzLI56WbY9UhS2WPle2P6DYrtXn4+xCEgvykKSE2l5RrFi0t22UbbFLiTKO4ziO864oHsPxxApfrIpwLYly81bHardWq7y1WhzXm4EbU1YBpbUibLYsaMTaGLZUHHMd8PDshiAOStqDsJKNCVCKT3CuwlWaDcvZdod5ek0YlNyZ72BIiiTzL54uErFtF8m6lbhGx3Ecx3EejKXJ2fdTN8Os4AWl9zeWXd7yNW/NDWsrjIVRLZlWW45B7mZ2btL6iIO3T155dkIQy1qIK6avXFZ0yzcEKT0/YW2JdXXbAyi55Pa9BmCWVDNZtZXm+7SY/cIylJf7M5mPF4giZE+ocRzHcZy7EtaWVCcZyMUilSKS19Df1o3qsMyen7dLBoofcS+Me8WeVYRuZe0StWXCXrA9Mew64HzsRG29P1aWb1bP4bbi01hSZ1YW5yy5rRLn06FV9Oa+/CwmgKsRN2q/o27HaCxV6PJax3Ecx3EuF8kmXnNvsz61b7ju/Ia0KYYnIVyOqcP1Gm2gvhbM8qI4UZ2pgc0FNVtxmXDOw05UiMH6esMIw43Zdska1jfg8HPlIohnYhpLhjm0x2v+uZQeY+0szzyM2nqJEsiZpJqt/I27sb5xHMdxnHeNlmLWuFfs1warpnYnpguqOK5iGFo/8dRCGSltETIJYrSk3S5MZ4RkN37NEHJpp9zS7dl1wMOzE4J43BfGfRO8p88nuiM7YeKxsHirTZHmCKGc2FUA16xzrRdBL6RyfJi1UNTKcCUMOlWWt4EC2adLHcdxHOfulMS4tK8MNxMyFrvVl6Ldr2UmilNxlDjjFlWLZ8OhtGpymS+adEA9fvZe29CprgPOx04IYrATcjwEScJw05Z68bQz32CwkzbbiS+152csbRI08Wu2KvZ9XUFWw+5pS6T2HcczotlxHMdxnEtjyhlIMgniGsgx2avVe/qsbXKDEtcc11Yo01gim+cexdSqcnmxa4Erz24IYrWTVEaIp4L2drZX7+Gpr1jaBZEjUz9RfQ9rnAdJtpQMY+srTgtpFm3lYtlu/7CQPKHGcRzHce6O2H28OxE0hI1CF7RWiTDQhuyLHpju7wm6lbK8lTl5Ok7zRtW9KncytUxUHbCdlDr7A10HPDxeW3ccx3Ecx3F2mt2oENO2M/q3hBxLD/GKqW+ImjJTPIbrNOmUSR6sUT4M9m9cW3VYijWbdpurQElsdWXovUOO4ziOc3+m3AGdhWXMd3NnThI1kXZjtxizXZVRCaM5U1T/YaQM1NVOzDqMF1qL5UXiOuB87IYgFkjL0gwvsPfqLGK5ilap/T8mhi2Rrk2LkgUNggYlrEtueYl4TguB8e2OEtvbJnEcx3Ec50HENaYcF2y2OUrTCHVofgrkAkTbDT53wnAtWFtFJ9NxOrN2hWbfVlsvnKvNbgji0v8bRrNbGa7bmbl4U6YhOhRSz4ZlSqy9wLSLQQXSMpgIVjt26h+a2a7VlJttLta8d8hxHMdx7kEpgKUlrG8q8dQenqfSzgO3qhjWCLlGMmt1ppIiiJvYzdGcqKTc9+NaN95zG7gOeHh2QxBXFE7elwjrMll6KhtDcxbMoWixjbDo5ZJ1HsxrGIo4rgl1sVwUAnlZhvVWioxMFi9b+dNUfKvEcRzHce6FQlhZ28Q8PW6qBOumo1SOELLZTmzkE6iJ3fX1sJFoG0a759ef49rEdqiD9xf957kOOBc781+uO7aT9fDFOhIqkGtqnSJqbRBQhHBolePqOmEtE2KCua4ii4l3jYKcqsQzoe04juM4ziUT7P4eBggrIZaveq+fil659fymhWzkCVgAhx07HNjOMjDd68Nabad5ZNIP84Kac3XZiQqx+QHXjPHmORhXJoSr1UqOxS6lGGwP+5ZNDiBZrKWi9g33s0b76mGcW1uFqFWNayreNki+MnQcx3Gcu6M2J5QWds9evGUPyzgL12Azpa62TtTKcR2oX18z2zaNTDvCtcosYZ7kwWTvug1cBzw8OyGIwwDjgVhc40LZe6W0TKwyKjL1B9eTORefYgKMy9peYe4TedF6jOeryLnwnarDAuP+Rf91juM4juM8EIG01/IDajDXNFiPTMEcNaFOqVHN9ha1DSJ3Qn+sDAdiLlYllW7uSKGhvDbKDu3HP77shCDOnQnTsIa031ZqGtrJj2LWKNEeyx1TnzBAKsI4rCH1UlaDShyYjLlrLvq0Gty27Zo30zuO4zjOXdFYHB+qI0StfdWdXrWt3dwBJYJZo4nfSrVfjYOJYRuy0xL13DRA/X1gx+YtqC3XAedjJwSxxtnqbWgrtTDoJI5FQYs4DsmqwWkh01CcrpXutB0rZRVYvYjnlio5Yr1KqWylbAXxrRLHcRzHuQcqsLgFKJw+x6QFJFc/4SJus5TsAWBVRXF5j/qaOoAfivVastbM3EtzrehnO8lbaSF2HXAedkIQEy1I4+Dzyslz1j80PTU0j8CAWaiFBH1Su1jW5cAa0lF6jTXAcCiENcRsr6vV4DjAcFBWnX5uOo7jOM7l06llEqi1T047v0Npo6C5TWVMK2iwQbnuyJ6vVV+NQhzMyjUtbfcYpAzgl/eq7ROl2uxcbXZCEIcuc/SBDBpYPa30t+wiGA7bSs6a5m2gbnVDbMWntjUCVumtzfX9cWbcj4y96V0pwnc63xUWd+yn1G9n+8ISanyrxHEcx3HuRoiZ21+8Jtzq6G+3pLraIlmt2HKxW61FMGYBHYi5TEgqAV+lnUJC23Geu1JILjvF22qZcB3w0Hj90nEcx3Ecx9lpdqJCDPDMr3ydV/du0r/WWQ8Rm5Zp3cqa3jUIy1tK7mzpWFeKcVCGfYEOhoPAcCDEtb1mZLORPi0xb8MtRzcnX984juM4zj156rnbnH7mGZZvzKxSa9xysWCdwjcGRVa20zvNCSWsN1jLUB1m4RpKQm3dVQbTAlMM9Ozxi8R1wMOzE4JYVThcrHnjTuTwRZkCOGrfTyiJNNWHOHdzU+1yFWgboouD+RdrhHhaJ0iFsK4+xNZHFOfbLBf9NyK+VeI4juM49yBn4WS1KDNBLYk27UF3rHa7r8JXIIsQBrUI5nJ7zZ0Vu5ZvZVY3O2uTCJY7gMCwbL9Po6BlxqjqjovEdcD52A1BPAQ++/mnyNcTGgO5JsqMkPZsKRiREtBRo5q1NNfbsdWqJQw1yU6QrMWfUKwhv75WirDO5l/sOI7jOM4lMwaGISIHrdA1/avNQnVxx4K8wmj3+jiouUeVY8PA9HxailWQszb/4UkLlHCPAOO+C9Wrzk4IYjLko94CxWeMezKt/MZ9S7Cbp82FWfAG0irB9eJJS6EbzY2iP262LGnRLoxtXgTZt0ocx3Ec557k15cs1lYMCyVQqzu2uGUVkGKjFkZL5QhZN1PmxAbwx31hcVs5jTKl0YlCOLWCGpjXcRXd27JgdR3w8OzGf7moyFpYfL5juNYe7o+U7sSquLV/SHKLadQSvjHvM6q55SEp3YlaZTibGJZkX/1RiYLcYjCH4ziO4zj3QZR4FJDRkmtFrf0xjFb1rfHMuZuFbBQ0tgpyjkJcW1FNIxbGJdIqwrFFOtedZtcCV5/dqBALcGOke2lp6TShtUWg9nN3ais+OWHKJw9D6zGq/cXTBRNtwM62WXTKModiyTLvRdoCqpC8d8hxHMdx7op0SrqWOfylOO30QhOrc9GaOxCVUiTTSSDX4bruJHP6nmg/57a7TNgM6pLURPJF4zrgfOyEIJaoLA7WxNWSxcs6GXBDnQC1PiHKxVD7gk0s22NxbQk09fiQSltEvZASG6l3G/6GW8Kb6R3HcRzn7mgWFq8Fa5EY3p4+F0ad/IUp3ZGCFc022ibUqsNzDRAGOziuaOFf0oRw3oIgBtcB52EnBLGOgfEXD7l2W+lWipzY46m37Q4ZgGzV4NSLWaQUgVtNtzWaKJZgoR0qkHuhP24TpPWCSQuZtks8ncZxHMdxrgAK3ZE5R0i2VFloYjVHKfNBVtjqj+y4+jhQhu9h3A/0xzZYP7Vaphb9DCX9rli7imuBK89OCGJEyQslDNJ8Bmk2arUaHEZrgpd18RTciGPWKbK59hjXLZJupZxdlGksNixbwuxWdqMl3HEcx3HeNUEZD9vQexW5YWytjipnxKsWkTsbissLoT9SUm8iONf+4gzaSWuPkOI0Ee3xi8Z1wPnYCUEsUdFlZtwL9KmdlCEp4RjGpUxtDpJMIMdBLbGxtlcUSxZJIKP1Eo971a/YLoDpPKzDeSMb7RmO4ziO41we62cyd04j+y+3maCNCm6we3damiWr5HLvr05t5T7fHWdWL0RzpErWMqHRqstzoU0Rx7IFH2LnfOyEIN4VEt475DiO4zi7iuuAh2cnBLEmQfYSb/xrwrWf7+iPbKXW3ymN8gLk4hk4I0drsgdbQdZ4R+0siS4ka6CXrKTQtknCULZOAsT1lv5GvJnecRzHce6JChyOHP+KRO4XLN+wh/vbxXGqKy0OfdsRtmAN2Uitzb3NEInazJD5GJsOCOs2SKeBtzlZXOifh+uA87ATgrgSrg8cfaGw98t2toZRiaf2nPUS24m/uNMG5HKxXav/paodW+6LCXdSxmXNMy/HlOzy3PO23mLHcRzHcbaPBLWALhFWz1hyLQAqLVVWLHK5O9WpBzh3wKwHWJK5TCxvZdIyWEbBQqZwDinDelMrpYC39l59dkIQd4uEBCWvIxLg9HlbqsV1IKxh+ZZdCNYDZLnlKjNbFUzchllTfVzbCT7sy9RUPzXdV6+W+rUVvJnecRzHce6FiEIW4us23LO+Wau+wv4rQjzVqaorqUYvb1qwSoY41l3j4kqVm/itg3nT79QiCbYSzOE64DzsxH+5nG3lFt7q6G6FySLl6IOZ8cBO3rQQa6Kvq8FqpRbb9of5FZcq8WhfcWgnv/kXlwsgQFjbReU4juM4zuWiQL8/kJ5dk26MkxZIe8q4PxuCD+Y6kRbWVpn2bMjOvuw+H9fK+rDlF1QxzEz4SmZysPKkuqvPTlSIVYWDwxV8eIWqcPKL1wHoToS8gOG6mHtEVtJCCAPElbVCjAelHWKtDIf2HFqjHYHOLoTuZGbyXezactd8DrdB9mZ6x3Ecx7kruo4sFiPL5YCqcOflQwAkRSuIqdAd27EhmaVq7gSNMhW+qiVr3QHWEnqHAKntHkNxnIBi3badv9F1wMOzE4JYjgJHb+0TF4nYJRYfOALg9LV9NETCWuhOTNTGldrXAONBG6pDSxIN5kEIrRIc13bhpLpSjEJaFF9jPzcdx3Ec59IJazg5XhJiQgTo7f6e3jNychBYvmKSqDtmsk/NESTpVNyqVeXhQEp7pbQe4TOtkpLKDnOwQXvnarMTghgFHYWwn8lZGE/LvkhQ+OAJq7BHvmVnc3/bLpBpUK4M3VWXiTpxOjlOiG2zjHstyWaaLFW2kl8OeIa54ziO4zyAPAT2D1YE0ck4AhXGRWRYB/JSkJeF7rS1T4Z1myGa64DqTFFbIzVC6ls1uPYPq2xnnMh1wPm49B5iEfl2EXlZRH5y9tjTIvL3ROTnyr9PzZ77YyLyaRH5WRH5qnf0O4D+FRPB1w9PeebZ2zzz7G3itYE0BjTC6ftG1jcgL1qDvIxtNThlkou1VlSj7lDimS3lRhHVKc1O43YrxFnDI/1yHMdxnG2wDS2AwvIzS05OFgxjZFx3jOuO4dYCbvdIsn5iLXHMKjIl01YhXJVtd2p9x7UANgnlM0WwuLbXnLV1vSgetQ7YJS1wFf7S7wC++sxj3wz8mKp+BPix8jMi8muAjwFfXF7zl0TkwTXYDAcvCenFA24f7fHqSzd59aWbpFsLwueXyCgsXo/k3pJrRK11wrLO1VLrZj7E0KxYquDVYAl3uRPiqkQ/B0+qcxzHcZx3wHdwwVpAFBZvQbrTs1536ElETyJ7v9Sz/2Jk+Vpg8YbJIiuGtXt/SPZVxW8YtLRNyoYIluJQIakN0+Vo2sC52ly6IFbVvw+8fubhrwG+s3z/ncDvmj3+Paq6UtVfAD4NfNkDf4lYtbd/K4AK0mekzyxeiVz7RWHvNWH/8zJ5DtrQnLQIxvLVnTBFPCPt39y1fuG5TUu9ILaBZZg/2i/HcRzH2Qbb0gIA8ZYpWBkCMgT6I1jcgv62fY17tusbyrA9Yr3EeXKcgvU1s22dvIdrj3C2Ybq0LEWxaNXksIXo5ovQAbukBS5dEN+DF1T1JYDy7/Pl8Q8An50d92J57G2IyDeKyCdF5JPj6REabetiXEd0HdB1mFZyy9fVPISFaRUX10p3UuzYFjYkB+Ukj2XqtPQXTRdCaaZPS/u3O1W3XXMcx3Gch+ORaoFhdVSKVUKMbcotLQC1+/58V3c+M6Rd+YqtPSIkJUebITKL1qYLEBPWtXi2reKY8/A8bkN1d1uq3HXZpaofBz4OsP/eD23J8ORycbsVx3EcZwd4KC2w9/4nXwu4Dnh4rqog/ryIvE9VXxKR9wEvl8dfBD40O+6DwOce9GaWO14mPYPC0sq2YW1LQUlAR4ljbrHNU+witnWSu2a9BtYXFNDJaWJ6TlpP8bbwDHPHcRznCeORagGNLXU2ffaAxZHd4Ltju/fnXkj7mz3AGhTJMxepSKn4KuO+WBtlbZUsxFU5VprjxDZuz64DzsdVbZn4AeDry/dfD/zt2eMfE5GliHwY+Ajwjx74bsG2QeIa9j61z+E/2+Pwn+3R37H+oPFAinOETt6D3cp6fuLavqq9Sm2el9RyzmvLRE2v606sVUL0iV+MOo7jOM5F8Ui1gCTobyvXPwPXfyFw8JJy8JK2doZsx1S7NRUrdHWn+rYBewvtmLVMqg3aVZep+nidS3KdevW59AqxiPx14MuBZ0XkReBPAH8G+D4R+QbgF4HfA6CqPyUi3wd8ChiBP6iqD+zSDSshrmDvNZ0qxVBWiglA2youCCFlcid0Ja0Oygme2sltkYwmiiUxNd0DBKzniCzl/bfDLtmjOI7jOE8O29AC1ULN/tXmEhWbgJ2quyWKud7zU28H10TaHMUqzcHcqHIvZKQk3pbfl1qGwbZwHfDwXLogVtWvu8dTX3GP478F+JZ390sgnpri1dhOaEk2NBdGi2dcvCmIKuNeiXIOMm2DVIu1UMSvCnSn1oyfF2wMz2XsAkn9dgWx4ziO4zyObEsLhKHaprXimGQriuWoxBMhDHbPXx+Kid1OJlEbV5SBOSGuIXc6iWnTAjIN5sWVVZ9rm4Vztbl0QbwNQoL+CHN+OFG6Io6Hg4Ac5eIjaEtB2y4RgrbeYLCLR7IyHMpUCa5otCVkKtXk7lRJYhdQ2lJSHTtmj+I4juM47wYBhmtCd6ItdpkWuGFhXIocF8u11GaMOFNNFm2pdDWcI6xbgFd93xrQJdsQxK4DzsVOCGINcPK8EE8gDm1rJPUQQrsgulOlP249QHn2Xyd3Jny7U2urmAd1hEFbOwXWOhF0u1slik+XOo7jOM79WN8EkCk7AMq9PtTCV9EJqQV15QTUlshSYV7cyZw8HdoM0WAJtiSZiWdBtdi6bkELuA44HzshiFFb+Y0H0L3OZJAte63aW5PlVEqPUbKKcRW0khXWkHspg3YyCWcyZWivtWXUnmPJd/k8juM4juNsFzVHCXi7a9RGYVU3K7p1mA5KZTi22SINphlyhLyooVz24jDQ2ia9ffLKsxOCWLBWiTxKaYMoJ3NZ9Ulq1d5psjS3CGcoF0HpFY7rDIRJAA8LsczzchF0J9ZzhLCVdJqKb5U4juM4zr3pjyxMIy1ngnhWva2RzZPNWrVeLU4U1Vki9cLyrcxwGGxOKWOWbVnpTsub1VRbtlcccx3w8OzEOKJStkFWOvXzzKdLbULUKrxmnda8iGW+UtRyMcx6iyRv+g/XtLs6narRT07HcRzHuQrMbdFqxHIVrjnWqq9s3PtVWjqdRpkcKKrYzX3RBqP1JdeY5xrfXF/vXG12o0JcTuppZbeQ9ni2adG69ZEidCflpJfN105BG1qEdbFi0WgRjpW0Vy64ka15D7oht+M4juPcn9y1kI1QsgKqQM4LYABimxUSLe0S5TWSTPR2x5nj56IJ3XkBbVSLb66/S6zaXO3cLhLXAedjJwQxQFpaS0Od+oRiI6wtZa6u/MYqaO3sAkCy2a9YpnlxkFiYKwWJDePtaufWjbrVHmK/EBzHcRznHtSqbmlnPHt/rvap8/5hFRO188dCsqKYKBvhHCpstl8k8yeOJ946+TjgRXzHcRzHcRxnp9mNCnHJHZ8bcUPpFeognjL1Ak3bIrXiG2c+hXWRV1eDpTfI+oZnq8SSXDP5E24Bxf0HHcdxHOeelF7eeKp0J0yJcrXaW63YJsvUmkyrrWVS1Fomxv1Af7wZ2mHvb2FflXg8i4a+YFwHnI+dEMQaYTwQZISDV/IUwTgemJF2dZaQemwnbbhuLpRn3oJhLN6FytQvVLdb6oTpfFLVcRzHcZzLowpXjS2aGWzex54wD2LLHSgFrwzITGTm1oNcxfN0nxd7kzpoL6kM7UfZyDVwria78b9IbEWoYidlXRUubivj0iZCu1O1HuLYjLetqb54CwdTzJK0TaOmYscWIM6COeyiUiQp4/72ulLckNtxHMdx7kGxUq0Wq9NwXbJ7OmL6oM4ZiQIBNDc/4ZpdsDhSVteD2bSW+39Y2XvWIXtJ9TkljNu5P7sOeHh2ooe4Lt4Wt00Uh3KS2okLffENrpZq9YTXMmmqwcI4cjQP48m2pbxxd2oxkP2JfcWZ93CNid7GH5lLbOOj+noniMhXi8jPisinReSb73Pcl4pIEpHf/cj+ZsdxHMd5hwjWIll3gLtT+6qBXDWprlquyYjtAsfqNFUqwlJ2gGt7hTTBLDq3dRMT2EEmkX2hXIAOeCda4EnRATshiEXLiV7s0iph1KkCXP2EzUKtrfjSwr7GPZlEdF1dTq8t/UXVmzhHGJfCuBcYDp7c1ZqIROAvAr8N+DXA14nIr7nHcf8d8CPb/YSO4ziOUyi7t5R7dv0yETuzYBsprRK2W5z75lk87tt9ftgPU8FLciukQfMsRmtRjSe2ZeJJ0gFP6P+iM5QVW3+srA9bA3zuzWcwdzKFcYR1G6LLi9ZbNFwT4mmzXqmvq3Zr1nLRfmVISlzD+nA7gviS/Ae/DPi0qv48gIh8D/A1wKfOHPeHgb8JfOl2P57jOI7jNOoOb1oyKaDutA3HTfd4bcN25NYSmRfCiILYznF9v5pcC2/POmA+lH+RfxuuA87DTlSIq/dwWlgfsMbWMJ87C+AY99pJNO7bai8M1mAfB6sm58Ws2b78l+tWShyqWXdLp1MRUs9WfYgvgQ8An539/GJ5bEJEPgD8+8C3bfFzOY7jOM4GGmA4lDYQXyq5uWQQ1PmgemwdqutOoD+yr73XM/0RdKXVklALYNVZSjZ+X1qWvuQnt/z4xOiAJ/d/0QxRE8On77ETxr470gAAIABJREFUOvf2+JQcI7XVwS6K/si2T0Tbqm9xxx4b9kOLY9baO6SkpZD7zZVZ7rc7WXoBK8NnReSTs58/rqofn/18t194dh3854A/qqpJ5MltH3Ecx3GuNpLrzrBCptmhnbFjpfQU1/5hSVb4guIsoUoYlPUNgczkXJV6KZarTSPU989bKj9eUIX4flrgidEBuyGIE5MvYEiKJCnfw7Cc+QdH6I90ErE6m0LNEXQRphVkHcCzFWdAkl0g9guLQ8XceeLx5FVV/eh9nn8R+NDs5w8CnztzzEeB7ykXwbPAbxeRUVX/1iP9pI7jOI7zILLdv+Oq9fV2p6VQVoSwjKYVxr0yDDebGRr3A/1JNmGdTDfElQ3s13aLMBus1yhT18RjzP20wBOjA3ZCEFcrtXHPJj4nkdvNohZLNdia34XxABa3dKoG2woxMy7ttA6jXQRgq0Vo1eQchbRsA3pb+Rsvx5D7E8BHROTDwC8BHwN+78bnUv1w/V5EvgP4u1ftInAcx3F2gzBq2b3VKTsAmOqcuTNJkJGpbUJD2+2Nay3tltY2Me5JCfRQ9GwUtJptMWWw/6JxHXA+dkIQ7wq65QtBVUcR+UPY1GgEvl1Vf0pE/kB5/kr3CzmO4zjOk4TrgIdnNwRx6fXVrk2YAuZJPG421sfSO7y4pS2+GdDeeoRzhP5k9j4CMqqtIGM7ESWXLZQne6gOVf1B4AfPPHbXC0BVf982PpPjOI7jnEXLUH0YdYppBrufx1XxG+6tLbIblMUdpqF7XZZWy9FCNuJaSX1Lra3+xVZVtmOnvmFhsxr9hPGk6IDdEMTBvrojnSzYANKe9QxrsKE7jcAA3Um2tok92XSTWClSJkgXd2yrJK5aW0WdTq1tEuZzvL0/0xNqHMdxHOceSEuYlcyGVaqoomr3/GmALtignGUN2GN1eH7/NSUfFKu2GtWcpzcrr7chu4RYyu0WcB3w8Dzmfd7vDJU2BQpYr1C5MOoKUUroBsDqRkAyLO40NTvuCasbgbQQwlpJi2K1FmZJNWNpyC/Z5qHGQzqO4ziOc7lU29WSMFeLZbkrLlMzS9Vxz0I7EMswqOlzGuH0WWE4tPyCGvIx7tkOci26SbZCmiTrO37Sd4ufBHajQoxtg3QlsnG+KhwPhP6Okg4gru2x/kQZDoX+GPbfsLP49GZgfVNY34T9V5m2SdJSyMWUO8xWgHHN2zwJLxLVSzHkdhzHcZzHgwzjvtjAfGiDbrkrLhNqX7lkFYQTtfwCVSSX0K6SXrd6T2D/lYzUynF1l1KdimtWNCvBX/Hi78+uA87HbghihbyEfMLkJgEtqW44kKl/aNgX9l/PU/LM6kZxlagVZLWtlWq7RtdCOWoPcUgl0U7Z2jYJbL+Z3nEcx3EeFwQrVIWh+guXx2sSnVSvYlqPscL6Wph2e0UtqKMr7ZZ1l7h6GptPcfk+mOexxu1lErgOeHh2omWiNrofvpwsrrGc+PVEnprhxSq+aWnbH0cvBBZ3Mos72WzX1sryzRbZnBZifoNS30eRbOk1NeQjd35yOo7jOM5lowLxRJudWqkEz9scao9xGC3VbvW00K2Ug1cSB68kmy0qbRTjvrS2S53tPheNkTvTALUlw7na7ESFWIOd5OvDQBhn/cTanlctfUJFPB+9zwTtMz9pfRRHH1hy+4vg2mdL73G0bZDclTzzCKl4FHcn2eKbt7gq5HL8Bx3HcRznsaG2SWhsLhC1XTJ3Vjgb9gO5sx3kow9kJAXG4jKR9uDoC0f6O5EwzIphJaQjd7OAjlGnynINB7tYXAech50QxKhZqkhuFmsVUcgLOHwx0d8Zuf2hJeNSWD1tJ+8rX7IPWPJM2lPe+uia65+NSFZCgm6VzaEitBM+LWQS1lM0pOM4juM4l0up3ta2CcCsWbtmmRZGi2UerslUOX7mx28BcPThaxx/SLj9K+C5f5ytnaLYt1Lu+y3tVqbd5zhsr33SeTh2QxBjiTLrG8Litg3MgcUz1raJ3AtpGUkLOH1GuP4vYdyDF/7hbQDi0Zq8eIaTFxZoyMV32Brm68RqXUHO/Ytr3OM28N4hx3Ecx7kH1Xb1rO3aVMW1tskwKv2REga49kvK+jrc/sh1APZfWfP+/7Pn6HlhfT2UQtvmvbcK4nHP2irjGnLe1oC964CHZTcEcbFYs+xyIdTtkRLjrMGG6daHHdc+N7L/emDv5RWnzy353G+2i+CZTw28/4c/z+e+6gXW18LULhFKu0TuZPIfPn5e6E6ax/E2UHy61HEcx3HuRRhh+VYGKQWsGqDRYVogwvFzJnL7o5I9UPqKT54uBa/UIxn2X8+MS5ncpXKUjT5kAF2AdoKO29ECrgPOx04I4riGw18eiavMcD3aFgcQTkzIdqfWHD8eCGmvA4Wj5/dZ3FGWb9rJfue9HadPPcfh57O1TyyEgJqLROkjrqvCg5c30+8cx3Ecx7lctIPhIEw2a6ns6k4WbAJpabu8wzUTzCrQH0Fa2HvcXlpZee8NLe9ThXKzbquew3FliXjMgj2cq4v/L3pSUBsMdBzHcRxnB3EdcC52QhCnJdz+UGf2J33bzsiLur1hq0Dt4HRfpr7i8UBab9Ea1jcFSbD/amY4FHJv/UGSQBatH0lrbnlovcSO4ziO41weGmC4BmlPIDO1OSpND4QRxv3mUxxms0bAlGy3vmYzSeubph/iWm0uKTa7VdGWZosL1SvPTghiypBb7uxkjasaI1NOWmspIgtItGG7sD4zhUr7OaQyUVoMuONQ3ruK3/K4DGz1IvAMc8dxHMe5N2lPSIsidosgrrkEGkrbA00MS4L5rTXHKpxbX7DU4K5QQjmqs2sQa6ssz20D1wEPz24IYoG8ENI+G/09UC6CaCd5rejWpJq5GK7nWFzZeyBFWOcihmcryLQsUY9nLqSLRPHpUsdxHMe5Hzna/TquWxErd2zcq6ekuWjP1eRZaPf6uILxAFbvMeEc1kIYTRdMA3TSimfbCOlyHXA+dkIQTyenVp/B9njuZkl1zBrrF6XKW0RujWPUsgI8eU6sWjzA8g177zptahnorSrtOI7jOM7lI6VVIkfoSkhHXJfUulIgq4WxKojDwFThrfohDsobHxbSXqK/HZAEi7dks09Sm7iW5D0TV53diG7O1jfUncy2M8TaIrrjzcjFas49bXnU81vae508I5y+dzQbt4Vy9AGdVoC5E2QsE6nSEnC28FeS9dF+OY7jOM6ThHbWFpl7kFHtK8/aKqUVwmqxbJo3ykxtEetrwvI1mzNKH7YK2MnzlnY7/0oLs2Pbzm7xo9cBu6QFdkIQ1x5iSfZvGEtvkCp5WQRwvQCSCWWN5YLRFrSRy8px+aZy8NmO9/7bL5KWkA6U3FuaXRiUkCywQ3KzanEcx3Ec53JJZQBeZgVbu3dbJXheIINaNbZ7fO4xraBW7LrxLzP9W/8/e+8aY1l23ff9197nnHtvPbp7embEx5C0GZqKJQUKnRBKABmJ8xAk+QstGEboBIqB2KAMSIAN+IvkD4mBgEAM+PEltgk5EuwARhgBlmEiYWDYjgHDMGyJUWjJJM1oolHE0fAxj37U4957zt575cNae+9zq6t6htVdp6r7rB9wUVX3WdV9Ds5/r/1f/+UQT1qwZyzfpKI3eGSXyPFuxs1mFhv67ABod6kLwLAnB2ezZrRHjLgg6SolgFvAJ51i14xGMKrnyOsgjr1vMf6///sVdEeE5W+LwT52uUmPS57hpH+n7cgYhmEYxrmU3V7Vp/1t1QKnIoqpjJhVPeClQBaXtNMgn4tkx6+IVWLvt1rRB2t9fJRk5YJ+3041qW6Sj3kumYUgzt5fdhKwvXlJH3AEv5GqbsOktgdotReIvtqBnCZGhKVErZy8Qli+BbTHKNstTmeVp0b8xWACT/gvbGZ6wzAMw7gYirUxLhetUgAokV7DqQzfkhfoSGfd7c3X+/aUEfYIHdfKMiVG6up1OCdbUdxt5r9KTAdcnlkIYkAqvsTSFbp4W+4bDiWD2CeG1xOgPebiF8IR0B/qJBvdQnERaE8Slm/lnEGdfd5zWXUmPXG4nfAPNAzDMAzjsaRGBKpPUtACgLAUjZArYHExmjGQAL+p1WQQypjnZs1gnUbLBKQF7VSgJc9Vvp1idLPxZMxGELMHoJ2l0FVhe1QFbV41pk7iVHKQduuk6hv2pFnODXICpK5uoYi9gnYyCdmN8oqn+PvYVoaGYRiGcRE0quaOE6e6I/05zxPQnqNmgxKzSupFGA5kqEdsCc1GBnMg1dfkZApAG/Fy/OoEl2fTAU/GbASxG3QF2IyqvYNsb1CAZBA3AG1le8T3kiXojuS5FHVyHdQjnE+SpCdKrMb5MpXGvDyGYRiGcWMomcJj3Zgb51spZOWiVmxl8BYlRnOqT9W5BlkjQOPZsle4pFFA3w9qmYgwbjizEcRzYE7xKIZhGIZh7GI64PLMRhDTqKLrNIw7tWKViCv5uTlhpJYQYzXWt6e1zMsOperrezHUU5IOVNdrBRpSaX5kBWoYhmEYxrVDDCDWOQE5iQoOoB5o8nU/x622tOsLZqDZME5fdtJ0N+hAD6/TbJUcvZYfN2428xDEI/tCzhgGAJfENhH2RNC2JwBx0iBtiWMbDqi+B6odInlJqJAxz/J8uPrcuECZhT7Zn2kWDcMwDMO4kGJphOYKQ67X7UP53m+lEBaWVKwPYUHFQ5zTJ4Z9HdW8kYZ6joS4VP/w6Focl1qEmyxlYprPeR6ZhyDOhvZR/BogvqD+FqF7wDqSmRHb3DFKO0M1uBHhK4Z5ggtcTPSur9VmQNIl2OnJNmFnqZnpDcMwDONiUlMb5/M13q91Gl2AxKsuCHGhvUfaMJd8zmCVa3tYSQRrf9uBSd6zVJxzbawRHZzaKRvsTQdclmsPAiGiXySi7xDRvx7d9xeI6HeJ6Mt6+8Ojx36OiF4loq8T0Y9+t5+XD3BKImzhNHSbZOUXl1JBjp2OYu70ptnCSe9nB2zvkIhelviVMqmu1zHRqXabGoZhGIZxPpNogXGze6pagHX+QFxIc7zoAELsCGFFRdCy0wjXKO/T74sWoKjT7vracA+S5/qtPNea6m4+N0Gu/S0A/yOA//nM/X+Vmf/S+A4i+n4AnwbwAwA+COAfEdH3MvO7HmpjD0/2+IQVJD5lAcieB4OJwMSI++Ih5iSrLZcYiahEsoQ9Qvcwj3uU7OIynnEU7ZIm8g0xyFaGhmEYxrPK38JVawHSOFQCHO9aGqNOleMGct1XYctO73f1PcAymOP0ZYfYAY32ElHQolm2ZWrFOfcvXTWmA56Ma68QM/M/BfDOe3z6pwB8npm3zPwagFcB/NC7f4h+1XiUsW3CbzWoO0mWsIvy5PHKkRs52LNXCJDV5PYFwrBP6G9RnWoD1BnpjBvwL2wYhmEYN5tJtAC0yquV4uRVvKZqo+AzFstcDU5trSLnHWYXRR+EJdDflsFf4yIYZc3hsWPBNG4mN1mu/QwR/bpuo7yg970C4Buj57yu9z0CEX2GiL5ERF8K6xMAsjJ0vXZ+jjpFxQvMZTslNXXyTBbPrNYKFwAXuIx+DPu63aKry7wiJNaxjVN6iJ/yzTAMwzCumaemBeLpSckbJlZNEOV7v5Hn+170gO/FAlmqxIPu/Ea5tscOWL6TSmpVHs4xTpdKDcq0uqksE09bB8xJC9xUQfw3AHwMwCcAfBPAX9b7z9sLOPf/i5l/npk/ycyfbFb7YJJVHHuUg7Y9lk7S7iHqtkaULlPKTXOpRrVlHxFFoDkVce2CHPSpAYYDuZXnTekb0gk1T/NmGIZhGNfIU9UCfrVfptTl/h9w3Q3221wIo2KVoMilcsxU/cZgmVaX7ZF5Mm1cjm4LeY0M8LqCf51z/gWetg6Ykxa4kYKYmb/NzJGZE4C/iboV8jqAD4+e+iEAb3xX7+2xs+zhRscxa2RaFsx+q4JXG+XGpvq8BZIFb0mXcPVEi51NpzEMwzCMy3JVWuBsHGryOp2OakErX+cBEcXNRm6ul2t/e8oY9qm8pviGexSN4QYV1WyWiWeBGymIiegDox9/AkDuOv0CgE8T0YKIPgrg4wB+5T29pxra86xx9hKVJk11tDN/PHYkY5pH/zrdAzkRoKtEF7QjtRtlDvdy85oyERciuCfD9kkMwzCM54Sr0AJAHc5VLA5U/cHQKjB7QliS6ANXq6T5Ou+iRLcSo/QnxYXaK9SKUZrqsDuw40oxz8SlufaUCSL6XwD8IQAvEdHrAP47AH+IiD4B+a/4bQA/BQDM/BUi+iUAXwUQAPz0e0mYMAzDMAzj5mJawLhurl0QM/MfP+fuX3jM8z8L4LPf7ecQS9V2bHqn7Pnp1CPcSNg2N9nqQCNrBYu3mKsXiAIA9Q+nttoj8gjnyVaEypy8PoZhGMbzwyRagOvUuDylDqixark5Pqzq9Tw30yUd2pV0PDM7oDuSSbd+K55hikB3hNJMny2YbgCmujybDrg81y6Ip4BYp8k12Jknzl7jU7TjNKn9wUWASJrrsiCOC5lE1z1IJYbF90AE0K7lueNtldShmPWnwkY2GoZhGMb5EGrjHFC9xG6o1+yxTaA0w7mRoCVNmtJmOaeDOvKUurBXP685Vnumx2QGVdMBl2cWglgiTxg+AnFFtYLbM1JDUvUNADoRw34DJJ1Y4/QgF3ErgztA0nXqtyzPbQjDAckJBWBxj9GeZAFuqzXDMAzDuHZYtABrhvDZpvdcKXYBZSc59xfl4lZumnMR2N4SPeGCDu5yOalKnkvMSI5qupVxo5mHINbM4NTRbiqE5gvmOebZaJ8WNWbNb2S55aLEtKWGEHQi3Th1onvI1YqRgLCk8tgUMGyrxDAMwzAeh1R1dQptuWazCgKUzOGok2yzSG7WogWaNRBbAMyICydN+qmK5sU9LpaJsEeIq9p0f9WYDngy5iGIczxaYBlAM6rakoZp57GNINne4Oz70SEd/oS121TiVuIij4OmOhJag73jsvqTpvYRG4ZhGIZxDiTFKqe2x6IdPUnKlENJmSiTbRnAAAz78mQKrJPnCH7LWL9M6BIV4RwXo+FeTj4np1gYN5t5CGLIlDkxt3Px2GSTfOzU/0NA0moy64kRVvJcN4itoj0Vr3DqpAHP9wyKIor7W3rCjKPdpsoeZEzn2jcMwzCMZ5AyIGPU45MtDUw6zVarvb4fTZtT8m4yMdBqoUx6iHSccy8VaHkyACaEfY10u2pMBzwRsxHE4/nl+WBlllUenEyeSw3AC63q5nGLrr7eMRAWhOWDhNV3WLKI2/p+fqtbKhtGWBPW76OSbTzJ32hmesMwDMO4kFys8hsu/USJCaTXahfEAiFDu9RiOarwppYkf1hHODdrGdyV+4qAUc4xi52SG5qsQmw64PLcyMEcTx3Ovl7xEWco1XGLuWM0NSp+ewBuNJb5UCbSSDIFww21ekyhTqWjKFsyfmDsfYttUp1hGIZh3BBy3Co7KjGsxFzSqLL4TV6EbXckTfRJ0yLCKg/fkMi1EuUGTZwYJUv5geEHxuIeo3twHX+t8d0wjwqxNrflrOG8KnQBIMdoTglukK0PSvI837PmEYqADisg7BOaE2ms628TDt6IOH3ZgXqpCke1YDQbiWtrAmP/mxP+nbYyNAzDMIwLYS8aIDUo9oJc5ALENukCivUhefm+yakTDQCWwhdYxLMLEM+xy6JYLsbJU7lv+c5EF2jTAZdmHoJ4lCkohnn1DusqTzzDpEkS2oGqCRK5wtse15VfauRE2NxxdUXZkAhhyM/DHpWVqGEYhmEY1wsDYoXMWiCnQNHYJCz2Sm6AfiFWBzfsxq7FBWFgsVoOBwBHtVQQ4I7HPmUG0rTWSePyzOO/KUerqIc4i1QXGcmTGN8BIAFxTw72sAe0J3UrhD2ACGzvivDNwdzwQH9IWNznEsuSWkKzZsSOJvS3k8WtGIZhGMZjoHEFdRS7xl7tD0GTItraXM9NLbxSqhFq7CRdilh0g6RUcBXPAUheGvFpksqt6YAnYR6CeC7YVolhGIZhzBfTAZfGBLFum7THjGGfSpOd72WFGEexaX4LNCfy/eIBIyxIu1XFjxSWUnr2A8P3YrvwwzmfaRiGYRjGpBDUCpl270+NXMvbE0ZYSjW3WWsDnd+1PpLOK2jWNb3KDQw/SNNdf0h1JHQPNFupGGdfsXFzmY0gzicABS52oXyQhlb8vnXYBnasFYAI5Ow7chHoD+rUOz9IQ92wLwf8AEJ7IkkTk3mI2SbUGIZhGMaFqG0yNXWQVnnI7X6fb0ULjJrqQquJVF4GdrieiuANe4DrtVmvk9xiioALE1yfTQc8EbMRxDkyzSWCi3LgUhLfT3vC6G+px0djWMa5xYBUiimhnFCbF0UQt8cMF3UAB+VJNsDm7khEG4ZhGIZxvWi8KnvJDI7LPEyLQYnEB6yxa/l5uaKcG+yTB5qN3h+A4VC+dg9I4ttibdZLrQwFa9ZAilYhvunMRhAT5ziVmg1MRezWFVXqNG+wxU6GMCVZUeatEL/V53YEfyQNdUFPLm5ytZnAU45rtPPNMAzDMC4kX8u5kXhVQCvGW0bqqO4Qk2iA1O424jUbEcC5otycaAOeB0jTKPKE2qjJEwCmmyBnOuDSzEMQq8cHiYrlARCvT3ek1eFYY9Z8kAOaG/EJZfxWJs70Bzp1Rgd5xKU8XifZyMnR39aR0IZhGIZhXDt+I7u6YTmq5DYE8nUYR2rqMK9SKR7p2XYA2lPG+iVRxS6g7jBD3gcQDZE6SbBgb0r1pjMbQUwJWD5MGPYJ/aEctdkqAYiX2G9JxG2q88zDnrwFBQAHhGYjJ8JwQDLvPAJM8v143rlUiRlpMaWfx7xDhmEYhnEumi88aERa0GKWCwCiiFoKDDcQUqfFM41hy3N9UwsM+0D3kIpOiAtpwKNIO17k1EofUVzyhPZJ0wGXZR5jI5zYIrZ3ZBJd2JPb5i6J0NWFm99wCe12QWwRTLoyZDnohwM9SXKnqpMKcR79TDqtJvmcSTzhqpCf8s0wDMMwnhNylZdYpsnmnqE8lU56jajaJbl6iNkx2HHJJd7ekf6jnEQRF2qb0MEexIBfk8wyoN3Eqqv9I6/gNhNmUyEGUEYxt8f5ZwYFMb0zEeC5pEyElQ7lUPMQNyKeYycnVfLyfsO+2CJ8D8Smfg4PhNTN6EgyDMMwjBtOWElhjEmKXoDsEBNXOyWx+oe7R2PXxPpAZYAHMYAkVksaCWhA+uwpEIgYzuTAjWcWgph9njojSRLZ3yNhgjqlRhMiXASCxqTIFBod87wAuFMzvkdJomhPAJlrrtsq+h4y6nFiUWwnnGEYhmFciAsihl1CmVLLjgDW6bO5IqzJVCKMuXqISa7v7TFj+4I20uvzGLnZXp46HCZ09x2aUyq9RleO6YBLMwtBnPOCAchKbhx/ov7itIA03WG0RdIAocsRbbL14WJ9TVxU8esiAG2g296VJIvFPQKdmJ/HMAzDMK6bXMEtiRCsO8CekGhklaBaJXYRSKPiVvECa2WYHTAcit2yOdXxz6oFmhOC18b8NJVlwrg0sxDEYIlXiQtJh2g0kJtJ548D6I5YrRKyFZIawG115QggNTrrPKFMs+MG2B5KM57rUbzs7ZH8TFGsF1P9jZPFuhiGYRjGM4gbGJGoFLYA1QIHcs1vT/jcHeUSvaa7w8M+odkwwh7BDaRN9Fp80/d1W2neK37lq8Z0wBMxD0E8E9i2SgzDMAxjtpgOuDyzEcQ5ASLfAFmwRfUJJY1QS/ovkjqJWktNtUykhpFaQmo0rDsAzVp+5gZ1a6SVm4uwBBTDMAzDuEHk5Ifst2WPUtWVOQISn5bnDQDSHCePM8JKZhIkEMIBg4KMb06e4RLB6wCv1MlwDjKR+kwwD0FMaoHoGamhchLEpUylcYP8nDr5fjiAeIN8nT9OARhuAbwBYkvSZOcYLuq2SgOQDuZwg8S6JfUbT4addIZhGIZxLux0CEeScc2pyTlskjhBSa7f0dWptKmB+IJVJ1CU1zQnjM1LYpcIK4lxc0Ea8JCb9AdpxA8r8SxP80dO9DnPIbMRxEwEOAYxl+k0TEB7JOMaAZQYlfZEvsZFNccTA8u3ZNXYbBh+Q2BHCPsS4p1HPObPa9bqJfKP/DaGYRiGYUyN+n8p1pkBmWbNaE8Y7WnC5o5HWBGatVzXw6pWeWlQoTtobBvJG8dFLjejVp5dnXpbGvaMG8ssBDHr8Ay/IbhQl09uYIQV4e7XB/S3PLqHEfc+3iKugPahjlvMC8ikBzTXzEIkwA1UHi8rMzXQb+9yiW2b5g81f4ZhGIZhXERqVQzrtRwAmGvCVFg6NBu5iOfJtX47KnhBG+pvy2CO/o4W1Po6njmLZxdqhGu2X145pgMuzSwE8dgnlIjg+7qS81vG5gWvXaNOVnIBGG6RTKrTFWTsAPgc2SKWCQqjbZWOi8eIkvqLT0ji3CbCfEqGYRiG8RhcFsUE0gjWZs1Y3k9qh5TCGekwu7gQQZyHeISVWjAHIAUgrLjYKfxWhbVq0hzh6jdiuZwC0wGXZx6jm6Hmdh2tGFtCbMVLfPu1Ac1WvESpkYpwXtHFrmYYuyCrwriU2eRhxQiHjKiB3c0JlTGQaSGvd0O1XBiGYRiGcX3kKbOplV1jp0Wt7pjht6zXbUZYUIlPc3linat2C7nmM4Z9qtYLViHMADcMblisl1pZLvnFxo1lFhXiMkqRZHW3uCdLqP1vR3AjvmAXGX6QVWFSIZyfD0h2cUyA7wmxA7oHUv3lLILbKn5FOOsKcqp/4ZnNHDcMwzCM7xZSS2MWr4BohP7Qye4uy81vGOkW7TwHqAkVfivX+dV3ZMc4NWrF8Cgl4vx+3EzkZDAd8ETMQhDv+n/rlsLRKw2atQrhBIQl4fAbEUcf8jK/3NfZ5uyA9liFb2As7kMi2FpddXbVY+Q04L0HAAAgAElEQVSCfBbPpv5uGIZhGM8GTEC7FqEKAJs7Dqt3EsAynCt2wPKeDN0g0mzfHL+W6qTa7iHrEC+SKrIHwChjmrN9Mi6tqe5ZYBaCGKT2BzXHl0Y55pIaEVvC9g5h7zsMv2GEfRnVXCbORE2dCLJCZEdwPYs36JDQPawCeNgHfK+e5cnGNdJES1DDMAzDeEbReDS/5bKD64JogNipRaIhUEqyy9vt9gsxyW6w06SKsJK+JHby+uZUboBqhghgNPX2ajEd8CTMQxBDDtxmDex9J6E/lAPGr+VEOP6Ax/KdJB2hScQsSIZwlO0S9Rb7jdwhRnsCN9UWkU33zalUod1pbbSbBNsqMQzDMIxzybvD7TFLX5AWsfxW+4sWBH/MWDxMYE9YPGD0h4SwDyDkN0Fpvs+vTw0VP7IIbm3WUy0gInoiLWA64NLYpr5hGIZhGIYxa2ZTIc47CesXHdwgSygXJFfF94ywJCzvJQz7hO4oYUgOp7eB5kRfrmb54YCw91ZC95AxHIitYnGPARpPstGUiYjyWZNgK0PDMAzDOJfc77N9gZBOqq+XEiMuJIYtecCBEFu9jgcGE5WdYGKpDvuB0R/IpNr+FrB8R2YXUGAd1rGbPuGm8hCbDrg0sxDErGI47EseYD4JtndEHPe3Cfu/mxA7wrCX55UD7UOgvy3PbU51rGOQ7ZHtHWmo64I018nnaMi3I1BiBE92cBqGYRjGTUC1QKM9PqXxnaqdgb20DoV96RPKnuMschEBboH+gErSRPYTp7Y+DQC4IcSsskwL3HhmY5lIrYhhmTVOSB2VXEG/lsovO/EQbW87jWqTwO0cs8I+d4symtOaOtEeszw36onBDG6ojGycDH7Kt/cAEf0YEX2diF4lop895/FPEdGvE9GXiehLRPQHn+hvNAzDMIzLUCLVALfV5rheJ8l5eSys6tS67V0qEWvj6yIx0N+mkmDlN0DYI/gNl+l0Occ4vxf76f7GqbXA86IDZlEhJpaDs1nnqTRyf1iKAHaDRqh56TzdvkDwW0JzymWbI3eY9odA7LQpr2cEJxmEbtBmPADQSBZMGcTNmLy7lIg8gL8G4EcAvA7gV4noC8z81dHT/jGALzAzE9EPAvglAL9/0l/UMAzDMAhyXdYMYmLRBMlRGZzht1xyiId9Qn9IEq/mNVtYI9iWb0tKRVzItZ+iiN488wAQu4VUjqlEsV0ppgOeiNlUiNmjTJUJS7mllorg9VudQR6B1ZssVgmqK8jM4r5WmReSW5xfB8iJERfyumbNaI+f+z2SHwLwKjP/FjP3AD4P4FPjJzDzMTPnf4h92MaRYRiGcU1wq9f/hVSDw0r8wbEDkqfSA0QJ2LwvodmMeoLyPIMoRbDtXQLKBDuxV8TR7jOQrZZc+pGeQ54bHTALQVzGMXs5WONSbtzIJDpKDCQx2zdbxun7CP2dJGI4sJrqAQoMisD2NsGvuaz4xFc8/kCxSrAH2qPp/t/zhJ2ndQPwkm5v5NtnznzkKwC+Mfr5db1v9/ci+gki+jcA/ncA/80V/fmGYRiG8VhYY9PG1+wyWMsBcaUDtxzg37dG95DRbADXs3iKkSPa5Pru1/Ja9rXAlod3AFUY5yi2q+Zp64D3oAWeGx0wC8sEoOHaDMRVrfimRg7WzYuE5duMuCJQALYvJuy94dBsEkJpF2XxFpF0kw4HhOZUTPMcxVoxXhECdWTjM8xbzPzJxzx+3t7MI2c9M/89AH+PiP4jAP89gP/8Kf1+hmEYhvGeyNdmSZKovl4muV5vXgJSw/BrwuIeMJy0xUebm/HdwEhESC1h8SBh2BcdkBr1Ew9ctAA7FHtGap7pgRmP0wLPjQ54tuXae4SSmN4ByJbGQr8d1cdPXgGGuxG3/o1HWiQ0J8CwV1d8bgAakpBu3zPiQKBOKsxMhPaEdwz3rCfRZEZ6/dsm5nUAHx79/CEAb1z0ZGb+p0T0MSJ6iZnfuvLfzjAMwzBGZC3AhCLlcqwqDcDwcgJ97ykevrGP7ptt0Qvl+q7jmP1W/MFZLIcV5A3Vgwz9ESzJU5M21U3Lc6MD5mGZ8DUjOFeFJV9Q7iMGFvcIH/n4t/HgBwdwl+B7lqpyrCtDCpCtkygWimbNxV+U1CLBXrdeoNXhZ3pR+K78KoCPE9FHiagD8GkAXxg/gYh+H5Fk2hDRvwegA/D25L+pYRiGMW9YtABpscqNRjJnFt/x+OGPvIbP/vj/iriU9IjYiY0iX9tdZLSnNYfY9Yz2SN6bm+ohTi1V0X0jXbNPhedGB8yiQpwTIFzQAzqL1FzB1arx73zt/Xjtj30Of+Q3fxSvffVj8pwDLs8tr3FixPeDVIXbY67dpZDtmLSQCJZn3DLxWJg5ENHPAPgHADyAX2TmrxDRn9bHPwfgjwL4r4loALAG8F+MzPWGYRiGMQkUJUKN1sBweM4TnKRQ/Z//6vuw/ndatEeSDuG2ACVNl9pKL1FqRQzHhQzjcIHFFqEaIX9eHtbBz2l17HnSAc+xXKu4ICs4bgiuB7YvyP1ZJOcpMge/4/Bv/fJPoXvHwR0C3YN6YLuB4XsgLsQfFJfyWt/X/9OUt0Sy7bip1eUpuI4VKDN/EcAXz9z3udH3fxHAX5z69zIMwzCMMQS1UPaM2BM2L2sU64kK2ACAgYPfbPHl178PzUaKaG5br6+UpPobVmK/GPYAl8QbQUmm1ZV+Ik2lYD+dGDYdcHmu3TJBRB8mon9CRF8joq8Q0Z/R++8S0T8kot/Ury+MXvNzGgD9dSL60ev77Q3DMAzDeBJMBxg3gWsXxAACgD/HzN8H4D8E8NNE9P0AfhbAP2bmj0NCnX8WAPSxTwP4AQA/BuCvazD0hSQvK7TuIcNFRjiQW3+LZfUXavLE4W96LO4R/MgbnBpNqOhyxqDkGccF7RjzuSGwdpI2a1YP0VP+13oceUb107oZhmEYxtVz5ToA0Gl0kZE8sL3L4N+zBv8emdRFsQ7xIpZJtLlhjhsUD7H4iSWvuESredlNJtYepabeij6YSm09bR0wIy1w7YKYmb/JzL+m3x8B+Bokw+5TAP62Pu1vA/gj+v2nAHyembfM/BqAVyHB0Bd/hp4mm7uE448Aw0sBw0sBzakc1Dt5hG3tPk2LehKUMc+9REj4jU64yVEqNP48QtiT9x4P9TAMwzAMY5cpdAAAgGTk8uZlwnDIcL+1ktugDfH+THM8yXU/LmpxLOsJvxXrZLYopFYGe7ggcw2QUCbZgZ7rprrnhmsXxGOI6PcC+AMA/iWA9zHzNwE5WQB8jz7tPYVAj2EP9LdU0J4SmnsNmnsNuiM9iBeaU5xHLpI24m12V4UgnWTDUg3OOcNxoavFyDKtJomoDnsyAGQSLppB/iQ3wzAMw5iQq9IBgFyX40Kqvu1RvTaHfUbY0yZ4hghaFk3QbFQPbOWWurp7nId+5aoyUD3KvucyEGzYn6jQehU6YEZa4MY01RHRAYC/C+DPMvNDTeg496nn3PfIf5lOUvkMALS3XgB7nUrHdZsjLIH+dkJ7TLKy66th3m+AsAd0D+W5qQOikwxidiQTa3LgtorlMvCjZaSWprVLALM6cA3DMIzni6etA/Q9qxY4fAEUgbiUZCi/lbdpj4Cjf3sAvdkgDaIFwCJivV7X/VbePjfaA5JIMXhNnQCVinLWGC5CzCB8wW98FZgOuDQ3okJMRC3kJPg7zPzLeve3iegD+vgHAHxH739PIdDM/PPM/Elm/mSz2BdPcAP0txnLNwnLN8UHDO0WzaOWKdZRzC5o1vBaLRI0yiTWcG4AsjXi5b3gRHT7LaM5rSeRYRiGYRjncxU6ADijBVb7aE8lQs1vCe2RiGFKQPt2AybZEQ778pVYi2Ed0B8S+kMqswykuMZSSIsogjeNcohl11i0RVw+9X8y4ylz7YJYw5p/AcDXmPmvjB76AoA/od//CQB/f3T/p4loQUQfBfBxAL/ybp/TnMqB36ypzBmnBBz8tpPoNQcMB7K1AVarRAMcf4Rw/BEx0csBjtIs5/uRqd4RYis3CrIyTA0wHE4bt/KU55cbhmEYxpUylQ7IjW3dfdnR3d5lbO9Kc/3+NwjdA7l+5+fl/qK4qt7ioh8iMBxoNvGg/UVUG+zYUbFTiL/4Cf6Bvguetg6Ykxa4CZaJHwbwkwB+g4i+rPf9eQD/A4BfIqI/CeB3APwxANDA518C8FXIZsRPM/Nj035TAwy3RBSvvs1YvywH8XA74cV/JYkSwy1ZBRKLVcJv5UDOYx4pAEktF+IdIlBKaDbiL3aBd0cz6hbJjBo0DcMwDOMyXLkOAKqftz0CFu8wwr7cH1tgdcpwQZrucqmQPYAkAnr1VtLnErZ3CXGhnuR9Ak4ZsSUgya5y0tzhXCVuj6mOgDZuLNcuiJn5n+Fid81/dsFrPgvgs+/5MxwQ9hiUCBQJi3tyf1zJVJkcu5Y8dqJR2mPG7dfkHIudwyk5HH+YcPtVGeu8vePgBhabRBp9ngrjZs1ww4SKeEYrOcMwDOP5YAodAABIwHDI8D2BmLB4R+4+/UBCeIfQnIoW6O8wKBAW98VOmRrg/sdFHLTHco0/+SDh1muM9b5WirWPiBJAWlYdD+Sg8MhvczWYDrg01y6Ip8ANmhTRAq6rByYl4P7vB176cgJFh+GQEDtGdyT2iLf//Yi39cCmnrF6Q1aWeTxjaqShzg0oHamAbrOQVJEnPTjtRDAMwzCMc3EBgCZNIIk2AID2oUN/C+iOEtpjFbhOGu/DJ46xWAzYfktmPfd3HBbvOCzeAZqtFL3KeGdNmcq9gOxRmu+dCeIbzywEMbGMZowrhguEdiv3L9+SJIiTD3jsfzOiP/bwjXiMl//pm2ijw/1v3JH3CIT+BYbbAoevyxF3+n5Cs5ax0MT1OHRBvUbM04VxG4ZhGIZxMQx09wjcqC1Si1jdQ6gWcLKjvM8IBwnLD57gYy++g6+//j6Q7vb6DSEuGKkjhIUM/Aq9RLGmTkc4lwg2EQbsaNdSadxI5iGII7B8m9HfIvgt4KJuZ0SCS0B/Gzh5xYGbhPhCwAc/+A4Ouy1ev39H8gghJ4HfiFjuD4D2hLH/hviR4kKEdbOW9x32qYjkqQzpczO/G4ZhGMZ3A0EtD5oUkZMfUiNN8rETe2XYZ+DOAGbC//udl8DvLNCsRRCPh231tySKtVlDRHIj1omsG/LOMTFLLNtV/32mA54Iq18ahmEYhmEYs2YWFWI42R5pTmW1ljtAt3dltdceSUB38oQ+tnijfwkA4I88OhlzDooE9hrm3TNO3u+w/+2E5m3G5q5D8nVLZPl2Qn8rm4gm/Dst0sIwDMMwzoUhO7nDgfh+8zV781JCd9+hOQW6h4TuIWF4sECiBbgFPMkuMSCX2dRJVFt3n7F9kdA9kDxiNzDCHgFcm+ryCOjmdCIxYDrg0sxDELOY5ykCm5eqbyguWDpON04eD8DyOwS81UgQ9+3dA1giVEiiVpbA0UccKACrNxmUErZ3pOBexjw3QHc0oSK2rRLDMAzDOJdsJ4gLuZ7npjrfE8KBpE94LYJ1D+Razw2weZHBTkUuaWO+vldqgM1d0qIb71zz40L7iBjT9ROZDrg08xDEOk6RG0jkmi6gKJFMpOmA5gRwXBdXzSkAorrYcoBf12xBQGLaiIDtCxLXcut3pI10c8cjtZpR2E32VxqGYRiGcQFyTdZm+EE9vwAWbxHCgVyvXS+N8TRI8SwsgcU7tBMKx16eE5dU0iO4AcKeVJ7bY1GlfivTauOCJIHCuNHMQhAzZEXoBhmnPOzLkd09lG2Q7YsMJkJ7kifQyInSPkQ5CdjXaXbsZYZ5amTFGTsgtYT1y/LPuf8GY/EwoTlJOHn/dP/EZqY3DMMwjAvQKbPNRtMftGBFCWgfAsMhEFZAeyrVYya1W67ldfJk0ROURlVfrQBzly0VOZECWNwHmnUCu2msDKYDLs8smurYy8oNyAcygxLL91FWf/nAzl/zbHLXj26hTq7LWynjiXRukNvmRcLJ+zzWLzc4eGOY+s81DMMwDOMMTBqLql/zNZsbuVHIfUZqjUg6nbZFGcNMsc42aDaSO5xHNlMSAc2uplhsXiRsbzu0J+ldfz/jeplFhXjsIc43QMRtHr9ISSq9XifNwDEoEkjN8a4HUiL4LJiTBHGnTirF7XGNcCEGwr4Y6t0w4T+xrQwNwzAM43xIdoopZfGq1oZBRDAOSTQC554hwAUGRdQKrxa/OI0EthfL5VhnACq01aaxvT1R/dF0wKWZhyB2WhXWA933KnKjeIXjQrY4sl0iV3spculCTV48QK4Xz1CzYYQVoTlBsVU0asYH60ozoniUrhzLHzQMwzCMC8k7v3l6XKOCOCzFI9yc6ECtPHLZAVGHdSHtvo/fslaDGeyovC7lnWPoKGeW2QeTXJ9NBzwRsxDEFHQV1wCIKAcruxq74nrWbQ+CGxjDvpwEbtDRzZDHXWCJVlnK8/JIxthV03xcEdxWn2tGesMwDMO4EaSWgAQwMVJUK2Un8WiUtJgVWJOiCNsXABCwfEteL0UzghvUgskAEpfH3Cj2jFQI+w0QW4tDu+nMQhDDAWFFRdzGhRyYqVHzu06ScT0AkpiUuAK2dxndPXnu6i0urwHkpPJbLiuysfD1G0Z7Is/N0+smwbS3YRiGYZwLO7U5sOz+7lRT1fdL2jOUWoDUNrH5ngjXS/Vs+TYAx8UznGNWc0GMIpdoVzfIjjQx0B9Mt1tsXI55CGKu2cOUarRa3gZhDyBSEbVxIcM6+jvA9kV5YffQwW/qkZaryxRyggVK/ApYu0o94eT9s+hbNAzDMIwbjdgiUeyT2etLjVR9iSGJESp2XWC0x4S4cDLOGcCGCcu3ufYOLWolODmCS7yTOdxsGJsXXNmNNm4u8xDEc8FWhoZhGIYxX0wHXJp5CGJWjzDqihDQ6DRdtVGSUYy5Gc6Bsfomlbi2zYvA3rdRqs3iJdZpN0EjVhb1fffeTDj+QLPzeYZhGIZhXBOj/iEk7SvS+0mb4VOjFojE2kcErL5DGA70qWq19OvdSjBr837UBn1ArJTsxFZhFeKbzzwEMVC6StmfsUxAxK2EbROcU/8PA+0pwwV58vYO0N8iLO4xXGCwd0it+I7Zi6d47COmJFNsJv0TbWVoGIZhGOejCRP5Wlni0XS8ch64IVnDImLdIKK2OZXrediT6XV+vTuOeSeujWvPUVwQ/MAYOhvMcdOZhcGV/Sj+TCu8pGHagDTcDQcypjk18pVifq4M8WhPdFLdIckBvq7GeZA0zzHlqjNh84KX8Y3WWGoYhmEY107eFRbRWm/ckMwU6HKlV0RtalVgksS0ugi0J9J0v/4egovSNOeCDvDwmlusGiMuJL845xMbN5tZCOLxOGYXqiCmICdAeR5rp2i2UuRpNqOJNpsXGbHV98nvFXPShN4CY3uHsHwQS7KFYRiGYRjXR26aSy2JfUJvFBnJjwpluqPsYp5aJ2lUUO3QnObBHazDO1QPjCNdVVj3h4Rmw1jcNy1w05mNZYJYZ4w3Mo0OQKnoQqNVYgc0AcVnlBo9ESAHfnsMDd7msjJkJ0Hc3OqkG8hz/JZx+pLH4evhkd/lyrDzzTAMwzDOh3MBi0vVFwBStk6IfRhxKdf+ZiN2CR4N2/A9azGMEFZZHwAuARwkri1Ptct9Sf0BoT2d6AJtOuDSzKJCnE+C7BXONogyPUZtFNkSUSwVXGecuzzlbgvAAcMBIbUEdnXsY64i5wl3qQFO3jebNYdhGIZh3Fwo2yJ0OIcbRahy3fHdabiD2ixUCyQdsNE9EEtF2JOhHkB9j/JxQXRHaoBh3/yTN53ZqLWwJ+J1cZ9L1Td2csBC7RHNGggrOfj9VszwuQEvLAm+Z/gtY3E/Yf2iQ+yk8S5PrRkvLyjJSjNN9S9sIxsNwzAM42J0l1g8wvVudiTDODoVx5pVzCRDOtiNruU6nbY9ZTRrYHu7PibNeLWpjimLbkKa4gJtOuCJmIcgJvULL6WSSzllIh88DAz7QMsiilNDoMCyNTIa6OE38l7taUJYOsSlbIW4QcK38wQ8ry6JsBIRPRl2IhiGYRjGuVAEuodcmuyzkPVbLgUwboBmkK/Rkewmh6oF/FbfjIHuOCG1HmFZEyfGw7+gTfYggHmiCrHpgEszC0HsArC8nxAWemDm1VyQSJXQEHyOTxtFslGs2x/NWg7yLHB9LydQd8RILUk6xShnMGnV2VImDMMwDOMGwMDiISNqM/14BzfPFeCmxrOyl8LW2apvmUqrFswcz5ra3ffMDf3sLYf4WWAWgpi1Y7TZ6MpwkPtTK6K3WTOGAyoCOLWSIex69R1Dcgebjca3aSeq34qfKHnNMNYcYtcDPgJMtWo8zR863UcZhmEYxjMFQQpjmdxA3+ou8BaIVC2PgNgoKMl1Pb9HagEexEqZ34dHTfVZN5QIt8Tgkbf4SjEdcGlmIYhBdfrMeNeCSQdqDAy/FXGcWrFGpFZu+QXNWleVmkqRt0dSqx4jP7JXMBBbWUV6i10zDMMwjGuHqQ7PAOoOcNijkkDVnKBc41mj2HJ2ccYNEOFJVARobsz3PdfEKUc6zZZNqD4DzEMQzwCCmekNwzAMY66YDngyZiGI62qQ4cbbFkTolwBAaNZcQreZaMcjlN8j5xenRqPZiOF62R4Jqzq/PPuH8zjIybATwTAMwzDOh/JAjd3d23iabZIM38vu7rAPcBArZGmkQ60CA9KDFLu6+5znHZSP413rxCSYDrg0sxDEgMSpdcco02cAzQdsa7xKs2VgAwwHgNuI5zhPmosLAmvHac4pDvsyujG1JCdCNs2zxrm5OqzDMAzDMIzrg0n6gbojSY7I4nXxAAhbLWLlnqM1EDsRyH7LpfJaBnZ5GdoRFlStFVlw54l1+XM9Wen2GWAWgjiHb8eW0K5ZxjMr3REQO4lbSRqPkuPVAJSMNhcAdlzeq9+vXaWAeIryqjGsqK5EJ1wV2vlmGIZhGBdAZwZn6Pe5IT4sqWQV553lsJJd4Z0qsQdo0N1itQfngpiLtUpMqO+Tp9ddKaYDnohZCOJ8gPhBJtSNDfV+qzPMgdJ8xx6ICzk5nCZS+F62P2JbBXJcAGA5eRzX7tW8JYORed8wDMMwjGtktJMbltUamZMgmu0oMlUJK5IdYq+Rq1tNnYgqnH1uupMGO8dVY7DX0c6JpxHExhMxC0HMBPR31N7gqfh6JIYNaNfiA2InAtkPjGHlZCzjsh7QlOqyLy70vXMVmFDEcyKxWOSZ59P9oRN+lmEYhmE8Q3ADHH0UWL7psHxbp3Ih5wVTmUKXbRMuavk3AXGlE24X6h1eEJpTLZKNC19j3csAXJ5pMNEF2nTApZmHIPbA+v0Jwz5h/w2gPRmNbo4A6ySa7BFOntBsGSnW9xgOa97g4iHVqXRblu2TgJ3gbb9hEckmiA3DMAzj+tFRzHEB9IeExQO9OwEOXHqMWG0TeaJds+EiemOng7gIaJP0IeXBHUy0Y5WkWPOHranu5jMLQQwC/JrgAqG/jeIhbtYEIgZIEyLUEpG3O5pNNd2DdZTjgspJAlRLROxqs17yIoRzLqFhGIZhGNdMAtygPuGu9gD5bbU6cvYZe22Yj9If1B5rssRCfMUUgWFPtYADEFUUO9pNmkj8yAwE42YyC0FMAVh9izAcyEFZqru9dI5mgUwsPuPYEWJLsvIrHiPGsEegWO0VOW2CHcAtAaEuzfKAj0n/TlsZGoZhGMa5UAS6h1UI97rz6zugPQZ87jFKgM/jmnk3OcIFGdWcdFiHi2c/Y+QXHonsqTAdcHlmIYizj8frdJns/z05IKzelO/rFof4gyRVoh5cFOXAj57gQhK/UT7m9cTJM8wpyevySWcYhmEYxvVC6gcGatawPKBVYp93i2tOsUysqxPpQDmGFegecHkPplG0RH5qrA12k9onjUsxC0HsItAeMeJStjKy1zd1ddvDBbU5OGmeY4fiBwJ0HPMCI9GblbLcnxvrMteyPWIrQ8MwDMM4F4rA6u1UKrjtw/qYH1D0gQt10Ea2SPJILbEDXM/ojhPi0u0U1Jjqc91QhfBk9knTAZdmFoKYImP1TpKGuWbk7yGC7xNSQwhLWRnmyjATQKhjZ1xguIHQBkZ3lMDeVbuEozLAQ95XTfoD73acXiW1YdYwDMMwjLOMrsfJ666x4nvZ5U1EpbluZ+psfoskYljELmN5T6rA+bm5fwio02pdlMb9K8d0wBNhLV+GYRiGYRjGrJlFhTg1hOP3i09C4tXkfhcBil6GaFBukJPRzKmp88kByS+OnXSdppawuJ/QHzq4ASWwu6wA1UvEntCsp8paMTO9YRiGYVwEe2Bz1xWfcH1AKrtuQJlUF5e7aVLjiXOpAUCMYd/BBX1+S2qx2K0QMwEuTOehNB1weWYhiAk6KaYhHbUoR0zyUiLPB2/s6njFYp1Qv3H2CLMDtrddmW2e2vqasKqvTV4OzGFvFE5sGIZhGMb1oNNpYyfDNIr3N9UGuvz1rE0iC2gXJKo1tQRoM70LQGy1gDayWFASjRAX1lT3LDALQYwko5dZI9Nwpmg7DtJ2QxXCMrlO7g9LMcw3a5aKMuTg91tGf0A6FrquAokfPamuHFsZGoZhGMbF8Oh6r7u6FCACmUeP5wZ8HfecJ9GWIlmUJIqwpDLMK7a0I4jH1/80ldoyHXBpZiGIs7E9drWCC6jYHR28MoiD5IAPu4I2b6XEJaF9J8oADy8G/GbDZcQzAM02RskpNAzDMAzjZpCHcOTilgtSyc12BxcApJo6kaNbAfk5eaA91sFdLA34qSG4cQYx5DGngnqyBnvj0sxCEOdqLaUasi0PaGdo+V48QJR0+2O0KoTXFR7XnGFS7xBFoD3lMrUmp1n4AeAJt0nMO2QYhmEYjydbHrMWGF/b89cyhyCqhlBBy46QOimOpTWVfn4ev9QAACAASURBVCMR05JWlYX22E88lSA2HXB5ZiGIQTpNRld9mTSy9469REwkzx9PoIlAOJQToX8oZ1G2VWTLRbNRb3JDuyfUVNiJYBiGYRgXUoQwj6/7o6pxyo1wdcc4Pw4APEgVeDgAFg8etUVQBPxo+AdrP1Gaqp3IdMClmYUgZujWB8n2xfiAyZ4hMcrrCQEulonyHo6wuM/Y3tGRzsOu9xhAsU20JywNdqNVpWEYhmEY1wufaXoD9Geq9xFL2hQci78YtXne98DyLUZc1MJXFs2ADOWI+v5+U9/fmupuPtfucCWiDxPRPyGirxHRV4joz+j9f4GIfpeIvqy3Pzx6zc8R0atE9HUi+tH3/FnZy1PukC/Z35ON9G4nho30Jj8v7rNUgtU7lBm/b1iSNN9Nl7hWA7mf5s0wDMMwrpgpdQCglgauhbJx81xqUMYwM0nDXJ5WB9QEqfaEd4ZvZasExbpznFod6jWVGL4KHTAjLXATKsQBwJ9j5l8jokMA/xcR/UN97K8y818aP5mIvh/ApwH8AIAPAvhHRPS9zBxxAVkInx2riISyJAhLgu/FW5QaPZibOr88NSqUASzfiTj+QCOryqQ+40EOfkBeGxcEv91ttjMMwzAM4xGuXAeU1+YEKBpZGsfFMklTk4l1+eHxZVyLYc2Wi+eYvbxm7EMGcrIETVscMy7NtVeImfmbzPxr+v0RgK8BeOUxL/kUgM8z85aZXwPwKoAfevcP0q/jBlBfV4h5tRgXdWU4PohdwM5qqdkw4ERcl+5UXR26KD+HJU3mIaYruBmGYRjGVTOVDiCGFMKyYPVSAc47wvl6LfMF6vMAfTxVwctORzJHwPU1VWosnos4foZ1wJy0wLUL4jFE9HsB/AEA/1Lv+hki+nUi+kUiekHvewXAN0Yvex3nnDhE9Bki+hIRfSlsTq7wt75B2DaJYRiG8QzzNHWAvt+8tIBZJi7NjRHERHQA4O8C+LPM/BDA3wDwMQCfAPBNAH85P/Wclz/yX8bMP8/Mn2TmTzaLfYlAy16h/Jwz2YCpk1Ve7EjjUiSahVhWgoD6ivPrEspqkxhlOTXuVs3JE4ZhGIZhXMzT1gHAGS2w3C+VYBdlh5eb6vkd31Ij02sxtlbopzj1CSdPZVfYb9WbfOY3Kw17JgVuPDfBQwwiaiEnwd9h5l8GAGb+9ujxvwngf9MfXwfw4dHLPwTgjcd/gHzJUWol/kQP0DyCGQzAAxhvi+SD21GZapMaAlP1EOUTo5wIXD8zLKbbcLD8QcMwDONZ5Mp1AFDFbZCvpH0/uaEeDB3JDBDJ9T3p40UKBIBYI9kcS+FM7ZWppTK+GRj5lEea4KoxHXB5rr1CTEQE4BcAfI2Z/8ro/g+MnvYTAP61fv8FAJ8mogURfRTAxwH8yuM/BOWgpZHlPk+fG+cR5mxh8QNRqRDnKrDMJpehG4B6hnMHqT5vJ65tquxBwzAMw3gGmUQHnCG12QPM5dpNCXADi+B1kkFcbmcSKXyvu8ZcRWjeGXah3qyh7tnhJlSIfxjATwL4DSL6st735wH8cSL6BORQ/W0APwUAzPwVIvolAF+FdKb+9Lt2ljJKjMo4LYIdkLSwSwHgnBKhiRSxqwe631bLhN8yhn2q4pf05Bg37LlHG/OuHFsZGoZhGM8eV68DgFr8cvIqp9fM2I2ek4tkKooJZyJUWYdvcU2ZSA2VlClKQBpNw2WqFotJMB1waa5dEDPzP8P5mwlffMxrPgvgs9/VB+UqMdXkh3E3KI9sFHn7ZHcEoz65BdoTea2MZ2bEluqWC2pO4Xi6zSTYiWAYhmE8Y0ymA6A2hig9QyV8KqptktQXnPuHIle9cEbkAjJvYNgXm0SecJc9yPKiqgMmG9JlOuDSXLsgngJiCcdOnuB7re6ibm/kEwFA2TopMWtnfcEMpI5KY11s5QmxG1knaPT0azelGIZhGIYB5GIVw/e0UwjL1ghi3vH9MmHHXMr6HqlBmVnAeWTBWQumCmSciXAzbiazEMR59eajDN1oT0fNceOGukw+iLlWjnMnaRa4sR1VmvOs8zPNerGd0ODOE36WYRiGYTxr6E4xe4IbWIZpYHQ9z6lR45HO+rpc4c1T7pqexTqRspDG+aI3i+Eprs+mA56IWQhiYJQscSb+hHN8WvEN5QdGcSnQFeFI8BZxTLXSXKDaXZpm8y9sGIZhGDcYlljVXM7NIjdPnR1HrD0iijMkRTQ/oAzqcDjfK2wa4NliNv9VqSUkL1FpyctZUCJREmScMwEYzSKXJ+kX3RbJkSvnrvayeE4M1mg2eneb/9PDVoaGYRiGcSGpFREb2t37x0Wt3Fif+4hShx0tAGgPUc876ROcRrpi9Fw4adyfBNMBl2Y2gjgfsKmhnWpxDtLOIpgJoFzd9dUXzF4O6LzaGwvmR4K3HVXv0ITYVolhGIZhXEy51rvdHeFshch6ILV6/c8Nc273ueykN4k9ISwJpx9kdPcJy7e5aIwSyXbOwI4r+/tMB1yaWbR8kTbJNZtRA93I6tBsakYxe3lO8jJ5pnSMnt1OUSH89o9s8PD3jUTxyC7hB8D31/VXG4ZhGIaRKYWq8cAM9Qzn6XXjZvo8l+Bsj1EWybEjsAP6O8BwyOiO6nTbrBGyrWLS3WLjUsxCEM8Gfsq39wAR/RgRfZ2IXiWinz3n8f9K59D/OhH9cyL6d5/obzQMwzAM43yetg54D1rgedEBs7BMMI2Ct9NoSyFXdEeJEiVuDTU9Yvw+8lVyCuOCsPqNFRb3WAZz6OtSq2Oen/OOTyLyAP4agB+BjNL8VSL6AjN/dfS01wD8x8x8j4h+HMDPA/gPpv9tDcMwjFkzqhCfvT6n8VRZ3r3278SmjeyQ2UK5uMdYvk3wG7VROKrPnSp/+Jp4nnTAbASxG/mGitU35xB7KlsgLqLMIueRL5gYO/X01BDcACzf4UdWUVkMs6NJT4ZrEN8/BOBVZv4tACCizwP4FGR6EACAmf/56Pn/AjJz3jAMwzCmZSR0z14vd4ZpcRW946IZoK8fiWWKAKkfmT2QQDIOGkByEudmOuDZ0AGzsExkL0/JFFZPTw7VLtEpUVeJpF+zl0h/HsewxA5wAwCdW54aEgHsaOd1k3V8Xs02yUtE9KXR7TNnPvUVAN8Y/fy63ncRfxLA/3Hpv9EwDMMwLknuJzoravM1rxTBsnDOPUGp3spLnPQI5RzirCfiQnaRZSdZUqd2qs1XyVXogHfXAs+NDphFhRgM+G0N0S4jlmkkcqmeLHllSNUFUQz47AAXuUy4Sy2AQT8jN9CRfF7sULdOnk3eYuZPPubx8/64c5cARPSfQE6EP/g0fjHDMAzD+G4ZN7g9UgUex6ONK8LjK53uFucLXWpqg35JqyifpWLYT1wmfvo8Tgs8NzpgHoKYxNdbI9bk/6rRym6eVEejYRsu1tcCGsPS5JvMMM/V4CyiKcn7ktolpHI84f7F9FslrwP48OjnDwF44+yTiOgHAfxPAH6cmd+e6HczDMMwjB1EvGa1W+9PnrTghZJGUYTzyF+cNL+4Wcs0WhdRJt5lqtAmEDOYJhTDpgMuzSwEMSWgWTOaTVJrg9zvoh7QQ/UPRY1lYyeNc6mRAznsifB1g3iE46GDixLZxq56hvLnATV67TnmVwF8nIg+CuB3AXwawH85fgIRfQTALwP4SWb+f6b/FQ3DMAwD2kjHj/iIU6MaIMeundN4l4Ww9A9J1nCzAXyfZPod1R3hcTE4F8vYP9MV4sfx3OiAWQhiJjngt7d98QMVqDbbAagTaiIQF1RWkG5AmVceO4d2zWjWCXHhELvaRAcACeNK8TR/I2F6Mz0zByL6GQD/AIAH8IvM/BUi+tP6+OcA/LcAXgTw10lWyeFdbBiGYRiG8fQporVOrM3kQR3j4VuUtCVI7RDI35fEKfm22TJiS6VUnN85N+n5AQgTaAHTAU/GLAQxgGKVcEnsE+XuJN4eSZsQ/9DZWeYAZLQzV+uEDPHIK0U5K5q1vCB2VFegU5nprwlm/iKAL56573Oj7/8UgD819e9lGIZhGI/A2vTma8GqTKQtSrY20WX7hAujNyCI3zjpCOeBxTqRqpAGVFxTbdZ/XnledMA8BPEo7SE1tHPQM1XfUBnRqAc7GPCDvDCCwNpE5x8yhj0nNgmW1WFY7IrgXInuD59r75BhGIZhPDMQ1/6fcVN9Eaw5WWGUIez7UbN9lAJZLp4xxH983o5wFsypE3vlJJgOuDTzEMTqAypGea73y3aGmN59L9Vebuq2iN+qIG6pdKf6npHaaotIDUlkWzMSv3qCtSfTHZ101tlvGIZhGIagl8icKoVz7JMlpnXkI06jkAjWSNaxD5ndqLqsYhoYvQdGjXxXjOmAyzMLQVyCsSkb58cHDJXuUmKqWxyaPBE7Nck3KBmF7GULhEmEcOxEKLugIjnKijG1ZAenYRiGYdwQ8q4wUItj4z6iccRqyR+OQFzJ4zmFYscWAeiOsjbmj3eLHeASFy1h3FxmIYhnQQ3QNgzDMAxjbpgOeCJmI4hdBIaVNL853dvIq0IXxQ9Uukod4Lf5cXluzjCWF2Inas1vuVgn8uMuyNZFbCf+Qw3DMAzDuJhsYxjZJ0uKhHqEd/KIedxUJ69PDeBOuDTR53zjZqMJVfq+lDSadSLLhHF55iGI1erQrhlhSXWARk6cOJsqoSfHWARTBIjq9oiLvNNNShHgrr4PE5Cc+I2n4hpmmBuGYRjGMwFBeoZys1th10UpNssE0Gg4R2nA0/vG19vkCY4ZCaSJVqMPZACJp4tgNR1waeYhiLWpLvldgZo8wQWu45WzMOa8IqxZhYTqG3I5eaIjsGNQlAiX/N7JSwxLamla35CdCIZhGIZxIS4wXAAGV6/NBN0lznMJxqJYK78u6JwBnUcwLobBc23Y593Pyo367CbSAqYDLs1Ea5brhyJj8SDu3pdY7Q11jCNr3Fry2JlJ7gLD6coSJNsnfstYPEj6sxz4yROabSqRbM9z9qBhGIZhPCsw9Hqfr/u52T6xzClQsZxvfpBY1RzTBtaIVm3Ol6FfhGYjr8vNePl9i7B2ZEL1GWAWFeLkq9B1WrkF1NqgESq+593OUUgMW/YCUUJZPqRGJtIQV3+x3CePMxHCkkqu8VTYVolhGIZhXExYyIVcZg/IRZNHY5f9wOIPVs9w9zBi/eKuVMqFMeJsn6SdNKuiBfR+ec1UsWuTfMxzySwqxE6nyvWHvgjfYpbPk2j0QPa9jGk+/EZfKr8uyAlSVohBjPRukK+SY1ytFmEpVowyF90wDMMwjOtFI1VTA4QFlV3dcW5wamohy/eMZh11mMfYTiGzC6rwre8PrpoiNXLLvmXjZjOLCnEJzSZg2HeyMsz3q0E+dqRbI5ADuhH/b1jqqnFbTfHsqKRR5FxjSrRzUuRhHRTP/jZX+YdO+FmGYRiG8YyRPI2sj/WiSZCmuDKES33Cp+/rpF/I6xPVAuF7LjvMsdXxzWrH4PFu8siXPAmmAy7NLAQxoAI15KY3uY8YaE9l7LIf5GuOUlu/1CC29SSIXd0SoSSrvbByoMTwWx3xzPmzRh9M0xnpbavEMAzDMC6mxK0GlKb5XMF1gRE1QjV5iWrNjXJ+W1+fh3blccyleuzETrnTQKfXZTdFhdh0wBMxD0GsWYCxk9VaOYhHtgkQSoW4bIskoDlVEd1oVVj/xSgx3CAe5NRSGQEtD45PBjs6DcMwDOMmkPOFxyQvEWuL+xHYdxLL5qUgJo142JlDkO0PzTohtR7NVpry2NPoiSjV4WzNNG428xDEeoy6wDurJ8qrvDXDbxL6W748llraySFutjW7OHeaZpO8i4x4JmJNGvkIfrjiv22MaW/DMAzDuJCS/DBKksqV3bh0+rP4hJGkUjxusstDOeQ9qBTcciFs3Jxfdo193X2++j9wmo95HplFU51hGIZhGIZhXMQ8KsSjSXTsUHzBpOb3sCSwd1rxJTBJE13sCEg5jFt8QamV9wkLTZJIwLByxX9UP5PAfsLpNDDvkGEYhmE8jtwIDyapAitMhH5fo1T1fooMIm2MqxvIYJI0qrBypfkuN9iVeQZAjV6lOuTrSv82mA54EmYhiAmQYRuNen11olzeMsmiOE+Yc0GiWbIVAgAoVXM9e7m5rZ5YJcZt1zsk+cQT/qHmVzYMwzCM81F7g+QDa3QaUGPXKE+1Vcuk+n9l1LM+Nc/zSFx8wY4BRAY3oiGybsi6wEUGhaksE6YDLsssBDGgB3FOmSgil8s8cxnNKKs4n2pmYBXPVN4ntQSnYx69TrsZ9lxp1oudxrowo9lM+EcahmEYhnE+VL8yEWI7ekgHdMkUut3XyPhm+TEX1pgIvk+lqR6oIjk32MdWdpLH1WXj5jIbQZwP4GyIB+RnYkmdyJ2grmekFqWTtBzY3UgUB2mac0G2TAB5j9xk57c13DsL7imwrRLDMAzDuBh2hNTUIRyAXNv9piZK7ewij6bSAfXncsNugkS2UAA1jpWG6bSA6YDLMwtBzKNVYZ4ck3+GZhES1xxhN6DYIPJB7CKQdARNajWU28kKEZCVIFF+rgzqcCWGxTAMwzCMa4V3v2bh6gIDDmUoRxmoQRLHRqnqg/w6inWHOSdMZDtGmXWQeGd+gXGzmYUgzpNlgLwlkg9e1AiVRHAsWxupkSgV3/Mj3iIw6xhHB98zwtLJFDtP5YCPKphBgBsmOgnU32wYhmEYxjlk/Zpqkz1Qh22UqXJO84odkBzgIj1yfc2lrtiixKtKcQ0lYi0X45IK7SvHdMATMQ9BrPPFXRSP8E6e4DiL0NNOhmDsanZg7hCVlSFw+j0Oq7cSGk2j8FsulWcXxiMbJ7RM2Kx0wzAMw7gQzlYHfjRbOH8PiNDNI5z5TMqEiGlC7Bgn73c4/EaCCwynz62CWIZ7uMCTaQHTAZdnHoJY49Wik1VaOVgd4IbRMI0md5iOXqdLPHYowz3yiZEaAjas9onagOcCI3YOLjD6Q7NMGIZhGMZNgFiqvsQ1Xi05bZR3VTBTrhgTZGID19dnn67vgcU9rs8vr1Hd4GtRLFlj3Y1nFoKYIOb27Pep1V6GG9kcXK4U5+iVZjfBJPuGKQDdERc/su/lvbM9IqxEDA/7brdb9aqxrRLDMAzDuJBifwR2soHFJ0xgnUqXfG2q45EgznnDRMCwRwj7JE31DUluMY+qyawFspaqx+LK/8CJPuc5ZBaT6jjPE4+sJvfsA64WiWySHw/SoIjqySHsHNTbO+oJImmycxEY9h2GfafbL7TjQTYMwzAM4xrhmgJRvo4tBlQzh7lBsVaMC1sUUK7rfsvoHkgPUeyqcC6aYmyhMKF645lFhRjIs8p3u0VlFUdIUCFMVL1CaXewRt4+SR4Iew7NmpE6IEYgNQ7LexFeT5ph36F7mEDMiN10+yQWt2IYhmEYF5ALVFkDaAHMBYYfgNjJcA0+Y5FIvgpndkDqZHhXG+X7PPhrEeXNx35hdppiMVFxzHTA5ZlFhdgwDMMwDMMwLmIeFeJcGc6MolcWRxGxJaSmZge6QVZZ0kBX0yWSB7gF0gbY/1bEyfu9vK4B+kNXOlZdAOKCiiVjqr/RRjYahmEYxvmwA8IKj9ggUitpELHFKI9Yr/+s02j3Rw34rQzgAjGaU0Z/S673m7u0swvtolgs2FNNsbjSPxCmA56AWQhi9kBYEZDkAHWhTqFZ3/VlqlzpKIX8HLt6ELOjWk8naZgDA2Epr+sbAucx0IHVfjGtgdi2SgzDMAzjAkjjVAPLNZ/r/cN+FcnEKoZHcWvl+qr2SUDSJFKrU2xLjnHNLE7EQFPHPU+B6YDLMwtBDGhT3f/P3rvHy3KWdb6/31vVvdbal1wIJIQQEmCCclFAGXQGFRQUvCDiODNwRhBHRY8yXsYbOjrCEdTD4HjmjCMIB44oKuLtiB6EERUdRwUCAhIwELklEAJJSLJvq7ur3mf+eJ63qnplrb131t6r96V+38+nP6u7qruqu1dV1+993uf5PQAyfDQIeCTYi+3QieGhUXdq0DtPmCHDR5G5NOEY+BnSAIbLRCnGyxNDs09VdUIIIcQZxwYBsbQcJS4zvEvNOgYF8uzsWom0KDatYTcxEMtLXe0GXXLlD3z2MxpBnBZAO/Ugb5rHssbcIaLxKRAA3agQBnBuO47qhqkU26ZFhLVLNT/dn+Q4aGQohBBCnBAOI70pAmQDm7UyawyE60TM+KbG0NQ+Y8zoZlfNw1oNrg3Y9MExq9yNYmXXZ+mAXXPGi+pIrpN8O8n3kLyO5Atj+b1I/gnJD8Xfiwev+TGSN5C8nuSTT7gT2+InGJHdxYFlJcsc1aYzHw36VEjvO5ga29aKJbXLoricJGyBySEdnUIIIcROrEQHBJY83zdX6No0sxlYprUujquZRfojlm/o2zq3U/r1nh4tTo2/pjTvKDqgmp3Ob0vsFWdcEAOYAfgKM3skgEcBeArJLwbwfAB/ambXAPjTeAySDwPwDAAPB/AUAL9E8vjeZuEhXDwBc+W3atNPhFz7/fqYeUFdM+g6M+gNTgOQ+5OoCOPiO1juWxUpFC3Qrp3W7+p4H7E7CU/XTQghhFgBe68DhpRc38HjXPeiuD5mqGaRNrkwpMZTIZijwVd2zWCpzzFOCyzbupkvS40X8rXre58+uRc6YExa4IwLYnMOx8NJ3AzA0wC8Jpa/BsA3xP2nAXidmc3M7CMAbgDw2OPvJNIjKvQHLPx+nvrdHH3Lq5kf/NUMqDf7A8KSi+jUYqkALzVYSrIHItK88G2WfOU9x+z034QQQog9ZiU6oNvZcvF8dz1P3mQrTzxvmOYBsrTw4FZ/63OQu01GgGzrdq3ug2L10RVcU/dCB4xIC5xxQQwAJCuS7wbwaQB/YmZvA3CZmd0MAPH30nj6FQBuHLz8pli2dZvPJXktyWubzSMxkou83xC5JUrcTt0ebXHA84dTa35glxFeuUWKRGfSXbrVLGKfbZ8usdKTQAghhDiH2QsdENvttcCxI31qQ7Y+JRLolyWg2Qc06+xqgZj763s1B6qFp0XUm1E4HxX7Hjwj2jW/ldbQJdoszm7OiqI6M2sBPIrkRQB+n+QjjvP07UKud1OdZvYKAK8AgH33udJKfjDQnwCAR4irhVuwGPwksATUx9D7EMJPgnbq0yclFzlPPHm+nfoJ066V4aEn2WNrW8g9ZkxTG0IIIc4f9kIHxHaXtMDwWV3tT3L3qBTR31wRVhmaOvKJF/CYNYA8CCO2U3orZ8PAns1gKEX31tUYLS5YzWyxdMDuOSsixAUzuwPAW+E5QbeQvBwA4u+n42k3Abhy8LL7A/jk8bbrTTYiR2iQSL/VXsUqF7U0dNMm5Xme/tBvL09i6iSqUS25sK4WhmrTxTAAzFd0EgghhBDnOnulAzrYF9YVihjOdS9kc+W9BayOZh7dG4zNtNaJ4Dxllx7J6HXgN0TEmcvbEGclZ1wQk7xPjAhBcgPAkwD8A4A3APiWeNq3APiDuP8GAM8guUbygQCuAfD24+3DSr5v+AOzQTeq8yd49BfwZPpc++Nmn4voIqSreUSCJ1GMN4mpkWlsL8etWLVMPIdoZdhpvgkhhBB7zCp0AABgmDqZrcv1rcJi1ZJrBV/ujhGlP0E7oVurFZepXJ4bUeVtaoZy3QfZ1j67oovq6dYBI9ICZ0PKxOUAXhMVognA683sj0j+DYDXk/w2AB8H8C8BwMyuI/l6AO8H0AD4nphqGT2aKhFCCHEOIh1wmpAO2D1nXBCb2XsBPHqb5bcBeOIOr3kxgBef9E6Sj9JKDnFXDDcD2vW+8xwtus+U4rgK3RRKyS0yDpKXYnN54hWpQ3u2dupd7eRDLIQQQuzMSnQA/BLdTqPALROcW7eimgFpbv28+SCl0lJ/4c/RoMMSsHEo4+gFCWkG5HUvuEc9FAl9xLmrMRJnLWdcEK8CY0xnTIk076c4mL14zlJvydb5Dw/8BYFIno91CdZVngJYbueMSMiPvORmY1W2awCyxLcQQgixE2nRR1GLSGUbxfUNuhQJG3gVp6bPLy4uFGxdFxT/Ybax3bBoBdyFAiDyFEs2bXuGdMApMQpBzOwHZjHdLnk+eeqPJ4e9EjRXvqzLK5r1zhTNRm/DAtAL6sJqJYXtmpVocow6cyTkCyGEEOLMQoQwze4Yldd6kZun9B4Ex0pOMLtmXGyAetHnCgNEux72qlFkn+ZlVrnXBGkexffGs6BiS5yIccg1upiluVhNi2K/RsCAxUGiPuom3EXAthOiMutGemkBTFrDYj9RzQ3VHFhcANRHYuQ4ONh9tAm0E8NKs5o0MBRCCCG2xRDX+BZoE7tZ3mFfAatdAFezwQU1gl9ArDMbuEoYWGaIE7seBkAfFS6uFStBOmDXjEIQG9B1kcmTPpe4pDigobdVpE+ZAEDV2N02wtz3MO+qUc0rVNt1dp6GVgNFB69kmiRQMr0QQgixAyy2aMvX5lz363JFJDMwLuLddXXgWZyaPqUyT9xpykjvZNsOAmuJSAvvZ7AqLSAdsHtGIYgROUGTI542UXKBgJjqWMT0SQUkixMmUijKwZVL7Wv2vGBLvq3ZRUS16aPJko/UTj26vEoxLIQQQoidobm1KsMitfQWKLnCeQqAQFsT1aZ11mpFQwB+fQddRDfrLoJBIK8BmKOzZQMiaBZ2r9u2EhFnFaMQxGyB6Z0RFUY/FWIJsFnk/Ux9+mSx5idMu+ZCedi8w9sxervnzm8wDvRmP7spldS66K4W5ifPqhhRz3EhhBDiHhH5wGDxH/bFRvNUiU3XAu0a0K57F7phgAzom3R1Db8msb3Ybp6gT6/IUXjX9nnFe450wK4ZhSBGjPCY4akPXacZT4pPjadPLA64VdoipjeGOcUAOucIAKiPekS4mnl0uSX7KZTUp2cs5SEJIYQQ4oxhVaQ1ROoDENf5GguEVgAAIABJREFUqJPvXCZixtiSry/Xd0uuISy626aF64gu9DXMR65iHzZI0RRnLeMQxOYHZnciDPyCi+jNlduntJWP8Ko5PEXClreDOEFg/UnVdb4p6RW1R5Grud2tc81eotwhIYQQYgdSzO5OwkptuCrs01LlmqCJjrVpoBkAF8IlslwfMzTrxORIOFVMlqPJpdierXUFfHuNdMDuGYcgZgjexrpoMQAgfAO5AABDU/kUCcqJsOhzjMq0SSpTLDHyq2Z+QgBAtRm2LAd8yqXe9EjyShhZi0UhhBDinlBaLTPSGlFcpFoDFx7MKtHiat5HhZmj8A7A5LALYUv9X0uRMgF6QG0W+6vQFduvJDgmHXBKqOxLCCGEEEKMmlFEiEunuslRH+WVQrdE6/KK2zX2neoaIMXyqiTHt311alr4iHI26Eaz2M9uBFhtRn7yhGC9muEaAW8fLYQQQoi74bm/bqs2dI4w81lhq9hbsGWfES55xiVCnBYeFS7uEWzgDT5iGVO4VSB6Esysc7HY888H6YBTYRSCuOQKw6KLTFMac0ShXSrL+3bMJednaL49dJzgwk8sxvzE5Ij1bZrDaqXe7NMphBBCCHEGyXH9t+W8XmZPqyzXbrD3Ks6T5cYd7Zo3+ao2w2o1edvnYs/GsGb1DZftW1drJM5exiGIc+T6VAj/wIjkzs1zhK2vBC0NPGzCrhgPAFD74/qYv2ZRhYNEadCRezFtKU4AeiR5ZawoaV8IIYQ41+is0mrABv0IjOj6DKS47udSiF90wLCwLrktG2a2pBsKJYfY2ztzycJ1z5EO2DWjEMRAHNTwqG4hF6uVDKSZ+UkS0ymljznn5fXeorldd8PuXPv0Spk+AbwtNBDTLtPoTKMsbSGEEOLsYKtzFPz6njb9fq69UYfVUXTfYskhIlcROa6BySGg2cfOwaq0eC4Wa9UsGn6sbdmvOCsZjyBu4VMbM+ujvugP9jzp7dPa+Jta6w7iPF2OGFczoF2zQdc76423F27y3axzpd1plDskhBBC7EBcjz1ia/31uXSWi8hvDjFcAmZlnb94WSCzNYDs/ImBwfrIVUbFJd2xl0gH7J7RCOLSVCO1QNslx/c9zPOU3RRJNUP4DQ/UbDkx2tLhxry7XeMR4tQOxPLc9+Ed71akiGW3IoQQQuzMoDFXKZYDXAfkiadT5hpAgkeMDd7ieTqwYItaI7Z9l7qlIrtIlwR8Xzl5eqVs185+RjOh35lVR4FdWnhVKcNhovgPVpteRVr6kefpoGI0W1SMxmsmXOpeU8iDYUa9qaNTCCGEOBtIC/chZjtYGJ1l8xQuhmdeNJditjfXfZ2QF+kvN9ooXeuASLeIW9m2Ja6sMYfYPeOIEMeoLTVuhVboutTVpRDOI7+MEWGz0XekSY31I8qwZHMRHKkU1hfQlRMtT1Y5WDOoh7kQQgixPS5cB5HabYK2ZTa55A83oZLKdZ1N/zpPnRw08SrOVWXzORyqKq5IDEgHnAqjEMSWgGaN3eiujORy1VedNlP41MgEmBwB2Bho7CO/Fu0Y6Qc/zYvoLNwnmM1tWwBgat2UyipziIUQQgixM+U6bgmdSC1dZ60CZhfHjPEw37jui+ZLNNlSdKILAY0UhhM26E4XkWVkidRzgVEIYsAP+NSaN8uIkd76oXicM8CEZt1dJhb7fERXzfrWy+XgbycEzLq+5zA/WQzR9hnRJjpGaXmF3oPqYS6EEELsTJ6i6w9QornTu9w5qtqMngSLkhbp2qE+OiiwDw1RivE9SObea6WNczvtn1ttuvZYle2adMDuGYcgjs4xxy5MsAqdG0SufcqjWgDzg14YR/PoroUjRTkJuu52C0OKZPpqBljlCfSWel/DPPETYXLEVutDrKkSIYQQYltSC8wvJI7eL4MtcMGH/JrdbBSrNb/+10cBGDCZW1eI1zXmCk/i0uCjeAyz9UCZVX1aRq7gkeOtRfp7iXTArhlNUZ0QQgghhBDbMYoIcbXp0xWLCwzNfsOBj/o4wJLnC/F2j/ZODkflqAFV604T7ZpvI7Vuoca59zcHgGYfumpVq4DqWKRJ1OxaRa+sO41BVaxCCCHEDqR5xkUfatGuVdh88AzHDvkF/tJ3Nti8qIJVwPpt7kLRWaflPmUSQOc+Vc2BehOeapmWbdyKRmCJKpNL7lN7hnTAKTEKQZyOznHp2+7EZx9+AW550gLzq7yv4n1fvob6aIPbH7oPa3f00x2Ap0bkum+s0UZjjjwl0uHs0x9lumTgXQx4qkVq3OCbSqYXQgghzjhcNLjgHTfhzgddhQf8dsLRe/v1+cD7bsHGJQcxu/cGZhdWaKcEzbrOthapD0AEx9o+hRKRj1y8iC15EA4oLlZuz5paiLOcUQhim9bI734/Ltj3SOz79Rprn/Ejsz3Qor7+Rlx6x72xuGQ/Dl21hskhw/xgcheJ1mCNH+yThXX2bLmOpPoyUtzsG3wAfUJ+Nbcu93g1H1TiWwghhNgWM7S3fAYX/eMV2PjYIRy59GJfvjmDvesm7Lv8Mhz9iqsAhPvUWnldX3vks8g+m9w13ormXtWm1xgV8WsGGKOT3aqkgHTArhmFIOaiQXXZpcjzFtNZgzsfeiEAYN8tc7SfvRP1dIp8+UEAngxfzS1Er6GexUiP7hhRb25twkFw4lMswxFgaswrTVdpu6bzQAghhNielJAuvgj7bjoKzua49E0f8eV1jbSxDgDYuLXBkcvr3qY1Ir/LLZHdgYKNC+Vm4oK3XSNSY15MBxfPqQHaBNhkRZ9ROmDXjEIQI1VA24LXfwzt5z8YF77hvQAA3u8y4OILke86hOltx3DkvhOP9JaTIMObcACo54ZmnWg2fPlk5maDw9yikroztD3hKl0mhBBCCLEjdvQYqlvuQL7kAqD2i3dz8T5M6io6bAHTQ341bzaif0G2LrhlyVMp5xcSa7db183WkucO55peg4QQ1Aj3qpWFiMVuGYcgNj+4OZ0gTxIml90HAHDX512KAx9aA2/+NI5evh/TwxmpMSz2pT5JvuQQT/zEWGz0PsZ56idGs+G9yksCfUmuZ+snx6qgpkqEEEKIHSC4vg47sIH5JRtop/sBANU8o64SbH0NbAz1poHZ+xGUa38JfHmQy/sOsPVaoVLMlsN/uGgAi+ZcbNH1JtjzTygdsGvGIYirCpxMgLrG4mCNfI0L4mozg4sG+erLUW+2aFh5ysTCT4RhakRqDKn1HCHQR4LtFJ0rRTvlQAh7HhEBYK6DUwghhDjTWEXYZZeguXgDqcmoNkt0y7C4/CIgAYuDFYzuIjE91GLz4qpLmwAAEKiPWTeLnBbm4jhHB7uq71tgyZZeJ85uRiGIjUR7fxfB67cc65wf8lqNxX0PwhKxeckExtKVpoUldu2egZgGmVtnn1LPrBsN9h1s4nEC2jp6nq9SD2tkKIQQQmxLXq9w+JoLu2BXFQErq4BmLUVgy1s45wn75lwc1A7R0yaqTU+JqI/2nenqxrwxV2nMUbObOV4Z0gG7ZhSNOdqNhNsffgCffegB3HnNfhx60AG/PWAddzxoHUcvm3pC/JRY7CNmF9WY709Ibek6A68irXwUOD1iYOujwmrTR5Jp0d8sRRHeZFCFutcYPIn5dN5OApJPIXk9yRtIPn+b9Z9L8m9Izkj+0Kl8RCGEEGK35AQcuSxhsT+hXSPmBxPmBxNmByvvXDuP2d22tGCm64BEjy5HCkUe2LPS3Ga162ZnJWfYUM0j9WLoY7yX7IUOOAktcL7ogFFEiNl6a8b6WAjWkhxfAfWmIS38gK3mBksEze/nqs8Fmh3sUyJyDVStr8uTMO4mumhwWkS0OKzbzldIVgD+G4CvBHATgHeQfIOZvX/wtNsBfC+AbzgDb1EIIYRwIrrbrkUe8OD6XIrhiz5IJQ2i9jTIUg9kUXgP+mvasGHNk1i2RUAafb2dp2rrfNIBo4gQl1Hb/CDRTj3lIQ8O6jwhwHJD5A/FqC5uVYhcq8JoO7lxdxcRTnffHw0r8x4krBuVnq7bSfBYADeY2YfNbA7gdQCeNnyCmX3azN4BYHH6P7UQQghxcrgN2iCvd0CuXBswdAHg129GYVxqDSlmhoHSk6C/nxbwaGpc+8t2mD1KzOZuuzzt7IUOOAktcN7ogFEIYrFr7k3y2sHtuVvWXwHgxsHjm2KZEEIIIc4PjqcFzhsdcJ4G8e/O5Kh3jHFPQV9WIrsl5aFa9NMibrDd5wCnRbRxjFzi1BjatdS3bx5EiS1yjZkBrCJvqHD6k+lvNbPHHGf9dvHv8zdHRAghxDlNvYmwSXM3KQDdbDEQKRK5rxkqM75lfTV3p6lcsyu2b8NdwmosO1IgIsW2Qju0vdnP8bTAeaMDRiOIU2M+/TFsmmFAToTRkDLQrEUC/SAfuOQYWQWs3ZUxP5BQZfTCuPW/eTLY9kBwbzc1s2esvrr0JgBXDh7fH8AnV/0mhBBCiBNig2ZZkc4AlDzfuB9F8V2RHCIFoqQ8sBfKadE34MiVp11Y7dmXgKdZlK63W3OL9+4zSgfslvGkTFgkyUc+UBnTpCZ6kk/6nN9SEWqV5w5XC2ByxDA/mFDPoghvcFKA6LwH26mPFFGWn9/f8DsAXEPygSSnAJ4B4A1n+D0JIYQQd4f9XyM9b7j0HBjEOS15K2a/trMroM8Tv843ay5y603ra4Za1xOwiBTXYb9mgEU0+TzlvNEBo4kQWzVIaUj9gZla61IcupFhpDt4hxofbc0uSH1f8xDXVQjjdo3ez3xjsL+Vew/ipK3STtsuzRqSzwPwZnhyyKvN7DqS3xXrX07yvgCuBXABgEzy+wE8zMzuWu27FUIIMXasYt+KeVA816Z+htgqt1NlzAAvNrhldtnQrrF3psp9Bb27Vvn9KhysLC/PTu/dh4N0wCkwHkEcB23JA/aFxTEiUirKQTzPyBN3kciDb6iaG+b7fbRYb3riMWM7MI8iAwNnCfa2becrZvZGAG/csuzlg/ufgk+hCCGEEGcUo0dsy30Aff5wqf9pXRsks04bFC3A7E9v14C5+ayxJU+f7GaFuZx+UV53vnK+6IDRCGJmQzvlUk5v7xFMGIm0MOQJu4K6tDAs9qXuuc063X4tutrkCbscomrhaRf+XO9cU6ZZVvYZ1aFGCCGE2BEOoqhdbGxLykRqXQQXe1WagZndizpv4Wq59qjrSTAUwYMo9CqQDtg94xDEUSQ3OWpLkeB2Er3Hia4rXYn2loO8THPkqjffzjXQbLBPrI+TIYXYLmkVwIqL6oQQQgixPVau1bbUJyC1xSkqcn7Z5xC3NYHUF9XRPDoMoE+VHKRX0IA8iDznyttBi7OfcQjiMMlup95ysQhiZk+K76ZEKgDJ76fWrVTS4CQoxXOMBPo8ARL8BMLAUaIz667CrHtln1MnnRBCCLEtnbUal+p86pk36+BAKOeoIeqeGxaqFs272nVi47YWlhLyBKgbnyn2uqMIuk2LvdsKUyakA3bNOAQxi9+w+w4WwVrN/W87iWT4FkALtFPPNbY0SHmw3kli/bMZbL2YLleeOM+mP+Bzy86pop2u6kOaTgQhhBDiOJQZ3K4FMwZew/2frqA+NX5pLVqgPLfaNGxenFDNl63airNEeW3piruaQnvpgFNhHIIYkfOTbfnAT4Sl8BEsPctzsWfLqBZuowb4VAgIVDPDYoO9hRsQYjkizBjkDZlSJoQQQoizga4XQfgKc6AFOq/gcu3u7hsy+/ojNh48a9b9cbFsLZPFQ7vV4lg1TM8QZy+jEcTnPQaNDIUQQoixIh1wSoxGEPuIjUtFdcwxIiwpP+b2a27UzS7nGPARYdp0F4r5QSId6XONSvvHpSmRGIGe5405hBBCiHOCLjoc0d/OEg0xYxyF9aU/AdDnG5eUyFJXlBaedjm7iKhm1vUpGNYN5brfZ9meOHs543KN5DrJt5N8D8nrSL4wlr+A5CdIvjtuXzN4zY+RvIHk9SSffMJ9WJm2sLulMHQ9yCOvqJ30XWuKc0TXzjmWTQ8Z2jXPD7bKq0zL9AmArp850DtPrIR8mm9CCCHEHrMKHdC9LnswrHSpZfaUyKIDSq1Q6WBX0imLAxWiTTNQiuZcNOc6XjdB1/TDKtcUufYivZVwunXAiLTA2RAhngH4CjM7THIC4K9I/nGs+wUze+nwySQfBm8N+HAA9wPwFpIPMbPjSk9m6+3RwgKlWfe8oDZsUdoJvUAu9/YphdR4tanbsPgoMNdxEhXfwWHrx2jf3K7wG5b/oBBCiHOQleiArRQtkCe822yuB8RKs67+uR5hdtvVPAHqo4Y8Qdfgiy26OiW2JUDmUeRVIB2we854hNicw/FwErfj/UefBuB1ZjYzs48AuAHAY4+7j/J30KEO5qK2WKh5m2b/2068TWM1t/65bX+wpyY8jRdAmvvjUmTHFkBJok9AfWy334wQQghx/rMKHQCUFMfyoF9eosVWRaArDe6zeAxH1Lhi19Sr3vQW0H3hnEeIrQ4XC4RbRVLKxLnAGRfEAECyIvluAJ8G8Cdm9rZY9TyS7yX5apIXx7IrANw4ePlNsWzrNp9L8lqS1zazI2jWB3lAEc1lNu9OFwdvu9afBO00rNPi1qwxOtG4d/FiY9C1Jvnz27XesBvZT6BhKsWeY3Z6b0IIIcQK2AsdENvttEB77AhypESU2h/AhSzQu0nlSXkO+tSHuL7niPjS+pni1PQzxaXZ19LMsaGrXdpzTrcOGJEWOCsEsZm1ZvYoeK/rx5J8BICXAXgwgEcBuBnAz8fTtzuq7vYfM7NXmNljzOwx9fp+P2ArotlgN9IrSfJdv/E48K2OqY7SzrEsL8VzlRt5VwvroszdOyu3NGwNLYQQQoid2AsdENvttEC1sR9WF8EbgawQvl1DrdKxrugCW9YCpT2z0V/TFd0N9m4RSe6np6EC+3OAs+pfZGZ3AHgrgKeY2S1xgmQAr0Q/HXITgCsHL7s/gE8ef8PwkVvtI0A/iEs02E8GRn5xSZbvKkyryCcuJ0GKhh21i+t2ii7pfBhRttK5blXegwYg2+m9CSGEECtkz3TAdvuKa3VpuuHX+dLOLhptTPseBZ0rFXzd4oD3Nygzw6XXQdEYuQ5HK7Mlwbxn7IUOGJEWOOOCmOR9SF4U9zcAPAnAP5C8fPC0pwN4X9x/A4BnkFwj+UAA1wB4+/F30ucPpxaDylIvkkth0G1VrI+0/HbK7rnVvDftTgs34+4sVRAnAtl1uAPQCe3VoGkSIYQQ5x4r0QFlX6Uhhw3qf5ot1+oovjeiE8bFcYE5bNkI1Ee94K4IZW8Ahi6anKe+vNQW7T17oANGpAXOBpeJywG8hmT4MuD1ZvZHJH+N5KPgY56PAvhOADCz60i+HsD7ATQAvudkKktp/exFSWWwxPAidr/hctAWuzV/jv9tp+5EgbBgqWeGRR1TIml5++2aV5SmBVYXIRZCCCHOTVamA2iAFR/i1C8vXeyKo1SeePc6IJ5X1FIC2LATxosD7lbl9q6GNOst1vKk32fnUiHOWs64IDaz9wJ49DbLn3Wc17wYwIvv6b6K7VqZxjACdTM4EebuKcw2iuOsn0oBgLTwls3VwtBM+2YcaQGgAVj5Ae8OE27h1hXZrYIRjeSEEEKcH6xSBxS6SC6AHAX3bIBkgFXewMtFcQTRIsKbK4Bx3c/mucLMBjZAvXD90OwLIU3PUU6LFaVMANIBp8AZF8QrYXB80PzgB9DZrDG7yG0nHvHNtSfUVzPD5Kg/N1eRiF/5FEueoLNgKxFn5j4c3Oz3Zh0rOwmEEEIIcUKY0VmrAf0Mb6kVSgu/1nvAy5Dm6MRz13vA/BpvKYR0U3KICUY3O1auBRBdcsXZzTgE8VjQyFAIIYQYL9IBu2Y0grjYqJWGGd1yK+4RfVFdm4DqmKGemRfeoTTmANp1zw8qlm2l6M4G+UhlJNnsJ6pjKzo4S3WpEEIIIe5OeAyneeT2RiTXonUzogkHDGBjYCLqzWjSVbDeozg1ADat0xW5ZtekA/DZY7ZEsw+oj62goEg64JQYhyCmew6mxg/aatGvKp1rcu2FcJaIduJFc/WmeZcZADBgsY+oNj03eH6QnZ3bYj9RzQa7y0B9zJ+Tp6qqE0IIIc40Br/mt+t9zjAAd48AvGAuXKVaY9eOud60zqmqXWPX1a6aG9o1IoeQdvcqdEExNq4F2kx1qjsHGIUgLpHbHJ7BLJFcG+T4DuzTUrRp5LCxBn1UWHKN09y31xXVWW/snRq/PzlsKxTEpXRWCCGEENvBHN3kEsDUX+M5cJYqxXTMfr2vKusac5VGHMVmzUgQ1nWka9e5lJuM3GuCvUc64FQYhSD2ojk/0JsYGXbrrD8RQC+yq4/58zqrNQDNOvtRnxmQ2blQpIWPONvOaiUS61tbFtVCCCGEOCN0fQNac2u00oQjLNMAeCe74jy1AJqNaD0bFLeqInJLOoVVgE3KdvqGHLlm179AnN2MQhADfQc6b+E8WFFaNBs6KzVEvvDmvYj6qC+bHM0uimMb8wNAvek5xrn2g30oftupi+KVVpYqmV4IIYTYlmKxikhr7PoEZHStmi3+ctGnTzQb7IRvRvEW9h4GqUHvaVx8jiPynGt01+WVCWLpgF0zDkE8SI1gA7fxLsuj0UaXTB+khS/vfYRT142mnRCTo+7Tksz8hEAfeW7Ww7S76dftOUqmF0IIIY4LGwDVskC11Bfed800ojjOIuDbRsCsmpn3KpjQi/DXvFtdWthge4M+BS1W17FWOuCUGIUgttQ7QQCDilF6rnCJGrfT3p+wFMyVJh6psThhiNQamjV2voMlHWPoXpG2toIUQgghxJklcn0tefE8UCLD7AJknmM8aD8brZiHr6fFzLP57HAR0V3XWwD1Zh+VpoTqWc8oBHGhdKYp0WKDN+Qw+igwV+yFsWE5ryi5kO6aeWxpxVjENtA/D8BqK0s1VSKEEEIcl65rbRTKVQtDG6kSYDTYIDshnBbWtWMuKRKpiY612QNnbtNatr+sDSwRzdqKCuylA3bNKAQx0Ud9S3QXACbHrDsBmvXIBQ4PwjKFUm/6NjwC7C0ay0ixnQ5OAvYpE4Rvo9ngUtRYCCGEEGcOKzVy1gesbJDamKteCJegmCWg2uzzgn0mmZ420RjQDuzWIpgGeGCtXSMW+/uifHH2MgpBjIGThLFPjrcUVmpzWxK0VqY8EtBOBtupAM+eBxYHickh6wRvEdYdySPNK0UjQyGEEGJnooVAatFds3NNL5Jr+ieUKLGx2K359bU04fDeBrGsBaxil55ZFHE7oeuAVYph6YBdM5r4Za774rnSlSZXfXpDl++bB8n1w3SH6GTXrgHNPvcYrhYWOUMMCxd0t8UG+8pTIYQQQpxxhtdkY9+cy2eGPeLb5ftuzR1m71PcrrNrxpFaC1/iZd1gg9cqh/jsZxwR4lFgGhkKIYQQo0U64FQYlSBO7fIozQ2zrbNfS00/WvSWjX2OEVvvcNdO+w52JU0iNQazfjRYGoG0aYUzJQYgq0ONEEIIsROpQecixYF2bNfYNdwo1/ZcezqlkZ2VWpcf3HoTjzKjzGyo5p46UdyppkcM7RRo8op8iKUDTonRCOJqHh1jEpeEazlwu4I4AxDeguWkAfzAB7zaNDXhRNGyF9jWew+m1jA9nLHIg5NICCGEEGeMkv9rKazQtvQQ8LbOvWgubhSkLTlJFXGbp74No+cgdykVoRca8xqltFD65LnAOASxRf/xCsgJqGa+ODWGTPbVpqWBR+QRD32ES7J8WvQnjKXl3KMcBXh5wm6fWOXshaZKhBBCiJ0pMaxB5+bUGszYOUgAfv2vZm63ZuyDYsYQyqEJ8rRYsbETw204WVllaKfsdMJqPp90wG4ZhyAuUd5yAJdPTY/w5or9FEpjSK13nskTAGUkWLMz2K5mwOwiYnpX71VY0iwAT6vIE3TTL0IIIYQ4O6ANiukAt2JN8L7Mw2Yc6EXv0sxyXN/ZAHndm3gwrNe6xl4Amn0usqtZL7TF2cs4BHH4D1ta9gJ05wmi3nR7tdT449QYcu0nSRkVVptxUtR9k45cu3De6iZhFdBGdHil0yQaGQohhBDbUqK7aCLgVaLF1ZYZ4dKgI5rV5dpzgwGPGlt0sa2yIU8BHHUxnFqgrftoMhA5xgPxvedIB+yacQji5NZok2PuG1waczCHoLW++wyArv94avzgBuKEiVFlae/MQY5xNTc067Hdgb/h6qxWTD3MhRBCiB2gAfUxt1WbH0zdbHF3zSaQ5tF8Iy6nqTGkBZHZN+YotOse+KoWHnDzBh4umoHer7hr6rXnSAecCuMQxNm70rnRNmAbUfy2QOT6FpcJ64rg2Jr3IY9RYZ70QjjNgPm9iPVbDamJHKFE1NHJpj42yBtSTZ0QQghxVlBSJ9kOcoibPhDmAteDWcy9a1SORlt5yi6azMZQtR4tLn0O0qJv2GGlRinZkqOFODsZhyBGn9djFf2gB6KIzoUvwqA7LcyFbBMjwTiIc0Uk+AliNTC9w7qWz8ByfpBHnqNpx2RV/csBMyUpCSGEEDthFZDJTuwCvc1qEbKIlAhPpYxGHSWJuMwMZ88NzpNIqUi9i0Uu6RVz69Iq21VoAemAU2IcgpilRXPk+TRl6oPgIkaLESkuAtbzg61v7TjtRXWaRQpF7qPLnkzvT54cNaS5R5vrYzo4hRBCiDONse9GZxP0M7gRHCtpkV0R/eC1JZBmCV16BcOZiu1W/2J/brUApodbd6pI8l072xmFIC7tGZl9imQY1WWGH8BRHVqabpTobvEpTov+IC9Vp8yGahZnjfXrcw2s35UBAxYHVngSKHdICCGE2BFmQzshmn39suldhnrTexW0a71NWlq47Vqu4A4U8Hzhdo1gY2jWQygzUjBi+0NrN0+nWKEFq3TArhmFIAb6aG4Rw0CZJvFRYbNGNGvoBMCKAAAgAElEQVSeTlHNPG2infJuVimdGG59CqSaR26QRYEeABBY7E+eXlGtMIlY1aVCCCHEjhQNYFXfdZbm1/t2jV2OcTV3YWscRIVR7NasizSned+PoOiBHI4SuSYW+1KXSrESpAN2jWL4QgghhBBi1IwiQkyLxPcJO7cIwN0grCIWa33fcbdgMzThRFFGdV07R4Rf8aARR7FYKSPIZp2wafIuN6v6hs3Uw1wIIYTYiZLfy0iPLA001t0lIi2iEG5mXec5wHOBlxpz1ECeGOpjQLsWzhKthf0awFSi0ECbSgHfCiK30gGnxCgEMRBTGqULTRwv7YSeA9R4rlCZPrGK0cvc+kT66DRT7NSquecWVYv+fhv5xhYnUru24tbNQgghhNge85zfXC2nTLTRfnlyzFwLmDfrYmuYNN7Ntp0OnCMWQJP7LrVY9LasxY8YiKYcFfomXuKsZjSCuJrFAW7LHWOKGJ4c7Y20AfP8IQCLfX5kextmPxFaEFb5QW8Emo0EmHUjwNQCXACLjbvnIO8pyh0SQgghdoSN54pats5Vqtq06FUQdT+tReArnKeGbZ7XPXd4cjR7rVAbEeOmNODo91XNLQQxV1dPJB2wa0YhiC1cJKpoxNFsxPLwG2b2aC5brzItYrhZT900iUeG6eL5WMaRyypvwLHmzx+eCMV+pZ7ZSiPEpqkSIYQQYkeqhcFanx3ufNVSFNtFYRymRK76BhwWTlJA38bZKqLZ8BlhmiG3DPu2gZ8xgHrTYLQuwrzXSAfsnlEIYnad6jxq2+UFhWH2Yp+3dW6nqet13laR95P6bVicNIv9LoKbDUYbSPjJUkoUzfOF6s3VnQRCCCGE2BkXq6WdMpZELpNHj9s1djqg1A4NRa5V7ByrUmPIawAykSvr0iJsoAUAzy9uKS1wtjMKQQwAsDgJqt5gO0/CUHsRnWUMqBpDO0UU16Fv0VgDOYXlWgNPys8WaRNE1VhntYJo0pFarDBlwjRVIoQQQhyHdsKuTbMNCt3SAp1lWmm2lScEG5/tLT0JAPQpFE1pzBH2qyWSXHyIK7d0BbmaTnXSAafEOARxKgc6w2w7lscBnCJVonSxaddcCE+OLZ8ExZ/QoqNdfdTck9AGYnhAO6EixEIIIcTZQjTq2tpsC/SgV3lsVRTFJWCxn10xfgl0pWjQkRpzMZ1xt+52pcCu3MTZzTgEMbygrlq4gO061ZVRXOqnSBBtGLemO7BEezNgE3hrZkZeMtB1qunuW8k1WtEHNKhDjRBCCLETcYl0e7Q+R5jWz+aWCPHQKWIYSMv1sgVbihnjkj/sryvdcK2zd23WVhAckw44JUYhiL0K1HOCq4VXkwKDds4GYOoFc4t97KY+UmMw9n7ExX8wLTx1IkV1aTWLvKIQv6n1ClRY71KxEkzJ9EIIIcROWOr1QBHBxSfYu8/11/tqDk+3XPObb8BFcLPhmqHb7qC9c9ECk1l0vq0NuV6RFpAO2DXjEMTZRXCzTm+aEcelj/gMOfUHNi0O4OR+w91IcJAX1K5HvlEbbR2rLU08QFgiqlleqjYVQgghxJmj3jQ0a+wFLsJ+tYkiuSiaA8KhKmqPihZILZA2garyVIl2OhDWZZY4BLFViBQNpU6eC4xCEI8Bw3KBgBBCCCHGg3TAqTEKQZwrop0A08MZswtSNzL0Zhs+Kiw5v547xHhdvw0LS5Z609Aau9Gir/RUDAyqSI1RoapjUwghhDjzRK5vat03eHFB35ij5Ap3DhIE0DlK9VFgi9bPadGnVpSiPOawYS2R4pJTTJ95Fmc3oxDELO0ai2NEHJeLA8T67S6GLaY1cgVwkCZRb5YmHW6jZik8iNc9paI/EaIRB3wKBfDtDXOM9hQz5Q4JIYQQO1GK3cmuSB6Ia3WbkSfsaous8rxhSwNnKgBpjuhKG88thXnZg2CpsS6o5ivckWIlTbqkA06JUQji4oNiMUrrrdT8CN3qL5gn/aivc6Ro4ySIEWR9rG/7mKu+yw3ggriaGaqZgfIEFEIIIc48RGe1ChBrd0YQa839glPrxfR5Ep3qskeOrXIhDCwH10r0t3Sn9ZnmvsAe9I639aZE6rnAKARxTn6w15seDZ4cLZFcol0fpEKssasgLQd4IbXWuVVY5b6Ek8MW6/oKVV/g7hK0vLyRPUa5Q0IIIcT2GEP4NnFNb2J56T8wQxf0qhbWieA86W1a08IFc7Ph2qHZ8J4E1RzdrHHpcGss3XGT27Ot4jNKB+yaUQhiwA/UxT5GG0Y/YKqZ50p0o7w40BGjxHY6yBVuejHcbMT6NWB6OLrVJcY0ClDN/EXN2opziDVVIoQQQuyIVUBbA7lh10egaAHQA2Uln5hhucZBgMxt2yJdMvW5wTQX0Llil2o5jCSvxIcYkA44BWgjmNIn+RkARwDcuqJd3nvLvq4ys/vs5Q5Jvin2ezq51cyecpq3KYQQQqwckocAXL/CXa5UC+yRDgBGogVGIYgBgOS1ZvaY821fQgghhDgxq742SwucW6i7thBCCCGEGDUSxEIIIYQQYtSMSRC/4jzdlxBCCCFOzKqvzdIC5xCjySEWQgghhBBiO8YUIRZCCCGEEOJuSBALIYQQQohRc94LYpJPIXk9yRtIPn8Ptn8lyT8n+QGS15H8vlj+ApKfIPnuuH3N6d63EEIIIU7MXmoB6YDzg/M6h5hkBeCDAL4SwE0A3gHgmWb2/tO4j8sBXG5m7yJ5EMA7AXwDgH8F4LCZvfR07UsIIYQQ94y91gLSAecH53uE+LEAbjCzD5vZHMDrADztdO7AzG42s3fF/UMAPgDgitO5DyGEEELsmj3VAtIB5wfnuyC+AsCNg8c3YQ8PUpJXA3g0gLfFoueRfC/JV5O8eK/2K4QQQogdWZkWkA44dznfBTG3WbYnOSIkDwD4XQDfb2Z3AXgZgAcDeBSAmwH8/F7sVwghhBDHZSVaQDrg3OZ8F8Q3Abhy8Pj+AD55undCcgI/CX7dzH4PAMzsFjNrzSwDeCV8ykYIIYQQq2XPtYB0wLnP+S6I3wHgGpIPJDkF8AwAbzidOyBJAK8C8AEz+8+D5ZcPnvZ0AO87nfsVQgghxEmxp1pAOuD8oD7Tb2AvMbOG5PMAvBlABeDVZnbdad7N4wA8C8Dfk3x3LPtxAM8k+Sj4tMxHAXznad6vEEIIIU7ACrSAdMB5wHltuyaEEEIIIcSJON9TJoQQQgghhDguEsRCCCGEEGLUSBALIYQQQohRI0EshBBCCCFGjQSxEEIIIYQYNRLEQgghhBBi1EgQCyGEEEKIUSNBLIQQQgghRo0EsRBCCCGEGDUSxEIIIYQQYtRIEAshhBBCiFEjQSyEEEIIIUaNBLEQQgghhBg1EsRCCCGEEGLUSBALIYQQQohRI0EshBBCCCFGjQSxEEIIIYQYNRLEQgghhBBi1EgQCyGEEEKIUSNBLIQQQgghRo0EsRBCCCGEGDUSxEIIIYQQYtRIEAshhBBCiFEjQSyEEEIIIUaNBLEQQgghhBg1EsRCCCGEEGLUSBALIYQQQohRI0EshBBCCCFGjQSxEEIIIYQYNRLEQgghhBBi1EgQCyGEEEKIUSNBLIQQQgghRo0EsRBCCCGEGDUSxEIIIYQQYtRIEAshhBBCiFEjQSyEEEIIIUaNBLEQQgghhBg1EsRCCCGEEGLUSBALIYQQQohRI0EshBBCCCFGjQSxEEIIIYQYNRLEQgghhBBi1EgQCyGEEEKIUSNBLIQQQgghRo0EsRBCCCGEGDUSxEIIIYQQYtRIEAshhBBCiFEjQSyEEEIIIUaNBLEQQgghhBg1EsRCCCGEEGLUSBALIYQQQohRI0EshBBCCCFGjQSxEEIIIYQYNRLEQgghhBBi1EgQCyGEEEKIUXPOCmKSLyD52jOw34+SfFLc/3GS/89g3dNJ3kjyMMlHk/wckn9H8hDJ7131ez0dkLyapJGsz/R7EULsHSTfSvLbd/naB8TvXnW635cQQqyCEwpikl9C8q9J3knydpL/k+Q/jXVfS/KvSN5B8lMkX0ny4N6/7RO+5yeQvGmHdWskX07ylvg8f0jyit3sx8x+xsyGF5CXAniemR0ws78D8CMA3mpmB83s/97NPoQQ4mxjGBgAADP7ePzutXuwr6tJ/jnJoyT/YbjfbZ573N93kv+c5NsjSPFekl8yWPcEkjmEfbl9y2D9r5Ccb1lfDdY/leT7Yvlfk3zYYN1zSLZbXvuEwfrnkbyW5Izkr2zzub6d5A3xujeRvN9g3Q/Hfg+R/AjJH97y2j8n+RmSd5F8D8mnDdZ9Ocm/j2v4bSR/f8v3tUby1fHaT5H891u2bSSPDD7TMED0DJLXh3b4NMnXkLxgsP61JG+ObX9w62CM5D6Sv0Ty1tjGX979Py7E6eO4gjgO3j8C8F8B3AvAFQBeCGAWT7kQwIsA3A/AQwHcH8B/2qs3e5r4PgD/DMDnw9/3HfDPdzq4CsB1x3l80igiK4TYDdv9dpzjvye/CeDvAFwC4D8A+B2S99nhuTv+vpO8F4A3wK9RFwF4CYA/JHnx4PWfDGFfbq/Zsv2XbFnfxravAfDrAL4rtv2HAN6w5Xv/my2vfetwv/Br6au3fiCSjwfwMwCeBr8OfyS+k+4pAJ4N4GIATwHwPJLP2PKdXG5mFwB4LoDXkrw81r0fwJPN7KL4vj4E4GWD174AwDXwa9mXA/gRkk/Z8hYfOfhMQ1H7PwE8zswuBPAgAHV8xsLPArg63tfXA3gRyS8crH9FfN6Hxt8f2PrdCHE6OVGE+CEAYGa/aWatmR0zs/9uZu+N5b9hZm8ys6Nm9lkArwTwuJ02RvL5JP8xRrLvJ/n0wbrnRLT5pSQ/GyPdrx6sfyDJv4jX/gmAe+/yMz8QwJvN7BYz2wTwOgAPP857fhbJj8Xo+T9sWfeCGOWukTwMoALwnviMfwb/AfnFGDk/JJ73UpIfp0cwXk5yI7b1BJI3kfxRkp8C8P/G8q8j+e4Ywf81yc8f7P+jJH+IHum4k+RvkVwfrH9avPaueE9PieUXknxVjM4/QfJFjEgHySre460kPwzga3f5PQsh7iEkryT5exHRu43kL8byRPIn4rfo0yR/leSFsa6kNX0byY8D+LPtlsVzvzh+R+6gRwufsMP7eDDJP4v3cCvJXyd5Uaz7NQAPgIvJwyR/hFtSq0jej+Qb6FHaG0h+x2DbLyD5+vgMh0heR/IxO7yPhwD4AgA/Fdef3wXw9wD+xQ5f4fF+3/85gFvM7LfjevZaAJ8B8I0n9c85Pk8G8D/M7K/MrAHwf8IDSI8/mReb2e+Z2f8H4LZtVj8VwG+b2XVmNgfw0wC+jOSD47UvMbN3mVljZtcD+AMMrsNm9t54TwBgACYArox1t5jZJwf7agH8k8HjZwP4aTP7rJl9AH6Nf85JfqYbzezWnbYdn6cE1yxuDwYAkp8DF8nPNbPPxP/rnSezXyF2y4kE8QcBtPSpjq/eMpLeji/D8SOi/wjgS+GR5RdieaQKAF8E4Hq42H0JgFeRZKz7DQDvjHU/DeBbsDteBeBx8YO9D8C/AfDH2z2RPuX1MgDPgo+eL4FHwZcws5mZHYiHjzSzB5vZVwD4H+hTKD4I/5F8CIBHwX8YrgDwHwebui98JHwVgOeS/AJ4xOA7Y9+/DI86rA1e86/gUYEHwqMiz4n3/lgAvwrgh+ERiy8D8NF4zWsANPEeHg3gqwCUkf13APi6WP4YAN+03XcjhDi9xKD0jwB8DMDV8N+H18Xq58Tty+HRtgMAfnHLJh4Pj6Y9ebtl9Knw/x8epbsXgB8C8LvcPtpKeASvzP5dCY8WwsyeBeDjAJ4av20v2eb1vwngpnj9NwH4GZJPHKz/+vhsF8Gjtls/S+HhAD5sZocGy96DnYMYx/t9Z9y2fs5HDB5fGsGKj5D8BZL7tzz/u0Pkv5Pkv9iyHW7zeLjtR8fg4oMkf5InH7XfbtvYsm1f4dfLL8WW6zDJPyK5CeBtAN4K4NrBugeQvAPAMfgx8ZJYfjH8//eewaa2++7/kp5O8Xskr96y3y8heSeAQ/BBzP+1Zf0vkTwK4B8A3AzgjbHqi+DnwQvjO/v7Ld+3EKcfMzvuDf5j+CvwH7cG/uN12TbP+0oAnwXwkBNtc/CadwN4Wtx/DoAbBuv2wUeM94VHIxoA+wfrfwPAa3fY7hMA3LTDugvgP9YW2/w7APfa4bn/EcDrBo/3A5gDeFI8fsHwPcQ2/8ng8VsBfHvcJ4AjAB48WP/PAHxk8J7nANYH618GH50P39P1AB4f9z8K4JsH614C4OVx/5cB/MI2n+kyeMrLxmDZMwH8edz/MwDfNVj3VfG56pP9v+qmm273/Ba/B5/Z7lwD8KcAvnvw+HMALODT0FfHOfqgwfrtlv0ogF/bst03A/iWuN/9Xm2z/28A8HeDxx8tv4Nb9lfDxXML4OBg/c8C+JW4/wIAbxmsexiAYzvs91kA/nbLsheXbW3z/B1/3+FBhTvi924CD6pkAL8c6+8b7yXBAwx/WdbF+i+IbdQAvgYu8h4X6z4X/vv+BABTAD8Z2/6xWP+g2GYC8HnwVIUf2+b9v2jrZwPwRAC3wgMeG/Df9gzgmdu8/oVw0bq2zboJgK8G8AM7fHf3imPki+PxlfE9Dq9JXwngo4PHXxaf9yL4oOZ92P74vSL+73fTB/CZ1S8B8BMAJrHsx2PfL4jtPx7AYQAPPdPnqW7n7+2ERXVm9gEze46Z3R8+Ir0f7j7K+2K4QP0m80jotpB89mD6/47Y3jD14VOD/R6Nuwdin581syOD537sRO99B14GYB3+w7YfwO9hhwhx7PfGwXs6gu2ntE6G+8BF/jsHn/9NsbzwGfNpvsJVAH6wPD9ec2W8r8KnBvePwr8vxPP+cZv3cRX8h/HmwTZ/GcClsX7pM2P337MQ4p5xJYCPWT+9PeR+WD4XPwYXZpcNlt2IuzNcdhWAf7nl9+RLAFy+9UUkLyX5ukipugvAa3HyaWr3A3C7LUd1PwYXRYWtv1vrO0RMD8NF7pAL4GJ0O3b8fTez2+B5uP8ewC3wmbW3wIM9MLNPmdn7zSyb2UfgRdHdDJl5WsJt5qkJb4TnDH9jrPsHuMD+RXik895w0Vu2/WEz+0hs++8B/B84ydk3M/tTAD8F4Hfh3+NH4/MvFY6TfB48xeFrrU9FGG5nYWZ/DJ8t+Ppt1t8Onz38g/hfHI5Vw+9/6bs3s780s7mZ3QHPVX4gPIi2ddufgF/vXrfNutbM/go++/q/x+Jj8AHfi2L7fwHgz+EBGiH2hHtkuxYn/a9gMFVD8tHwqPG/jRN3W0heBc8/eh6AS8yT+N+Hu09hbcfNAC7eMn31gHvy3gc8Ej4Cvz1+NP4rgMeS3O7H/mZErlV8hn3wH9rdcCv8JH+4mV0UtwutT7UAfEQ85EYALx48/yIz22dmv4kTcyMiH2ub5TMA9x5s8wIzK9NgS58Zu/+ehRD3jBsBPGAHYfhJuKAtlFmzWwbLtv5+bF12IzxCPPw92W9mP7fN6342Xvv55kVP34zl3+rt9jV8r/fisuPQAwB84jiv2YnrADxoy7YeiZ1T8477+25mf2Fm/9TM7gWPPn8OgLfvsC3D8a9PS+vN7HfM7BFmdglcwF4F4B273Pbyk83+m5ldY2aXwoVxDb9+AgBI/lsAzwfwRDPb1mFpQI3trw1l3aUALjCvC7oZ/p0WjvfdA8f/XMfb79b17z3O84TYE07kMvG5JH+Q5P3j8ZXw6aa/jcePgI/6/p2Z/eEJ9rUffrJ8Jl77rdgmB2o7zOxj8JynF5Kc0q1ynnqi15Fc33Ij/Afq2fTCsgmA74ZXFt+6zSZ+B8DXRR7UFD6q35V3s5ll+IDgF0heGu/vCpJPPs7LXgngu0h+EZ39dKu7k7G2exWAbyX5RHpBzhUkP9fMbgbw3wH8PMkLYt2D6ZXMAPB6AN9L8v6RQ/b83XxeIcQ95u1wAfJzca6vkyzFUb8J4AfoxcUH4K4Dv7VDNHknXgvgqSSfTC+eXacX896tLgLAQXiE8I7IPf7hLetvgacB3A0zuxHAXwP42djH5wP4NnhE9R4RM47vBvBTsa2nw1MHfneHlxz3953uDz+hOyi9FJ5a9+ZY94TIp2Vc634OXqCGWP9NJA/Eb+ZXwQcJbxis/8L4Xu8Dn3X7wwgigV6Dc1nc/1x4SsVw2zW9ILoCUP43pUBxneQj4n09AO6+8F9CsILkv4EfD19pZh8efhlxDf9qkhvxub8ZnubwF7H+G+l++Sne93+Gp8bcHpv4VQA/QfLieN/fAQ+KgeTDST4qPvMBAD8PH/R8oLyvwfd5FTzV5U9j3aV0W7YD8fonw7XFn8V+/xKep/5j8d08Dp6O8uYd/u9CnDInEneH4MntbyN5BC6E3wfgB2P9D8Kn/F/F3odw29Gjmb0ffsL8DfzH9PPgtiwny/8W7+V2+Oj7V0/w/CvgEdnh7cHwooFNuL3MZ+C5YE/fbgNmdh2A74Gng9wMz5E+0ej7ePwogBsA/C19GvIt8AjFtpjZtfAfoF+Mfd+Ak6/wfTuAbwXwCwDuhP8AlgjTs+F5We+P7f4O+mnTV8J/dN4D4F3wKUchxB5jbuH1VHix68fhvzX/Ola/GsCvwYXCR+C/Yf/uHm7/RnjKwI/Df/tuhAvd7a4DL4TnzN4JL8Tb+jvws3ChdAfJH9rm9c+E5xV/EsDvw10i/uSevN8Bz4AX+H4WLlK/ycxKYOVL6Q4/hRP9vv8IfLbuRvhv3nDdF8CvT0fggv59AIYNlb4PLvjugFu3fYctW6f9l1h3ffz9jsG6JwJ4b1xH3wj/Pn9msP4n4Neo58OF9rFYBngKyG/AByhvj/f4k4PXvgg+c/mOwXX45bGO8DzcT8f38X0A/rWZvSvWXwEPah2Cu3fkLd/JT8FT7z4Gv4b8JzN7U6y7DMBvAbgLwIfh/++vM7NFrH9YfI+H4df66wfficHTI26C/19fCuD7zewPAE/vgB+rXwM/Bl8J4NllgCHEXkCz4818CSGEEEIIcX5zzrZuFkIIIYQQ4nRwzgpikk+ht4W8gaTyXIUQQgghxK44J1Mm6Ab2H4R7It4EL6R4ZuQpCyGEEEIIcdKcqxHix8KbeHzYvJXl6+AJ+EIIIYQQQtwjTrZ15NnGFVg2nL8J7kDRQfK5AJ4LAFybfuHksuh/QQA5bBJp/rg4Jw6D5Ts4KXJB2Fr2bRgAY//68hqLbceG5h+/6VYz26496mnjyV++3267vT2t23zne2dvNrOnnNaNnufc+973tquvvvpMvw0hhBBCbMM73/nObTXZuSqIt5OrS7kfZvYKuF8j1h5wpd33x909hy1hlfVbKfdbuuHMxMDNBJsYQAMbD6LbNAMtsfGJGvnzDmH+6X2ojiSkOTC/pAUm1r+DsvlZgk0NH//OH9nzbm+33d7i7W8+vT00qss/dLKdqURw9dVX49prrz3Tb0MIIYQQ20ByW012rgrim7DcTe3+cL/LnUmuUi1ZJ6e5mQASSIBVBmYCs1iZAVqCTbI/rsxFM4DFvIbVhuaSBXi0AmoDGnb74DzBaoPVBjYn3YzolDAAGXkl+xJCCCGEOJ84VwXxOwBcQ/KBcKP0Z8Abd2xPSW2AR4hLBNfqEMEtQBBWGzDJwDwBtcFS7qO+mwlpntDsM1RVRq4zOMnAhRmYV0ibCRYZ2Tb117HhuZulLYQQQggxEs5JQWxmDcnnwTuqVQBeHV3ltqeKSG0GbGroAqkJ4JzIGy04S8DEkA7VyAcbF8KLBNR9hDivZ1THKuScgCa5xm4JZCKvZdSHKgBAbj3lwupVOngYWlOEWAghhBDinnJOCmIAMLM3wltgnsSTAZtkcJbAZpBDnOHil4CZp1Lk9QweqzyFYkEYI7LcJFiI486pbubhX2ZieluF2WVNPJcAIxq9moyJSJk49yz0hBBCCCHONJrQF0IIIYQQo+acjRDfMwiuZRi94I1tH7a1RQKSIR1LyGvZo8UpcouJLvfYKnecqGYEJy3mqfaocWWwjRaz+1hntWb7PIc4Har7aPQKUFGdEEIIIcQ9ZxyCmAY7WoGLBFtveys1hlWawcVwHU4StcHMwBDLgFuoIRPtmiE3FbiZMLkroV0D8j7CNlqkifsA52M1uJmQN9rOmUIIIYQQQpydjEMQF1/gdotZ8cQFcDpSIW/kvsEGAFQGy9b5FNvEwDnQbkQPjkw0V2+iqlu0R6fAgkifWffn7s9evGerzCE2tOdgG24hhBBCiDPNOARxiFpmeErEoBkHW3ZuEFwkL8Dbn8GjXljXdbUzF8XVnQltiRzfOcUi9dZtzcVeVJf2NbBjNdBwZT7EgIrqhBBCCCF2wziK6pqE+nCFvGbLEdsMYH/j9msltSEBWIQTRck5jrxjNkSeGlhnf14LTG+vfDsJLp4zYXd6xHipTbQQQgghhDgrGUeE2IDmosYjxfPUp1Bkem7xoJDO0qBdc2Xe2Q5Amrm3cGqItkldxLh5wCZss/Y2z0erfp9TeO7ydDWFbgagVYRYCCGEEOIeMwpBzGn21sqZnt5QBPGcLlwBT6WYZI/w0vqocaQ8GNEV4KVpi5xqsCH46TVwYrCN7C1CEMV6GWBOK27OIYQQQggh7imjEMTWuhA2GKzOXQQ4HwgXCMIjvPPkwngjqu8WVV9UN3WxnKeGfHgCJANbot2fkY4l4GgFW4toMK3brnKIhRBCCCHObkYhiGGe/2vrhnSk6jKnLRGYZvcLnnjOsE2z5/+aR5I7kVsZuJncPYIY+BQDVtuy37Ax0jPcjm0lHxGQy4QQQgghxC4YhyCGIe9vwXnq7dV8MXi4Rp5m0CJVonUhnDYj3aFEeDNhaxncrMCDLbCZIrLsgrh7PoqLBZHmRDsZR92iEEIIIcS5yjgEMdEVvHFQ95ZmRLsvA5MMizQKzgmbmkeMDb1l2zHEcOkAACAASURBVCzBJvCmHYdrX558PY9WsIsXsGNVv7+GyDWAtdVEiAGoT50QQgghxC5Q+FIIIYQQQoyacUSIU+QCRx4wF54GkWt4cdymF8RVd1Zo92dfnyKfOPyJbRrpEButW7fBXSgsw+3VjlV9KkakUZAAV9mpTkV1QgghhBD3mHFEiNuwWGsJm5gXwdXhETwJr+EFkdetc5kwWpcjDIYFWwof42RepMeSNhFuEgl9GkVL2CTDVugyIYQQQggh7jnjiBAjIraNN94o0V5kuMCtzXOEoyguRaEd2r71stXWRYu9Ix1AestmtP32AfchtonnJpdo8t5/QKBVgFgIIcSY+NCHgLe8BfjEJ4ArrgCe9CTgmmvO9LsS5yDjiBATwP9i786jJLuLA99/43fvzaW2XqSWkFqgtcXm92QYse9GYCEwgjk2BjN+YDACD8sDjG2Nx/bzGQ/Hg48XeBiQZSQMNnoY24ORAQshxoBAyEhj9kVICFm0tlarl9pyuff3i/dH3KyqbrqlblSVWV0Zn3MumXnzZuYvk8MhKjp+EVndGq1RB6qFZYcBpBnRTJFSkFa0DXWDX6YexrGkkSz7OyihCJZJDv1gAXO0lm3SD4T9xXIQvcYUi+9X83DOOefWrZtvhssug7k5OOUUu73sMjvv3FEai4BYqrqvMFjbtPncjk49gjnJ0mhmFnKLLoMujWfWQiFTssUAZbDyC0BKIXTtPdJUhTaTtWbrWwY5TcSloHujEpHzReQmEblFRC4+xPOPEJEvi0hPRN520HO3icg3ReRrInLj8FbtnHPumHfNNbB1K2zeDCHY7datdt65ozQeJROpbqdWWN1wWFiasWyZ3l5mQS91xrc2qAMevEcq1NqoLVodcWrZRj2iIAv50rXaSlZnXCR0cVg/sRAZbr2yiGTAe4DnADuBG0TkSlX9zorL9gBvAl50mLd5lqruXtuVOuec23DuuMMywyvNzNh5d0z62tfgnnvgZ392+J89HhliBZ2MDOLFwaa6NB0toG0kK3vohKU6YOkF23Q32Cg3iDWjkCYS0rPMsPSD1RdsKhEVO/oB6WRoJx/q6OYReDxwi6reqqp94CPAhSsvUNVdqnoDUI5igc455zao7dthdvbAc7OzcPLJo1mPe1A++Ul46lPhzW+Gqhr+549FQKwTidCMFrhWgg7qieupdPQCoSsWKNflEToRbUpdWR9JbFxzYCkYzmbK5ZHN+4sDPzQAiaXM85p/RyDp6h7A8SJy44rjooM+djvwoxWPd9bnjmbZV4vI/z7EezvnnHOHd955sGcP7NsHKdntnj123h1T3vMeeOEL4eyz4bOfhXwE9QvjUTLRC0x8vQ1Af0ZJDTsdJxPF3ox8UYhtpZwGKij2Z2QdoZrQ5cywgAZgf05jXyA1lMbtbUIJveMUSULo26XllKJ1Vcbg3DCsQcnEblU9936eP9QHHs1fAE9R1TtF5ATgMyLyPVX9wtEt0Tnn3FjasQNe/erlLhMnnwwvfrF3mTiGpARvexv82Z/Bz/0cXHEFTE2NZi1jERBLKzK/o7TwLSjN6R4ARVCy7QlVIfVytJ+RNyL5SRUhKFW3QOvNeK12n1ZRMbvQIp4EscyIp0GKgvYDUiTypuX4G41IVQVSko0+KmMn8NAVj08B7jzSF6vqnfXtLhH5GFaC4QGxc865I7NjhwfAxzARmJ+HN70J/vRPIctGt5axCIjHgbImGeIHcgOwQ0ROB+4AXgr80pG8UEQmgaCqc/X95wL/bc1W6pxzzrl14e67Yf9+ePjD4ZJLrEnIqI1FQJyqQPPunHxBWDi9ojfbBKwFWzYfCCVQQAaUM5EqtWxDXa5LDXnn5wsWFzNCV6hmIqJCyusuE0nQxZy022oxFgslnw/EtpKaG7ejr6pWIvIG4NPYz3e5qn5bRF5XP3+JiDwEuBGYAZKIvBl4FHA88DGx2dY5cIWqXjWK7+Gcc8654fjWt+D5z7eGIF//+voIhmFMAuLQFSQKi9ujdYZYtJy8NpRqS2WBby9AM9mtYJvosohWgxZt1pItTitZJ5CailQBrXefkamNfgY0T1RAKGWo2xaTDr+jhap+CvjUQecuWXH/bqyU4mCzwDlruzrnnHPOrRef+Qz8/M/DxAT81V+tn2AYxiQg1gzKmWQdHxTLZcLS/TCX2+jlRiSVAc0jBKxtWj1pTouEJCEViVSsGNXcTFCKdZ4YENCpSKzk0NvO1uI7MpKSCeecc865B/T+98PrXgePehR84hPwsIeNekUHWkex+doZBMKSBApFxQ6J1jNYBYr9Ad3TtAl1UaAXkFLQoDaso7Lpc+TWUUIqa9M2aMmmhS71N8735UtZaJ+B7JxzzrlxFiN86EPWEe+LX1x/wTCMSYaYKKSm1QPn9+VLfwbEyXqyXDPRL0AmK2SusEBXsDrimpR1KUUUJEJq1KUSamOfSZAmIwDVcSVZuyIuFFZjPASKEMfj7xvnnHPOHQM6Hej1bKr2P/2TlUoUxQO/bhTGIyAOik5GWrc1SIVSTda1vgcFq7qYI3UMbBlglrvqKiA2zjm1FFFZqh/W3AZ9LJVN9CHN51ZO4Rli55xzzo2Ze++FCy+EVsuGbWzaNOoV3b+xCIjb7T6Tmzt0NuekyUg2X5cziILatLrBiGVt2RhnzeoyiUFZbgAdRMcK2orQD1ZjnMSC7noqnZRCKCGJMLQiYkazqc4555xzbqWbboILLoA774S//mvrN7zejUVA3FlsUn5nhkYldCeSBbBgtcKF1QxrM4EK0kg2aKMf0CItxbMKNrZ58Np6jLPmCSnDUl0xQJqI6NYKLYMF3EPgm+qcc845N2qf/7wNDMxz+Jd/gSc+cdQrOjJjERADxJbS31oi8xlxxibKkQQqQUrL5GpDYTZHQt1FohesnhhAIFsMxMlkpREC0q/nOWdWMqGhvrYSUsc21skGH1XnnHPOOQdQlvCrvwonngif/CScccaoV3TkxiMgTqAn9qAK6GSk2GUV3elhXWRni2o6IlEInYAkrMdwX5ZKIMAywynHSiPqzXZSCamVLHM86DoBIIr0beBHag8rIhai+qY655xzzg2XKqRkG+Y+8Qk44QTYsmXUqzo64xEQ50qaLyj2Z8RTuuipliGOnZxmV6i2KpQBFOLW0kopyhVZYKy2WFKou0wI2kikyWjdJ5IN7RiUTKBWV5za+mMb95xzzjnnNop+H17zGpichPe8x8YxH4s8pbhBKJAIq3o455xzzh3O3r3wsz9rPYZPOmnUq3lwxiNDrFb/W00nwj1NdGnashBbtqkuVJAKJewt0Mx6EyO6vDWy3kRHpssZZK0n2HUzRFgqmZBKluqKGWKC2DfVOeecc24Ybr3VOkn88IfwN38DL3/5qFf04IxHQFzX/+pERcwysnnLfmZde1p6tlkudANpSwl92ygnnWx5OEe0qXUksZ7DUWCqRDv1T6gc2LPYUrbLXSmcc8455zaAfh+e/WyYnYXPfAae/vRRr+jBG4+AOFO0FZHFHG1Hso71IW7sF+Z3lNBIhH0F2lDC/sJaE09EtJGWh21kQILQE9K2PswVaD8DUXTKapKlfl8tdKn2eKmueI2p+qY655xzzq29RgPe9z7rInH22aNezeoYj4BYIJvPaJ0+R/+mGUK0ILWagvZxHfq3TaEBQsdGPGs7WllEEguEARWbTpeaCvuKpbZsUgmpWY+AHnSZyJNNuasCqeGj6pxzzjl3bFOFP/oj6x5x0UVw/vmjXtHqGo+UYiVoBvHrm6imE73Tu/RO79I9oaJ75yRZT8j6NpJZGwmqYHXB0cojNFObXhfUxjTnaqUQAqkdrRtFKXXNsRLmrAcxopAPr4g4Iat6OOecc86VJbz2tXDxxXDttSztxdpIxiIglgT5gpAvwtmP2oku5uhizsTOHG0l0ukdpLLyhtDJyPdl1m94skKq5Ql0ogJ5IlusfzbFWqxlakM9okAUm1TXULK5bJiTm51zzjnnVtX+/fD858Nf/iX89m/DBz94bIxiPlpjUTKhuVJNKP1tFf9+31aKvVYH0TkpQq40WyXMTVBNKNpUqmY9srmXHbhRDqwMoqV1oAzxuMo6ToBttANQQUWJ0xHpD+dvDhvdPBZ/3zjnnHNuCLpdeNrT4Lvfhcsug1e9atQrWjtjERAPWqDle3PijCB1WW9zd0Y3VxZ2TdJqYlPnwILhRrJSi6J+j0HXiF5AW5HQy4hb62A4WbnE4FqJVk5BrujQ/l3BN9U555xzbvW0WvDKV8I551hXiY1sPALiaCObtReYmewyW0wCUM5EUAg9G9kc+sE6TDQT2X0FqZ3Qoh7THG1KHaJIFUiFwqCDRLBRzYNrtVDrVyz2nHPOOefcseLjH7fNc09/Orz1raNezXCMR0pRgSjEycieOzYvnW7uyWjclxFO6CIRqBPE+f6M1Eq2wS6rg9uIZYEnom2ua9XPAVRC6ASkDPUo53pwRylDG8zhk+qcc84592C9613w4hfDH/7hqFcyXOs6QywitwFzWDhaqeq5IrIV+FvgNOA24CWquvd+30ixLG4KSFeojisBSLM5eUfQu1qECgi2Aa86rrQ/FRK2aQ4bsCERdC4nTVhmeTC4QyYrYsC6SmCvkb6gUwm6Hlg655xzbn2LEd7yFnj3uy0g/pu/GfWKhutYiNaepao/rarn1o8vBj6rqjuAz9aPHRBVVvVwzjnn3MbX6cCLXmTB8NveBn//9zAxMepVDde6zhAfxoXAM+v7HwQ+B/zW/b4iU6TuGBH6sjRAI00kZC5HSogNDugkIdE6RQz+ZNDMxjZLnTWWsn4fBV3IlzbRARBBG0AvIBNx1b74/VHEu0w455xz7qg1mzA9bdPnXve6Ua9mNNZ7QKzA1SKiwF+o6qXAiap6F4Cq3iUiJxzqhSJyEXARQLZ181J9cGwr2T0Ne/MAoYJyk1IBqZmsBjioBcPCUh9hVaw8om6xNhjWgcpSuzWVOiBNWH1xrrCw3n9i55xzzo2jb3wDZmbgtNPgwx/emP2Fj9R6j9aeoqp31kHvZ0Tke0f6wjp4vhSgedopmm/r0GhEqipQ/fsUAKmhxAbolj56T3Mp2EWwbHAlaN0lInSDTambiIS53CbUad1urWldJqQ3eL2SzWfEdrKOE0OSvO2ac845547AVVfBL/wCPPnJ8OlPj3cwDOu8hlhV76xvdwEfAx4P3CMiJwHUt7se6H0kU8r5Bgu7JinvnCROR+J0JF8UqklFu5llfHNFW7EeuZzQrA50+wFtKKEvkIRUZ4qlEgiQzWZoO6Kt+mgn4kN6aDMtb7RzzjnnnFsH/uIv4AUvgLPOgssvH/Vq1od1GxCLyKSITA/uA88FvgVcCbyivuwVwMcf6L20spHK2XwAhWw+kM1bL2FtWJ2wZlgviyTQr3sOJyuT0IYFxymHbF8OLXusmdat1WwQxyB4zmYztG8t2MJEtTY/0MHfEZtUt5qHc8455zaOlOA3fsPqhM8/H77wBdi+fdSrWh/Wc8nEicDHxHL4OXCFql4lIjcAHxWRVwO3A7/wQG+UNaIFxB2hv72kfavVEPc3KVKBhrq1WhQrcQjUQzVY2mgnUdBmIm2uoJ/Z83Uf4jgdye8tGFQsSL2PTjOF/Y3V+C0ekOKdIZxzzjl3eJ0OXHMNvP718M53Qr6eo8AhW7c/hareCpxziPP3AUc1QDD2c6Z+mFM+bg7ZNYHW3zo1BtEuS7XCBJtEZ5vqlMGuOq1LH7SX2XV5QoqEdjPComWbQ3/QsxgLlsullzvnnHPOjcSuXTA5ace119rtuNcMH2zdBsSrKkI5bQFtc1dGbNv9xn6hd3yETC0RXHeN0DzZ/UoIfUv7Sl1OESJUk3XLil5mL2koYUufaq6wa1v14I4kS4H0MPh0Oeecc86t9J3vwPOfD094AnzkIzA1NeoVrU9jEUEJ0Dsh0tvTJk4o1Uyyo439AkFtEl1/0GNNljO7yY7UUlIzkXK1muJmInQEKntdqqzjBKJoP0DPNtqNxy/snHPOufXmf/0v6yLR6djADXd445EhbiYbytEJlJsjUlq0m7b1YTGDfkBKITWTlTpE24QnKqRWnQ0OQGbxsbQjupATJxMUCVWxfsMr/vlBoqCE5WEfa0wVorddc8455xzwgQ/ARRfBwx8On/wknHrqqFe0vo1HQDwWhOQFy84559zY278fLr4YnvUs+Lu/g02bRr2i9W8sAmItAzQTqR6/PNCc7FPuniQ1lNRIhG4gTVn9L1J3iSjqDHE/EHqBNBFhcTkbLAu51SAXibBoG+5SO9lY58TysA/nnHPOuTXU60FRWAB87bVw+un22D2wsYnW8t0FxZ4cKYV8PpDPB3p3TZDadS9iqUc3NyLSD0sDNWQxQwaBbisRFrIDh21kiuaK9AJpU0XaVEGeINTt27Lh1EwoVjKxmsdGJiLni8hNInKLiFx8iOefKSL7ReRr9fF7o1inc845dyR274bzzoPf/V17fPbZHgwfjbHIECNK6EEqoNgXKGfqjhO7M/pbEqJCKIXUULQq0CJZHXG23HZN6kEd+YJQbrF2awBaJKQK1lGiWweRmdrGPLBMsVtXRCQD3gM8B9gJ3CAiV6rqdw669FpVfcHQF+icc84dhZtvhgsugB/9CN74xlGv5tg0HgGxCv2TSlq3N+gdl9Apmx5X9QpCKVRbKrLduW2gEyzdWm+uk1gHtkFtMEce0DLYprm6BEMztVZtjbq8Ioq9RynQjkP7mj5d7og9Hril7nWNiHwEuBA4OCB2zjnn1rVrr4UXvQhCWO4q4Y7eWERQEutaX4BMl8og8kVBEmSzGSlnuUuEAkmQUtDcSiLIbDSz5gpqAbJUdg15glyRzI6l98gVquH8xIqQdHWPDWw78KMVj3fW5w72JBH5uoj8s4g8+nBvJiIXiciNInLjvffeu9prdc455w5pzx7rMbxtG1x/vQfDD8ZYBMRgZb/lprQ8kQ7obYtUbSVfsMBXKrEjWYZXC6stHmyw02ZCA4R2Ba24dC7M5WT7crSX2SS7+jVhMbNssVtvDvVfysG1Lf8GnKqq5wDvBv7xcG+mqpeq6rmqeu62bdtWcZnOOefc4W3dCn/7t3DddXDmmaNezbFtLALixlSftKm0st6iDmybyYZqqNUWa8M6RWhQtLWi00Se0DxZ6UQU8o5YFribWVlFwzpKxJkVpRH9AKVY8J2G9z0jYVWPDWwn8NAVj08B7lx5garOqup8ff9TQCEixw9vic4559yP6/fhVa+yQBjgec+zwNg9OBs66hkIUtf6HtcHQBoJaSS0HZm6LVBNWdBLEvtFqmC3CaQXkF6w4DazcolGowKx5+hlVktcytJUO8BGPg+xy4Q7KjcAO0TkdBFpAC8Frlx5gYg8RMQmvYvI47H/Nu8b+kqdc8652r59cP75NnTjBz8Y9Wo2lrHYVNdZbLKpDOSTfcpOjtTT58L+BuX0itKIOisspQXGUi5PqpNSoB+IbaVcaECqO0hI/aKw3HNYM12ecJeGUzKhQNrgrdJWi6pWIvIG4NNABlyuqt8WkdfVz18C/DzwayJSAR3gparqf90455wbiR/+0OqFb7kFPvQh+OVfHvWKNpaxCIhJQJEoZ5ugkP2oBUAxL3S31YFrJUv5cs0sG6yZWBaYur2agCZBq0DoBwukS0EbirQrdNF+TinFwqxYB9tu3anLID510LlLVtz/c+DPh70u55xz7mC7dsETn2jlEldfDc985qhXtPGMR0CcKdnenNS0THAxZ1nbclqRZD2GtbCa4jDoJFHJcq3xQAXN+wK94yOpYdPv6AULnmNY3qpVb8KTtNy2be0J0Uc3O+eccxvOCSfAW98KF14Ij3jEqFezMY1HQDwGvGTCOeec2zhU4Z3vhGc8Ax77WPit3xr1ija2sYmg4uYKqeq+wgE0QGyrdZ6o+w4TIE1GyxbXfYelHs4hvQCZvQ6sXjjbV5dI9AOykCFl3Zd40LYts84VzjnnnHNHqqrg137NssKXXz7q1YyH8cgQq1DsLizubUPntBKAxt055eZ0wIa6MJ8hCVIOocLKLFYop5WQKVU7EqNAUDQCrRUt1lRss92KEc7D4CUTzjnn3LFtdhZ+8Rfhqqvg4ovh7W8f9YrGw5gExJByJW6umD5+gfnbZwBsAIeAitqkuU5YGt8slRBbabltmlofYs2UuFBYFlhYfl4URJY+T6qAFhH6HqQ655xz7oHdey+cdx58+9tw6aXwmteMekXjYzwCYiDrCHEz9L65maIue4hN6yssKmgFqI1y1jxBDIgEBp22pA6G8wUhPaQk3te0jXO9zHoUqyzNOpOqDpa7KzbarTFV8Rpi55xz7hi2eTM8/OHwx38Mz3nOqFczXsYjIA5KNWVBazyjS/PbbQD6m3VpAAeZtVZDgFDfL5bLIDQDikTWzZmaXmTPbMMC4Ihll/srgt+6fnjQy3hYogfEzjnn3DHnqqvgMY+BE0+Ej3501KsZT2MRQUkppOkKaUTibEHWg6wHqaHLwSsrMruiUKhtpFOxzHFfoBcop5VOr2HXJ9ukN9hENzgkyXIpxUw5ku/snHPOufXv3e+2gRu/+7ujXsl4G48M8US00c2dnGwh0N9kwWpqJOsTPBjIMcgI9wPkasFusGt1MkEZEIXuQsNeVwkSWN6QV9cLp5bVJJNAO8P5iRVIvqnOOeecOybECL/+6/Cud1l/4T/7s1GvaLyNRYZYY0C6AfKE5ixnfctgZQ1RLBAWDqj5lVa0gDgo9APSC2iAUCQrlcgULeojV+JkIk4mtJEsa1wu1xVvVCJyvojcJCK3iMjFh3j+ESLyZRHpicjbjua1zjnn3Ea0sAD/8T9aMPyWt8A//ANMTo56VeNtPDLEldC+K6OaDJRbVvQFzhXp1aOXERu1HBRRga6gcTmgDf2ABiVUAlkiNhTyhHQzK7toJKsjpk4Y9+19l0on1pwMvYZYRDLgPcBzgJ3ADSJypap+Z8Vle4A3AS/6CV7rnHPObTjdLtx8M/z5n8PrXz/q1TgYl4A4KL3jE+0zZilUKPda2zVtRZhKaBUso1vXEMtMH8mUdF9zaVOcZqANJRVQzdcb6sqwNKaZTNHWYAcehF5GyhIMaXSzTaobesnE44FbVPVWABH5CHAhsBTUquouYJeIPP9oX+ucc85tJDfdBKedBscdB1/9KjSbo16RGxiLkgkATuqyZaJD9wczZB0h6wiykKELOTKfI4vZ0oCOcFcL7miTLQYkgSQs0xuU2Eo0p3vIRLW8IW8wjS5PljVuJOLmCs3T0oa9Y9TxInLjiuOig57fDvxoxeOd9bkj8WBe65xzzh1Trr4aHvc4G7YBHgyvN+ORIR4TcfX/vtmtqufez/OHSkkf6V8AD+a1zjnn3DHjL//SRjE/+tG2kc6tP2MSEAt6T4uFz7eZzoXZHZbR1YZaSYQoOhGhCkgUqploG+hW1gUXtlEuWwyoCswWNpyuEtIEhG6wLhVgYV3dZULKDZ2E3wk8dMXjU4A7h/Ba55xzbt1LCX77t+Ed74Dzz7cew9PTo16VO5SxCIglQuveQGcbdE+I6GS0J4KiZNZ3GKxNWpFAxYLhtLLlBKAwcbfQ/j8XuWdfk7CQkaYi0oroBDTb1nO41ylgPrcOFomhUGQUNcQ3ADtE5HTgDuClwC8N4bXOOefcunf77fC+98HrXmf9hvOxiLqOTWPxX40GWHxYxeQPc0JfiLllbcN0idxXECcTNBJZJxAnk2WHmwkpZXloRxkgQTkNjWQDO1I7WR3xYo6UQm+usM/L1IZ2oLCtN7TvmYZcEq6qlYi8Afg0kAGXq+q3ReR19fOXiMhDgBuBGSCJyJuBR6nq7KFeO9Qv4Jxzzq2BuTmYmrINdF//Opx6KoiPCljXxiIgJlOyuYx8EeJUPYwDCHe2rKtEKUi3IDW0nlZnU+pSY0V6V0AQelsT3T1TSF+QKJYh7gpa6PL7doMN9QD0vo1dNa+qnwI+ddC5S1bcvxsrhzii1zrnnHPHsu99Dy64AN74RusxfNppo16ROxLjERAnyPowf6plfwfT52J7UB6hULE8UEOxoDiKtWYDqPsQN/dnZKf2WOhmlj3uWe2wVII269rkAohiwfWQqEIcfsmEc84552qf+xy8+MXQaMCTnzzq1bijsaF3fC3JLGDNF4X23cEywqUQyuVssE5EwnxmwfIgsG1G601ciW2uU5AS2o3Szs3nSFlPsMvqsc8JpG+fQWA5oHbOOefchvWhD8FznwsnnQTXXw9PeMKoV+SOxlgExNIPHP+NiEShd3wiVEKoBA3YLxAUSit7IChUQuiEA4Zq6GRlJRINENHlYFdsKIf06+EeSdBgo5w1UxsTPSRJZVUP55xzzj2w738ffuVX4GlPg+uug9NPH/WK3NEai5IJzZXZUzOqCSU1Fc0tAyzRMsWpHW3iXG4BrHRsHPPKsgdrr5YoZxK775u2DHM/kJrWuk0baq3WYDmQDmpB8jC+I0Ia8uhm55xzbpyp2ma5s8+Gq66CZzzDyiXcsWcsIijJlNiCVKi1T0vLhwr2H4JtnOsHG8PcsCwwKst1xkAxGwiZvU+ciWiRSBPRssH1pDpCvTkvyvIUO+ecc85tGHv2wHnnWSAM8JzneDB8LBuLgFjLQG+L0j5tri5zwDLCjYROWSnEUoDcjkh/OcMbKgiVZY+lErIubNs6i7Yi2WxGNptbFrhI0MuglyH9uqY4V8s2D0lEVvVwzjnn3I+75RZ40pPgi1+E/ftHvRq3GsaiZMI555xzbjVcdx1ceKGVS3z2s/DUp456RW41jEdAHJTUTCzsbRMakTRoh6YCPdsEx2ADnChZTyhbNnQjtXTpbTRXUgGLvYaVVQhQ1K/thaUxzdq011LJUj/itabgG+Gcc865NfSd78DP/Aw87GHwyU/Cjh2jXpFbLWNRrZUJKwAAIABJREFUMkGweuB8d4Huadrmt1ytzKGV7H5lbdXyexukhtUISzOheVrehFcJsaXM7W8jizkIpKnK6o5h+X0TMBjqEYYTEDvnnHNubT3ykfDf/zt8+cseDG80YxEQD/oCN/cIxZwgCxmykJG1K8K+3Da/BYVGQoPawA4B5vKlTXWhU/cWBkKullUWkF6GNhKigjaj9S4OWJ/iaD2Mh/QtSRpW9XDOOefGXVnCm98MN91kHSXe9jY47rhRr8qttrEomVCBNBlJj5unkSXK22cACP/ets4TzYjM5VYS0apLIIBwXJ/YsZ8oAQSlubsgn+wyt5iTSJYJLoNdUJdMIEAU21g3xCqG5BvhnHPOuVWzbx/8/M9brfAZZ8DDHz7qFbm1MhYBcdYD6QW6d0/aic19AMpmTlgMdbmEZYnzWZvWUW6OxF623CUigVQBBIos1hEy0A+ErnWVCD0LiFM7Qm6Z6WHVEDvnnHNu9fz7v8MFF9jQjQ98AF75ylGvyK2lsQiIY9tarEkVoBKat7YA6G4v4YQeUgW0KaAQW1YDLBMVupjbxDoAsQ11/U1Kb/+kvSYokoTUtkyx1iUVSxniRhreYA6F6JvqnHPOuQfte9+DZz4Tul349KdtI53b2MajUFQsS6uNhM6U9E7v0Tu9RzZVIne10G6GTFR2jYAWinYzQscyvJorcSJZKYUoKFYOARBt4h3l8nAP4opBH9GDVOecc+5YcuqpFgR/+cseDI+LkQfEInK5iOwSkW+tOLdVRD4jIjfXt1tWPPdfROQWEblJRH72iD5EId+bk81mFHc3mPhuk4nvNilumqCYF0I3kN/ZpNibUcwLWijSrVuoNWxqnaiNcS5mA5s2LVrJhIC26y4UK4PgSpBeODBwHgLfVOecc879ZFStNGLfPmi34YorrKuEGw/rIer5K+D8g85dDHxWVXcAn60fIyKPAl4KPLp+zXtF5IhGwVUzETmpS2wqnYckOg9JtHZD1oXWPQEVyHpCyrBAtplIK9quabC+wxIhprCc/a1riaUMSMcOzeuNeWLvNQyKkHR1D+ecc24cVBW84Q3wqlfBe9876tW4URh5QKyqXwD2HHT6QuCD9f0PAi9acf4jqtpT1R8CtwCPf8APEaCZkGDt1Br7A439gf4m6JyUiG2l2hSpJpXUVCQKoZORdYIN3igUgqLNRH9rotsrbO1BIYnVJmf2vDbtMzRXpBeQIdUQO+ecc+7ozc3Z5Ln3vhd+4zfg4otHvSI3Cut1U92JqnoXgKreJSIn1Oe3A9evuG5nfe7HiMhFwEUA+fGboB9obIksbimp+g0AQm6Ba3+Lks30kX1tQl+oWgltR8vu1jXAUgU0T4RSKDuFBdl1QKyNdGAmuL6vE3G5S8UQeNs155xz7sjdcQe84AXwzW/CJZfAa1876hW5UVmvAfHhHCriO2RRgqpeClwK0Dpzu/c+c84559yP6fXgE5+A8w8u3nRjZb0GxPeIyEl1dvgkYFd9fifw0BXXnQLc+UBvpqXV/Ha7BdNfa9LcZ/Hx3MPE2qNNV2yaXqSzMEE1qYROIClW7lBniLWZlkY0t6Z6dBYnWIrPU11DXE+lkwipUFTDcr/iNabgdb/OOefcEfjXf4Vzz4Xt2y07nA3vH3PdOjXyGuLDuBJ4RX3/FcDHV5x/qYg0ReR0YAfwlQd6s6wL7Z05xfcmKKdh9gw7ymmFXAn7clQFzUCDtV0jUwuGs/q+2ia6Yr/QapTLuep6YIcGRTM74lREG3WrtyEO5vAuE84559z9e+974clPhj/9U3vswbCDdZAhFpH/D3gmcLyI7AT+H+B/AB8VkVcDtwO/AKCq3xaRjwLfASrg9aoaH+gzYgtSAb2HlAd2fghYhritLHSa5D2gba3SpB+sjngQ+OaJJBBbgV5pP5v0gm2kKyJUwlKCNglSWvs2L+t1zjnnRi9G+M3ftED4BS+AX/u1Ua/IrScjD4hV9WWHeerZh7n+7cDbj/ZzUkNBhWw+LA3LiO1E1gnEiUT/vhahaefIFA1AkWysMxD2WWeJ/paIdBpIJWgrWvYYIFcIdX1EL0Nb9ZS6MKy+a94qzTnnnDuUxUX4T/8JPvYxa6/2znd6ZtgdaOQB8TBIkdBTOzBXWGA8XQKgixnVpsrG02VK1VayxYCWVj6RRK2lGpAmLFtc7MlhW6JKYkFwJYTFjNROSzXEGhSQ5Ql2zjnnnBuZm26Cq6+Gd70L3vSmUa/GrUdjERBrJVTdHCkD2UKgaliQK1GsPKIUNFm9b5xMhE79fLW8KS6bzyAJoSdUg0xsL0CA1EyQJzTVdbeDsgxlaCUTirddc84551a6917Ytg0e8xi49VY44YQHfo0bT2Ozc0o6GcX+QJxKltnN1b59EkJfIFiALFHQXC3I1XoMcyWkHOJMhSQoiog2E6Fv45kpFPphaQNe6ARCLwy9ftgn1TnnnHPmmmtgxw7467+2xx4Mu/szHgGxAJVQtdWGaPQD9IPdD0pq2HQ6DQqDLXrF8sQ5zdXqhaNQTSjdfS3bUJertWYbtF3r2ZEmEpoppOF2mXDOOeccXH45PO958NCHwjOeMerVuGPBWJRMgPUR1oR1j5is7GQ/WFnEdEW2q0E1E61EQrDuE0FhUHRfd4wIfQitCu3V76MCVb15bpBULRJKQEpZ2sC35t8P70PsnHNuvKUEv/M78Id/CM99Lvzd38HMzKhX5Y4F45Mhzq0YWHOFMtihgoqilRAnEjRt45y06+4RggXICWQxQ7qBxpyw7bg5ywCDBdWllU5oI1nWecXjYQ3mcM4558bdtddaMPya19j0OQ+G3ZEajwxxAulltoGuF5ZbodUlEbKQo0WCXoaUAilfbqdW/8mgYjXH1QR0+oU9380g16XyiANqhhXLMmfDK5nwDLFzzrlxFKO1UXvGM+CLX7TBG+L/l+iOwnhkiMeAsrob6jy4ds45dyz4/vfhnHPgS1+yx095igfD7uiNR0CcKdq03XJZZ/l/JdpM1nKtmZZbpWWgubVRQ7Fscqg3zwUoJ5X5+ZbVDddZYGuxJkub6qQvhK51mpByPH5i55xzbti+8AV40pNg1y4ftOEenPEomVAhzOaIQnVctdxbeH9GalqHCemJdYcQrYNcqWuIVwzbSIAoWln/YSkFRW1Us4q9DiBTRCEVasH2kHgfYuecc+Piwx+GV70KTj8dPvUpOOOMUa/IHcvGI31Z1/dKtKA1dDJCJ7OexApESJsraCTLBB+q7leAAKEvNCb6B3SPCHVWOOvYEbqCCmgrId3x+Imdc865YfnMZ2wU85OeBNdd58Gwe/DGJkOczwv9bbEuZ7BgNk0qkoIN4ejaIA3N1EYxt9Ly5juwYHkxJ1+0YBcFLWzKXZqISBWIdc9hnYjWaSJT2FwO6Tv6pjrnnHPj4dnPhne/Gy66CBqNUa/GbQTjkb6sO0TYfUgNG8ZBniy4leXnQq8OkFsRGslet+JXKmeUXqewB1q3cZO6fGJF2zXpC3QDujgef3M455xza2nvXnjZy+BHP4IQ4A1v8GDYrZ7xiNYEUgEyUaG9DB38D6iT2QS6JDbKWbFgOLNRzNIPaFFniasMzROpEEKmxEZCOhkSWR71PJhKVwFBkImIdoZT5e+DOZxzG9LNN9sM3jvugO3b4bzzbB6vGyu33grPf77dvuxlNoHOudU0HhlihWoqois6QqDWNYIkS6OdESBTwkKG9AJh0C2iG2jem9G8u6C5JxCyCAqhxMosGmrZ5CR2qFigHRSGuanO26455zaSm2+Gyy6DuTk45RS7vewyO+/GxvXXwxOfCPfcY7XDL3zhqFfkNqIxCYitC0TYW0BQsrmMbC6zgFVZniYXBelkaEPRiViXViRSI9E7saJ3ckk5qRRFhGjT7VIrUWzqId0M6QQ7+gL9gM7nVpvsnHPu6F1zDWzdCps327+Rb95sj6+5ZtQrc0Pyuc/Bs54F09Pw5S/D058+6hW5jWosojUpEpop2rRa4jSRSBPWe3jQX1hUyBaC1QE3I1LUz9fBNIWSzWZorvR6RX19ZtnnPS0LrFd+ZrQyDEnDybT6YA7n3IZzxx0/Pnt3ZgbuvHM063FD99jHwstfblnihz981KtxG9lYBMRarfiaUSwjnICyDvrUukvEqbqzRBLkvoYF0WIH/UCcTMSJRJwrULGNeSvbr2k72dHQuk9xWM4+O+ecOzrbt8Ps7IHnZmfh5JNHsx43FGUJ73gHLC7a3z/vfz9s2zbqVbmNbiwC4nGhKqt6OOfcSJ13HuzZA/v2QUp2u2ePnXcb0v798IIXwMUXw8c/PurVuHEyHl0mMhu/nO3LiJMs/xkQsHrfKGiRlrK62UIgNeuNcoNexD3baJd1hGpbSTabEzdVthlvMMhjUDbRSGg/gDK0kgnwSXXOuQ1mxw549auXu0ycfDK8+MXeZeJoHENdOm6/3TpJfO97lhV+2ctGvSI3TsYjIE4g/UCcThCtPAIgm8uIk9ECZFFCJ0MLpdpSWf3wfL4UEOtEpNjSRX8wSWhG0gmJsKdBaiWkF5aGdADoZCR0A6mVrE+xW3dE5HzgXUAGvF9V/8dhrnsccD3wi6r690NconMOLHhbpwHcujfo0rF1q3XpmJ21x69+9br7Tb/6VbjgAiuT+Od/9n8EcMM3HiUTdScJFUUqsSA3KHEqWnY3U6QdbUjHZEW2P4fc+gpLJfaafmCi1be64aDoYk6aiNCKaDMhlaCFWt/iXkCibawbBMlr/hXV264dKRHJgPcAzwMeBbxMRB51mOveAXx6uCt0zrlVcAx16Zietpj9uus8GHajMR4BcbJgVVRIEwnpBjuSIL2ANCLMFWgjIXkiNRNhf4H0BTmhh5zQg0ZidrZNe1cg9TMbwhEFuhnSzSzrXAfakoTU0uVJdm69eTxwi6reqqp94CPAhYe47o3APwC7hrk455xbFcdAl46rrrKEzllnwVe+Ao9+9KhX5MbVeJRMCGTzNlUuTiW0HpYhVUAbiswW6IT1FtZ9DdhckspAMdmn3NOy92hHdLZBY5/SaURi14Ji61ucLAtdd5zQzDLL2kxQDK/NhG+EO2LbgR+teLwTeMLKC0RkO/Bi4GeAxw1vac65DW9Ydb2DLh2bNy+fWyddOmKEt7wF3v1uuOIKqxcW/78wN0LjkSHOE829Qn7mPKET7FsHIGKBbKBuxyYU+wIhT7RmeuTfniKfy8jnMqY2d2CyopwSWq0S8rQcDNcT7pYOqd8zU5uINxTeh/goHOrLHZzKfyfwW6oaH/DNRC4SkRtF5MZ77713VRbonNughjl9b5126Zift72R7343vPWt8JKXjHQ5zgHjkiGurONDd18LJiNIvVEutxIHVWjfXtA9IRKbWHZ4okcp8Mgn/hCAfsy4pdOgc6IyERJSZ4jz2Yxqk8VM0re/L7SRLA4uA9Ibj785jjE7gYeueHwKcPC/IZ4LfEQsZXE8cIGIVKr6jwe/mapeClwKcO6553qNjHPu8FbW9cLy7TXXrH6WeB126bjzTvi5n4OvfQ3e8x74z/95ZEtx7gDjERCLUk0q0oiE/Q20DlIlQpyOhH6gc0pF+/hFOrMtJqZ6zN8+w3E7lc7vnQTAPee2iWdWbNop7N86ZaUQKlTHl5YpLhQ2lfZ5nWwp965eMrEe3QDsEJHTgTuAlwK/tPICVT19cF9E/gr4xKGCYeecOyp33GGZ4ZVmZuz8WljZpWNQqvHBD46sBdtNN8Gtt8KVV1qLtTV3DLWdc6M1JulLIbaV7O4mcTraP5gLtvEtCRKBTOl1Cop7CsKXNzG5M2PhZKH8r3sp/+te4pP3s+m7OaFSmpu6yxPvegENViqhVUCrQDZVWUlFlEP/47wbKVWtgDdg3SO+C3xUVb8tIq8TkdeNdnXOuQ1tVNP3hlmqcQi33263z3oW3HbbEIPhEX5nd2wZi4A4dKGxNxBKCAsZmquVS0RBoqAZtLd0CJky+ai9LPxUj+Kp96HnzHHPdSdzz3Uns7ivzSNe+j3KKaG3uw0CoR+gkRAVpJctfZ7c0bLOE3n68crUNaJ427WjoaqfUtWzVfVMVX17fe4SVb3kENe+0nsQO+dWxajqekfYgu3SS62LxFVX2eNNm9b8I80x1HbOjd54BMSbKuQx+znxcXez6ay9NE9cpHniIu1T5mC6JDuxQ4yBar6gX2VMbV6k02sQQiI9cp70yHlCM/Kte05i/pF9Jk5cINvUR7f0ydoRbUdkpm9RqUI8sYfmilRheL+wWuua1TyOhIicLyI3icgtInLxIZ4XEfl/6+e/ISKPXfHcbSLyTRH5mojcuHo/hnPOrVODut7paftn/Onp4QzKGEELtpTgN38TXvtaeM5z4ClPWbOPOrRjoO2cWz/Go4bYrYkVAy6eg21Uu0FErlTV76y47HnAjvp4AvA+Dmxx9ixV3T2kJTvn3OiNYvre9u3wgx9YMLhvn2VLTz4ZzjhjTT6u04Ff/mX4h3+wjXPvehfkw4441nHbObf+jEVAXHVzOndP8aNiAulkS10mQilQKJUUVuvbjizeN4H0A9K3+t/BmGdtKAtzBdl8xmI32GCOSkhJEIVUhOXuFWVu9cURJA2v9CANv2B5acAFgIgMBlysDIgvBD6kqgpcLyKbReQkVb1r2It1zrmxddZZtpluUEKwd68FyM99rj2/ypvP/vEf4X/+T/iTP7F+wyPpMXzeeVYzDJYZnp218pQXv3gEi3Hr3ViUTBAUJivrDJHp0ojl2E5oQ9F2hJkSyRMkAYU0kUgzFdqsB3nk9tqsCzTsulB3q9B2PLBWWBQC9jnN4XWZWAPHD/rr1sdFBz1/qAEX24/iGgWuFpH/fYj3ds45t1puuQWe9jTYsgX277fbpz3Nzq/i5rN+325f9jL46letz/DIBm6MqjzFHZPGIkNMErQfkDKQLQSqLZWdFyyTWwV7XgXB+ggT7HVLSVe1wR0hCu3NXbIsoSr0+xkpZsTSNtgB0M2WB36E4eyqU9ak7dpuVT33fp4/kgEX93fNU1T1ThE5AfiMiHxPVb/wkyzUOefc/bjjDjjzzAODwZTs/Cr1Rv6Xf4FXvAL+6Z/gnHPsGLlRlKe4Y9JYZIglYcFtVcdmqQ5u5zMb6VwJ5IoGtb7BhYLWgzbqjXKDEC5fhNc/6vMEURZ2TVLONYmDDhNRrHNFv95MJ4osDOtvjpFMqjuSAReHvUZVB7e7gI9hJRjOOedW2/21e1uFzWcf/KBVX0xPD7GLhHOraCwCYg2Qz2ZoMxGnEtIXpC+kZiJutrII60dcB7O9YFnlVkRKyywDkKB7vPKur/8M8z/cxEue8BWkE6AfkGSt16SXWVAtipQBnaxG