User Manual 4.0 Environment Models

User Manual 4.0 Environment Models

Introduction

Scope

The scope of this section is to present the physical models available through the Patrius library.

Javadoc

All the classes related to physical models are in the

fr.cnes.sirius.patrius.forces

and fr.cnes.sirius.patrius.math.parameterpackages. The classes related to reading potential files are in the package fr.cnes.sirius.patrius.forces.gravity.potential.

|=(% colspan="3" %)Library|=(% colspan="6" %)Javadoc |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.atmospheres |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.atmospheres.solarActivity |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.atmospheres.solarActivity.specialized |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.atmospheres.solarActivity.specialized |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.gravity.potential |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.gravity.variations |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.forces.gravity.tides.coefficients |(% colspan="3" %)Patrius|(% colspan="6" %)Package fr.cnes.sirius.patrius.math.parameter |(% colspan="3" %)Patrius|(% colspan="6" %)Class fr.cnes.sirius.patrius.utils.ReferencePointsDisplacement

Links

Some useful links are given hereunder.

• • IERS Page**

(% style="margin-left:30px;list-style-type:square;" %) IERS Conventions (2010), Technical Note No.36

• • Project Pages**

(% style="margin-left:30px;list-style-type:square;" %)

• NASA - Crustal Dynamics Data Information System (CDDIS)
• GFZ - Grace (Gravity Recovery and Climate Experiment) Mission Homepage
• GFZ - International Centre for Global Earth Models
• Groupe de Recherche de Géodésie Spatiale

Results Pages

(% style="margin-left:30px;list-style-type:square;" %)

• EGM96: The NASA GSFC and NIMA Joint Geopotential Model, Lemoine; F. G., Kenyon; S. C., Factor; J. K., Trimmer; R.G., Pavlis; N. K., Chinn; D. S., Cox; C. M., Klosko; S. M., Luthcke; S. B., Torrence; M. H., Wang; Y. M., Williamson; R. G., Pavlis; E. C., Rapp; R. H., Olson; T. R., NASA Goddard Space Flight Center, Greenbelt, Maryland, 20771 USA, July 1998, Available here.
• GFZ : Global Gravity Field Models, Available here.

Useful Documents

None as of now.

Package Overview

None as of now.

Features Description

Earth Potential Models

The

fr.cnes.sirius.patrius.forces
.gravity.potential

package provides tools allowing the user to read external gravity potential data files. The following file formats are supported :

(% style="MARGIN-LEFT: 30px; LIST-STYLE-TYPE: square" %)

• EGM96 ASCII format data
• EIGEN-GRACE format
• ICGEM format
• GRGS format

Below is a diagram showing the architecture of the

fr.cnes.sirius.patrius.forces
.gravity.potential

package.

For a detailed explanation of the Data Management System, please refer to the [SUP_DMS_Home Data Management System section] of the Support User Manual.

The

fr.cnes.sirius.patrius.forces
.gravity.variations

package provides tools allowing the user to read external variable gravity potential data files. The corresponding ForceModel is also included in the package. The following file formats are supported :

• GRGS RL02

Below is a diagram showing the architecture of the

fr.cnes.sirius.patrius.forces
.gravity.variations

package.

