{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Orbit determination example\n",
    "This notebook does the following:\n",
    "* Download an orbit first guess from SpaceTrack\n",
    "* Get laser ranging data\n",
    "* Feed the data to Orekit\n",
    "* Estimate the orbit\n",
    "* Propagate and compare the orbit to the first guess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OD parameters\n",
    "First, some parameters need to be defined for the orbit determination:\n",
    "* Satellite ID in NORAD format\n",
    "* Spacecraft mass: important for the drag term\n",
    "* Measurement weights: used to weight certain measurements more than others during the orbit estimation. Here, we only have range measurements and we do not know the confidence associated to these measurements, so all weights are identical\n",
    "* OD date: date at which the orbit will be estimated. \n",
    "* Data collection duration: for example, if equals 2 days, the laser data from the 2 days before the OD date will be used to estimate the orbit. This value is an important trade-off for the quality of the orbit determination:\n",
    "    * The longer the duration, the more ranging data is available, which can increase the quality of the estimation\n",
    "    * The longer the duration, the longer the orbit must be propagated, and the higher the covariance because of the orbit perturbations such as the gravity field, drag, Sun, Moon, etc.\n",
    "    * In some cases, the estimator can only converge if the time windows is small enough\n",
    "    * In the case of CALIPSO, it must be kept very short because we don't know if there were orbit boosts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Satellite parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_list = {    \n",
    "    'calipso': {\n",
    "        'norad_id': 29108,  # For Space-Track TLE queries\n",
    "        'mass': 560.0, # kg; https://atrain.nasa.gov/publications/CALIPSO.pdf\n",
    "        'cross_section': 5.0, # m2; Estimation based on satellite's geometry\n",
    "        'cd': 2.0, # TODO: compute proper value\n",
    "        'cr': 1.0  # TODO: compute proper value\n",
    "    }\n",
    "}\n",
    "\n",
    "sc_name = 'calipso'  # Change the name to select a different satellite in the dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_weight = 1.0 # Will be normalized later (i.e divided by the number of observations)\n",
    "range_sigma = 1.0 # Estimated covariance of the range measurements, in meters\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "odDate = datetime(2021, 4, 19, 21, 0, 0) # Epoch of the orbit determination\n",
    "collectionDuration = 6000.0 # seconds, approximately one orbit. In CALIPSO's case, only small windows work\n",
    "from datetime import timedelta\n",
    "# The data is taken before the epoch\n",
    "startCollectionDate = odDate + timedelta(seconds=-collectionDuration)\n",
    "\n",
    "# Orbit propagator parameters\n",
    "prop_min_step = 0.001 # s\n",
    "prop_max_step = 300.0 # s\n",
    "prop_position_error = 0.1 # m\n",
    "\n",
    "# Estimator parameters\n",
    "estimator_position_scale = 1.0 # m\n",
    "estimator_convergence_thres = 1e-2\n",
    "estimator_max_iterations = 25\n",
    "estimator_max_evaluations = 35"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the ground stations from a CSV file. These ground station coordinates will be used for generated simulated measurements, for instance RA/DEC or range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude_deg</th>\n",
       "      <th>latitude_deg</th>\n",
       "      <th>altitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Svalbard</th>\n",
       "      <td>15.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>117.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>-65.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>-73.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunka</th>\n",
       "      <td>102.5</td>\n",
       "      <td>51.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            longitude_deg  latitude_deg  altitude\n",
       "name                                             \n",
       "Svalbard             15.0          78.0       0.0\n",
       "Australia           117.0         -32.0       0.0\n",
       "Argentina           -65.0         -35.0       0.0\n",
       "Antarctica          -75.0         -73.0       0.0\n",
       "Tunka               102.5          51.7       0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "ground_stations_file = 'ground-stations.csv'\n",
    "ground_station_df = pd.read_csv(ground_stations_file, index_col=0)\n",
    "display(ground_station_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API credentials\n",
    "The following sets up the account for SpaceTrack (for orbit data).\n",
    "* A SpaceTrack account is required, it can be created for free at: https://www.space-track.org/auth/createAccount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter SpaceTrack username clement@jonglez.space\n",
      "Enter SpaceTrack password for account clement@jonglez.space ·····················\n"
     ]
    }
   ],
   "source": [
    "# Space-Track\n",
    "identity_st = input('Enter SpaceTrack username')\n",
    "import getpass\n",
    "password_st = getpass.getpass(prompt='Enter SpaceTrack password for account {}'.format(identity_st))\n",
    "import spacetrack.operators as op\n",
    "from spacetrack import SpaceTrackClient\n",
    "st = SpaceTrackClient(identity=identity_st, password=password_st)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading CALIPSO range data file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the nearest date at which a weekly data file is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "reference_date = datetime(2015, 1, 7)\n",
    "delta_time = startCollectionDate - reference_date\n",
    "delta_weeks = int(delta_time.days / 7)\n",
    "week_start = reference_date + timedelta(weeks=delta_weeks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('CPSO_10second_GT_2021_04_14.txt',\n",
       " <http.client.HTTPMessage at 0x7fa25a68faf0>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import urllib.request\n",
    "\n",
    "filename = f'CPSO_10second_GT_{week_start:%Y_%m_%d}.txt'\n",
    "target_url = f'https://www-calipso.larc.nasa.gov/data/TOOLS/overpass/coords/{filename}'\n",
    "urllib.request.urlretrieve(target_url, filename)\n",
    "#response = requests.get(target_url)\n",
    "#data = response.text\n",
    "#print(data[0:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing the downloaded file. The range data can be decimated using the parameter `decimation_factor` to reduce the computational load."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat_deg</th>\n",
       "      <th>lon_deg</th>\n",
       "      <th>range_km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime_utc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:00</th>\n",
       "      <td>-45.3315</td>\n",
       "      <td>-67.2500</td>\n",
       "      <td>704.3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:10</th>\n",
       "      <td>-44.7363</td>\n",
       "      <td>-67.4658</td>\n",
       "      <td>704.0738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:20</th>\n",
       "      <td>-44.1407</td>\n",
       "      <td>-67.6781</td>\n",
       "      <td>703.8208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:30</th>\n",
       "      <td>-43.5448</td>\n",
       "      <td>-67.8869</td>\n",
       "      <td>703.5671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:40</th>\n",
       "      <td>-42.9486</td>\n",
       "      <td>-68.0926</td>\n",
       "      <td>703.3129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:20</th>\n",
       "      <td>-42.4227</td>\n",
       "      <td>-92.8997</td>\n",
       "      <td>703.0635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:30</th>\n",
       "      <td>-41.8259</td>\n",
       "      <td>-93.0995</td>\n",
       "      <td>702.8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:40</th>\n",
       "      <td>-41.2288</td>\n",
       "      <td>-93.2965</td>\n",
       "      <td>702.5487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:50</th>\n",
       "      <td>-40.6314</td>\n",
       "      <td>-93.4906</td>\n",
       "      <td>702.2910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 21:00:00</th>\n",
       "      <td>-40.0336</td>\n",
       "      <td>-93.6821</td>\n",
       "      <td>702.0331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lat_deg  lon_deg  range_km\n",
       "datetime_utc                                   \n",
       "2021-04-19 19:20:00 -45.3315 -67.2500  704.3260\n",
       "2021-04-19 19:20:10 -44.7363 -67.4658  704.0738\n",
       "2021-04-19 19:20:20 -44.1407 -67.6781  703.8208\n",
       "2021-04-19 19:20:30 -43.5448 -67.8869  703.5671\n",
       "2021-04-19 19:20:40 -42.9486 -68.0926  703.3129\n",
       "...                      ...      ...       ...\n",
       "2021-04-19 20:59:20 -42.4227 -92.8997  703.0635\n",
       "2021-04-19 20:59:30 -41.8259 -93.0995  702.8062\n",
       "2021-04-19 20:59:40 -41.2288 -93.2965  702.5487\n",
       "2021-04-19 20:59:50 -40.6314 -93.4906  702.2910\n",
       "2021-04-19 21:00:00 -40.0336 -93.6821  702.0331\n",
       "\n",
       "[601 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#variable space separator\n",
    "df = pd.read_csv(\n",
    "    filename,\n",
    "    sep = '\\s\\s+',\n",
    "    skiprows=5,\n",
    "    index_col=0,\n",
    "    parse_dates=True,\n",
    "    engine='python'\n",
    ")\n",
    "df.columns = ['lat_deg', 'lon_deg', 'range_km']\n",
    "df.index.name = 'datetime_utc'\n",
    "\n",
    "usable_df = df.loc[startCollectionDate:odDate]\n",
    "decimation_factor = 1\n",
    "usable_df = usable_df.iloc[::decimation_factor, :]\n",
    "display(usable_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up models\n",
    "Initializing Orekit and JVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import orekit\n",
    "orekit.initVM()\n",
    "\n",
    "# Modified from https://gitlab.orekit.org/orekit-labs/python-wrapper/blob/master/python_files/pyhelpers.py\n",
    "from java.io import File\n",
    "from org.orekit.data import DataProvidersManager, DirectoryCrawler\n",
    "from orekit import JArray\n",
    "\n",
    "orekit_data_dir = 'orekit-data'\n",
    "DM = DataProvidersManager.getInstance()\n",
    "datafile = File(orekit_data_dir)\n",
    "if not datafile.exists():\n",
    "    print('Directory :', datafile.absolutePath, ' not found')\n",
    "\n",
    "crawler = DirectoryCrawler(datafile)\n",
    "DM.clearProviders()\n",
    "DM.addProvider(crawler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The orbit determination needs a first guess. For this, we use Two-Line Elements. Retrieving the latest TLE prior to the beginning of the orbit determination. It is important to have a \"fresh\" TLE, because the newer the TLE, the better the orbit estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 29108U 06016B   21109.54066165  .00000226  00000-0  54070-4 0  9999\n",
      "2 29108  98.2706  62.1875 0001315  79.7201 280.4148 14.62531336797058\n"
     ]
    }
   ],
   "source": [
    "rawTle = st.tle(norad_cat_id=sat_list[sc_name]['norad_id'], epoch='<{}'.format(odDate), orderby='epoch desc', limit=1, format='tle')\n",
    "tleLine1 = rawTle.split('\\n')[0]\n",
    "tleLine2 = rawTle.split('\\n')[1]\n",
    "print(tleLine1)\n",
    "print(tleLine2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up Orekit frames and models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.utils import Constants as orekit_constants\n",
    "\n",
    "from org.orekit.frames import FramesFactory, ITRFVersion\n",
    "from org.orekit.utils import IERSConventions\n",
    "tod = FramesFactory.getTOD(IERSConventions.IERS_2010, False) # Taking tidal effects into account when interpolating EOP parameters\n",
    "eme2000 = FramesFactory.getEME2000()\n",
    "gcrf = FramesFactory.getGCRF()\n",
    "itrf = FramesFactory.getITRF(IERSConventions.IERS_2010, False)\n",
    "#itrf = FramesFactory.getITRF(ITRFVersion.ITRF_2014, IERSConventions.IERS_2010, False)\n",
    "# Selecting frames to use for OD\n",
    "eci = eme2000\n",
    "ecef = itrf\n",
    "\n",
    "from org.orekit.models.earth import ReferenceEllipsoid\n",
    "wgs84Ellipsoid = ReferenceEllipsoid.getWgs84(ecef)\n",
    "from org.orekit.bodies import CelestialBodyFactory\n",
    "moon = CelestialBodyFactory.getMoon()\n",
    "sun = CelestialBodyFactory.getSun()\n",
    "\n",
    "from org.orekit.time import AbsoluteDate, TimeScalesFactory\n",
    "utc = TimeScalesFactory.getUTC()\n",
    "mjd_utc_epoch = AbsoluteDate(1858, 11, 17, 0, 0, 0.0, utc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the propagator from the initial TLEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.propagation.analytical.tle import TLE\n",
    "orekitTle = TLE(tleLine1, tleLine2)\n",
    "\n",
    "from org.orekit.attitudes import NadirPointing\n",
    "nadirPointing = NadirPointing(eci, wgs84Ellipsoid)\n",
    "\n",
    "from org.orekit.propagation.analytical.tle import SGP4\n",
    "sgp4Propagator = SGP4(orekitTle, nadirPointing, sat_list[sc_name]['mass'])\n",
    "\n",
    "tleInitialState = sgp4Propagator.getInitialState()\n",
    "tleEpoch = tleInitialState.getDate()\n",
    "tleOrbit_TEME = tleInitialState.getOrbit()\n",
    "tlePV_ECI = tleOrbit_TEME.getPVCoordinates(eci)\n",
    "\n",
    "from org.orekit.orbits import CartesianOrbit\n",
    "tleOrbit_ECI = CartesianOrbit(tlePV_ECI, eci, wgs84Ellipsoid.getGM())\n",
    "\n",
    "from org.orekit.propagation.conversion import DormandPrince853IntegratorBuilder\n",
    "integratorBuilder = DormandPrince853IntegratorBuilder(prop_min_step, prop_max_step, prop_position_error)\n",
    "\n",
    "from org.orekit.propagation.conversion import NumericalPropagatorBuilder\n",
    "from org.orekit.orbits import PositionAngle\n",
    "propagatorBuilder = NumericalPropagatorBuilder(tleOrbit_ECI,\n",
    "                                               integratorBuilder, PositionAngle.MEAN, estimator_position_scale)\n",
    "propagatorBuilder.setMass(sat_list[sc_name]['mass'])\n",
    "propagatorBuilder.setAttitudeProvider(nadirPointing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding perturbation forces to the propagator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Earth gravity field with degree 64 and order 64\n",
    "from org.orekit.forces.gravity.potential import GravityFieldFactory\n",
    "gravityProvider = GravityFieldFactory.getConstantNormalizedProvider(64, 64)\n",
    "from org.orekit.forces.gravity import HolmesFeatherstoneAttractionModel\n",
    "gravityAttractionModel = HolmesFeatherstoneAttractionModel(ecef, gravityProvider)\n",
    "propagatorBuilder.addForceModel(gravityAttractionModel)\n",
    "\n",
    "# Moon and Sun perturbations\n",
    "from org.orekit.forces.gravity import ThirdBodyAttraction\n",
    "moon_3dbodyattraction = ThirdBodyAttraction(moon)\n",
    "propagatorBuilder.addForceModel(moon_3dbodyattraction)\n",
    "sun_3dbodyattraction = ThirdBodyAttraction(sun)\n",
    "propagatorBuilder.addForceModel(sun_3dbodyattraction)\n",
    "\n",
    "# Solar radiation pressure\n",
    "from org.orekit.forces.radiation import IsotropicRadiationSingleCoefficient\n",
    "isotropicRadiationSingleCoeff = IsotropicRadiationSingleCoefficient(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cr']);\n",
    "from org.orekit.forces.radiation import SolarRadiationPressure\n",
    "solarRadiationPressure = SolarRadiationPressure(sun, wgs84Ellipsoid.getEquatorialRadius(),\n",
    "                                                isotropicRadiationSingleCoeff)\n",
    "propagatorBuilder.addForceModel(solarRadiationPressure)\n",
    "\n",
    "# Relativity\n",
    "from org.orekit.forces.gravity import Relativity\n",
    "relativity = Relativity(orekit_constants.EIGEN5C_EARTH_MU)\n",
    "propagatorBuilder.addForceModel(relativity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding atmospheric drag to the propagator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atmospheric drag\n",
    "\n",
    "# New CSSI loader for three-hourly atmospheric data\n",
    "from org.orekit.models.earth.atmosphere.data import CssiSpaceWeatherData\n",
    "cswl = CssiSpaceWeatherData(\"SpaceWeather-All-v1.2.txt\")\n",
    "\n",
    "from org.orekit.models.earth.atmosphere import NRLMSISE00\n",
    "atmosphere = NRLMSISE00(cswl, sun, wgs84Ellipsoid)\n",
    "#from org.orekit.forces.drag.atmosphere import DTM2000\n",
    "#atmosphere = DTM2000(msafe, sun, wgs84Ellipsoid)\n",
    "from org.orekit.forces.drag import IsotropicDrag\n",
    "isotropicDrag = IsotropicDrag(sat_list[sc_name]['cross_section'], sat_list[sc_name]['cd'])\n",
    "from org.orekit.forces.drag import DragForce\n",
    "dragForce = DragForce(atmosphere, isotropicDrag)\n",
    "propagatorBuilder.addForceModel(dragForce)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up the estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.hipparchus.linear import QRDecomposer\n",
    "matrixDecomposer = QRDecomposer(1e-11)\n",
    "from org.hipparchus.optim.nonlinear.vector.leastsquares import GaussNewtonOptimizer\n",
    "optimizer = GaussNewtonOptimizer(matrixDecomposer, False)\n",
    "\n",
    "from org.orekit.estimation.leastsquares import BatchLSEstimator\n",
    "estimator = BatchLSEstimator(optimizer, propagatorBuilder)\n",
    "estimator.setParametersConvergenceThreshold(estimator_convergence_thres)\n",
    "estimator.setMaxIterations(estimator_max_iterations)\n",
    "estimator.setMaxEvaluations(estimator_max_evaluations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the measurements to the estimator. A new GroundStation object is created for each measurement point as it's a \"virtual\" ground station, i.e. the point on Earth hit by CALIPSO's laser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from orekit.pyhelpers import datetime_to_absolutedate, JArray\n",
    "from org.orekit.estimation.measurements import Range, AngularAzEl, ObservableSatellite, GroundStation\n",
    "from org.orekit.frames import TopocentricFrame \n",
    "from org.orekit.bodies import GeodeticPoint\n",
    "\n",
    "observableSatellite = ObservableSatellite(0) # Propagator index = 0\n",
    "\n",
    "for receiveTime, data in usable_df.iterrows():\n",
    "    ground_station = GroundStation(\n",
    "        TopocentricFrame(wgs84Ellipsoid, \n",
    "                         GeodeticPoint(\n",
    "                            float(np.deg2rad(data['lat_deg'])),\n",
    "                            float(np.deg2rad(data['lon_deg'])),\n",
    "                            0.0\n",
    "                         ), \n",
    "                         \"bla\"\n",
    "                        )\n",
    "    )\n",
    "    orekitRange = Range(ground_station, \n",
    "                        False, # One-way measurement\n",
    "                        datetime_to_absolutedate(receiveTime),\n",
    "                        float(data['range_km']*1e3),\n",
    "                        range_sigma,\n",
    "                        range_weight,\n",
    "                        observableSatellite\n",
    "                       ) # Uses date of signal reception; https://www.orekit.org/static/apidocs/org/orekit/estimation/measurements/Range.html\n",
    "    estimator.addMeasurement(orekitRange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing the OD\n",
    "Estimate the orbit. This step can take a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimatedPropagatorArray = estimator.estimate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propagating the estimated orbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 300.0\n",
    "date_start = datetime_to_absolutedate(startCollectionDate).shiftedBy(-86400.0)\n",
    "date_end = datetime_to_absolutedate(odDate).shiftedBy(86400.0) # Stopping 1 day after OD date\n",
    "\n",
    "# First propagating in ephemeris mode\n",
    "estimatedPropagator = estimatedPropagatorArray[0]\n",
    "estimatedInitialState = estimatedPropagator.getInitialState()\n",
    "actualOdDate = estimatedInitialState.getDate()\n",
    "estimatedPropagator.resetInitialState(estimatedInitialState)\n",
    "estimatedPropagator.setEphemerisMode()\n",
    "\n",
    "# Propagating from 1 day before data collection\n",
    "# To 1 day after orbit determination\n",
    "estimatedPropagator.propagate(date_start, datetime_to_absolutedate(odDate).shiftedBy(1 * 86400.0))\n",
    "bounded_propagator = estimatedPropagator.getGeneratedEphemeris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariance analysis\n",
    "Creating the LVLH frame, computing the covariance matrix in both TOD and LVLH frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the LVLH frame \n",
    "# It must be associated to the bounded propagator, not the original numerical propagator\n",
    "from org.orekit.frames import LocalOrbitalFrame\n",
    "from org.orekit.frames import LOFType\n",
    "lvlh = LocalOrbitalFrame(eci, LOFType.LVLH, bounded_propagator, 'LVLH')\n",
    "\n",
    "# Getting covariance matrix in ECI frame\n",
    "covMat_eci_java = estimator.getPhysicalCovariances(1.0e-10)\n",
    "\n",
    "# Converting matrix to LVLH frame\n",
    "# Getting an inertial frame aligned with the LVLH frame at this instant\n",
    "# The LVLH is normally not inertial, but this should not affect results too much\n",
    "# Reference: David Vallado, Covariance Transformations for Satellite Flight Dynamics Operations, 2003\n",
    "eci2lvlh_frozen = eci.getTransformTo(lvlh, actualOdDate).freeze() \n",
    "\n",
    "# Computing Jacobian\n",
    "from org.orekit.utils import CartesianDerivativesFilter\n",
    "from orekit.pyhelpers import JArray_double2D\n",
    "jacobianDoubleArray = JArray_double2D(6, 6)\n",
    "eci2lvlh_frozen.getJacobian(CartesianDerivativesFilter.USE_PV, jacobianDoubleArray)\n",
    "from org.hipparchus.linear import Array2DRowRealMatrix\n",
    "jacobian = Array2DRowRealMatrix(jacobianDoubleArray)\n",
    "# Applying Jacobian to convert matrix to lvlh\n",
    "covMat_lvlh_java = jacobian.multiply(\n",
    "    covMat_eci_java.multiply(jacobian.transpose()))\n",
    "\n",
    "# Converting the Java matrices to numpy\n",
    "import numpy as np\n",
    "covarianceMat_eci = np.matrix([covMat_eci_java.getRow(iRow) \n",
    "                              for iRow in range(0, covMat_eci_java.getRowDimension())])\n",
    "covarianceMat_lvlh = np.matrix([covMat_lvlh_java.getRow(iRow) \n",
    "                              for iRow in range(0, covMat_lvlh_java.getRowDimension())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the position and velocity standard deviation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Position std: cross-track 5.749e-01 m, along-track 2.971e+01 m, out-of-plane 1.046e+02 m\n",
      "Velocity std: cross-track 3.156e-02 m/s, along-track 3.634e-04 m/s, out-of-plane 1.440e-01 m/s\n"
     ]
    }
   ],
   "source": [
    "pos_std_crossTrack = np.sqrt(covarianceMat_lvlh[0,0])\n",
    "pos_std_alongTrack = np.sqrt(covarianceMat_lvlh[1,1])\n",
    "pos_std_outOfPlane = np.sqrt(covarianceMat_lvlh[2,2])\n",
    "print(f'Position std: cross-track {pos_std_crossTrack:.3e} m, along-track {pos_std_alongTrack:.3e} m, out-of-plane {pos_std_outOfPlane:.3e} m')\n",
    "\n",
    "vel_std_crossTrack = np.sqrt(covarianceMat_lvlh[3,3])\n",
    "vel_std_alongTrack = np.sqrt(covarianceMat_lvlh[4,4])\n",
    "vel_std_outOfPlane = np.sqrt(covarianceMat_lvlh[5,5])\n",
    "print(f'Velocity std: cross-track {vel_std_crossTrack:.3e} m/s, along-track {vel_std_alongTrack:.3e} m/s, out-of-plane {vel_std_outOfPlane:.3e} m/s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing residuals\n",
    "Getting the estimated and measured ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "propagatorParameters   = estimator.getPropagatorParametersDrivers(True)\n",
    "measurementsParameters = estimator.getMeasurementsParametersDrivers(True)\n",
    "\n",
    "lastEstimations = estimator.getLastEstimations()\n",
    "valueSet = lastEstimations.values()\n",
    "estimatedMeasurements = valueSet.toArray()\n",
    "keySet = lastEstimations.keySet()\n",
    "realMeasurements = keySet.toArray()\n",
    "\n",
    "from org.orekit.estimation.measurements import EstimatedMeasurement\n",
    "from orekit.pyhelpers import absolutedate_to_datetime\n",
    "\n",
    "import pandas as pd\n",
    "range_residuals = pd.DataFrame(columns=['range'])\n",
    "\n",
    "for estMeas, realMeas in zip(estimatedMeasurements, realMeasurements):\n",
    "    estMeas = EstimatedMeasurement.cast_(estMeas)\n",
    "    estimatedValue = estMeas.getEstimatedValue()\n",
    "    pyDateTime = absolutedate_to_datetime(estMeas.date)\n",
    "    \n",
    "    observedValue = Range.cast_(realMeas).getObservedValue()\n",
    "    range_residuals.loc[pyDateTime] = np.array(observedValue) - np.array(estimatedValue)\n",
    "        \n",
    "#display(range_residuals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up Plotly for offline mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "#pio.renderers.default = 'jupyterlab+notebook+png'  # Uncomment for interactive plots\n",
    "pio.renderers.default = 'png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting range residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "trace = go.Scattergl(\n",
    "    x=range_residuals.index, y=range_residuals['range'],\n",
    "    mode='markers',\n",
    "    name='Range'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Range residuals',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Range residual (m)'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propagating the solution \n",
    "Propagating the solution and:\n",
    "* Saving the PV coordinates from both the solution and the initial TLE guess.\n",
    "* Computing the difference in LVLH frame between the solution and the initial TLE guess.\n",
    "* Computing the difference in LVLH frame between the solution and the CPF file.\n",
    "\n",
    "Also logging:\n",
    "* Latitude/Longitude/Altitude\n",
    "\n",
    "For analysis purposes, the propagation time window is here from one day before to one day after the epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propagating the bounded propagator to retrieve the intermediate states\n",
    "\n",
    "from slrDataUtils import orekitPV2dataframe\n",
    "\n",
    "PV_eci_df = pd.DataFrame(columns=['x', 'y', 'z', 'vx', 'vy', 'vz'])\n",
    "PV_ecef_df = pd.DataFrame(columns=['x', 'y', 'z', 'pos_norm', 'vx', 'vy', 'vz'])\n",
    "PV_tle_eci_df = pd.DataFrame(columns=['x', 'y', 'z', 'vx', 'vy', 'vz'])\n",
    "lat_lon_df = pd.DataFrame(columns=['lat_deg', 'lon_deg', 'alt_m'])\n",
    "deltaPV_tle_lvlh_df = pd.DataFrame(columns=['x', 'y', 'z', 'pos_norm', 'vx', 'vy', 'vz', 'vel_norm'])\n",
    "deltaPV_cpf_lvlh_df = pd.DataFrame(columns=['x', 'y', 'z', 'norm'])\n",
    "\n",
    "date_current = date_start\n",
    "while date_current.compareTo(date_end) <= 0:\n",
    "    datetime_current = absolutedate_to_datetime(date_current)    \n",
    "    spacecraftState = bounded_propagator.propagate(date_current)\n",
    "    \n",
    "    geodetic_point = wgs84Ellipsoid.transform(spacecraftState.getPVCoordinates(ecef).getPosition(),\n",
    "                                              ecef,\n",
    "                                              date_current)\n",
    "    lat_lon_df.loc[datetime_current] = [np.rad2deg(geodetic_point.getLatitude()),\n",
    "                                        np.rad2deg(geodetic_point.getLongitude()),\n",
    "                                        geodetic_point.getAltitude()]\n",
    "    \n",
    "    '''\n",
    "    When getting PV coordinates using the SGP4 propagator in LVLH frame, \n",
    "    it is actually a \"delta\" from the PV coordinates resulting from the orbit determination\n",
    "    because this LVLH frame is centered on the satellite's current position based on the orbit determination\n",
    "    '''\n",
    "    deltaPV_lvlh = sgp4Propagator.getPVCoordinates(date_current, lvlh)\n",
    "    deltaPV_tle_lvlh_df.loc[datetime_current] = [deltaPV_lvlh.getPosition().getX(),\n",
    "                                                 deltaPV_lvlh.getPosition().getY(), \n",
    "                                                 deltaPV_lvlh.getPosition().getZ(),\n",
    "                                                 deltaPV_lvlh.getPosition().getNorm(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getX(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getY(), \n",
    "                                                 deltaPV_lvlh.getVelocity().getZ(),\n",
    "                                                 deltaPV_lvlh.getVelocity().getNorm()]\n",
    "    \n",
    "    \n",
    "    date_current = date_current.shiftedBy(dt)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spacecraft's latitude/longitude/altitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat_deg</th>\n",
       "      <th>lon_deg</th>\n",
       "      <th>alt_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-18 19:20:00</th>\n",
       "      <td>81.534088</td>\n",
       "      <td>-179.484191</td>\n",
       "      <td>697594.926055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-18 19:25:00</th>\n",
       "      <td>68.232100</td>\n",
       "      <td>124.298114</td>\n",
       "      <td>696008.853261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-18 19:30:00</th>\n",
       "      <td>50.789342</td>\n",
       "      <td>112.031613</td>\n",
       "      <td>692532.689928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-18 19:35:00</th>\n",
       "      <td>32.830495</td>\n",
       "      <td>105.928791</td>\n",
       "      <td>688921.312645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-18 19:40:00</th>\n",
       "      <td>14.697556</td>\n",
       "      <td>101.500749</td>\n",
       "      <td>687183.710030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20 20:40:00</th>\n",
       "      <td>70.030270</td>\n",
       "      <td>107.795182</td>\n",
       "      <td>696290.287243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20 20:45:00</th>\n",
       "      <td>52.705196</td>\n",
       "      <td>94.048206</td>\n",
       "      <td>692978.606638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20 20:50:00</th>\n",
       "      <td>34.776813</td>\n",
       "      <td>87.620379</td>\n",
       "      <td>689317.171181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20 20:55:00</th>\n",
       "      <td>16.656291</td>\n",
       "      <td>83.089021</td>\n",
       "      <td>687320.786977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20 21:00:00</th>\n",
       "      <td>-1.531643</td>\n",
       "      <td>79.139757</td>\n",
       "      <td>688502.705612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>597 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       lat_deg     lon_deg          alt_m\n",
       "2021-04-18 19:20:00  81.534088 -179.484191  697594.926055\n",
       "2021-04-18 19:25:00  68.232100  124.298114  696008.853261\n",
       "2021-04-18 19:30:00  50.789342  112.031613  692532.689928\n",
       "2021-04-18 19:35:00  32.830495  105.928791  688921.312645\n",
       "2021-04-18 19:40:00  14.697556  101.500749  687183.710030\n",
       "...                        ...         ...            ...\n",
       "2021-04-20 20:40:00  70.030270  107.795182  696290.287243\n",
       "2021-04-20 20:45:00  52.705196   94.048206  692978.606638\n",
       "2021-04-20 20:50:00  34.776813   87.620379  689317.171181\n",
       "2021-04-20 20:55:00  16.656291   83.089021  687320.786977\n",
       "2021-04-20 21:00:00  -1.531643   79.139757  688502.705612\n",
       "\n",
       "[597 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_lon_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurement generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating measurement builders to later retrieve the simulated measurements from the estimated orbit.\n",
    "\n",
    "The PV builder is not strictly necessary, as the spacecraft's position&velocity can also be retrieved directly during propagation above (the position&velocity are direct outputs of the propagation and do not need the same post-processing as ground station related measurements).\n",
    "\n",
    "No noise generators are passed to the measurement builders, which means that the sigma values are not used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_position = 1.0  # Position noise in meters\n",
    "sigma_velocity = 1e-3 # Velocity noise in meters per second\n",
    "step_pv = 1.0  # Step time for output PV data\n",
    "\n",
    "sigma_range = 1.0  # Range noise in meters\n",
    "sigma_range_rate = 1e-3  # Range rate noise in meters per second\n",
    "sigma_az = float(np.deg2rad(0.01))  # Azimuth noise in radians\n",
    "sigma_el = float(np.deg2rad(0.01))  # Elevation noise in radians\n",
    "sigma_ra = float(np.deg2rad(0.01))  # Right ascension noise in radians\n",
    "sigma_dec = float(np.deg2rad(0.01))  # Declination noise in radians\n",
    "step_ground_station_measurements = 1.0  # When a ground station is in view, take measurements every second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from org.orekit.bodies import GeodeticPoint\n",
    "from org.orekit.frames import TopocentricFrame\n",
    "from org.orekit.propagation.events import ElevationDetector\n",
    "from org.orekit.estimation.measurements import GroundStation\n",
    "from org.orekit.estimation.measurements.generation import PVBuilder, RangeBuilder, RangeRateBuilder, AngularAzElBuilder, AngularRaDecBuilder, EventBasedScheduler, SignSemantic, Generator, ContinuousScheduler\n",
    "from org.orekit.time import FixedStepSelector\n",
    "from org.orekit.estimation.measurements import ObservableSatellite\n",
    "from org.orekit.propagation.events.handlers import ContinueOnEvent\n",
    "\n",
    "meas_generator = Generator()\n",
    "meas_generator.addPropagator(bounded_propagator)\n",
    "\n",
    "# Add PV builder\n",
    "meas_generator.addScheduler(\n",
    "    ContinuousScheduler(\n",
    "        PVBuilder(None,  # no noise\n",
    "                  sigma_position,\n",
    "                  sigma_velocity,\n",
    "                  1.0,  # Base weight\n",
    "                  observableSatellite), \n",
    "        FixedStepSelector(step_pv, utc))\n",
    ")\n",
    "\n",
    "for gs_name, gs_data in ground_station_df.iterrows():\n",
    "    geodetic_point = GeodeticPoint(float(np.deg2rad(gs_data['latitude_deg'])),\n",
    "                                   float(np.deg2rad(gs_data['longitude_deg'])),\n",
    "                                   float(gs_data['altitude']))\n",
    "    topocentric_frame = TopocentricFrame(wgs84Ellipsoid, geodetic_point, gs_name)\n",
    "    ground_station_df.loc[gs_name, 'GroundStation'] = GroundStation(topocentric_frame)\n",
    "\n",
    "    # Range builder\n",
    "    meas_generator.addScheduler(\n",