++XX1tKACxFpYAMurjzomiuB/6vuNvFEYL+q3iUikyIyDSAik8BzgW8Nc/HOOTc27q/d24PojawKv/d78MpXwjOfCV/6Epx22oNY5803w/veB7/zO3brPYPdkIxHyURQ4mRC2hXsL9BGvamuG5DFQCpAW9HOJ0FaEe1kUInVBwOUgdAL5ItC794WHNfnc3/6JLKfgunvwOyZELfUk+rUMsVMl8h8MbSveaSt0lbv87QSkcGAiwy4fDDgon7+EuBTwAXALcAi8Cv1y08EPlaPRs6BK1T1quF+A+ecO0Y82E1vDzTG+XCbzx7gcy+7DP7gD+BVr4JLLoHiwfxf3qCWeetWq2WenbXHXvfrhmA8AuIkEIF9DasPrscppzxBFZBKCJ0MqSybnIIilaAF0LPSgdAPaKFUbWXL6Xs5aXqO27afTugr+/6PhBZpqTxCJyukCmgnt814G5iqfgoLeleeu2TFfQVef4jX3Qqshwoz55xbPw4VgMLqBIqHq6c9XLB8BJ/7ildAuw2/9EursHlulWqZnftJjEdALBD6NpGOiQq5rwGANhUdtEordGnUcr7XfhZRqCbr4LlpU+cm7hIWz2hyy1ePQyeVcksd8GaKTlt5hGQJrd9LesOrSlmDTXXOOeeG5XAZ0nZ79QLFw2V8DxUsv+99h/zcW674Cm+8fgcf+hBs2wYvf/mD+M4r3XGHfe+VZmbsvHNrbCxqiEGJU4nUTrC/sJZqEwkqQVRslHMzEicTaboiNWy0c2yp9UgQ22AnUajaEEKid1JF/8QKbdUt14SlzXfazWxzXW+Ik+qcc84d2w43avj665c3vd1zD3z+8/C5z8GVVx5dje3Rtlc7xGa7L+46mye+40XccAPcfvtP9jUP60HUMjv3YI1HuKZCNpchZV3+0A2Ebt1DWNSGcFQBEoTZHIlWBqwCoSeEngxmblDOKIv721YjXAmUwYZ3rOxIkVm2WQtdGuyx5l9RLUO8modzzrkhOly3B7DA8J57bNdapwOtlh1H0y/4cAH3Ndcc+vqDAtSP3HAmz37nCzhuuuT66+E//Ief4Dven/vb+OfcGhuPgFh0KeuLgFR2aDPZJLmZCoKSdQKplYjTkTQd0SKRCiUV1pIt9IWsI4QiWv/iwHJ2OCjSyWwSXqD+LKtFHpYRtF1zzjm3Wg6XIX3CEyww/Ld/s/IJEVhchMc+9v4D2oMdbXu1FQHqX3/5TF72/mfzxJNv58ufuI+zzjr6r/eAfJCGG6HxqCF2zjnn1rvDjRp+9avt3JveZMHwli3w0z8NJ564PFzjSAwC7kFNMNx/ScKKzXbnb/kKv3HeVv7gndM0H70W0fCKz/QA2I3AeATEKpDVwzaiWFYYkL6g7QSLGdmiJcvzuYw4ldCgyGRFa9LmUHYXG8TFnPZdgdZMh/0xEHYX1te4Lp/Qoi6PSNRT8GRoJRMw/LZrzjnnVtEDtUZ74Qut7vdIA9qDnXce/PEfw65d0O1aycUJJ8Db3nbIy/ftgz/6wA5+//d3sK0Bf/Qgv55z69l4BMSitslNIbWS9VYDwnF94nxOthgIpRBbStxUERYytN5Q19k9YW8xUUGe6B2naMyQfQWpoRb8Yn2MpZ5Yp/WvKtE27DnnnHNH5P4ypIfLIA9apB2JQeZk0CPtMJmU226D5z/fypMvuACe+tQj/wjnjkUjD4hF5HLgBcAuVf2p+tzvA68B7q0v++263y0i8l+AV2Odhd+kqp9+wA9RsXrhXiB0bUIdAJ2MbC4jbqnQ2Zw0Ga19WqF228mW+ghrFexcgIXdE8hWyxzTD9DNCPM5GgY772yIh+YKYZgZYq/7dc65Dev+MshHMrjjmmvgrLPg3HOXz+3bt1yD/OEPw7/+K1/ZdzY/982308/aXH115sGwGwsjD4iBvwL+HPjQQef/TFX/eOUJEXkUNh740cDJwDUicraqxvv9BGVpc9tSWQOACqkAklhLtqDQCzZ6OdS75fqWTZbSzucdIW2vQCHb2SI27f3SdN1+rf48DfUGvu5w9i0q3hnCOefWzIOdFLdaDpVBPtIJb4fr8/uNb8BXvwrf/z6f6D2Hl3zl13lIuJfPn/XLPOJz58D2l3tdr9vwRt5lQlW/AOw5wssvBD6iqj1V/SE2DvjxD/iqcFD7s8wywNlshrajBa2KTa2LAgphLre2agHrGjERIUC+AJs3LZD6GeWJJXpcHz2+T2hXNhEvSR08q/Uozo72F3HOObeuHG3/3mEbtFPr9eDaa61H8U03wRVXHHjd4bpY7N0L3/oW/OAHPPTbV/FUuY7rT7yQRzRuha9/fX19V+fWyMgD4vvxBhH5hohcLiJb6nPbgR+tuGZnfe7HiMhFInKjiNwY5xcsCMY20g0C4kHwmy1aOYSUdc3v4Fep26kRrFZY2hWaQa8sbPrdbE7erNAyWEnFgNrrpEg2KnpIdJUP55xzHH3/3mG74w7rTfylL9lmuS1brEb46qsPDGQP0+e3ml3k7762A2LknPzbXD3xIk7Y9317vt9fX9/VuTWyXgPi9wFnAj8N3AX8SX3+UDUBh4zdVPVSVT1XVc/NJqcsSzxZ2aa6MthRV0VIwjK7ldjQjoWsLnlQwmJGWMyQXkD7Gf1NysLuCcJsTmokqvvaNpyjm0GRoEhoM0GuaDcb6uhm55xza+Bo+/cO2/bt8LWvweTkcp9isA4SKwPZQ/T5nfvFX+WFX/xNXtL5IF+qnmCBcqNhgf/+/Rb8r6fv6twaWQ81xD9GVe8Z3BeRvwQ+UT/cCTx0xaWnAA/8v1LByiEqywAP6omrTRHpB6opqxnWUA/rqMS6R6zsEjFVQSWEEvKpkiqJvSZi17bjUr0xuS6VTwyty4SO56Y6Efm/gQ8Ac8D7gccAF6vq1SNdmHPDsl5qWzeyo+3fO2zn/f/svXmcXXV5x/9+zjl3mTtLZkL2zZAFQpCAFEVFBcrigoq1VEXbunXzJ3Wr/n7aShGpVSvd7JJWK5bauFKpgLZAUkTFSATZlARCWJJMMpNl9pm7nXOe3x/POXNvhkky2WaS3O/79bqvO/fes3zPCa/hc5/5PJ/nUvja1yyXWNWqxSMj8LKXPV/I1nmQt2+3JIlfDSzmSzP/jAvkpyamw9CEMcCKFcfXtTocx4jjsnwpInPrXv4G8Mvk59uAt4lITkROBZYDGw5+xKTaWxazSQj28BV/xBut6KZZxeqr5RP7NtZZKoL0ZaDqke0TWppLEIPfHCJNlkwhIwFS8ZCKh9cf4A364Ctec/Xo3pyDXGYDeibeo6oDwOXATODdwOemdkkOxyRxvHtbTxYma6Tw5s2wejV88pP2PNF/x+XL4fLL7ee+PqsSX3CBPe9HyD70kA3Ae+YZ+MGFf8Xvr/ixCepMxgRxLgeLFtmzG5/saACmvEIsIt8ALgJmiMh24DrgIhE5B5NlzwJ/CKCqvxKRbwOPAyHw/oMmTNhZwAOvLETNcc3G4AlRmi6RjljOxBBJbbhG8nbcEkLVwy9BGJvnOBpKbp8HWlchlkiI8zFS9NBy9gjvkOMgpGXx1wFfVdVHRKTxSuWOxqTe2wq157VrXZX4aJPPwz2grxuIAAAgAElEQVT3WAX1/POP/kjhiSRFHOivAe94h/mHp0+fUEbxM8+Y9r3vPjir68Vw7W123he+0ErH27bBqlVmr6gfDuJwnKRMuSBW1avHefsrB9j+M8Bnjt2KTlwa0TIBPCgidwGnAp8QkVZGx6U4HCc5+4vRmugo3xOZybKK1AvVK6+sCc2jzcG+3GzevO+UuV/9ynzDH/1ozQYxNqP4RS+y1zffPHqPNobLOeMMePObbeBGPg+cdZmd66abTAgvWADXXguXXXb0r9PhOE45Li0TRx0FqkJUSBIjkpQJgmQIh8po052Xt3g1VJCSh2YVzdaSJ8odMNSTTK+reiDg9/tkOzMEAz7BgG+Ne77to5M4mKNBeS/wceDFqjoCZDHbhMNx8rO/GK2T3e85mVaRyUqYOFjj3te/blFqnmfn97znR6stXw7vex/ccIN9QVi3bvQeRf1DfPjt3axapTz4oG2ez9eda/FieNWr4MIL7Xnx4qN7fQ7Hcc6UV4gnBQFJq6eRIFX7WUJ/1NowmhdcFSQRsZrV0bg2yh7+iEdlemzHq3pIRfCHA6K8IvOKxHFy3J4sXtWz0c6T+JVjPxM4T3YUWIlNO/w00AzkD7iHw3GycDRG+Z6ITKZVZLKq8OM17m3ZYvaFT34Sbr3Vvug0NdlnTU12D+6/f/xqed09Gi4HvOObv8X3HlnMBy9+lHPOWbXvucfaNbZsgWuugaVL4ayzXKOmoyFojAqxWLNcXIhGxTAwWsmVUNCCpUhooGhraLtVEy9xDF7RG30v31qG2PYPp4doPiLelSe3qYncpiYQiGdU0JbQPMmTgGKWiaP5OEH4Z+BlQGq9GQT+aeqW43BMIuPEaB11b+vxyGTGoE1WFX5s497mzTZkY8ECe4QhPPfcvmtRhe5uE6+33GJC9umnTdw++ii0tbGzv4kLb3wDtz+6iC++5Sf83QXfwR87MKr+C8bu3fDYY+D7thbXqOloEBqjQpxOnxvyEYU4nwzpCAVViFuShjhRi1eremhTbEkRsf3m8EtCdU6V7I4MImrxbSpoECHDARJCpcPEr7aEFgNZ9fCG3Ki6Y8z5qnquiDwEoKq9IuI6GR2Nw3ijfE92JjMGbbKq8GM9wNu3wytfWfu3Pe008w1v3QpnnmnRap2dJmAHBmBoyKbNtbTARRdZ7NrAAN/4+dls6m7ne//PXbx+0aPQOs49qq+Cb9oEhYJVoPv6XKOmo2FoDEEsIO0V4uHMvpFigSK5GI0EGQrQTAwlf3Rgh41fNpFbzQpUPNSDMPQgG6MqFrcWClFLTNBWseOWfbTogwpxxyTFrinmhW48qiLik/yrishMXFOdw3FyM5lWkfGa1Y5V6kL9l5tPfnJfq8ZLXmIV8O3bTfw2N1vVuFSykc3ZpA7Q3w93383QKS+g5bnn+HDhbt708kUsefwZ2JyxJryx1H/BSEVwqVQTw43SqOloaBpEECvxUAapWOawRGmWmtho5bQ6DMm0OjH/r4A3bBXeOGv7ha0x2pMnvysg9pWwWREFf9BHes3blRFMOLdFiN+Yxt5J5IvArcAsEfkMcBXwyaldksPhOKZMpkhNzzfZ1dF6kdrdDRs2wM6dEAR2vaecAg88YDFwvm/vA2Qy/NvOK7h256f58dWrWbb+ayypPGlxaitXWqPd4sX7Xs/YLxi9vWbbOOcce68RGjUdDU9jCGKwim8+siqq1t6j5OOVPfMTJ37hOKvgJwM6CknMceIvzu4KqC6oUJ4u0BaiI7412MVKlNzNuNkm4PmDPtEk6uFGbKpT1TUi8iBwCfYv+iZV3TjFy3I4HMeaE80qcqgxcalITT2927dbVXjJEhPB554Ljz9u1gnPgzAk9jP8Wc/H+Fz4EV7dsYFZpa2WJaxqFojly60CPNb+UP8Fo6PDqu2rVsHMmbUhJCd7o6aj4WkMQZwUhKWU+HmTccoqFsMWDAuVPNAUIf0ZvJJNs4sKMZpJ9g09NBvjF4V8+wgDpVaCXVkb9KFWEU4TKTI9AWEhJmqOyPROooe4AQWxiCwCRoDb699T1a1TtyqHw+GoYyJDN8aSitQ//3OoJta7FStg2jTzB2/aBMuWmWBubaVYEt7V9Vd8u3Ilf9j6df7h1f9Lpn+PCVwwYQv7tz/Uf8GoF+/HuvrucBwnNIYgTqvCMkYxekAshAWFXARln8yAEOXN7gA1ES1li1irtipR2TKHvRcMo9sLxBnLM/ZHLImiOi0y0S1KVGiMII8p5PuMur7JYwM6ngDOnMpFORwOxyhjY+LKZcsQ/sAH4I1v3H+1ePlyiz678EJLnCiV7P204W3lSrNRDA3x2eEP8O3ylXyh/TP8yfLbkJWvhw29tSpya6tZL3K5g9sfTrTqu8NxFGgMQSxqvuG6UcyQpExkYjTQ0bHL5VkRXnOV5uYKxeEs8YjdIvWl1qnVn8PLKNHuJnRaEtE2HBA113q5/L6AqDmaxD63Eyoq7aihqmfVvxaRc0lGfTscDsdR40gm49WnODz+ONx9tzXEBUEtJm1/1eLUS7xihc1ZBvP3ZrPg++jnPo/89D4+/tO7uaB3J69+2QCUzrZ9+vqsyS4IrLK8bp2lVXzsY0fnnjgcJxGNIYjTZAmxyXGjTXUAsQllCT20KYKyR1wMKO3KEbdESC6JUit7EAv5roDS3BCvIsQKGngE/T7qK1HODukPmSdZQsGbpJAJh6GqvxCRF0/1OhwOx0nE4Vge6klFbblsYljEos3A/MHz55s1YunSmtgGE+CPPmqiedUqeNnLbFxzdzdcfjn3Lv89rvvLBdz2t0tomz2bV3d2wvwzzEpx000mms9M/lgWx1Ylnj/fVX8djnFoDEHcKDSmh/gjdS894Fxg9xQtx+FwnIgcrPp7pJPx0ga5J56wynChAJWKCeBSCdavh7lzrQp8113wpS9ZA93pp5tfuL8f7rjDGukuvxwuvZSv/Ww57303LF1Yoe/L36ZtsV8T6+vWmXf4wgut4S4ljl18msOxHxpEEAt41kQncS1lQrPWVJfp8QkXldDhDF7JI26KiQqxeY+H7Rb5RUGDpHnOU8JZFfyejKVS5NSm4OXMdxzlBFSQok/c1IAqdXJprfs5xDzF/zVFa3E4HCcaE6n+TmR883iiGmrv5fOwZ4/ZF1RNDLe1mQe4UjGRvH69CWFV6Oqy3OHUR9zbC3v2oJdcyvVrlnP99XDxxfBfV3yNjsh/vlhPp9pNxvASh+MkoDEEsZpVgkDRKojI6Pv4NtbZ8xUtmxjGV5tuh0WvAURtMZKLiEdy+M2hTbg7pWojoT2QfIRWk2/ioSC5GJlWIS5O0i1WGtVDfP1Ur8HhcEwyR+LnHctEqr8Hm4w3nqi+8UYTtsuW1d4rFMzD29lZE8a9vbWhGs3N1jBXrcLwsGUN9/VZ/Jnvw5YtXP+6+7l+y3Le9eYB/vUbbWQ//cz4Yj2NT9u92yLbdu2yc443mMPhcDSIIMYa6IgFf9AjbE+yhWOQEZ/KnCrSm7Oc4rr4NGJGhbFEHt6Aj8QQh2LiN/SQSEw0D2ZqKRa5GK16ybS6SbzIBipGi8jtHOCKVfWNk7gch8MxWRypn3csE6n+Hmwy3tq1EEXwyCMmcDs6TIC2tMB559k27e3mA370UXvevt2qwJkMvPzlVg3O523bbNbsDSKwbZsds78fOjp478K7aFsxjw8vuwt57r37F+tnnWVi/MYbzaYxe7ZtO95gDofD0TiCmFgIhoWwJa4N9s3F+HszRIKNbVZBygKRpU8g4JWt6uuVxTKHY8HPxITDgdkvIoF8BPlaVdnLRsTFAGkO0dDFrh0jbpzqBTgcjingcPy8B6ooH6z6CwefjPfYY7Bliwngjg7zBW/ZAgsX7ruOpUvNE7xkiYnfV73KROu6dbB3r8WjgR0nl7NzFQpsGZzFPw18mC803cjCll4+8oanoG86rFljovnOO03wnnOOVZi3bDFR/Y//aGL4tNPgjDNsm/EGczgcjgYRxJ7iVUEikrRaU8Qy5KPzS9CTRVK7QwxMqyKDAQjE7RYTEZd8yMRkunyqC0woq6/4ZSHyfUuoCE0Qx2rbatkn01qexAttHMuEqt471WtwOBxTwEQquvUcrKJ8sOpvyths3s2b4VOfgvvvt+SH5mbLBW5qskehUBuGkTIwYCOU3/e+fd9fvBi+/nVrqJs1y9Z0552wcSM/jV/Kld3/Suz5vE9uZXQFxaJt/6Y3watfbWu4806rSPf2WoU6DG0tTzxhFebLLjP7hWusczieR2MIYmzqXHbxCJWB/Ogf2r0ZZXRXHjyF2KrCEgta8vErQpxTJGmqU0+TyjEEmYiyr2ighO2hDeEIpTYRLxNbZTiG6kBuiq64MRCR5cBngZXYYA4AVHXJlC3K0RgcTR+rY+JMpKJbz8Eqyger/o7H5s3whS/Ak09aRTifNxH92GMmeDMZO8/wsIniAwltsHNddx28/e22jh07YNo0vnX+3/DOu9/BwmAH35/+uywPdsIzOYtde/hhq/i2t9tj7lw710MPmQd5+nSrRlerVnEeHLTpdhMZzOFwNCCNIYhV8EoepR3NSFuI9Ng8Zr8rQ7XFhK0okFFLlhAlao3AV2QkGb2cTLWLcko4nEUL5jf2shHxUMZEtpco7cEMEoPm49p7k3Kdk3eq44ivAtcBfwtcDLybRiqVO6aGo+1jdUyciVZ0UyZSUT7UyWxr11qz2vTpVgmeNcsa1opFeOYZOPtsywxuabHs34kK7bp1fPEtP+GD33kFFyzayn/PvYYZXVthqGzX+x//YaL7rW99/nVt21Zrqpszp2afqFTMs7xw4f7vlcPRwDSGIBYlbo4gG0PJN6EKVDOCZmOzSwgQJg1yHnhDviVOpNIqBjzI9gksDqmO5MBX/O485NWsFcmYZw0UqVjsGoETxMeYJlVdJyKiqs8BnxKRH2Mi2eE4NhxpLq3j8DnUiu6hVpQnQmenDdno6LDXc+bA0JBl/i5aZIK4p8cqvum60r8o3HzzhP6icN5vvoB3bXyA1af+FfliGbqwxr1p06zKWyzaOesZGDDBWyrZ521t5lvets3sE7NmuS9tDsd+aAxBHAr+kE+c9SxirZJEqgWWMmHCN6kOZ5OOO8F8wLlEZRZ9vBGPoAi5liK9e/JoRqlOD23bSEarwdPn9dPT2Y43YnnEjmNKSUQ8YLOIXAN0ArOmeE2Ok51D9bE6ji6HUtE9WEV5otaX+u0eesgqwU8/bQJ1zhzbt7vbotRaW/cV6RP8i0J/P9z+5S5+u/lWXt7Zyct/M4B1O229HR0mtFtb7RxPP22JFTNn7ntd73kPfOc7ZudQrY1tfslLLHLNiWGHY1waQxB7EOfVqr+x5QZDLZuYWPCGfTRQNAAq5v+l6tWqrgKaUTRxUGg2xi+EiChRxUeVUfHbv+kUCr1CcV5EcEppcq6x7vwNxoeAAvAB4AbMNvHOKV2R4+TnWFQdHceGA1WUJ2p9qd8uk7EBG7t3m+Dcs8fE6dy5ZpMYT3RO4C8Kzz0HV1xW5sktM3j5Rz2WnFqXXRyGlkyRZuiXSnDqqfb6oYesArxwoYnhyy6zJr0vfhH+7/+skn366XDVVU4MOxwHoDEEsSjqKRIKXlmIs5q8LagXIyUPzSWCWQBfiZtivOYqcdkUsCTPpRmKFHMgEA1mLN8YIBMjScRa3BQz0qzgK9ViZtIvt8EIVXUIGML8ww7HsedQfayOqWV/FeWJWl/qt3vkEfs3b2kxW4LnmWAdHoaXvnR8W8RB/qLw4IPw+tdDsR/+5ze/wpLtP4L122yK3e7dduynnrJs4Y4Oe93RYUL4kktsRHM6snnxYjt+czO88521/z5d/rDDcUAaQxA3CNqYHuK/EZG5wHeAb6rqr6Z6QY4G4HCSCRzHjsNN/Jio9aV+u74+e3R0mH3h7LNrFoZvftME6thqcxBYJFqlYqJ6xYrRtIfbboOrrzbnw7o3fp6VvffZ8Z591sSw51mKxdAQbNgAL36xRaulAz7GE/PgPO4nEy7RZlJoDEEs9pDQotTSRjnNR1D18CpC1BLb+OWyh1cIYU+OOOPjDdktigsReIr0eqiKbRt6KGavkNAzCwaYlziy8c2j45wngwYUxKp6sYjMAd4CfElE2oBvqepfTPHSHCc7h5pM4Dj6bN5swynuuqs2mGJwcP+JH2OFRSYzMetLvUWmvd2SG9KsYTALQ7VqleKxInTNGqv09vbWotDWrbNhGR/7GHt+bEltt90Gsz+00UY079lj15FLYjt930R2X5/lCy9ZYgJ56dJ919nWBj/6kVWx06a/l78czjzTedxPVFyizaTRGGPUEgEc52MktGQJzcZQ9ggGfNQDr+TBcGBRa7tyZp+oekgIEgKhkNuRIbdX6Ggdgd6sCdBsDB62fd0jvytAK97osA7HsUNVu1T1i8AfAQ8Dfz7FS3I4HMeaVCg88oiJYc+D9etNCE6fXquUjt1+cNCExeCgCcQtW0xoxrE99/RYBa6eSy+19/v6TMhGkXXAzZplAnXjRti61WwNa9bAvfdag11bm4nfXbus6rttG/T0EE47hV/Ir8Hy5bznPXDffXYJtLfbOgYGTGBDrTGurc2ua/p0G+yxapVtV8/Pf273QKRm6bj9dvjVr5zH/USl3q7jefY83n/fjiOmMSrECRKJNcslIlVi8xM3dXl4FSjOUUJfiTNq24o144H5jSvTlPzuZF8FrXomsJPmPCnZ9wsNlNKckKClStifnbwLbMCmOhE5A3grcBWwF/gm8CdTuiiHw3HkHOzPxKlQSG0IacPZpk3wylc+vxo6nl942TITqWMb08ZW3uotMoOD8JrX2D67d5vQbG42Aex59l42a6OY58+3dcyYYSK8WGRoIOZtj17PujuW80T2Cyxa2UKQXtuqVSZk9+6161G1KnQ+b9cpUrNujOdj/+EPrZpcqZiAb2+3/X/4Q3jta53H/UTEJdpMGo0hiCNBIssY1kIidgHNxGR6A+LzBhjpbsbrKMNIAFUfZpaRXTmLZEspxIzMF4qDBbS9asI69K0C7dmAD2B0DHTclYfWaLKvttH4KvAN4HJV3THVi3E4HEeBifyZOBUK7e1WCU2FY1/f+NXQ8YRFsWjl2Re8wPzApRLccsv4zWfjjW7+0Ies+tvZaWJ1eNiquX19Zgr+6U/N1uB5IMIO5vH6DR/kkcEl/OPZX2bRma37Xlsqci+6yKrQW7aYuG1qMsHu+3bczZuf72P3fTv/nDlmtUjX0dxswnqif2J3ftXjC5doM2k0hiD2kgQJT5GyZz8D/pBPdlAY3tmMZpRoJICKZ/FqxQAK8WiKRLo/MYgkxwpAczFS9PCGMsQ5E8+aMRuFjXOePGPvJJ7quEFVXzrVa3A4HEeZ+mpud7dVfbu6LJvs0582gZYKhRUrTNSmZLPPT/zYvNkqut/5jgnFJUssl/cnP6kN1AgCa2T7xS9g+3YTu089dWBhuHOnCdGmJvMjF4smQisV27a5GV7xCli/nkf6XsDrf/Zn9JXy3DHzPbz2N5eBN3vfhrf3va8mcoeHTaQ/+6xdz5w5cPnlJurrvxyka1q92nwXYWiCuLW1Vlk+66yJi2HnVz2+cIk2k0ZjCOKkoU59yw8O+uyyM0PC0LIqkosIunNEs0NkKCAuRBbFllHzGgPEAlWP5u1C4dxhunbMMg9xYE16UXtYs2JEguYjNAAvP0kVYqUhm+ocDsdJSFrN7e42sVsomNjr7n5+NXX6dPP13nuvWQ3OPtuSHuqHYtx4o1kZ4tgquU88YcfaudMEdRhaSkQ2a41ojz8O115r1oulS8cXhmvXWhU3nze/bxSZMI4i8xafc45VZpua4IIL+Po3zkFj5ScLr+bsMyOYfYEdp7vbzrdtm72+9FITximrV5tNo75CmJ6/XqR2dlr82h132OtCwcRwb6/ZQCaCm8B4/OESbSaNxhDEUeLzFfCG/dEc4vJpJTJBTHUgi/qKn4mREUEDb7Td0Bux/OE4FyNVQWJl70AzwbCgAnHGRHAsoAUTv1ry8PoD4taQeDJTJhwOh+NkIK3+btpkwq5QsOrrnDm1hqK0mrpmDTzwgFV93/xmE6D1mbtr15qtYeFCG57R1WUCuFw2AdzSAjt22M/ZrInIUsl+/uEPLepsZMQqyPUV6s5Oq9jt3Wvr6+01sV2pWNU4mRrXc8dPmT6/ib+8Zgd/0vVNZt1fhpWr7DpTwZ+OfB4vIWOiHtL58+39N7zBrBo9PVahfvObbVjHRHB+1eMTl2gzKTSGWkvTH4IYryJoVu3Rl4VnCgT9AVFrhKpQ7YhMPCtIJWnCi8Ere2hryMhcodyfJywocdasGHFWCQY8/J7AHkUPibBJd5XJusXJmOij+XA4HI6pIE116OqyCmyxaBaCFStMoO1I2gWWL7fK8ZveZE1jc+c+vwu/s9MEbj5vNoLly+HXfs0E5Pz5JmT7+83yUKmY+AWrLD/5pD3SbZ591gTr5s22b9os19Jix/Y8+/nUU4nf/V4+eudlrPrWn9Kts/B3bmfWXN8m2fm+WSsef9z2iWM444zxEwTSLwf1jOchTe/Z/PkmqP/gD+yefOADE7/vEz2Xw3ES0hgVYgWv6CMR+GUhnGYVYq/soQHIwhEYzhD3Zs0nHCiUxb4uJHo2zsfIQAZEkUxMXLDsYZlWQYsB1ayMWha8smfV6HJdNrHjqCIit3MAk4iqvnESl+NwOI4WaVPXwICJ0OFhsy2cc46J376+fQXawaqa8+db7FipZNVjMIGdy8H559t2qeAtFEzQ9vTU4s5ETCSLWGU5Faz5vA3H2LPHxPCiRVZVPu00Rq75f/mdP13Gd78L739/hlPeeAH8sFhb0yWXmD/5ySdrEWmbNtlnM2fuW5GdqIf0aPxp3flVHQ1MgwhiQQPFK3uUF1SQotkg8CBsi/B3NEEhJtPnUZlTxe/NEBditCmCxDIBoM0hUTEDnpr1oilGBzJk+nw0gDiTNOsVrboa5RWJJ7HS2lja+8bk+c3AHOA/k9dXA89OxYIcDscRUt/UtWqV/cn/xz82cTdzZi0nuF6gHawL/9JL4eGHzTc8fboJ3d5e8x2//e1W9e3stDzjfN6EYFNTTQRns+YRHhqq2RK+/3345S9NZIP5k3fvhtNPp7vSwRuvbOXnzyl/93fCB167GblpTKPaunUmimfMsGpxR4cd6777rAGupcW8w2lDXyqgDyZ0j/RP686v6mhgGkMQNwoNJIhV9V4AEblBVV9V99HtIvKjKVqWw+E4EsY2daVCbPt2E6vjCbS0qrl7t4m4ri6zP3z0o7VjfPSj8PWvw/33myC+6CJ4xzvs83XrrBntJS8x4fzzn8O0aVbx3b69ZrfwPBOnW7bYNmm1ubnZRHO5DF1dfPyRt/HLzg5u/Y2vceXrXrb/RrWbbjKBvH69HXPaNDvP+vWwcqUdv15AH0nSw6FEqTm/qqNBaRBBbGkRUWsVioHFogHaHJMtVKlGTXglj+qiMjKQIZ5dtqEbQz6ar8shrngUugRdUWRIwQ8U3ZWjOquKlHwbBQ1oh9r+2QiNGsOmPYXMFJElqvo0gIicCsyc4jU5HI7DoT5dYtMmqwi3tVkF9YYbxt9n+XKroN54o1kaZs+2Y9Q31i1fDtdd9/x9V682sVoum32hXIZTTrEKbUuLVZG3b7fkCN83Qf7oo/ZzHJulIgigWCQWHy+K+Ntl/8wHrngNL2rrqonQTMYq0H19JohPO81E6imn2Fp7e0347t1r1ehstuaZnj3b1nq4SQ8uSs3hmBCNo9YUgp05G9GcjSEbI0MBlb4cuW6fuL2Kln00F0MsJnBbQ4LegKA3QMoeBEpxtjLUV8DblSPek7Oc4tCzBIqyj5R9dMS37OOSP7mjm/UoPyaAiLxGRJ4QkadE5OPjfC4i8sXk80dF5NyJ7jtBPgz8UER+KCI/BO4BPnSYx3I4HFNJWoG97z7z+ba3m7d3yxYTduOxebNVW8PQUihWrjQR/cQT1lC2evX+9+3srA3nKJVM6JbLJnoHB+3c3d02lrm319IsOjtrY5+7umDnTr46eBUXD91GMTuN9o3redFDN1nu8WOPmWC+557a9RSL9jo936xZcPrp9lC188+ZU7NQpCOgdxzm3CE3+tfhmBCNUSHWROCCNdbtsXHKcZMN0Ki2KoSeJUuUPaQpRGOg7BPnEmUYC0RCWLChHFFLBAqxCAQxEnlosqkgtbHOwcnrYxARH/gn4DJgO/BzEblNVR+v2+y1wPLkcT6wGjh/gvseFFX9XxFZDqxI3tqkquUJrP01wN8DPvBvqvq5MZ9fCdxAMuwb+JCq/uRQ1uZwOA6RSy+Fa66pDbsolawSu3Ah/PmfW3Nd/Z/80+pnd7eJyGIR7rrLjjVzpnmAn37ajrl0qflzly2r+XG3bLGmuLa2WhZxEFgldedOs2HMnm37bd1aa3xLcocVuDb8FJ/hz7iMu6lWoSmTMdHZ22t+5yD532w6Wjp9zuXs2kZG7Fq3brVt29rsugsF227TJtv2cJMeXJSawzEhGqNCHJiIDadFxDklmlEhmlFBCxFStMl1QW9QG7k8kEEzilRltFqq2Rh8ZdpmD40EaYogF+NVrUIc5xRtDu3hK17JBDGZ+MBrO1ooUxG79hLgKVV9WlUrwDeBK8dscyXwH2r8DGgXkbkT3PegiEgB+Bhwjao+AiwSkdcfZJ9UjL8WWAlcLSIrx2y2DjhbVc8B3gP826GuzeFwHCLLl5twbW83e0E+b7aBbdssS3jBglpW7+bN5gt+4gn7bONGszYMDdkDTFg/9pgJ5Q0b4OabLY7sgQfsWAsWWPTZwIAJYBGr0i5bZvvMm2e2hpER8/iK2LZRRMkr8A7W8Bn+jN/jy3yf19EW9doxUyG/apWt8+KLa2Ol83l7nQrtpqbadLtFi+x6R0bskctZFbqnx8XZQq4AACAASURBVL4EHA4uSs3hmBCNIYhjwMMEbkuI+Ir4NoZZmyMb25xTVGx4x+9ecB/Nz9i3+jSzWGKBUCi3w5KFu9GyhxSTIR+xjXCm7Nsjhrg5Ii5E+L0ndBF+hog8UPf4gzGfzwe21b3enrw3kW0msu9E+CpQAV5Wd5y/OMg+BxXjqjqkmtb8aaahWhYdjinkrLMsYu3KK63ZbfduE7Zz5uz7J/81a+DOO02knnqqeW43bjTBnApKMHG8eTM884yJS7CBG9//vonh1larzvb0mDidPdt8w/39Jmx7ekwcR1Ht2GHIH+pqvsHb+Zx8nC/xB2S82KrMg4Mmei+4wMQ92HEvvBBemkya/9GPbN39/TZZ7yUvsepwau04/XTbp7vbLBVH4vdN84n7+kykp0kdhyuwHY6TlBNarU2YpMIrZavuplFq0hyixQDNKORCKPnQElLwKsx6sMxzczNmnQDLJ26JqLb5PP3kHPCtghwnTXdSrcsc9jDvsEDUOkmjmwE5+pJtj6qed6BTjvPe2FXsb5uJ7DsRlqrqW0XkagBVLYrIwcrb44nx88duJCK/AXwWmAVcsb+DJV8U/gBg0aJFh7Z6h8OxL2OzcHfutCazFStq27S1mQ83bTibNs0GW2zdal7bOXNMkN5+u1kO0hHL/f1WiU0n1Z17rtkxHn/c9gerRqua8B4etoeICetq1bbxff4s/AvewG1cJbeC55tYT4d/XHihbdfXZ1nHPT1WyV6/3s6fzcLLX27H3bYNHnzQhG9rq631iSfsi8HChUfe/Oai1ByOCdEggliQkg3LoOxBImK16ln1OKMsXrCHrd3TiaseX934UnIvzBE3V5G6SXMyGJiTQIBIzIOcSriY0cxhzej4cu9YM/k1zO3AwrrXC4CxnR/72yY7gX0nQkVEmkiuXkSWAgfzEE9IjKvqrcCtIvIqzE88bklFVb8EfAngvPPOc5Vkh+NQYr7GI583wStildKVK2viF2oWgIUL7TxRZKK4qcnEpudZFfbpp03EZjJWZR4asiqp59kxfvxjsyX4vlWP+/vtOKeealXix+taGqKIH+sr+C5v4m+iP+E0NnMam81eJmLHzGYtrSKOa0Mt3vteyzr+wAdsLWmD2/btJnq3b7dJe+3ttXSNri57Px0TfaS4KDWH46A0hiAWrSUnZHV0nLJUxbzCJY9dAy20Txtm+fQ9PPb9FYzMU6RSX/VV8IXcTmGkI7ZfgpFVgYmxIR5hIp4zsY1tzsQn+x/afw4sT6LOOoG3AW8fs81twDUi8k2sCtuvqjtFZPcE9p0I1wH/CywUkTXABcC7DrLPRIT8KKr6IxFZKiIzVHXPYazR4Tg2HKnwPFZrOtyYr/p9r7zS9n3qKXtOI9hSobl8uaVBpLFlu3fbNitXwuWXw3e/a5YHzzOhXC5b5TcM7VyZjAnpatUqyi0tJmjb2kyU9vZaY1u1CsUiX4/fxru5iVPlWT6pN3AKPbV1p1aLIDBxPbYSu3atvZ4zp9ZUV0wm123bVqsoz55tjzi2z6b639LhaCAaQhBL1YSrhIJGgldMmudaIkuPEKhW7FZ889T/Y6W/gmh2GQYzeC32J7K4FEBk2rptxjADXa0QxHiDARIKUUb3Fb+SCO+TeHSzqoYicg1wJ5bWcJOq/kpE/ij5/F+AHwCvA54CRoB3H2jfw1jD3SLyC+Cl2NeTD05AtB5UyIvIMmCLqmoSFZcF9h7q+hyOY8bxmi+7v0EUE8nRHW/fZcvMt9vauq/QXLPGtmlpsUSJatXE5vTpMHeufUEQMU/vwIBZH4LAtvO8WuxZENTGRKdjmjMZE8+lEqrwGfkk13I9F/o/5rv5dzC91A9+tpYZPG+eieeeHnjPe56fedzZaZaIYtEEeVeXnadateurn7TX3Q2/+IWtb/Xq4+NLjsPRADSEIFYfi1QL6ywNYCK5KqivRF1NhB1VXnfxVZw68DSP37AAiSEezgBYfnEMUQ6Ik0g19fCqQji9asI6jVhLI9eylml8MqOqP8BEb/17/1L3swLvn+i+E6U+zzhhZ/K8SEQWqeovDrDmiQj53wR+V0SqQBF4a12TncMx9RyJ8DyWHEnM14EGc3zqU/tuG4aW1vDkk7ZdGFp1OE4aPzo6THAODtqI5DA0/24QmCAGE8BhaPt7nr0ulUwQJ59/sHIj/8Af8zveGr7MH5ATr/Z5S4vZOwYG7L2FC20c9Fjmz7cK9c9+ZtfW3GyCW9WE/lNP1ZIt7rnH9rn44lqixlR/yXE4GoCGEMRkYrNGjAC5GEka3bTqoVkso9gDBgM6XzuLyrRZeIMxcS6uiWgBbQspZ3xkOItUBC0oYUtk1ok6JEzsFPEkxq5xTJrqjmf++gCfKfDrB9p5AkL+88Dnj2SBDscx5XjNl01jvlKBDhOL+dq82XKB162zau38+VZV3bbNpry9730WY5bmCD/4oInRc881m8G995rNoaPDjrdihd2LJUus0a6jw9bR1GTid2jIRGqhYM9p5FpqpfA8CAKu8P6HGdLDtcHnkHIJRhKvcBDYesplyzKeP99GRI8nXNNGwXQE9I4ddo5Vq0xEt7SYML7nHltnek0pU/0lx+FoAKZcEIvIQuA/gDlYi9uXVPXvRWQ68C1gMfAs8BZV7U32+QTwXiACPqCqd07B0o8/JpYdfFKgqhdP9RocjinlcIXnsWZsSkTq+f2N39j/Pqn9Y8ECE79RZGJ2ZMRSJhYssCru009blvArX2kRZvfcYwJ61SqrvG7caFXinTtN+J5+utknNmyw5rlFi+AHPzAB6/u1CnEcmxAOQ8jneWZkNj/hlfxOcAuvlnt59cj/1P7al1aXW1pMvI6MmEg/UANcmvTw059alXrePLumctni35qa4Pd+z0ZNn3127Rzd3dbYt21b7d46YexwHBOOhxziEPgTVT0D84G+PxmS8HFgnaoux4YkfBwg+extwJnAa4B/TgYtHBCv5BHNLbNk0S7OXLiTMxfuJOjO4g8m45oTu8Pg8ojS3NDi1DxQT+3RZH7jYNAjLvtokAzu8LBflL7ac5xYJaZVkUisuc5xzBCRvIh8RES+KyL/JSIfEpH8VK/L4TjmHK/5sqn4Sz2/ra32GswT+8lP7jtOefNmm0K3YYNVToPAbAupwJ03z6qoAwP2+fTp9jx3LlxyidkivvENE43LlplX+Oab4Y47TAQDvOAFJlyHh626G0U1EVyp2M9J1Nr91XM5P7qPj4Sfp086ak14nmfH8zx7b2TE1vniF08sDWL5cvsSs2IFnHmmHaezs1aNHhw0wb9li23f3W2jm/v7TcjXDyRxOBxHnSmvEKvqThL/p6oOishGLCf2SuCiZLObgR8C/1/y/jeT8bzPiMhT2KCF9fs/icDMMoEf81z3KbTc3wRAvDhGc8lgjSBJjkgdDgJ4iiTJEYpCKEgMubYyZc0jxaRpTkga6Gr7apoyMamT6ibnVMcZ/wEMAv+QvL4a+BrwW1O2IodjMpiMfNnDTbEYG/O1vwbASy6xCu+uXWZd+MUvLKKstdWuZ8cOs0E0N5uQfeQRE65bt9ZyiXt7LSrtvPPsONu22XS5nh743vdMxL7mNXbedetqzXephzd9lEr8F7/Jb5e+xDzp4geZK2mfmYE9TfvaKYLABHS1auv753/e/z0Ze/9EauOa08l4vm/X3t5ule5HH7UmwccfN6Ecx5aRfLx4xB2Ok5QpF8T1iMhi4EXA/cDsRCyTxHTNSjabD/ysbrdxJ5zVD0vwOzqIRwLiRJwOzzPlOG1ZL727WpHhAClD3JR4hgWrDgcx2R5TueW5acUYPAEpepY7XPZGq8I1v7FCNjYfcemgxWvHkXG6qp5d9/oeEXlkylbjcEwmxzJf9mimWIzXALh7N3ziEyZmd+0yT2+a/NDba5XRIDB7w+CgVXkzGfu5VIJ//3cTkkND5rdNJ9INDNixw9C2j2OrIL/whVZp3rixVhVOq78i/DUf4WPFv+KlwQN8b/77mXnWC8wWcd99VsVNG+jAmt/i2Naxdq29N/aejHf/9u6tjXbu6THLRUeHCWCwyXZposa2bVYZPuOMmp/4ePCIOxwnKceNIBaRFuC/gA+p6sABho1NdKjC6LCE3MKFKiUP9RU/H40eoO+ZDvyZJaKqV+cPw4SsKn5/QNhshx4VwB6U+nOQUaiIWScqJqI1qC1DRgK8Ea+WaDEZNGaF+CEReamq/gxARM4H7pviNTkcJz5HM8VibANgdzc89litSrpjh9kZVK0SnFZtw9AexaL5gYeHbWBFoWCf795tYnVkxIRuuWxCNY7tuGnz2/CwVZ9nzKiJ7rRa63ng+0gJfktu4d+bP0jTKy+Fr33N1nr99XDTTaMjm6lWTazn8yZW65Mg0vvT2WnWhwUL9r1/aQX4kkvs3P399pxWuwcGTLi/7332enDw+POIOxwnKceFIBaRDCaG16jqd5O3u0VkblIdngvsSt4/pKEKAPiKVxXioo/flaHaltgYYsHf0oRkIWyL8NsqxBUfLfpILERNEU0dRQDKxQzam0U98wlLxbNYB0/NPyxaa2pLKsZRU4SfD4/GLZoQDZYykXI+Fo+2NXm9CNgoIo9hqW+rpm5pDscJzNFMsRjbALhpkwnMODaxm8uZmC2V7DmTMTGbVnDD0Cq1YWgCN4qs2lqtmjjeO05EuGrNM5zJ2HGHhkzIlstQrTIQt7ApOp2XhD/nw97fQ3MzEobw3/9tk+X++I8tRm3jRhu7XCrZuvN5q+aef37tmr7+dbs3u3fb8bdsMfHe1lar8C5damtqbbXKcE+PieSZM2se8LT58HCaEx0Ox2Ez5YJYrBT8FWCjqv5N3Ue3Ae8EPpc8f6/u/a+LyN8A84DlwIYDniOIidpDvMGAOIDWhTb2c2BXC3EpINsnaOARaRav7KEtEd6Ah04LiTa1AhDPiPArJngzrRW0r0BUUKSaTLPzsEox1KbbiRL1Z4/0FjkOzGumegEOx0nJ0Uqx2LzZKsJ33WXC8JxzbDDFnj0WidbXV6vYZrO1iXLpY2Cg9nmaE5xOoPN9E5j7I4rsAXasctma8cpltm0XrohvZQfzeEaW0coQjCTDO1paLMatULDK7w03mOC9/3745S9NoLa0mLAHE7R33FFrvOvtrQncIICrr67dv7POqlWA6z3GYz3gk+ERdzgco0y5IMZG7f4O8JiIPJy896eYEP62iLwX2ErSJJUMUPg28DiWUPF+VY0OdAKNLe1BAyXylIG9zQA0P51heFFIlE9GOSdT66TsgYLuzdG8PTmG7xPllKg5wo89orYIyUd42Yh4b27fyDNPIRL8gczRuUMTpQErxKr6nIh0YH81COre3+9gDofDMQGORoWy3kf76lfDww+bMG5vt2MuXmziuLfXKsWqNbtD+ixiFdmhoVoaRPp5+vpgpNPpTj0V2tt58GGPN4T/yTAFbuEqWnXAfn+K1MY59/XZuteuNQF73XV2PddcY0K8o8OE+X33mcjdu9dE6/btJopnzzYh+8gjcNFFFq029v4dzAN+LD3iDodjH6ZcEKvqTxjfFwxwyX72+QzwmYmeQ0qeNblVfPP0Jn7h4aVVvKYQ2ZknzipeR4VoOCDbHVBtj8nPHeaqi38OwL27l7OlayayN0vUnzHPcNkj9pLjiaJ+8ovZA1SJWqOTflLdVCMiNwDvArZQ+0pw0MEcDofjIBxuhbK+6lnvo21vt+psX5/ZDtavt8axPXusElso1JrVkqEYQK1SnIrj+tcTEcOqJmA9D7Zu5fbnVvG2/tXM8Hq4r+0NvLD/PqwSInbObNaa7iqV51tE1q6161m/3q5t2jQT648+auJ5924Tw5mMPWbPNqF8//3whje4Cq/DcRwz5YJ4MlAfi0XzqVWAgWDIwx/JUFpUgYqH7s4RlIXKzIjrLvpv/vWZV/KV/0vmPyjErSFByUNCCJsVbQ6RvVkb8VwfN5zkE0skk1u1bcAKMfAWYKmqVqZ6IQ7HScehVijHJiusX29V0XofbVubNYt99KPm061W7TF7tonjdFBG6iH2/do45nomOkk9jq3qm8vB0BC3Vi9mZWYzty