Atmosphere models and solar activity model

iVBORw0KGgoAAAANSUhEUgAAA7wAAAO4CAYAAADx0vohAAAAIGNIUk0AAHomAACAhAAA+gAAAIDo AAB1MAAA6mAAADqYAAAXcJy6UTwAAAAEZ0FNQQAAsY58+1GTAAAAAXNSR0IArs4c6QAAAAZiS0dE AP8A/wD/oL2nkwAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeNrs3Qd8FEUfxvEnhQAxNFFA KQoCIl16kSYdpakgRUFELPSqFOkvIkVBmhQVBBEQlKbSe6+iICogIIJg6KgIIcm9NxvvTLkkdzGQ S/L78jn2dmd2d3b2brL/my0+NjsBAAAAAJDC+FIFAAAAAAACXgAAAAAACHgBAAAAACDgBQAAAACA gBcAAAAAAAJeAAAAAAABLwAAAAAAKYU/VQAgKYWE8ihwwJsE+PtQCQAAAl4ASIxgt/f8o1QE4EXG tihA0AsAIOAFgMTSqeb98vflABtISqHhNk1e9xsVAQAg4AWARG2I7MFuGj8CXgAAACQubloFAAAA ACDgBQAAAACAgBcAAAAAAAJeAAAAAAAIeAEAAAAAIOAFAAAAABDwAgAAAABAwAsAAAAAgDfzpwoA JLUbt8J1K8yHigCSUGiYzRqGhNqoDAD4jwL8Oa4h4AWQ6jkOrKdtPEtlAF6i/6JjVAIA/EdjWxQg 6CXgBZDaOf4QDG2aT2n8uMIC8KbvJQDAc7fCwtVv4c9UBAEvAPzrrrR+HGQDAIAUIOIH/BMnTujh AvmoDq/ZIwAAAACARBMcHEwlEPACAAAAQMp0/fp1KoGAFwAAAACAxMc1vAAA4Lbx8Ym4Pt9m+2+P O9qzZ4/at2+vH3/8Uf7+/vSaJOG+AAACXgAAEKu8r30ca9qJ99umygAsvmCsc+fOOnjwoPbu3avS pUsTYAIACHgBAPBWrgLbuALhlMiTQHDfvn3WMLkEuwAAAl4AAFKt2IJbT3p/L168qNdee03Lli2z xhs1aqRp06YpS5Yszt7Fjz76yMpz48YNnT17Vs8995y2bNmirFmz6ty5cy4DT0+Xa04v7tu3r2bN mmWNFy9eXO+//77Kli0bZbkff/yxXnjhBfXo0UPvvvuuyx7Q2HpFw8LCnOkmzdMyXr582Srj3Llz FRoaqsqVK2vdunVxln3Hjh3q3bu3M9jOnj27fvnllxj75d5779Uff/yh8PBwFSxYUG+//baefPJJ l9vkeD9s2DC98847Sps2rSZNmqQjR45owoQJunXrliZOnKjWrVvHuY2xlc2xfFPHU6ZMUc6cOfX5 55+rZMmSzvIMHTpU48aNk6+vryZPnqyWLVvGWQ+e7nN36w0ACHgBAEihlgxq4lH+JsOWxJjWtWtX LVy4UOvXr9fNmzdVv359pUmTxgrqHF588UXn+549e1p5TdBkppu8rni6XBNcTZ8+Xd9//73SpUun hx56SG3bttXhw4edeQ4cOGAFSw0bNrSC3djE1+vrSPe0jCYAMwGbCSZffvllqy7iK3uTJk2sx4qc Pn3aChxjc/78eWt4/Phxa/4OHTpYPy7EtU0m8DdBrcnfvHlznTp1Sm3atFGePHmsspm0uLYxvrK9 8cYbqlevnurWratu3bpp06ZNUerl+eeft9Zt1mUCXnf2obv73N16AwACXgAAUrBsGTL9p/lNz51R o0YNq3fRMS1y0Gd6cU0Pm7F48WJr+NJLL1m9e4m1XNNzaxQpUsSZbnosI2vcuLHV+/npp58mSt15 WkbH9I4dO1rbbnpV4yt7YGCgNTRBY/Xq1a0A1GxDZKYX1vSYrl27VidPnnSuNz65c+eOddzx7M64 tjG+spntrlOnjvXe3PArtnU5yurOPnR3n7tTbwBAwAsAANzmCGCjB7KOAMVV3sRerjkd19xB2RXT C/rII484g6Gk2vbYuCr7mjVrrN7ozZs3W6f+Ll++PMapua+//rrVc/zZZ59ZQb05RflO7F93yuYI kj0R1z50d5+7UzYAuGN/H6kCAACSRlo/91+uNGvWzBquXLlSq1evtt5HvnY0uooVK1pD0xsZ+ZRb w1yn6bhW09PlmtNvjVGjRsUaZJme3VWrVlmnvbpirks1IgdGkcv0X7fdkd+chmvKOGDAgHjLnj9/ fus6WHOtb+QAMnK5rl69ag2rVq1qnZYc3zZ5Iq5tjK1skYNdR69206ZN412XO/vQ3fzxlQ0A7iR6 eAEASCJ+Pv9tfnOTIxNMmGsmjVatWlnTYmN621q0aGFd22lugGQF3S56JBOyXBPcmdOEBw8ebPX4 mVNYv/vuO2ces6wxY8aoT58+1nWd5sZOkZlra01v6YMPPqj06dPH+5xdT8to0kzvsrmW1zziqFq1 avGW3VyXeubMGesmV+XKldP48eNjLNdcK2tOGc6RI4d1je1/2SZPtjG+spUqVcq6nrZWrVouy52Q fehufnfqDQDuFB8bD4cDkERCQm3qPf+oxrYooAB/HyoEqYa5E7O5adVDWd2/hrdYt48T9Rm95k7N plfSXGO5YcMGdkpKObDj2b+AVxzbdCrvryyZghQUFHRbLuWA++jhBQAgCbi66/KdkDlzZusxMuZa UHNToRkzZrAzUpDbdQ0xABDwAgAAtyRmT62nrly5wg5IwcyPGQCAf3HTKgAAAAAAAS8AAAAAAAS8 AAAAAAAQ8AIAAAAAQMALAAAAAAABLwAAAACAgBcAAAAAgBSD5/AC8Cph4Tb7i3oAAADJh5+veflQ EQS8ABC31YcuacV3F6kIAACQbNQvntV6gYAXAOJUp+jdqln4bioCAAAkG35cKErACwDu/cHw4Y8G AAAAEgWHlQAAAAAAAl4AAAAAAAh4AQAAAAAg4AUAAAAAgIAXAAAAAAACXgAAAAAAAS8AAAAAACkG z+EFACCVCgm1UQnJVIC/D5UAAAS8AAAgtmC39/yjVEQyNbZFAYJeACDgBQAAcelcL5/8/bjCKbkI DQvXpJXHqQgAIOAFAADx8fX1EfFu8hFui+jVPXHihB4ukI8KAQACXgAAEBubzWZ/UQ/JaX85BAcH K1u2bFQKABDwAgAA1wGUCHiT2f6K7Pr16woMDKRiAICAFwAAxAyg6OFNbvsLAEDACwAA3Amg/nml RJvXrVKfji/qrz//0KEz11LM/gIAuI/bVAAAkJoDXquHN2lf+bOm+U/psb1GDemr9+cs0sHTV71i OxPrBQBwHz28AACk6oA36a/hPXI+JN4yJKSMPx/9SSXLVEhRp2wT7wKAZ/yG2FENAJJCWLi0+tAl 1SmaVX6+PlQIkATfv9L5MulOfv1u3ryhwX26qHL1mtbw8bpP6OFsadW5z5vWMNwWrteef1ofT5ug nLkfUP6HH9GkMf/TxDHDrZfJd+nieXV/qZX6dX1Zn8/7WEVLlNJ9OXNb87fp0En1KhVV21e6asKo oVHmM6qVzKcxwwfokw+mKFPmLCpcrKTOnjmtds0aaMgbXbVjywY1ffb5WNfxZs/XnGU3Qz8//zu8 32zad/yqyuXyVfp0AQoICFCaNGn4QANe1rbyHfUe9PACAJDK3alOw19/OaHer7ZVjToN9HyTOnq2 TXvnuh3D3A/k1Z5jwfpu/251fqG56jZ62pr+Y/BNZ74Rb/ZWy3avaurcJTr4zV717dJeX2391ko/ sH+P1u8/5lxe5PmMjQeOO8vSqFppPd26nYb27arqtRvos5Vb411H6fKVrLJXq1VPbZ+qp1GTPlSe vA/xIQIAAl4AAOB1we4dvEtzmya19fqQtzVyYG/1Gz5W9ezBrOOaVMewcfPnrGGJ0uV1Ifj3GOnG 6uWL9eXn853jfn5+zvTHatSOkjfy+0sXL2jy2P9p28a1+v23M7px428rfduGtZo0a6Fb6zDl80+T RqOH9LW2pU3T2trwzc93dH8BAAh4AQCAlwW8c5auVZ/X2urZNh00a+p7uhUSoiefbhElkIse0MU2 /dBvf8nX1zfefJHH3xk+QI8UK6HOfQYqY6bMKnr/XVHmc2cdyxZ+qgWzZ6jZc+0198P3NevzVXc0 CCXgBQDPcJdmAABSdcB751735cyjmfYA8eyZX63h7u2bncF25GHkG2mZYUBAWus6W8f0GnWf0LyZ 0xQWFq7Lly5pcO/Occ7veP1x7aoqVaulDBkza/WXS5zpj5arqI8mj4uSN7Z17N251Sp78Lmz1jD3 gw/d0Tok3gUAz9DDCwBAqg54bXc0iPL3T6MhYyZb780wes+sqx7a6fOWqXPbZ3Ty+DHtOXZeb741 Xv/r311jhvaz8vQePNKtHt72nXrppeZP6OL5YLV9tZszfai9HD06tNKE0UNVsnR5zfpidazrcJR9 8OiJLtd3J/YXAMB9PjZaTgBJJCTUpt7zj2psiwIK8OcuzUBSfP9erJ5L/n58/5KL0DCbPtp4Wp3K +ytLpiAFBQUpMDCQigG8rG3lO+o96OEFACAVu9M9vPjv+wsAQMALAADcCqA8uy505tpvdPL0Nfs7 0ytsD5btQ+udT8QUxzId8ubKoHa1HqWiE3F/AQAIeAEAgFsBlGc9vCd+vaYJL1d1O3+X6ZvolUzk /QUAIOAFAADuBFD/vNwVHhautAF+1vudP55ShUJ5VKHfAmf6zpHPOqdHzGBTcg/RHs0dZA2/+fVP r9hfAAD38VgiAABSsXCbzaOX4e/nawW13T/eYb3fO7qlNd0MzbiZbtLN+9jWERoWpholHrCCyZol 81rjjjQzzbw8LdvteiW0rm53eQAABLwAACAOEdfw2tx+RQS8PlZQe+CdVtZ7x12eHe/N9Ihg2Oef dcRczo5N63Tl0kUr/dLF89q1ZUOUdcQ2X1K8vKs8fGYBwBOc0gwAQKoPeD2bJ42fr8a3raiSvT7V 9+OfizLdKNL9EyvdMe5q+SuWRJwGnTf/wzpx7Cd99cU8la/yeJQ8ZR7IaA33nLymsg9GvH+l5wDN nTFRAQFp1WfYWJ06cUwLZk5VaOgt9Rk6VvWaNNfVy5f09ps9tHnN19Y8VWs3UL+33lPGTJl1cP9u jR8xQD8ePGCl3X3PvVq+/bBz+a3ad9KiOR/o3hz3afTUT1SwcHFneaaPG6lPP5wsX19fvT78XdVt 9Ixu/P23Jo0apOUL5yrk5k0VeKSI+v5vvAqXKOVc5qAxU/T2gB7aduR8nPnd3V8AAPfRwwsAQKoO eD3s7bS/0vj5qEGZ/P8EtT5WgOsIdM24mW7Szfvw8JjLCAm5qTXLv5Cvn58GvzPVmnfdV0us6ZF7 VHefuGq9Ik978pnWmvPlFqtXuF+ntmrwVEt9vHyTrl29oneH97PyjhnSR2u/Wqz3Zn2uMdM/td6P HtTLSuvVoaW+27dLizcd0NafgrVs2/dRlt/m1e5654P5OnPqpMYOeSNKWsPmz1vrNusa98+63h32 hhbMmqaZi9dp4bq9+sEeSA/u9UqU+Yb16ejctrjye9rjDACIHz28AACk5oBXnt0IyWYLl59vxKnK DcsVsIY/T24TJY9jujOgjraMVcsWWT2y5SpX1yPFS6lMparau32zNf2Jp1tFKVt02e7LGev4ZXsQ bObZsGKZNV6qYhV7wB1uvTfTbOOldOnTW+Nd2j6lUhUe07MvvKo8efM7l5Hlnmwq909P8+Hv9kcp Q+R1XTz/u5X21efzrPFn65R3pple58jzrdhzVHfbl+tu/vj2FwCAgBcAALgVwHp+XWijISag9LGC r8j/O/z7hF77ex+fGL2SKxZHnM68e9tGlc+bKcp002MbuWyuyhvfuC3Se7N+q0y+EeWYOGeJPv1g sg7s3qZFs2do69oVWrL1UJT5HUGytRXRruGNbV3bjl6Qn5+/y7xZst7rHHcnf9z7i88sABDwAgAA twMoT4OouX0buJ33uVEroiz/yqUL2rdzi/z902j1NyeVPvAu/fXnH6pXOp81/eL5YGXIlFl/XL2i s6d/VY6cueMM+FyN12zQRCuXLNCOTWvtAW/E1VuVH69npeXMk099hr2jSxeC9UT5glZwG3kZYWHh WrX0M+t9tToNo6S5Wlfdxs305cJPNPv98WrzWk/r+t64glR38hPwAgABLwAASJSA9/bf+Tdy7+Xp UydUt1Ez62ZQ6dIHWmmBdwXp5R79rZtXmWtnew4arcmjBqlp1WJKmy6dNhw663JZsY33GPi2PZAN 0+uvtLam1Wn0jHoMGmWlPVPjUZ3//aw9sA1V4RKl1f3NkVGW0bZRVZ08+pPKVq5uT3sr3h7e3kPG KkPGzFo0Z7pmvDfS6rXNk/chzflqm8v53MlPwAsAicfHxt0PACSRkFD7weL8oxrbooAC/H2oECAJ vn/PlMsmfz/35xs5e7PH6+rXpqrX10fl/Fms4bZjl726nKFh0qLdwepU3l9ZMgUpKChIgYGBfKAB L2tb+Y56D3p4AQBIxRLSw/vJG/XdzhtxSrP3/7aeJiCtsz68e3/xmQUAAl4AAHDbAt6ErMPbrT/0 GwEvABDwAgCAlBXwehZE5c+X1eq19SQ/QRoBLwAQ8AIAgDsfQMmzHt6mFQpJFTwN0ojSEm9/AQAI eAEAgNsBFEEUAS8AEPACAIAUGEHZOE2WiBcACHgBAEDKExZuAl4eC5Z89hd1AAAEvAAAwC1Hzt2w v/6mIgAABLwAACBlaV81p/x86eFNLkJCbeq/6BgVAQAEvAAAID4m2A3wJ+AFAKRMvlQBAAAAAICA FwAAAAAAAl4AAAAAAAh4AQAAAAAg4AUAAAAAgIAXAAAAAEDACwAAAAAAAS8AAAAAAF7MnyoAACD1 Cgu3KSSUekguQkJtVAIAEPACAAB3rP3+ktbYXwAAEPACAIAUpUahTKpVODMVkUyYHt6Bi09QEQBA wAsAAOLjI5v8uaNHshHuyynNAEDACwAA3GKz2ewv6iE57S8AAAEvAAAg4CXgBQACXgAAkFqFh4fb Xz5URLLZXwS8AEDACwAA3EIPb/LbXwAAAl4AAEDAS8ALAAS8AAAgNQdQnCabnPYXdQAABLwAAMDt gJcgioAXAAh4AQAAAS8IeAGAgBcAACQHP584KT9u0pxshIab/9NTEQBAwAsAAOIz94fkEzylv/y9 /s5SxGvKE3jxW13PWoIPEQAQ8AIAAG8S4O+jsS0K6LvvvksW5fW1v5Yv/06VH0yr+3Pm9ILyhNvL c1iV8t2VJOVJ45eBDzEAEPACAIC4gt4ypUro+PHjXl/Wffv2WcOjR35Q0SKFkrw8hw8f9qryAAAI eAEAgAv58uVTcHCw15bvzztILyli1bav78+Tprzy5s2bZOXx9fXVsWPHVL9+fa1cuTLJywMA IOAFAABxyJYtm65fv+6VZduyZYsVlBcsWFAlS5bUtm3bVKxYsSQrjzkN3NzdukyZMjp37lySlwcA QMALAMleSCjPI/F25hTh5CwwMNDrymR6T3/88Ud16NDBGq9evboOHDigU6dOqVChpDmV+OjRo1bg bXp6vaE8tH+gTQMIeAEg2R/s9Z5/lIrwcuYmUBwgJi7Tu5s/f36rB9rImDGjSpQooa+//jpJAkzT o3vy5Ek1bNjQK8pD+wfaNICAFwBSjM718snfz5eK8DKhYeGatPI4FXEbgssffvhB7du3jzK9Zs2a +vbbb62e3zsdZJqbZ5kAPHPmzF5RHto/0KYBBLwAkGL4+vjIjx/bvU64T8ROOXHihB4ukI8KSSQ7 d+60rtvNkSNHlOlBQUEqXry4VqxYcccDTHM6c7169bymPLR/oE0D4mk7qAIASEYHITYbLy99OXjz 3Y6TE9O7+/3336tatWou0x9//HHr7s2mV/VO2bVrl3WzKhOEe0N5aP940aYB8aOHFwCSEXOwbfPg 3i0Tl+/SydPX7O/Mr/X2ee1D651PxJSIZf6bP2+uDOrSsDwVnYD9Epm527E33gAqOdm+fbvVW+q4 djc6c+1s0aJF72iv6pEjR5w3q/KG8tD+0fbRpgEEvACQokT88u5+/hO/XtOEl6u6nb/L9E1RftmH +/sFicecRnnw4EHr/aFDh9zKf7ufg/v7779bzwI2r82bNyd5eWj/aPto0wACXgBIeQch9qO9cA+u YQsPC1faAD9PjnKsdcDz/YLEY4LFwYMHx5g+a9YsvfDCC0lSpuzZs8coU1KWh/aPto82DSDgBYCU x8NTmq2G3sO7mkY/la1Q9nS6L2duLV63S5mz3O2cvmX9ar3W5mmF3rqlH3+/oSuXL+ndEQP11eLP dPPmDT1aprxyPZBXI9+bYS0jMk/zO+aZNPZ/2rF5vQ5+s886rfThIsXUc8Bwla9cTdf/+lPD+/fQ iiWLrPx1nmyiIaMnKvCuoASnebJfcPt5W3BJsOvd7V9itH2Gn5+fAtKms9qozn0GqlS5ilHSXbVV 7rabtGkAAS8AIBJPT2mOOOjz8Xgd0Z0986t6vPycPvzsK2vcHKB1atvMOmhzzNPtpVbatXWjZsxf rsrVa0VZ3uFzf6twjvTWuHkfX34Hkzfy9D07tqpa7Qb6ePEa7du1TW2b1lHnF5pr15FzmjhmuBbP n6NxM+bK3z+NurRrrqz3ZlOfwW8nOO2/1BmApG3/EqPtMw6e+VPnfjutLva2ps1TdTTb3v6UtAe/ jvYpctsWeTnutJu0acDtx12aASBZHfB59jLS+Pm6/XK1DqNsparWwdqksSO0e/sWdWzzjEqUKR+l XHt2bLHeZ703e6xlibz8uPLHtr0fLlyhFzv1knxMT0gJK0/RkqWttJXLvrDGq9d9Uo/VrGu9N9P+ S5qndY3by5xCTHlo/+5k2+eYnu2+XBow8j0rWH3v7SHxtlfutpu0acDtRw8vACQjnt6l2WRO40Ev h7luy+ZiBWPen62mNUprytj/6eOgDNbdOkdNnqXHH83nLFfZilWsHtunapZTtuz3qWaDRnqt5wBl yXpPjG2wDgbdyF/0vvTO9wd/ux5lOcPe6KoHHyqosdM+sZZ5PvhcxB82/zTOPGbaf0nzZL/g9vvl l18oD+3fHW37In+/i5QoZQ0P7N3lMm/0ae60m7RpAAEvACDaQZknN62y2cLl5+vjQX7XN27JkvVe DRn7vrq92Fx//fmH3p61yOqZjVyusdPmaubkd7RxzVc6fvQnzZs5Td9/+43mLNsQYxsMd/J/e/qv GPOZ69P6dm6nb3Zv17wVWxWUIVNEmi1qvogN0n9L82C/APCe9i+x2r7I3+/I6a7yRp/mTrtJmwYQ 8AIAIh+EJOAa3kZDlsk8edIW7X+Hf59SaX/v4+Py2i0zrWrtBvrm1z+jTIv8PihjJnXpN8x6/Xry uBpVKa7D330TY3mOcXfyR5/3h4PfqG+nF3T+93OaNGex7s/9oDNPthz367fTv+hmyE1nfjPNpCc0 zZP9gtvvgQceoDy0f3e07Yv8/T54YK81LFe5WqztpKftJm0aQMALAIjEloC7NM/t28DtvM+NWuHW qXrxpf9+9ow1LF3hsRhprpYVW/7oeZ9rWF3p06fX+I8+06NlK0ZJr9mgseZMn6D1K5bJxzfimrwa 9RpaeRKa5sl+we3HXZpp/zz5qiVG2+dYb/C53zRyQE+lTZder/bs71Y76U67SZsGEPACACKJuJmI 7Tavw+bWtOjprepV1i8njunmjb+VPjBQjZo/ry59h1ppZR7I6MxbKk8G7f3lWpz5I+d1MPOEh4Xp rz//1CstnoiyfpPWoXs/Xbl8UYN6vGJNa9istV7pOcBaXkLTPNkvAJJ3+xfbssvkzay0adPq0XKV NWPECj1ctKTbPbzxrY82Dbj9fGz8hAMgiYSE2tR7/lGNbVFAAf4+VIgbddWyUg75+7k/37CPNnq8 rkEvVqfCPRQaJs3bfk6dyvsrS6YgBQUFWTeoQeIyd0X2pl5VbysP7R9tH22a93xeqTvvQQ8vACQj tnD7y8PfBj55o77bea3T+sKp54TsF9x+3KWZ75kn7R9tH20aQMALAMlMQm5alZB1wNM6ow6A5N7+ 0fbRpoGAFwCQzA748ufLavVceJKfgz4ODr0Vd2mm/XP3u0bbR5sGEPACQDLk6V1Kn6lcWKrs+Trg aZ1RB3cCd2mm/XP3u0bbR5sGEPACQDIUHm5TOPf38sL9Qh0AtH+0aYA38qUKACD5sJl/Nl5e9xLd IXeCuSsy5aH940WbBniCHl4ASEbCwsPtByN0cXjffqEO7gTu0kz7R/tHmwYQ8AJACvbTuRs6cvZv KgIA7R8AEPACQMrSvkpO+fnSw+FtQkJt6r/oGBVxm3nbXZHTpk2r0NBQ+ftzOEX7R5sGEPACAP4z c7AX4M8BH1Inb7sr8s2bN3Xs2DEVKlSInUP7B8BLcdMqAADg1b7//nurJ9XbZMiQQTt37mQHAQAB LwAAgGdu3LihZcuW6fPPP9dvv/3mdeWrW7euihcvzo4CAC/GKc0AACSS9WtXKzj4d+u9ud7UcQqu eXyN446+THd/+vjx4xUSEqL69esrT548Xre/ixQpEmWc/Xb7pmdMl83+fwEaGQAe87GZh20BQBIw N8XoPf+oxrYowHVZ1FWK2D+dyvsrS6YgBQUFKTAwkIoBaP9o06g76i6JcUozAAAAAICAFwAAAAAA Al4AAAAAAAh4AQAAAABIXNylGQCSkbBwm0JCqQdvY25SAoD2jzYNIOAFAPwHa7+/pDX2FwDQ/gEA AS8ApCg1CmVSrcKZqQgvY3pDBi4+QUUAtH+0aQABLwAgoXxkkz93X/A64b6c/gfQ/tGmAQS8AID/ xGaz2V/UgzfuFwC0f7RpAAEvAIADPg4OAdD+0aYBBLwAgKjCw8PtLx8qwuv2CweHAO0fbRpAwAsA +E/o4fDe/QKA9o82DSDgBQBwwMfBIQDaP9o0gIAXABDzIIRTzbxxv1AHAO0fbRpAwAsA+M8HfByI cHAI0P6BNg0g4AUADvjAwSFA+wfaNBDwAgCSg59PnJQfNyn1OqHh5v/0VARA+0ebBhDwAgASau4P HIAAoP0DAAJeAEhBAvx9NLZFAX333XcpZpvMMzV37dyu8hUqydfXN0VsUxq/DHxYAdq/FNO+0aaB gBcAcEcP+sqUKqHjx4+niO25ceOGLl28oKDAtEqXLh07GECc7d/dmZNP8EX7BhDwAgASKF++fAoO Dk7+f4D8I/4EBQUFcUAIIEW1fbRvAAERzWubAAAgAElEQVQvAOA/yJYtm65fv56st8HPz88a3nXX XUqfnmvzAKScto/2DSDgBQD8R4GBgSliO8zBYErZFgC0fbRvgHfxpQoAAAAAAAS8AAAAAAAQ8AIA