    "        EventBasedScheduler(\n",
    "            RangeBuilder(None, \n",
    "                         GroundStation(topocentric_frame), \n",
    "                         False,  # one-way, this is important for telescope observations\n",
    "                         sigma_range, \n",
    "                         1.0,  # Base weight\n",
    "                         observableSatellite), \n",
    "            FixedStepSelector(step_ground_station_measurements, utc), \n",
    "            bounded_propagator, \n",
    "            ElevationDetector(topocentric_frame).withHandler(ContinueOnEvent()), \n",
    "            SignSemantic.FEASIBLE_MEASUREMENT_WHEN_POSITIVE)\n",
    "    )\n",
    "\n",
    "    # Range rate builder\n",
    "    meas_generator.addScheduler(\n",
    "        EventBasedScheduler(\n",
    "            RangeRateBuilder(None,  # no noise\n",
    "                             GroundStation(topocentric_frame), \n",
    "                             True,  # two-way\n",
    "                             sigma_range_rate, \n",
    "                             1.0,  # Base weight\n",
    "                             observableSatellite), \n",
    "            FixedStepSelector(step_ground_station_measurements, utc), \n",
    "            bounded_propagator, \n",
    "            ElevationDetector(topocentric_frame).withHandler(ContinueOnEvent()), \n",
    "            SignSemantic.FEASIBLE_MEASUREMENT_WHEN_POSITIVE)\n",
    "    )\n",
    "\n",
    "    # Az/el builder\n",
    "    meas_generator.addScheduler(\n",
    "        EventBasedScheduler(\n",
    "            AngularAzElBuilder(None,  # no noise\n",
    "                               GroundStation(topocentric_frame),\n",
    "                               [sigma_az, sigma_el], \n",
    "                               [1.0, 1.0],  # Base weight\n",
    "                               observableSatellite), \n",
    "            FixedStepSelector(step_ground_station_measurements, utc), \n",
    "            bounded_propagator, \n",
    "            ElevationDetector(topocentric_frame).withHandler(ContinueOnEvent()), \n",
    "            SignSemantic.FEASIBLE_MEASUREMENT_WHEN_POSITIVE)\n",
    "    )\n",
    "\n",
    "    # RA/DEC builder\n",
    "    meas_generator.addScheduler(\n",
    "        EventBasedScheduler(\n",
    "            AngularRaDecBuilder(None,  # no noise\n",
    "                               GroundStation(topocentric_frame),\n",
    "                               eci,  # RA/DEC measurements are defined in Earth-centered inertial frame\n",
    "                               [sigma_ra, sigma_dec], \n",
    "                               [1.0, 1.0],  # Base weight\n",
    "                               observableSatellite), \n",
    "            FixedStepSelector(step_ground_station_measurements, utc), \n",
    "            bounded_propagator, \n",
    "            ElevationDetector(topocentric_frame).withHandler(ContinueOnEvent()), \n",
    "            SignSemantic.FEASIBLE_MEASUREMENT_WHEN_POSITIVE)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Propagating, retrieving the generated measurements.\n",
    "\n",
    "Warning: the cell below cannot be run a second time without running the cell above again before. Otherwise the result structure will be empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from orekit.pyhelpers import absolutedate_to_datetime\n",
    "from org.orekit.estimation.measurements import ObservedMeasurement, PV, Range, RangeRate, AngularAzEl, AngularRaDec\n",
    "from org.orekit.utils import PVCoordinates\n",
    "from org.hipparchus.geometry.euclidean.threed import Vector3D\n",
    "\n",
    "generated = meas_generator.generate(datetime_to_absolutedate(startCollectionDate), datetime_to_absolutedate(odDate))\n",
    "pv_itrf_df = pd.DataFrame(columns=['x', 'y', 'z', 'vx', 'vy', 'vz'])\n",
    "pv_eme2000_df = pd.DataFrame(columns=['x', 'y', 'z', 'vx', 'vy', 'vz'])\n",
    "range_df = pd.DataFrame(columns=['ground_station', 'range'])\n",
    "range_rate_df = pd.DataFrame(columns=['ground_station', 'range_rate'])\n",
    "az_el_df = pd.DataFrame(columns=['ground_station', 'az_deg', 'el_deg'])\n",
    "ra_dec_df = pd.DataFrame(columns=['ground_station', 'ra_deg', 'dec_deg'])\n",
    "\n",
    "for meas in generated.toArray():\n",
    "    observed_meas = ObservedMeasurement.cast_(meas)\n",
    "    py_datetime = absolutedate_to_datetime(observed_meas.date)\n",
    "\n",
    "    if PV.instance_(observed_meas):\n",
    "        '''\n",
    "        PV objects are given in propagator frame (ECI)\n",
    "        We transform them to ITRF and to EME2000 frame (depending on the user's needs)\n",
    "        '''\n",
    "        observed_pv = PV.cast_(observed_meas)\n",
    "        pv_eci_jarray = observed_meas.getObservedValue()\n",
    "        pv_eci = PVCoordinates(Vector3D(pv_eci_jarray[0:3]), Vector3D(pv_eci_jarray[3:6]))\n",
    "        eci_to_itrf = eci.getTransformTo(itrf, observed_meas.date)\n",
    "        pv_itrf = eci_to_itrf.transformPVCoordinates(pv_eci)\n",
    "        pv_itrf_df.loc[py_datetime] = np.concatenate(([np.array(pv_itrf.getPosition().toArray()),\n",
    "                                                       np.array(pv_itrf.getVelocity().toArray())]))\n",
    "        \n",
    "        eci_to_eme2000 = eci.getTransformTo(eme2000, observed_meas.date)\n",
    "        pv_eme2000 = eci_to_eme2000.transformPVCoordinates(pv_eci)\n",
    "        pv_eme2000_df.loc[py_datetime] = np.concatenate(([np.array(pv_eme2000.getPosition().toArray()),\n",
    "                                                       np.array(pv_eme2000.getVelocity().toArray())]))\n",
    "        \n",
    "        \n",
    "\n",
    "    elif Range.instance_(observed_meas):\n",
    "        observed_range = Range.cast_(observed_meas)\n",
    "        range_df.loc[py_datetime] = np.concatenate(([observed_range.getStation().getBaseFrame().name], \n",
    "                                                    np.array(observed_range.getObservedValue())))\n",
    "\n",
    "    elif RangeRate.instance_(observed_meas):\n",
    "        observed_range_rate = RangeRate.cast_(observed_meas)\n",
    "        range_rate_df.loc[py_datetime] = np.concatenate(([observed_range_rate.getStation().getBaseFrame().name], \n",
    "                                                         np.array(observed_range_rate.getObservedValue())))\n",
    "\n",
    "    elif AngularAzEl.instance_(observed_meas):\n",
    "        observed_az_el = AngularAzEl.cast_(observed_meas)\n",
    "        az_el_df.loc[py_datetime] = np.concatenate(([observed_az_el.getStation().getBaseFrame().name], \n",
    "                                                    np.rad2deg(observed_az_el.getObservedValue())))\n",
    "\n",
    "    elif AngularRaDec.instance_(observed_meas):\n",
    "        observed_ra_dec = AngularRaDec.cast_(observed_meas)\n",
    "        ra_dec_df.loc[py_datetime] = np.concatenate(([observed_ra_dec.getStation().getBaseFrame().name], \n",
    "                                                     np.rad2deg(observed_ra_dec.getObservedValue())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Position and velocity in ITRF frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-04-19 19:20:00</th>\n",
       "      <td>1.924822e+06</td>\n",
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       "      <th>2021-04-19 19:20:01</th>\n",
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       "      <th>2021-04-19 19:20:02</th>\n",
       "      <td>1.925378e+06</td>\n",
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       "      <td>-5550.817674</td>\n",
       "      <td>5162.107314</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-19 19:20:03</th>\n",
       "      <td>1.925651e+06</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-19 19:20:04</th>\n",
       "      <td>1.925922e+06</td>\n",
       "      <td>-4.590723e+06</td>\n",
       "      <td>-5.020764e+06</td>\n",
       "      <td>269.092496</td>\n",
       "      <td>-5540.625371</td>\n",
       "      <td>5173.449799</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-19 20:59:56</th>\n",
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       "[6001 rows x 6 columns]"
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     },
     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
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  },
  {
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   "metadata": {},
   "source": [
    "Position and velocity in EME2000 frame"
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  },
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   "execution_count": 29,
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   "source": [
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azimuth and elevation, here selecting only the Tunka ground station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                    ground_station               az_deg               el_deg\n",
       "2021-04-19 20:02:23          Tunka   15.115783617032385     7.66812839220923\n",
       "2021-04-19 20:02:24          Tunka   15.122700514659531    7.751957209168841\n",
       "2021-04-19 20:02:25          Tunka   15.129634927361755    7.836097603463941\n",
       "2021-04-19 20:02:26          Tunka   15.136587045799004   7.9205522614634365\n",
       "2021-04-19 20:02:27          Tunka   15.143557063116962    8.005323899386005\n",
       "...                            ...                  ...                  ...\n",
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       "2021-04-19 20:14:22          Tunka  -164.13383659179337  0.24119900191966986\n",
       "2021-04-19 20:14:23          Tunka  -164.12951251781402  0.17884562380385266\n",
       "2021-04-19 20:14:24          Tunka   -164.1251985181108  0.11663496456349737\n",
       "2021-04-19 20:14:25          Tunka  -164.12089453374423  0.05456605756710139\n",
       "\n",
       "[723 rows x 3 columns]"
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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    "az_el_df[az_el_df['ground_station'] == 'Tunka']"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right ascension and declination when the spacecraft is in view of any station. Although the RA/DEC coordinates are in theory independent on the station's position, here the time-of-flight to a ground telescope for instance plays a role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
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       "    <tr>\n",
       "      <th>2021-04-19 20:59:56</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>-3.1530873942438413</td>\n",
       "      <td>-19.996196891839794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:57</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>-3.0612274494554192</td>\n",
       "      <td>-19.865481796190636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:58</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>-2.96971573053403</td>\n",
       "      <td>-19.73473397801875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 20:59:59</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>-2.878551349264586</td>\n",
       "      <td>-19.603955737852363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19 21:00:00</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>-2.7877334142899812</td>\n",
       "      <td>-19.473149365982014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2812 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ground_station               ra_deg              dec_deg\n",
       "2021-04-19 19:20:00      Argentina   -73.66852095441548   -76.67135779903485\n",
       "2021-04-19 19:20:01      Argentina   -72.86381262705333   -76.74465684573238\n",
       "2021-04-19 19:20:02      Argentina   -72.04453000559761   -76.81573644924268\n",
       "2021-04-19 19:20:03      Argentina   -71.21063690261401   -76.88450069522003\n",
       "2021-04-19 19:20:04      Argentina   -70.36212129471667   -76.95085159654056\n",
       "...                            ...                  ...                  ...\n",
       "2021-04-19 20:59:56      Argentina  -3.1530873942438413  -19.996196891839794\n",
       "2021-04-19 20:59:57      Argentina  -3.0612274494554192  -19.865481796190636\n",
       "2021-04-19 20:59:58      Argentina    -2.96971573053403   -19.73473397801875\n",
       "2021-04-19 20:59:59      Argentina   -2.878551349264586  -19.603955737852363\n",
       "2021-04-19 21:00:00      Argentina  -2.7877334142899812  -19.473149365982014\n",
       "\n",
       "[2812 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra_dec_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with TLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Rectangles to visualise time window for orbit determination.\n",
    "\n",
    "od_window_rectangle =  {\n",
    "    'type': 'rect',\n",
    "    # x-reference is assigned to the x-values\n",
    "    'xref': 'x',\n",
    "    # y-reference is assigned to the plot paper [0,1]\n",
    "    'yref': 'paper',\n",
    "    'x0': startCollectionDate,\n",
    "    'y0': 0,\n",
    "    'x1': odDate,\n",
    "    'y1': 1,\n",
    "    'fillcolor': '#d3d3d3',\n",
    "    'opacity': 0.5,\n",
    "    'line': {\n",
    "        'width': 0,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the components of the position different between the TLE and the estimation, in LVLH frame. The grey area represents the time window where range measurements were used to perform the orbit determination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WRQrndxVFT6BKUHS/x9vib3rPf3qPX16PCTABdQJBJ7zSBU0SxkUrP8GEOSvx9dY56V7IoggePd78ZttsEa3RK4dqmChyVLrqOx7fkqfy123dB8qhlC90sX+2dkdMGNxRRGZJiqfMW4PDO+dnSAdo3XOskEp6NEbbNWw3VBRF6Rm0rfLlCr1tUl7o9NaB9k0XA0phmDK8S4YLMaUqKC/Q0kqS8NJLgY1fvRuRlZYx05fhy6+PinxVWp6u0V5EiimaLV9IkiiyTI/NaVET3i27D6DfyDk4sHmW4EN5sCUfKipm9aD+/3LDNCEKr7ceJCKTFKGkR8cN2w0RnJ8v998b2vQos0bTvpg5ujsqlS8lniRcuXYTy2cOSlcvikT+/vcZrJozRPzdW0byQq0SXrqYx2b/77E77YMiT/I0Da2TIcndHnpJ9P9TfKTxQ7N80NMQaaGIKKWJSAJKTyXoZiZu6gARQZMvlHpCx6G7WRr0Hsc02wg9wSEZe+2ViiiUP73IGxVeiuY3ee2/pxRSP1BuPz0tkha6+Zn50QYc2DzTLT6184Re4X2yelu0frOWeIoiXyi9gaSRxuATJR9wCa9WX6hV0p3w0jHR9PWq6Tah44VSRSiNQ23RI6m03apNezF80kf4dM1kETGdFbdBpBF9vn6qGA+06ClLEl61lzKpjGXrduHrw8e8Ft7YmKwuKff2+KDjhY41aaEnGx+t3o6vt85OV6QYEyEhoGsYLXrPf1r14t+ZABPwTCDohJdmJug+eLoQLYrMUk4hTc+lnH0gJTVNRJwookc5aXrlkB590cVt7/7DIsqYkpIqxPTSleuawksRIIoGKpcnqrQS0tbsjWqivh9/8oXIp1UuSpG6dTsByz/eLXIM6VE95c12bfW665Ge3jYpL3RG6qCWa0j1psjTN4ePYd384aojTBJeihxRBEm+zFm8CQtXbhPyQH1UplobIWRZZLmztH5CYjJqvFROpKbQoia8dLGs+FpXzBjVDU+UvF9Epj6aMkDkP1Z8rQumjugq0kxoWi/pjfEvvj6Kdn0miEfiytk+KCpMkVvK6WvScTh+PHZKrCdfkpNTRB9IfegtI3mZVgmv0ZfWjvx8EovX7MBPv/6OazduiRsuitzfUzBfBuGV54BT3SXhnTq8q0gJOPDdz6CbNumYk7ev66CpIG7uhFfvcUwR9q17DojI2fc//iZuWHq2b4iHit0VcaPCq5w2T+qHbUs/QJHCBVxNoHPCjEUf4+CWuykKes8TeoRXEmWK+En55dKOlTdhaulJan2hdlC6E161qQNJeJ8u/bBI91Bb9EiqVC86Jnu1bwTKma3dvL94+iJP8dJTltkcXqqLpwjvww8USfe0R2qzmeODhHfNls+E7MsX+Zgwcv7zfCnnX5kAE9AiEHTCS/lPh44ex961U4SskOzO+miDEK8QlZnK6KUZkim9ckgRJMq9Hdn/HZR97CERHZSitJ7mQaXy6eS2f+N/cwgT/MtXSci6uCK8dMc/YfZKfL9jnuvxvdRJJAthoaEZIg2Uu/jF1z+C5rakF9ykx/d626S80Bmpg7cyJwmvMoJGbSVZ3v/Nj64IL83XSxdD+eNyiQlFgaQX7dSEl9Zr0HYIni5VAg8WKwya7mnv2skICQlB297jRf5c+ScfRb9RH+KrTTPFjdEPP58UMktRa3nEUtpn7pyx4kUlkmJqB+VyKpewsDCRk0eLt4zkZQZCeEkOKdL9Zr2XRcSUIvnEjSLw+7/9ybDwSulGS6a/hzKPPZQOWfOuo5Ata7Rb4dV7HMsLpfzKsTOXg6SEJJvGib+EV+95Qo/wUpvK1WyPFo1quI3wSu8sBJvwUtveHTRN3Ey93+Nt1GneP8MsCXYQXuULhFRvs8eHHuGl/eg9/2ldzPl3JsAEPBMIKuGV0hQGdH0LTV9/WbRMykelR13PPqmeX0vrKeXw97/OiGiD8uJM6Qf0CF56CYq2pZe0WvcYqxnhpVQBEm/5ix7SG8lbl4wRESPpsa8UFZO6h2YDoNy2ji1eEy/gqC0UQaaLx/c75goR19sm5UXSSB28lTlJeCnPmvKtpYXknbg/8cgDoJw3Wih/kaLZcVP7C+Fyt9BURtTv9OKafKFH8DTfKc03my9PTtdLZ5TLS4+h6WU4etuaZnOghaIqVFaTelVEzrS7ZcGKrWKWjK1LP1Cd7F7azltGgRZeiuyS3B7aMTddFJvk9Or1W4aFl6KeFM2jmxc5V+rbKg17CAl2F+HVexwr+4pEqkKdTi6Jcndc641u6o3w6j1P0EtrV6/fdI09d2OGBJpenKRUCvkxQLnn67ftE08T5Dm8WtF2tTGtlwFta1WEl8rave8Qug+ZLmYmoHM4RTzlT1bsKrxmjw+9wqv3/McywwSYgDkCthVeKReR3pT/85+z2L73a5G/RsJDwitf6CUCikjRNFn0+JoWejmC5r+UPlKglENJekiSadowSl2g1AfKNbxy9QZG9nsHOWJjxKNemmaJvszzwcD2br90ReWv3PipiDL1bN8Ixe4rKKZOoqjsyxWfcskd1W3I+EXYue9bMcsB1ffMuUsi1eHW7Xis/nCoiC7SrBC0fbnSD4sySYgnz1uDW7fixUtCtOhtk1pUSE8daB/eypwkvDSzAb0YRF9bo8fRi9fuwCd7DooX1Ig3LRRJoTe3KRLbsO7d6YFIEijX9sUKZUTk9q4YT8A/Zy6Kl+BomqMC+XILUaNc4nd6jhU5gZOGdXZNeUbM6CWqXDli0LpJLRHFlBaS4RFTFqNZ/Wp4oXwpkf9LaSPfHz0hconpgkyiRhFQSrUgyS56b0HxuP/Un/8KsZZeOvSWkVHh7dLq9XQv29D2NEZp2jzpkS/lJcunzqJ1KBItRcnV9kk3d5QrTfOjrty4R8x4kj0mm2HhpbJpKqo5SzaJR9g0jR8dS5S3SdOSUU6opy+taR3HJLdz4jaiUoXSok00i8fqzZ9h7ZbP8cmyseJNe3fHtV7Z0yu8es8TdBM8feF60A3ug0XvEbMT0DGhHDP0whs9qaDccUp9ohe99nxxSDy96t+1qSsP3m4RXk9jUhprlLJBN5h0Q0Q3mVJOvvS7JLx6xreZD094SmlQi/BKY8Hb40Ov8Oo9/5m71PPWTIAJ2FZ4pa6haWPoYl360QfRsM6LGS74tB7J6qJVn4hIyN+nzyMkNETICeXDSdNmqX1pjS7s9JnJf/49j0eKFxUvhvx1+ryYnoryA9PSUlHmseLigtN/5Ido1aSmR+E9eOgXtHmrNmYu+lhI0d15eJ9Bj3YN0s3DS+JHOYGUAkH7pvVeeLaUiCpLYkLzd34wfRmOnfwLN2/GI3eu7EIIaT5gmrtSTXjpb2ptUrtI6qmDFcJLbyjTvMR7vjwkpvmiF4F6tG0g8gPlC03NQ2Lw7eFjYr5WSt14utTD6NOxsWuGBIqA0SwKJMIkvKvnDhWzD9CsCxTlI/mlyJd8Gh8SabpxoC86SeIs7Zci5jSNFl1wku/cQf68ucTcmTSnqxRlo4vxtAXr8emXh8RNB82nXLJ4UXRo/qrriYK/hFftdEWzFdBLY95+WpjSW2gaqvMXriBf3pxoVPdFFL//PkyYs8or4aVxRf29ZvNeXLh0Dfnz5RLT0qWmpeHoL6c8Cq/WcUzjh/qfbgavXruBbNmixbzInVrWSzcvsp5jwN2XAPUKr97zBOWEDxm/UMwQkpKSgtED2orZQdTGDD15mTxvrTgGEpKSxdRYrRrXSPfhFrsJr6cxKf9NmrFA+QSM1pGEV8/49qfwUn3MHB96hZf2o+f8x7rCBJiAOQK2E15zzQnc1iTU9Ga7p2mLAlc73jMTYAJMgAkwASbABDIvARZei/peLYJsUdFcDBNgAkyACTABJsAEmIAJAiy8JuDJN2XhtQgkF8MEmAATYAJMgAkwAYsJsPBaBJSF1yKQXAwTYAJMgAkwASbABCwmwMJrMVAujgkwASbABJgAE2ACTMBeBFh47dUfXBsmwASYABNgAkyACTABiwmw8FoMlItjAkyACTABJsAEmAATsBcBFl579QfXhgkwASbABJgAE2ACTMBiAiy8FgPl4pgAE2ACTIAJMAEmwATsRYCF1179wbVhAkyACTABJsAEmAATsJgAC6/FQLk4JsAEmAATYAJMgAkwAXsRYOG1V39wbZgAE2ACTIAJMAEmwAQsJsDCazFQLo4JMAEmwASYABNgAkzAXgRYeO3VH1wbJsAEmAATYAJMgAkwAYsJsPBaDJSLYwJMgAkwASbABJgAE7AXARZee/UH14YJMAEmwASYABNgAkzAYgIsvBYD5eKYABNgAkyACTABJsAE7EWAhdde/cG1YQJMgAkwASbABJgAE7CYAAuvxUC5OCbABJgAE2ACTIAJMAF7EWDhtVd/cG2YABNgAkyACTABJsAELCbAwmsxUC6OCTABJsAEmAATYAJMwF4EWHjt1R9cGybABJgAE2ACTIAJMAGLCbDwWgyUi2MCTIAJMAEmwASYABOwFwEWXnv1B9eGCTABJsAEmAATYAJMwGICLLwWA+XimAATYAJMgAkwASbABOxFgIXXXv3BtWECTIAJMAEmwASYABOwmAALr8VAuTgmwASYABNgAkyACTABexFg4bVXf3BtmAATYAJMgAkwASbABCwmwMJrMVAujgkwASbABJgAE2ACTMBeBFh47dUfXBsmwASYABNgAkyACTABiwmw8FoMlItjAkyACTABJsAEmAATsBcBFl579QfXhgkwASbABJgAE2ACTMBiAiy8FgPl4pgAE2ACTIAJMAEmwATsRYCF1179wbVhAkyACTABJsAEmAATsJgAC6/FQLk4JsAEmAATYAJMgAkwAXsRYOG1V39wbZgAE2ACTIAJMAEmwAQsJsDCazFQLo4JMAEmwASYABNgAkzAXgRYeO3VH1wbJsAEmAATYAJMgAkwAYsJsPBaDJSLYwJMgAkwASbABJgAE7AXARZee/UH14YJMAEmwASYABNgAkzAYgIsvBYD5eKYABNgAkyACTABJsAE7EWAhdde/cG1YQJMgAkwASbABJgAE7CYAAuvxUC5OCbABJgAE2ACTIAJMAF7EWDhtVd/cG2YABNgAkyACTABJsAELCbAwmsxUC6OCTABJsAEmAATYAJMwF4EWHjt1R9cGybABJgAE2ACTIAJMAGLCbDwWgyUi2MCTIAJMAEmwASYABOwFwEWXnv1B9eGCTABJsAEmAATYAJMwGICLLwWA+XimAATYAJMgAkwASbABOxFgIXXXv3BtWECTIAJMAEmwASYABOwmAALr0mg/16KN1mC/ze/J080grHe/icVHHsMDQHy54rG2cvBNxb9QTgtLRUJt66Z2lVk1liEhoaZKsPIxmGhIcibIxLnriQY2YzXtTGB8LAQ5IyJwIVriTauZXBXja5tvDABdwRYeE2OjWAURxZek51us81ZeD13CAuvzQZsJq0OC6/vO56F1/eMg3kPLLwme4+F1yRA3tw0ARZeFl7Tg4gL8DkBFl6fIwYLr+8ZB/MeHCe8P/36BybOWYXjp/5GdFQkWjSqgTfrVRF9lJqahjHTl2Hr7gMIDw9Du2Z10fjVl8RvV67dQP9RH+KHX04hd85YDOvdCmUff0jzNxbeYB7+zqg7Cy8LrzNGsrNbwcLr+/5l4fU942Deg+OEd/22fSh6X0GUeewhXLh0FQ3bDcHccb3xYLHCWLvlcyG7M0Z3w+34RDTrMhJjB7bHoyWKou/IOShcMC86t3wdPx47hR5DZ2Jz3GhERUZ4/I2FN5iHvzPqzsLLwuuMkezsVrDw+r5/WXh9zziY9+A44VV2Rpf3puDVV57HyxWfRPu+E9Dsjep47unHxGqL1+zA2fOX0bN9I1So2wmfrpmM6KgI8VuXgVNRv+YLeOHZUm5/q1yhdFC+/MU5vMF8yGasOwsvC6+zRrQzW8PC6/t+ZeH1PeNg3oOjhTc5+Q5qvCPCKDsAACAASURBVNUXi6cOQKECeVCjaR/Mn9AH9xTMK/ps38GjWLpuJ4b2aom3uozEzhXjXX1JaRE5c8SgVpXybn9r1bgmC28wj36H1J2Fl4XXIUPZ0c1g4fV997Lw+p5xMO/B0cI7df5a3LqdgP5dmoo+qly/G9bOG4Y8uWLFv785fAzTFqzD8D6t0XnAZGyKG+3qy5mLPhY5v3WqVXD7W+dW9XAz/k7Q9X9MdJagrHfQgfZThUNCgKyRWXArIfjGoj8Qpaam4vq1y6Z2lT02F8LC/DctGd3EREWG4XZCiql688b2IRAaCkSGhyE+kfvUV71C1zZemIA7Ao4V3pUb9mDnvu8wa3R3hIffPQhqNO2LOWN7oEjhAuLfe778Hqs27sGw3q3RqP0QkdIgLR/MWI68uXOgTtUKbn9r3aQmrt9ODrrRFZs1PCjrHXSg/VThEAAxWcNxIwjHoj8QkfDeuHbF1K5iYnP6WXhDkC0qC27EB9/5xRRoB28cFhKC6Mgw3OQbU5/1Ml3beGECmUp4N2z/Ems2fybkNmt0lKvtnQZMRoPalUG5t7QsWLEV5y5cQb/Ob+K5up3xyfJxiI3JKn5r33eiWPel58u4/a1KxbKc0sDHVsAJcEqD5y7geXgDPkS5AgA4pcH3w4BTGnzP2Oo9HPn5pJhZ66Mp/a0uOkN5jovwbt/7NZas3YXZH/RAtqz/yS61fPPOr0CzOEizNDTtNAIj+rbGk08Ux/vjFiBPrhzo0uruLA2d35uCbUvHijI8/cazNPh8jPIONAiw8LLw8kFifwIsvL7vIycJb1paGuLW7MDqTXtx9vwlZMsajeqVn8aArm/5HuT/7+GfMxdQ/533xb+Sku8gLTUVkZF3X+yfMqwLnn2ypOm6sPCaQFjp9Xdx6cp1UF6jtDz39ONCgGmZMHsVPv5kH0JCQtCi0SugF89ouX7zNgaMmotDPx5HbEw2DOre3DWbg6ffWHhNdBZvagkBFl4WXksGEhfiUwIsvD7FKwp3kvAOnbAIv5z4E0N6tUTx++/D1es38fe/51Gq5AO+B6myh7jV23H67EXXO1FWVYKF1yqSfiiHhdcPkHkXHgmw8LLw8iESWAIhxw8j7NI53Clf3W1FWHh930dOEd7f/zqDhu2G4pNlY10v2cvpte09XgTsxs9aKV7M3758HG7eiseoqUvw7ZFfERYWigZ1KrsCevSCPr2XdOt2PIAQDOjaFOWfehTDJ8Xh6++PITk5GQ8/+D9MH/Wu205SE161evzw80lRjyvXbiI0NFREpCs+87gol9o1bNJHOPXnGUREhGPe+N5C5KWUBvo+Qotuo9G+WV289HxZyweM41IaLCekUSALr7+J8/6UBFh4WXj5qAgcgYi48cjy1XZRgaTmvdxKr1x4Q37/Ban3PoCQ8LuPh3mxhoC3wnvmHPD1If/PnnFPgRA8XTY0Q+OXf7wb+7/9CdNGdFUFQ6JJojj+/Q6ul/Ap9TI2ezb0bNdQfFiL1mneoBqqVy6HN9oMxugBbfBQsXtx7cYt8YT7ux9+xcbtX2LS0M5iH5S+cG+hfIaFV1mP8xevIiExUdRr38EfMG7WSmxcNBIpKal4reV7oNmtqE5Xr90U9T167JQQ3oWT+qHLwCko/+SjeKt+VWsGhKIUFl6TWFl4TQLkzU0TYOFl4TU9iLgArwiEXDqL6IHNXNumFi+FhO7/zecuL1QS3qtL5yJ8y2LxE61L2/BiDQFvhffQkTTMXOD/aR3LPhGCjq0zTqVG06JevnoDA7v9N7bkhEhmq1Z6SrxYLy3larbHrlUTXS/e7/z8WyG000a+i97DZyFnbAw6tngNuXJkF5uc+P0fdB4wRXyHQE8urrsIr7Ie8nom30lBhTqd8M222SI9Y8DouVi/YES6zqaUhslzV+ORB/+H1LQ0MYmArxYWXpNkWXhNAuTNTRNg4WXhNT2IuACvCIQeP4KoSb2EtIZcOgcS4PgRi5GWp2CG8kh4Y+MvIf7dRq7faD1anxdrCHgrvHaL8JJcHjp6ApOH3Y2+KhcS3tZv1sIzZR4RP1FaQ8XXuuDQjrmuVSm1YMSUxVg1Z4iI+C5auQ3rtn6OSuVLo2f7hmIGK9rH7LgNoKhsrw6N8Hy5x9GuzwQR/aVl00ejxUe7aHEnvPJ60HoHv/9FfNArISFJbHfo6HF8+8mH+OyrI1j+MU0o0DOD8HboNxFhoaFo8loVIeW+Wlh4TZJl4TUJkDc3TYCFl4XX9CDiArwiEL45TkRrk2s1Q8jl8yK1wV1aAwlv1u1Lkbx2oUh7CDt+RAiypzQIryqViTfyVnjthuzHX3/HOz3HYceK8a6IrbyOJLzvvFkb5co8LP5MMzpIEd4c2bOJv8kjvNK2SUnJGD55MWKzZ0XvDo1dRf706x9o33cCNseNQY7Yu9srF3fCK6/Hteu3xBdtl84YiGJFCuF2fAJeqNdVCC+16f2xC7Bu/vAMwksv6M2b0AcN2w7G5OFd8FiJYj7pEhZek1hZeE0C5M1NE2DhZeE1PYi4AK8IUHSXoryUmhB66Swon5dkliRWuZDwhrd9WfxZz/peVSiTb+QU4aVu7NBvEuITEjGoWzMUK3IPEhKTcPnqdZFnqxReWn/Q2AXIni0avTs2duXwUi5sjZeewbHf/sLDDxYRo2PJ2p346/Q5tGxcE9GREciZI0aI6astB2LVnMGulAdvhJfygBu3HyZEPWt0JOYv3yoiyN9sm4M7KSmo3ay/EG36hgG9ZBcaGoITv592vbRGOb/0ct2auUMR9f/Tn1k5pFl4TdJk4TUJkDc3TYCFl4XX9CDiAgwTkPJ35WkJWTvcfdnm9qydGYX36jmE939LpDtQGoOUDuFufcMV4g0cNS1ZfEISpsxbg08+/RrXb9xCTLZotGxUAy0b11AVXkproBkQDh76BdFREahfqxLoa7D0ghrlzh449DMiIyJw3z35MKp/GzHF2cAP5uPOnRTxNdq3G1ZPlxPsjfDSNuNmrsDWPQeQO2esmCli1cZPXVHdX0/+LWaGIOGOjorEnLE9xUt08g9PULQ3S5YwvPeuev6ymWHOwmuGHsBfWjPJjzc3T4CFl4XX/CjiEowSkOfvSi+q0Qts7vJ4I7cuRtimuHQRYE/rG60Pr++seXi5P60nwMJrkilHeE0C5M1NE2DhZeE1PYi4AMMEKF+XUhjkMzNIU5Sp5eVGT+6FkF/vpj9IMzN4Wt9whXgDR0V4uTutJ8DCa5IpC69JgLy5aQIsvCy8pgcRF2CYgPyFteTazcX2kgQr83jl05fJ0x2kMtzl/RquVCbfwEk5vJm8K33SfBZek1hZeE0C5M1NE2DhZeE1PYi4AMME5C+syefSVcvjldIfQvMVws1hca59uRNkw5XhDQQBFl4eCJ4IsPCaHB8svCYB8uamCbDwsvCaHkRcgGECUv6t8uMRanm5UiQ3S6UauN64R7p9eXrRzXClMvkGLLyZfABoNJ+F1+T4YOE1CZA3N02AhZeF1/Qg4gIMEXCXokCFqOXlStHgqIFTcLlwyXT74hfXDKH3uDILr3UsnVgSC6/JXmXhNQmQNzdNgIWXhdf0IOICDBFQm5JMKkCZpuCS4zwFkW3aKly4lsjCa4i2/pVZePWzyoxrsvCa7HUWXpMAeXPTBFh4WXhNDyIuwBABtSnJ5AVIaQo03y59cpgivGklSiFm8LQMwsszNRhCzxFe63BlupJYeE12OQuvSYC8uWkCLLwsvKYHERdgiIDW7ApSCgN9cjjsxA/iIxMpLXojtlqdDMKrVZahimXylTnCm8kHgEbzWXhNjg8WXpMAeXPTBFh4WXhNDyIuwBABtSnJ5AXIv6JGf6evq90ZvQQ5YyIyCC/P1GAIPUd4AdUvrVlH0f8lqX0q2Re1YOE1SZWF1yRA3tw0ARZeFl7Tg4gLMETA3ZRk8kIkKRay+2xV4NW3VYXXUz6woUrxyo6bluzqtZuoXP9drF8wAsWKFHL1sL8EkXY4ZPwibPv0oNj3zVvxyJY1Snyu+LGHi2H+hD6WjDp/tYeF12R3sfCaBMibmybAwsvCa3oQcQGGCOgRXlFgyh2ExN9CWkwOhIeFqAovrcZTkxnC73Zlp6U0LF23Ezs//w6lSj6A7m0bBER45bCfqdUBO1dOQGxMVms67P9LYeG1FKfvCmPh9R1bLlkfARZeFl59I4XXsoqAN1OJeRJeb8qzqi1OKsdpwtuw3RCMHtAWXd6bgs1xYxBKJ3tFSgNFXUdNXYJvj/yKsLBQNKhTGa0a1xTrzV26GTdu3sZfp8/j2o2buH07EYN6NMdjJYqJ39dt/RwrN3yK+MQknPzjNPLmzoEpw7ug9KMPqg4LNeGt3bw/urdpgJFTF6NcmUcwZkBbrN+2DwuWb0VCUjLy5IrF+Pc74N5C+ZCWloZ5y7ZgzebPcDs+ATWrPIv+XZqmS9HYvOsrrNywB/Mm9EFkRLilw5MjvCZxsvCaBMibmybAwsvCa3oQcQG6CXibgsDCqxux1yt6K7yp//yBpP27vd6vtxuG3VsU4RWqqG5+/NQ/+GD6Msyf2Af9Rn2I2i+Xx/PlHs8gvO+PW4DY7NnQs11D3I5PFPLYvEE1VK9cTgjv8o93Y/38EcgRmw2f7v8ecau3Y+Gkfrh4+RqadByODQtHImt0JLoPni4EtOoLT7ltjprwlqvZHhWfeQJDe7VETLZose2vJ/9Gwfy5kSN7NkyYvQrxCYkY2K0ZSGaXr9+NWR/0QPZs0bh6/SZy5cjuEt4sWUIxdOJH+Ghyf+TMEeMtVrfbsfCaRMrCaxIgb26aAAsvC6/pQcQF6CbgC+HVOzVZlmPfI2JKH9DsD8m1m+uuc2ZZ0VvhTT74GW5NeM/vmMLLVUK2XiNV9/vBjOV45KEiqFvtORw49LOIilKklBZ5CgAJ565VE11pBjs//xYbt3+JaSPfFcJ79vxlDOp+d6yQ5FLUeM/qSTh09ARmx23Ah+N6id9oXVraNK1tSHhLV31HSPP/7i2gut3+b3/E0nW7MGNUN3QeMAWv1XgeL1d8Mt261B6SbYr+zhzdDUUKq5dltoNYeE0SZOE1CZA3N02AhZeF1/Qg4gJ0E9Cag9ddQZ4ivFqzPlCZJNpRk3qL/6eF5vilF+J4+Y+At8JrtwjvnZQUvFi/G27cinc1LjQkBHvXTRFiKwnvoyWKouJrXXBox1zXej/8fBIjpizGqjlDhMQmJiajc6t64vcr127g1Rbv4fP1U3HpynU0aDsYa+cNQ47sMejYf5KIDFd46jE89Upbsf49BfJi40ejXGWrRXhJeL/fMVe8yCYtqzfvxa7PvxMpDNeu30LuXLGYNaa7kO1B3Zrj8UfuzyC8lHaRlJyMNXOHIXfO7D4Z1iy8JrGy8JoEyJubJsDCy8JrehBxAboJSNOIpRYvhYTu43Vv50l49UxNppzqLKl5L9wpX133/jPDit4Kr93Y7PnikHhZbfSANq6qUZ7ug0ULo2HdF13C+3TpEpAivJQ+QIsywpuUlIxOLTMKL61LQkx5vPfdk1+kJTR7o5pHFO6E9/DOea7tPvvqCKYvXI8FE/sge0xW7N1/GCs3fiqEt0O/SXijViVUqVg2g/BS7vEff58FCTtFp32xsPCapMrCaxIgb26aAAuvc4X3yqShQJZwJL3Vw/Q44QKsIaAnGqu2J7PCK5/mjKK8JLskvbz8R8ApwksvqTWs+xIqPnM3Z5eWIz+fxJjpy7B85qB0KQ2Dxi4Q+bC9OzZ25fC+Vb8qarz0jBBaT8L7eutBIkeY8mj1LHqEl15Y2773GyG4iUnJ6DtiDpKS74h/02/rtu4T/035vucuXEGBfLlc7XnyieJo3nUU3qhdCfVqVNRTJUPrsPAawpVxZRZekwB5c9MEWHidKby5/vkZt4d3FY2jSCJFFHkJPAEp39ZoHq0n4dWTFyyfCo3+m5bbs3YGHoiNauAE4b189QbqtRqI3asnIktYWDq6r7zZBzPHdMeYaUvxzpu1Ua7Mw7h1OwHDJn2Eg4d+QXRUBOrXqoTWTWqKFANPwpuamoYO/Sbi4Pe/iNkQSJpfe6WiK/1BrVv1CC/NvvDuoOn445+zYtaHZvWrYdPO/UJyaZ/TF67Dxh37kZiYhNpVK6BvpybpBP6v0+fQrMsoLJs5CIUL5rV0dLHwmsTJwmsSIG9umgALrzOFN2bZRCR/tlU0jqN5pg8TywrQPQevYo+ehJdW9TQXryTEkuTyNGbq3ekE4bVsoGoUNG7WCuSMjUGLRjUQniVM5PQ27TRCvMRWpHB+f1XDr/th4TWJm4XXJEDe3DQBFl5nCi9JTeqFM67GcTTP9KFiSQG+El5PEivl79JLavSymrQuR/7TdykLr/4hTjMmvF6zIl56/m4+7emzF9G8yyjxkhp9Tc2JCwuvyV5l4TUJkDc3TYCF15nCG9nu5XQNY+E1fai4L+BOMrJ8s+fuJ4BDQj3uyNvoqlaE11O5yhfl9E5j5kNitiyahVd/t9A8v8MnxSExKQk0A0RkZAQ6NH8Vzz5ZUn8hQbYmC6/JDmPhNQmQNzdNgIXXecKb5co5RAx4yzXtFD3S5mmoTB8qbguQora0gtaNhbefAdYSXk8Sq3xRztsX53xH0B4ls/Daox/sWgsWXpM9w8JrEiBvbpoAC6/zhDf8xA8In9hTvKiWmqcgKMLH01CZPlRUC5Dnx9IKntIE9Lxc5q6WZoRXmUahZxoz39Cyd6ksvPbun0DXjoXXZA+w8JoEyJubJsDC6zzhjdiyGFk2x7nmWSXBMTorgOmBlUkKkKKlUnM9vSDo7UcnqGwt4fUkscqcXTP1cHK3svA6uXfNt42F1yRDFl6TAHlz0wRYeJ0rvCS5aXkKgB5380wNpg8V1QKk6CnxJen09EEJM6LprfCqRZXNRJp9Q9EepbLw2qMf7FoLFl6TPcPCaxIgb26aAAuv84Q3anIvhP56RDxeD710loXX9FHivgD5y2L037S4y+M1kzurJbzuJFY5Q4PUEm9ziX2IMuBFs/AGvAtsXQEWXpPdw8JrEiBvbpoAC6+zhZdaR1FIo5+yNT2wMkEBSsnUmoHBl8JLuNUk1l1UWauumaD7MjSRhTcz9rr+NrPw6meluiYLr0mAvLlpAiy8zhNeucxQ6+jf0hyspgcMF+AioIyeak33pfW7J7RaEV6pn5UzcriTbBbejLRZeDMyeaFeV2xYNFL354PpE8YT56zCR1P6O+5MwcJrsktZeE0C5M1NE2DhdZ7wKiN9/Pja9GGiWoAyeqo1+4G3H52gnRsRXvlMEe72aUa+fUMz8KU6RXivXLuB51/tgphs0eITwXlyxaJFo1fQoHZlw5Dlwtt98HQ0e6M6yj7+kNtyWHgNI848G7DwZp6+tmtLWXidJbzSY/bQfIVwc1icaBxH83xz9Cmjp1rCa6Yf9AivmsS6+6oaC69zI7wkvLWa9cP+jTNEI+kjEa26f4CFk/vioWL3GjoY5MJ79JdTuP9/93j8khoLryG8gV/5dnwC+o6YIyoybeS7rgodOnocLbqNQUR4FtffPl0zGdljsoIGWP9RH+KHX04hd85YDOvdynUX5Ok3Ft7A93dmrwELLwtvZj8GvG2/Uhrlc/KqvbhmJtKuR3iVwu1pNgYz+cTe8rL7dk6K8MqFl7i36zMBDepUxssVn8T6bfuwYPlWJCQli+jv+Pc74N5C+UT3rNv6OeYu3Yzw8HBUr/QU1n/yBVZ/OESkNLTtPR7vvFkb5co8jB9+PolRU5fgyrWbCA0NxYCub6HiM4+Dhdfuo1xWvzPnL6PzgMko/eiDOH/xSjrh3fn5tzjw3c8Y1L15hhb1HTkHhQvmReeWr+PHY6fQY+hMbI4bjajICHj6jYU3iAaHQ6vKwuss4ZUes4c9WgY3Oo8VjTPzKN2hw96SZqlxdSe1ZqcC80Z4PU2DphWNtgRQkBXirfD+knAFKy//5vfWPhKdC41yPZhhv/IIb1pamgjEdR04Fas/HIr8eXPi15N/o2D+3MiRPRsmzF6F+IREDOzWDKf+OoOW3cZg5ZzBKJgvNz5avR1jZyzHFxumZRDe8xevIiExEUUKF8C+gz9g3KyV2LhoJAuv30eBiR3eup0gBkNScjKWrt2ZTnhXbtiDy9duiO9Fy5fU1DRUqNsJFO2NjooQP3UZOBX1a76AF54t5fa3yhVKg4XXRGfxppYQYOF1vvDy42tLDpUMhailKLhLW/CH8CojzJ6iuCy8GceEt8K77uop1D+53TeDzEOpr+cshrUPvKIqvJTDS9HbhMQkxGbPhqnDu6Bk8aIZ1t3/7Y9Yum4XZozqhgUrtuLchSvo36WpWC85+Q7KVm+Dz9dPzSC88oKS76SgQp1O+GbbbBZev48CC3b41bc/Ydn6XemEd+GKbeKOJzIiHNFRkWhY90W8Wa+KGCBvdRmJnSvGu/ZMbynmzBGDWlXKu/2tVeOaOH81wYLa+reI/DmjgrLe/qUUmL2FfbUTaWmpSK1Q3XMFLp29+3uegggJAfLERuHiNc9jMXxCT4T8ekRskzR6SWAaGIC9pqWm4vbNq6b2HJ0tB0LDwkyVoXfjsE1xoP9FvtESN6rdvXBlWTQOofu3406L3tpjQ++OeD1EtH1ZUEj6cJeLhjvWdOzQMZRWohSSe04wTC9LaAhis4Xj8o0kj9u66jR6CcIXjRPHLO2P9itfzNbHcAOCYAO6tnmz2DnCe/3mbdRu1g8rZw9GoQJ5RPNWb96LXZ9/B4r+Xrt+C7lzxWLWmO4YN3OF+O/WTWq6MFAwb8viMRmE9+D3v2Dpup1ISLg7Hinl89tPPmTh9WYABXobNeFNSUlFYlIyskZH4q/T50QUt3PLeih+/30iDWJT3GhXtWcu+hgU+a1TrYLb3zq3qoc7KWmBbqrh/WcJCwnKehtuaBBtkHrhDBJnj0bKz9+LWkfUb4mIN1qptiD5s61iXVrCSpZB1kFTEabRp/JtaLto2qZkmSAi5H1VU1NTcfnyJe8LAJArV26E+Ul4k9YsQNLahYhs0Arhr7e8K2T//zdP48JUAzPhxnTM3e7aEPRyYNapq1wE6Nii40XJWuqD8Eo1Edne+JRNdGMaGhKClFTP14z44V3FeYD2T+OAlpjl+zL0kLv6Z8KudDWZrm1OWJQvrc38aAP++fc8RvVvg8++OoLpC9djwcQ+4v2jvfsPY+XGT4XwUoT30uXr6N2xscBAzlO2WhvsXTc5nfCWeOA+1GjaB0tnDESxIoVA7z3Ry20svEE6etSEV9mUVZv2inxdyttt1H6ISGmQlg9mLEfe3DlQp2oFt7/RXRSnNATpALFZteXzgdJjTVrkUxNJ1ZXWk1ef5mfNMXMNzl6Od9sqKVeRPl5AZWSmz9RSxDzh1jVTPR6ZNRahof6J8ErpC1Ed+uPyEy+JevPja1Pdp7qxuy+YuWNt9iUxPTm88r6m45rOBe4+OGI2xcJ6ooEv0duUhsDXPH0NlMJ781Y8qr/ZG4sm98OPx37H9r3fCMGlAB69oJ+UfEf8+7ffT4uX25bPel/k+i5bvxsjpyzOkMN7T8E8aNx+GHasGC8CgPOXb8XsuA34ZtscjvDabTDoqY8e4V3+8W78/tdZ9O/yJp6r2xmfLB+H2Jisovj2fSeKOe9eer6M29+qVCzLwqunM3gdTQLyiymtHL5lcYYLHV3gIuPGC2FNrtUMd8pXc/07vHJNXGvUXXU/ygujmTfNNRtiwxUytfDeugFky27DXgl8ldy9EObu72bzqPUKrzyPlyglNe8lblDVlsx2LGuNGqcKL7WbZl74/scTYkaGdwdNxx//nBVBuWb1q2HTzv1CeGlZs/kzsW5YWCherFAGlLowd3yvDCkNlP6wdc8BMSsVzf6wauOnWDd/OAuv1iCz4+9qwkszNDz+yP1iDrp/z15E2z4TMLjH23i69MN4f9wC5MmVA11a3Z2lofN7U7Bt6VixrqffOMJrx94Pvjop3xaXXpwhsU2ufXdWESnyJP/ilvziqBYRFvK8OU4ItBTVdTevZ/BR01fjQAov9U/4liWi7+InbUBa1N0bak+LNBYoVeXiPY+k63sjnxeW+t3INlp1c9LvnqLmaiJpdqYMvcKrdsy6487Cm56MU4TXSceZndri2C+tqQkv3fXQ24y00FuP77xZE3WrPSf+TYnhA0bNxaEfjyM2JpuYuuy5px/T/I2F107DOTjrovZoUp66QCKblqcAoib1Fo84lWIbsSUOWTb/J7RKCsoLtRSpkst0cJLTV+tACq8knVRTvbzVhNfT9FTuKEjl0O+eooT6KDpvLU8pCkZmb9BLxojwijJTUgCNvHEzH8LQW+9gWo+FN5h6y/91dazw+gslC6+/SDt3P+5kRp53m/LQEyJKK4/uSkRoWrKo9lXFP+NH3F3H9dvxI2IOV/l2mS0fNJDCK5dOvXnTksRkm7YKF7LkEl1pNF9TmeutV7ade5RlbJmnFAWpD6QbBa0PUujhZlh4dRTKwpseEguvjkGTiVdh4TXZ+Sy8JgHy5q6UAzUpkQsToVJ9kS0EyL5iEpL3bs3wMppaNNeoPAV7F9lFeImj2te7lHylx9TZV+zDuSv/TTVn5PG1FL2UXnzSK9vB3tdG6u8pRcHdF9jUbjj17tMXwms2zUJv3YNlPRbeYOmpwNTTJ8JL03nRNBc01y0lTjt5YeF1cu/6p23SRUvtsbOUAxpGMys8W9WVzyuvGUV4c/97DLeHdRF/lqTKU36vEXnyDwXf7SVQwiu/sZCitMoIvLLV8m1iZ6z2WnjlY4rkTa9s+64X7FeyJ1lUPgXxJqVE2WJfCK/ZF+ns1yvmasTCa46f07e2RHhpaowde7/Bni8P4fBPv+HCpWtiQmRa6EshpUo+gMoVyqDWy8+KT/U6aWHhdVJvBqYtZqM00pfWrnV8Qzz6lsRZumirvbSU1WSgSAAAIABJREFUmR6FBkp45dNeUQ42/Vsrl9aT8BrpM/mYopk9qFwt2Q7M6A/cXj3xVAqu2SnJqJUsvL7vaxZe3zMO5j2YFt7Nu77CB9OXIVfOWFR5viyeKHk/CuXPIyZEprnjzp6/LKbS2PPFIVy6eh39OzdF7arlg5lZurqz8DqmKwPWELPRVkl4L275GBTxocfXybXeck1ZppYGkZkiQ4EWXrrhSM1TUMzU4C/hlY8pI6IcsIMgADvWOu7kv9tVeK2oVwDQ+2yXLLw+Q+uIgk0Lb+MOw9CnYxOUffwhTSD06brxs1Zi2cxBmusGywosvMHSU/aspxX5tJLw0ocnpIs0SS8Jlrucw8x0oQyU8KrNraz18pgrsliiFHIMnZ4upUHvkwDlmMpMNzd6j3I9x518+r6ILYtFhN7d1H969uuLCG9mewFVizMLrxahzP27aeGl1IUQ+mZiJl1YeDNpx1vUbCtyA+XCK58Gi6roLqKYmS6UdhBeSmmQou/UJ+4WPcKrFSVWjikW3oy09QivxE26eaRSzKSFsPBadNL0UAwLr+8ZB/MeTAuvvPEn//wXx0/+jVu3/3uzWPr9jdqVgpmT27qz8DqyW/3WKKuFlyouRQI9RRM95ff6rfF+2lGghFcumpTSQP2i9REIqV9SKlRHzq4D00V49Yqr8mYmM0Xz9Q4pPced/EMvJMhmZmigerHw6u0d79dj4fWeXWbY0jLhXbhiG6YtWIfi99+LSJUX0z6a0t+RPFl4HdmtfmuUFTIij/C6Kp6aCoS6nyHFinlF/QbJ5I4CJbzyFASK8NIjci1pcn0Vr3Zz5HqrjVfCqxxTmSmar3eo6BFeKktKEaL/Nju1my+EV2879HIJ9vVYeIO9B31bf8uEt0qDHlgwqS/+d28B39bYZqWz8NqsQ4KsOnqjdp6apSq8OjhovbSjo4igWCVQwqv8hLMe3p6EV+/NkXLuZZaijMNU702AxJJuVBKb343Qe7v4Qnj1pGZ4W99g3I6FNxh7zX91tkx4qzXuhR0r7s73mJkWFt7M1NvWt1Xvi0i+EN7M8vZ+oIRXKbh6eLvyRt/ujVw16qaL8OqVNOWYYinKePTovXmgLcPXzUXyq600P/OrdXbwhfDSPvXcSGnVzSm/s/A6pSd90w7LhLfTgMlo0fAVPF36Yd/U1KalsvDatGOCpFp6JEirKd5GeK2Qba262eH3QAivmmTq6WtfCC9LkTnhtWoMs/BaRdJ9OSy8vmcczHuwTHj/On0O9d8ZjCceuR8F8+dGqCJ/cHifVsHMyW3dWXgd2a1+a5QV0RlvhdeKdAq/gTKxI7sIr54bDNcLhz0mIHe5cl5FeNXE2opxZqILbLdpIMY+C6/vhwELr+8ZB/MeLBPe7oOn48Tvp1HhqccQFRmegUmPdg2DmRMLryN7L7CNsupRs7fCa+SxbmBJmdt7IIRXLW9Wj2R5El49ubjuXkbUE102Rzm4ttZz82F1i3wlvNy3//UUC6/Vo9ZZ5VkmvM+/2gXbl49DtqxRziKk0RqO8Gaq7ra0sXoERs8OvRVevTmheupg53WCSXgleUkatQR57v9fugivnhskd+uwFKUfoSy8dj5iva8bC6/37DLDlpYJb42mfbBt6djMwCxdG1l4M12XW9ZgFl7LUHosKBDCq3YzoSeiblZ43Y0pPdFl//SGPfYSiBsAX0V4AyHv9ujFjLVg4bVrz9ijXpYJ79ylm0WL6MW18PAs9midH2rBwusHyA7dhR4B0tN0byO8eiKGevZv93UCIbxqfasnoi7l2ibO2YW8OSLTRXiJs1YuLguvvtGoxVFfKcbW8pXw8s3Mf/3AwmtsTGa2tS0T3vZ9J2DfwaOIiAhH3tw5EKGQ3i2LxziSLQuvI7vVL40KtPDqESi/gPDxToJFeOU3IJTS4I3wupNqq8aaj7vKL8UH6kbPFsKbmICsfd5A/KC5SMtbyC+8/bkTFl5/0g6+fVkmvHv3H/bY+soVSgcfHR01ZuHVAYlXUSVg1aNIbyO8VKlAPNr193AIhPCqRd20Ulj0CK9Wf7kTWz3RZX/3S6D2l5mFVxqXxP72rJ2B6gKf7ZeF12doHVGwaeGNT0hCdFSEbhhG19ddcIBWZOENEHgH7JaF1z+dGAjhVetbLdGShJi+6uUuwsvCa37MaPWD+T2ol+CrCK/e6L18Bg+qYVLzXuJzyU5aWHid1JvWt8W08FZt3AtdWtXDK5XLiXQGd0tiUjK27TmIGYs+xk4HfZGNhdf6QZlZStSSF70czER4rZJuvXUNxHp2EV5qu6fcUXkEOLnnBNWUBq3+cpfPKUV46dO4Cd0z3xcx5eNOK9LuqzHqK+HVG72X2i21j2SXpNdJCwuvk3rT+raYFt6jv5zCsElxOH3mAio+8wQee7iY+PBETNZo3IpPwNnzl/HDzyex7+AP4u9DerVEqZIPWN+SAJXIwhsg8EG+WyujTGaENzO88OIk4dXqL3dCHCjJs+NhGigWLLy+Hw0svL5nHMx7MC28UuMP//Qbdu87hCM//4ZzF67g5u14ZI2OQv48OfH4I/eDcnifLVsymFmp1p2F13Fd6pcG2UV49T4O9QsUH+0kEMLrLnrvKaovj9SltOitGuH1VnitHG8+6ia/Fas3Imp1hQItvNKxTlF+kn5KnYkfsdjqZga0PBbegOK3/c4tE17bt9RHFWTh9RFYhxdrZZTJTIQ3UBd/f3ZvIITXXeqCJLyUVkDiIV/kNx+pdd/2Sng9CXUgpuLyZz/r3VegbvJ8Jbx6zyXy6D/9Ny1Oe3GNhVfvUZA512PhNdnvLLwmAWbSza3MqWTh9TyI/C287j7vS7X0FKHVI7xasuZJall4744TLYa+OiX5Snj1Ru/lwhsZNx60HUV4KdLrlIWF1yk96Zt2sPCa5MrCaxJgJt3cyouuGeHVGx0K5m4KlPCqPTL2JLzy39Kee0U1wuspIq8lPla9JBnMY0HrpsOXbQu08MpveLRefvQlB1+WzcLrS7rBXzYLr8k+ZOE1CTCTbi7JTXKtZkiu3dwUBTPCqyVJpipmk43tJLyebnRYeP0zYAIle74SXqKmFb1XHudaueD+6Qnr98LCaz1TJ5VoufAm30nBxcvXUCh/bidxctsWFt5M0c2WN9LKi64Z4dVzsbS88X4u0N/C6ylq7kl45WMi5OHSHiO8atOLaUXrrRxzfu5CS3cXKA4svJZ2o2phLLy+ZxzMe7BMeK/duIXhk+Kw47NvkJaWhqN7Fgoum3bsx1+nz6FTy3rBzCk4hTctDZGz3kdiy75AdIyrDXRSCEZRd9IAsvKia1Z4nf6o207C6yklQY/wepJaLeF1alTP6HnB04uDRssysr4vhVfrGFaODStTqoww8PW6LLy+Jhzc5VsmvP1GfYjExCR0alEPb7QdjMM75wkyx0/9g479J2HXygnBTcpN7e0sjtJJTTnBOAtv4Iei1gXKSA1ZeD3T8rfwepJaT7/Jx0RovkKqEV5PKShaM26w8N4dJ1Yee0aO00AKr3JsaI0VI+2y07osvHbqDfvVxTLhrVCnE7YsGYNcObKjdNV3XMJLkd9K9bri8K759mu9BTWys/BKESNqpvxtXBZeCzreRBFW582aFV4ro80msPhsU38Lr6fomacorB7hJUju8jW1onZav/usA2xWsFa+q6+q60vh1TqGlX3PwuurXuZy7UzAMuEtX7sjNiwahfx5c6YT3m8OH0OfEbPx6ZrJdubgdd3sKryevpvOwut1d1uyod2E1+mRPzsJr6e+l4tYWGiIaoTXjPA6VXKMHJRWH3tG9h1I4VUe41rpL0baZad1OcJrp96wX10sE96RUxbj33OXMLBbc9Ro2gf7N87AoaPHMWLyYrzyYjl0a/OG/VpvQY3sKryevpvOwmtBx5sowuqLjdkIr9Mjf3YSXnfCqhQxT8Lr7pG81o0LCy/E3LPELxBfGfOl8Gr1vTICHEgOJk6dmpuy8GoiytQrWCa8ycl3MHX+OixbvwsJiUkCakREON5uUB2dW9VDlrAwR4K2q/BKFzcJujyPl4U3sEPRbsLrdBHyt/BqyYfaI3UrhFfrsbaVHzsJ7BHk/d4DKXp2El5PTwq8pxv4LVl4A98Hdq6BZcIrNZKmJfv37EWkpKbivkL5EB6exc7tN103uwqv8rvp8mmMWHhNd7upAqyOqJqN8LLwandnZNZYhIbqu2nXEk+1CK3yJshThNdd+Vr7tfpGS5ua/dYIJANfCq/WOUVtzAUql9mXo4KF15d0g79sy4T32yO/4qH770WO7NnSUfnnzAUxL2/pRx8MfloqLbCr8MqjTPTftEjfTWfhDexQ1Lo4Ga2dWeE1IgEhF84gfNdqJDXparSaAVvf3xFeLfE0K7zuIshasw8EMroZsM5X7NjIWLe6zv4QXuWMPFIb1ORWa7xY3X5/lMfC6w/KwbsPy4T35YY9MHFIJzxR8oF0NI799hfeHTQN25ePC15KHmpuV+GVX3Tpv70R3pAL/yItSziQK58j+y5QjdISIqP1Miu8ekVI/iJkUvNeoItrMCz+Fl6teV7VhFUZZfcU4XUnvHoidnrWoT4NPfMncOsmUh98NBi6WHcdA/k0w5fC680np1l4dQ8bXtEhBCwT3lJVWuPTtZORO2f2dGgCMS3Z7fgE9B0xR9Rj2sh3XfVJTU3DmOnLsHX3AYSHh6Fds7po/OpL4vcr126g/6gP8cMvp5A7ZyyG9W6Fso8/pPmbXYVXftGNjBsvXtaQpibTE+GVy01C9/GglAherCFgN+GlVukRIfk0d/IbKGuo+K6UQAmvfCpAeevUhFU5Z7Yn4VV7QqD3pkVPP8vz/5127Fv9dMXIqA2U8LqLalt9HjLCwlfrcoTXV2SdUa5lwlu9SW/07fwmXnquTDoy+w4exbBJH2HniruP1X29nDl/GZ0HTBYpFOcvXkknvGu3fC5kd8bobrgdn4hmXUZi7MD2eLREUfQdOQeFC+ZF55av48djp9Bj6ExsjhuNqMgIj7/ZVXjlFzbliU2P8MpneXD3mMzXfenU8q2OrJiN8MqF152k0TpK4fW0rp36zt/CqyWVatKl/JuvhFfP2JP3s9onjO3Ut0br4lTh9eYLfCy8RkcPrx/sBCwT3hUb9mDqvLVo2bgGShYvipAQ4Ofjf2Lhym3o0PxVvFW/ql9Y3bqdgF9P/o2k5GQsXbsznfC27zsBzd6ojueefkzUZfGaHTh7/jJ6tm+ECnU7ibmCo6MixG9dBk5F/Zov4IVnS7n9rXKF0rb8RK8y2qOMKOkRXmkbqdOk/F+/dKKDdyKPnFvF1Arh1RIh+ZhKKV4KFAUMlrQGuwmv2uNnI8Krtr3e3FQj/Ux9Tkuw3NjoOW1ozaChpwxv1/FlhFfqf7Xp1tylOwSShbcMtbbjCK8Wocz9u2XCSxg/3f894lZvx6k/zyAtLQ3/u7egEN3qlZ/2O+Wvvv1JTJEmT2mg+YHnT+iDewrmFfWh6PPSdTsxtFdLvNVlZLoo9MQ5q5AzRwxqVSnv9rdWjWs6VniDNZrn94FmcId6Hz0bKdYK4dWK9siFKuWhJxC+ZbHI4SXptfviT+HV079qAqKUD08RXjPCa6SfU/MUDKobGz3jUKv9esrwdh1fCq+ncecuqs3C621P8nbBSsBS4bUTBDXhrVy/G9bOG4Y8uWJFVekrcNMWrMPwPq1FGsSmuNGuJsxc9DEo57dOtQpuf6P5hROSUuzUbFGX1J8PI2nUuwgtWRoRA6bgzrqFuLNuEbK83gJZXm+JqIgwzXonvFUpXbuoHCqPF3MElH1jrrS7W9PTlIgsYUhM9n4sJn84Bimfb0N4234Ie6FGhmrJx1BI3oKg9Wk9Wt/uS2pqKq5euWyqmjly5kKYjrnE0y6cQWL3xgjJVwiRk1ao7lNtDNDxSn+XjrO7fRqKxOTUDGWobU99p6dPnNzPejpYyVnPNlatQzemWbKEIkmlT63Yh3TOjlryWbripD6Xzv/Sj8rrghV1CHQZdG3jhQm4I2Cp8CYmJeOff8/j5u2EDPsrpZi9wdddoh7h7Ys5Y3ugSOECYvd7vvweqzbuwbDerdGo/ZB0nz/+YMZy5M2dA3WqVnD7W+smNXH5xt2PbNhqOXYYoeN6AA+XRmrviQj58hOELBiLtOdeQVqrPsidPcJzvS+eQWjfpkDeQkgrUUpsT9vR9ryYIyD1hdQ35kq7u3UIgJzZI3DFxFik8eGpn8V4OnZYjCfkLeAaH6kfLLWiCT4tIy01FTdvXDG1j2wxORGqQ3iJkfzYU92p7PiS+KXj+3BphIaEIDZbOK7eVDm/qGwfsuEjhGz8CGl130baq2+7bauhfqbZGmTnEVMAbbKxkrM/q0VR+5ioLLh2O9knuw1tffcF7NT5e9KV767NeseMTyrro0Lp2sYLE3BHwDLh/fzAETEzQlLyHURGhmfYH31q2J+LmvB2GjAZDWpXBuXe0rJgxVacu3AF/Tq/iefqdsYny8chNiar+K1934li3ZeeL+P2tyoVy9oypUH5yFP5b60cXvnjsTvPVg2qx9f+HGPe7MsXL81YkdKgNV2TMvdT68Usb9j4aht/pjTozaVV8lPy9ZTSQJyU2+t9PK01/oK5n/WMH60cZj1leLuOL1MaqE7u2uYujUPrmPe2nYHcjnN4A0nf/vu2THjrtngPXVu/jpcrPmmLVqsJ7+adX2H9tn2uWRqadhqBEX1b48kniuP9cQuQJ1cOdGl1d5aGzu9NwbalY5Eta5TH3+w4S4PyomZUeOUXbRJeupgGS76mLQafh0roFRMj7fC18KrlBwZSHIywoXX9Kbx6JUIprMp/awmvkr/e3FSj87UGUz/rGReBvFELlPC660O9Y1UPV7usw8Jrl56wZz0sE96qjXv5beoxPSjVhJe2mzB7FT7+ZB9CQkLQotEroBfPaLl+8zYGjJqLQz8eR2xMNgzq3tw1m4On34JBeJVRJ60Ir1yY6Y18upg6bXoiPWPIF+voFRMj+7ZCeI1OaxRMImRH4ZXzk6Jz8jfsAyG8amPAFzdoRsa2levqeaHQyv0py/K18Lo7t7iTfBZeX/Y2l21HApYJ72stB2LJ9PcQky3aju30WZ3sKLzKi5TyRG9EeO+UryYelalNd+MzqA4uWOsrXN403Qrh1fOWtzzKH0wi5E/h1UoZkPpXPg7ob8qbSi3hVcqN3hsQTzc2yo9fUL30tsebcevvbZwuvGrHpKc2602/8Xc/mdkfR3jN0HP+tpYJ765932H5+t1o81ZtFLknPyIi0ufx0gtgTlzsKLxqd/ryu3wt4VWeOAP5GNBpY8YXLK0QXuLsrm5q0sPCqz4y9QqinB9N/2VUeOXb01MYvTelem5skms1Q3Lt5qKBTooCsvAuTjdoA83DF+d2Fl5fUHVOmZYJ71OvtEV8gvsZC37au8g51GQtcaLwKoXZF5LmyMGg0ShfXWCsEl4jL73oFTs79LM/I7x6bwTk/NLyFMiQJ68V4ZX2Q3Jq9CmMu+NZ7UZZEl4npDQFOqLp65QGtWPSU5t9dT4K5DHPwhtI+vbft2XCezs+41Rk8uZnjY6yPw0vahgswiuXmULFi3mcXcLbx6Ve4MtUm/jqgutr4VUT4WCK/PlTePXmaMv5peXOL2ZCkUdWtYRXvj29WGokz96I8DpJinx1/Ok9iflaeNWOSa3j1GnBDBZevaMxc65nmfBmTnyw5bRkaoJiRHiV2+vND8ysY0Bvu7UuPnrLUa5nlfAayQEMtDwYYWVn4aXIKS3EU/6pZiPCm/rQ4yJCrDcK6+54dic/TpEiXx1/esdiIIRX60mMU/pW6gMWXr2jMXOuZ5nw0lfJ1mzeiy27D+D8xavYtvQDQXT/tz/i1u0EVH3hKUcStmOEV+0kJn9JpmD5Zz2KunJ7vVErR3awhY3Suvh4uytfCq87sWXhVe8tvceKPHJKKQ3EM6H7XWmlRUt4aR3pOKXIsDJC7GksqdXRUyTXKTe8vjr+9B63gRBeeeqLlJed7slrh6rin7dn7dTbDFuvx8Jr6+4JeOUsE95ZcRuwddcBNKn3MsbOWIbDu+aLxh06ehxDJ36EDQtHBryxvqhAsAiv/CLnSXjVLnx68xJ9wddJZeqVIaNttkp41SJgkiQoo4fB9KjbnxFeI3IoCavU33LpMCK81DdKYfY0htSOZ083ML4at0bHudn1nS68an2o1XdGxqtZ/v7YnoXXH5SDdx+WCW+VBj0wb0JvFCtSCKWrvoPDO+cJKlev3cRLDbrj0I65wUvJQ83tJrzuRER+kctf+zW3EV4WXt8NU62Lj7d79ofwyvNLpXoGy+NQfwqvESaSbBBP5bR/eoRXvj2VET9isShHa1GL+qlNSSaV45Qb3kC3w9cRXuovo0/nWHi1jhb+3UkELBPestXa4PP1U8U8vHLhPfnnv6Avmh3YPNNJ3FxtcZrwqkUJAh0ZccLAkW4kqC1WPz60SnilvpfLlydJly6uekUrUP1oV+GVBIy4KCPoeoRXvr2RebI9RfLVbmy0HosHql+N7tdXN5x66xEI4dW6AfNmXnBxLhvaConthyKl5NN6m++X9TjC6xfMQbsTy4S3Q79JePzhYujY4jWX8F67fgt9RsxGbPasGDeoQ9BC8lRxuwmvu0eT8otWvrfbuY3wqm0f6Jc9nDBwfJkCYJXwKiNEcklXk9pgiQ75S3iN9rF0XBF3+Qtr9G89wivf3sinv9WOZ08y6JTjPzMIr9YX/JTnUqNM5OcEI2POX+dwFl5/kQ7O/VgmvGfOX0b3wdNFCsM/Zy7g0eJFcfLP0yjxQBFMGd4F/OEJ/wwQd8Irj9J6El5vprbxT8uCey++fMnLSuE1csE0erEMVA/aVXiJBzFMrlQXKWVfSIdHj/DSBtRftCQ2v/v5bz2L2lj0FOlzivAG+gbNHxFeI8cvjRWjaR7S2JHGmdVPq/SMX0/rsPCaJejs7S0TXgnToaMnhOimpqTigaKF8VSpEo4mGCwRXvlFK2/3QW4jvGrpC0654PllIF69iPAvtoqPAcjzKX2ZFuIL4aWIY8ilc+Ltf3fTXRm9WPqFv8pO/CW8Vt7U6BXetKREhCbcRlpsLt14lek1Wuk2VrZLdyV9sKLW430f7DJdkf4QXq0v+CnbaPQYls5jUjnKJxO+ZqhVPguvFqHM/btlwrt03U7UrfYcssdkzVRE7Sa87uTUjPA65YLn64Epf8RM+5JPM+XLaKiVwitPfQk78YN4+18tr9Ob6JCv+bsr38nC6y1Tef41leHp08RGUzW8rZOvt8tswivdsLo7fr05hqXzA93M07jwVLav+1OtfBbeQFAPnn1aJrxlqrXBlrjRuKdg3uBpvQU1tZvwuoskmhFep1zwLOhuj0VIUitNEyW9SKQVQTNbLyuFV/4pWZJdpbjL6+rLqLVZJvLtrRDeqIR4ZJ3cF4mt+iP1/pKq1bPySYjeCK+3nOSPvkmMtL7UFmhZ9Lad0nZ2OIf5I8IrPyalG1ZPUVijx7B0jqP8XRrvdsvjZeE1e6Q4e3vLhLfXsFl4rEQxtGj0irOJKVoXLMIrj9LmHjHDbUqDka9teezohFuIHtYG8YM+BKJjHD8mlBdUufzSp1+Jq5E36Y0As1J45XJOdfD09S6jF0sjbbJyXbPCG3L7JnLMGoqws3+KarmblcJKHr4WXvlxricSGOj8V7PjIbMIr/ymK/TSWc35mY2OWenGhySaxhALr9mRydv7k4Blwnv4p98wZPwilHjwPpQr/QiioyLTtaNmlWf82S6/7ctpwuvu0buRCI/d3+T1xeBQfqBBKY6eIqVm62Ol8FJdpDFA/+0pOmRlRNMsA0/bmxbeS2eRY1IfhCUmiN24u8gblQdPdfa18Mrn3dUjRsEuvHZIy/JHhFftvOPpxTIjx7D8piGh+ziPaTC+PJ49lc0R3kCRD479Wia8DdoO8dji1R96/j04cGWspd2E191LCPKTVa5Za9xGeK0QXuWLDXZ7k9cXY83d51oj48aLKIuvorvUFquFV5IDreiNkYulL5jrLdOs8IYdP4LscRMQGpNT5C3Sojamjb4AFEjhVUtd8TSfsi9z0PX2o5n17DBW/SG8xEj+BT+t844RLsoouZEgiJm+M7ItC68RWplvXcuEN/Ohu9tiuwmvng8F5Fz1hdt6u5ueyEiERz4hPjGy+4cJrBi7nqZ1IoHUO2WUN3WxWnipDmkpdxASlsVjdewQNdPDy6zwkhTEbFmK5I4jQDcwFxOuYlOvwWhc8Kl0u5eOPSveXPd1hFcpRp5SV2hdK2VeT59ZvY6V0Xdv6+Yv4ZV/gU/rpTIjwqs83o1cE7xlZmS7yHFdkWeMM7/oaoQDr+uegKXCm5iUjCM/ncS5C5dRp1oFsdekpGSEhIQgPNzzxTNYO8mpwquUVCMnN+nCL73Ja4UA2Hl8BDo/0BfCq4d3ZhBeyt/Nsmcdsh3YhcTJm4T41Suagi+KFMCBwm/gvvD/8tOtjIL6Q3jlN6ZaYhTsX1vLTMIrny1GPlOM2jFt5BiWPxWgco1cE/ScT4ysE/rzt4jYvkLMPy1dZ6g+FMzhhQm4I2CZ8J784zTa95uE2/EJuHHzNn7YvUDsc9Wmvfjy66Pi4xNOXJwmvO4eUxm5mEtlSG/yal1Mg31cGLlo+KKtgRLeQIu+XpZmIrwu4f31KBIHz8OB04dRP/mw2PWkPM+hYfaHXNUwcoxo1d0fwotrlxE97l2kFC+F5FpvpZs3Wlk/I5FArbYF4nc7RKj9FeElviEXzyDsp29wp1Jdj7iNnLuUNw2BYir/+IWUsiGNTxbeQBxdwbNPy4S3RbcxeOHZJ9CqcU3Xp4UJw9//nsebHYdj38fTgoeKgZraTXg93XVLv8XOWI2zoTlVW6klvFrRWrkE0UXUjm/yGuheXasGOnoUKOElOHbM41OVUhp9AAAgAElEQVR2minhvXQWWb7agawXziKpy2hMvHoYE67eFd6eOUujR87Srt1ZGfHyi/DqGt13Vwp24bXyZsQAtnSr+lN49dbRyE2rXYRX+QSRAivSi5csvHp7PnOuZ5nwPvVKW3y+fhqyRkemE94r127gxfrdcHjXfEcSdpLwejr56b2bl0cMkmo105zf0wmDItCPe1l4PY8iM8JLL6zRmI6MzYuUmk3R4Own2J9w98W1hjEPYVLe51w7t1L+7Sa8RiKBdjymWXjVe8WI8CqvAYG40ZfPQkFpFdSv0kLRXnohmxcm4I6AZcL7csMemD6qGx5+sEg64d2440vMXbIZm+JGO7IX/C28m27+jtW3TyEufxVDEVpaWYpAxQyehvMFH86wvdXCSyckKyXArgMo0BfTQAqvlVFNX/WvKeE9sh+hf/+GiKKPIPXZaumE974sMThw7xui2kbEQU877Sa8VrdPDwMr17HDOA32CK/yPBeIqL/yxkue3kCpc/nebmflsOGyHEbAMuFduWEPFq3ajo5vv4pBY+dj6oiu+O6H41iydieG9GzheonNYfz8PkuDFGGqEFUQqwtm/MiHJ8GUTljuhNdTFEfv3bxyPTtcaHw95gLdRhZe30V46aJOH2bIUrYyULwUnv1nDf6+c9O1w9NFWxgS3jQAjc5ux5z8lZErNP1c5fJW2E14qW7BfPNqh7rbUXiN9KvVwhs1qTcS2wxEWkwO3adot9ehW9eBbLHgacl0o8yUK1omvERv38EfsGjVJzj5x79ITU3FA0XvwTtv1sZzTz/mWLjeRHgXXP8F1aLvw72yN7z1Air8xyLXqsq3xLWiMNIjqawdB+Di4y9m2CULr95e+G89LebGSzS+RSCFN9DRbT20zER4w3evA+JvIuzlBgjJew/kxx/tWxJevY/8pRxgZTqEsh0svHp6Vt86djhGqabBLrzKG3u9Y16tl4zMJCHfXut8w8Kr75jIrGuZEt73xy1Az3aNkCM2G65dvyX+P7MtRoV3f8IZNDi7XWCSLpZ6mf2dfBPPnv4vR8lq4fX0iErv4ytlnpfWCUpv2+26nh0upiy8nkeHKeHdHCcKD23YGadT4sXxV+TaLfG3v3Jkc01NpvfiL88B9nT821F4A/0kw9tzgN6+8bZ8vdvZVXj19qsySi5x1fq4hZIPnTOlD/LQb1ofuJFvr1VXFl69ozFzrmdKeOlFtf0bZyAiIhwV6nYS/53ZFqPCK3/LWymsWuzkskzrKqdF0pIvrQivFcKrFFy9L7tptd2uv9vhYhpI4Q2G/vVWeKUpyRAdg9A6LVzCS7m7lOYgF175p3ppJhO1ReuGVb6NHYU3WG9e7XCMUt8Gs/CqXVvkL5AZ+ZqmvCxPXy5UE2USXk+CzcJr1yulPeplSnjrtRqImlWeRcVnngBNS7Z42gC3rXqo2L32aLHFtTAqvN0vfolVN0+IWiiFVbVqqalAciIQGY1VN06g+6UvXaspt9c6sUsX5agGrXD5pSYZducpT1dvhDezCa/e3GaLh1264oJNeMMO7kLKMy/7Ekm6sr0W3v+fkiwtTwGEVWmAA0nnxdMZyp8PuXwOX2ZNw+qC1VEhqhD0jAOtG1a7C28w3NyoDSo9feOPweg04SVm3uRGy69TdONI0qvna5xa1zeqDwuvP0Zy8O7DlPAePfY7Rk5ZLHJ26YMTnr6mdnjnvOCl5KHmRoXX07RGaruRBJJ+GzF6nGsOUPq3ch5QrROCGeHVKluqu3QClE5gdrnY+Grw2aF9gRReo+0PxBRuVgnvmlunxA0n5d+GnTiM5YWyuW5a9cggC6+vjkLP5Rodo76qpV2FV0/k3t35XyvFQOsGJOTyeTHHs9b87lSOnn5k4fXV6HVGuaaENyUlFWFhoYJE4/ZDsWL2YGdQMdAKo8Irf+nF3UwL0u7lj4zobx26tceKyBsiwkRzgSpffNGSUilKG1G5Jq426p6hlZ4u2nrztZR3/HojwwaQ22pVPRcLX1c4WIRXOZ6NPAY1w9Bb4ZXm4E0tXgpZSlfE5OtHxQ0n3Wj+feeWeFIjPWXRMw6UT2iUN6x2j/DqSdsw00++2lZP3/hq3/JyWXjv0pBfZ6R/68nj1XOzzMLrj5EcvPswJbyvtnwPCyb2RZ5csZgdtxHtm3v+jGHwYnJfcyPCq8zhk8/jqbYH+RyD9HudJi/jiyIFhOjSxVYpvFpyaUZ4tfKDqX5q62jVKdjHhB0upoEUXiP9qxzPeh5jWjE+fCG8VC+S316phdD9/upiAnxqH809TYKstkj5+3Tc09RmnmZqsGMOr5G+tqLfrCpDT99YtS9P5dhVePU8nXDX91KE19O4VzKR9wd9IY32T8cMleFpkbbb2aEnmuQ4hxP3vYXI0LB0m7Dw+mMkB+8+TAkvvbT2xYbpiIqM4JfWdIwBSXilCx5t4ulNbSmiIhUtCS9FlejRqlKYtR75aAmv1oVBK19LTXi1os46sNlylebnd2Fc7goo1uVVUT9/yZvqjVEIkD9XNM5ejvc7KyMSpBzPeh5jWtEgb4VXmoP3TvlqiLiveLoIryS8fc6H4t1yzV25jJ7GQbALb7Aey1rnLSvGmJ4ygll43V1btK4ZalyUaW96+4fk+u87N1Cq/WuiWLV3YFh49YzEzLuOKeFt12eCSGko/+SjmDJvDbq3beiWZNPX/feSij+700iEV8rhk1IStIRXOpnQ3S9dbMwK7/+xdx7QVRVNAJ6EJLSE3kLvRaRbQBCw0QXpRaSJCAoWpFl+EQtKsSAIggoC0hUERSlSBUGkF+m9pNBJgJD6n9nn3Mzbt3tL3gOSkHuOR827Zcvs7rezU2jBCqhcA671H+3RTFaTl9XEpFoQ7WiG72R/+eJb1I+44dj90WRTr2FffM/qHWlFwyvLM2ZGimvRzap6Xv/uK+B949Jmw4wBC4Wbzi57jsHYR14SWQytwjORwyqd0JiZNKVGDW9aHMupqczpEXjtaIf5AFb1hx07YHruk7pVYFS9quKVqhOSDOD1erpM1y/wCngvX42CaXN/h6Mnz8HGLXvgoRqVtI01ZYw6VE9ab92UAi8eaeI/ZqHJCBBQE4YTC+5sKRQSxePlGmIrDW/SxXDotG86nMoZDL/d/zzkzpTFrflvB/DiB6xAOa3JAA8t98ucP6DudT+h4b1b190EXidaP1rY0F4Ptad27PZ80aYpBV5KOhH/eBsIylcYOkauFLbzGJnhTFy0Abyf56tr61iWHFZ1JzS8rqkReNPiWHYin76QNbN3pFbgtVo3sE46sPUF8FqtO/h96seXm9WB2VVKZwDv7RbWdPp+r4CXt0nfoZ/B16MGptNm0lfLCfCS0wruTDfFhFkCL9/54n/nHvqsKAhCLjm/ceC1mny404zKYcZqp231u86pJb0BL4+0MXTDbhhy3t/S/ux2Doy7Cbx2NWj8vlvdBgmbVzt2e75otxQD739JJ1ALnTlbDjfgxXJhiLJ6pyJgyYq9wn7dSmNNcoPAbJV8JrUCr9Uc4Iv+cvIObPeATSsgYPNKiK/9lMeJQWpytEuPwGsHlnl/qjYgVusWB1465aR3yiaBGRpeJ6Pn3rvXZ8AbFxdvGpYsvTatE+AlzSDC5qaYcENbhHE8VRcHRVxo8vR+wgDe2md+9ABmq4mDayZVx0FWYGq12OkmP6vn0pps8EgbeKQ97nIOEVbnbl1pDXhRG24la75sy5QAL086EfdEG1PgRS0/XlaOO3zMqk5oeJ0zgNdaAuSMXao+cApk1l9N+R1pGXh1WlgnNvwcXPlm186mhO6RgVc+Ic0A3pTL573wpNfAO/qrOVD3oSrwx/qtpu01/I0e6bI9Uwq8clgjuXFkzVnk1PegxuMlDUc1FfBaHQ3xpBcq+0ErCLF6vy5sTHoCXjnSBmr4Fl4LvSO2qLoBdDeBF8tkJTeqhe5OyoSvgPeRc4uMTSbWidIM7/r6Z2G/i5prXYQGvJ+fyqjGb1oAXqtN9Z2c5Am2sO0TMHTcpuUepwa6OWvDzTD4+1aECDF3p67UCrx2oNVXwKvagNj5PskdnXKS43cG8N4p6U0f3/EaeCd+/7Ow3V2zcYdpiwx+qdNdb7Htew6JjHBBgQFGWdb8+AWEBGcDtEd+c+QU2L3/GOTJlQPeH9wLalYpJ+4z+80J8BJwog3fpluRbnE85caRj37CF0+CWtWyegW8/Cgev8ePg+wcTdsFXtn73uq5uy4YDgrAHdbQBrv41evwz42ywh71bl1pAXjlRe1uAu+4q7sgv39W6BJSXttldFSOWdawb9GkwQx47dgj+wJ4Xzn/J6yLOQe7inW8K+KWWoCXa3dJsy6HyNLNaXzT6jS9uzeNnpaBVzde7cAqbzMV8NqJ8Y5ryJmII8KPBWEX/yF7en5CmqHh9UZC0/+zXgNvWmqileu3wuZt/8L/Xvf0DB/60WQoUigf9O/ZBvYeOAYDR0yEX2d8LEKumf3mBHi5Dd/mmAgjiP1AhZZBBt5/1s6EZ0omiKQTCwo1Af4uGvBWYElaJeozPtnbAV6rxU73favn0pIMqSJthMXWMNXs3e763W3gtQOv8kJnJau+bDOu4cWwRvOjj4rXdwguA8UCQpSf8j99BDLt+gsSi5WFhGqPCOAtdsrlmEgbRQLY8KiKkFCltmmReUjCzUXbKccvf4HKpOFugRovl1PA8WU/8ncdObkLCn/5NuTNmttwGJXLpnNYc5ze3UeVSI/A69QpULcWWJ0S8ZBkCLt1soQqFUYZwOsjYU2nr7mngHfe4tVw6WoU9Ovmip1KV2JikogjjNrerFmCxJ8HvPMltG1WH+rXrqb9reEj1SGlwEte3rrg8zIgbD67E9rG7TSAl2uLO4S4NNFm4MEXS9RKUrSHYoHB4tkM4LU3wrnjISb/wOtcSBNxpH23rrQAvPJCdyc3QRx4yXYe+6pyUB5okq24stt4ljU8LjcDXrNY2vRyXwAvt8E3y9J2O+UwNQAvjUGVORGBE2p9sQ8Dl870cCbkJ12qWK63q/1SK/DagVYdkNpZN3h76ja6ZmsXfQOTLqENLyp9igaEZADv7RLUdPxer4D3yY5v2G6aP+Z9avve23UjhlCbvmA5ZA4KhKxZMkOHlo9Bl9ZPQMT5y9B1wEewcm5yppfPJs+HXDmDofkTdbS/9erUzBHwcrs9jNKAcTztAi8HLQyDlFLgRdjFhQJDu/DJ3s6kZ+UAopu0rJ67Xf19O95L0IEZtuZHHfHYONyOb1q9My0Ar7zQ3S3gnR99RNjh4oWaog7BZdXAu+svQC0v2uQi8F4IzAS1wxa6JXtRRUrR9RU3hUENr2r88mdVGl4OamZZ2qzkxZvfnQKON9/SPcudRuXTFZIril2O75CdCflJ151sx7QKvGZ97lQevAFeXLMwLBn2WbGA7MoT0gwN7+0YcennnV4B76at+4yWOH46HKbO/Q06tnwMShUPhcCAADh5Jhxm/LgCXnuhHbR4ss5db7WEhES4FRsH2bJmhlNnI4QWt3/P1lC+dDHo/9YX8MuMj40yom0yan6fbvSI9rf+vVrDpahY2/WqcXw+nIqLgh0lO8DGm2HQP+JP6JyjPEwo9KjHO/ymjga/jcsgqdcQSKrbBOZcPSTu7xKXA8ZXbg/9w/+EOdcOwYSCj0LnnC5bRP/nHxf/Tvxutcf7Ntw4B63O/C5gF6EXJw/+LBzYCf5jBgJUrA6Jgz9T1slv8XTwWzIdklp2h6RW3T3u0X3f6jnbDZgKbhx9cQeMurgdhkJh2Hhqh0j1vLhoU6iXrfBdK52fH0Cu4CC47EAWfVlYITcHdrrkpqLaCUi+507KRFJiIkRHXRZV/vryPriamDxmh+atoZb1jcsBLoRDUt3GAPkKwaXMAVDz1I9QPDAEdpRyJdjh47l4kNo0gl5ujL9sobC4aDPl+OUF8ffzgxzZA+FKdHJZ6Xt038Xyz/uyG22/y2yesf2SFN54KjYKapyYbzytGntUPnFTvlBIHDXLuF9+nvdnCotk+zHcxARnCYCrN+JsP3NHbrwQBv4Y8lJqK+PbFr87kQfxnQthrj7JlxydSF7v3Or939pEwItrZt2sBZXrZ54Q1wltxpXRAqoW8Ap4+Qs79XsfhrzU2XD0ot92/3sURnw2HX769v1U1wPzf1kr7HXRbrdj3/eESQNdo76aA/ny5ISnn3pE+9vznZtBTGyC7Xpl3TNF3HuzSh9YH30OGh//FeoHh8LyUk97vCN25KuQ+O9OCHprHPjfVx0+itwGH0ZsgzcPnYd3275t/P87BWvB2wVqiedjujYQ/87ywzqP99H3EHjrnooQ2Wr4swnrf4e4KZ9ApvpNIbDPMGWdzO5JOh8Gt17vBH75QyHz53PdnrfzbtuN6IMbEyEJPojYBu8WqAV+SIsOrj5n1sHMywdhcnQuWHd8u9g4TCnSAJ7LU8HBW3x7K9YgKDAT3IqzL4u+LAHKDfYxyg3Kj+pC2UAZQdlAGbmTMpGYmAhXLl8Sxfowcptb8d75b+zIZU7483dIOh8OmR5tCn75C8G+wBhodPI3t/Fa8eAcOBkbBQfKd4YSmc2BVx7vJEc62UGxDArwh1txiUbRaP6gP+A8cjcuuS/vZBmoHembqvbDeRPnT5SzoLc+F/+m6+StKKh4aA6UCAoRfUfz8Z2oA57EBAT4Qyzr0zvxXatvmM3d+KzV72brjvxt3b00hwS06QEBbXq6PRa/cBrEL/weKMsarlvFA4Khz9l18FzuCjClqGvdwytLUCar6mb8fg+3gM+At/qTz8Nfv3wF2bK6Z++6cTMGHnn6Zdj5x3eprpnn/LwKjp8KhzcHdIG6LfvDsjljIEdwNlFOTKTRvkVDeLxeDe1vTzxa05FJAz8C5c5P6IQmX/LRDx2lY6KDV7p+BjymLzq9WR0tkUkExo0tdjVaAC+3A7RjdnDu3w3w5ZFlEFqiKrxazT2NtNn3U4PdH7Uv2lK2j1gmjrVTYgdJx8qLroXCgpMbPUxD7Ag5luGzq7sA/Fz54L297rZJg5V5AskG1vPGpJWiundSJsiG91pCLHwT9S/k9HdpgVDT+0LIfZAjk6dWiGdZS8oWDDv9b0K7yBWGDT0+bxVajPerbJIkj19ZBmSTBm4DjPdaZWn0VqbMnrfq79v5bW7HjN9xapLgNL27L+uSWk0asI5mTmNW5m52nFbxGyldI2htIuDFebtopuxKk8AMkwZfSmz6e5fPgLdJlyHQr3sraNXYfQFfumozjP9uISybPfqutx5GaKhSqTRkz5YFzoVfgD5DPoXhA7vDg9UrwrtjpkLe3DlhQC9XlIb+b4+D32eNFvea/WbXaU12WpH/X24ceRKRgVdeQK2Al55H4K17OsKwhUJ7YLzsAC+3IZQddcy+bzVh3knBsEq+YVUWaoPFW8/DvMxRAnidgjP3Erfj8GRVptQCvLpMYyrZuJMyoQLeHP5BAhp1kRpwPOCFWdbwUgGvKlKKrq9uB/BixjZd0hormfHmd128bW/eafdZGr/keKuKJ272LqfZLu2Wy859GcAbLhyr0cFXTsVutgEm5c9L3Z6BOaHZhZKgaGCwyFYo938G8NqRxHv3Hp8B7/K1W2Doh5Ohdq3KULFscfD394PDx87Ahn/2widvvQCNGz5011v5m1m/wqyFrqxIOUKyQ+8uzaBlIxfwXYu+AW+N/Aa27z0EOYKzi9BldR+83/K32wW88o6bIOmr3zZB644jYF5QlNsO1y7wooa4+LXrjoFXTrggx680A5g7CTdWQsZhE+91Cpyk1dux+gTMDbrmoSm3+j7+zp1ufBEH9G4Dr9VmSdX/VvJqpx3t3kPASyHJ0FkNgXdf7CVokrUYVM6c1+1VcpY1/PG3hPPwyuW/3DSKToBX1ujKACzXRdbwcs2kzkPdbnt4e9+d1M7LZaU2x3kQHZiwL9EJ0O7F+4EcGH0xBu18P60Cr1V/+0LDazYfEPBSljXc6BXLFCISv8j9nwG8diTx3r3HZ8CLTXjqbCQs+v1P4RCWlARQvEgBaNOsvvh3er3sAq/KhEHn5a0a/Bx42zz5EmwoXsBth2sFlYaG+EAk1Nux2wjvQuYUVseUMvDK4XzMvn8n4cZKzuTkG061ZNRnFxfthbmZozw2Dlbft2pHq+dVv99t4LVaEHWyYRV7MyVtoXqGgHffrYuw7OZpEY4MzRowcD1qiOpkcQ8pJyed0AGvVaQFXhZvgZcDMr4XQ+LdyZBavC4qExVf9ZXVe2j8YjpnBCCnm1beZwuuH7VM725VHie/p2bgNYNWqw2t3ZjaVmuUbj6gvzcf/oZbf6nWzwzgdSKR9969PgXee6/5wLYNr7fAyyf6hxo+B+trVHUEvDTRTzh8Ax5dtcLIWEPaEatJi8pPfSzbzllBz52CGzMZ5LCJ5U8JNHgLvFbtmJIxlNqBVycbXsnElQsQ9OPXENe6t2UMZAJeisGLkJvDL9CAXzkWrwp4v407CZ9c3elmvnIngZcDM8rIp1fcy5ISufHmGeo7PJq+kzGoafxhOmfMuuUN8FpluzRrn5U3T8NTWYs5asL0CrxWyhJqJCvgVUE3V5a06P9sBvA6kriMm+UWyABeL2XCFxpeO+YBVsBrBZzc2arhpLFAOcnpSN8KeGnBxSgPGIpLtp2y0gJ4BTde9hE9zu2mMf6qU2jgz+/6ejHMKZzNsYb3XgRenWzYPQpVdT/JK/5mBV1OgVdOOoHf8BZ4ZTi2clqVTRo48OocduwMkx23LkCNzPns3Gp6D7W/nEbc6xebvIBvWC+PmgVV337RsfMeN0NZEH0sRZte6gunDnP3OvBarVFyamgUBQ7J1dvXdutv2vzw9TNDw3s7R2Daf3cG8HrZh3aBV2Wzp/PyVu2EOfA+XKURHGvcxs2GyWoy8RZ4acFGG2CM8CDbTqUF4OWQ0T57GdPEHyqx4MC7+6PJAvwp848q0obqHbRY4oYBj9Sd2iCq3nm3NbxWmhtfAy8/Usf2sIIuAl6y2URHNbwwxbAq+YRT4LXjtOgt8PLnsexmSWt0UxrJni9MIazmGy+nVeXjNP7QYW3ngs1QrW8rr4DXKr27rg5mzrtm9U7NwGum8LDS4Np1YrRaI1Tf4XKWv75ro0ZKGtX6mQG8t2PkpZ93ZgCvl31pF3hVWgEd8KoWE7oXj/IK31cPjnbu6wa8VpMJPf931npQ/r1+hoaXdsdW2jYr2zmrSdHq/V52g63H+aajTuYCjqGBL7jYDykBXgIXhCTUMPMJ3FYlFDfdbeC1stHWyUZKZYJkHY/S8dvxdRoL6NVdToEXx5/fxQhIqPYIJBZzZWJTaXitQovx8sgOblZRWmQNL38e36vyULeSn5SCmuq9d8OOlzaseMq0eNt5sdnETaMTgHeS7dJs00u/Ofl2WgVeq9M/q7WH2srqPtXv/G8F7nfFpM4AXquRnvG7rgUygNdL2XAKvFwbpPPyVg18GXhxgedG+1aTCZ/oK7zSWti/ncqZHQh4rUwOOPCidzR/FpvQalJEuImMuQq9X3sB5hX1TLThZTfYepwDSu0sBQU0ONGw8gUXnWZOli4DeMzm5B28z/H79wLw6mTDSmZ0ncqP0xGm8aL4vqpnVMCb0y+zEZO3d4773B4j4I2v08iwT33v5n744fohN7i6W8Cr81A3GwSyKY1TZ03Vu2nDYqVhtzU4bdwkA2+/51o7Nkngc6ZVpAxVkeR2TC/Aa6awsBqndrX9VkoR1Xvo20cGDIcHsx1xm2tV62eGhtfGQLqHb/EZ8N6MiYVZC1fCv4dOQPT1mx5NOmWMXgOTltv/TgEvTdRou4Z54jE/PJ+8rSYTfi/CrVPg5cD9dOenHAMvTlytauUXWlEni4QvZUM+VtZFydB9UwZe7Ie8rV2h6+yGN+OT9MALGx0fyVLZtsREwENZCor/vdsaXiyD2YaJFq0prw2CxTmT4It89aBIQDBYyawV8OIYyDxjrNDymtnxEvB+e+1ft2QTpGHHTSi/5KQT+JsKeJ0Ak2pxNpM/WcPLN6xYHgzJ5ETuqKxUT1+MQQIUmo98OVZV7+LJc77wKytMq5za4TtJ/mNWBvrNiR1vatbw3gngtQJnMo3icXppU5UBvLd7dN0b7/cZ8A4bOQVOnomAJx+tBVkye2YueraNK4RMervsAq/Ko1vn5a2afDjw0oSQUuANfrcbNH+ysoBP1PTUve6nDQhO/cWjExC4ci2R1fF05NT3oMbjJY3utwuIvpQXGTqcAi9fcDEOKB6ly3ZlVuXl4EIZ35zGAaV6ELSkduBF2diYPckII0WQkFLg5XBt5x0EvAS4lF1NC7xS0gns0z5RW2D1rTAxXijZgxPgVZkvpRR4iwUGu212rWQOf/c2Q5nuGzTurcxK6Pkfrh2EyMSb0D57WcB6OLl48p2BZRrD6EoFHQGvbEZi5TioKhuVAU91XIlLygEl77GqS1oFXqu53a6G1wp4sf342OamUqveGe1hxqNaPzM0vFZSeG//7jPgfaL9QPh15ieQNYsn7KbnJr4dwCtPDLLtKLYnHuHyRbTchPeERytqvVDjwi95oncKvPz57atPwKu5r3mk1LUKU/T5seUw1j/MKJZTyMMHbyUmQINzi2B54ZZGelgnsiVDh5PUsPgdGXgxs5hsV2ZVHu5ZPPDiBsdxQOXQarjYpgbgNVsUUTYoLSi1D/Z/6eULIXDpTNBlaFO1pWwvbMdhRgZe0ujKGl/8nirpxO0GXtVYkDW8Mhw73azRJomcJZ2AmplMk1YO78GNeMzrY7RhylSyazVe+O8ceF97qDvMDc3myA7fabZLVdkIsiisoZNMb3cbeOOSEiHQz1/Z5GYmcVbmblZOq/RBO8DL5xG0o8dnUK4ygNfJSMm4V9cCPgPeRp0GwYq5Lnu6e+myC7yqI02dhlcHvKhVwOgA3gJvjglD4MUSWQxo7XSre2cAACAASURBVBR2Q0wsuqNJGXjH+p/zyDBmNSnKGc5ScqRK73BiM8tlUYYEp8DLF9xhG/cIR6kHikU5MkvgZXASx5XqIWvqUFN+p4AXvz3u6m7YX6wzZPMPdBvmOuAlQKUsSaQZw/5/dt0mx8ArL652tEsIvJHXznvY7CqB92I4BGxaAUl5CwoNPl0qDa8TDaEKUM3kjwOvysHNqezS/EPOkk5AjXd0TFICZPHL5Nb3HHrxB515iSy7Tje9cvIdp8Ar95eV46BqHfPGBv9uAy/JoGqzYzaO+NwenRgLmcAfsvoHGM1jF3itNMX4Qn5ik5i3kLEure73hoeGV2VDn6HhvZfoy3ldfQa8Qz+aDE0eewgee6SG81Kk4Se8AV6d04sZ8GL8V7JZfPjmBgO2nGh4nQIvXyh+3hoJC05tdIs/a+Wlj91LCwWGFEKHN6fA622GMtXi5iQ1LNZBBl7Upj+U7bBt4JXLkBLglTPFITSUCAqGArmzQvglT9t5Xw0t7qyjWjBVMTTx2ygb58f0NZIEYL9TSK3xh6PFAmf3OBzfJy/MdhZbHfDyMGXFAkJEU/mfPgKZdv0lojNglAa6ml9eAwcTrhlOnvj3tAS8HJCd2v/Kmy1V/2M/oz019odu40yyyzc9HULK2RZR7jhb880fjBMXu9pqJ8l/