+6hjnxDijnTfym45grlZrV4s47rQq8erVVzB97zCrACxZYVXtgwK7l3HPhtNOs+W769NpxwtDi4M491/Zfu9Yykg8lwm6iHG5EnsPhABpEECPgD3tIKHghVKfZL9ewSQmbLEf4rDO3sr1/GjObh3n/ov/jIxveSu5XTTDTtvUqgvRmyPYKI0srSMmn6bks5VNioo7QkinSEc5e4i0OdNI63WTyTnW88UugnVrTpcPhmCrGJlPMmWPCccMGE8J9fSZ0zz4bLrsMLr7Y3tuwwSqt6RjlUsn2DUMTnH19JmhLpYmvRWQ0NziqROyKZjE37uRfch+k2txOs2bMxuD7taa+VIgXCiZ2OzvhiitqSRLPPWfbT59uY6XBBH8cW/Pd3XfbWqvVWoV45Urb52hF2I3H0YzIczgalMYQxJ4SNccE/T7VFiU4xX6pxrFHNBTg9wVsXH8qYXtEL9P4+A/fRfMgZIaUKGel37CgRC0RYcm31IrpZYp+Fq/o4fUFIJZjDBD0BcQ5U6fqYoiPNZ/Fotd+CZTTN1X1jVO3JIfjJOFQq45jkylWrDDP8NatViXN5UxA7txpxz7rLBObg4PwxBNmQUgtFHv32nNzs9k1enqsOpumTKQVYs+r2SjS10FgojSOGa5kuDr6FpvC03gocz7NWiQ71A3DiQ+5UjHx3NJi6Q+et29Ve+7c2vUMDNh6RkZMTKcWj44Ouy/XXQc33mhCftYsuxdpQ+DRirAbj6MZkedwNCiNIYgbBW1Iv/LNwOeBx6jNGXQ4HIdDvQDOZOx52bKJVx3HJlPMnm2V3/Z2E57t7ZbBm8vZedLGvVLJPh8aMiGbz5swTT24e/bYNmOb6AqFmiUik6lZIxLBvLM6g9dzOw9zDl/kAzSH/bZfeow0717VRHmlYo13YFXseqtGsWhCOQxrYn3BAotSW7LEtrnsMmsUXLvWspXnzbNrvPnmmmUk5WgO2TiaEXkOR4PSGIJYBYmEsDXCa6siSSyaJxGRZohaIryijz/o44XgFyFsgqHFCrFtG+djS48ACD2iSMi0l4nbPGR7nqg5HvXweouHCXvzkI2teuw4luxR1S9O9SIcjhOOsdXfZctstHH6Z/c77zS7w8KFJk4nUnUcL5lieBje/GartHZ3W1TZtm0mKh991IRiX58JTc+zCLPmZhOn6RjngQETy2Ob6UZGbJ/0vUrFhGq1ymPV07mC79PDdG7jjVzBD57fZ1HfrJdWi/v6bEJcWvkFW/c995jATePe4the+75dd8p4vuujFWG3P4718R2OBqBh1JrmI5tS15lHky/9YUuMVxHilsh+UXpKWIAor0goo/nCgDXJ1Y9hDj2q/TmkKsi8EjqcId9ltzMcaMb3FS/0qU6fvMEcDdpU96CIfBbLp663TLjYNYdjf4znOb3xRhsSkYqqctkE4aZNEx8dnCZTrFljAhLsGENDJirvu89+fvJJE5T33gsve5m9N3euic3eXnt0d9vrMLT1pSJ4bDNdWsUt2hCldODHn/DXRPj8mFfyIh5mv9SnVoDdk127rCJ95pl2/F8kv05e9Sp73rTJ4uK2b4dPf/rgtoRjPWTDDfFwOI6YBhHEmuQFZ/E8JcrZu/6QBx7EnhIXLCJNYkFDIT6lir83M1oVlpJH2BKhvo+XD9GeHBJh+w9n8Fqq5F9kAz/6t00j0+cR5ZVs9+Td4gZtqntR8vzSuvdc7JrDcSDGek7LZavGbt9ugzNWrDAhWyxaxTTlYFXHu++Gv/s7y95tbTUhXSzCLbfYfiL2WRSZZ3fPHvje9+z9ri6rDg8M1NakWssnniDVipLRKl/jd6iSYQHjCPjxIttUa97jbBYuvNCsEJ2dJo4vvrj2xWD2bBPKnZ0mhg/mtT7WQzbcEA+H44hpDEHsgUYeEglReziaBhEWTOgGPRnClsjSkGPQXIwM+yBKnEkEcRLZlhkUIkBbQrTq2djmSIhHAgZ22/9cgjlFqm0+KPjbc1NzzQ2Cql481WtwOE446j2naeW2qcmEX7For08/3cRrR4eJv4NVHe++G6691qq9LS2WydvVZQL7lFPgoYdqU+MymVo6Q5IEgeeZjWJ42KrC+6sI74cY4RN8lof1HO7g9cw+UPDM/o6Zy5kF4pxzrMEvnSi3erV5jOtJvxxMNOHhWA/ZcEM8HI4jojEEcSR4u7PEWcXvC4ibkgQIT9GmiKg1xOvJmHWian5j9ZU4h0WnAaaCoTQzJh7MgK82qrk9hMEMUvSsygzE/TkTykBlprNMHGtE5ArgTCCfvqeqn566FTkcxzn1ntNNm8yzO3u2eXtFTBxv3WqiOJeD224zEXn++fs/ZprBOzxswrpQsOcHHjDhm1Z803HLqUUhjm27XM7WlM+bID4EiuT5Xf6DW/gt/ojVyOH8MmxqsueuLhO29ZXwA1kSXMKDw3FS0BiC2FPL6Y0ZrQIDBEM+EvmEbTF+WQAfzdnEOSl7o5YIAM3GyUS69BiCZhXpzxAMeoQtMUF/4iFuifCHrUKcim/HsUFE/gUoABcD/wZcBWyY0kU5HMc79QKvp8dEqIilJOzZYx5egKuuska7iy4yIbhli40uXrLE/MbLlsFTT1nFecMGOOMME8K7dlnld2TEKs6FQq1xLRW79SOYfb/WVJfEpU2UXczkSr7H/ZzPX/MRPszf7nf06biI2PmDwH5Om+bGNsrtz5Jw880u4cHhOAloCEEsFSHOKnE2xivXRsqF0yLIxFDxiDOKZtUa6QCmVYkrHlKx7aXqoZ7iVQQNBM0o/rDtF7bGaDYmzKR/4oM4AC8EKU9SFFrjDuZ4uaquEpFHVfV6Eflr4LtTvSiH47imXuClldoLLqh5ZPv6zAP81FO16md3t01q8337/Omn4V//1Xy/QWBV3kcesepzWiVOLRL1TWv1pA1t7e0mwmfNsucJ2iQUuIpbeISzuYWreDO3Hvq9SEXwtGm27unTx4+Wq7ckpJ7hm2+2Lwnl8r7bu4QHh+OEoyEEsWaVuDWEqkeciyGfVB9ErbfCUxukEYNX9YhbInLNFYJpEUPdLbZpRRC1Ucze9Apxb9b8xWLV41QwA2T6fEuqqArRZKZMNCZJazkjIjIP2AucOoXrcThODFKBl1aLc7nne4Xrq5+ptSKfN0H8wANmsdi502LKZs+GjRvNO5xmA4MJznQc8ngEgQlRVfMQH4JdQoC/54NUyfASfn5o15/GraWCeMYME7Gvfa19vnr1+E1yd98N119v1xkEVg1+/HH7bOlSl/DgcJygNIQgRrEmOc/EL2mVWEBFIVCkaNULzap5hfc2QTZGkgY8DSyCTTNqsW2RoC1Jg56vULUqMjBqsxCgbdbQpF5nA3KHiLQDXwB+gd2Ff5vaJTkcJxAHsgMEgeURp5nAS5aYcPU8qxZ7nlWCt22rCd4wNHGdCuHUBgHjpzuEYc0uMUExfDO/y0bO4HN84sCRagcirVp7njUOLl5s1e4LLth/kxyYGN6xw4SwiIn4XM5EcT7vEh4cjhOUBhHEFqdGBPiMTnTTpigRsx4SQtQa2bYlD83H+HszeKnd7QVFwn6rCutwxtLYRnw0o0jFM8GcNNVVsx5eySOaX2Jwa9skXufknep4QVVvSH78LxG5A8irav9UrsnhOOEYm1CweTN86lPw7W+bp7i52UTtww+bcGxvN0FZKplXOJ83YVgu10Ynh6F9BrUJc2lTHTy/YjwBm4QCn+JTfJrr+HXWUSFDlurhX3c6RW/pUrj8cqsEH6hJDqwynMtZlbxSqY1mjmO44Ybxz+NwOI57GkMQO05aROS3gP9V1UHgY8C5InKDqj40xUtzOKaG8TJx4cA5uWP3/8pX4Ec/MjEchmYBKBRM8EaRPYLAmuZaW00sl5O5OGmihIhtk6ZLQC3BIh2iUc9BBHGZLO/lK6zht3k3N/Ev/NGhi2HPs/Wlk/dmzoQ5c6xpMBXDa9aYBeSMM54/kETVrr2vz7KKs1m7P3v2PH80s8PhOKFoGEEc55PRygKjpdRYbBhHVWz0sq/gKaqepUgESphPtt2dh3yEX/IJW6rWBJ0x37EGQGDNeQCZXp9wQRmt+KNV48mgQZvqrlXV74jIK4BXAzcC/wIcIB/K4ThJ2bwZvvAFq2KWy/CrX9nEuI4Oq4IeKCc3Ze1aE31PPmkV3mzWsoWHhkxA7tplDWiFglVKVS2j1/P2HWvs+yaURewYqZgsFvfvJ94PCryR27iLV/MZ/pRP8NlDS5IYPZDWEiXS6vbFF9s1pTaJhQtN8N53X63RsL5JLpOx9af+41TIt7YezoocDsdxgnfwTU4C0mlzkSCh4I14eCN1lz6tijRFEHomaj1riItzsXmGM4oXglf0iXJKkA+RqoeUbdKdhIIUfYtzSwRyoaVMsDdjx3QcS9L/s14BrFbV7wHZKVyPwzF1rFljQlbERLCIDcR4+mmriKaV0enTaxaAsXR22sS61NowNFQTf6WSCcCPfMQqzdmsicN0upvn2edxbHaCatUEZBqjtr+0iYMgwO/xb3yDt/GnhyuG02vIZGxwyIwZZv9oarIKeGqTWLmyVkXeuNHEcU+PVZAvvdS2b2mxaxkZsQrx4sX2cDgcJyyNUSGOLR1CAxO6mvyeJ4iRQO3z/gziqQ3laK+akE0/A9SDuCUEXwkHsrZfhDXX+ZY2IVXbNs4qw3sLSCFGwkmKXWtcOkXkX4FLgc+LSI5G+aLncIzl/vtNCBcK9rpQMGG3Y8e+2x0oJ3f+fMseTifH+b6JSN+3qvPixfAXf2GRbAMDVm2FWlJEWilOq8BRVBPKvn9I1eH7eDlbWcTVfJPf4pZDuxdQq1ar2jWImG945kyrmI+MmNhtb7d7MEpeiAAAIABJREFUAlYRvuACa5Lbtg1e+cp9m+SuvBJ+8hNL1wA49VQbdb1kyaGvz+FwHDc0hiAGs0PElh/slRKRK6ChWCOdZwM5ALTsmf+gzoKgWUVKPsGAR5RX4kKMhOkv+SR+Ld1YgKqghYjszszkXWNjWibeArwGuFFV+0RkLuYldjgak7EV2PE8uwfKyV22zKa1pcM6wtCEcFOTicZSySLY5s0zwd3VVRu1nFomUrsEWKU4CEwITzBfGOBbvIV3cjPLeIqruIUMhxFhmVamPc+sEAsXWnX36afNXrJsWS1hI53cByaKczl41atq45tT3v52u5/Tp+87ta5+kIfD4TjhaJxKWih4JW90LLP6ieCNxaq8gQlbLUQWn5beGbWHVZaVOGsDPAA0ZxVgKXtQVwlWTyGjyLCPLhmZ9EttJFR1BNgFvCJ5KwQ2T92KHI4p5KUvNXFWLJr43LXLBl2MjMD//I9VNestAOPx1FPw679es0ykld1q1ewTzzxT8wbHsSVQpEI3ikwA1wtwkUOqCivwl3yCt/EtXsIG7uXCwxPD6dpTC0dnp6VklEpw2mkwd65Vw8HuRU+P3Zs4PvA9SmPqWlvtmK2t+/djOxyOE4bGqBB7itdWJR4O8Iq+NdgBfiEkGszURG/Fg5LYmGZRyMZQtv8pxC0hhB7BsEe5Jaw16MWC5iM7bqv90paSDyNWZa725SbnGht0Up2IXAecB5wOfBXIAP8JXDCV63I4poS3v93sEbt2mQ/42WdNkM6ZY1XRZ5+FN73pwAKusxNe/GLbfvfumrhOfcGVitkydu0yYRxFB84PPoSqsAK/z5f5Cr/H21nDTbyHHJVDuQPj09ZWS8V48kl4xSvg3HOtCrx2rVWB95fFPB5jY+qmkvFSRY6XtTkcJxCNIYgF4mKAN+LbpLrkr2hRKTDfRCaG0EOzEX5vhtgX07tDwagNQUYCEEZziSVMKs2tIVQ8VEBG/NHP4qbYmvki5yE+xvwG8CJsKAequkNEXLu3ozFZvhw++lETSN/4hgm+U0+1BrJi0aqeqgcWTP39cMstNpI4jmsJEWlDnIjZBfbuNd/wISZGHAgB5tDFtXya67nu8JrnxuL75nvevduupVqFCy+0z9LKMRxfIneipBF54w0ROdGuxeGYYhpDEFcFv9/HqwrqeVYBBqTom3XCV7xBD/WFaEbFGuoSa4UkTXUSQ9QSEzZZTBtekloxEJgwbg5HEyW0KUICRUd8qzJPFg1YIQYqqqoiVh8XkeapXpDDMWWk1cLHHrOf0zHL+bz9aV/VGu/2x9132/5dXTX/bSWp0La3m5e4XLbc3UzGKsPxkf+Oe5YXsIcZnMeD3MC1R0cIp6TWj1zO1l6tyy5OvdQnapX1QENEToT1OxzHEY0hiH1FA4g8xauCxolwTbzAlKxyLKFAybcyhZ94IlJrXFuElK2hTn0b4+xVPbNJaLJ9Wj0u+1BMLAyFSVSpjSmIv52kTLSLyO8D7wG+PMVrcjgmn7RaGEVW3U3tDIOD5v1dtqw2VW1/3HSTbTN/vh0jDGuNdb29tSpxtWpCG2oJEodgjajn55zHG7idafTzOCvxOcpFhDg2q0ihYPdh3jx7L22GmzkTrrnGrmnOHBPNJ0qVtbPTKsMp3d21dAw4cYS9w3Ec0BiCGIgL0Wj6w2jD3Kjptlb1RcAreeYzTnKFAbNbFCK8so/XWiUeztg2VfMKS8kfFaSaZhoDMtAwt3hKUNUbReQyYADzEf+5qt49xctyOI4+B6tiptXCRx6xJIW5c62JbnDQLBNbt9pAjYsv3v9xN2wwwXvKKbZPb29tAl0qeFVrYtj3j0gM38qbeAdrmE03/82bjr4YBhPzIyPmIc5kzB/d2WlrHxmxcctNTRabtmePNd5NmwbPPQef/vTxLSjnz6+lY3R32zARz7NJe4ODJ46wdziOAxpDrYk1zKmXWCCSSq43o0Jc8dFY8IYC4iY1MdwcQSj7ZAjHhQhvxCcz5FGeKZZWEfz/7L15uCR1efb/eaqqt7PNPgMMM4AwbMqmyBI0QABxQXBXXDDRvL4hLiGRRENIRI15jSEmvj+VRMVIDGgwGkXAQOAFVOIGiCAwOAgIDLPBzJk5W2/1fX5/PFVdfQ5nmzl9+pw5/f1cV1/dXV1VXVUDM3c9fT/34wgHQ+IldWvGKyRWjJxD60Ej+7hNp9hxTXUiEgI3qeqZgBfBnoXLeF7Ryy830VuvmzC67z445hgTsUuWmHd4ZMSEUbVq651wgjXeTbTfnh4TzkNDmRViMo/wHvqHFfgH/piLuZwX8zOu41xWsXWP9vUcREbnIIMJ9nLZhOHSpSaGb745y07O520IB5g4rtez6XXzWVCeeaYdI1hlOB1NfcQR3j7h8ewmnSGIPQsSVY1FZFhEFqnqzrk+Ho9n1hjrFa1U4OGHTbSdfbYJ5EcfNUG7ZIk1kO3caaKwWLSUhQMPtIa7VBxt2AAXXWRCqly2bUsle10uW5W4hQ1zzSjCHZzK6/gW/8oFdDEy9UZTEQRWBXbuuakXYWjXZdcu+OY3bRrdqlUmgstlu2Eol01Md3XZTcGKFXaNr74aLrts5sc3G6QRcLfcYjaJtWtNDK9aZZ9PNoDF4/GMojMEsdJokGuu+saDOcKdSVNcCOFQQFxySDkgKAdEw9Iou9b6bLtaj0OHIvMMh0rc7WzCnQNN1tVaYFaLgEZTXrvOswMpA/eLyH8DQ+lCVf3A3B2Sx9NixnpF1683gVypZOOYjz7aqsRr1pjYzedN4C5fboLvXe/KxPB//zd89KNw993ZmOJ0RHNfn1WWn302s0u0iAF6GKSHfdnM13kLBSoEM/2LKx1TPTycTaZLq9vNgzlSH3RqJTnmGLNGxLF5idPtNm2y9dPJczffDG972/ytsjanYwwMZDdNMPkAFo/HM4rOEMSCiWJRa4RLrQ21AFFwSRNy3OXQvCMYDtGcUl2sWSJFMpZZ80CohD114l0567uriw3iqCbNemE2vY52jW7u0Bxi4Ibk4fEsXJq9omDCrlDIvKPr15uoK5dN0O2zjwnAri4T0vvtZwM3zjrLxPLll1uSxFjvb7lsorJQsOdyuWWn8BSrOYfryVHjJ5xIiRbtO4qykcyFQjZOOq0Sh6G9T4eFlEp23iMjdp0GB21ZpWJV9e5ueMEL7MZg61azVXzgA3DuudmgjvmYSNFsn2ieoPfa187tcXk8ewmdIYhjIaha1dblNWuqqwtxIZlQFyiiAk5sCEeoSKBZlFog1nCXiGW3I5+Mg8ZyiCNFupLBHAM5lHRUdBtj1zoQVb1qro/B45l1xoqdQsFE8dq11kjV3W2CsFg0gfzyl1slFOz9Qw/Bj36Uva/XTfypPnekcn9/1iwHJjb3sGku5V6O4VXcwAC9XMubZl4VbiYdLb10qb1OY+LSUdL5fDYtb/VqE8JHHGEV5a4uu1nYutWqxUEAhx8OK1faskcegYMPtu0HBuDv/s5eH3zw/Mv9bbZPNA8XAbjiivkn4D2eeUZnCOIgiUoLQbtiSCq5BEl82kgAISAQlgMktnziuORGD7eOIRwWWB5bL3Q1gJyzmLUYdCQpNYcKkUJVbKftojMrxB7PwicVO9dcA9dfb9PoymWr8q5Zk1U8TzkF7rnHkhL23Xd08sCaNSbqbr7ZBPXQULb/Wm10Pm86mS59PQNu5BW8iWtZwg5+yEs4mvtntL9RpBFy1aoJ+bVrs4a6NHauUrEbhmXLTDQ/+aSJ3p4eu07Dw/C611mz4TXX2LL+frvhOOSQzFu9eLF5swFe9CJ7nm+Na2OHi/jBHR7PtOkIQSyR0r3/ANVqRLW/0PARa8FBXXDdMVIOkulz4PLOhm64LIc4WFzFxUItVALFKsOFGGqBVYHrkg3hqAfIiOUZa4f6GDweT4t5/HHz/j7xhAmctCls585M7K1fb6LwrrtMDN1+uwm7fB6OP94i2Z591pZFkYnq8QRvC4ZtAMQEXMLfcCi/4nrOYT82tWS/QFa5TpvpKhUT/JWKidzly7NmwfRRKsF73mNe6yeftJuEyy4zKwmYKB4ZsWt5221Wcd+2zYTvd75jfwbLlo0+jvncuDbR4I6rr7b/fnzV2ONpEEy9yt6POqF232Lc490mUsOkYpzM3kAwG0TR4XrqaN7hStZdrYHZKdhcgB15gqEQNxJBLMhAcj8RJ2K4FtgjbrJKhG0ezNHKh8fjmR+kvt8dO0yQBYFVMZcssepoLmfirlw20fu855noS8Xc0qXwwx9atm4+byJ6qoa5dMLbHhATUCFPiOMGXsX3+e3WimGwc87nTbSCVYR37coq3Tt22OuVK+G00+Cd77SUjZ074bjj4O1vt+dbb7XrC1klvrfXBPfgYPZdixfbss2brfKeMp8b1zZuNMHezMiI/UowMGBV4zSvOL0GHk+H0hGCWKqCyytxUQkGQ2umSx+xINUgqwgndgqpW+U3qAQElQCXU7RgleOgqw6aDPKIk5/sak2X0gFqOcXtdEyItvYxnxGRUET+t4h8XEROGfPZpXN1XB7PrHDLLdnUuFzOHoWCiZvULgFZtXf7djjjDLNQHHCACcMosulzqiaqo2i0T3gsexi5NkQXb+A/eDv/hgKreZoehqbcbreJYxOuaYJEFJmArdVMBOZytt7/+T/w+c/DhReaJzitmKbpHEuX2vVNWbfO1v3MZ2yd3t4sjm7JEnvcc49V0fv77VqnzXbzjbQZs5l777Xq8GTXwOPpQDpCEGsItUUxYTkZqFGKbZpcX82qxXmHRkklGJBySDgUmChOqsmaUyQ2Ya1OkKrY+sk24XDQqDYTgFSCRjSbZ1b4Z+BU4Fng/4rIp5s+e93cHJLHM0ts3GiVzijKKqC5nFV5V6400dbfb8Lt9NOtCtrXZ17ZoSGrFG/aZJPYHn/cBHCzCJ6hTzhlM6s4jdu5jnN5KT9ofT1AxKrCYIJ4eNhEXXo+YZiNaB4ZserxnXdm249XMe3rM0/2WNats+a5xYvt2haL8LKXwStfadd940a77vPZj3vmmSbY+/szAb9lCxx77Oj1JroGHk8H0REeYhwElYC4yxFUBUm8vuGmAnHJxHAjMcIJWoypF0nEbVpBASnE6M48Wg5N+9YCNF9HCo44UBsLDRCk/uQAqfqmulniBFU9GkBEPgt8XkS+BZxPW+vyHs8MmWokM9jySgWeeir7ub5aNQG4aBGcc042jKG/3+wTu3bZslWrzC4xPGyisVKxn/3HqwrPgAc4kldyI8+wnG/zGl7N9S3dP5ClYjSzdq1ViDdutKq5ql2Tnh5735wjPDa+Dia3PBx11HOzffv74dWvtiryfGe85Imzz7Ybp2bms+3D42kTnSGIBYKKEO2w3GHdVQAgvxOG1ggyElmiRJJXjIhVhOtiXuMEp5DbFaBBMsAjBkZCwmGzVKQyTBwEiRDWjqjBzwn59IWq1oH3iMhfAf8P6Jmzo/J4dofppgCksWsnnQQ//ak1z6XNX7t2wR13mPgrl62K/Ja32JS1hx+GG2/Mmuea0yNaSI2Ic7mOKnm+z2/zIu5p+Xc0SAVxmj38zDN2Y1Cv2/OBB5ro37EjyxxOp83tblbvQsj2nSh5Avbec/J4ZoGOEMSSd8T7VNF8DCq4xO9bTlMkBPMOq02WUzErhAaWJQxYBVmFerdVlKOB0ESwgiskojl9CqDebZFtQaV9gzk6rEJ8l4i8XFX/K12gqh8TkaeBK+bwuDye6TNRCkDq52yuHJ9xBnz3u5aO0NVlFdDVq80SkTZEHXSQWQi++EVrrPv+962iOUsjmFNy1Lmat7EfT7OWJ2f1uxpEkZ1XvW42isMOszHUW7ea6O/pMVvJypWjq8TjZfVOZHnY3fX3BhbiOXk8LaAjBLFn4aGqb59g+ZeAL7X5cDyePaN5JHPzxLmhIWt+OvhgE3U33wxf+IJV8/bf36rD6Xhl56zRa+VK21+asXv33SYO04ltLcYh/CUfp8QIl/IJTuIns/I9E1Kv2w1Bb6/ZQQ46yK7JwIBZJhYtskl0UWTXI80KHlsxnYrdXX9vYCGek8czQzriB32NBS2HxP154sEILYdoOTTPby3JDHZAzkF3naA3aVoRLE4t75B8DKIElQDpiqmvrOK6HCyq4bpjXF8dXVKzx9IqLK6hpZhgv5G2nWeHpUy8WET2aXp/gYh8R0T+r4gsnctj83imTeppTQdolMtmA9i50+wOTz9tE+aCwH7637nTLALpiOFCwSrA9XpWCS6V7HnjxtHDNlpImQJv5Rr+hr/gKfZv749TQWA3CWkOcaViond42JreRODII22oRhTZzcWxx/qmMY/HMymdUSFWaaQ+aEDDWiBxMpa5aQQzFbGPCzbRTsrpOpYi4fJKuCVP3O1AQSU0W0U9eaT7i+xR21lo43m276vmAf8MnAkgIr8NfBJ4P3As8AXgDXN3aB7PFKSNdPfdB48+alXe3l5LR3jySXsWge99z+wRtZpVg0VMEFar9ohjWzf1Ba9cac8PPGBCcRb8wttYzmv4Nv/DKXyKP+ViLp+dLtY0PcI58wanCRvpMI5cztIzFi0yAXzCCXaDcMwxtk5/v1lQjj3Wlvf27vmxTKfx0ePx7NV0hiAOLEOYUE3gJr7goCq4orP8YbG8Ys1ZzJoMRSagk6a6oBrgumM0iJHhyJanoRKVAFdsGvMc2HemArkTSaq0/w4cCDwOvElVd4yz3suBz2DDs7+kqp9Mll8G/C8gmZXKJap6Y9OmoapuT16/GfiCqn4T+KaI3NvyE/LMLQtJkDQ30h1zjP2c/81v2mS1wUGzPwwN2US6gQFLUVi+3KwUcWxV5CCwz7Zts2VDQ9lgiieeeG72bIuokuOl/IDfcADf4A28gW+2/ksKSRGhq8tEb1eXVdDLZVueTqbr6TGLxGmnwYtfnDWHXXSRDdtYurQ1TWN+/LHH0xF0hiB2Yh6AWtpEZyI37naQS8qqsVj8WtoY1123oRtqotZ1x0ghJnqqQL3PqsNSDdCCwxUd4VCAS66mhVXYsI92iuJ5ZnP4MHCrqn5SRD6cvP9Q8woiEgKfA84CngJ+JiLXqeqDySr/oKqXT7D/UESiJGHiDOA9TZ91xn/XncJCEyRpI12lAj/4gVUye3vNBnHAAfDYY/ZZWvndtMlEYnd3VvnM5ezzctkaymo1s004lwnHWSBPjUv4G9axgZP5ceu/IK0Kr1xpYjeXs+gzEaucR5EJ5H32MYtELmcpGrffbt7piy6yMcwHHjh109h0b7Ima3zcG//783g849I5wsEJQdli11w+UY7JAA0NbehGY+RyADISElSFuCv7yVFEqfc4tCsGhw33UBPbrlQn2GZJYHGXa+yXwvxSqW3kPOC05PVVwO2MEcTACcAjqvoogIh8PdnuQabma8AdIvIMMAL8INnHIcDOGR67Zz6x0ATJxo0m5H70IxN3ixeb/eHHP7bnoaHRebsjIzZhbdky+7xetwayBx/MJrX197dsuMZ4XM1b6WGQ87iOC/jq7HxJPj962Mjhh9s5PvywVcPToRurV9s12LHDzn3lSjjiCHv/jW+YGJ6qaWzsTdavfw3ve58lcxx99Ghx3Nz4mNLXZ8s9Hs+CoSOa6gAIFVdyWd6wYDnDgeUNEwskryWpJGto66Sfu+HI/MiDoQ3dGI6gnDwP5Ih7Y+LeGBlpEtlBGwWxtvgxM1ap6iaA5HnlOOushlEZTU8ly1LeJyL3iciXRWRJ84aq+gngg8BXgJeoNtRAgHmJPQuF3ZkutjewerUlSHR12UPEzmf1amuYi2MTwWFojyCwZWnzXD5v6RHVqlWVn33WPp8FQazAx/hL3s7VfJH/NXttCoWCVbeDwK7FokV2bQ4+2PzBaWoEWAV8eNiaD8PQ1u/uNnG7bdv0RhA332Rt2wb332/76u838X3llVmU3Xjjj/0gC49nwdEZgrj5b/EAG9McJf7g5D1RNpFOIxvVrLlkrHPegbPJdNGwEA0Fmb84oDH+GSfJpDvXGOoh1TZd4laLYbscy0XkrqZHsy0BEblFRH45zuO8aR71eH6S9E/rCuBgrEluE/D3z1lR9ceq+p+qOtS07FeqOotTATxtZ6EJkjPPzKbNqZr4HRqyCWJhmFVKU5EYRfYeTPimntrUIjFLVMnxu3yFj/AxLuAqvsXrZm8EZBDYuYuYSN282UR/rQaveAWceKJVbpcvt/UqFbsZqFSyZrlSyd5P50ap+SZr/XoT1EuWZFPsli7NhPV444+3b7flHo9nwTDnglhE1ojIbSLykIg8ICJ/lCy/TEQ2isi9yeOVTdv8uYg8IiIPi8jZ0/6utNrbWJA8K2iUjHAGi1oLFC0klggVG8whEBfUvMKabSuxWHRbYAM9cILmnVWI926eUdXjmx5faP5QVc9U1ReM8/gOsEVE9gVInreOs/+ngDVN7/cHnk72vUVVY1V1wBcxe4WnE1logmTdOhO/qR+4WIRTTjHhf9xxJuqqVRPB+byJxFQQp+9rtZaPXm6mTIGzuYl/5Z18lL/iK/wueWYnws2+sGznk89bhXZgAH71K6vc3nFHJoSXLbOKbr1uNwpLlph4HhiwG4tCYXo3Ss03WTt22J/ByEhmx2n+BSIdZNHba0K6t3fv9a97PJ4JmQ8e4jrwQVW9R0R6gbtF5L+Tz57TVCUiRwJvAZ4P7AfcIiKHqurEo5gCq9RqLgnYbRaqoUIgSM1i1bQUQy2ZWlcPbGodSURbANGwUFtbgcHIqspOTDhHyT6gUS3W7rrZKdpA4gKZT1wHvBOLQ3sn8J1x1vkZsE5EDgI2Yn+ubwUT0anlAngt8MtZP2LP/GQhTtZ661uz0cvNSQiXXAIf/ah5Wp3LbBDFYmaZ2LlzVivDAAUqHMfPeTdX8nauntXvAqxC7FzmFVY14ZkmSTz8sE2iu/12E8WHHmqCdWDAtn/8cRPHhx46vRul5pHMixebKHbObkjgub9A+EEWHs+CZ84FcSJ6Uq/pgIg8xGgf6VjOA76uqhXgMRF5BKse/mjiLxHLGU5fp5XgumSiV0BzDqmEZoVQs0JILckWThIoXARaCyDvCAYjXN5BPbAmu3S/WLMeNWnEtnUgnwSuFZF3A08AbwQQkf2weLVXqmpdRN4H3ITFrn1ZVR9Itv+UiByL1eIfB/53u0/AM4/Y2wTJ2ASDQw6xxrjmRIPJRP5HP2qpCj09VhGtVOx1Pm/iDbLBFC3kx5xIN0McxS/5NB9s6b4nRTXzEFerdpOQ3gQ8+KBVfuPYrA2vex3su6/ZRn76U8txHhy065eOZ56K5pusJUvMh3300bBiRfYLxJ7GtHk8nr2SORfEzYjIgcBxwE+AU7CmqguAu7Aq8g5MLDfn/YxtxEr39R6SKK5w6eJZPe55wzzS3qr6LBaHNnb508Arm97fCNw4znrvmNUD9Hhmgw0b4Jpr4KabYNUqGwrx6KNw1VXw0pdak9iuXfB3f2fCuFYbHfm1YYMJ5zVrTPBt3GhiMZ+3/aVDKrZuNZHcQr7BG7iAf+W3+B9upY12lDRqLSWOrRLe1WUV9P5+q4r/+tdwzjnmFQa7Hq9+tX0+PGzLPvABez7pJKvCTyaOm2+ymm9gFsIvEB6PZ7eZN4JYRHqAbwIXqeouEbkC+Dgm8z6ONVW9i8kbsbIF5nf9AkDhwP1V8zaNLqgEuMXmhZNKlDXGYYM00uqw1AK0GEOQTt9QpBxaZJtiVWGFoBygBR1dCXZi2zpBShM7OVrNPMsh9ng6izTK6+GHLScXLFotlzNh9/TTJrIqFfPHbttmXuI0V/mMM2ygRBxbdTiOrTK6zz7Z6yiynOJqtWWHrcCn+DM+zN9yCj/k33lzy/Y9KalNIq1yh6FVhfN5u0bFokWqrVxpglfV7BLbk3k8qdXkkUdMMG/ZYtVeEbjtNrveF188/YqxF8AeT0czLwSxiOQwMXy1qn4LrKmq6fMvAtcnbydsxJqQpPEN0jHN0lg+qq0wfS2JDzgg8xCXQ4tuy9n2UpPEZqENEd2Q5VE2pU7L4fQvhMfj2XtpHriRCrOtW60xLJezx/LlFq22ZImJ2iDIGrm+/GXLu7399mycc7VqTWO9vSaS63V7tKihrkbEe/kcX+Q9vIWv8S/8HkVaW3mekCgyoZ9aP2o1E8WHHQa/+Y1duxUrzEM8PAwnn2znPtZqsnq13VwsXWpVZciu/d6aVe3xeNrOnAtiERHgSuAhVf100/KJmqquA64RkU9jTXXrgJ9O+iXa5OUtOmuawzy/UrMJdeIskxiwpIhk4EZaIdaCRakF1QDVJGatHJpvOB0JnQriWmDbhkDrCjlT4yvE02aikdFNn7+NbJDIIHChqv6ivUfp2atIB248+STcfbeJt3TaXKFggu9b3zIBHIb2+R132ACKFSvgvvvgF7+wyubgYNZclkaMNfuFW+QdVoRHOIRL+AQf5y8J2vGXSBDYo1SyG4OnnzYBm+Yxr1plz5s3mx1i8WJrdisU7MZgbDX30kuzm5CUYtG81ntrVrXH42k7cy6IMa/wO4D7ReTeZNklwPnjNVWp6gMici02zawOvHfShIkEiQUVRYbDhulCI4VkSy3ESCW0ZjrsW2UouzxacGjOoaFA3iHl0JrwijGqoF1N/5BIctS1wOSWZ14xjZHRAI8Bp6rqDhF5BWa/ObH9R+vZa8jl4IYbTMyCPVerVgl1zqqX/f0m9HI5axC75x4T0Iceao1dixZlYjeOTRRDy5vnnmANXQyznGe5ibPJUW/p/ifFOXuktoglS+xa5XImeE85xawQ990Hp546OoVjvEa31avhgQeskpxWiMvl6UeweTweD/NAEKvqDxnfF/ycRqumbT4BfGK3vsiBBGLRa2klOPUMR0lFmOxIpG5T5jSfxFMkecRSE1SChoDWamD2iEizbatBkm3c5pKtrxBPlylHRqvq/zTZvwujAAAgAElEQVSt/2PMmuPxTEyaK9zdbcLumWeyamhvrwnj7duz4RrVqlkDnn7a/MdRZNuMjGQ2gtRS0ELu5oWcw/W8iLu5nlfPrhgeLwkjn89GUG/ebDcB3d0mbFevNiEbhub/TZM5Jmt0O/NM+PnPzZetat+5fbtZL/bWrGqPx9N25lwQt4XAps4FZRupHAwmVZcAi02LxTzGjkagr5acCUyX/WMksaCFZCJdasNwNrRDKkEmgJPINnIOKu2bVOeb6qbNeCOjJ6v+vhv43kQfNiearF27thXH59kbqdetAW5kxCqU+bwJuyjKps2lE9lSC0Xqo039ss7Z+zRNIt1vi7iOV3M+X2MF2/gUf9ay/U6bKMpuEpyzCvELXgBHHGEV4SVL7OYhFb9nnTX1Ptetgz/9U7j6avjJT0wQn3761CkTHo/H00RnCOJEKLrumGAwxBWTqq9ggznSQR1OEv8wTcuSVRMBHA6GxIvryFBo1WZsgIc9p4kUtm8Z8X6Jecq0kkoAROR0TBC/ZKKdNSeaHH/88f62ZKEyNls4jUpLWb3aUhBErAJcqZgdIghMCA4NZX7iQsGqoqrZGOd0El/qHU6rq2FoInmGfIYP8Mf8A8dzF9dxLvuwZeqNZkI6gnpoKFuWNgqmY5qdg3vvhRe/GD772T0XsOvWwWWXteSwPR5PZ9IZghixaq0TXMGZCE6pSyaP0sg1J2iUVI61KZ2CJK0iqRpLum1aOU52Ky4b5iHtqhCDt0xMn2kllYjI0cCXgFckucqeTiWNVFu61JIg0qi05hG+Y3+6P+AA++leNcsS7uuzCvLOndk45nRMc5oekUaRNVeGZziEY4Ae/pGLOI/vcDVvo4uRGVyMKUiPta8Pnvc8WL8+myiXCnxVu0lYutRuJNavt6zm8W405pqpboQ8Hs+CoI1qbQ5JItGkGiDddaTgkIIz32+AVYVTu4STZEJd0IhdS6PXUIi7XJYgIYlPOK0qJw+NGmXlzK/smU80RkaLSB4bGX1d8woishb4FvAOVf3VHByjZz6RRqotXpxFpS1dastT0p/uTzvNLBO5nP1s/9a3wvOfb6kKQ0P22cCAieq0Klyp2HO9Pv5Y5j0Uw0N0USOil0Hu5BT+gzfMrhgOArM95PN2bhs3mgUitYmIZIM4wtBuGHbsMMvI/vvbdbnyShOhk7FhA1xxhSVMXHHF1OvvKemN0MDA7h2fx+PZ6+gMQSxY4kMMGgdoxR5SDpFkfDMBNn654Ow5UKSaNMtFatVgtXWkElpEW2+Nk4/aQNfqQasch9qoPksstv94vF/nZ+k0tbWPhYqq1oF0ZPRDwLVJeskfiMgfJKv9FbAM+LyI3Csid83R4XrmAxs3WsWzmb6+rCEuFWe33GLjg7/3PfjMZ0zI3nCDZRE/9ZSJ3dRHm4rftGmuxUkST7MvL+UHvJ//D4D92ETIOGK7lfT02HPqEd661dIzDjzQBGU+b+fb22vL0tzmffaZ+EZjLO0UqdO5EfJ4PAuCzrBMhA6KMVoERsLMBhGY0NWksY4QG7ihQEjWQEdif0ia71xvDeoBy5cPcP+3j6DYr4w8X9HGVDus4hxpe10MC1jEtprxRkar6j81vf594PfbfVyeecrq1VbRXdw0Bn7XLhO341kpzjgDvvENG7LR1WWDIyoVE4P5vDWTlcsmilUzAdki7uMoXsUN9LOYv+bSlu13SsrlzPLR1WUCeWTEbh5OOMGE749+ZJ+VSnDXXXZdDj8820dfn92ATESzSIXseTaGcGzcaH+uzUx1fB6PZ6+kMwSxx+PxzIQzzzShC6NzcYvF8cXZl78Mjz8OmzZZQ9xIYlNIB22kU+rS6nALxfB/cTZv4lr62MUPeCnH0qZ5MlGUJWfkciZ6i0W7JoODZqE46CDLGV6/3t4Xi7B2rQ3jSNm1a/L84PFE6siIjWtutc93ohshn2/s8Sw4OkMQO0mmx2FV2/TfHpc0ywXJOOa0SS4d2axNdgdRNIDACRIq6pRnHl1KMQ+7Dk4H2yXNduWkeU9oq/dgIdscPJ45Zd26544MPu44+PSnMz8smPhdvBh+9jOrCqua+B1LmjEMLUuRAOhnEefzNZ7Ho1zPOexPGyqZaWJEGiunCoccYhVgsPNPbwBuvdWq4itXwpFHmuAUMXE81QCOlLEidcsWE8NLlkzc8LinTHQjNNnxeTyevZLOEMSapUFIvXlEc/J5Ncjyh/OuEbXWmDiXvLZtk7cjIQRQXhVbKkUy2hmwWLfUb6ydYdP2eBY8zSODUx9rsWjVz0ceMWG3337wxBP2SIXiVN7gFlSHFfvrajE7uZFX8gJ+SS+DM97vtMjlrCq8dq0NFknHVYMJ4aEhWL7cPjv6aLuh2LLFhOXFF5uXuPlGY6IBHCljReo999jrF74w8/lCaywU490ITXV8Ho9nr6RDBDEWf+YwcZwM0JBKAKFaUoQmnuJykIjiREQ3/VvVWG84IhoMiPetoJUQdQKRg2qYfV8tqS63K4o4TbnweDyzT+pjfeEL4etfN19wuQw//WmWFlGrZRXTyZhhpNoIRd7BVzmd23gvn+dkfrzH+9pt0sxlMMFbLptPuFq18y+Vsoa0o48efVPR3283EmedtXsCc6xILZdtEEez7aKVPt/mY/Z4PAuWzhDEgaI5h1SDLBKtaTk5bUyUs9HOJmg170bbJpom08UlRYciosGQuKBQk0bFWaoBWowbI5w9Hs8CI/WxBoFVP7dvt8SJWs0qpmGYNcxNJnZzOft8D6vEW1jJeXyHn3ICL+GHe3gyM0DEUh56e7Pq7IteZKJ4wwb7/MQTbUTzwQeP3nYmorVZpF5xRZZznOJ9vh6PZzfpDEHshGAobBK69g+UhmqRa2VBYtCCZQ5LHJitwkk2bKMrRutCNBBQWxGjxRhUiAtqdomaZKObI/Mfa9HZ4I924cW3x9Memn2sqW81CEzgFosmBON46spvrbbHh/AgR/AqbmALq/gWr+M1fGeP97VHpJnCpZKNnQ4COP54E6cbNsC552bNbVdcMXvNad7n6/F4WkBnGFxDxfXE6JIqrjdGI7NJSHcdLZq4dd0x9NQgl1SNwZrt8g7yDi2HUAuod1uzXGFzjmDYLBcSS2OSXWM7MLtFfpZzPxPS/j2fQ+zxtIEzzzTR1d8Phx5qr9Mqb72eVYdniWdZykv4ISOUuINT2y+GwXzDaXPgvvua9WHrVvssrRyn+cDN18s5e96+3ZbPlNRC0dubDQJpRUOdx+PpKDqkQow11O3Im1hNrb5DUaNZjgB0yHKGCWzanIyEDb+xFmOzVTiBSkCt1+F6YkunqASIMGoIh0Y27U5zXll6PAuOZh/rwAC84AXw4IMWs9acNzxewkQLWMZ2LudizuBWDuCJWfmOBuMNDkkr4ADd3dYkd999NnWuXDZRWqlkQywuvHB2m9O8z9fj8cyQzhDEgiVB5BS662g1KYwnMWvmGzaLRDam2TzEqciVcmgNdQVHMBKghVTwOsSBBoIrWYVI6ol9IkhSK9qF194eT/sYmzpx+eVw/fVW/azVMiHZIhS4jMs4lTv4HW7jXfxLS/c/8Rcnf7GMFfj5vH22c6fZFH71KxPHixbBAQfAnXfCySdn/l4vWj0ezzymMywTTZPptBzaVLpYECcWjZZMp5NYGuJZ6kJQDhr2ilQohwMhrugIRhKbhBNcX3201yDV1MWY4lO5uTprj8fTLtatswixgw+26meplI1obgEV8rydf+NjfITrOacl+9wtgsDEcD5v5xWGJn7D0Jan0/ZUTQDv3GlV42uusSSJ2Rir7PF4PC2kMyrEjc44kmxgaSyWetL8pqCl2J7T+wQHUZ9VROq78jbII6dQcLhYrKocKVQDNJ806EHWXAfUettXIZZZ9Cx6PJ4pWLcOzj/fBGGlAp/7nD3PkGdZymv4Nj/kpfwNf86H+WQLDnY3yeVGV4fTv2vSgRs9PVAoZJP4HnrImg1rNXtu1aAMj8fjmSU6QxCnRZogSX9Im95ySTU3qQirU6sQx4JUhaAq2VyOJFc4qIiJ4WTQBwrRrpB6TzyqsU5igUqEa1eB2OcQezyzz4YNmQ92vBHBaeJBHE8vZWIKtrGcU7iTJ1jL13kzb+baGZ7AHlKtmih2zh6q1hSnatXwXM6sErkcPPWUCeF8Hp73PLs+/f1WLV65svXjlT0ej6cFdIZlwuPx7DkbNlhs1qWX2nOn/vydTqcbGLCqZ3OKQvr5LbfYlLprr7UJbTMUxMt5hpdxM7dyxtyJYRg9mjl9XSiYZcI5E7ppDFsQ2BjlVavghBNs+5ERuPnmia+dx+PxzDGdUSF2SeZwLRg9eS40+4SGatXdpsl0ritGXEg8YpdIAtuHK6g148UgmBc5LqhVmNNECbV1KcTIcLtG1fmoNM8skIrApUuzvN1O/fk7nU6XZulWKvDww/CBD8BJJ1nlc9EiePbZTDBWKnskir/BG3gRd/M8HuOzvL/FJ7IHdHXZuY+MWNZvqZRFq23aBI8/DscdB9u2WXV4zRq7Bj/+sV2vLVtMNKfXrpXjlT0ej6cFdEaFWCXLClYajXKjrBRpkG8ifHGChkAtgFo24U7qyS5z2hDSmnNod0wwEhCMBEgtQOqCjIRoVzwXZ+zxtIZmEZhOIkujtDqNjRtNDIIJvDvvtNcicO+9lrKwfr35aet1E4u7OYFOgU9wCW/iG3yCv2jt8c+Enh54zWtM5J9wAhx0kFlCFi+GtWtNBP/613DggfChD5ldIo6za/OTn5htYsuWbJ99fTbdby7wv3p4PJ4xdIYgFqvqRjtDq+IKjfxhFUUqyYjlgIYXV2LB5Z0J27qYz7iWeIcjJagEhIMBUhOkFhAMRLiiwxWdTb3LORPd9TZeYm3xw+NpFoEpcylk5pJ0Oh2Y8O3qyuwBlYo9P/qo+W23bbPK8G5Er1XJ8W6u5FI+wdv5Kp/nD2fpRHaTZcvgqKPMCrJqlQnbzZtN9KaP5z8f3v9+OOccE8kvfanZKR56yD5fvtyu2Ve+At/9rgnjuRqvPJX1xePxdCSdIYgBiaG+vGbitrEwGcohINUAqaQVXiHaFdiUOYc9JEmPSIfQ9dStQa+3TlAHV4oRFUQF1+XsykY6aljHrJ+jn1TnaTXNIjBlroTMXNM8bW3HDlu2datdjyeegMceMzH8wAM2nCJtPpsGO+njFXyPf+FdfITL+FcuoMDsDPXYLQoFE8Pp8I33vteuwfbtFr82NATDw3DKKdmN0saNFj/X1wfHHGP2iTC0m4Zi0a7VrbdaHFsrJtXtLv5XD4/HMw4dIYglFrSgBAWzL6RVXw0Si0Rkgzk0UHNN1IRCvxCWmuwOaYKEkHiIk+EbKri8NhInUCByBIOhrefx7M3M5sjdvY3mEcGq2Y1CLmdpCrt22eM3v9ltq0REnRFKXMUFXMZHmfO/OaLIKsMnn2yVcICXvQx+7/fg4x83EfnMM+YlPvdcOPLI7EYpvYnq77fPN2+25/33N2E9PGzV9NWr58Y/7H/18Hg849ARTXXmGXboYI5oWYV6fx5Iptelo5lzilSFuOQIh0KG93NoLGgh+YctqfRqCISKlANcV2wZxEWHjGRjmqUSWlNePYB2Woh9VdfTappHFM/GyN29jXTa2plnwvveZ+K4VLKK8LJlJgLTARXTqA7fw3GsYwO9DPJDXkIw1/8Tp8ceRfZnfdRR8OSTVh0WMVvBWWeZVzhttuzry26UXvta28+VV5pVYnjYxHEQ2HWLInt+6Uvtv6e5IBXsaWMfdO6vHh6Pp0FHVIhRSEsu9V35hocYB0HZLoHEYg1wOcUVHYX9B227ZKodeWdDOJoLP3ln3oLYMovTpjrq5ivWUDNB7fHsraxbBxdeaJXBCy/sXDHczLp1ZgtYvNjEYLEI++wDK1ZY9TOcOl3mW7yWU7iTi7kcYG7FcBRZJbi720T+8uUmEv/zP+3zs8824Z96bZur5Rs32nOaPJJ+dswxJqQLBROhqcXi8MPnVoD6Xz08Hs84dESFGKwpjryDtIEOs064kiMYCXAFh5RDNG/NcCPPdCGxEA2YYI5rTVPoQkupkMHIhHSkxN0us1W4ph882+Uh9r5fj6e9HHWUNWSllcYvfcnsAeWypUxMgAJ/zwf5Mz7FifyEv+bS9hzvZDiXTaKLIqvsrlhh1eJXvGL0umlUWvoYj3Xr4LLL4G1vg6uvtgzinh6zYBQKo6vJ7cb/6uHxeMahQwRxYmUYDE0Ed5uPwRUcBEpuV0C9B1wx8QKnGtZBfVHmO07TJxiM0LwDJwRlwRWSb2ka2UzkLGEibKNK9YLY42kf6VQ6sOlsaUrB2DHHTdQJeR+f5Z/5A97ItVzFOylRbtMBj4OIiV7n7HUYWlxavW7Nb4UC3HGHVXVXrTJ7xO5YHZqF8S23mE+3t3fuBehkYt7j8XQkHSKIDc0rWnSZcIxsWEd1eQy9NXQkhFiQpMKrRZd5h9NUiVpSbQ4VIqW+yFnluRYgI4kDJQDiMBnQ0f7z9Hg8e8hUo5mbaa403nqreYhrNWs2m4CtrOQ7nMeH+CR/wyVz7xlu9jmrZpPoKhV7nctZxfvOOy1JolDYM6uDF6Aej2ee01GCeCGTzhXxeDx7yEym8u3aZT7i/n4Tk2PYygqW8wz7sYlf8gKWsX2WTmIPELFHPp9FxeVyJvDB3nd1wT33wGGHzZ3VIWV3blo8Ho9nmnRGUx1kecKxNbxJLUCqiS84VDQWa5oLrXFOcw5qY7zAsRAOhoSLqgSDIVK1z4OByLYpOnvknU2783g8ew+7m0/bPOChWLQc4l27njOQ4+ccy3H8nI/wUYD5JYYhs0yAVYDD0JrqjjjCGuPSFI1yee5HdvuhGh6PZ5bomAqxuMQDXEsGZ4AJ5NCyhBkJEbVsYi3FyEhoZdc0Ni0OLac4FuJ6QBBbQx6BpVJIJUAl2W9oHW4aZSkWbWGaQwA8Hs84bNxoIquZyTyzqYDeuNEGdMTJXxZNGcTX8yrewtdZynbezL/P0oG3ABFrpsvn7WbggANs2QknZNPpenvnvhLbfNMC2XPa6OfxeDx7SGcIYsGGcISKFsia5iJropNq1kgXVANcMpWuMaq5aT8uUhjIZQ10kUIMQVVwUbJy6kHOuUYDXzvwlgmPZxpM9JP7dPJpm7e9+27b7pZbxm2i+yzv5Y/4DMfxc77Lq9mXzW04uRkgYlaJ7m6rCh91lCVNjM0YTpkL68Lu3rR4PB7PNOkcy8R4BFhecKRmcVBLnpCamBgObWCH5hQVbeQZa+SQROfKSIjUAlyXs+pyYOtKTZByQDDku+o8nnnDZD+5T5VPO3bbYhG+9z2zEsCo7OFHOYgP8ve8mu9yB6fOfzEchrBoERx9NJxzDnz2szZ9b2zGcMpcWRf8KHGPxzNLdEaF2InZIRSIaaRISE2S6DUsEaJgwzdUyAZypL9+5jTzFEsS0aZZEoXUm6rJgQlsjRSq7cshnuuGdY9n3jPZT+4XXjh5Pu3YbV/4Qms0q9fNZhAE1GOIiHkej3Enp3AcPydkHg3nSWPWxhJFJvCPPtqE8FSpEHNlXWiOuuvrMzE8l5nGHo9nwdAZgjjQhhDWnDZ8wa7kGtnCGqhVjMtW5dFQIZfFrqV+Y01q6pqzf+SCoRDXHSPloCm/WCyn2DfXeTzzi6l+cp9MCDZvu2ULrF9vFoPBQQA2xys4l//kj/gMb+MajufuWTqJPSD1CPf0wM6dVgEPQ1ve1weHHmqT9sIQDjkErrhicivEXFkX/FANj8czS3SGINZkaEbeQbVpUl3iHdZcOmHO7BBWIjY7hBYT9VwPbChHqEQ9NeojEcFAhOaUsD8i7o4JE3uEKzqkFth3tRGZR4Uoj2deMh2f8FTbViqWy9vVBStXwjPP8MuRg3mVfpdnWcYids7e8e8pqhajtmyZjZx+9FETwUuX2vkMD1uixCmnWKbyVNFzM7mOM8VnGns8nlmgYzzEUhekEjYEcPpobpqTWoDUA6RqFggNtWGdkFgaTXb1oRz5TTmkjvmOiw6JLb3CdTm0KzZfcndsItrj8cwPpvIJT2fbe+6xprPBQdi8mZvdGZyiP6BGju/z25zDDbN/HlMhY/7eSSfQxbGJ4te8Bg480Gwfr389XHWVTZR75JHpRc/N5Dp6PB7PPKQzKsRi/mCpp36HZHmQpE+AWSLSSrKAqE2sS1c1T7BVfruWDlPtziFPlUBNWAcjAbqkZl+3yyrHMhiaR7ldeHeGp9OZKvlgd39yH7u/M86AX/zCBOeOHTy83+m86rF/4ojgYW4IzmWNPpFFNc4FYWhivVaz5Iti0Y61XrdHFFkVeMUKO6ePf3z09tO1QnjrgsfjWWB0hiB2Nm45Fb+SWCJUNfEVO/u8FCeRaQL1ZNu0j64SWMSaQqWcp/BAifJKG8QhlQANFdmey7ZR8xG3s0LsY9c8Hc10J81N9yf38fZ3661w4olml7jjDg5bXOaL6/+c1+36Cn3RMISlhqd41kknzBUKJoALBYtMS78/FcZgXufVq606nOYKj2dv2B0rhLcueDyeBUTnWCaSJAhxZoXQUAmHA8KhAKkEDZGMM2uE1MWyiKtJTnGoaDFGi454MKK8wmLWglKdcDBASw63qI5bVEccyLIKFGPfVOfxtIvdnTS3h/sbqed451fP4GdDR8LwML97+I/pc/1ZFTZI/loda1toJWlmcD4PS5bYsRWLJnb328/EcDogZMUK2Hdfqxhv3WpCfyJ7g7dCeDyeDqVjBPGCR7HGmVY+PJ69iY0b7ef9Zvr64OmnW7a/bbKSM656B1994IXc3XOqpU2USpbVW69bJrFzJlR7eky0zkQYBxP8Fa1q1eC+PhPty5fbutUqHH64JUaoWrbwi15kjXRhaOs/9dTEI5hTK0Rv78QZxB6Px7MA6QzLROSQxVU0FjTOUibixY5cV41AhXg4SsY4K9JTww1HSCFGuhObRQBB4KjtLJjfeEUFduZRJ9SX1Qm76oSRmQe1uwoq9r5Yn+CgPB5PS2l18sGY/a3fvIhXfeZlPD3Qyze+Ibz+9efAhsPgoouyam0UmSCuVs260NVlQjStHk+HQsGSH0ol27ZaNVFdrZrI7ekx4T00ZCOWazWrAudyts6jj9oxLF4Mhx1mjXTLl8NLXpJ5hycTuN4K4fF4OpDOEMROcNUQ6uYlJnKN5bWhnE2bC5Ls4UBxZZs+Ry3AJZYHqQtxMTa/cN7hKqHlFA9FIBAP5NCRAgCaT4Z2JPtrF95D7OlYNmywau3NN5tt4NhjTVD++tdmF7j00t0fL9w0BGL98FpO/tR55KXG7dc8zYmvX2vrrFtn4nPffc2fOzJiotQ5syeAVXBV7fjcNJpso8hEbuoLzuVsv6lN4nnPM8H85JNmZzjwQDjpJPjNb2DTJhPmK1fCtm0mwk86ya4JTOwd9ng8ng6nYwQx5aBhEAkG7LRdySHVIBGuybpKI4fYhnEkiyOFujXWSU3Qgu1Xmpr10jxjjZw11gVKlDbatQMviD1zzVQpD7OxbXPz29lnw733mjB+0YtMiHZ1ZVPNxmuym4imJIVDdj7EBSceyUV/2ctBv3PQc9ctl60im1osqlX7vlSc5vMmXuv1LPVhIgoFO+44NnE8MmLLe3psP0FgfuE1a+x7TjjBRHJPDxx/fFYh37LFmgDvuceui5/q5vF4PBMy54JYRIrA94ECdjz/oaofEZGlwL8DBwKPA29S1R3JNn8OvBsLOPqAqt40+ZcAeWdT6BRct1kbwu46uqWAdlnznObUKsOJKNauOBviUQ7QokNFCYcj4pIjKAcWq6aY3SLOGvMIFBkOvUj1dA7TTXlo9bbNzW+LF1u1tr8ffv5zOO64PR4vrAqfvm4d55+/jv32g89MtOJJJ1klemjIfL21mr3eZ5+sIjw0ZPYHVfPlDg5alTclnRoXBLbNsmW2Xj5v5/LMM/b+0ENN1A4MmAg+5RTLDwargoPFwvX32/kefbRlC/toNI/H45mUORfEQAX4HVUdFJEc8EMR+R7wOuBWVf2kiHwY+DDwIRE5EngL8HxgP+AWETlUVSdO/5S0cqtIXbJhHJsLaHIF0ng0iQVXdLZNTbLKcYBtF4uNfI4Fl09GPzsb3tHYb6TISEBQFepL2+MhFrxlwjPHNAtT2D0BOpNtJ8rOffJJOPXU5y6fxnjhSgV+//fh3/7Nir9/8RdNH46tZP/Wb8FDD1kltlYzq8aSJWZlULVllYpVfLdtMx9vT49VcMHWDwJbp7fXRPxJJ9ln69fDjh22XZopfMQRdr5hCG97W3ZcUQS33ZZdx5ERuO8+OO20TDR7PB6PZ1zmXBCrqgJpcGcueShwHnBasvwq4HbgQ8nyr6tqBXhMRB4BTgB+NPGXgJRtDLO6oFHJjXtjq+RWwsQ+IQ3BHJQDNNDMBpEz8RsOB7hi4it2tr4rOnBNono4RGKI2ySG5yOTVfjHrPdl4Bxgq6q+YHe398wjpjvUodXbTtRMt2bN1E1249g0ti9bx2tfC9//Pvz1X8MllzB6/fGyid/9bhOqP/mJieATT8zE6i23WNLFCSeYwK1U7LF6NWzebMd36KHwrndZNXdgIDvmNDO4t9csJOm+9tvvuZaSNM1iomePx+PxTMicC2IAEQmBu4FDgM+p6k9EZJWqbgJQ1U0isjJZfTXw46bNn0qWjd3ne4D3AIRLF5sHOPH8kvyKqXmFWoAWY3vuinEh4KyarJFmgzWSUc6uqGhaihXMMhGqjXxO1nVFhxZiqITk+sNWXqqJmX9RaR9mnAr/OOt9Bfgs8K97uL1nLhjP7zuTlIeZbNvU/NbwCm/fbgLz1lufuzz10I4jbh//9Lc4+6Y/5vGNea65Bs4/f8x3TVTJfuQR+MhHxj++ZtGaXrdU1B5ySGZpeOQRez/RMafpD+k+rrpqtNe6VoPTT4df/SqzTPHjqIoAACAASURBVJx++vTTLTwej6eDmRc5xKoaq+qxwP7ACSLygklWH6/c8RwlqKpfUNXjVfX4sLsHqQvBcDhK7FIX8xYPRpYGUU/EcS7xA6uNdtbAUiMkFoghtzOksHQElUQwR87Ecl8d11e3FIuRkHAooF5qb8pEKx8z5Dyssk/y/JrxVlLV7wPb93R7zxyQCsmBAROSAwP2/pBD9nyow0wGQkyUnXvWWZNn6o4zeGPRqiIrg2e59dZxxDDMPOt43To7p/32g/vvh8svt5i09DreequNh57omCe69hs2mDgulcwmct559lwq+VQJj8fjmQbzokKcoqr9InI78HJgi4jsm1SH9wWSDCOeAtY0bbY/MPm/RpII22Ki9JrHKdeT0c1J4x1Vq/Rqd91SJVJl2BejlcCmOgswkiMcDoi7TfwSgAxZNTioCi6nuLwSLS/P/MLsnUxU4W/X9p7ZYrIqaZLKsNtNXE2JDnvUADZRdu5kmbpNNo2bHtifUw/dxBLp5/uHvAv5rxfB/eMkXcw067i5Kt3fbz7g++83UZ1Goz3yCFx44fjbT+a1nqhS7lMlPB6PZ0rmXBCLyAqglojhEnAm8LfAdcA7gU8mz99JNrkOuEZEPo011a0Dfjr5t6hVgishGiRNcCRRanHSOKeYGK4m6RL1ABLbA4CWQ8sgzjnzFVdC4r6sj0+aYt1c0UZDS1ederVNlonkNFvMchG5q+n9F1T1C+kbEbkF2Gec7f5inGWehcJkft+ZDHVo90CI1avRnbv45I9P45Jvn8DHfud2/jL+KrJkycRJFzMVnc2Ctr/fmu9GRsxbDPDgg9YMmH7X2Osx1bWfyU2Fx+PxdDBzLoiBfYGrEh9xAFyrqteLyI+Aa0Xk3cATwBsBVPUBEbkWeBCoA++dNGECkkxgyweWetDIDQ7KgTXERc4i0gLQkkOGEgtFLWo01eFAC0nzXE3QLtsmyx7WzMwRKEFXHTeUI/9MGwVx63lGVY+f6ENVnfD3bBGZqMI/XWa6vWe2aPVEuDmiduqZXHh+P1fe92LOP34Df9r9edgFvPCFDQsFMDrpYqais1nQLlliERalko1TfvZZ+961azMrxNjYuamuvZ8y5/F4PHvEnAtiVb0POG6c5c8CZ0ywzSeAT8zyoe11zLPYtYkq/O3a3jNbLICf5nfuhDf80TpuuQ8ufcVdfPSF1xHcPWBNaKl1AcZPupiJ6GwWtIcfDnfeaRXiNMPYOUurmCh27swzzXe8dauJ6WLRBn9cfHG2zkyGo3g8Hk+HMi+a6mYdtQY5cmp+4QRXdIQDIdGzOWuOcyCVAAK1IRx5qwjjsCsVmPUiGgjI91SRmg3gaHweqj0CcMMRwXBAbf9qm84RcNrax8z4JHCWiGwAzkreIyL7iciN6Uoi8jUsMu8wEXkq+UVgwu0984CJmtj2ItG1ebPNr/jyl+HjNx5P8Ncfg3PPtWptM62ufDc3D65YAUcdZd+xdauNXs41TbacqFkvTZNJ49Sa02Uma7rzeDwez4TMeYW4LTgb1yx1iLscRMk/IJESL00m2CWiVlNR6yy7mDBZNwZZXMPVAzSC+kjO0igUpBaYeE7WDXdEaARaUGvE60AmqvCr6tPAK5vej9fLP+kvBJ55wF760/xjj9m8jMMOs3CHnp6mD9tR+R5ruejpgSOPNHEMZpm4806bQFcoPFeM33KLpXkc3+Rk6u/PKskzGXDi8Xg8HUxnCOIkEo1qYFVdTSor1UQA99bQ4ShrSovF/AehNceB+YZ5tgClGA0VjQVyagM/0iziJGXCKtIKMQR7d1Odx2PM1c/wLfzeb38b3vpW+NSn4H3vGyOGofVNaRMde/PNxBVXWFV6zRoTwl1d9rjnHlPtY8X4VANMZjLgxOPxeDqYzhDEaYJELGgyVQ6ssgsg2/O2XLBpdU6QOrhCMuoZIEzEcyy4ohLkY9gV2WAOxSrKrnkwh2t8tcezVzPedLbxGr7a9b1nnJENs5iGSFaFf/xH+OAH4cUvhje+cZLvbFXle7rXLBWwQWBV4XRUs+r413eqproF0vDo8Xg87aZjfs+XmhCUBakGSQVXTLTmHa7gCEaChvjVUHG5ZBCHYDnGoVV8KcVmnwgVV3BId93yi2NBczbqWVORHKl5jNt1jvNrMIdnoTDOAAuWLrXl7f7eOLamsml6ZOt1eP/74U/+xIqtt902umduVo89js2ofN119hzHz71mqYAFO7BTT4XTTjM/83jCfKoBJjMZcOLxeDwdTGcIYifk+gM0gHAoINoVEu0KCQZCcttyoOB6YrRgGcMaqY1xLjq0FKOlGCnG0Fen0G1NcvHOvFWEB3JQC5CeemOqndQCcALlNl/edHxzqx4eD8x8Olsrv3fjRlO50xTnd91lroSLL4ZvfMPcCG3h/vvhvvssCSKNV7vvPvjlL0evt7sCdqqGxgXQ8OjxeDxzQWdYJgRqi52lPyRT61LqqS0iFuito6EQ5GJcNbR84jSzOFJcXag8U0JUsG66JJM4UnQka8DTKEmeCNXyjT2evZm5+hl+vO/dvPm5Jd5xPLJpItlJJ5k2PfLI2T3U57Bjh02hS1MrSiWLV9s+Zkr5nviWp7J17KUNjx6PxzOXdIYgVrM8iJqtgWSMh+ZNDGukUHRQDiFQXCVny3PaKJQ2pj0LJnwLNrLZlWKzRtSCUYbhcMiqw67QXsuEx9Ny5ip3eLzvzeWe2zQ2Rpz/4hfmOPjc5+Ccc+ZADIOJ+B07YHg4E8POWbV4LF7Aejwez5zTGZaJJBtYm1IjAKsMOwgqATKYVHIFyyHuis3/m8awFWKkZh7k3M4ACR3hYEAwkmznMEGstl9XUFxBG8LY49lrmauf4cf73osvtsrrBBaDG2+El7zE7Lpr1szu4U3K0UdbxnCpZMdYKtn7lSvNw3Hppfbs84E9Ho9nXtAZFWKSgRtiDW+aTxcqUg9wxRjJO6gEUBOzRNSSRro0snh7nqgs1LqVsCzUBnJoYPYIGYwaqRIAQdma9uLemPqKWntOMBHjHs+sMFdVzPG+98ADx7UYXHGFxakdcwx897vmuJgz0ur2Mcdk1e1HHrHnrq6JUzOiyAZu1Gp+ypzH4/G0kY4RxB6PZ4Ewjki+/Xb4wz80i8TXvjZOxnC7afYG33ef2Sc2brRhG08/bSXsJUuscnz55SaKczmLwQAbIZ0maPimOI/H45l1OkMQOyGoQdznzAZRS0eeilWBI0VrgdklAKkI4gARXHKFNITKCjMfVyJBu+s4iRrbByMBrts+j7ud2TBqgrr2WCYEEJ8M4elQTj0Vvv51eMMbzFExZ4wdxnHIITaS+cADbdnjj1sF+LDDrPPv5z+HRYvMc/yLX1hihgj86ld2UuCnzHk8Hk8b6AyDa+ZmGJX6EJQD8wdrstwJEguu6IgX14kX181iUbD3WopRUVzRERViguEAqQRIVSyTuBYkjyTPuOgawzradp6tfHg885ENG+CKK9h80Sc5+8gnePB7v0EE3vzmeSCGr7xydEby5ZdbNXjxYmusKxSsfL11q1WHq1WzR0DmNS4W7TW0J97O4/F4PB1SIQ7AFU34al4bYs91xxYf0agYYykUAPVkil36WVVsWl0phmpAfVce+uomop0kEWtJskTJobnYBnrkvbKcj4jIy4HPACHwJVX95JjPDwf+BXgh8Beqenn7j3KeMJPxya0e+ZyIzgdr63jlV9/LtoECv/nyv3HkIS/dvf3Oxijq5kEiYM+1Gjz1lO27qwuGhizje2jIEijyefMNp+uPjFiFON2HnzLn8Xg8baEzKsRN2cMSJ6OZnSC1wJIiqoEJV8GmzuWcjXCuSiOZQksOjWwiXTiQlKGSSXbWfJdNqpNYCHZFSaW1fRViUW3pY6EiIiHwOeAVwJHA+SIyNpxrO/ABoHOFMIxf9ZxkMlzLtp2IW27h1u3H8ltXvJ1KPeSOi6/nFS9+Zvem5s3GccH4g0T22ceqwWDf1dzpVyrBySebZaK/Hw491BIznn3WXvspcx6Px9M2OqNCDCb9Y0FzDuqZSHUF0IJDCjEaByZiazbG2ZWcZQxj26JNFePIWRVZadgiNF1XFA3EbBh+Lsd85ATgEVV9FEBEvg6cBzyYrqCqW4GtIvKquTnEecLYqmelAg8/DB/4gIX9TlZZHa9imi7fw2rs7f+T5+XXvJHD9tnJDe/7Lw5YNgjuuYM5duucWnBcwPiDRFavNoGbCt6NG00An366CeLt2+Etb7GUiYEBG9ssYtP4enunHtLh8Xg8npbQGYI4yQYG0CR3GMwyoQFI3jXEcLgzIi7ZCOdGDjFktgoHcXdM2FUnHo5MZINNrUumd2jBJeJbMwHdhnP0sWvTZjXwZNP7p4AT93RnIvIe4D0Aa9eundmRzTc2bswGYWzZAnfeaT/9i0ydgtC8bco4U+V2h5NfXOdPnryLS177IItKifd2d20FrTyuZutFFMGmTXDwwVnUWhhadvJUgvess3b/uz0ej8fTMjpDEEtW2dVAcUlmsFQD6KmjzgZ0hDtylk2c2B+kFMPOnO0jAOpi1eSaEA/lrNIcZFnFjaEftaBhRtGuuE0nqbCAbQ4tZry7lD2+eKr6BeALAMcff/zC+kNornquXw/d3fbf2ZIlU1dWWzTyuVyGj3wEPvQhWPqK3+Fvn74SKkuhsIdT81o1ijq1XixdmuUKq5oPeGBg9Bjmdgre2fBHezwezwKnMzzEiQh2qTgVe2jBoXWBagBqSRGumEyzC9TEcLIuClq06XVB1ewT4XDQ8AiPSpOwDDRwIBXvmZiHPAU0zzHbH/Ct/ONx5pkmOFM/q3PWDHb44fb5ZCkIzduOM1VuOjzzjK3+qU/BTTfRmql5LTguYLT1Igjs+ZBDbBrdxz8OF17YfiE6W/5oj8fjWeB0RoU4Fmucy5nYlcQyEQxjdopYiLsdBFgjXXcdKiESC2HZhG69N2m6CxUNgJwj7korxIKKyxroUg9FANrGqq0srNrkbPIzYJ2IHARsBN4CvHVuD2me0jxgQpL/vk85BVatsteTVVabtx0zVW46bNgAr3wlPPkkXHstvPGNTfudidCc4XE1mAVLyIyZLX+0x+PxLHA6QxCL4vrq9CwZplLOUa9Z1TZOKsPUhNzSMvVnS7guhwSKYjaL2uKkqpw048lgZBaMQJFyYI10TkyN9tUBzIJRtcY8n+c7/1DVuoi8D7gJa3v8sqo+ICJ/kHz+TyKyD3AX0Ac4EbkIOFJVd83Zgc8VqQBNxxEXClZZnY5dYQ/F689+Bi9/uRVeb7sNTl6+Aa5ooQ2gFaOoW2W9aCXzUaR7PB7PXkBHCOKgLJT6yuzTN8DOfJGdP18OQG2JQ/qqhL0x8aYucoNCbamDgRxhOU2ZSBRtLUmgCBQVYFfOPleQGlYNriYOFCfZ5Lp2DubwHuJpo6o3AjeOWfZPTa83Y1YKT8ruVlZn4GVdswaOPx4+/3k42I3j1Z0PI43TGwTImuh218/cauajSPd4PJ69gI4QxB2BYuOmPZ7ZZLqV1fEazqYQsarwta/Bm95k8b033ZR8cMUs2gBm0oDWKutFK5mPIt3j8Xj2AjpCEGsE0Y/6eGyfXrrX9ZN//k4ASv9vMXGpxOBBdYo7A2o9rpEcoREQCzJi9gotJh7imlWBtbuODJvPWPNKUBbiLqvQBv0hrugssi3wVVvPAmF3xONuelmrVXjPe+CqqyyR7IILmj6cLRvAHoj259AK60UrmY8i3ePxePYCOkMQ55S4AEuOeJal5/yKoKsLgC0XHMPQGlj8QMTwPorLK0HNYiXikkM08wBHOyJczhImXD6xTQi4rhgph8SL6lBOLBMBkFOoBOMHfM3aiXrx7Zkldlc87oaI3bEDXv968wpfdhm84x1jVpgtG8BCbUD7/9u79yjJ6urQ49/9O6cePdPzYABhQF7KRCP4Jj4wMT4wAYwiudxEV6JJ1GBuFPPQKGYRr7kmd+Umvi65iuLVazQmhKXRS6IJCsEbjYFIEAEjOkDQDIOAMjM9Pd1dVef89v1j/+oxTfdM9XRV9aP2Z61a01V1qs6vjjXO7s3+7b3agnTnnFsDxqLtWmgIMycXzDYr7H7L2Zz5lVnO/MosmgvZHMw+Stn+jPshQDlZohmQq417jgIxBcHBptFprimLrNa+rV5CMxDm7IZCmM6goge3Y3NurVqoxdi2bYuPTG4Hsb0WCGLvuQfOPhu+8hX4xCes37DM/yszqDZp8y00avlQbeScc86tW2ORIY51mxg3c98klU3KLW9+GgDyI0rj+AJpBr5777FQixCxcodC0oCOlCJW+1e63FzAXAaNzLK/hUDVWrbF0D4We64UC6pHxRPEbliWWrbQZy3rnj0wPW1x9XOfu8i5h1UG4BvQnHPOJWMREFMIoSHo8Q2YqrP3t/YDMDtXI8zlZJsimyZnmfrW0WhQyk0loZlZDFxLUeZEC6YrVDe0aE1VyI6doywCOpdZsCztKNjGRGvVxjeTj7IPsUfEbkiWEjy2a4337YPvftcyyWeeeVAQe/vt8MQnwtOfblONa7XDnH8QZQDza6BPPx2uv96eG8UGNJ8g55xzq9ZYlEyQKbGuVGsFzW0lJ26e4sTNU+i3Jgm769TqLfbetY1YVWJNLbNb0W52GGC6gmZK84d1qvuEYiZHZzMrqSgFqUSkkNR7WDrt1sKMT6pz60C/ZQu9k9Ke/GR46lNtmlwK/lThT/7Envr0p+0lhw2GB2GhCW7XXw8vfOHypt4t5/w+Qc4551aN8cgQK+RHz7F1coa9d2zizj2nAZAVQvWMfRzYX6f26AM0ZirWg3g22OS6aMM5wMY8hxnLGmuaWBf254SG2LjnRrWTDdb2iGgg5uUjljO8z+kZYjck/ZYtHGKjWnHaDt7wBvjQh6y12otfPML1L7auu+6yEcsrdf61voHPOefWibEIiGv1Fls3zxBE2fLMB2kWlrWda1ao5iWbj93H97+3DXJFJgt0S0TKgM5maCUFmaUQN5bIXCDWBFoBDVBOKKKg1djdQBfU2rNV4mi7TDg3TP2ULSxSazx1zw/4uZ+x3sJvexv8wR/Y3ryhWKg0YaUnuK30+Z1zzh3SWATEjUaFhx7Ygkxbva/WuqUQramMffWIBJDZVCoBhJlAeXQL2tPnqjaVTjOl3GA9hkUhTpRoljLDZaohnrENdxpkdBvdFB8T7VbeIrXGX9r/dP7hH+DDH4bXvnaI51+sPdzExMpuoPMNfM45t6qNRQ1xVimRmQytR3SiW8IgpfUb1qqimVp3CYBo7deYC1Ar7dYItklObBS0VKIFwqXVENtoZ6sd1kpqyaZYID0CgiI62JtzSzav1nj/AzPw8MO89LdP5zvfGXIwDIu3h1MdTuu2fg2rdZxzzrmBGIuAuGxlSCFUH8iRudDZ/KbSDVylTJ0i2vFrnnoIN4PdBKQZ7DGxbLCmmmFp2XuidG8RC5AbvqnOjZF2rfGmTfzNDZOc8ke/xpef9HrYsYNTTx3B+RfrLVyWnXUNfQPdQnquy4qc3znn3CGNRcnE2PCsrlsNduzgT/9+B7/5KXja02DHC04a3bkPVZowqAluR9o+zSfIOefcqjUWGWLUBmQUk5b11Wq0PsFCp8bXps/Z5jitKBSWAQ4zGWEmQ6JYxwlNXSZiarGWK2xuwZaW/dzuO5xGO3tdrxsnZQm/8RvwxjfCS14CX/oSHH/8CBcw7NIEb5/mnHPr0ngExKRgN1diNR7cJ7hho5YJijRDZyJduzuElHYLc9ZlAgGJ2CS7QqyjRBS01XMpBZt6B6O9wqqDvTm3RJ/8JFx+Ofz2b1uf4Y0bR7yAYZcmLHWEtXPOuTVhfEomBBu0EejJ2gpxwu5IK6B5tIA4DZ3LpgPl1gKAfEOBPFRH6yWhyClTBlgaGVq008zdc1EI5LETeDu3nqmCCPziL8Kxx8J5563gYoZZmuDt05xzbl0am4BYRVPQqwcHrpVo5RFVhTwiMzlE0ImSsie7VT5Ut8xwvSRWMsKGAm2F7ohmUcKsbaDTgGWOwduuuXXv9tstCXv11XDqqSscDC9mUGOTvX2ac86tS2NSMgGi0g2G2/W9YgM0pGUt1WQ6R4Oi9Yg0Mus80S6vCEqciGgjs8l0pdUlUwpSCtIKxHok1iNaS7eKHtTzePif0duuudG69lp4znMszpyaOsSBO3fCFVfAZZfZn6OsuR1k3a+3T3POuXVpbAJiFSWbDoS5QJi1GwBBLbCNVmMsLUGaVj6hdZtMJ3MBrai9Jk/HC1ZDnCmUqRyj3XJNUj0yVorh3Hp05ZU2fvkxj4GbboInPWmRA1d6I9og6369fZpzzq1L41EyEQUClJM2Ya5dxiCzGaFIZcMZkGmn2wRqWWGtpl7DhRA3F9AS8qmMohqhYhliraVa4faY5xRcI4qO8gp7VteNyMc/Dq97HZx/Plx1lcWFi+oNSKH753XXjSaQHHTdr7dPc865dWc8AuJgwWln41yZsraixHrK7papa4SANAXdUFptcG4lD1q146Wm1PYIxdY0prklIEJoiE23g+7GPLqvd27N66nD/dmjT2H3my7kzX90DPnh/l9kpTeied2vc865wxiPgDhpT5brbD4TbPxyIVZj3O5NXIjVELeEuCEFuUGhkRH2Z8z86JzVHh/IbLhdNVJuUMs+A7RSKUUhUIyqZMJbpbkh2rmTBy+/isv+5aW85xVfY7L5MJfm74J/76NcoFKxYuNGA446Ch7/eKjVDg5IB7XpbSHnnGMlGmCB+NSU1f1eeOFg3t8559yaNx4FrkKnv7DMZZ3RzcDBPzesXhi1AR1xc9HZgCezGflURrmpRGdyyx4Htbdtl2EUwW7twR3lCFuuKd6H2A3Ntz5xM8/6i0v486+fwa27j+2/DnfnTgty9+yxIHh2Fq6/Hu6+u7sRbdg1xl7365xz7jDGKkPsnFu6G26An/2Tl1GtKF9609/wjNMesif6KXu47jo4/XQ46SS4807rynDUUbB9ezcgHUWNsdf9OuecO4QVD4hFpA78I1DD1vMpVf2vIvIO4FeB9K8vv6uqn0+veRvwGqAE3qiq1x7yJFGQpm2s00rPsAzFsruZTZsTevoKZwrN0Mkeo1BsLUChfn+F1uNmKA9U7NhqhHabNugM+GhvrBsZL1d2g9BTvvDXD/44P/9/fpofOXaWz73qak49rec/Kk1NQZ5bG7XFSh3a9cMhwHHH2WMxHhxIr3SNsXPOubG3GkomGsALVPXJwFOAc0XkWem596rqU9KtHQw/AXg5cAZwLvABEckOeYbUJo1Ip5RB2kGvSqecQidKO0axOuA8opmimXZbrKkwd2KLcqpqx/b0Im4fi4oN/KhEssli4BfMuaGZV77w1G3f5aId3+Cf3v8NTuXeg/vv3n23Ba2HKnVob2jrNX9DWz/HOOecc0O04gGxmul0t5Juh0qrXgBcpaoNVf134C7gGYc9T5ZaqPWU9YaZDJROz2Ca1meYPAW1zdS3eM4m3MlshjRtCIc0xWqOmwFpZIS5bqAtpUAa9sH3a0d0XY6ED+Zwy3bddTQ2H8sHbj2bSOC0xwh/+ctfYOv373xkHe727VYOcaj+vv0MsvBhF84551bYipdMAKQM778CpwPvV9WbROQ84A0i8irgZuBNqroHOBG4seflu9Jj89/zYuBigOzorZApUgRU0mANgAzbQLehsGA4jXG2N1DIILYnzVVSNwoRpCHothY6l1lbNRXKnG4f4qZtzCNAedQIM8QexLqF9NvBYedOfvipG7jw62/ny3vO5Iz63fzksxrd8oX5dbiXXWbP9Zpf6tDe0NY+/wknWHeH3vfp5xjnnHNuiFZFQKyqJfAUEdkKfEZEzgSuAN6JhZbvBN4NvJqDcrzdt1jgPa8ErgSonfJolVawARqiyFyqsMiA9lCN0noQd+qBm2ngRjUFxGninIpCFbQZkEboBtcqMJeWFoDcptVp6UGqG7CltChrl0Bs22ZlDVNTdn9+l4WdO7nrXZ/l/Jvex/dmj+Evf+zd/OTuL8IDz3lki7S2hfr73n037NplwXLv2uavb/5nOP10e9x/qXPOObcCVrxkopeq7gW+BJyrqg+oaqmqEfgw3bKIXcBJPS97NLD7kG8sqT44j1AEtBLtViuR9ia7dphd2Ea6gybWqRAaAUkT7wBqW+fQDSVSL7uvDemm2LS6oFZjPAoKRB3sza0+S21R1ufY4q9+6Hae9eev52HdyvVnXcrLT/oqTEzALbcsXr4wv9Rh50748pdtXYda2/zPcM898Hu/Z3+uxGhn55xzY2/FA2IROTZlhhGRCeAc4E4R2d5z2IXAHenna4CXi0hNRE4DdgD/ctgTKZb5LQQpAlIE6zrRTL2HK5YJlna5Q64WLLfEbgDR+hazuUVz1jpM6Eze7VbRDoCFTp/iTkcL5wahzwC34777Fi5r2H3w75DNB/Zw/JY5bnzbNTznZ46Cet0GaTQai/fsnd/fd9cu+ImfsMcPtbb5n2H3bru/e3d/n8k555wbsNVQMrEd+LNURxyAq1X1b0XkEyLyFCzcvBd4HYCqflNErgb+DSiA16eSi8UpVtebJtN1WqnNpTKIarQzt8Qyu6leWMtu84pYizamGdBGhswGQhRiPXYzxO0/ayVyILf3qI4q0+rDNMbCUluUHWJssSrceCM8+9nwvLObfOOMj5Bt2wIcZy3S9u61YPdQtby95RCXXdbf2uZ/hr17bX179/b3mZxzzrkBW/EMsarepqpPVdUnqeqZqvrf0uOvVNUnpsdfqqr397zmD1X1sar6OFX9u75OJFhgLKC1mOqJOXgjnIBuKG1D3URp9cO1dAuK1iNaUWQmIzum0a0fLsWyzIXYxruZ3ALrXEc8rW71TKoTkW0i8kUR2Zn+PGqR4z4qIg+KyB3zHn+HiNwnIrem2/nLWtB6/uNU+gAAFtlJREFUsdQWZYt0cGj95Dm89rVw9tnw1a/acdneHy6v00O/a5t/XDsYXiBod84550ZhxQPi0UhBaZaCvBS4dp5tWGs1aqVtnsvVRjC3AmEqJ0zlSCN0yh+0Follz7jnllj2OI151kwtcN5YdIPm8XMpcL2q7gCuT/cX8jGsn/RCHtGHeuwttUXZAmOL9/7nX+W8N+7gox+Ft7/dMsQDGW/c79rmH3fCCXb/hBO87ZpzzrkVsRpKJkZAkSjWiiIohJ4AubQNdFotO50kKIUwnSEK5WSqxlBBCiHMBuJxDWIrs/g3JVNjPXY30KU6Yi3Cwj0xhvYxV1XwfQHwvPTzn2GbJd86/yBV/UcROXVUi1rzjqRFWU9Zw733wotfbPvVPvYx+KVfWvi4oa5t/nGPeQz81E/BXXd52zXnnHMrYkwCYrcCjmuXuajq/SLyqCN4j4X6ULtlBK7XX2971669Fp7//CM8/0It09rB7OHawLUt9Ble9KIjXJBzzjm3PONRMhGAyZa1TdM0Ya6RJsll7bHO0p1Yl9qwxRzyfTn5vpxsf4YGG82s07lNrUub6nSyTGUWqRSjZeUWNpxjTbddO0ZEbu65Xdx7ShG5TkTuWOB2wQA+0RXAY7Fx3vdjfajdEXroIfvzNa+Bb397mcGwt0xzzjm3zoxHhlhTZ4jJlvUhlp4gtRQLlNut1URt4EZu3SjKDdaOTTeUMBeIkyVEQVF7n6Bke3LKTWXn1wspBc2UMJ1bKcXIPuTAz/UDVT1r0TOqLlrkKSIPiMj2lB3eDjy4lBOr6gM97/Vh4G+X8npnVOE974Hf/334p3+CJz4RHnUkufq23pZpcHDLtB07uo9fd92hs8RLGS7inHPODdl4ZIhLy9xqM7VRy7XnFi3zW1HrPKFiE+wCxImI1ku0XpLVSkLqUSxNIWxsIWpBdKwp2cbCNuXVSmRL0x7fcOhucOvcNUC7QvWXgP+7lBcfog+161NRwOtfD29+M/z0T3eHwS3L/L7Gi7VM232IWTlLHS7inHPODdl4BMRBrXValsoaYroVgjQyez5TmzoHNsUuU2RDQWVji8rGFuVUBRWQVkDrkdjIIJJ6DUfKqQqyP0f25+jDVXsujG/bNeCPgBeJyE7gRek+InKCiHQ6RojIXwL/DDxORHaJyGvSU38sIreLyG3A84HfWu6Cxsn+/fDSl8IVV8Bb3gJ/9Vc2eG7ZBtEybanDRZxzzrkhG4+SCbCa3lQOIal9mpQQ62maXK7oTG5dJDZEC2j3V2jlllUOm1rwYA0NQFDyH1QoN0WkFMJMQHNLLoMF1FIKMp0jY5okVtUfAi9c4PHdwPk991+xyOtfObzVrX+XXw5f+AJ86ENw8cWHP75v55xj2VywTPAJJ8Ddd8MZZ1jLtKkpa5l24YWLv8dSh4s455xzQzYeAbG063oBlW7gWrdNclIKqlYPrLlaMFxRNHazpHG6gmSg9YjMZpTbG+hchjQCsZbarKUMM6V0WriVEyOqIW5vqnNjrSwhy+Ctb7XY9ZnPHPAJBtEy7RDT85xzzrmVMB4BcbRNbpCytylDTLRAuVM4Ihbwkqs1GG6FTucJDe3HBJ0okekKoWXBtVZS0JuC4DCbXpNBaIxHVYpbeZ/7HFx6qcWqxx03hGC4bbkt0+ZnmfvJKjvnnHNDNF7RWnt0cyVaEKtpjHM1EiaKTvs1mgEKC4Y1KBqUbDag1ZSBVUGDEjemMc+Z2ma7NLFOMxvUofWSuKkY4edbVTXEboTe/36rGa5WrXJhVRvEVDznnHNugMYjQyw9fwbtZn0r2hm3HGdzC4YzK52gGdBqd/pcmakVCQeQWomSWba4YjXIEnsyzapQj0gWu4XFo+BB7NgpS/id34H3vhde8hL4i7+Aycn05GpubbbcqXjOOefcAI1JhlihkoLbQjqt1BAIM5nV/LZjyTJliHPt3i9toAcKMlGgjYxsb27lECnI1kw72WSdKAnVEp3LUC+ZcEP0zndaMHzJJfCZz8wLhr21mXPOOdeX8cgQQ6cXsaig7RrioMR2O7Z2UNyuHy6l030CsA1rKZucP5xTbC0OmmwnRTdDrAJxf8Wy0a1RBcRe5jCOLrkETj4ZXv3qeU/MH6DR78AM55xzbgx5+tK5NeaOO+CVr4RGA44+eoFgGB45QAMOPzDDOeecG1NjkiGWzjhmDdopj5Cip064leqAS7FMsKbuEe3BGpVo3Sqmc8p6t56YiE2sEzqdLMK+3O4HyBojqiFW1sBuKrdcX/wiXHQRbNgA3/se7GCROmFvbeacc871bTwyxAo6WXRGNbcn1anYBjuZy6CeRjhniuZpMEeU9CfIbEa2LyM0AjpZEGaCPZcCYa1GC5orkbipJE6WaCVSbBrhZA7vMrGufeQjcP75cMopcNNNKRherE74nHOsldnevfaL0t69dv+cc1b6YzjnnHOrzngExKlrRGfDXEidJHozwc3QGeARZjJC0+qCpd1Bol1KPBGhFYibC3tMsZrjoEhmNxSyjS1oB8nOLdO73w2vfS284AXwla9Y3fAhRyB7azPnnHOub+NRMhGtS4QUYpPotLupjokS5mw8M4qVVWQKtWijnlPbNZ3NKSupW4VC2J+jVUVit/WatlsOB7VObyqdtm0j4Vnddevcc2HXLvjjP4ZKJT14uBHI3trMOeec68v4ZIg7nSPoZnZVCD9M3SCaqQdx+/lmsF7Ecxk6l1lw3LROFWEqR49q2RCOSoRWaslWiUglQhEopqrWhaIYYR9it26dcYa1V+sEw9CtE+7ldcLOOefcko1HQFwKlYdzpATqsVsyEaHcmEY1BzoDNiSKjXRW6fYhztQywSrELZYKDk3brGelF0J4qEp4qEplb0Z2IECJ1SePhFpruEHe3OrmdcLOOefcQIxHyURQWltLpBQkjzCd0mwBNChhb975WQqxior2BrxeFasfRqGyq0rrqAgbC2gGpBGINQsi44QF21KKBeGjoKDq9cpjpV0n3O4yccIJcOGFXibhnHPOLdF4BMRg3SAqEeYypF0ynGm3rhiQViBuKK3MIVOkkdlEO+huxhPI9uY0jy7tfiNY/XCmnXy7NIXQFGIFdHyusFsJXifsnHPOLdt4hGsC0rISCK0qWrMgV+YyK3fIQEUhi1b3q1iQW4v2fPttioCKEquKlJZJzg4ENLPsMu1AO1fCdEBKKLYWj1jO0HiZg3POOefcko1HDbFimeCNJdWj5uxTBwhzQnYgWLCcW/ZXa1Z2oPXSNuI1gt0E6zUM6KbCRjVnSqxrp2lFNh3IpgNSCK1tJeVxzRX6wM4555xzrl/jkSEG4qYCySOtRm6dJIBQCK2tJWFjCxoZYSonFEJZt04RgHWeAMsyN22ynRzIkVZ3Q10oQINQTqaAOY+WjU7lFCPjbdecc84555ZsbALidU/VRzc755xzzh2B8QiIU1s0LdKYZrFMamtbQT7ZothbJcwFa1VcUxu4UYj1GU6J4nxWKOuKBmvDVk6WZDNWPxxzrC1bO5us1rZNq4o0vA+xc84