AAAAQMALAAAAAAABLwAAAAAABLwAAAAAAAJeAAAAAAAIeAEAAAAAIOAFAAAAAICAFwAAAAAAAl4A AAAAAAh4AQAAAAAEvAAAAAA8ExoaqgULFujBBx9UQEAAFQIQ8AIAAAApw3fffaczZ86obdu28vf3 p0IAAl4AAAAgZVi3bp3y5ctHRQBegp+dAAAAABfM6ckhISFu5Tt58qRWr16tsLAwNW/enMoDCHgB AAAA77J582Zt27ZNuXPntgLZX375xa35fHx8rOt2TbDLqcwAAS8AAADgVfbv36+NGzeqcePGKlGi hNs9vIa5QRWBLkDACwAAAHilrVu3Kn/+/Fawax0o2wNYglggeeOmVQAAAIDdlStX9Nhjj1ERAAEv AAAAkHKY05fNacn3338/lQEQ8AIAAAAph7lW9+bNm25fswuAgBcAAAAAAAJeAAAAAAAIeAEAAIB/ nDlzRn5+flQEgBi4zzoAAACStVy5clEJAFyihxcAAADJms1moxIAEPACAAAAxpw5c3Tq1Kk485h0 kw8AAS8AAACQbFy7dk3Lly+PM49JN/kAEPACAAAAyUbDhg114cKFWHt5zXSTbvIBIOAFAAAAko08 efLonnvuibWX10w36SYfAAJeAAAAIEn4+PhEGbortl5eeneBlIPHEgEAACBZS+hdmiP38rZr1845 nd5dIOWghxcAAACplqOX98yZM9a4GdK7CxDwAgBSOXNQ6OfnR0UASNYcvbwrV660xs2Q3l2AgBcA kMrlypVL4eHhbufnmZcAvJXpzb106ZL13gzp3QVSDq7hBQAkiLlmzpMbxDieedmpU6dY88T3TEwg pQsJtVEJSSDH/bmVJcvdunz5kjU04+yLOy/A34dKAAEvACB5Mj0mM2fOtHpxXZ0q6LgrauQbxwCp LdjtPf8oFZFUwVZgSQVd3qBT9iH7IWmMbVGAoBcEvACA5Cny3VBd9fJyV1QgQud6+eTvx1Vnd15+ +6sy1ZAEQsPCNWnlcSoCBLwAgOQtci+vCW4d6N0F/uXr6yPiXaQm4baIXt0TJ07o4QL5qBAQ8AIA kieeeQnEz1wfb+PyUaSyz7xDcHCwsmXLRqUg0fD7IQAgQRw3rPLkxlUGz7wE4jv458Ur9b2A24Ue XgBAAg/KE3aEwjMvgfi/WwQA4O8JQMALAEimHNfyGuaZl1y7C0Q6+P/nhTtv87pV6tPxRf315x86 dOYaFXIHP/MAAS8AJIKboTepBC+Q/f7sypIliy5fvmwNzTj75s5L65+WSvDGg396eONV4J4AHb0Q kuD02Iwa0lfvz1mkR8tWoNfxDn/mAQJeAEiEYLfZ1AZUhJfIEJ5FBVRUu8I3a+3UpVRIElj46tcE vV558M81jfE5cj4k3jpKSB3+fPQnlSxTgfpPgs88QMALAImkRZ2W8vdNQ0V4icLKSyXcYaHhtzR/ 9TwqwmsP/unhjezmzRsa3q+HBo4cZw3/9+77ejhbWv0UfNMaduo9QLOmTlC6dOk06O33VK/R09Z8 Be8NsIYm36WL59Wvawdt37Re2e+7X6Mnf6RS5SpZ8+85ck6NHy+rDfuPKfTWrSjzGdVK5tOF88HK lCmzeg4Yrmdat9PZM6fVtX0LHT54wL6cipqzeE2s63iz52vOspth2rTp2KkuPvMAAS8AJBI/H3/5 +XKTeqTmg8uIP//mmZeFChSiQrxxH1EFll9/OaHer7ZVjToN9HyTOnq2TXtn3TiGuR/Iqz3HgvXd /t3q/EJz1f0n4P3xn4DV5BvxZm+1bPeqps5dooPf7FXfLu311dZvrfQD+/dovT3YdSwv8nzGxgPH nWVpVK20nrYHvEP7dlX12g302cqt8a6jdPlKVtmr1aqntk/V06hJHypP3ofYuQABLwDcrgPJiH9A av4OOPDMS2/8QYIeXoc2TWrr9SFva+TA3uo3fKzVe+voDXQMGzd/zhqWKF1eF4J/j5FurF6+WF9+ Pt857ufn50x/rEbtKHkjv7908YImj/2ftm1cq99/O6MbN/620rdtWKtJsxa6tQ5TPv80aTR6SF9r W9o0ra0N3/zMzo32mQcIeAEgUQ8m+eMKDi4drl+/rsDAQCqGgNfrzFm6Vn1ea6tn23TQrKnv6VZI iJ58ukWUz3H0z3Ns0w/99pd8I53dE9/8xjvDB+iRYiXUuc9AZcyUWUXvvyvKfO6sY9nCT7Vg9gw1 e6695n74vmZ9voq/QQS8uIM4pw8AAMCrDv55OV735cyjmfYA8eyZX63h7u2bnT8GRB5GvtGXGQYE pLWus3VMr1H3Cc2bOU1hYeG6fOmSBvfuHOf8jtcf166qUrVaypAxs1Z/ucSZ/mi5ivpo8rgoeWNb x96dW62yB587aw1zP/gQ+9bFC7hd6OEFkAoPJunhTWoHNh/UlNc/0N9/3dCcg9OokCT4DsDb2yjq wXmw6p9GQ8ZMtt6bYfSeWVc9tNPnLVPnts/o5PFj2nPsvN58a7z+17+7xgztZ+XpPXikWz287Tv1 0kvNn9DF88Fq+2o3Z/pQezl6dGilCaOHqmTp8pr1xepY1+Eo++DRE/n+0SYhCfjY+IQBSCIhoTb1 nn9UY1sUUIC/z21fn+OxRM/Wbil/Pz92gBtaFHhR849+lOD02Lz+5CC9NLytCj7KjVuSQmhYmBas mafRdSfr7kx3KygoiFOavahNfLF6Lnsb5UOFIBW1STZ9tPG0OpX3V5ZMQcn6vgKO77FjW2hfkx49 vABSH86fctv8Ix/qdjzs8szPZ1WwZD72Q1J+B+DFu4ceXqS+zzxAwAsAifWHVTzyw5VbN29p5rBP 1W5QK2v48oi2almwvebZg14zfLpzI309c7XSpAuw52mtCvXLWPO1sKcZJt+1S39oat+PdGj7YWXJ nkUdx7ykh0vlt+b/YM9E9W08WBM3jlFYaFiU+YxOVXvr6oVruivTXWrR8ynVaFZFF89e0rguU3Ty 8Ck9XDq/Bs55PdZ1TB/wsbPsZpgmLc9ajus7AO/+PcKT4/+Za7/RydPX7O98FHEPbp+Idz4RU6L/ xpE3Vwa1q/UoFQ2v+swDBLwAkGh/WLmGN7rgX89rUu8ZKlW9hIY9P1o1m1eLcX3bvbnu0Qd7J+rY t8c1rvMUla9X2pr+6U8fOPPNHjFPtVvVUJ9pXfXzwZOa+saHGvP1cCv96Lc/a8KG0c7lRZ7PmLRp jLMsbzQcourPPKaPhn6iUjVKaPjCAfGuwwTEpuyPViuu/7UZo9dGtVf2B3jcTmzfAXh7G+V+/hO/ XtOEl6u6nb/L9E18BkCbBAJeAEjBf1p5Dm80w+0BYus3mmnOiAV6fsCz9mC2jLOOHMMqTStaw/yP 5tOVC1djpBu7V+3TtuW7nOO+fr7O9OJVikTJG/m96bX9YtJyHdz6vS79flkhN25Z6Wa85+RObq3D lM8vjZ8+HbVQrezbYrZp4qbR7NxYvgPw7r3jyR4KDwtX2oCI+xLs/PGUKhTKowr9FjjTd4581jk9 YgZaQNAigYAXAFL2n1Z+TY5i0Cd9NLnXB6rZopq+/miNQkNCVblh+X+qK9ozQJzV6Hr6J4enycfX J958kcfnj/1cDxbOo2e6NNJdGQP1XOFXoj0jRPGuY+vSnVo3f5Meb15Faz5Zrzdn92I/c3iZLIXb P7fhHu4ifz9fbf3+pLp/vEN7Rz9of7VUmdfnWUPDTB/fVnqsyIPOdURWOk8Ga+jr56e0adOqeKny eqVnf5UoUyFKenT7Tv1hpeXImVuffr1VmbLc7UzbvnGNerz4rEJDb1n5po8bqV1bN+jwt/vt319f FSxcVJ1fH6Iylarq+l9/afSgXlq9/Atr3poNGqvfiPEKvOuuBKdFL7Mpw+3gWE/k5Udet7nLdPHS 5fT6sLEq8EhRr/zMudqGO/2ZB24XnsMLIFUe6vOK+sp6f1b1tweIF85e1AD78PDun5whkc1FvTnG 0wT42+e55Jxe6vESWjNvo8LtR+t/XPlLHw76JM75Ha/rf/ytYpULK9Ae7O5avd+ZXqBUfi3/cFWU vLGt44e9R6xtuBx81Rpmy5ONfRvHC17cRtn+vfTCnVdEwOtjBbUH3mllvXfc5dnx3kw36Y7prpZh 7D5+WYvW7dW1q5f1cvMG+nbfLit97y/XrJeDY9wx77kzv6pvpxecy9u1Zb16vdTSCnYd69u/a5uq 1KynrT/9rokff66D+/eoV4eWVtq0d0do+cK5GvruVI2Y8KG+/mK+Ne2/pEUvryd16mn9R6/TyOsd P3Ohte1vvNbmtpXhdmzDnX3xvcftQw8vgFR4MMk1vK74+fup/bDnrfdm6M6zLl//oJve7ThZ504G 64P9E9R2YEvNGj5Pc0cttPK0ev0Zt551+eRLdTWy3ThdvXhNDdrVdqabckzoNlWL3luqAiXzacCc 3rGuw1H2dkNbu1wfXNc9vDXg9WyeNH6+Gt+2okr2+lTfj38uynSjSPdPrHTHeGzLN9Oz3ZdLrw9/ V+2aPK73xwzXlE+Xu8wXWekKVbR76warF7d0xSrq0b6FipUqp307tzjzR15OwSIlrGHhEqWttLVf LbbGq9Z+wpnHTOs2YESC01yVt+yDGa3hy9376dMPJ8vX19fa1rqNnnGmtWrfSYvmfKB7c9yn0VM/ UcHCxZ1pe05ei7Icx7hR5oGY08x6y1d53Hp/9vQpZzlql8qr63/+qfDwcOXJl1+d3xhq/RjgWO6g MVP09oAe2nbkvFv5X+k5QHNnTFRAQFr1GTZWp04c04KZU60fHPoMHat6TZrrxt9/a9KoQdYPBCE3 b6rAI0XU93/j7fuglMttiCu/q3Ie3L9b40cM0I8HD1hpd99zr5ZvP+z2Zx4g4AWAxDqY5Bpet83+ YapVV45h9OkPly2g4V8McNZr0N1B6jyuQ4z6jm1+h7zFHtC49W85x5/p3thKvzd3Vufy41sHPPsO wLt/kPAoALBnTuPnowZl8v8T5PqoYJc5zkD3yMTnrWDXkW7OjojtRw/H9EeKRdzF+Tt7EOMqb/Rp IyZ+pBZ1Kmj6+JEKtAde6dMHavh7H6hB+Ydd5h85oLseyFdAIyfNstIuBP8ecWDq/+/d1c20/5IW V3kbNn9e9Zu2UNNqJTRueD/Vafi0M63Nq91VsVotdWnTVGOHvKFpC76OdTlResdPXI0xzbzfvDZi /lIVHnOmrd533BqeOXXSKsOIvl20YvcR53zD+nSMsqz48j/5TGvVa9zcSuvXqa0VaDZ4qqUaViqs d+3bV7dxM7077A0tnjdLC1bvUkDadFbewb1e0WdrdrvcBnfyRy6n6a2/fPG8vtrxg+7Ncb9HP64R 8IKAFwAS92iSv67gOwAv/kHCs9PObbZw+f1zTXvDcgWs4c+T20QN8P6Z7gyo41i3oq3fFkc+hyz3 ZNOboyeptz3ouf7Xnxo+4UNlzZYjRv6/r/+lgV3b68DeHfp42SYFZcoc57oSI83VeLb7cjrfXzz/ e5R0sy3l/umVPfzd/gSXwSiXN5PV61qzQRP1Gf6Olefq5Uv64L23tXvbRqvX11UZVuw5qrvvyeZ2 /sjbE33cBKEm71efz7PGn61T3plmeoJj2wZ38kcuZ7r06a1pXdo+ZQX3z77wqvLkze/2Zx4g4AWA