dIVKq8ArO9vJJmU64OVz+6HhkwzbcW7aY2f+x/a0UorI49v/YriQJzwBmtWgjoc2PwN4bQ+djBv/awGfAe+4b3+Cb2f/CtUrlxOphIMCk3eA+K3hb/RIl41+O4BX3gnzifrXCbMM4G0Xt9M4En/iwyFa5x15os8173N4P+60oaUdsj/CFDx8Aby04HbZc0xolu0uUiQ03iZsUAGvU+DkGiasBwdeO17vchmcePlTO8jAiwDZKUe52w68Ks0yH9C640pcsCKnDjcy+w3MWU0sXKhhXHTczzHwqo5erRZSBN7DV894xN1dduMU7Iu9BE2yFoPKmfOK6qhi8OLfUxvwOtms8c0igo5T7TDWX95w6mzwqS9UplWkYeSbHrv2r3zTvGTFHqjV51NHGw58XmVz7dQ0xBsb/Lup4ZVP2OT5yg7wUqYzbEv5lM1qDPIwdlcnLofw+BtaG256F26cyExPpc1X9WeGhjddYpbPKuUz4B0zca5poQa/1MlnhU5NL7ILvKpFRgc88uTBgRPDYdEk0DZHmAG8T77eTTSLKjyTFfBiMgkzW0o+saBW7scdC900vHa0bLSwoLMXhjVzCryyl7nTI1mVdsdb4MWjW77pIGcmnXzKZXDi9ETvpHYkW8w7BbwqzTJ3OtI5j6FsbP1pjFuCDqpDWGwNU1MaZTvOGANBf6+ExK5vGCYHVvbjToBXFZJMB7x2j8R195lBKzdp8FZ25e+nBHjlDafOHEEXuYGX4bN8dVMUR5jKjUkn8g/+2oBwuyZOTpL/6MYwB2Qnmw58HwFvn2ProGJQHngupPwdW8pk7bo8/+rmcPo7D8EoNkDx0W6nHXaBF+d+VHjgpZMhUvjgPeSgrVorM4D3jolPuvmQz4A33bSIw4p4A7yqAas6HuIL3uIVe8WxLtqPDigXbMSh7DHkJS3wyt/Js3oOTD2w1gDPCZuPmgIvn2xQG7xl7Uw3gLECXu50N+HvY9CyURWP1MRWzc7LkJKEDaq2dqph5UeqGPweNxdOFj1vgVeXGnlQ7uq3XcOrAm1+HK0DXpTVz44uF6cJtMgSuPxzoyyUHT9CewQuy0QSJEHRE9PFn/mCbWUbqAPeTTHhYsOIm4c6WQqJ9+qAt/qFXyEaEtzCz6UV4JXlzonMUh/IGn4zkySVllcug1PNKpaDR4pBDbLd9pfrwGOhO4F/b02SEHh3wXl46uivokh2ToWs5kU7v3PtvE67bgW8PMlO0YAQj/jHdoGXbPmx3DoZolMcvIfMnTKA105PZ9xj1QI+Bd7rN2Jg0e9/wpHjZwEXp9IlCkObpo9CSHA2q3Kk2d9TA/CO8ysH3T55z9gNy40pw16+PWvg2z/nGsCLWleCaO6oo1ooBkX6uR1Rby7azsOuUv6+HDDeaexafJ+38WvtTphmgsiBF0O5oYbXiZZY7gf6f7vaal1q5HH5695W4FWBtqwh0nl5499xg4LAS6BBbWYltzo5pr/TsbpVaDIE3m2XjsGym6ehclAeaJKtuHiFDLxGhAYAiGvhOjGh63YAr5nscA2varPGM/YNlOII68YfyZkTmaV3kexTdAKzExrqD26bLWeadAKaVAYC3vPrLxiZ9ZyAs2oOcFIOXwBv+4jlsC76nKiS01MufOZmYrybw5idhVOlXeda8R07/eD41vPQdVtnjzWENPYUZQXHcNFM2T3saa02ndy0iZdZZxqDJlIYpQHnWLxUfac6+cgwabAjEffuPT4D3gNHTsELg8ZAqeKhcF/5kuDn5wf7Dh6HIyfOwjdjBkPlCskxWNNTc9sFXtXEbFfDy+/j2tj+tcuInbYVOMiThQy8k2Yu0oY0w77iC2TH2BA3JyScsKyyvPFwXuOP3HCcrAHLQAsTakUWRB9znGHJF8DLk28ULVhW2PA6gQe5DE6cnrANVMCLEPNTaBPbwDsz6hAUCMgKjbMWsz0MdaDN7S91MoDw07pkghHzGc0+eJuZnUzIBdQ5PtoB3jUXD3poc/fduugGwbqQZNcSYqH+5RWQOyAr4AaPX3aAS9fPdoFXlZbcyemEPM84kVmqK8m+HeDFZ2QzE7m8cixpK2Hkp0RbT4cYGxI77U/vVtXbibZb7kcnfYBlQA1vgaPTjKraNcWgB07FR0GdMz85BmUz7frxEwDTZgRA7oRweDvSE3hpXJNmlrTScrvbAV5u2oQnK3jZjdSh6rsM4LUaNRm/yy3gM+B9bsBH8GT9B6B7++RQPvix6QuWwx/rt8HM8W+ly9b3BnhVA1Z1tCTb0JJnO9pDIfB+eTkHPDdlkvZoWJ6YC4QfgGXTPjTMErhdsBzDFztNXhRwMcs99FnRnwi8VrFQeXSDoQfOQ7W+rWxHNsBvyE43KVmwVc84taH1NfA6PZLl5UXnL/S2x0VzS7F2toCXt6OT41QOXO2DSytTMuucXniGPfomr8fk/70n5MgsNTBNHDo7cKvQZKjhtQW8mpBkqQV4+VG8E9mVx79TUJPlBlNiW51KkBMjaXnlOcTpGObAi6YwdBLF41pbJbJwkvxHtVh5C7xhidfhgVMLxJhFG1iaP+0ujNyO3kmUG7m/uVb7yF85YM06V2zesWGPeYxFHfDKmnGrGLvctAnlmCKk2J2HVBsT1fyZoeG1K0335n0+A97qT/WGPxd96WG+cC36BtRv/QrsXPltumzhCrvnwJoiz5jWTQc2doFXtqEl4P2k5ZPiuHhIpD+8OW2mbeAtlHgFfhvV3wBeHvkBnQTkywp4rSY7XwEvaUScLPZUF9WE6VTDyrPdmdmW6YRBXnBlkLcaIPLCZTh/lephC3i5p7YVsPCy6L7LjyPNgLfG46XcUlGnBHg58KDTUp7eTxjAYAd4fz6/xyMig6zh1UVouF3Aawae3KTB29MJ+Xmn40eev+xoVblXPoaVal06CTZmSwKMsIDmQKPqVoXRBRINMxcr2edmUQsKNTGS6zgxSXASC11VHp1Jkl3ThC2x4dD63DKxWUDglR2/zNpAjpJh95v4Ttn8hWvXry+sCMdP+lkCL1dw8HcSeFutAQjObXKEGSc9Tk/pMoDXaoRk/G6nBXwGvPVaDYAfJrwNJYu5A9PRk+eg1+ujYN3CcXbKk+bu8ds2yc2RRVUBHfCq/q5avPmChTa0lCRiZM+uAniHHoiEYYtXauOZ0oQ3PKAeBK6uAC92C4Rzw5qIUFEYF3fX1z9r7X+xPvJkg8dX1dvXNiDGLAYwnxzRVhjDeTUf/oajDGPeOnup6oB/c6ph5cBL2cGcaMtUGiY78EAyJT/vFHgJDuh9dtM7y/2vggyd0wvG4EVZwYu+x9ud4krv7fUDlH6woHb8c+BZsmKv2ylBieNHTaM9oIZXBl6E2zMRR2B2lTJC49YhuKzWYc0MeO0Alw4w7QKvt6cT8vNOgTelDmc0l2GnEjDhXINzDnrqO4nWwvt/fuXuRjY3O+1vtul1omn2duPwxbVdMObSDmGSgBeeztnV1HoTllGnXcf14MzUipA7F0DJkknw7O+Pi3Lx0xZUrpz7d4MRVpBMemQZsjIrkoH3TFy0oyx5OtMTef7M0PCmOYS6owX2GfB+MmE27Pr3KLw14FmoWLY4JCUlwf4jp+CjcTOh7oNV4NXebe9oxe7UxxB4reyQnACvyhaST7RvJBQCBE7UxE4f+q6YNBAiESZ1KVppUq+/sT6UO+oKhTMga28BInaAlxaVxYHtITg6GGrM7OoGvOVH9NPGAOawiaYTGN3gxQ/eS9FkT1pJmvyd2MCpFkYnwMs1jLhoY5QM1PI6gQfVpO0EeHXguaVoO6hVIL9p4gleVwH7Ulghs/FiBbxLl/lDwJHd0Hrf6x4bpwvDnvFYLPFbVO9L364SsvNRgTnQsHV+qFE9SVkUGgMo62gH3u+51oYMoV05jQlycuEvQeCdEbFN1LlDcBkh85hNDROgEPB29C8EAasXAmQNhrgn2riV4XR8FDx9ZR1UzZIfULvILzvAdTuA18nphNx/Tp4VYBZ12IidjPW3U2dqI+zbsA2LoFa1rC7zmxvlRKzjv/escIv0YjVfcz+AMU+/b9zuxAZXda+TDau3NvgEvHikfzr+uqM5kH/baZQaud70rrZXakCuJbWgVIkkqF4tESp92xXyJIS7ZcpD5Qra26INLz8VkmXCDvBiVkocc7henk6IchSaTidzGcBrNXIyfuct4DPgjY2Ng3Hf/QSzF60C/G+8smQOgk7PPA4D+3SATJnUObzTenfYAV7dAqMCLivgRY9scgj5fvREW8BLE16z5c0hNCJUNPlbkZ2hzCDXsfDlUbNMQ0PRZNNxYUcIjg6Bfhdfh9fa5jOOp8xiAKuAt99rfWFu5ijb2g3VouQEFDlgyVpNu++RgZcC63sLvE7gQQeePxVqDM8ULm0KvHyTUCdLqKPFVi4j14rhBmrRkkxKpxeEnW1TBinBht659pvNUO3SUZiU93NILFcNenZPMAVejBk95Lw/0OkG2bWahUWSgbfksWPCSfNq7twwqXIxyBkTCy9GxIssa3JKYdfm4PYAr5nscJMGb81x5OedbPSw/r5yOOMb1Mip70GNx0t6JDDQrQeGWdSBSHilyZAUAa+TWOiqcuhMkuxuvDtELIONN8NFODJvNJwDL2x027Bevw4QecEPcuVMEtpa+ZLtnEnuyh0tB/U3NrAEXpyrVdp4t7nzmzGQbfsKQxEglyF88SRj00NaYrtzL74rA3jTOimljvL7DHipOnFx8XAm7DzEJyRAscIFBPSm5wuB1+pYykyjIg961U5Znmi9Ad77boXC5SsAHa+MgkavuLJLWQEvlfH5Gb3F/fjs949dFceSVhEi+GRFx5kfDR4EY/3DbNvvqYDXCSiaLfB2J93UALw68PwiX10YULyqLeBFLU377GVsHyeq2o7Lo5kNoBnw8hBvqPVH4D0aVB1efyVeuWjz+x+u0sgj1aiZl7gMvKX++UuEPLr0cEOYEnhJAG+/rQeEbGM6YUwrzC9vgVenRUwp8CYlAZyJjzacFuXIEfJ8qxorduUe3yXPP07MAPB51fyH5g4FQ8NEUe2Y1lAbor/Cqw8lh4xzUhZVO3i7YXXSjr4CXtn+ddr0TIYd7vvvxrvLbpynnFCdQ8NDodmK5vBYg0TxT9irg6FM7E4RfYacl3FcjaqY3y2sIH2A2vPjox2h9LKfoFH0dO0po2qD48ThUNfOcp9mmDSkZ9ryvm4+B17vi5S23uBr4CXjfzoyVy04tLjPGPYevJp02DBp4M/wVuQa3i4VCwqv3EZR30PHl12TIwKvzhyCgAdNGTou7CRgpNEJZ8BLk9XRsavEkdm4fiPgvRyHbIfXUWm4fAW8dt/DbQjRNAOPztGsxMmCqfqWkyNZedKnxd4O8HLoUsXR1I06FfDSu3r51QC/6bUMQH17v7uXN2pRv9gy3S3pBH2Hyk523XNzDYWtWZtA65YJSrMGGXiPNW7jBnxWwDshbBNcTYyFFwKKQ961vxqmC3Q8PGzDbgG6CLzyZQa8dvrPV8D7TZYmcHB+UVG8B5pdgU4Brigdtxt4dUfiPGqE2aytGyN1dn3l5sxo9g6SlwmHb0Drp1xJdlRzo9k77IaG1L3DW5OkOmd+hFP/mRI5OdI3i//74MVyIqwYXfL4UY1feU7v2S0eSpUEiBk+CPJE7nLT0uK46v9wKaHckBU71B5oKtd57zHodGUUHCncGAr/b5BHE279cQy0eiC/m1mE3bkXX5YBvGmLi1Jrab0G3tFfzYG6D1WBP9ZvNa3j8Dd6pNY28KpcCLxWE78ZFMkDWeXtKk+0dM/afoOgdY4wYReLEKbKX4+Vo4nlxdUd4d1eWeHd9wPggZvL4ONOB8WCg5rXAr1cGa88Fvv/NAQIvMO2dYRcuQHy/zkdtlfbLkDGKkIEn1zXfvO30CAMfOglmPbYVT3wJiYCJCYABASK4qgWGjugQXUx07DbnXRlpymyFXViD6n6ll0NlRl4Yqa1UaXqmGp4eXsVyxRiWzuoqh/J80MXy0GVpa4jUZSLpiu6uNkAcuCVxwiP3DFs4x7YUm0ozI9sImAXF2354iHhCrXqJ+yn+dgxsyFEDS8Bb5+4PJDn77UewIvl010YzeHZqE3QLLg08NjDOtmU36MDXjPZ4SYNfPzGnwkRr8d26l/1O/HfVhpSFSzYlXtVHZ1s8vB5Xf1TUoZF10LhoarJoS+djh+5vZzUxZuNtxyRxYlZiXwvL3PVpfWFdhcVEXhyJ48fnYzxUzsC3ptfjoW8+11ZPCnsG54myjF4Sb65bwgB7z9Zm4jnZVv8RSsnQv9y2dzmfLt9lwG8XiFKxsOsBbwG3onf/wwP1agEazbuMG3YwS91SpcNj8BrFSLGbFKVJ307wEuL+7qO3eCZkgm2gReBdUDbrPDlhEDIGb4Dvm6/zhHwfn2xg7ATOz1vJZyo/JsAXooQgbCMwG0GzGsWnYCy55bDFxW6wYhnErSxPKkN6J3egCKWJ7UArwo87E76ZsDbMbgczC7/pG3gxeQPdo9izYCXbADxOBTlgpxeaOOF9uivJWEkBE/tEHdCQi0vLpJvrWwuxEd3LEsOlvR+Lhdl5nytzRaIwPvR2XXi3YOjgiHTrr8MW91vr/3r0vyG3Ac5MqnNr+wAr5lZk9zH0dEAwcHmcqkCXrShLxYQIsAGr++6uUI9mgGvLDenzvhBQnwSdA740bbjojz+nEAilk8n4wSQPwVWh9pF9BsODt0y8Np1OnMSGlK3UKnmIbvQTt8vGRQCGwu7HLhTOgZVwIvQSppePn50cx/3y/hsYFZRHnnTSKHldMBLbV9jV02YdDMSKqwbBQi8hxoO8di0frlsNIyqWMBNOSSHS9O1u9nmgOpB8XwzTBrSJWb5rFJeAy+V5OLla5A3dw6PgqEtb8T5y1CkUD6fFTo1vcgO8JpNyvKEqTqapYUBjzQjlheBcnunC3up9U80EsdEpOGlY3a5fWhi/XDd88IpaPnyINj4dyKsbDVOAC/GNc0/+Gtls9KEifZeOKng0df0d/ZCzsLjhSODVYQIer7SrVBY//ceASXfFnkZBne9ojyOpfBWVBiEm9Ag12aKL+x2Fzp8zmyBtqsp5oCGUQII7p1oanwNvFQmO8Ary5ndhZramW/qqE/JzAUX2ytX/KDQtEFCg08aIjkUEYI2XTLwoknN6GO9BMzJdrw6+2kOUs+u2wSBS2cqTXM48A6J9BMOa7iZSihfDewAL6Yg7he9FV7MVQXkNL52Niw8LCA6+aFJEWrACjY6o/VU58DLtXGkTcd0sPPazIXo4GjTKDFcPodu7wj4HF5/dVwK+zOHiTHN+0U1Cchy6+RUg8Oq/C1qF0yc07aGe2QMuRy6cWp3HvAF8HqjKac2axBcGGbnaySqZ3cMyvMXn1MfmddcaHdxzHz+ZYDH+LHSrtOpH5aHHKbP1+sO2Z/tKqKn4Hokx+CVxzBufNcXCxahAdEOH+3x5U3rlz8M9LADtrtxMptjZbnIAN7UREepryw+A95HWr4Mfy35yqOG6MSGiSc2/Tox9dXeByXyNfCqvM3lKAtojoD2UicerCs8nUnzpctWRRP1Dyd7CeeErf9khiW/J8BvjZZCWKEwWLz1PDzQbrBt4EWTiJr5nncDXp05xW/h4fBCjCvY+s9bIwWUrAjubtgPy9op0jJQYaiOsq2i3ckS32O2KNoFXn4Ej1ECnAKvbtK2u2CbaVrtAK+8WNtdbHXl4xBGgLpv4Kfw4M1lBvCq0gpTv+6+EA1No380ZBePUOflGiqATLZDlO2nSdY4bHbZe0xoqCghCBfmM7FR8H3kVsjpHwR9950WDmvxdRoJG+w7CbyvXq8P0T+5wgLi1e6lK6INVHa4OuAlbTpGxnACvLQ5oW/T2LdyuDWzAbVjP+wr4CV5/TtrPcC03jJ0WZ2yOYmUo1sWVMBrd/7wBnjNotSgIzFuglCRgTIhjx/d+KVydz9bH0bWLS2qLMeAx41h5NThyrCCYtMUEyY2bKjMWO1XyA145U3r4F/e9TjpsTuHZwCvD0Al4xWiBW478G7bfQgGvD0O/vrFE4bTQx8g8FplrXICXCrg5cdPGBZMBbyopVXFIMU2pol6A/QQGtrjRzLDtNnJwLugYGN4JGuy9o33C7fXXFSrrvgJNQm5Mr8N7zQuZWh4dcD71sZjML3IemH2MWHzUUvg5SlJcQJGDTQmyLhdwGtHQ8ehGcNiob1nXItkT3E7R5O3A3iNRTxrIfizUmutSYNZpAUMPp9/ewU4ccIPnusSD/mkgxhd+1CdBy3pLbRLeJ37YKwwWSENL/alnGWNZAu1nF1LTBX/i06THHjJa5zuteMwOP5wtC3g7ffPAYCb0RD/eBtIyhZspDjF+LxoLqC6vNXwEly8fLAZxPxd2PjEg82vQt+8rlSzsuMZAe/WyAvC3ppr0/EFeHxtB3j5CQ165KNmGaFofd11cLiMdeIDndzakXmqqG5zxTeRr3T9zHQ5oO+dC2liJJ3AB+xCk7fAq2sHp/PH/wo9AH2z3C/qaheWzRLWIPDSeMF+Rejldry68tHfVcBLjmdWwEubVjH2Q5oIbfClTIVgZIE5bptW1BR32DfdCGNJJwp2TwrM7pPrl6HhTQ9Udfvq4DXwdnxxBJwOi4Sr165DzhzZ3UqamJgE12/chNdeaA/Pd252+2pxF9+MwGul6bALvJhyUxVAnwMvLsp4bNzp6OviWDZva9fkeXHRXksb2mXB7QTwRl3KDGMmJNha9FTAi5NqjgOvQ7cO9xvmFDrt8nPrdsPqEtsFJA7ZHyGgBO282vQq4XEcS0doqHlDeEf431C8oDKOq93JEtvGbFFyumClFHh15bW7YKvu44vwiarPaYFX9W2+4AWsStY6ypoZ3aJMMkl24djO12f9APk3JIcmQlnmKYD5MEUZGlZ6npABdJosfF89+LvqYI8FW95soIMbyRpvk3GXko9U/d4aA4VY0jZDw0vhx1hyifnRR4yEFCkBXjsaevmEhqDTzPFMBbx9V3cyNhd4ykJaWjOzBBl40fzk5yUBsLiyPeC1cnqycpjjG275Xm7WMvaRl9xAlssKN2nZVO1lt9ne7vixAl58qRNbaCqE0/mDA6/dZ82c5dCue0CbrGJeJ+Alja8ZVKvkljS8OD9X/uwNYeJgliCEb1pl4OWbVh3w2jUHywDeuwg46ezTXgMvtsexU2EiffC4Dwa4NY+/vz8ULphXadubXtoRgddqsjSb2PhkpgNefnxM3q+UBpLsq+wA78H724my4i6496txtoCXZ+X5srorigNOdId3TBeOZ2g/vHjbeSVsoz0mAu+OavaAl+x3CXgRmOYUzqYMem53srTSpNhddHgYrTZPvuQW0cKOtstb4LUyLUio2c8R8HKHk5q7amq9vK2A9+uL7eHpWq6N7um5fwjHFTIr0GVZw3sxdugntZKBF4+pw3qOFacHeHEbQK4JHHrgvHGSwdv0nX9rQp0FzxoaJg7um2+GwR8XD0DxOIAuf+92y6a27MYp2Bd7CZpkLQaVM7viUssX3jPsxm74IG9t6BDiSgtLl1PgxTjYWDYEVrx0jmcq4MXNBSXmwHaa9ZDLJMkMeHmSgVb7Gohv42bju6QdxriU7ZJ5/ej5RvHl4N2AugKs8LJrEmM2TjnwYvQLigwgt7/ZO+xufJ1EylHJgLcnNDR/fFu0ITQNcDWiXact2TGLz2mYTOitxvmNfiG5ovGjG7+j9x2DcdldJ28UeYTmX7TDzTJiDBTYMAMWnNoo5l/VKSYfw5g9rcIrrUW9BoWucdMy43sfvrlBGYLOm7mTtyGZ5mRoeJVTWMYf/2sBnwAvvuvg0dNQoUyxe65hvQVeDlydwm4IOyg54gEHXrRvPHHK3wgBRcAbudff7ZidOkI1UeOkMHh4nC0tj2pBR03CX79vgpGdDwgbzK2nQ5TfPn4CoOfRjcbRKdUPJ9SGLzwstHt8saYJl+qP2uCx/ueEswOmwSTgprrZmSz54qACAzvAwidWjCigA16zFNN3E3hVdeQg1P1MfWjYIEHp5a0DG1pIx8Y0hc4VXerUa1t2Ccc17L9b3QbB+TF9tfZ/CGykZcQ27XQrRGyaaMHmwMo3G53P3XAz3eFjY2yYKw4wLrjcDtgA3qvRwgSHZ1O7E8DLT2iq5A0W0EoORlbAuyjsuLCTRKfRMbeaiuNrvBBaEViszBL4xgbHEH4bx+/ofUdhfV134FFN3vLGCO/BMrxZZr6tKA9mQMpNVTBSgw54z0QcEcCkNGP8dQAAIABJREFUOkm7U8Cr+47d+YPGy8oyLeC+BJfdEHcIxc0EmhWpUmubObu23FcfJjV32eDiJTuu6cYvNzUj4KUTNjRL2N/7B3h49xhj/lWF3sRvff24y3ES51bKuInjDy+CbpzXVY7HeA+Vb2OhdlAiczD4uXwq3S6zzYrc/hnAe88hmKMKew28qzdsh7KlisLeA8dNP9zsiYcdFSyt3Fx810zLid/MVssp8JJHPIWAsgJe1URNwLuqxHZLLY9KA4ogizaEtFiHxdZQxvDF+3CxJi1UvVORhmMDhrqRtVNyWmU8Yvvs6HIBvBj65omTNY0jXZSPOwm8PPFBrT5j3Y5f7Wi7dJO23QVbp4mmbx+t3BWyXE8OQM/HjxXwTivj0tzRYklxOc3auOeRjbAiwN0G9MrRCCg8tqtoGwTerT+NUZqjoOYfv0V2pAi8BLIqT3Pe9ngKwm3VdcDL7RgXXjsM/147A1UiLkHzw2eMCA1YP2+B186Rugp4ycEopcCLpywj4v90BLwILQirOC4ReJwA7+Mna8LjJ2oYGb3sRnkwk29uqrBt101h+6267ECzN34UdsavtxtWFfBy7Xmx2Q2MqvPxZ2U7zG1waSPEHdd0c+TI5ZHwVYXf3DS3BLz4no0v/gF11rwBo/MnKrOsyWMYNazdR70vIjt8VGAOXM5UyADec/9ugAezHVFuWGT/FG6OQQ2SAbxphYZSfzm9Bt7nB46GFk/VgdmLVpnWdsGU91J/a6SghL4EXpWnOU/cgJnOcNeMk825910e8ejQhY5dsvcyVUUHvF9Ni7V1rKkCG3w3auKs4oDiojw48+8G2JLJBmoQmrTu67FYyzGIUTPwxsGfhHcvZvPBkE4YgqdhfVc2LjsLFdciqDSwdoAF30EL1pIVe6BWn0/dJMVOOXTfsWuaQd+XvertAK8KlrmH9R8VXIH8CcIIFs3K1nrbRtiS19PpiZwuEV62rJ1pCryYvATNXdAumkwVVJ7mHHgRbHi8Z75gjjzWRyS+kBdcM+BFh7S/YsLFwl8nSyHlDIB2viNjDsC0Qk96hPCyIz+8jGj+gdCJYwP/0TmekUkD1/BSWEAsJELN/07usr1hxQ3j+GpVjaPvzrPsAS+Nf3Ru7FO0rGEnatfpzap9CMjO/7QXYt/wjOONdd2ye7lIsKOCWrvjx0loSPxmfDxAANs/6uphVT8SKJKBQxW7QPYYV7xnfspSf2My8OJvpB21MqWQT75IrnAMP9D0qjbBzMDPbsK8NvM8IJTG76ymq6Hz0ddhQNlsyjjapPTgciADL53SmAEvz9aG8zteHPjx/836Tm7/DA1vCiDmHnrEa+C9h9pKWdW6+xaJBdPMjs5Mw8sHMy78cixRDrzcaWXJm3+I0GRWwDvnQAQMyvK722KBkwICL04WVlqeJw8uV8brtAO8aOOFWgSuyaUJ9aF271kCL07eX/t9bHj3HpxX1NAw4YQ++6Gllm2PnWamCba7YHkLvLpJ2+6CrZMh+vuqci2hYlwepYyqnsUFqx58L+4nZx1yeqG4nrqyybbZ3AaU4kjj8fT86MNK+2taLA+XOSTkj2I5ozMaX7Ap4xrBAjq3oa0vB14eAeGDP99w0zDRgjvhyi64En0J6p0KBzxloJBkWPc7AbyqrFbU1lbAO/7UHnj94kbAWKcYJQX7Bi+zPuBCQJsd+eh7yPzzwgbYTDOK33jqQPIJDXnXc3MUq7BmVkf+1DYYqeOjSmvgmZauNLf8+mnHQngl9zVlgh+748fMVl/esNKmC9u6R7d40ebentBQPWOrvQjnr94S1eMOhc9uaS5Or3DOxMxp5PSl0yxPOXMERsRv8GgTHqlBB7yknVUpLGj8/tZoNjTdNhBaNrrfI7oCbbiwnWjTiqcHb4/9VIy/ieXnwLGoQga4mm1YuHz2KVLG1GlVZVaRAbz3OoE5q7/PgDcuPgECAzIZX09ISIQ9B45Bwfx5ILSAeiF2VtTUeXfHg3+Ihd1s4vcV8HKnlSVj94pIDQS8OvtR1cSIwLt0VYylHZ9uwcOe4PZbum/LTg3FAoNF5AW8Hm/yqYd2Sg7Jhs/PLjsV/i2SZGwoaELHd+xpvk6pZeSSYrUg2gVeWhR3rD4BBXq5n1bYCS9ktvDbMc3QfYMWjKnFH4PG/iVsAy+27TsNPFPTchva/VlccTZlKDI7EqcFEwvySd0qyuNQgtrztQ7BksruwKsKrcShSI6zyzWQ/Vf/LGKJzs01FLZmbWLY8ZoBL2ZRW3bzNFQOygNNshVXtp+s4aXy4/Fr9jYHBJCaxYFVxSwm6LALvJjGmcICUiHtaGn5huCtxgWM+s1YdRPeLOOp4eMNIJ/QEPDi3z+9stNSu4zvsnLM4puZmZm/hZK1Cnpk6Rq3ZQaMLpCoTeFuZ/zYdRxGW2nKWIblJ1tw3fglILWK1KMCXq7M+ORYR/Et2gzit1HLS/OTPAbJBle1YaEx3GTIaeX4NZM9Gr/LSw6FxidGadMKy2MY5X/SzEUiqcvcMp/D1hvVjbazA7xkmqGy4c/Q8KZO9kmLpfIZ8A5450to26w+NHykOiQlJcGLQz6FA0dOQcytWPhgyPPQuOGDabF9LMtsB3jNjrydaHgxtS93Wqm9aiC81jafcgdOBVc5J/gCeHlYKR3wIhSPbeme/pQm1B513ofF9Q8aoCCHJJMn5X9ulBWhq7h2wc6xqhXw2rWhpT5EW8NCrfq5ycWdAF6dDNkBXvlZswWPmxTcrHhOuWCaOT3xxCE64KVvZHn4nDgBoEyBqOGlBZ9s+bidJ2p4MSMbj4HMTW66LlosgHdLtaEwP7KJ4SkuAy9/3g7wYnKKCbHH4I8ireHCvhChhaIrpmKYpaZUFbMYnzcLLUYmDUOPbxZwiTa0MxtUdZO7fkuPiQ2DGWyrnAvxJX8eiYZOAT+K9+nCcak2rE5gG++1GhsceP/y6y82KnKWLl8Ar1k5+G90ioTyh5pWOu2we0KDY2vt+kwi1TbN1YYMBwTDkcrPGhpebB85RjopE/A9eLS/KNdO0f+ydvP9qTdh8uPqDQtBY+0+Z4ykPwsKNTFkh8aYarNF4xeTA2E2T51ChTTRwW0PGdEe0BYfxx/C8spbTQwttZmG/pWdu+CnXDuM+qlMmsw2K/L8nWHSYIks9/QNPgPeuq36w0/fvg+F8ueBtX/thNET58DC7z6Afw+dhBGffQ+Lp32ULhvaW+DlGkaaMChwPzYYP/bC1MLkxYsTw86dAJuf+Bb2FvHTmlTIEwq+EyeFjdtuwsjl5+G3xvpjTZwYm0T/qExfit//qIC7uQLvYJywP5jqaSemA145QoPsGBcRFurmxY0TLk6UFPJMF1rJCmitfqc60cKkioZhJ7SZ2T12NFS6e2ghHh76IPTJXFk5xuRnzRY8rmGNe0ytvUTtzuwDEUrZoVieWBAMZ4T21/LpBy2WuKCTWQUeaVNqbB5ayQx45WN9Mgk6fH93mHyxhwErwy5sguCYGGh++DRUuRYHcU8kp7F1Crx/zM4pQIhi6YYVDDMdQzqTJAIbXWgxGXhVUUpUoaVkAdCBHvZzi1zTTIFXtWE120irhM8MNLlT6y9z/oAz13q4aebpfaosXfxbdsaPHcdhtFM+M7WiMlXvNFCDJ4fWvbl6GGH18O+yLXxxCXhRfhH45fTQ3Jb+eP1tHsBrZpLAgZnDKEViwN9JO6uK40zAi7F40UdEl1aYxnClDmcNk7nFK/YqN5y0YVFtzORNm5PkGXx9JE13BvCmS8zyWaV8Brw1G70Afy+dBIGBAdB70Bho+tjD0LZ5fUDThgebvgjbV3zjs0KnphcNOrpJuQO3OyFz4KUjIZ61jAOv7LSCE6NV8Hme+IGgECeFcxdvwguTzYEXJ59OAQuUwMuPO1XmHFbA+36jfvB5jWS7PDmtJb7/l23XhWMFhj6TvbjthlayAlorDTD2I92D/y2DN/7NW+C14/RmBbx1LpeHl6/VM7RKJH+q+tFipUsvS8CZ65WtStnG5/dcjFY6vWC/1J38pPg8RuLAxCGyfTuPxHD/FZcdsQp40aaRzCpIC8w3g7JpBc+2NuzEMPFe1BZ2C18B5eMDlcB7Oj4K5kcfFc47HYKTU9by8Usa3gXBreHHibkMIMJ2QPt9s00jB15ukoTvNwstJgOvyn5x4t+R8FFBd097eW4k2fo9uB1UzRfs9rMZKOrGL72Apwzn2kPd9+VTIFWkjnJHyov00jzCBk9aoDMbszN+7AAvpX5WpeqdUXSD1nSN2vHDdc+LzRBph8XcwGS4btZCsKZ8K0PDq1MocLOt/X1+FTLG6y4rA2QNPQHz8QbbjaQ/XCFAwKs6IeMbVl2WS6wX35QajofrLwjgpQ0ntaNOQ48y8NJSd+dJJ8kzMoA3NZFQ2iiLz4C3U98R0K19EwgIyAQffD4dVs77FLJkDoKI85eh9fPvwF9L0m5q4ctXo+DNkVNg9/5jkCdXDnh/cC+oWcUVgP6LE7tt2/DhxIQD+spVPyhZIlE4ZzgB3pUVGxtOKyovWTkoPpavxeLkxA9OgZdn0pEnVazH6xc3aMMiYfnGL/TU8JIG4YsK3UTiCtqZyyHJOFSpgBfrZudI18pG1wnwYjkwGgbGmeWXHeA1W3BVJgc7d/lD3jxJULVKkgHcKjtBlac3j0Er149AA8uvs4EmIKUFUYYtqxiy2wZNhvK3/oFeb7fzcCqk79NRMT/SLtBrhGhbDsQy8PLNIMoIhkZDxzeUo0XH/Yz0wqOuDBOOXQgcr8a4gLfLHhfY8nivdoAXj5Rnxp+BuVe6CUilhRzHwMzVN5TgL284MDUwN0nC381CixHwdjm8SoAWRUngcmcFnVh/2lCozBbMQFE3flX1ooQ2+D2UW6wXOdfJJk1ic3MF4PsZAeLfPDQdxmIeEvWFsVHB/8CTHxw7qo0TlcUO8JrdQ+OXIsGoUvWaOcjyKByYrY+Se6B8ILwXbHRGmAbJwKty6qU6EVCqwr9ZRfggaOQOZRx4acNL45+Pbw68lOVSnnfkMcxPv9Dp+ny97jDqaPIJiw54UcZUp4x2k2dgW8nzW4aGN22A590qpc+Ad/ueQ/D68K8gMTERRr7ZBx59uIqo0/wla+DPv3fD+I9evVt19Pq7Qz+aDEUK5YP+PdvA3gPHYOCIifDrjI8F0DsB3h9O9hKLAV14pPtP3sMGMH/9xdfCy5WOdkX7Rbl+Vzmt4MRgZseKExOGj5KD05OG18wODL+Ni3v/qp6OTfibnFRChm2cdFEDJWu/CHhX1X0J2tW7aoTFoZBkpMHDutFxsS6bG2m4Kt0KhTE3m4iNBNnNURtbeYnjfVZHovxYfXPRth4pUHWxinPlBMiVC0Qwdbs2hOgYRAsSls0qvBDZaLfwqwBNdtUTGyq8CHplDTe3kb3cyvO4lEAM5VSlAeaLnQpm8HlasFQ2gnaAl9vxYSQHlH+K5CADL9ew/nQt1EjcMqfM56ItsB0+L7TcAF6M8pDw34YlJgZg17E42FB4HwTFBsGAAvcp5woC3mHreggNHg+bpAvvpALDufHt3RILmG0aCXib/bPKI94xvZvscHUOU3aBd258O3i0rLv218xshb5P4+bXKz2hZMkkA3bp9+jgKLEZQNgnKMbfeDSQU13WCaBHcxRMG42RGsh+FRUCCGBt43baAl7UoJ/fFyJgW472YMePAkO3YdZB6l9ezr86uDStqmg8HHifezybYe6Ccozjl0yDOoWUgxmlHzc0vGbRLmgMqMaQ2W+8fXUnOCr7Wzn5BL5Hl9ZdtrM3/BtO54DSsyeJDSU/YRm06SuYE5rdw7SJn+JxGZZjcevmTpSTH1bdFBkb6fkM4PUad9L1C3wGvOm1lRITk+CRli/Dmh+/gKxZXPETuYOeFfDKcXRxMkaHBnKIeLhPsift0hGu+K4q4MXUntPK1nVrZivg1UEpAe/4n9wnC7kPzX63CoukWzBJk4vHXg81jxefRO0Tj8F7sYBLy8eBF2380KmJX3Lb4m+kOfQl8BI01r3hB/Pv6+4h6jJU8yNJ0gbaBd6sBwqLjQbWA9sYL7KTk8GGa0gahhSGWXkbuXl548KNIeF4pAVa7BAECSZl2zoqv8pchi92k5+Yp0y6ImuA+XG2vFjyLGrtarQRiyUH3jUldwizCgIiAl4CZ4IqbBvUvqONOCa++KvdD0aIowk1f4THIJfQ8HLgDQ93Ae/e+1zA+9TlylCxQpJH/xLwdpzRU/zGgddq06gzScL3mG0aCXirbF6sDAuIz3P550BJFdh9IRqaRv+oDPiP95BMom9As0LuMYjtmGvwo3wEXtLsYigvUb541/cReCkKAfbbz4tdobe4Uxb176b2s+CnDYUNswacLx4oFqVMS0v1pHpgmt3QiFDxZ3keMNvU8mxycnIbM00rfZ+A78XVHeHdXlkN+aFnk7pvg6lJO2BwnhrwUbGHBPDKJh2yuYZZYhJZQ6sy9TBziKRyFe11QBnajPws0P4e7fB18wPNbSrg5ScsfXePUm5YqB5yeDTZcU23WZHbaAP0gLq1kts/vTJJRr1S3gI+B15MMXzkxFkRqaFMicJQqZw6VFLKi3xnn0STjK4DPoKVc5ODon82eT7kyhkMvTo1gyXhJ6DV2d+hbrZQWFykmUfhTsVGQc2T88Wk329NZ2j/TAKULu0Hk6f6wbHjAEWahsH7+ZeK538d7vpGwrerjffMuXoIBkT+CY0Ty8OsCo+6vX/UZ/7wR/FtwnFrSN6aMCRPDbffjx1LEmVD6FlcpCnUzVZY/J43RxBcvBYLCxb5Qb/7XVEULpR73nOhn38dPq4xF4oHhsD2kh08fn/qR5fTmOrbWL/l/q44q51zlIfxBV1l99u4DPynjYakuk0gT728xrczDXsW4EIYJHwyC7aeChVlk+O04m+Qz7Wg4UVti/89aW9vOHbcDy5dToIHagK0b+0ClwERf8Kca4eUZcTf8f7yF6aKe7dk6iX6Rr423jgn2rHeTX/4uaoLevg1+tIOGH3R1Q69/aoD9gu/sCx98syHU3FRsL1EBygeFOL2O5Xx/cz1IerHCqJML/ZMhKMn/eGP1QC3KoXDDw/+6iFj2Ma7z7u0aCWDQmBrCVcfrVzjJ57Lk9sP/HpsE2XDPhgRVE+UDf8+dGAikGzx/qGCDf2fn5EUgcsOvfvJxwEmlF8KG2+4yxY+j323dTsYiUm4bPHnn3osyegfkV74gXZCLvBZfAf2I9ohYvkJiEgGULYnT/OH0qUA3n40WYYz9X5cVOHvl1aLd+DvPzz+IzRMygn9/tkPOWo9DpCvENyMAdjwl6uvt1ffLv5dc2dNeOpxd+C9mhALX1/dBwuvXIYWCzsYbUfthPXpXFQ/hkh2MNzVuvubiuf5pRtD/v4AObMHQdHffvYYv/z5fIddJzCq8bvon2h4Idc87fgluRt4swG8VdXdfhnl5O88Bz3GL/92zRMume64sCMER