559xqNh41xAIyZ0MytBKtDCKzXsTlD2pU9mVkzRT85oo0AmE2INFqibVeWqCca+pTDGGyRcxBK4puKNMEPKxeuZkGgJSpn/GoqA72to6JyLki8m0RuUtELl3geRGRy9Pzt4nI01Zinc4555wbvvEIiNNIZikC0up+ZJnJyOYCMYfWUQWxFtFaaZniLQXl1qKzAS9ORKsfbgixHolzOYSUFS5tUl174IcU1oYtzISDulS41UFEMuD9wHnAE4BXiMgT5h12HrAj3S4GrhjpIp1zzjk3MuMREEch359BCbqhG+SiQrG5pNzWsql0VdsMF9M0O5nNupPq0kjnOFmyeft+ZCZDN5T2eCW1Z0vHalXJZgKhKd0yihHQGAd6W8eeAdylqveoahO4Crhg3jEXAB9XcyOwVUS2j3qhzjnnnBu+8QiIRSnrEZ0skd7AdWMBebTAtxptdHMjDeBoWXZX2mUP7SC6Etl/32ZrvXYgs+fnsk7nCrCBHxqg3BQ7bdyGb8DlEuu7ZOJE4D967u9Kjy31GOecc86tA2OzqU4nIjQDqnTrehs5UoJWbdQy0YJkEdBC0FaAdkDbSmURaThHOwCWQtCKWu1xM/1+EVJt8URJVhvV7Ga3BAsVssz/DaCfY+xAkYuxsgpOPvnk5a3MOeeccyM3HgFxaUM0aAnSCt2sba5oBOoWLIe5QAxK2J8RqwpbWuisjZ+T1JdYg0I1EqZyC6SL9JhKJ1zSvJsVLg+M6BIrPqmuf7uAk3ruPxrYfQTHAKCqVwJXApx11ln+P4Jzzjm3xoxFyYQokLK3WolII1gniblANhus9KFpo5ilGm36XC2i0znZdEY2nVkZRCVaaUUpaHtMc7Ub/2g9ovWIFIFsKid7OCccyFbgE7vD+BqwQ0ROE5Eq8HLgmnnHXAO8KnWbeBawT1XvH/VCnXPOOTd8Y5Eh1lytL3AeLSucfg3QCKL2uMzZJjmZzgkNIRLQiZKyUzIRuplmLMiOuXbHPpfS6WARZgKaa7cUY2QfdF1vhBsYVS1E5A3AtUAGfFRVvykiv5ae/yDweeB84C5gBviVlVqvc84554ZrLAJiyRQN1nZNs7IzmIPcYsgwkxEnIlIr0ZZQblTrHJH6CtubKGEuIypU92Q0t5UWHBdCmMnQeRWnUkLMPUBdrVT181jQ2/vYB3t+VuD1o16Xc84550ZvLALicaCAeg2xc84559ySjUVArEUgmwnEY5twIO92jigFKXqGaihWI5w20NEInY4UmqXyh0xpbY5IlG4AGoFqKp8Ay0ALVkIxqkl1ql4y4Zxzzjl3BMYiIEaUckOERnZQGUS2NyfWInGDDdbQIlgwnGmn6ZZutLZpYSqHQKdLRbY/I9asV7FW0ya8ifTGqWOFtMQn1TnnnHPOrXJjEhDTnSQXLBAGiFVFJyLSDMhcIG4oLVNciPUdztQCYVJXtXpp75Mp8egWTOW2JatMx6eOE1IKcSLVII8wIPaSCeecc865pRuLtmudTg9V+6GciJQT0dqolVYyEavdMopspqdUoqJoRZGIlUHUS+s4ccD6ELe7TIiKZZeL0B31XImj7TLhnHPOOeeWbDwyxG2pbZqUqSexaHdqXaorzqYyy+puaSFTlU5Aq6ndms5mZNOBWFVCIxDrkdAIaIDQ7NYbSyEQs87rRsJriJ1zzjnnlkysu9T6JiIPAQeAH4zolMfMO9cpqnrsME8oIn+fzjtIP1DVcwf8nuta+q59d5Gn538v3JHx67h8fg0Hw6/j8vk1HAy/jv1bMCYbi4AYQERuVtWz1tu53Nrh34vB8Ou4fH4NB8Ov4/L5NRwMv47LNx41xM4555xzzi3CA2LnnHPOOTfWxikgvnKdnsutHf69GAy/jsvn13Aw/Doun1/DwfDruExjU0PsnHPOOefcQsYpQ+ycc84559wjeEDsnHPOOefG2roPiEXkXBH5tojcJSKXDuH9TxKRG0TkWyLyTRH5jfT4O0TkPhG5Nd3OH/S53ep0uO+cmMvT87eJyNNWYp2rWR/X8PEi8s8i0hCRN6/EGteCPq7jL6Tv4G0i8lURefJKrHM16+MaXpCu360icrOI/PhKrHO16/ffYhH5MREpReSiUa5vrejj+/g8EdnXE3u8fSXWuRat6xpiEcmA7wAvAnYBXwNeoar/NsBzbAe2q+otIrIJ+FfgZcDPAdOq+q5Bncutfv1859IvR5cA5wPPBP6nqj5zBZa7KvV5DR8FnIL9Xdvjf88eqc/reDbwLVXdIyLnAe/w72JXn9dwEjigqioiTwKuVtXHr8iCV6l+/y1Ox30RmAM+qqqfGvVaV7M+v4/PA96sqj+zIotcw9Z7hvgZwF2qeo+qNoGrgAsGeQJVvV9Vb0k/7we+BZw4yHO4NaWf79wFwMfV3AhsTb9YOXPYa6iqD6rq14DWSixwjejnOn5VVfekuzcCjx7xGle7fq7htHYzSxuB9ZtlOnL9/lt8CfBp4MFRLm4NGXpMM87We0B8IvAfPfd3McRgVUROBZ4K3JQeekP6T2kfFZGjhnVet6r0850b6fdyDfLrMxhLvY6vAf5uqCtae/q6hiJyoYjcCXwOePWI1raWHPY6isiJwIXAB0e4rrWm37/TzxaRb4jI34nIGaNZ2tq33gNiWeCxofz2nv6z2aeB31TVKeAK4LHAU4D7gXcP47xu1ennOzey7+Ua5ddnMPq+jiLyfCwgfutQV7T29HUNVfUzqUziZcA7h76qtaef6/g+4K2qWo5gPWtVP9fxFuAUVX0y8KfAZ4e+qnVivQfEu4CTeu4/Gtg96JOISAULhj+pqn8NoKoPqGqpqhH4MPafOtz61893biTfyzXMr89g9HUdU93r/wYuUNUfjmhta8WSvouq+o/AY0XkmGEvbI3p5zqeBVwlIvcCFwEfEJGXjWZ5a8Zhr6OqTqnqdPr580DFv4/9We8B8deAHSJymohUgZcD1wzyBCIiwEewjSnv6Xm8tyb0QuCOQZ7XrVr9fOeuAV6Vuk08C9inqvePeqGr2ND/3o6Jw15HETkZ+Gvglar6nRVY42rXzzU8Pf07QOoYUwX8F4uDHfY6quppqnqqqp4KfAr4dVX17ObB+vk+Ht/zfXwGFuf597EP+UovYJhUtRCRNwDXAhm2a/WbAz7Nc4BXAreLyK3psd8FXiEiT8H+c8a9wOsGfF63Ci32nRORX0vPfxD4PNZh4i5gBviVlVrvatTPNRSR44Gbgc1AFJHfBJ6QypUcfX8X3w4cjWXjAApVPWul1rza9HkN/xP2C24LmAV+vmeTnaPv6+gOo8/reBHwX0SkwL6PL/fvY3/Wdds155xzzjnnDme9l0w455xzzjl3SB4QO+ecc865seYBsXPOOeecG2seEDvnnHPOubHmAbFzzjnnnBtrHhA755xb00RkeoHHThaRG0Tk6yJym4icP6xzOefWPg+InXPOrUeXAVer6lOxAQYfWOH1OOdWMQ+InXPOrUeKDW4B2MLCY6v/h4j8es/9d4jIm0RkUkSuF5FbROR2Eblggdc+T0T+tuf+/xKRX04/P11E/p+I/KuIXNueXCoibxSRf0sZ66sG+3Gdc8uxrifVOeecG1vvAL4gIpcAG4FzFjjmKuB9dLPHPwecC8wBF6rqlIgcA9woItf0M/FLRCrAnwIXqOpDIvLzwB8CrwYuBU5T1YaIbF3ex3PODZIHxM4559ajVwAfU9V3i8izgU+IyJmqGtsHqOrXReRRInICcCywR1W/l4La/y4izwUicCJwHPD9Ps77OOBM4ItpHHYG3J+euw34pIh8FvjsYD6mc24QPCB2zjm3Hr0Gy/aiqv8sInXgGODBecd9CrgIOB7LGAP8AhYgP11VWyJyL1Cf97qCg8sO288L8E1VffYCa3ox8FzgpcDvicgZqlocwWdzzg2Y1xA755xbj74HvBBARH4UC1gfWuC4q7BNdxdhwTFYzfGDKRh+PnDKAq/7LvAEEamJyJb2uYBvA8emrDQiUhGRM0QkACep6g3AW4CtwOQAPqdzbgA8Q+ycc26t2yAiu3ruvwd4E/BhEfktbIPdLy9UA6yq3xSRTcB9qtoubfgk8DcicjNwK3DnAq/7DxG5GiuD2Al8PT3eFJGLgMtToJxjdcrfAf48PSbAe1V17yA+vHNu+aSPPQLOOeecc86tW14y4ZxzzjnnxpoHxM4555xzbqx5QOycc84558aaB8TOOeecc26seUDsnHPOOefGmgfEzjnnnHNurHlA7Jxzzjnnxtr/B/Mewrq23dTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot Sentinel-2 8 ndvi for AOIs\n",
    "for desc, res_S2, og_L8 in zip(fl_gdf.filename.values[:num_fl], res_S2_means, L8_arrs):\n",
    "    \n",
    "    print(desc)\n",
    "    # NEON NDVI\n",
    "    l8_nir=5\n",
    "    l8_red=4\n",
    "    l8_num = og_L8.sel(band=l8_nir) - og_L8.sel(band=l8_red)\n",
    "    l8_den = og_L8.sel(band=l8_nir) + og_L8.sel(band=l8_red)\n",
    "    l8_ndvi = l8_num / l8_den\n",
    "    \n",
    "    # Satellite NDVI\n",
    "    s2_nir = 8 # 1-based... 7 or 8 for band 8 or 8a\n",
    "    s2_red = 3 # 1-based\n",
    "    sat_num = res_S2.sel(band=s2_nir) - res_S2.sel(band=s2_red)\n",
    "    sat_den = res_S2.sel(band=s2_nir) + res_S2.sel(band=s2_red)\n",
    "    s2_ndvi = (sat_num.astype('float')) / (sat_den.astype('float'))\n",
    "    \n",
    "    ## check shapes. if less than 2x2, fuhgeddaboutit\n",
    "    neon_vals = np.flipud(s2_ndvi.values)\n",
    "    sat_vals = l8_ndvi.values\n",
    "    \n",
    "    # hard threshold bad values\n",
    "#     neon_vals[neon_vals>1] = 1\n",
    "#     neon_vals[neon_vals<-1] = 1\n",
    "    \n",
    "#     # hard threshold bad values\n",
    "#     sat_vals[sat_vals>1] = 1\n",
    "#     sat_vals[sat_vals<-1] = 1\n",
    "    \n",
    "    neon_shape = neon_vals.shape\n",
    "    sat_shape = sat_vals.shape\n",
    "    \n",
    "    if 1 in np.array([neon_shape] + [sat_shape]):\n",
    "        print('array is too small for anything, continuing...')\n",
    "        continue\n",
    "    \n",
    "    # slice if necessary\n",
    "    if neon_shape[0] < sat_shape[0]:\n",
    "        sat_vals = sat_vals[:neon_shape[0],:]\n",
    "            \n",
    "    if neon_shape[1] < sat_shape[1]:\n",
    "        sat_vals = sat_vals[:, :neon_shape[1]]\n",
    "        \n",
    "    if neon_shape[0] > sat_shape[0]:\n",
    "        neon_vals = neon_vals[:sat_shape[0],:]\n",
    "            \n",
    "    if neon_shape[1] > sat_shape[1]:\n",
    "        neon_vals = neon_vals[:, :sat_shape[1]]\n",
    "    \n",
    "    # get the correlation coefficient\n",
    "    corr = np.ma.corrcoef(np.ma.masked_invalid(neon_vals.flatten()), np.ma.masked_invalid(sat_vals.flatten()))[0,1]\n",
    "    \n",
    "    # plot the NDVI\n",
    "    minval = min(sat_vals.min(), neon_vals.min())\n",
    "    maxval = max(sat_vals.max(), neon_vals.max())\n",
    "    \n",
    "    fig, ax = plt.subplots(2,2, figsize=(15,15))\n",
    "    fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05)\n",
    "    \n",
    "    im = ax[0,0].imshow(neon_vals, vmin=minval, vmax=maxval)\n",
    "    fig.colorbar(im, ax=ax[0,0])\n",
    "    ax[0,0].set_title('S2 resampled')\n",
    "    \n",
    "    im = ax[0,1].imshow(sat_vals, vmin=minval, vmax=maxval)\n",
    "    fig.colorbar(im, ax=ax[0,1])\n",
    "    ax[0,1].set_title('L8')\n",
    "    \n",
    "    im = ax[1,0].imshow(sat_vals - neon_vals)\n",
    "    fig.colorbar(im, ax=ax[1,0])\n",
    "    ax[1,0].set_title('S2 and L8 differenced')\n",
    "    \n",
    "    \n",
    "    ax[1,1].scatter(sat_vals.flatten(), neon_vals.flatten(), alpha=0.4, color='r')\n",
    "    ax[1,1].set_xlabel('L8 values')\n",
    "    ax[1,1].set_ylabel('S2 resampled values')\n",
    "    ax[1,1].set_title(f'correlation {corr}')\n",
    "    \n",
    "    ## plot 1:1 line\n",
    "    ax[1,1].plot([minval, maxval], [minval, maxval], 'b--')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_S2.band.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_ndvi.rio.to_raster(\"test_s2_ndvi.tif\")\n",
    "neon_ndvi.rio.to_raster(\"test_NEON_ndvi.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_S2.sel(band=s2_nir).rio.to_raster(\"test_s2_nir.tif\")\n",
    "og_S2.sel(band=s2_red).rio.to_raster(\"test_s2_red.tif\")\n",
    "neon_S2.sel(sat_band='S2A_B8A').rio.to_raster(\"test_NEON_S2_nir.tif\")\n",
    "neon_S2.sel(sat_band='S2A_B4').rio.to_raster(\"test_NEON_S2_red.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at the band means and plot the difference map as function of variance or STD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\n",
    "from matplotlib.ticker import FormatStrFormatter, FuncFormatter\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentinel-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S2A_B2', 'S2A_B3', 'S2A_B4', 'S2A_B5', 'S2A_B6', 'S2A_B7',\n",
       "       'S2A_B8', 'S2A_B8A', 'S2A_B11', 'S2A_B12'], dtype='<U7')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_s2_ids = neon_S2.sat_band.values[[1,2,3,4,5,6,7,8,11,12]]\n",
    "n_s2_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NEON_D10_CPER_DP1_20200913_170936_reflectance\n",
      "S2A_B2 1\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-313af0491ff3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[1;31m# extract values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m         \u001b[0mneon_band_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflipud\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mneon_band\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m         \u001b[0msatl_band_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msatl_band\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mneon_std_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mneon_std\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\xarray\\core\\dataarray.py\u001b[0m in \u001b[0;36mvalues\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    555\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m         \u001b[1;34m\"\"\"The array's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\xarray\\core\\variable.py\u001b[0m in \u001b[0;36mvalues\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    449\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m         \u001b[1;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\xarray\\core\\variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    252\u001b[0m     \u001b[0mTODO\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mremove\u001b[0m \u001b[0mthis\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mreplace\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m)\u001b[0m \u001b[0monce\u001b[0m \u001b[0mthese\u001b[0m \u001b[0missues\u001b[0m \u001b[0mare\u001b[0m \u001b[0mfixed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    253\u001b[0m     \"\"\"\n\u001b[1;32m--> 254\u001b[1;33m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    255\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"M\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\array\\core.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m   1303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1304\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1305\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1306\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1307\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    164\u001b[0m         \u001b[0mdask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         \"\"\"\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    435\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 437\u001b[1;33m     \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    438\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\threaded.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(dsk, result, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[0;32m     82\u001b[0m         \u001b[0mget_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_thread_get_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0mpack_exception\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpack_exception\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m     )\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\local.py\u001b[0m in \u001b[0;36mget_async\u001b[1;34m(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)\u001b[0m\n\u001b[0;32m    473\u001b[0m             \u001b[1;31m# Main loop, wait on tasks to finish, insert new ones\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    474\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"waiting\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ready\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"running\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 475\u001b[1;33m                 \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_info\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfailed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqueue_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    476\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mfailed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    477\u001b[0m                     \u001b[0mexc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\site-packages\\dask\\local.py\u001b[0m in \u001b[0;36mqueue_get\u001b[1;34m(q)\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\queue.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m    171\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mremaining\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m                         \u001b[1;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 173\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    174\u001b[0m             \u001b[0mitem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\software\\anaconda3\\envs\\gbdx017\\lib\\threading.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    297\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 299\u001b[1;33m                     \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    300\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# plot Sentinel-2 8 ndvi for AOIs\n",
    "\n",
    "# need to iterate through the correct bands for sentinel 2, as arcsi produces only 10m and 20m bands and omits the 60m\n",
    "for desc, neon_S2, og_S2, neon_S2_std in zip(fl_gdf.filename.values[:num_fl], res_S2_means, S2_arrs, res_S2_stds):\n",
    "    \n",
    "    print(desc)\n",
    "    if 'tarp' in desc:\n",
    "        print('tarp is too small, skip it...')\n",
    "        continue\n",
    "        \n",
    "    # iterate through the bands\n",
    "    n_s2_ids = neon_S2.sat_band.values[[1,2,3,4,5,6,7,8,11,12]]\n",
    "    for nband, sband in zip(n_s2_ids, og_S2.band.values):\n",
    "        print(nband, sband)\n",
    "        \n",
    "        # select the bands from the data\n",
    "        neon_band = neon_S2.sel(sat_band=nband)\n",
    "        satl_band = og_S2.sel(band=sband).astype('float')/10000 #arcsi scale factor is 1000\n",
    "        neon_std = neon_S2_std.sel(sat_band=nband)\n",
    "        \n",
    "        # extract values\n",
    "        neon_band_vals = np.flipud(neon_band.values)\n",
    "        satl_band_vals = satl_band.values\n",
    "        neon_std_vals = neon_std.values\n",
    "        \n",
    "        # mask bad values\n",
    "        neon_band_vals = np.ma.masked_greater(neon_band_vals, 1)\n",
    "        satl_band_vals = np.ma.masked_greater(satl_band_vals, 1)\n",
    "        \n",
    "        # get shapes\n",
    "        neon_shape = neon_band_vals.shape\n",
    "        sat_shape = satl_band_vals.shape\n",
    "\n",
    "        # if shape is too small, don't plot it\n",
    "        if 1 in np.array([neon_shape] + [sat_shape]):\n",
    "            print('array is too small for anything, continuing...')\n",
    "            continue\n",
    "\n",
    "        # slice if necessary\n",
    "        if neon_shape[0] < sat_shape[0]:\n",
    "            satl_band_vals = satl_band_vals[:neon_shape[0],:]\n",
    "\n",
    "        if neon_shape[1] < sat_shape[1]:\n",
    "            satl_band_vals = satl_band_vals[:, :neon_shape[1]]\n",
    "\n",
    "        if neon_shape[0] > sat_shape[0]:\n",
    "            neon_band_vals = neon_band_vals[:sat_shape[0],:]\n",
    "            neon_std_vals = neon_std_vals[:sat_shape[0], :]\n",
    "\n",
    "        if neon_shape[1] > sat_shape[1]:\n",
    "            neon_band_vals = neon_band_vals[:, :sat_shape[1]]\n",
    "            neon_std_vals = neon_std_vals[:, :sat_shape[1]]\n",
    "\n",
    "\n",
    "        # linear regression piece\n",
    "        x = np.ma.masked_invalid(neon_band_vals.flatten())\n",
    "        y = np.ma.masked_invalid(satl_band_vals.flatten())\n",
    "        slope, intercept, r, p, se = linregress(x, y)\n",
    "        \n",
    "        \n",
    "        # get the correlation coefficient\n",
    "        corr = np.ma.corrcoef(np.ma.masked_invalid(neon_band_vals.flatten()), \n",
    "                              np.ma.masked_invalid(satl_band_vals.flatten()))[0,1]\n",
    "    \n",
    "        ############### PLOTTING ##################\n",
    "        minval = min(x.min(), y.min())\n",
    "        maxval = max(x.max(), y.max())\n",
    "\n",
    "        # plot the images and scatter plots\n",
    "        fig, ax = plt.subplots(2,2, figsize=(15,20))\n",
    "        fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.15)\n",
    "\n",
    "        ## NEON resampled band\n",
    "        im = ax[0,0].imshow(neon_band_vals, vmin=minval, vmax=maxval)\n",
    "\n",
    "        divider = make_axes_locatable(ax[0,0])\n",
    "        cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "        fig.colorbar(im, cax=cax, format=FormatStrFormatter('%.2f'))\n",
    "\n",
    "        #fig.colorbar(im, ax=ax[0,0])\n",
    "        ax[0,0].set_title('NEON resampled')\n",
    "        ax[0,0].axis('off')\n",
    "\n",
    "        ## Satellite band\n",
    "        im = ax[0,1].imshow(satl_band_vals, vmin=minval, vmax=maxval)\n",
    "\n",
    "        divider = make_axes_locatable(ax[0,1])\n",
    "        cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
    "        fig.colorbar(im, cax=cax, format=FormatStrFormatter('%.2f'))\n",
    "\n",
    "        #fig.colorbar(im, ax=ax[0,1])\n",
    "        ax[0,1].set_title('Landsat 8')\n",
    "        ax[0,1].axis('off')\n",
    "\n",
    "        ## Scatter plot of resampled NEON mean value and satellite band\n",
    "        #im = ax[1,0].scatter(neon_band_vals.flatten(), satl_band_vals.flatten(), alpha=0.4, color='r')\n",
    "        im = ax[1,0].scatter(x, y, alpha=0.4, color='r')\n",
    "        ax[1,0].set_title('Sentinel-2 vs. NEON means')\n",
    "        ax[1,0].set_ylabel('Sentinel-2 values')\n",
    "        ax[1,0].set_xlabel('NEON resampled values')\n",
    "        ax[1,0].set_title(f'linear regression:  y={slope:0.3f}x + {intercept:0.3f}\\n corr.coef={corr:0.3f}')\n",
    "\n",
    "        ## plot 1:1 line\n",
    "        minval = min(x.min(), y.min())\n",
    "        maxval = max(x.max(), y.max())\n",
    "        ax[1,0].plot([minval, maxval], [minval, maxval], 'b--')\n",
    "\n",
    "        ax[1,0].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "        ax[1,0].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "        ## Scatter plot of difference values vs. std for that pixel\n",
    "        spectral_dif = satl_band_vals.flatten() - neon_band_vals.flatten()\n",
    "        ax[1,1].scatter(neon_std_vals.flatten(), spectral_dif, alpha=0.4, color='r')\n",
    "        ax[1,1].set_xlabel('Sentinel-2 & NEON Aboslute Error')\n",
    "        ax[1,1].set_ylabel('STD of NEON pixels')\n",
    "        ax[1,1].set_title(f'Absolute Error vs. STD')\n",
    "\n",
    "        ## plot zero line line\n",
    "        minval = (y-x).min()\n",
    "        maxval = (y-x).max()\n",
    "        ax[1,1].plot([minval, maxval], [0,0], 'b--')\n",
    "\n",
    "        ax[1,1].xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "        ax[1,1].yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "\n",
    "        fig.suptitle(nband)\n",
    "        plt.show()\n",
    "        \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'og_s2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-8dcb06158451>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mog_s2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbands\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'og_s2' is not defined"
     ]
    }
   ],
   "source": [
    "og_s2.bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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