JDqY/GZ3MY+fcflouYNUNLz6OwBv/j3C82saGw1ZZrVRtmj/O/zbetnf+/jE28N7+Nt91rBMxapu 9fCa8cdq1tfO41dc5jHvfzx0wAp2LwSf1biZn+v+3A/8+9izHPdZwVxIyE3nPGaaSU9oWnzljW3c vDenDju+LdHT4povusj14cgzceRALV/4iUZMmqWqtRuoysPZYsyfJeu9znF38ruzfY4p245ekJ+f f7zb4E7+KOWcs0SffjBZB3Zv06LZM7R17Qot2XrIzc8833sQ8AJAIh9Muv/XNSHPuCxZlr/e8O7v ALx5/3geAMzt28DtvM+NWhHnNbznfz+rMYN6K226dHqpez+XeeO46Xqsy32xaU2lS5deY2YsUIky FaPMU6NeI3uwNEkbV30pX5+I64yr1XnSypPQNE/KG3k8LCxcq5Z+9s+yGlppWe/NbvWqnj51Un6+ flHmy5Aps/64esUeeP9q3a06vnr584+Ia3xLlq2kc2dOu8wb+b2n+WMbN6c1f2kPnGe/P15tXutp Xb/s4Gob4srvaj058+RTn2Hv6NKFYD1RvqD1o4G7n2OaJBDwAkDiHk7Kkz6uhDzjkj40eP93AN78 g8TtDgBi+9GjcsGs9vYurUqWq2QPXr7Sw0VKuN3DG9/6wsPCrNOdO7duGCVt+7HLat+1r3Xq7rDe r1nTnni6lTp07x9xQ7oEplXKn8W5jooPZbbW40752zaqqpNHf1LZytXV/c23rLTOfYfr3WGvq80T j6ll+85R5us5aLQmjxqkplWLWT8SbDh0Ns56ee7lbtap0iYoNNcKu8ob+b2n+WMb7z1krDJkzKxF c6ZrxnsjrV7bPHkf0pyvtrnchrjyu1rPMzUetX4sCQsLtW5G1v3NkVzDC6/gY+NnXgBJJCTU/gd4 /lGNbVFAAf4+t319N0NvqtnUBmpcrXGUX+jj88WiBzWzZ+1Iz7iMeK5l5GdcmvfmpjDmGZcvjluj pk+fjLKMdkU7K+t9WTRkYV8FZb7LOf3g1sN6r/M0hYWGaeahSfrzyl/6/L1l2vn1Pt26eUv5S+bV vbnuUfv/PWctI7L48i+d8rW+3/mTThz6Rb4+PspdKJee7tZQj5QrqJvXb+qTtxZq94qI0xZL1y6p toNaKG1g2gSnuSrf7eBYT+TlR163udu0qYfW/Zspd8GcXvnZd7UNd1JYeJiWblqq0XUn6+5Mdyso KEiBgYE0Sl7SJj5TLpv83W+iNHL2Zo/X1a9NVSo8erD/T4C8LVJgjDvD/idQi3YHq1N5f2XJFKRs 2bIl+++xY1toX5MePbwAUp2E9L9GfsZl9OmGmW4eB3LgnbzOdUR38exlvd/nI/We0cUaP7zjJ03o Ot0Kdh3zTOn1oX7YdUS9pndSkUqPRCnzR/bg6MV/AiXzPr78P+07phLViqrvrO46sv+YRr3wniZ2 naHJO8dosT0Y3rpkp157p70VIE7qNl0Z78moZ3s3TXCaq/Ld7v0YnVnv99t/0DsvT9aUnh/prS8H ev1nMTWtF27unwT08H7yRn2380ac0synILo0AWmd9Y87/ZmnDkDACwCJfDDp2V9Xz59xGXP55hE+ Jsg1Pa/m/YTOU/VQibz6ac9R5zw/7Tlmvc+YNUOcZXSkxZW/9wddnO9zP5zLGuYtmsfKt2fVfmu8 ZI1izjxmWvNeTRKc5qp87YtFlKFxxwZaPXuDfHx99NyA5irfoLQzrU6bGlo/f4uyZMusjuPaK0+h XM60Dw9OjLIcx7jhCK4jTzPrLVyxkPX+wm8XneXoVrWfbvx1Q7Zwm7I/mE3PdG9k/RjgWG674a01 Z/gCTds3zq38TTo/oVWz1tsPkP3VekAz6znE6+ZuUmhomNWzXOGJMgq5EaJF45Zp29KdunUzVLkK 3q/nBz5r3wcPuNyGuPK7KufP357QZ2MX6+ThX52fgTGrh7n9HUDKCngTsg5Etf7Qb9QNAS8IeAEg RfxplUd9XAl4xqWr5b865gUNbPqWFfCmuyutAgID9Mqotur5+JvOcj1cNr/VYzv46beVOVsmlapZ XI1eq68MWYJcbIPczm+CpBz2wO21d9pZ8149H3ETFP80/96EJGJawtNclc/hsablVbFhWfWtP1QL xnxhD3hLOdPqv1hLRSs/ondfmaJ5by/SG7O6xbqcyOMfHpzgIo9NBzZE3CG7UNkCzrT3Nkc84/f8 6YtWGT4eMk/vbvifc66ZA+dGWVZ8+Ss3LmcFtSbt/V4facyaoarUqKz61B6sBaO/sKeV1vxRn2vT ou0avqS/0qRNY+X9cMAc/W/pAJfb4E7+yOWc1HWGrl36Q2PXDVcW+753XV9xfQfgzQf/ngQA+fNl tXptPclPgAECXhDwAkCK/cPqWQ9vgp5x6WL5Ge4O0gtDW1mByo2/bqrr222V8Z4MUeYzAemKj9bp wMaDOnv8d62ft0Unv/9V/T/pEWMbjPjym2tup78xW0e/Oa6B83srfYb0CX7Ehqc3SIksS/bM/wbI F65FSTf1Urjiw9b7k4dO/adHgLQv1lX+Af4qU6ek1fNq8pjrnJdPXanDO3/SxTOXXJbBBLOOXnJ3 8kfenujjJgg1ebct222ND2zyljPt95PnY90Gd/JHLmdAujQR016ebJ0xUKt1VWV/IJvb3wF4809y nvXwNq1QSKrgeTsIeM9nHiDgBYBE/cPq6R9Xj59xGct6S1Qvqhnfvefyj7x5nz5joJ7q3tB6BZ+6 oAFPDtcvh3+NtZ8zrvxmOP31j3X1/FV1e/9V3ZMrq3M+0xt88bdLunUrzLlMM832H9LiOniJa9za H+E2l/smvvmii1y3jjzmNOGti3fqlbHtrFOxXyvdM8b8GUwQ+c97d/J7sn3TvhknXz9ft7chrvyR y9ljeietmb1BR/Yd04b5W/TtpkMatWoIB5eptI0CCHgBAl4A+Ocvq+cPufT0GZduPbgynvQrv0fc KbRgmYfceuhl9PxvtXpXAenTqPPEDirwaL4o85SuVcK6pvabdd9aAbphToc2eRKaFmf54hi3hYVr 59d7oywr0z0ZrV7V879esAd/PlHmC7QH+devXbcH3heV9b67462X63/8HVEvpfLp0m+XXOeN9N7T /LGNV2hQWluX7NKKD9eqQfta1vXLDq62Ia78rtaTLXdWtR7wjK5d/EO9Hh8Y8aMBD71MMW0UuwhE vAABLwAk8O9qxL/bvQ53pkVPH9Z8jHUaq3nEUNr0AarcpLye6v6klfZyiX9Pa+5Qorumfzsuzvzh 4eHWqdPvvDQ5ynrMfA1fq6s/r/6lj96MuCa0UuNyatSxnjVfQtNclS+2bY88PqzFGJ39+Xc9UqGg mr/exEp7pmcjzXv7C/v2jVbtNjWizNey71P6fNxy9a03zLrWdfLu0XHWcb0XH9fJ709ZQWHRxx5x mTfye0/zxzbesv8zSp8xvdbP36xlU1bI199X2R+4V4MXve5yG+LK72o9A574ny4HX1V4aLh1M7Jn X2/q9ufaxtGlVwsLNwGvDxWBVPSZpw5w+/AcXgBJJqmew1u/cn2PnsO7/IuHPF5Xw6d+ZgfH45WS EacKTzvwLpVxxw8uw7Ri2wqew+ulbWLBHOl15NzfVAhSHZ7Di9uBHl4AqU5Cro/z+BmXVHP8f4AC /J37A3f+OwDv1b5qTueN8oDUwASJ/RcdoyJAwAsAiXO0b9MdeMgl9RyPSbtGUVdJ+R2A1zLB7p04 6wUACHgBICUe68uzaxgT9IxLHaWi4dXfAQAACHgBICUe7Hv4HN6CxXfYX56ug3qGd38HAAAg4AWA lHiwL3q4wHcAAAACXgBIkUf7NrpgwXcAAAACXgBIecJtYbKFc8CP1CssnIdeAgAIeAEgRTpx5oSO nz5ORQAAABDwAkDKUrfok/It5kdFINUKDQvVtNUTqQgAAAEvAKQ0vj5+8vej+QMAACDgBQAAwB0T Fm5TSCj1gNQjJJT7aoCAFwAAIFVYd/iyVh+6SEUAAAEvAABAylKzcBbVKXo3FYFUw/Tw9l90jIoA AS8AAEBK5+frowB/HyoCABKBL1UAAACQPNUZukY+Vbqp/uB1SV4WH5/bE6SPHDlS7dq1izHdTPvy yy/5EAAg4AUAAEgpag9bJZ+qva33a9Z9qS0Te2jlukURQedj3VV/6PrbGsiOGjVKRYsWVbp06VSo UCEtW7bM7fn37Nmj4cOHu73uy5cva/r06Zo4MeZjtMy03r17688//+RDAYCAFwAAILmrN2SN1q79 UuvHvmoFt7MGvKQbt6SZg7vIp0oPrX+3q1auWaI6QzbctjLs27dPX3zxhW7cuKGZM2eqTZs2bs13 8eJFbdq0SQMHDnR7XXPnzlWrVq0UFBQUI81Me/rpp7V06VI+GABixTW8AAAAyYTp0f3y7W66HmLT V6NeVbgtjW78Haoc6f21YmQX/RUSpuWjOqlh3ynSkBoul3H+/Hm1bdvWHjivVfHixXXgwAGFhoZa 081pwmZ6zpw5NXv2bFWuXNmax9FLa7PZ9NlnnzmXdfXqVVWoUME5PnjwYI0bN07p06fXpEmT1KxZ M2fa6tWrrR5ZB7PMXr16Wfnuu+8+LV68WCVLloxS1o0bN6pbt26x/wBQr57VA9y6dWs+HAAIeAHA CLeFKTSMekDqFRrGQ16Tq7At78mvSjctGNzVRJ9atXeffBSq8HCpbvkysg/UaugU2ez5YmOCThPo mutfTZCbI0cOa3r37t3VsWNHa7o59dgExYcPH3YGutGZgLVIkSLavn27c1q+fPl05coV7dq1S089 9ZQz4F2yZIlatmwZYxlVq1bV2LFjraC1S5cu2rJlS5T0HTt2RAmwo6tYsaI1HwAQ8ALAP/b9vFu7 j+6kIgAkK/WGrNOqdcv0Vsd2unYjVDsOfWOPRMM04+t9erl+Wfn5X9fjjxbSmaVva99Pvypjxswq cF+GGMtZsGCBrl27Jl9fX2XPnt05/fPPP9enn37qHPfz84uzPCYI/vrrr63TitesWWNNM0GyIxA9 d+6cM++KFSsUEBCgBg0aRFlGo0aNrOFLL72krl27xliHCchNOWM9kPX315EjR/hwACDgBQCH0g+V U9n8FakIpFqmh3fa6olURDKzat2XeqvDC1KYdOHSTYWHh+rjFQe06a2ntOKHyyr9cB6N2jFN43ev 0iP3ZFfdfCU1+r6YpwOHh4dbgRu0lCkAACAASURBVKIrYWFhcQaY0ZkAtkmTJvHmmzZtmjp06KBi xYopd+7cbpfp3nvvtdJiK5M5FbtgwYJ8OADEiptWAUh9DZ+Pn/z9/HnxStUvJD/1az+p/h/M1skL 13Xp+t/ysUntniillYev6NXGldVl3VsK/uO0Fjd9Rh0fLWm9b/RJ7xjLKV++vHWzKcOcMuxgelun TJliBZjmBlOvvPKKNT1t2rT69ddfnfmef/55a9zkM6cjly1b1q3ym2t7zTW7ISEhzmlnz561ltO/ f381btw4xjympzhyGaMzaSaIBgACXgBIoFEf+6nW8ys0dmaaFLVdtfLWv23Lnjdlgcb0eTfGdDNt 5/pdfKiABPh6UE3ZNo/T9M8XKPjmdSldbtlsPnqlSSW9980spfcP05MFCujvsFBlTZ/e/j6/Na3n