4eIutH8hvfxujdb0VzIHI5R7BdZBkmDn9hzCAxZ1lR8ZtQHSeA/diDkaeVyxlTVEcdKg72uOe7zuObQPLQgLFjkL+ZWPg+YtRONX9TwtrtWE17slSwD2A74DdK0qsZvq7O/iXHQ/3BzeK9Z8saBxsGJhjtgVfFtMCxfTfiw6ENw5XqcMUbJ7ECeQ3EMTFvpspPn8y+WhcbwmWfXibltfIFHoXNOV9IGPn5pw8rHL3++Xt8zyrWL5miKsiKXTR7DVP8l14vAoxNGQ1LF6vCx/xei3YZ1C4Oep6YK4NWVY8zTLv8e6l8+/nHuJDnjbU/jH+WInsc1oG/3QLd2yPifjBbgLeAz4L1yNRrw6B9NG4oXKSi+cepsBFStVAbGvNsP8uRyX+TTSjecPBMB/d/6An6Z8bFR5Inf/wyo+e3fqzWcuBUFpfb+IIA2qsFzHtXiv395tgv07OIKabTx70SYNjsBilS/Du9XnSMcszDskn/+UMjx1QLjPSPCtsJ75/6B9wo/CMNDH3B7Pz4/PmargE7V70uWJULfhCViMVhTviU0DCni9jz+3ir/ZPG3pFruobbwb81HnhImCag9RGcL+Wrzw7+wqNI66JG3Ikwr+Zjbz2PGx8Oc7C4Y52WLXfsb3Jg4EoIaNoPsD+cWzxwt+ATkGdjLqDs+e/BIkhHUnGAH2wXbx02At00yyn/wcCKMmZAg/n9w/0xQoZw/9DyxBr6/eACmlXgMeuSr6JpYLyXBtFkJcPGS601fNZwjHPNw4R7RI5d4jl9ro87CY4eWaNvh+wsHoOfJNaIdekc0EP1aoawfPPKQv/hvvFb1ngcnYqPg+P1doWRm97FAZXwhsiEkLisL+fL4wSfDXWmeho2Ihz2B50Q/3B9XGPbUdvUD1RXvHdXCtWDwPsTnsJ6nHtsBK4ttE31QfE0NIXd1H/YXcmhWL+wDlEtsF15m6hts308C13m0LZYD5Wr2n1cMED9epavRnNgeWIaWTTNByyb+QPKNfYxlzNK+lzE2sJzrH1kvvoFAjFCUa/4G8S78xpLfE8R7Xi0y22hblKPE82FiDPX5MJ+4d37376FrpiIwbMNuIXf++QsJ+Tp4OAny5QVYUSwZeGtU9YNiRZKh9EpCLHwRudsAXuzXwQOSU3BhXeoF6McQtTEC77nmz3iMoUY/bzH6Rx7f2H9VNy3Wjl98md9/8n++VF8hN/waueoMvJ3rF63ckty13t8AFnZNNufA76L8EIyp5hb8Tqk9P4h2f2ltJwiJDoGeXfzdxg6vOwIvtp2Q3SNJQv6wf2nsEPBi/48P72bcU23ZqxDSwlU21RyF4+DpHauF2RaNcSo/PoPjKDp7tJijcVPIZZHaisqAyT3G5nhcyCVdJK8EvKrx+/T21fBr0kG3OQafpzmezM74HETjqECXo/BxgGvukOfQTm9f8hhDNO6xLW+026ldG7D/aF7jZebzRt9hN5XtguXe8d1KmFo7l3JtobJTH5IcfXsjN7QdPwECKteABeW+EPXv0eg8PJt1ttDQ68qBbcvnRj6Pfzsu0JBx3v9UBhz/tIY9P6M34P0ZV0YL6FrAZ8A7+INJ4Ad+MPyNHpA9Wxbxves3YuC9T6cJOPx0+EtpshciL1yBjn3fEyYNdI36ag7ky5MTnu/cDA5FX4UKB2cL4MXUv80a+UPzRskLz/ros9Do2K9QOa4wbKv1tFsbvDsyEU7GXjMiESDw+uUPhSxfzDPue/3AVpgUuw3eKfgAvFOwltvzS1ckwYcRLuDV/d4ffhEL5orSLaB+sAt4Mwf6w624RNj8TxK0iJstoOZghS5QgoHYxUtJ8MJ3ZwVo1Q8uDCtKu5cd3/PyyoPwXcG18FzuCvBNsYYedUPQksuWsP53iJ38MfjfVwOqtn0YTsZGwYG8j0HBIb2Nun8xKREOH02CfU+vh825DxmwE/Tim5Cpvkv7Q1eFA7PFO6j82Ca/rUiEvHn84OEH/ODdvL/CvsBzRv2xXtju/KLFDIG3RsEc8Fo/d+CdcWg99InZr6wnvmfmpYPwwpm10AwqQOgMlyb7tb5+UK6sv/gWfpOO7WKqvugxDl44vRZmXj5opE+mZ/HGw0cSRTujphwX5Nb7G4p6YR3xQnlrW2iK+G/+buzbmfMSDRvvT7I3hMOTXFo8ev/JW1FCdksEhcDBil08ZEv13pcHuQD+/bf8Ydj1daLc3xRtCM/lqWA8j9+etPSqkGv53dS3VAZqOwSeyVkqQmDbnqLOX3ydBOXK+An5Wx99DtCkBW25s85aL77D39Mifq4hAwWH9oak82FiDA3/pqBoew68AU3aw03/YFiz3qXFq/0gwMS4ZOAtWgSg2v0MeONjYfzFPQbwyuMb35Fltwt4VX1L9at9uTysbeC+KcRnOs07AD9XWuchW2j3fewYwINhs5Tjkxqbvr32Vh+o/aA78A5ddBbGlflVO3653K3oV8noP2p/Al7V3II3Nzr2i+gbPrdwIVKNC/wdxybKD140P1IUDhzf22sOE7JbNmcENAt7XsSCVckoycF3hdYK4OVyiM+jHGKb1H/mulbO+fjF8bWwSkMxbumicWQ2fqsscgG3PA7wHTheSNO6skwLeCJnUQiLTBTzArZDub6HxdyhmkNfmHoWZj7g3n80v6EcXnrgkPZZrP8LlVwbYS6XfGzhPKeSXZojCdTlesljmORoSpZK0H7EO2IeX1Bnjmj/Z6vugh6l1grg5WsM1QPH+KTHkscvrUF8nilyxn1+ozqQHFEdMoDXbQrP+B9FC/gMeB9s2heWzvwECuT7L/flfx9DYGzRbRhs+e3rNNkBaJpRt2V/WDZnDOQIzibq0HfoZ9C+RUN44tGa0PvVOANmcMDhRbZN+N8jl0eK4PqqjDhog4SOJxR6C4EXvdR56lRVHF1qSKvQXLpYuWTDaxZ2DG3MhswzD1umC4tkZp/G4+1Wb19b2PnN318Bnlryrqh7WM+xwn4XbfDIUYS0ezwkFbWByr6LO33RYkN2ijzGZfWqCXDlih/0y7dAlIOOZnlYJPzOopUToX+5bNoA/9yxsMrSBm79T/awqjSeVAczG0K8h/+OTjV0kTe5zsaN2/G9HdEMwpcXdisbvkdn26hyiJIdznQOgTrva/ye7JBCbYfAO+5yDsA+xovsDCksEwJv3et+wr6dR5rAsGPcPrrh/FnC0Qnfs+B8MzG+fmv7E7wclx/67TsjYvBeuYKOUxhJIAmqV08CDDt2NTEW7v+3MgTFZobHGiZviK4lxMI3Uf8awMvtd6kfqA0xDitFJ6DfqH4qG3y8RxdLF214Dx0MhGa3XBtSXXIX6nuV45lV+mAuV5jUgspuFraKT+JW0Um4fDx2ooZhm84dyrhTJfkwoK0+ykmeyJ3QPP4dAbwqxzyzeYbb0b/w5hWofVZvy2zmGEyyqBu/Vr4MPBLDosJNoHn+EjBlZrxw3OMRHFTrg2pP5BCXAAAgAElEQVR+5TGsucMzj7OLZcaQjn3zLvBoN+pbmjt049/MBlgew7yf33lzsBCRjS/+Ifr7gWw7oV+7reJvPFII9yUYWeh3D4dA+v3B5lc96iGnX+ZOg3M/Sr9ZXdMkQKWyQvsMeB9o0geWzxkLeXPncKvihUtXoVnXoWkWeMWEN2Yq5M2dEwb0ckVp6P/2OPh91mihyebAi0bzlJYSF0aEKVWkAmogOZ4ixiKVgRejLGzJq05dbAW8ukxJBLy6wOdYPrP0sVT+OQcijKDjPBYnLUQqpyeeUa1F/2fFRPfFT+eh+5EVIovWofu7iTbETQNq9/D3xVvPQ/1Vrt95liwsh84ZDCd2jNowrPQ8w1wBPajJIYwvujwb1f75yWYftChYZXqSQ4PxsGBYRnL+k73VZShCDdPwgEdNI02gneGJE35QvVqi4fGvA16+2JJDjwxsugVP5RDFU+r27J7gFlJPXnAp7S1fyGVgxvoT8KCGb9GJTB7Au6LXXLEZwc0ghnxC4KVxQ45JHLwo2xoC78rYpmLhl4F3504/uHzFD0qVTBLRBWTgrVM7EbK4DqmAwpatOBsHrf9uJkJOyZcZdFqFxVOl/sb3I/Bu2hwIrQu4tFu6bGj07Y+PdoRuTyQ77KhinMrl5nL7ed56hjzJcZpVkQnwXU6AF8NioU49NjYJMgcla6J5WKldXy82otRsPx0K12fNhGx5lol4zioglEMz6sLn6TKOUXtYxeo2c0zEOfh/J3dpE+BwpcKCxPZQKSQXfD4pyYgkEVAkWgvjBJK9/GrAByWquW30eHxfVdvoInhw0ETg1s0dCLWqpCiqMcxlfPL/XGnXCXgLBm6AkZ0PeIA3B/cvgzd6xDimuSZ/5Si3KAx8s0tzWQbwpjKqTMXF8RnwYuSCvLlywJuvPAuZg1x2NJhW+MMvZsLNmFtp1qQB63Et+ga8NfIb2L73EOQIzg7/e70b1H3wflHHjz6PFSkecVFGLYycepQcr3TpP3k2IwRe9FInLZcurBjJk9WiZgW8+J4HNi8XJg/yosYncl3ZdZMqLUS6pBjZ+j0lqvDiB++JiY40uAi0Uy72FB7cCJsTK/wmgPfnE5mgwbwZbm1DbWC16BLQkfYdn5M1uByaK8WEuoVXKh0SDtfyj4RR9ap6pPeUF0y003x2S3MPKCJveQTeZcHtRPxlfnEN02d563r8bhVarX3EMvjrpmfIJB7YHrXXVfIGA4Iqv3QLniq6AF+k5BjSMvCqEp7IERoEUMa5Fnw51rIc6YFvBmkxpH7kMtBl7zEjFu/fVQeLOLkEvH2PX4boGo1h02bXkXWN6okibBwBb71zleFGZGaoVDERKGgBB96eB5p6tB++xwx4rfpOB8QIvEt/C4TnK9oDXtTgv/RwAaNrsa0H7PKMwa2SO9xoYdIF3KhxoDELxYXvsaqb1dikssiJC+gk59wHYyE8YKcWeEkedfFm5RTWKjDEMug27lS+Gatuwptl1Gl8eSxo3TypmoNoM43fsEoqg5vcX1tV9djomcUQ10G8PIZ1CgO+LqmirPAoGFyGKbTm/t4/wHdLi0BF/x9hcNcrbsArQ7NKTkhDLwOvag7JAN5UTJiprGg+A97w85fg1XfGw+lzkVCqeKiI0nDsVBiULFYIJn78epp1WrPqL8xYJkMDP1KPf+IQTC+iz3fPY93ios61mFbAi2Uzy5amSw1KGl58/smDy5VhjxAqMByTWepeXdgjs7BW+E0Ke9Pvtb4wN3OUAbxoyvHOXJedMu7eOwf8KDYS/9woC2XHu5IScHMPvM9qUZWzIOFEjWGT+NGz6h0UPunKsQg498CXyhS5KuB9O7KZAAd+yeHT8BheBR66Y2+rOg68sBHmRatPAaj+g5b09qg3hzX5yJxr/xCGGtZPMExNSMtpphlTAa+sIZaB5+KivUb/8sQjeB8HXllLxcFryP4IA3gRnHB8/NlkObwVnRMwsQGGvENzBtTgoiYXr/nRR4Sc1Y8uB9FHQiA0NMkIT8aBd0RMEw/tO29DWcuKv5mZJOHvujZE4P3q5xj4oOocbVgxfJ6H5PrmxfwpAl7cqKFTGcqlnbBz9BEr7bVZKD4u/ySjmMUQ43TTph9DFeL8oAqNxaEpqZs6IyDNQzEVw4S2Uge8VuEZaWOvOqHBOXZLXlc0GjvAi46FVaokuMmRDnh5QoyJzUvD2vWZhCmEHVimvsG+3Vq7sdHcclIH3dyig3wVcHIZ/nXCLENL/7+vikK23L/D+KfPurW9LjShrKHHsvKwg5uLtjNilPM2IDmbnbcxdC5Z2mrJzvj9Hm4BnwEvtiFC7rbdh+DYyXMQF58AFcoUg5pVyoO/v2eop/TS5irgxbrhoEaTBl2sU6o/P/LCY9vQem2MY3uea153rKiz06MFQWV7xoFXZzKBIP5dkivsmCqtqYAQTSYnq0xABLwf93wORhdINEJOHWjzKXy7qaYRQ5MWgnMhTYz4qnikzS8zLZOsAUlITIKEeD8IcoVTNi4zoFz/zV74pdB8U+Dl35kT76nB5dpSBAvZrMDMzptDjU4GdMBrJ4ucru6y3TBCPM8yhuUye7/qvbL9IHUAbRh3rD4BBXq5jkTN7NvNNM3jLgUbySdwc4TjY8nDycB7NKg6HD+RbM6gAl6y7RV1/C/1MJo0TC/YyEP7zvvn5YPN4K3GyVpWJ8ArgwkCLx6lT2w41xR4ZSjidrgj4v/UZkLksE3Ai3K5c3cyVHUt4QrXpzOnuF3Ai9/EcY7zBIXG4nMQj+XLEzuogBOhyeqUzSp5iC6WMs2x9H4VUPMxsq1Ee8gVHGQknpDlX9508s1Ms9BCRlIZPn/Y0Q6TfbbKHMEp8KrGME+9jptW9NMg5UXRHNPcMmpineV36OZwORb0lCxNxKYbL94GfGPVrkgG8KYXtrod9fAp8N6OAqb2dyLwmmkyrI79sH48vWqhVv2M1Kc4MQzO7IoxqYMdM8exD6Z6pvbF73Hg1U14ONlML7redMHEd6kmXF1AcepLyrY2q01r4QxGIYmWdlolJkNVdjEyg5CTTzgBXp0smQEvBr5vkyNMxJEcG9MUOld0hdzjl9mRpwosuEmFHedAK03Z51d3wtjLOz02Jr4EXqqvXRtgKjM/apdBVV7wdy7YDPkHu5xbeeISCtmHmr+IloM8NM0yeHFZwfHxZaEVQsPbIbgs7DgdKux3yZwBv7XsxinYF3sJmmQtBpGbXFpSclzbd+siLLt5GjYezgy/P/aIUoR0Wla82cwGH3/n2n/UYBGwIvC+OtY8bTE+rwNeO+NXPnlAu3k0J8LrqS5XoFOA3tGLy7VOc2on5S+f/xD4KrzSWnwf+xqdD1XAS8BEx+pm4G21cTdTDFBnc+DlJzikqaQEOSkFXt345kf1GOcYLzmhhq6N+YYV7X9xzlFpZ3Xzpy5ttXy6Qm2kMksZ809zSMw2Hj57Mqub9lt+h67/5Gydo2OaCP8OuQ34/P1ayaq6aT7j7xktAF4DL9rpjp44F9b+tQMSEhKh+RO14fUXO0BggCvebHq/EHjNgMkp8Bbo5Tq6p0XfCnjNjp7GL7z9wKuLkrDnoiv6hMq72gDeho9A/4dLCeD9YPlxmFR+jpFWVHbmIK0wz0KHbWS22Fk5o5BsmvURHqu2qpVfAC86fr3VOL+blg8XTN3GQl4wcUGsNKWF+DOZNeAipOsnet4KeBdEH4bXLmz0OFK1U39d3UmmhwfUgzNTXfGLZdtn/JtOw6QCXvk4Va7fhL+PQ+uOI8Sf+WJHIatkp0ayR9YBL8rKvKAoePfS3zDqQlZoEHwf/HW4sJs5A36LA++1HfkBUw6T4xoB777IzDCnpjXwju6Y34BWOyZJHDq5ffe1a37wxhzP1NzyfMqBd0jlMm6OZxgdxmyzzPuP27gj+D7Z5appZAN81mpDZZWym+rC55Ayc74WoEsXnQKRHavKaclsDrByKpMdh1XabPmEhsYugXfZulFaG18+Bn8u0lSp4dWtHypzLDllsm5u4HLRp2hZYQah0s7q2o7+LkO81aYVT2lK/rPRiJJyLHKCFnhp82zWf9z5Fedf3JDJTsEZwJveKct39fMaeL/45kdYvnYLvNq7Hfj5+cHE6T9D08cehr7dWvqulKn4TbcTeLmXvU7Da+Y4Zgd4ZU9gamqzsDS8O1QOOyrbK/4MLmgIvZS6EoF34B83YVLez43duwxrdwN4KaIEeokT8GLqUq7ltBN2jE/oVZfWd5u0ua2qanOA7WalKfMGeHWLDV9I62RGm3wAzP4lX7qyce3UZwOzujlDyZEOeHrhZ15yxbTlUULoBACBd0VID7Fwcxs+M1n5NFM4TLm6VwBv+cxPCHMGbrIgA2+mE/kgLMzPcFzbFBMuHCdzZs8NA3JXV85EOuh0Crxz49sbwHrqpB98sMwaeLkm73m/Gm6OZzqnUV4JgqoJu58Xmwyycd+fxT0ltW4KtoryoZNp/j4Z2khDjxv/fs+1Fo6t9TfWBwT6E6f8jZBeZCtvtbFrsVjvvGeWwpfKqIvCwuGvHnwvbpeBOaXAK28mIs+DiG6RM6d7JkAdLMvmENhWKljVjX+Sq7ZXasCX1V0KGLx0m1YjvfCum1B62UKhoZ+XayjsvvExTKldwDh9MovUotKQU9i+hy6WAwz5KGt3sUwZwJuKASmVFc1r4G367BB457XkqAX7D58UcWrXLRyXyqp6e4pjBbxWDkdYKpqcMNZorT5jISmvKz2lLsoCrwkuUi1yTfOYbBEKMCSTSsvKTRpUwGsWVkxuRR7SC4/76VnZ2UAFvH/WqAotG1URCQUG/FIE/inWQzhHoSZRXsRIKyzH4jVb7KxsDKlM1AayDSDFDMY4oBg4/cXVHSH+TIgx6WJdv58RIOpsFmeXl6PbmXqGLewzrZIdwca2dKWnVWmYrDRlBLzygmHndMEO8D6SxT27He9LHfByz3QEXAylJtsAy+2P2dZe6fqZ8XoCFcq0h33/zaFmYsPANx0yHHBZGV2poADez8NyQdIFV1IQHoUB/5+gFtuv1JVCsP+Av+G49nt4OPybxT7wkjYN32vHBh/vo7b64WQvw5lp1y5/mLA5QoTm05kM4LNmwEsJVXQxfPF53n+5YoIhOBgAk15YQSR1ki+AV54jcaOZFJwLIHMWN6e8+26hOYrrWF8Xy5eHR6Qy0vvR+XJIZXcbT4JAs7aSgRc14Hzsonzr2oGPrwkF6yk1vKpxaqU9l+v2ed660CGknDF2OPDiJh3LSPav3GlW9x0uVxghAi8VrNIH6Xs8og5GSflz7+tuEW6snN7k/pNjlGM4Rtzs8ou339gydW7PQp/x1nTRAl4Db41GL8Af8z414u+i41r1J3vDpl8nQrasmdNFI5lVAoHXDCycAO+M+XuhZp+xxpEo7qatFi0zx7HZB9QLJgdeVdB1q7BivD1ooqPFxI5dGz6PWhyESIRJtNFcHlsPgmtVM7SIMojpgNdsYbADfFgWHfSh/W7g0pmijFhWBIc/ZucUwIWLXsMGCYZNmRmwyuWgkFsI9rhhwXe90+C7FAPv5pgwaBu+3AOM7NRfBza0gJvBEhZYJd+ybShql3bu8jfCzckLFrU/anLHPvKSseGjSA8EvOgI8+myWobZCw/vxoGD+g01whhODoF3ZHhRyHK+hoc5gwy8dbIUgjVrXapsNGtYeCJCmAXY1fAidJDm0Y4NPgde1LLSs+iRP/n0YVPvfy67GFqs/sYGwlTGymmUj18rG1Bd5AF6h1VYOzNYt4I2/J3mFwz3l+WAa+Mlm9ZYwTnXwM95Nhl4uXkExXtWyTsfR34zHjCgG5+nslg5j6HT3dC8NZTAq5p/rOpEbafbrMv2v2TSoNKQqsrOgZnMdFSwKvfhl5dzwHNTJgmzvIP1B8OEc1+5OfyqIrXYmcMxFnGby9WNE5AM4E33aHVbKug18FZu2AP++f1ryJb1v0jtAPBIy5dh0XcfQsH8uW9LoVPTSxF4zTSJVvaXHBo+XH4cqj/8vhjUNBkT8Oo8pfmCKWeywaNYlYZIBby4YK59oq5oWprYrGCbl7372fowsm5pY7HN8vA5bYY5fI5MFHIPfVYJejKscYiRk0/YWWww8L3u0vUf2u+ilpeXkXuI0/twMXmzzHwjFnOxwGC3T8l14Vmg8EYegk1ecO1oes7GR8NDZzwdjOxstlTyY+ebZrAiAy+2D2p4Zc0svYPaHzX98yt3N4CXHL4oTnN4z7Ewdlkt5bEmlwEymcFjVQxp9fv14/BVdFXIG1McypZNgkySewHZ6VYOygNNshUXcXrRjhfDk21PCrcNvGhnikfAZFusy3QoyyHXhpP2bdqMTLAik3PgRU3ez4szibY2O3WgMujmJzubJXyHDnjtnq7wOUTWUvL3r83fFn79LjfkyunSrvKwglbyqjre5/McvgvnOoqlbjZ+KWMcPs81zVYbBzvAyzcHdttPdR+PmkCmKnyukjecqrKrgFcXZYX34Ti/ctDtk/fEGL7VbZDYsKA5GJnkkQkJN0nSrWH4dztymKHhTU1ElLrL4hPgvb9CKfBjocf+PXQCypUqCoGBrhAieM2d9G7qbokUls6XwIt2rKUL9BdaHjvOFFRkVWpTOaQLPyrSAe+iWnXFJE4Tm50FU9ae0BFhcNtDMC67PjYlaWx1wEvvpXBEHGIoMYdcf3lTYBf4VIsG2e+KhU2Ccm7KQBoTMztb1aRNEz+lodY9b7WYY/lw6IUe97QhtLPZ8hZ4VXWTnXyon1TaJfyNJ5/YXLStAbzc1AdhmMLW8dTd9G7efsWvRYsNFV4Y63lplpvwQ3BtqJw5r3KUy8B74KCfsOPF62TxE3AxzyWoFFIE2uYsr3yeH7ti6mcDWqdnEps+K8cx2d4ZP4LA+1NO87CAeJ/c1qRNN3Ma5ZXQjRG7Y8cqrJ0upCEvgxnUWNmvc/nR2QvLx/uy0xnP6qjylZDriHPz2nWZoPtzCcaJ1P/bO+/4KoqvjZ9UWgihFymKYEFRQP0pRURQRKoNUEBABJVmQZGqoAiCVAsdUWlS7KICdlGR14YgNoqCVKkh1ECS93MmznWybJm5e2+4N3n2HyV3p+xzpnz37JkZp3qqaTumnGfr4bXr4zqgp9pfhWU1vw+SbguEMtjtQc552NVdfQmTi8Scdmiwgqk8Xpi/yFiB12nRm9dexHYvQ7INqeP3ovOvC3ImR7L8oIBv4H3zg5VaOt18Y3b8XF67Qg28qTv65vgsqQOddgvH3DZEV4HXbo9JnYUc0o7qZM/7PcqY1oOtv6fXU3503MM3Zu9OKvRY5xzhAqpnxQprbsDrNNnoAp/d50MZvyvDLuwm0yNHiIoUyVbCbWJ2ggJeCMbxkm7pdT5tMvDW3f46/ZWeJsIupI66z2+tu06ZdpONPG1NnYAqL7hGfK3gy+rVUccCOeHtTK8d2KXECrzvN11An6wrb5uP9RnkFwRecPhTlbPonWINxdHEdpcVePkeGdYQDPCyl5XhgvvgtMYLxdHWOnG0fBreY92yjwfmtE4niKnPYAVeLpf1douhV9N7LXpyWiwr8wgF8Lp5M73i12U93O6zhkVYAY6/sDxacJlYnKgDvHZtyKmvqX+/pkgFW+Dl/Kz1133hsINla/89djyL3v8gjhpfm5nDM+5kQ+sXGhm24QSrnI9qQ3naGn9huezSQoFwsKQjSadtKSjroPPCoMYoqzYA8OY1qgrf8/gG3vBVLTpyZuB1AwTZkd0mDquHSH6W1PXSWIHXuiG6NQ7PCXhnFmwmwil0dg6wAg+HRAyLv1p4h3nSdTr9SLVqzInjdOWepbafEq0TiPS48qcy6+ET4QBeGUKxudktYtD2Wm3uBpc6k5dTeh34dAJeHe8Y28Natk6ZbsCreqc6pteiydOy9858sO+pAOBbe7esK5+qV6FGgxwvAXwgC8d5T77qE8ewCOszyC8IJsDLNua9evnKzMii9PQY+ixzK60/6e7htVvFf/bZWWJy1wkLUj28dzYuTDKtDvCqwNP+jdsDLxfWI1mdRlMnT6Luy5LXtnZunjm3NsS/6Xzd0AFeqe+0fW3p2/eKiSTs5ZU7DjDwPpf0ldgNwq6+OlrovDi4Aa+1r+qU6fTsJv2X87DW3foSxXOC3UmLTtA5de6bIhSMx+oS3ZuI2xaeuo0qxifZ7qPLv/sZwwG80cFKkVBLAK9PK+gCr5uHZ+Lm5TQudidxDCB/EjX9LCkHC3m0qQRepwlTBV71bX705vaibK9txewGOrlohn/jAfKLel84TiBqeqeB3e5FwenwCa88vBZe2U2sMn53Y99hdEXhjb6AV2fycpowdSYvBt479q6gz9J2BDxUJrBgLVv3cyrb0a5+OoDvBbxqHCIfK8zXI+U/tY3ftZu0pYdeB3jlaWoq8AZg7N9jh5uUOJ/qFq5gO1rYAW9KSpaY3HW+0FhjTGtdkr0Y8ov6n3se/KLaeeretmInDH65uPKebeJFxmvRmJN3Vfdlye8uHyyoUx4mbdjNw6s+y2tTU8RLgTxkQ4bZuLVZP/1XLbtqwaKOHl7reKervx0smvRfO/2lPdR9w/mlgNukXTiR9eXkh0/+CuylLMPBOJaY46/ZIWKXh46H3Gm3GHUM+uqi7INLcEEBOwUAvD7bBQOv28AsB67vfzpGFS6sF/hcqxb77P/NEUfsNt5Sh875vE5gMNb9LCkHC3m0qYz/NQXegd+3D2y3I08P8vJsWid7fi6vT4TqsztNNHYDvgRe6+ETJnnYmdtqP9Wb/PHQZ6itzQ4I1nzcJkU/E6bO4hUG3oGHvqFX9v0W8FCZwIJ1grTGT3t1ESts6DyvNU+rDdW4Xvbw7o8rR6PKvOo44dq1gZg9O6junvdof5FC9HnK9ZQcZzlT+t9KhBp4+YVPTu6mwMtbSMndO3SAlx/BDvZ0XpTcYFM3lCAUwOtUV91nsIM+tX2pzyLHRvm7DG/wC7xOkJlj95C4GEfgVcuvW7C8OPSDL7fFyvIZrGOlX+C125VCviDohCXt3lle7LMuw8GSDicRf32QeVhPa+TnkGOG0/Zqbl9IAbxeIzR+lwoAeH22BQZep0lH/Tt7qfgTz/GHxgYW5cii31w0TJw4xqEHRadcE6hRfMU0mt7Y/rQytdqqh2jmvaUDi86cJkzVw6vWkU9bkm/ybsdlqmXLwabmqfJ0w/stSO6TqAs9ThON3YTrdPiESR5O5rbb1opj0D5r11ELeP1OmH5iIVXglYuETGDBCi26tpNahgJ4rdsryfrX2B5DX82bFwBeu8mS6+E0yTMMHMw47gq8hzLSaWbaL1QsNpG6J9fI0UQWG3h45eb4nIGEVh3gVRd+Vt/038I4nYMjnMYenRclTmvXTkxelpzamYmHMjeBl5+Zd0nhbd/4koeg+F04Z7c9mFXHBBfgVctvW6Sa5yl3aiO1jh2m/Vf9msKArabfvaKi0EteTv1PbYcch89fyKzAK/NQ9wGWf/PjtFB13npp9mJVXFDATgEAr8924Qa87CmskLZMlLB/1sfE/2aIsu4y8NaUe8X2SQy88iQuTlPuhh00suz7np8l1QlzSouqYjDnQSoY4JWLXry2FZOyOU2OuhOezkQhy3Lai9dusjKZtNXBekfRZoEV/rzKeGH5wvTQvtOP7bU2G7/A6zTh6nhrGHg/yNhC3bZ+GjheWBd4+DnUT5i8m4fphGm1ta53UNXwtS1f0QNZG06r/x07j9CUOW/RpsRatPDciQFAservtB8p16VoTDz9WLKFY093A95Zh36h1Mx06lL6MqpUINk2DxXY6i1uGdinlT3HTsdr2wELb+0X/3E28HI//Lbj+/S5Eqbi9AB2fU2n3XB+dv3EpO9YYUnNk/9fx0PpVJ5JG3Yab3SfxS/wum0PJr+SuQGvmr5tUlWtl2zZHqx1N+2/6vjH4V+qd1ndQtEpnEHWQ7UBHxH9zfY1xCFF5XeVp+YrsvufUx4AXp8gguRaCgB4tWRyvskKvGq86K63pwYWPK0u1CAHSPHG3HwxBLOHVwLv4/H1xU4HfF15z99aA58KvHz8ptzk38lDZPXwqotmkg5nr2T32lZMVcQOcHShx2TTdSfg1ZlsvMws6ys/x8nFcbqTrhvw6sC/n8U/dsCrCzxSF9VeuraTadXPkfJzrFcojNUePDneenJNIFZa1v/Rf2Jp0Etz6djZtShrwFhHM7q1gQvii9HSlEauTWD8wTXid/aQq5cp8Hb4vxYBj5huSJLadra/dKEovvE1mTSm4rKwA68KOxJOTb4O2KXXhUyvMcSkDTtBnm5d/O4UoTOOuQGvqjkffsLtUWdLN9bQWrZp/+U81BhiDuFS+y/HPO/fT8QHvdgdL24FXhl+sOrQFrpt/6fCYXPhjJbiJU6epGntjDohIU4dGB5er9kNv0sFALw+24IEXivUMMjuGXuf2HYr8Ib/78ldDLvsPeSLF9e88dGUAPDKrZ34N92JRwXepqeqi5XsPLh83W6p7VY7XsCru8uCHSzx33QnGXWwVhfXOD230+ETOpONl5ml/dYs+YaqbN4k7MN20p10w+Eh4jo7xbapz8PA+0v8fmqy4Z3A1wDThWNuE56XdmpZFROStF7SrHnytnNly+8Uf1Y/q76w4Sh1fONNYQvZZ+zqEynAO3jXjWLhGF9+dkqIi40REGLdas7u2e1gz8T+VkDSfcmzgo582dcdt3IDeGVdvF7A/AKv3TNb/+YGvOqLA4+FTluk2dlfLWdCyQZG4RAyP9leWCc+gMNrsaNdPaxtTtV0fIn6rrDspL8uvMv7si7r6TVc4fd8rACA16fxnYCXQfaf2cNyAC8XJRdeSaBiiFu9boX49GMdZHQnHuu2ZlyO22bqTsDLm5RXKVCUkgpn0bNpa7S9DFbYNwFet03XrdupOe3FqzPZeJlZPgMvkKpYtloArkIBvCMQcA8AACAASURBVDqDtp/FPwy8ZYoXorgfpp4GjF77qEpdJDSx5rw9k8mEp9a9UnwR0W68jqS12oP7y5XHvhRxf7xwZcmRTWLSf+uvOLpm0RzbUCA1D7dYVBMPb4+iNXIsbpOe38EVGlJsrOWItn8rYG3DDLxr18VQkwe3Ufs9yzx3+LBrYwy85Ta/FLCnW/u1g1uTz9p+Fz1ZdxgIBnjtvoKYQLufXU7UF29ru9Udy3TGMS/glRpI6NTtuyoscxqdRbbW9iT7sPy7SdlWaJYLz3THTif9dbVXnx/A6zXT5e/fAbw+7S+B1zrBcNA+T9hWkJXQJj+ZO93nNghbq2wHvG7H3VqB16+HyDpZmU54ViB0GijlVlNWb59deV4vC/yiEf/Nh0LKU1ddT5dXShOwxR7eMt2yj8bkS3fSdRvcTYDXCpo64GIHvDphFGo7snp4dD+nWtupBF6T9JwH2/bh31+nBTWrClhm6OaJ//s1Rynhvbl0ssWdZD1SWq2/3eQo20WTghVoetIVrj1dhi6EAnhlQbr9wK6tnkng1W3zoQAdmYdde9Vp+051MLWBk62CgS4ZGmIdE7yAV+rOdffySFsbsxxjZN8x7X8qNJqWLetibccAXp9wgeQhVwDA61NSCbzqJNE+vaiI1+UzxO08t3KPVwY3nuidTvPSHTDkQNMqvhqd/0YjsXH9Ta0yqO7212wPdQg18FonJt2JXkpvcsqQ3V68bt4Vu4FfvnSopmc7qWe+e02k1mbjBNi6E6af1e4SeM9eO1fYW3p5TCYuPx4e9bMxl2nyOVbqqMayq59V3/ruHy3gVSdsayxqsMCrLmbrXe4qRw+vXdn8N91+YNd2dmQcoSv+XqIFPnaAavLC43eVv0wv+5ruuKX2oXABr9eLrxcY6/ZfuzZg1dULeGV74bxMv5CosMzpgwHe7aeO0FfHdhCHJTntees2XfrZ7cXvbiGy/cDD6xNo8nhyAK9PA9sB7+07j4ptWZyAV+7zKov+64r6VLvx2adNbroTh2n8kxV4/X4SdYvdUmOSnaS2TnZunh2nwyecoNk68LP2BeaMEy8a7DXkK27DWmpzWWlb4NX1MvkFXqeJVcc7LIH3jj8+Ep7RYMIS1Amb/9/0k6asp7Sxzup8tT3IPiE3qpeT9sB3PhKb2PPOJrzDidvlFIt6Z9L5NKzgBa5p5fZj7ZLODRxBHC3AazdOSC28Dl1hUfwCrx/QkUax62fWUAk3AzqNlX6BV/elhevmNY55AS/nwTpszzhCKyvcQnHy3HGNOcrPC6tG9lq3WLXSHTs5c7cvNDrhVQBeLRPl+5sAvD6bgB3wdp41XQDV/Ftupj7VC9u+rfPvie/NpZh9u+nYiDl01pZXRE1UUIhW4NWtt3Wyk7FfbhOd0168TnvBWjcyl2ER1iOKnT7j6g7afoFXBU61DZgA76ObV4n4WXkF6yUKxjskdeKyTTzLavfjl5nR9WvSmAaXiDwmlKpPjaeOF31Jxry7dVenWNRBKbXp7vjKuQ68fmDLxMNrV45Ou5GC+F3l7wd0ZB3s+p8JtPvZ1s8JuPjvuQ28wU5HKvAG2/+CLVtNp7Y7k68MduNfMNrDwxsKK+bdPAC8Pm0rgVcOuI9klqchY8eJGNBXBjyutYerE+zoxtK5rVC3G/ysHl67ycJksLKm162302TnNlk7bU3m5V2RZclwEqvH0C/whmLzfOtz635OlR7e57asFe1NXsGA6/6M41QirqBxr1AnXFPQloVJ7/3a55ZQtYQU8Wdpr3AD77KjW2l9+n5qVqgSXVSgpCjbr4fXD/CuPrGLbtnpfTQw19Najm67sQPefsUuDWqVv59t7bgebsCr87XASWvdl+9Q7AVsfQbrmKTj4TXueEoC+aymX2f8lGlNq76kmJwW5xd45Qs3gDeU1sx7eQF4fdpUAq8ccOVG+RyfO+quTto7HdhBni44msY/WYHXbrIwAV6/nzTVScnrlCEn4LV6Yu08s+qRwXw8sXo5TYy6OoQSeOVnaF1wkcC7a/+xwDGz/KKzpGwzqpSQ5LOF6yU/mZVJZ2+ZIzyzwZZr57138ujb1cpqc2lTHQ+vHfCqRw7fWaaOawyvXTvxA1u5Cbxq230o5dKgVvn72daObWnVSrft20G7GkalO4Y6OR10X1qs0G63H3W4gVevp4b3Lj/twOrRN9EewBteu+aV3AG8Pi1pBd4O6zbT5PdXiZjDZy4sqw28frblCQfw+vkkqhsGYDdZeZ0y5LQ1mXVis/scKtPa7enqZ+Nzfg4vD5FJHJrpfqYq8Mrn0CnPZ9MPeXI7uHWK2bYr3NoG5L+fL1Gfbowt5VrfSAPe1w5vpAf2fqm1eMna/00+BUtRZH/hrwLBbCsnx69g48dD6aUOJfDqvrRYod3uxT0/AK/sc8G0Az/b48ly4eEN+bCcpzIE8Po0px3wvrD6T2IPonX1sltRflYpmwb8e3l4Tb0r1vtNYJk1USdor1OGnKBVhR2n077cPo+bLvyz2tILeHU+8we7vZsKvFyvL45tp/oFKxgtevHZDUKS3M4+foBXvni9XvYGujTDPUxj1fFdYncJflHgNiheYk6l0eLDm4TXOpweXjvv4sTUn2jcgR+1V9urfc7EMyYNZwVW03AY6y4BOu1dbTTWOptCu9P9Ji/ffrz0/CzqM9i9uOcH4JUvCNK2Ji/eAN6QDKPIxEUBAK/P5iGBVw64Dbbupre/3yMW2Zh8TjuTwGudLEyBV52w5VnspgsnrB4mtwnXDoK8wiLcwhmsk5X0EJnqYAf6Jh4i6+SsCy5W4PXZpM9Ycmu4ipfNrBW1au0XeNef2EfLjv1NFyWWoDalLnYNafC7l7W17fgBXpM2JzW0AqtpHKjfXQJkeglIum1f1j9cwGsyhqt1aFvk3NPWb+QH4LW2A5MXJ2sfMtEeHt4zNmxHVcEAXp/mksAr4ahy6hFaXaiBOArVxLtgd69JeuuE6eYhsXp4rWBn6l1hCf3EbqnpdU4Zsvv07eld+fdYZ97aisNNrJfpwj+7ZuMXeK0DvC64AHizrWG1oWyTqyvcSiXTT7n2dBVumxXO3tEhN4HX+sJrCrxq+gmpP4nt6UxgIxSr/GX7N33ZZa2tY45u25dGdXo5NRlD/YSVcT3UOrRLqnZaOFt+AF7V+cH/b/Li5BSSZN1px64jy/aCkAafQJPHkwN4fRpYAu+23RvF0ajqYG8y2LrtQ6mzl6bTHqR2nxatwKsOUrwi2g/w8uDEOwWYfMri8lUPk9eEKYFX3WlB9RBJ74paB6/V/nbPbKpDqIFXNyQmrwAvn36nnqrmdLKeU5d12l5rW5XOdPxIqjHwqmEOjUqc5+rh9buXtRW2+u37ihalbRDHLLcrWt1zlFLT99v3pfHhH0czT1H1rfNEOaZ9V1ZOQkev5Jo0pMRlnnVWb7ACqynwWscwmbfuolO+36/TQa0Da2g9gCW/AK/U0WsctzYQP/tBA3iNulu+vRnA69P0EngZqErefLHITW6jYwK8dhOmyWBtEv/kBbymnxNVYJUeWhPvEqdXPUxeE67dTg1u3hWdT+OmcdB2zcavh8jpk7yXlySvAa/0wjstUHTqsuoLyoSSDQLba60665aoA962u5fR18d2aXvI5FjDgLzkyCZj4GVNUzNO0Lcn/qGGhc6ixJhYnyOjeXL1hdFk7JQl2b1wmqwn8PvSwvWQY4Csk+qsyC/Ay2Mpn/L5QMql1D+ltnZDCHb8U+cPeHi15c6XNwJ4fZqdgdd6SpQEXhNg9bsPZaiAlwfoJUc2au8uIeXzGwPI+cgBb33lOygltoCjZZxASE5u1vPk3XZnkIXYAa8p+PsF3mC3d8srwGu1q9Xj69VVVRvy1mi8Dyi/gOkAr7pAjT9H85WbHt7TttUzBF51/OCQBj5iWufLkJemufm7Xy91OIDXZAxXX/z5/60ezvwCvMG2GaeQJJ12LNMCeINVP3+kA/D6tDMDr/xcful9N9HWYkUCE43JYBlq4HUL+Lfz8Kp1DQZ4rYsVdAapYKVXj2Y+OvXDQDbyGaxxwNI+fJTwyZadHYs1CQvR9fCaeKqCHfABvP9ZQ9qQveJtdy0Xn+cXl23q6eG1A151q7I6xc9xDWnwe3iL16EFXn1Fli9f9vh+nQMbvPLNzd/VMYg93KbQbh1vTRed+v3Kpnoa+f+tX6oAvO6tySkkSacdA3hzs6dGb1kAXp+22/nHn8QxpXyy2qX3tckxSJt8TvO78brJClc34GVQmHjwp6A+iarA+U3F23wq655c7tTA27+x9lbvCv+bobvyocPCPnx5ndZltZdpHKHfGEBrzLBu+8krwGuN2fWKu3Z76VBjyXWAVz1VrXtyDZH1mQRek2N1VdCSL3um8ZNh7ayamathGfLEQB3Ykdn7BV67/q7bB9VH5DSsP59ap8ZfA3jdG0Kw4x/nKtPCw6vZ2fLpbQBen4bfO3EE8adYjjts0/TiAChWiitqdESnX+A1Cfi3A141fbAxgPPSfqcX036jOaWbhP2EL7s4XruFbxKiGIqtp6tZTW+FjNwGXqdP8l4vD3kFeK2eez/AKz2d/N8JJet6enjtgHfx4Y3iBbZd0rlULfksVw+vXfiLCSxZFyiapFUnfNmmveLgfQ57YUmuasCHX5hCu/WF03TRqd8xWIqSkZVluwc2gNe92QQ7/gF4w9Id82SmAF6fZj3YrkHAe/hAicNiOyD2LjkdfuBUnN+ThkIJvNEQA2gXx2u38M0kDtTqITLZB1L1MKsr603CWjgP61n0OpN+XgFefn51j2WTY4Vlv5I2UxdPPlTsEk/g5fQMWXzxgkvhNc1F4LXbR/rsxKL0VYVbtUco2XbkM/T79zm0MzjDN6qnBMpDQJaUa6ZdK7/A63cM9qoogNdLoeDGP85VwjI8vN4a5+c7ALw+rc/AK4+q1TntSxd4Tb0TJjGAdh5edVsvnmx0QMundL6SS+DlTGQcr9RMnfBNvIRWODWJv3UCXlNPnayDyfZueQl4VciVoShqnLZXo7Ge9MTwGg3Aq8IWfwrnBXemwKvuEOC1s4eXjmfid+s6ANPT2qxjoOkYCuA9E1bPWaZdDL7uSw+nBfCeeRtGcg0AvD6tw8ArY0NVL81VBcsGFs3odFi/R2taP8e5eRbzAvCq3kA1jpff9PdnHqdLC5QK7J6hE87A+Z22Un7XMqNYZr8LD7kO0m7qJ3l58ptTU81LwCtfUHiPZQ5b0bWd1MbuxK+6BcpqeXhnHfqFUjPTqUfRGpQcl0jqv0sXLeUa0uD3tEK7g1OuKVqBFpRsqj1CzU37gwbu+zriX1adHkh9YeV7dPcglvlZ+5/pLitWG5oCs5eh4OH1Uui/8Y9fVPmLi0loDoDXW9/8fke+At4f1v1BXR8cTYkJ8QG7f/raJCqaVJgOpKbRoFEzaO2vm6lESjI92b8b1amZveG722/7+3Wh40Omifu8Tvtya2zWwdV0sDZZ4WoHvNbJxmSgOVOdyC6OV62LDGdwOl3NWm+TsBC7Zw5FDKDdJ3mvT9N5CXilTdlm7MWXX09025jajuVXiqyszIgHXrX/P5TCpzQuJ1PgZY3SszIoMSZOV66Iu08NyzD1UlvjoE1j8AG8Z745yBd+eXCHyX7u522ZT4frdD/zD4EaRKwC+Qp4P/ziO/rm+1/osYdO35pqwMjpdFa5UtTnrlvo5982U78nptDSOU9TwQKJ5PabPHjCCrx1C5Q57Sz13AJe+UnUKSzBDni5bupkY/o58Uy0cK+DCUzCGbj+JmEh4QZemb/OpJ+XgFe+pMjn131ZUe2hTpr8dSVY4FVjegsUTjby8Jp6B9UFO9x/eZeCLiUuoFHJV52JrnXGygz2lC6usBVwTYHXuo2ZqdPBSzR4eL0UynniJt9t6uXnuQ0XFHBSIF8B76K3P6H9qWnUs3ObHHpkZmZRvda9ib29hQomit/6Dn2Obm3ekBpedanjb43q1SIn4K0UX8To8Aa/R2vaxQCaAq8aA2g60JypLma3PRnXxWR3Bll36wRpGn/rFJZiEg9t90m+XsHyrvLmJeBVY7P5ob32TrYTRmr4SpkmdF3hStrAqy5SqxRfNMciNi/gtfZfU+BVXzjl59zHy19O9xbIPr0xP12fHNtGp7KyqGnhSkaPbe1/potOVRvwdmimwOxVWQCvl0KnA6/OC7+aK4DXW+P8fEe+At6XFn5AryxZTgUSE6hQwQLUrvW11OHmJrR7zwHq1HckfbhwXKAtTJi+mFKKJVGLJnUdf+t2e/McwKtOcnULljMCXr+Drd0nUaewBCcPr7qtVzgPjghlh5OfwK1gJL27Jh5Ck5cGN9CS3vFgoEdtB7qgnJeAl59fvsTw/3vtnezUlo5knqQisQniZ10Pr7rvLgPvzLRfqFhsIvG+vF7Aa+2/wXgH5QuW/Jz7cbVWdMGpkqHsLnk6r2BPKlRFUV9yrSESfsUD8HorGMwLP4DXW1fcka1AngLe4yfSqUOvEafZ9sbGV1KPji0pIyOTTqSfpMKFCtDW7buFF7fPXTfTeVUrUZ/Bk+jdOU8H0k55+S1iz2+rpvUcf+vT7WY6dPRkIM2W9DS6ZNMiqpJQlBoULk/zU/+gKeUbUseU87TaW7FfZ4n7Ui/sTr12fGGUXpbN6bnMXju/oKuLlKellVucVnZy4YQc9ZY3zD/4h2s6rYfI5Zuyfl1DWWMeym7MAyZSzIW1SP4tplR5ooHjSfxX45LP37HYeXRHsWrUcuv7jhraZbfyyI4caaz/1qiCuOWSjYtoy8k0GlS6Dg0sVcczWUwMUVKhBEpT2qJnogi+IeuRDpS1d6ewW8y4Bb5rmpmZSWmpBzzzeS9tC609sY9aJlWhyglJNOXAekqJTaSeJS6mpOQUiotzj41V+6/alqZUaOhZtmp3Hj/Y/p9Ub02XxZfRSoubiKz9reXW92jlkZ20tHJzurpIBS2JZN9be2576r3rC5HeZAx3KyQuNoYKJcbR4eOntOqSH2+SNpTPznOhycVzGy4o4KRAngJeUzMvfvczEa/Lcbvt7xsuQhrkNWbyq1SqRDFqdX09x9/uvqM5HT6Wc/Aq+stMkQXDJg+W71dpoT3YyrRpNXpQ8y1LRfqp5RtSp+Lnaz2aTD+49GU0as/3xP9laLJeSYXiT6u3vGfV0V1Uq2ApKhT738I+rcLP4E0Zox8UkCsAqcENlPXlcgFMsTd1pdibu2rXjAfb5lveE7YbVKpO4P/fr9JSKw81PaeZd+B36rnzC+qUcj5N1YQeLmjNsT00P3UDjS1XT6vcGPaKFoynI3lkIs38egVlTh9Fsd0HUOzVN2pp4HYTA++h1P2e+Sxl4D2eDbwpcYk0L3WDAN5eJS+mosnFPYH34g0LBaj+XO12WpC6wbUP2lWm544vaN7B3wM/ZdS5j44ez/CsN27IVoBf+i/euFA4HH6ufjup9qiSWFRLJjVNz52fG4/hboXwl5gCiXF07ARs6qaTnMekHbUM9+9NPLfhggJOCuRr4H31rY/pz627aFDfDlS/dR9a9upYSk4qLLS6b8AEatuyETVuUNvxtyZX18kR0sDp1ON1+ZQmkxgkdSuxfvu+NNoSSy1bbmnltMLVKaQhmruJDGGQz2C6up/T2S0cMlm85zcOO1j981pIg9Dh2BGiQkWClSRHOt2QhlXHd4k+xyEFFeOL0OLDm8QWX+2SqmmFNKj9d8mRjcYhTdaTAv+s2Yl2HzgeEg3ySyZqSIJpDD5rpB5vHOyJk05aI6RBrxXKfmQyd8qcEcOrp3F+vStfAS/v0FDzwqpUpHBB2rFrL93z6Hga1q8LXVHrAnp87GwqWbwY9e2WvUtDnyHP0gfznxH3uv2mLlpTB0zZoII9C94P8MpTppwWnuVF4GW9+Wja+FUrKKtkWXHUczCXnCTlwiGTbXG4PHWSDfWiF6fnyZPAG4zxHNLoAu/6E/to2bG/6aLEEnRRYvGggZcn6iWHNwdOXWxXNHt7Q68rx0mBhcrRFxfcBOD1Es3yezAnFapZqAvdQn3iJIBXz5iHs9Lpr/RDdHGBUnoJlLsAvMaS5asE+Qp4Z85fSvPf+EgYOLloEereoTm1blpf/PvQ4aM0eNRM+uHnPyg5qYjYuqz+FdkrpN1+swKv1UvzTcXbtBuUevBBv71fEXuITRaPqWVzoU5vyHkVeLWFdrnRzz6QAN5QWCD0eQQDvJXiigTgt1nhyloeXrX/Tjz4k/EXGvmFgBV4pHhtGn32lQBew+agegd5L2PdhZ+yGHWhmtyWzsRp4VZdAK+hMYO4HcAbhGj5KEm+At5w2NUKvOrRpqaHN6jeBd6Hky+TwdYKvE6wDOB1bgnq1mxuLw1OOageXvXzqK6XL5g2Cg+vu2q6wPv3qbSAV5dBSYY38I4rOrs0qP032M/hDbe/SR2TqlOv4jWpVLECAF7DDmE9mtsUeOX4LcPCTNMDeA0NFuLbAbwhFjSPZQfg9WlQK/CqnyVN4j+5Glbvgulgq8K2W1oAr7PRdV8anHLwG4cdTHME8IYWeHkrMg5rOBPAK5+EV/QDeM17g3zJDOZoWi5Njt8yLMzUaQHgNbdZKFMAeEOpZt7LC8Dr06ZW4OXPkm13LxPhCEvLt6TaBnFIfr0L6rGqbgM1gNfZ6LovDTrAK9tBMIsvTJolgDc0wHsoIz2w927F+CRan76fmhWqRBcVKKnl4VVjtuUhFiYhSepTAHhNesB/9wZzNLdakvWIddMYfgBvcHYLVSoAb6iUzJv5AHh92tUKvDK7jKwsiuMNUg0u6V2QG8+beni5KNXD0S+llm3pAF5no+RYOFSwHPHRtCaXGsfJMYR8BQs9uuUCeEMDvJyLjNuUHr52SecSH0KhE9KgHnyw+PAGUSmTkCQAr26L13th5buCAVb1iPVg0jvVDjG8/u3rlQOA10uh/P07gNen/Z2AN5hsQ/E57UDGcXpk39c0q/S1FOMA3ABeZ+uoC4eCmezCuejFqdYA3tADL4c1pGamUzDA6+eFVT4JPLzBjKD/hSTI1MF8XVHj+INJD+ANznahSAXgDYWKeTcPAK9P24YSeMP5OU19TACvu9EZendnHKXaBUtTHJl56f2GpQTTHAG8oQPeWYd+EaArrx5Fa1ByXKKRhzcU8Z8A3mB6ApF1DA3m64oaFrGkbDOqlJAUXGUsqeDhDYmMrpkAeMOvcTSXAOD1ab1QAi9XJVyf0wC8Pg2tmTwUXnrNogK3AXhDB7wy9lbmyF5+vnRCGtSvA5zGz4InAK9pL/jvfjmGBhMSxrnsyDhKI/d/RwNT6oQMdjlfAG/wNtVNCeDVVSp/3gfg9Wn3cAJvKD+nAXh9GlozOYBXU6hcvE13WzKu0rKjW8ViNb44rKF7cg1t4A3lCyuAN/gGIkMSgglJCr5U75QAXm+N/N4B4PWrYN5OD+D1ad9QA68aPxbM5zidx0FIg45Kwd1j/aRqujVdMKXCw+uumgnwytPWOEd5rDD/v46Hl+9T+68f4ALwBtMTstOczMqkTMqiAjFxwWcShpQA3jCIaskSwBt+jaO5BACvT+uFGnglMAX7OU7ncQC8OioFd4/1s7Yf6NGtAYA3dMArD5/gHDkkgQ+dMAFedR9nP19oALy6rT967gPwht9WAN7waxzNJQB4fVov1MDL1fnw6DZKiU2gKwqW9Vk7++QA3rDIGshUjcP2Az26tQTwhg54OSe5NZncoSEY4OUXVj8LngC8uq0/eu4D8IbfVgDe8GsczSUAeH1aLxzA67NKnskBvJ4S+bpB7sXLmQS7D6tJBQC8oQVePmHtaOZJuq5wpUDGuiENx7MyaPj+1TSqZF2KNdzhQ30KAK9JD4iOewG84bcTgDf8GkdzCQBen9YD8PoUMA8mVxeufVPxtrA/IYA3tMBrl5su8IbK2ADeUCkZOfkAeMNvCwBv+DWO5hIAvD6tB+D1KWAeTc7bW12eWIaqJiaH/QkBvADesDcyFOBbAQCvbwk9MwDwekqUr28A8Po0P4DXp4BI7lsBAC+A13cjQgZhVwDAG3aJCcAbfo2juQQAr0/rAXh9CojkvhUA8AJ4fTciZBB2BQC8YZcYwBt+iaO6BACvT/MBeH0KiOS+FQDwAnh9NyJkEHYFALxhlxjAG36Jo7oEAK9P8wF4fQqI5L4VAPACeH03ImQQdgUAvGGXGMAbfomjugQAr0/zAXh9CojkvhUA8AJ4fTciZBB2BQC8YZcYwBt+iaO6BACvT/MBeH0KiOS+FQDwAnh9NyJkEHYFALxhlxjAG36Jo7oEAK9P8wF4fQqI5L4VAPACeH03ImQQdgUAvGGXGMAbfomjugQAr0/zAXh9CojkvhUA8AJ4fTciZBB2BQC8YZcYwBt+iaO6BACvT/MBeH0KiOS+FQDwAnh9NyJkEHYFALxhlxjAG36Jo7oEAK9P8wF4fQqI5L4VAPACeH03ImQQdgUAvGGXGMAbfomjugQAr0/zAXh9CojkvhUA8AJ4fTciZBB2BQC8YZcYwBt+iaO6BACvT/MBeH0KiOS+FQDwAnh9NyJkEHYFALxhlxjAG36Jo7oEAG9Umw+VhwJQAApAASgABaAAFPBSAMDrpRB+hwJQAApAASgABaAAFIhqBQC8UW0+VB4KQAEoAAWgABSAAlDASwEAr5dC+B0KQAEoAAWgABSAAlAgqhUA8Eax+Q6kptGgUTNo7a+bqURKMj3ZvxvVqVldPNG63/6koWNm0Z59B+mCcyvTmKH3UumSKac9rdt9unlEsYQRWfXPV/1E/UdMpTnPDaYLqlUWdTx5KoNmzH2H3vxgJZ3KyKTq51SkJx/tRuXLlIBNI9KK/1UqFH3MLQ9uE8/PfoPS00/RdVdfRo891Jni4mIjXJXor55dP927P5XGT1tMX327juLj4+jqKy+hoQ92poT4OKN+CptGf/vAE0SeAgDeV21CKwAAGkRJREFUyLOJdo0GjJxOZ5UrRX3uuoV+/m0z9XtiCi2d8zQlxMdT804DxEB79ZU1ad7rH9Kq79fT5FEP5sg7IyPT8T6337QriBuNFXh50TL69Osf6djxE+IFRgJv6qEjtPDtT6jjLddRUpFCNOWVt2njn9towvDesKmxyrmXIBR9zC2Pv/7eRd36jaF5zw+h0qWK04CnptMlNapS13bNcu8h82FJTv10zfqNxDZpeX1dyszIpL5Dn6NG9WrRHTc10e6nsGk+bFB45FxRAMCbKzKHvpDMzCyq17o3ffraJCpUMFEUwIPrrc0bUsniyfT08/NpwZTHxN/53mtuuZ/enzeGEhKyYXjamIfpxIl0x/t40HXKo2hS4dA/EHIUCqz+8VeqfVE1uvvhsTTkgU4B4LXK89vGrcK7/+bsp+iHdRto8NMz6YP5Y+jn3/6ETSOoLa37dXNQ9tjw53Ytmy5+9zNKO3yUHuxxm3hqbhePPTOblswYHkEq5L2q6PbTOUuW07ade2nw/R21+ylsmvfaC54oMhQA8EaGHYxrsXvPAerUdyR9uHBcIO2E6YsppVgSlSlZXHh0Rw7sHvjt9p5P0pAH7qSLzz+bRkyaS7273kSrvlvveN+Wv3c5/lbzgnOM64sEZgp06jOShj54pyPwsrf3lz/+El7g7bv20tzXVtDAPh1o6YerYFMzqcN6d7D2KJFSVMumS979lOrUPI9uatZAPMeJ9JNUr1Vv+n75jLA+FzLPVsCrn/YbPoUa168tPL66/RQ2ReuCAuFRAMAbHl3DnuuWbbupz+BJ9O6cpwNlTXn5LeHNLVumOP26YSs9/lDnwG9dHxxNPbu0oStrXxj425Klnznet3X7bq08wv6g+bQAt4mUX3buemg0zRrXnyqUK5VDIdg0shpMKOzhlsfrSz+nxg3qULNr/xd48IsadaWfP32JYmJiIkuMPFgbt3769Xc/03OzXqd5k4dSfFzOGF7YNA82BjxSxCsA4I14E9lX8J+9B6n9fcNFSIO8xkx+lUqVKEZlSxenld+sFQvV5HVr98dp+MNdqeaFVQN/W/rRKsf7tmzfrZVHlMoX8dV2mkgPph6mux9+hvrd247qX3Hxac8Bm0aWaUNhD7c8Xnvvc6p5QVW6reU14sEPHzlGDW++n35YMTOyhMijtXHqp7zIcPCoGTRjXH/bhaWwaR5tEHisiFYAwBvR5nGuXFZWFtVv3YeWvTqWkv+Nqb1vwARq27IRlS9bgoaNezkQx3cqI4MatOlLyxeMpWLJRQKZ8idxp/u279qjlUeUyhfx1babSDlWs0f/cXRX+2Z0Q6P/PHrqw8CmkWXaUNjDLY83l62kPXsPUv9et4sHZ9AaNnY2vfHiiMgSIo/Wxq6f/r7pb+r/5FSaNKIvVa1c3vbJYdM82iDwWBGtAIA3os3jXrnHx86mksWLUd9u2bs09BnyLH0w/xkqVLAAteoyiAb17UgN/pe9S8MnX/5AsycOECEPTz07l3p1aSO2MnO7z+m3KJYsaqpunUiPHjtO9z46QezSoH6+5gfatnMPzX/jIxrQ+w5hX9g0cswcrD10bcpxoV3uHyW2sMvepWEaVa9akXp2bhM5IuThmlj76eatO6nfsMk0fngvOrdKhRxPDpvm4YaAR4sKBQC8UWEm+0oeOnyUBo+aST/8/AclJxUR+2/Kz9zsZeCV+zt376OqVSrQ6CH3UMXypcWiluYdB9D0Zx6mauecRU73cYluv0WxbFFRdetEyiu3nxj/MsXG5ozLnP/CUMrIzBS25l04OG4TNo0sEwdjjx9/zt55Q8em73+8msZPW0THTpwQ+76O6N+NEhMTIkuEPFobaz/l/bPZHmo/LZCYQN8tm0GwaR5tBHisqFEAwBs1pkJFoQAUgAJQAApAASgABYJRAMAbjGpIAwWgABSAAlAACkABKBA1CgB4o8ZUqCgUgAJQAApAASgABaBAMAoAeINRDWmgABSAAlAACkABKAAFokYBAG/UmAoVhQJQAApAASgABaAAFAhGAQBvMKohDRSAAlAACkABKAAFoEDUKADgjRpToaJQAApAASgABaAAFIACwSgA4A1GNZ9pPv36R3pywiv08eKJp+2r6ifrLdt2U+/Bk6hdq0bUue0Ngax27NpLj42dTZu37KBSJVJo2MNd6OLzz3EtiveSnD7vHeKjbEukFKXB93eiK2pdINLwaU5Dx8yiPfsO0gXnVhZHGJcumUJ796fS+GmL6atv11F8fJzYE3Tog50pIT77HHk+HW7a3HdozuLltGrpFD+PirRQIOwKPDP5Vfpn30Ea93jPkJUVTL9yKvzkqQyaMfcdevODlXQqI5Oqn1ORnny0W+AoW/7787PfoPT0U3Td1ZeJfbrj4mI9+ykfcjLgqemi2OdHPhCyZ0dGUAAKQIEzqQCA9wyo/8Bjz1Nq2hHq3qGFOAktFNfqH3+lkZPmUrVzKlKti87NAbxdHniabm3RkFo3rU9ff/czDR3zotjQvmCBRMeip815h1o3rUcVypWib9f8Rg8/MYU+f+NZcZJX804DBMhefWX2KW6rvl9Pk0c9SGvWb6S//t5FLa+vS5kZmdR36HPUqF4tuuOmJsSTMx+3WaZUCvE58l+/MzkUj408oEBYFODjuG/rPowSEuLpxQmPBo7v9ltYMP3KqczUQ0do4dufiNP3kooUoimvvE0b/9xGE4b3Fv2wW78xNO/5If+ewDadLqlRlbq2a+baT3f+s5/6DJ5EtS6qRv/sPQDg9WtwpIcCUCBiFADw5rIp2GPa+f5RAhgXv/tpDu/RDXf0p1bX16O1v26i/QfT6IZGV1CPji1FDe/pP466tm9G46YuoiNHj9PyV8fmqPnGP7dTUlIhWvLuZ1SsaJEA8B47nk7NOvQXsCqvPoOfpVtbNqRr69XWfvorW/SkFQvH0dZtu+np5+fTgimPibQMwNfccr8A6KJJhXPkN2fJctq2cy8Nvr+j+PvK1evESXB1W/Wi1e9N1S4bN0KB3FaAv8J8+tWPVLZUcfH1ol3ra0UVfvplE82ct1T0tX/2HKDDR4/R/XffGnhx9eqn1ucIpl85afHbxq00aNQMenP2U/Tiq+9T2uGj9GCP28Tt/Ntjz8ymJTOGn5Zc7ac8tvDJcOknT9L81z8E8OZ2w0N5UAAKhE0BAG/YpLXPmD2iaUeO0n13tqZWXQYLcEz+FxRrXd+dHrmvPXW69Xo6fiKd+NjKR3q2p6vq1BDAe/DQYQHIlc8q61jrZ2e9TsWLJQWAl71At/Z4nD5aND6QZtRz86li+VI5vMBuMmzaskN4fT6Y/wwt/XCV8OiOHNg9kOT2nk/SkAfupJoX5AyT6Dd8CjWuX1t4fOXFnrP6rfsAeHO53aE4MwXuf+w56tK2mfgiMXDkDJo/eWgAeDv2fkqA44XVq9C2nXuI//3unKdFP9btp5xZsP3K6UnY2/vLH3/Rk/270eNjZ1OdmufRTc0aiNv5SPF6rXrT98tnnJbcrp+u+m49LXjzIwCvWbPB3VAACkSwAgDeXDZO23uG07NP9hGhAhxfxx4k6T1i4P3stUmUUixJ1Gruayvo7x17hIeUJ9Lrr7mc2rZs5FpjK/DyzW3uGkIP39uOGl51Ka39ZZMINeBwijtva+r59AyoPR4ZS51uaUpNrq5DS5Z+Rr9u2EqPP9Q5kLbrg6OpZ5c2dGXtCwN/49CJ52a9TvMmD6X4uOwYXr4AvJ6S44YzrMCB1DTq3HeUgFi++IvME4/cRedULi88vAyTb780MlBL7k9tbqgv4mR1+2mw/cpJmt17DtBdD42mWeP6i7Hl0RHTqHGDOtTs2v8FklzUqCv9/OlLFBMT49lPAbxnuBGieCgABUKuAIA35JI6Z8ifCm/t/rhY0CWurCyqcd7ZgfAABt7vlk0PACLHuvJn1fHDeomJ9O4OLXJApV1JdsC76a/t9PQLC+ifvQepzsXV6dDho2IibHrN5a5Pz+EKA0dOFx7lPt1uFvdynVZ+s1YsVJMXP9Pwh7tSzQurij/xorbBo2bQjHH9AwtoALy52NBQlC8F+EVz7NSFFBsbK/LJzMyku9rfSA/d01YA79gpC2neC0MCZQwb9xLVqF6F2rdprNVPg+1XTg/FYVJ3P/wM9bu3nQgZ4ovrVPOCqnRby2vEvw8fOUYNb76fflgxM5CNWz8F8PpqQkgMBaBABCoA4M1Fo4x+YQGdf24luvnGqwOl3txtqFhkwt4jBl4OGyhfpoT4feb8pbRnX2rAw9u9Q0v6X+3snRKcLjvgVe/NyMikVl0G0ZznBlOpEsUc8+EdFXjSLFyoIA3s0yFwH38yHTbu5UAsIHuqGrTpS8sXjKViyUVE/B8vTps0oi9VrVz+tPzh4c3FBoeiglKAX+AmPdmHKlUoI9JzPH27e4fTilfH0brfNgvvqRpD3+ORcdS+zbUBD69bPw22Xzk9CMfp9ug/ju5q34xuaPSfN/flxctoz96D1L/X7SIpw+2wsbPpjRdHiH979VMAb1BNB4mgABSIYAUAvLlkHN6l4IY7HqG3XhqZY8U3Qy17X9h7VLtpD7q9TWN6tNftYpLt0GsEPdH/rkAMr1/g5bjgCdMXi/hgjvPja+v23XRWudJiuyL14jhfoiyxHZl6sXeKgXlQ345ioQ7HJH/y5Q80e+IA2rx1J/UbNpnGD+9F51apYKssgDeXGhyKCUoBXtw1fPzLtHDq4znS8xcW3uqPF2Zyv3z+qftFyACHCN03YAIte3VsIIbXrZ8G06+c+ilvH3bvoxPELg1q6ALfv33XXupy/yjxYlu6VHEa8NQ0ql61IvXs3EarnwJ4g2o+SAQFoEAEKwDgzSXjfLzyB3pr2crTFoHs3L2POvZ5ij5aNIHqte4tdmV464OVdOz4CfGJVN2lIVjgZW/PvNdWEMXE0I3XXkn3332L2G6Jy27ddTB99fYLlJiYEFCCtzRqcefA0/YIfrBHW7r7jubCOzT46ZkifdUqFWj0kHuoYvnS1H/EVOJ9RmNj/4sRLJCYQN8t+2+hDIA3lxociglKAd6B5Kxypy/ofGfFV/TFN2tF3PvEGUuofJmStGb9BoqLixNfQOT2ggzGTv002H7l1E8Xv/sZPTH+5dP66fwXhtIlNc4VfXH8tEV07MQJsSf2iP7dRD/X6acA3qCaDxJBASgQwQoAeCPIOOzh/VGJsQt31T756kex68KE4b3CXRTyhwJ5QgGO4WXgfXnSwFx7HvTTXJMaBUEBKJCHFQDwRpBxOYZ3zYezcq1Gz734Ol1aoxpdU/fSXCsTBUGBaFaAgZfDgl55dlCuPQb6aa5JjYKgABTIwwoAeCPIuLkNvBH06KgKFIgKBc4E8EaFMKgkFIACUCDCFQDwRriBUD0oAAWgABSAAlAACkABfwoAeP3ph9RQAApAASgABaAAFIACEa4AgDfCDYTqQQEoAAWgABSAAlAACvhTAMDrTz+khgJQAApAASgABaAAFIhwBQC8EW4gVA8KQAEoAAWgABSAAlDAnwIAXn/6ITUUgAJQAApAASgABaBAhCsA4I1wA6F6UAAKQAEoAAWgABSAAv4UAPD60w+poQAUgAJQAApAASgABSJcAQBvhBsI1YMCUAAKQAEoAAWgABTwpwCA159+SA0FoAAUgAJQAApAASgQ4QoAeCPcQKgeFIACUAAKQAEoAAWggD8FALz+9ENqKAAFoAAUgAJQAApAgQhXAMAb4QZC9aAAFIACUAAKQAEoAAX8KQDg9acfUkMBKAAFoAAUgAJQAApEuAIA3gg3EKoHBaAAFIACUAAKQAEo4E8BAK8//ZAaCkABGwWua/8w7dy9T/ySEB9H5cuWpMb161CPji0ppViStmZDx7xIXdrdQNXPqaidpv29T9Ci6cMC93/z/S80adZrtHDq49p5+Lnxh3V/UM+BE2n1e1NPy+bufs9Q4wZ1qFG9WtT09kccixk9+B5q1bSe+P2rb3+mlxcto/V//EnHj6cLLW9q1kBoiQsKQAEoAAX0FADw6umEu6AAFDBQgIF3UJ+OVPfyGnQg9TBt/HM7TXn5LTp46DAtmPIYlSye7JnbyZOnqFmHR2naM/20gXfz1p3Uqc9T9PU7kwP579qznz7/eg21b9PYs8xQ3KADvHfc1IRS0w4Hirv2todo2uh+dH61SuJvRQoVpMTEBFrw5sc0Yfoi6tnlJgHJiQnxtOHP7ZSZmUnXXX1ZKKqLPKAAFIAC+UIBAG++MDMeEgrkrgIMvMP6daWrr6wZKDgjI5Pa3jOMapx3Nj014G7x9/W//0WTZr4mvJeZmVnU4H81acSjd1NCQhyxp/a3jVupaFJhio2NoSf7dxOQt3vPAXpy4iu06rv1VKJ4MrVr1Uh4O//YvI3uGzCe/tl7kIolFxH5v/vK07Tu1800dupCem/uaPG3tvcMp26330gL3vxIeKHj4uKof6/bKe3wUXpp4Qd09NhxOqt8aXp6UA+qUK6USMPwPXbqIlr64ddEMUSN6taiQX07irpZLx3g7XjLdTmS1bq+O817YQhdfP45OUCdgZ/rcWPjK3PXgCgNCkABKJDHFADw5jGD4nGgQCQoYAe8XK+lH62iERPn0DdLp1BMTAxt3f4Pbd2+m+rUrE7HjqdTz4ET6MZrr6S7br9RPAaD4JIZw3N4eDv0GiHAuHPbG2jPvoPUd+hz4v8ZfH/6ZZPIQ/Xwfvb1mtOAl+H2lWcHUdnSxWnJ0s9o1HPzqf7lF9P44b2oQGICPfbMbDp+4gSNfaynqMfEGUto41/b6fGHulBiYjw9OeEVio+PC/yuah4q4H1lyXJa8u5ntHTO05FgUtQBCkABKBDVCgB4o9p8qDwUiEwFnID31w1b6LYew+irt1+wjeWdPvdd2rJtF40a1MMWeH9Yt4EGPz2Tli14JvDg/Nl/+Wf/JwBWF3gbN6hNPTu3EXmkph2heq1609znB1OdmueJv61cvZbGTH5VwObJUxl0VYuetHTuaCpfpoT4nUM0bun+GH2/fKaIUQ4H8D4x4RVKPXSYJgzvHZlGRq2gABSAAlGkAIA3ioyFqkKBaFHACXg3/bWdWncdQquWTqHkpML08+9/ijCCv/7eRVlZWbTvwCG6/NLzafywXrbAy97YJ8Zne1cDV1YWlS9bij6YP0YbeDmkgBd+8XUi/STVadqDPlw4LhDC8H8//kYDRk6jT1+bRFu27abmnQZQQkJ8DvkzMjJoxcLxAQiWP4bKw8te5iNHj9OE4dla4IICUAAKQIHgFQDwBq8dUkIBKOCggFtIw5gXFtDKt54XsbYtOw+koQ/eSS2a1KW4uFia8srbxFDsBLyL3v6E3nh/ZY5dGNQq6Hp472p/IzVvkh0XK4H34yUTqFzpbA+uCry8EK5V50H07QfTqXChAp4257jkO/uOpB9WzDzt3k59RlLrpvWoXetrc/xmF8PL3u73P/mG3n5ppGeZuAEKQAEoAAXcFQDwooVAASgQcgXsgJfBkuNvL7vkPBp8fyf65MsfaNTz8+mjReMD5T86YhplZGYGgPeyG+6h+ZOH0gXVKot7vvvpd7Ew7eMlE6lY0eyFaeq17rc/qccjY0WMsLzsYnhNgJcXrP2vRU96YeQDVP+Kiz21Yq/s/5rfJ0C12jlnBe7nxXBN2vajiU/0oasuq+EJvL9v+ptuufsxmjr6IWp41aWe5eIGKAAFoAAUcFYAwIvWAQWgQMgVULclO3zkOK39dROxx5Khl3cj4HAG3oHh9vueoLnPD6HqVSvSRyu/p1HPzqO6l18UAF4Of2h53VV09x0t6FRGhlhQ1rH3U5RctIjYWaFEsaK0eesOOn4inepdfrEIiWh06wP08qRBdMmFVSkmNoa+XL3utEVrJsDL4vBOEss/+1bsLsF13b1nP/22YWtgr1yrgE+Mf5nWrN9IA3p3oHPPrkC79x6gF2a/QfsOpNGiacPErhPqZefh5d+fmjRXLPR7oPutVP+KmuL5eZu1+Lg4uuj8s0NuN2QIBaAAFMirCgB486pl8VxQ4AwqoB48wZBWpWJZur7h5dS1fTMqXKhgoGbT5rxDc19fQSdOpFOjerWp6TVXiAVoMqSBD10YNu4l2n/gEI0c2F1sz7V3fyqN5rCI1Wsp/eQpqliuFPW9+1Zqes3lIt9ZC96j2a++L2KCX5v1JG3YvM038PLCtckvvUlvLfuSDhxMo5IlkumWGxtSn24326rMXuEXX31fwOqOXXupTKniYh/dXl1vErBvvZyAl59h0Tufit0aOLSCMblcmRIin5bX1T2DFkbRUAAKQIHoUgDAG132Qm2hABSAAlAACkABKAAFDBUA8BoKhtuhABSAAlAACkABKAAFoksBAG902Qu1hQJQAApAASgABaAAFDBUAMBrKBhuhwJQAApAASgABaAAFIguBQC80WUv1BYKQAEoAAWgABSAAlDAUAEAr6FguB0KQAEoAAWgABSAAlAguhQA8EaXvVBbKAAFoAAUgAJQAApAAUMFALyGguF2KAAFoAAUgAJQAApAgehSAMAbXfZCbaEAFIACUAAKQAEoAAUMFQDwGgqG26EAFIACUAAKQAEoAAWiSwEAb3TZC7WFAlAACkABKAAFoAAUMFQAwGsoGG6HAlAACkABKAAFoAAUiC4FALzRZS/UFgpAASgABaAAFIACUMBQAQCvoWC4HQpAASgABaAAFIACUCC6FADwRpe9UFsoAAWgABSAAlAACkABQwUAvIaC4XYoAAWgABSAAlAACkCB6FIAwBtd9kJtoQAUgAJQAApAASgABQwVAPAaCobboQAUgAJQAApAASgABaJLAQBvdNkLtYUCUAAKQAEoAAWgABQwVADAaygYbocCUAAKQAEoAAWgABSILgUAvNFlL9QWCkABKAAFoAAUgAJQwFABAK+hYLgdCkABKAAFoAAUgAJQILoUAPBGl71QWygABaAAFIACUAAKQAFDBf4fvj4p+JNXD9cAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "traceX = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['x'].index,\n",
    "    y = deltaPV_tle_lvlh_df['x'],\n",
    "    mode='lines',\n",
    "    name='Cross-Track'\n",
    ")\n",
    "\n",
    "traceY = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['y'].index,\n",
    "    y = deltaPV_tle_lvlh_df['y'],\n",
    "    mode='lines',\n",
    "    name='Along-Track'\n",
    ")\n",
    "\n",
    "traceZ = go.Scattergl(\n",
    "    x = deltaPV_tle_lvlh_df['z'].index,\n",
    "    y = deltaPV_tle_lvlh_df['z'],\n",
    "    mode='lines',\n",
    "    name='Radial'\n",
    ")\n",
    "\n",
    "data = [traceX, traceY, traceZ]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Delta position between TLE and estimation in LVLH frame',\n",
    "    xaxis = dict(\n",
    "        title = 'Datetime UTC'\n",
    "    ),\n",
    "    yaxis = dict(\n",
    "        title = 'Position difference (m)'\n",
    "    ),\n",
    "    shapes=[od_window_rectangle]\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "\n",
    "pio.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}