iqjfRdPbOmbMGKtHderUqUqTJqJtmzx5sjZv3qzAwEDrplWOQHLVqlVWMOq4U3LhwoVVs2ZN6xRl c6ry/Pnz3Sq/mf/NN9+0rhV2aNiwofV4I3Pn5/Hjx8eYx1zja06bjs3KlSutG1cBQGz4iRcAXHh7 to/Wrl2jtbNrOZ9zWaXzKPVu11i1nrMfYNVupN5tQxI1+Fx7YkWUaebUwqYlmsnH11dffLMgzuvp tq3eoY1fbtKACX2d04a8Oly1n6qlynXu7Onbf1z9U1/NW6EZq96PkdZ56Gvq2LCrSlQorvSB6fmg AR7yqdpDjRo8oSt/htlHbsrXlk2h8tOE3Ws0uEol/XT5fKTMUtFsd2voljV6t35P52QTsP7444/W e3OTqG+dZ6b04fdnWDqGrVqmnv3O8X79+lmv6KLf2MoxHnm6uVmW6UV22Lt3b5zba3qTzZ2b 33jjDWXMmDFKmnn+rrmzs6uyAIADPbwAEM2YWX7O51ya4Dbycy5rPb/K+ZzLUbPS3tZy7NqwR6Ue e9R67Vy3O8685WuU1Y51u2QLtzmD5T2b9lrT77R1S9fr8cY1XAa0ZlqV+o9p+2puGgYkRJVqj2vZ 11/pys0bWrriKwVf/1Nh4dL9QRl18uIlXfzj2j+vq7p47Zo1zaRFZ66jNT2r5rTl999/P0m2xZwq HZ8sWbLo1VdfVY8ePWKkmWnmlGpXz+gFAAd6eAEgGk+ec/nGC65PC7566apG9Rqr/VsPKN8jeXXs 8M9afewra/roPu/ap3+je3JkVd93+qhImcLWPI5TjB09vdtWbVfJSiWsIHbHup3OnlqTr1mHp7Xk 42XKanpvpg3SQ4Xz6dGKJbRv2zcqU6WUFeyWrFRS/mkimvmWFZ/X5QtXFJQpSO37vKD6z9Z1ljV6 77JjPLayrluyQdPemmH15D5Wt1KUXmXj250H9VS7xrHWb9lqpfXVpytUs0kNPmyAhzYPb6jyfW5p w4Z1sm2ZKJ8q3RTWsarq5iuho8FHVD9vPslmde7KZv9v/2/BVlp0ka/JTSo3btxwK19sPbgzZszg AwEgXvTwAkA0q+fUV6O+E/X3DV9dv+6rpVv2adm23VpkDyL/uBGqv27Y1Nge7K6dE/s1sFNHzFC+ Qnm14qdlGvHRUIWbLhi7ycOmqdFzT1rT35zYT+/0+/eaNRNkOgJPE+RuX7PDHsSWVPFyxazg19F7 axQrV9RaRsuOz2ri4IjTAyvULK9tqyOeh7lj7S49VqeSM/+8HXO08uhyTfxinL0MU92qh9jKOm7A BE1e+p6+/mGpCpd6JMZ8h/f/oKKli8S6XDPPiZ9O8kEDEmjXmKfswe446329mk01+8vter1CF/n7 Z9DmU6eVKU1a3bS3Oea9f5ogzWjan0oDkGrRwwsAkYyZlcbt51yeOX/Bes7lqZvbYixn45ebtfzQ F/Lx9VGWe7I4p29ZsVXrl25wjvv6uf7d8eCeQ9ZjOB4okMcaD7cHu9/u+k4lK0b01FSqVcEaNni2 niYNiTgdsVLtCpo7ab40XNq9YY9e7B3xPEzTUzv7vbnau3m/Lpw7r5s33Lv2OLay5nkot6YMn65q DaqoXvM6Mea7evGqtd2xMdcinz5xmg8bkAhWDKnufL+2/UR1XzZGXbdusB5LVCNXYY1v1IdKAkDA CwCIkJDnXNYt9HCM5Zje2NhuMrXm56/jDAiNrau26+rla1HupLx9zU5nwOsQbgu3B+ER6zGB9b33 3aM1i9cpe67synR3Jmv6jFEfKX+Rh9S2+3PKkCmDaj/UwO36cFXWd+aP0q71e6zeZHM35mlfT46S nilrJmv7Y9tGc31xrry5+LABt4EJcAlyAeBfnNIMAJEk5DmX721dG2M5hUo+rJULV1vvzSm+DqZn dukny62A8Jo9oB3Xf4I1PU1AGp0/ez5KwNvr7e7O05xfH9vL6nF1uBR8yVrGh6NnqVLtf+/CXLlu Jc0cO9s6vdnhrz+uq0yV0lawu3nl1hhlvfveLFYZzfIcZY6rrAtnfKHyj5fVa2++rDO//BZjeeaU 5cjbHJ1Jy/vwg3zYAADAbUcPLwBE0qvNLfurrmo9v0DtnmkiX/Ocy5Dfoj3n8pEoz7lc+OOP+uqH w3rikcLO5fR4q6v1WCBzvWvNxjWs510aXYd30oSBkzVtxAfW+CsDXrKGo2aP0MAOQ3X6xBm9t/Ad Bf8WbF2n61CsbFGdP3dBP313xBp/86UhOv7DCXueItb1tQ4VHy+n6W99oMq1KzintXi1mV5/rr8u X7hs3ewquo6DXtWgl4cp5MZNPftqc+f02Mp69tRZPfXos7orw13qMaJrjOUVt5d718Y9zptxRbdn 0z6VrVaGDxuQCOoMXaM1a79UvccbacXQmqlim318fGI8AikhRo4cqSNHjmjmzJlRprdr105PP/20 nnzyST5gAAEvAKQ8tdqsStBzLiMHvOba25nrIu4gaq7HNcGpYU4zHjg55g1kipcvpqlfTnKOR38m 7315ckSZNmXZBJdlz/1Q7hjzPly8oD7d9rFz3HFtryNf9SerWi+H1p1bxFnWN97pbb1irb+mNfVK g05WoB0YFBgl7e/rf2vrqm1q2bE5HzQggWoPW2U9J9y2eWyU54TLHvD6PNbdek74isGP37YA04w3 btxYS5YscU4zz8v95JNPEiUQjbye6BJr+ZcvX9b06dN18ODBGGkTJ05UmTJlVL16dR55BBDwAkDK 43jOZY0atbVpwypVrlorynMuM6UNcBx6WRHv1ZshLp9zaR4FdOXSVStY7T2qR6KVz5z+7M0yZApS w9YNNGX4tBjb/f6w6XqlfweXz+gFEL96Q9Zo7XrznPBuVnA7a+C/zwn3qdLDek744z3Hq47NR6uH 3L5Hf50+fVo//vijChUqZD3i6Icffrgt60nMADqyuXPnqlWrVi4DWjPN9PAuXbpUrVu35kMHEPAC QMoyuH0a9b9RzXrO5do5T6jW8ysS9JxL8yig28E8JsjbmccludLz7W58wID/wJPnhCuWgPf8+fNq 27at1q5dq+LFi+vAgQMKDQ21ppvTec30nDlzavbs2apcubI1j6O31RGA9u/fXyNGjNCcOXOsHlEz boJEx/JjW07u3Ln1+++/K0uWLHrrrbfUvn17a9mDBg3SuHHjlD59ek2aNEnNmjWLty5c9TybcROk mleDBg20bNkyLViwwApwI9u4caO6dYu9PapXr57VA0zACxDwAkCK9FanQKlT3YgDH8dzLht10Wtf 97eebdkw30M6f+Nv7fztN+s5ly0eLUWlAbjtwra8J78q3bRgcFcTfWrV3n3yUajCw6W65cvIPPG7 1dApstnzxaZ3795WoPvll19awWmOHDms6d27d1fHjh2t6Xv27LGC4sOHD0cJdB2eeuopK8g9dOiQ FdiOHj3amRbXckxvsHH8+HGrDCbgNfLly6crV65o165d1rIdAW/005rd6fE1AXONGjVUsGBBvfnm m9qwYUOMPDt27NBnn30W6zIqVqyoLl268IEDCHgBIOXr/cIN+/9+1vN2+9V8Qsu/OxjlOZcNixej kvB/9u4DzomiDeDwm6twUgQEsaBSjiK9S+8gRQQFpSOgSBMBQRBEpH5UQZp06aCIKKggTXq3gTSp igrSQUTgSr7MHAm5XPrljrvk//ALSbbOzuzt7JvZ3QGS3LPvb3C7n/Dvj57R/YRHPpI+wXJUi+f1 69d1X98PP/ywZfjy5ctl8eLFlu+OulYz6969u9SpU0cGDBgQb7ij5ajgevDgwbJ27Vp9SfR///1n mUYFxeZA89y5cx4FuLZU6/GwYcOkTJkyMmfOHMmSJUuCaVRa1PY7PEEOCdEPtAJAwAsAAUcFuAS5 AJKbN/2Ej34k4WW7sbGxlifH21L9ZDsLBK117NhRNm7cKJ06dXJrOf369ZPixYvroFcFpa4C6sS4 c+eOZVvtyZo1qx7naFvVJd6qhRhA6kc/vAAAAKmAN/2EN1yY8InqZcuWtXTFoy7tNWvYsKFMnTpV B4KXLl2S119/XQ8PDw+3XIpsTQXNn332WYKg0dFyrl27JrVr19bBrmoFTix1KbZKv1qPdddCap3q nuA9e/bIkCFD9HdbqiXZetttqXGFC/PDJkDACwABYNS8YP3gqrEfhwbMNtfMWdcny1ky9RMZ0+eD BMPVsF0bd7NzAR745r0aYtwyXmYs/0TO374povoJNxps+gmPjNdPuBrWa3X8v8Hp06fLmDFjdMA6 bdo0CQ2NO7ZNmTJFtmzZIhEREfphU+aA79tvv9XdELnbRY+j5fTt21dq1aqlh6sHZSXWhAkTpFGj RvLggw/KX3/9ZRmu7iFWlzTnyZNHPxirW7duCeatXLmyfPPNNw6XvWbNGv3gKgCpn8GYVM97BwAX 7kQbpffSYzK2WaSEhRiSfH23o29L02n1pGvdnhIS7PyOjpHzDbqfy/Xza0rNNqtl68S4fi7XL3xe arZao/u57N32jk8DTOv+c9X38rXKyZAZ791LU88xsv6LjQn62fV1YKuWb5seb/xz7YZ0btBNZn77 UYJuiFR/vF2e6y5TV02ki6L7INoUEE1ZPV5G15kimTNm1oGMCkKQ8o+Jhso9pWHduhIcG2T6Ei1B xlAZ1aGU5JveTPcTHmSwasswxF3SO3jrDokeZP/veevWrfrhTL4IQFMT1Q9vsWLFdD+8GTLE71bu xo0b+v5f1UJMP7zJu+93LRsimTKmk2zZsvnNtnB8vf+4hxcAbIyZGyzrN67U/VxWbzU5Xj+XNVvP tPRzGWNsLH1fuZ1k6bh47qKcOXFGcuTOIRfOXpDfT5xJkvX4MoC2tuHLjVL9+Wp2A1o1rFLdirJj 7S6p0agaOx3gJl/1E666B1IPblJPR549e3bA5aO6rFrde9yzZ88E26+GjR07lmAXIOAFAP/kST+X fV+xf+nvtcvXZNRbY+WHbT9JrgI55fihE7L2+Nd6+Og+H5iG/ygPZc8i/cb1kYKlntbzmFtbzQFo iy4vy6LJS6Xf+D6yYu5K/f39zsMsy3e0nOblWsuVi1clXcZ00qHPK1L35Tp62a27t5Dls1dIWJpw eWNIF6lSr5LLvLDX8qy+j+gxWmqYgtmy1UrLjvW7ZNNXW6T/hLfjzfvzrgPyQrvnHS67dJWS8vXi 1QS8gAe2DH1OyvaJ0v2EG7dOEkOlN73qJ9zePbmB5p133rE7fObMmexoAAEvAPivtQvqSu3Wk1z2 c7l+geP7XKcNnym58ueU4bOHyNXLV6Vp6RZ6+JQh06Vhqwam4YPl6P5fdVA8Z92MeIGuWcVnK8js MXPl9K+n5YftP0rHdzpYxjlbzpKdC/T72d/PyWt1O+uAV3nkiUfky/3L5fBPR+T9TkMtAa/tZc3u tPi+MbiL9G7eVx7P9Zh8PHaejFsyKsE0h344LO9N7u9wGU+XKCCTB33EDgd4aPeYF0z/v6A/u9NP +MzG/ck0AAS8AAB1OXOo2/1c/nnhou7nUvXPa0u1eK765XMxBBkk00OZLMO3rt4mG7/8zvI9KNj5 swMbv/K89G3zrrTs2izecEfLUS2/8z9cJPu2/CAXz12Q27fu3Wdc+8WalkDz8oUrHgW4ttJnTCft ereRbs+/Kb1H95IMmRJeMnnt0jW9/Y6oLkn+OPUHOx2QCKvfr3rvb7nDJOmxcky8fsInNOxDJgEg 4AUAxPGmn8s6+fMlWI4x1uiwj8l1J75xGghaq9+irvy482d5rmV9t5Yzc9QcyVMwt7Tt0coUlKaX WrnrJVleRd+Jvrut9vu5zJglo84HR9uq+ul8POfj7HSAD6kAlyAXAO6hWyIAsOJNP5cfblufYDn5 i+WTNcvW6s/q0l6z8jWfkS8XrtKB4PUr12V8/4l6eGhYqH4wlS0VNA+aOiBB0OhoOf/+c1NKVSqp g90ta7YlOj8yZ82k06/WY94eRa1z7vgFMvnLD2XBxMX6uy3Vkmy97bbUuJz5nmKnAwAABLwAkBze ahMl6+fX8bify68PH4q3nJ4jusunMz6T2nnqy6pFX+v+LpXuQ7vKgd2/SL0Cz8vLz7SSnPnjAr5R 84fLwNcGS4OCjd1Kp6PlNOvUVN5u1V8PP3HoZKLzo8t7neS9jkPk+SIvyqW/L1uGTx0yXdq91VYe e/JRad/nFZk0aGqCeYuUKSS7N+11uOy9m7+X0lVKsdMBiVB78Dr94Kq6gzak2DQaDEnT7dz//vc/ adeuXYLhathXX33FzgFA45JmALBRs8230rBefbl6I8Z0pnZbgozZJFqCZeKedbqfy6NXrFpiTedx hbJllsFb10n9Ak9bBj8Z+YR8vCHuSZ8H9v4iJw+f0p8zZs4oA6ckfIBMkbKFZdpXky3fHd1Xax7u aDn5iuSVxdvnWb63793W7vLM312tp2qDyvpl1rJb3L3E6snRZupJzeqVIB8b15DX63XVQXhEuvh9 EKp+eLd9u12ad3mJHQ7wUK0h3+p+wo1bxuqnym+dFNdPuAyuIYaKPXQ/4asHVfdpwGo0Gi3fZ8yY IdOmTZNDhw5J/vz5ZcqUKVKhQgXL+GXLlslLL70Ubx5Xy927d6+sWbNGBg4c6FaaVD+6Kh2qH11b kyZNklKlSknVqlXpWggAAS8A2PJVP5eqe6Crl6/JI09kl96jegZcPqoHWz3Xsp5MHTo9wfZ/NGSG vN7/Nbt99AKBLjY21vSy3ypab8gGWb/xK91PuApurfsJN1TqaeknvLbRIGveq+LTNJmtW7dOFi9e LHnz5pUVK1ZIixYt5NSpuB/1fvrpJx0A284T6+BefzX80qVLsmnTJhkwYIDD6WwtXLhQmjdvLhER EQnmUcNeeOEF+eKLL3TakBr2eSOZAAJeAEgugzqESv9bVXQ/l+sX1JearVd71c+luXugQNa8y8t2 h/ca+SY7GuCAavV01DjqST/hxoGV7S7jwoUL+rLfDRs2SOHCheXnn3+W27dv6+EdOnTQwx977DGZ O3eulC9fXs9jfghfdHS0LF261JLOhg0bStu2bfVnNX+nTp10oKnmt27hff/992XChAmSNm1amThx ojRp0sSyjLVr10qvXr0s06tbQNR3FTg/8sgjsnz5cilaNP4xVgXI3bt3d9iKXKdOHd2frgqKkTr2 eYCAFwCS0YiuESJd4/qvdaefy2bFS5BpAHzCWQvvrY1jJU313i77Cb9jms5Ra2mfPn10oKsCUxWk 5siRQ0/bs2dPef311/Xwffv2Sfv27WX//v16njt37ljSZm3q1Kny8ccf6+Eq8FWXE2fNmjXBtE89 9ZRe1549e/TlzqoFVlHrUt9tl1uxYkUZOXKkzJo1Swe23333XbzxO3fu1K3MjraxTJkyej53W4xx v/d5Al4Q8AKAz6S/kklCg8Lcnn5IQ/X/A3L17BH5X/E28cY9m/3uh8vkK1KPOzG3yYQUzFELb8MR W93uJ3z/yXO6n/Ans4QnWI66x/bixYv6Htps2bJZ1qkuTza33iqqVdfc8mavBU49GCoyMlJq1qxp aalVL7OwsDDdcqy0atVKv5ctW1bOnTtnWZ66bzc0NFSeffbZeMuuX7++nka1RKtA3Hb95vQ7ahlU aT927Bgth6lonwcIeAHAR0bues/vtzHDnUxyPewKhQ34UcDrTT/hQ6t1SrAc1eppHcxaBxw3b96U oKCgBMNtAxLVMqsuPa5Ro4Zl3K1btyzj06RJo787mt/8ffLkydK5c2cpWLCgPP744wnGq/661Xps 53/ooYf0OOu0WlOXXqtgnECKgBcg4AUQMMJDwmVZp28sl+j5qyDTv69WfSUVK1eUDBkzUPBwKCw4 jExIgRxd0mzuJ7zjC00lU5rg+P2EN6ogb6wfIZnDDbqf8D9u/CM7//hDmi4bIJ+8ODTeckqXLq3v z33llVdk9+7dlnWqVlX19OWOHTvK1atX5b333tMBaXh4uPz++++WgHTjxo363l4VdDq7ZNjZQ6vM 39X7qFGj9L2/c+bM0a3Cyl9//aVbn9VTmxs0aJBgftVSrC5rLleunN11q3EqiOaS5tSyzxPwgoAX AHwW9JYuUVpOnjzpt9to7qbjzzN/Ss4nc1LoQCrjqIX3s96m4M70eqDOO9KuSSMJUv2E3/nLpp/w AvH6CV925IgM2PSRDKtyr6VX3WfbrFkzeeONN+Tll1/WlxSrdY4fP15fPvzOO+/o6UaMGKGHf/nl l9K0aVM5fvy4nD9/Xgegtv7991+722Hvs/V39f7AAw9I37599b3F6sFWyosvvii//PKLDqznz5+f YH7VDZK6HPqZZ56xm4fq0upatWrRcpiK9nmAgBcAfChXrlz6xM3fqPvljphOcNWTST/99FP9oJnM mTNT4IAfBLxKurrvetVP+NDKr1sG58uXT3788Uf9eceOHfpHMrXOLFmy6ODSNi3qAVLbt2+3fL9x 44bLgEVNYx5m/dn6u/Vw1RqrAm7z961btzpdvjrGqdZd9TTn9OnTJwi+V65cKb179yaQIuAFCHgB BC51uZy6X82fqCerqmBe9Y9ZqlQp3Sem6jYEQOo6+Xd0iae3/YTbXtpboEAB/eAn9fRk1f1PSrr0 V11C7So9GTJk0E+Rfvvtty39/pqpYcOHD9ddIHFJc2rZ58kDEPACQJKIiIjwm21RrSWq1ea1117T 39WlgB9++KEebn4SK4DUEfA6CgC+6lNFat2K0v2EX/t6uGSsP9CtfsJtW9AOHTqUYJ0phbr6xp30 qNZde2lX/fymtG0CAS8IeAEAiaQuAcyTJ48luFUtIKqVV/Vfqe7TA5D6A15l7bs1TP/HPR3ZnX7C x1bvSvAHAl4Q8AIAUq/r16/LDz/8IB06dIg3vFKlSrqVV7WY0MoLpA4nTp2WYIN70w5tmMn0fya5 dvakjCz+arxxde/2E37sxGkyFSlatL7yPC0ZAQJeAIB9qgsOdd9u9uzZ4w1Ply6dlChRQjZv3qyf sgog5Vt02Lcn/qE3z0pUxCNkrB+gLAECXgAIOKp1Vz2synzvri3VfYd6qAutvEDKFhZikLHNIn3a V3iQ6bVq1SapVLmaZMiYkUxOxQKhLEOD01PQIOAFAMSnugvJnz+/w2BW3ctbpEgRWnmBVBL0lipR 1Gd9hZv75f7jzGmp+GRFMjgVoywBAl4ACDiXL1+WPXv26M+//PKLW9PTLy+Q8vmir3D65fYflCVA wAsAAUmd8AwaNIiMAPxQYvsKp19u/0FZAgS8AAAAfsfbvsLpl9t/UJZA4gSRBQDgfzZt2kQmAAHM Wb/coCwBAl4AQKqmHlAFIDCZ++WuUqVKvOGqX+7jx48n+t5gUJYAAS8AAADuC3f65QZlCRDwAgBS LdvWAACBwdwvt6NjgOqXm5ZByhIg4AUApGpVq1YlE4AA5Em/3KAsgUDAU5oBAAD8AP1yU5YACHgB ICCopzTTygsEFkf9cg8ePJj+uilLIGBxSTMA+CEucQMAAKCFF0AKEBsba3oZyIgkyFcAqFy5MscD yhLJVvcayQQCXgCIz2g0ml7kg69PioxkKgCOB5Qlkv2cBgS8ABAPLby+V7FiRVoBAABI9nMaAl4C XgCwQQsvACSdrVu3SqVKlcgIyhLJdE4DAl4AIOBNYtu2bdOtvADA8YCyBAEvAS8A3Edc0pw0J0Xl y5cnIwBYjrOgLJEc5UPAS8ALADZo4U26fAUA9eMXxwPKEtS9BLwAQMDLSREAjgegLEHAS8ALAL6t HLgEyLeeeeYZLnsDACDZz2nIAwJeALAT8FJBAEDS2Llzp5QrV46MoCxBwEvACwAEvP5h165dupUX ADgeUJYg4CXgBYD76MSp0xLMQ5p9flKUJWt2MgKAduzEaTKBskQyiNZ3E6UlIwh4AeCeRYepGHwt i+k1/xD5CsB06p2pEMcDyhIg4AWA5BYWYpCxzSJl//79ZIaP/RqcX+rn+4+MAGCSx/TieEBZIjmF BqcnEwh4ASAu6C1VoqicPHmSzPChZ8qWJhMAAAABL1kAICXIlSuXnD9/nowAAB/bu3evlC7Nj2CU JUDACwD3VbZs2eTmzZtkhA9s375dKlSoQEYAkH379km1atXICMoSIOAFgPstIiKCTPCBHTt2SK1a tcgIABxbKUsgoAWRBQAAAP6rSpUqZAJlCRDwAgA4KQLgf6pWrUomUJYAAS8AgJMiAAAAAl4AAACk Cps2bSITKEuAgBcAwEkRAP+zefNmMoGyBAIWT2kGUqA70UYyAYk+KSpfkft4A1lYiIFMAAAQ8JIF QMoLdnsvPUZGIFGymF7sR4FtbLNIgl5oPMSOsgQIeAGkOF1rPCohQZyswjv7M5WXIqUeIyMCUHSs UaZs+IuMgAUPsaMsAQJeACnvj9MU7IYGE/DCOyXLViATAABAwOOhVQAAAH6Mh9hRlgABLwDAr/y4 ZzuZAEDjyb6UJUDACwDwKz/t3UEmAAAAAl6yAAAAwH/xZF/KEiDgBQD4lWKly5MJADSe7EtZAgS8 AAC/UrwMT2kGAAAg4AUABceyngAAIABJREFUAPBjPNmXsgQIeAEAfoWnNAMw48m+lCUQyELIAiBl uhUVK1ExBjICXlFPac5XrBwZEYCiY4z6/U60kcyABfsDZQn4SlhI6jo/JeAFUmhFNn3TWTIDXsti ek3d+BcZEcD6f3acTICWNlMh6b30GBlBWQI+MbZZZKoKegl4gRTGfAAZ3DiXhAZz1wG8s2NbZSlf MQ8ZwbEEMIkkCyhLINGiYmLlnWUnUl26CXiBFOqB8GBOWOG12jWrkQkAAMCH4hpiTp06Jfkic6Wy VAMAAMAv8WRfyhLwtfPnzxPwAgA4KQJw//FkX8oSSAo3b94k4AUAcFIEAABwv3APLwAAXjIY4u6z NxoT103I3r17pUOHDnLkyBEJCQlJNb+a+2NZ+GP6q1Spwg6SAsrBF8umLAECXgDAfT4pytl5nsNx pz5qG5Anzq5OdLt16yYHDhyQffv2ScmSJQkw4bHY2Fh5+OGH5eLFi5ItWzY5e/asBAXFXchXtWpV t8vQ3XL1ZP9OrftrStwuR2UJgIAXAALK/T4pshfYOguE/ZEnJ8nff/+9fk8twS5SnrVr1+pgV1EP k1m/fr3Url07yfbb1PZDhzfp5cccgIAXAAC7HAW3nrT+Xrp0STp37iwrV67U3xs2bCjTp0+XTJky WVpe5syZo6e5deuWbtFq1aqVbN26VbJkySLnzp2ze9Lq6XLV5cX9+vWTuXPn6u9FihSRjz76SEqX Lh1vufPmzZNXXnlFevbsKR988IFHrWcxMTGW8Wqcp2m8cuWKTuOiRYskOjpaKlSoIBs2bHCa9p07 d0rv3r0twbZqHfztt98SlEvWrFnln3/+0S2IefPmlZEjR0qDBg3sbpP585AhQ2TcuHESHh4ukydP ll9//VUmTpwoUVFRMmnSJGnZsqXTbXSUNvPyVR5PnTpVHnvsMVm+fLkUK1bMkp7BgwfL+PHjdevm lClTpHnz5k7zwdMydyffHE3jbJvdzXd76VVU2SsFChSQw4cPy4IFCywB75o1a2TFihUJ9g9nZXji xAnJnTu3pE2bVq5evarz86GHHpJr167J0aNHJV++fA73b9t51WX65tZnNS5Xrlwu88pZWTsrH0d/ C7Z/e672a0f5YhsQO0vLhQsXpG3btvrHBzXcF9QDCWnlBQh4ASDg3e+Toi/ea+TR9I2GfJFgWPfu 3WXZsmWyceNGuX37ttStW1dCQ0MtJ/ZK+/btLZ979eqlp1UBhBquprXH0+WqE+4ZM2bIwYMHJU2a NPpEXp3EHjp0yDLNTz/9pIOP5557Tge7jrhqMTKP9zSNKmBQAZAKJjt27KjzwlXaGzVqpFsC//jj Dx1MOKJO2pWTJ0/q+V977TX944KzbVKBvwpq1fQvvfSS/P7779KmTRt54okndNrUOGfb6Cptffv2 lWeffVbq1Kkjb775ZryHtKl8ad26tV63WpcKeN0pQ3fL3J18czSNO+Xqbr5bp/fOnTvyySefSHBw sP7hpUyZMno9s2fPlrCwMNm9e7feHtv9w1kZqqBU5e+3336r05cxY0Yd7KphKkB0tX9bz5sjRw4d 7L744ovxgl1neeWsrJ2Vj6O/BU/z19G2jR07Vvr06aPT5Wpf6dGjh6xevdrlMckTavsJeAECXgAI eCnhpChb+oyJml+15ijVqlXTrTDmYdbBgWrFVS1CimrBUl599VXLvYu+WK4KIJSCBQtaxqsWS2vP P/+8DgIWL17sk7zzNI3m4V26dNHbrlpVXaU9IiLCEpiofUUFY7aBjGqRVC2mqoXq9OnTlvW6ogIc R9/NfTc620ZXaVPbbW69VA/8crQuc1rdKUN3y9ydfHM0jTvl6m6+W6d3yZIluvW8Zs2aumWxevXq OqhWw1XwZWa7f7iiAjYVtM6fP18HvOYA1B1qOjXvrFmzJH/+/A7n9aasnZWPo78FX+zXKoh99913 pXDhwjJs2DCXaTGXt6tjEgACXgBAgDOfLNqeNJpP+O1N6+vlqoBCXZppj2otUpeSmk/e79e2O2Iv 7evWrdOt0Vu2bNGX/q5atSrBpblvv/22bi379NNPdVCvLlFOjvJ1J23mgNETzsrQ3TJ3J23uTOOo XN3Nd+v0Lly4UL+rAM760ls1XAW86jJ/b6iWVXXpsrpiRLUeq88qWHeHmk5Nv2PHDv1AtvLlyye4 DSCxZe2sPJ3xZr9WaWjWrJn+vHTp0gTrdZYWXwa7PKUZ8KKOIQsAwP+khJOi8GD3X/Y0bdpUv6v7 D9UDeRTbe+yslStXznLSb3tpogoCzIGAp8tVl98qo0aNcnjirVp2VWuWdWuaNfM9mtYn8tZpSuy2 m6dXl1aqNA4YMMBl2vPkyaPvjTTfy2keb50udQmrUrlyZX1Zsqtt8oSzbXSUNuvgw9yS17hxY5fr cqcM3Z3enXxzNI275eos3+392PLdd9/py2XVPanq0ls1v/quhv/999/yyCOP2N0/3CnDTp066Xd1 n7l6mri7+7eiWmsVlS51GbC9fd+bsnZWPo7+FrzNX7P+/fvrwF217D799NNu7Svq/mFHxyRvcTkz 4DlaeAHAD6WEk6JgQ+LmVw85UieQ6h4/pUWLFnqYI6p1SLXAqFYp9TAgHXTbabnxZrnqhF5dGjlo 0CDdiqMuudy/f79lGrWsMWPG6JN6dR+iegCONXU/oWpVeuqpp/SDfFz1s+tpGtU41bqs7l9UQYn5 Bw9naVf3Gv7555/6wT7qns8JEyYkWK66BFVdRpo9e3Z9v2litsmTbXSVthIlSujLS9UlvPbS7U0Z uju9O/nmaBp3y9VZvttS96CqoKt48eKSLl06PSxDhgwydOhQnUdqvKP9w50yVJfj/vjjj5bP9jha Tv369aVr164SGRlp2WZ388pZWTsrH0fb6m3+mqmAVlF/4+bgXf244Cwtapza7lq1aulLtgHcHwYj z1wHUpQ70UbpvfSYjG0WKWEhBjIEqY56ErN6aFXuLO7fw1v4zXk+7aNXXcKpWm9U4K9aueAnJy30 /euV+/UQO/XAKPXEbBX4qXtqKevUW5aA9Tlq17IhkiljOv0jW1LcyuNrtPACACdFPmfvqcvJ4cEH H9Rdg6h75lSLysyZM9kZ/EhS3UPs7+7HQ+zOnDmjLytWraiOWoYp69RRlkBqR8ALAJwU+ZQvW2o9 pfr8hP8y9zmLlE89LfvGjRuUNYD7jodWAQAA+DGe7EtZAgS8AABOigD4JS6BpSwBAl4AACdFAAAA BLwAAABILdRD7EBZAgS8AABOigD4HfUQO1CWAAEvAICTIgAAAAJeAAAApAY8xI6yBAIZ/fACKVxM rNH0Ih/gmYqVqsidaCMZAUDKV+R4QFkC3gsOUi8DAS+ApLH2l8uyev8lMgIeelS+XHqMbAAAAIlS t0gW/SLgBZAkahfKLDWezkxGAAC8snXLZqlUmUthKUvAO8Gp/CZYAl4gxR9kDKn+QIPkp57STF+8 AJRtWzdLjeocDyhLIDBxGg0AfoinNAMAABDwAgAA+DWe7EtZAgS8AABOigD4JW5voCwBAl4AACdF AAAABLwAAABILdRD7EBZAgS8AABOigD4HR5iR1kCBLwAAE6KAAAACHgBAACQWvAQO8oSIOAFAHBS BMAv8RA7yhIg4AUAcFIEAABAwAsAAIDUgofYUZYAAS8AgJMiAH6Jh9hRlgABLwCAkyIAAAACXgAA AKQWPMSOsgQIeAEAnBQB8Es8xI6yBAh4AQCcFAEAABDwAgAAILXgIXaUJUDACwDgpAiAX+IhdpQl EMhCyAIA8M+TokC5rPlOtJECB/g70cJCDD5b1u3o2ylyG1NquuCe8JBwMoGAFwAVJvy9jH1V4auT +N5Lj1HYgBNpMxUKmL+Tsc0ifRL0quNn02n1Utz2PZL2yRSZLrhvWadvCHoJeAEkRyBEhenfUvpJ ka8r/G7P5pKQYO7SAezL4/dbGB0TK5PXnPT5cpvVbi4hQaHsQkj8PhobJUvXLiEjCHgBJCcqcvhL hR9kMEiwgfwFAlWsIe4AcOrUKckXmcuHx5ZgCQri4AIf7EvGYMs+mj8yPxlCwAsgWQ6+VOTwkwo/ 1mg0vchfIGADXuO9A8D58+clW7ZsPlmu0bRco5GDC3yzLyXFPgoCXgBU5AiACt/TgHfyqt1y+o/r pk/qBx/T34HpXX8yxA2JS+u96XM+nl66PVeWAgRSQcCr3Lx5UyIiIpK9nty5PdLjY0u5CjyHINDq P1/uoyDgBUBFjgCo8I2maNfowcUKp85cl4kdK7s9/RszNut1AEihx5Yk+vv0tJ705tjyDD88B2T9 BwJeACkw4KUiR0qt8D1t4Y2NiZXwsGBPVpCgBSm55csW95Cvo+dvez2ft8vwRTqApJRUf59GY6zp 5f6vad4cW9Q6YF/zfK/q9yVHZ/lB/Uc5E/ACSP6D791/SVqRCwFvoiv8vB3iKvxfZ/vFPpdUgbSn DTyePtXZ9oS6wMNp5JHHcsjnG3bLg5kyW4Zv3bhWurR5UaKjouTw37dSzIm99Xzq89Url2X88IHy 9YpP5fbtW1KsVFnJ8WROGfHhzKRNR2ysVCyYQ65cviRZHsoqWw78JkFBzstC5bVinZ/2hrma1915 bOe15sn6koKnaUpM3nmTrqRej7NjQFIds2I9PG55fGyxWX7LfK/p9yDTckLDQiWyeC558Y2GkrdE nnjjbS06OlOPy/JoZhmxYqCkezCdZdzPW3+RcZ0mS0x0jJ7u88mr5Jcdh+TkgdNiCDLIE/lzyMu9 GsvTZfPLrZu3Zd7QxbLrm7163jK1S0q7wa0kTUS41+NuXL0hn3ywQnZ8tUeibkeZtim3ZMvxkLz+ v3Ze5ZE7XOWjO/OqvErp9R8IeAG4OEHw9CTB04rcdvkt8r0qD+nK+L0ElfHYTpN0Zbz46CxdOS79 4HObyjGrdDJVji3u/uJr5un05nmWT15pqvAPmyr8U6YKP8hU4T8uzXq9YKnw58aruEtI+8GtLZW6 N+OcpTGpTujM236vws8tTdys8M3zLvbhL+sppYU3bl82eLwOW2f/PCO9OraSWZ9+rb/v3LJRurVt qoPdxASFyRHw9ni1hezetklmLl0l5avW9Hj53qZj23frdLCrXLp4QXZs3hBv/e6u8+C5/9xKh73p PE27eRnJkT9JlSZv8i6x+1lSrie58lv/bOtxPenZscXR8ucfnCaXzl6WCd0+kmGtx8q7C/tIZLFc svDIDD2+Vf6O+t383bycS39dlkk9Z0i/OT31d1XPje8yRdev5ukO7/1VilUtIu8u6CNHvj8mw03L H991qszY+6Esn/ilbPl8h7wx4XUJCQmW8d2mSoYs6aVF36Zej/vwzelyaNcR6Tu7hxSu8LTHdUJi 6g5H+ZjcdRYBLwEvgPvCs0u1vKvIE17Cc9FUGU/sOV3esaqMP+gy2aoyjjVVjtPkoKly7JegcoyV RabKveXdil59djW92aK7JwXm4UdMFX7xqoVl4ILeusJXFeEHXafITFOF/9nEL0wV93bpbqq4g60q 7pamitvbce6k0dO8dNeCuxX+eFOFP7T1GBnoUYUf69N9LklOSj28h1cJ9fTHGzsRdenylXWQO2XM MClj+ty1TRMpWqqs7N2xJd48lQrlkH9v3DCdlMfKU7nySM/+w6RKrbpS6NG4+5eHjp8mQ/u+KT+c viI/f79bxgx+Rw7t/1GPy5I1m6zbe9SyTrWu+TMnSZAhSN793wSp1+glp+uwl371ee/OrXeX/3CC bVOtv0P7dZfvvo0L5KvVqS+DRk+WDBkfTLA8T7ZNWbV8qX7PFZlfTh47Il8uWyzlKtfQw65fuyrj h78rX33+iel4EC3Fy5ST2Z9+Y1lnwexp444Zf920LH/NzoPybLmCEp4mrew6elbnS4VCj8uN69fl q20/S4OKRS3z2C7Hdt7g4BCpUuRJHZCrcY7K3rxu8zJtvztKq73p7eXTrf9u6tb3Lz5dqFvf8xYo JO+NnCiFipV0mCZX+42j9Jg/d+szUOZO/1DCwsJlwPDx8tvJY7Jw9lT9403/4R9IgxeaubWfOVqP s33KPF2XtwbY3be9/fu8Xz8Mhybyh2Hr4ZmzZ5K27zWX918eKcsmfCHvfNzT5fwFyuTV9apqxVWf Vf2ax3S8P7znV8v01st5Mn8O/Z6z0FN63O413+vvJaoXsUyjhjV/u4nX41R9q2Q01Ye26b1x9V/5 ePAi+WHjz3fnLyrtB7eSBzJEJNjGLuXfkv/+vaXLO/tTD8vLbzU21eNFpHWB1/X414a31cv6+Ocp buWjo+WZmX9UWHB4ulvT348ffEHAC8BZ6GE6+BqSuCK396u7uTJefrcyHmdTGcfe/fVZUcGis1/u zePcmd52eL+7FZ4a+oRVhR9rVeEXd1KpezrOWRrdqfBjE1Hhq3kzWVX4n3pQ4bf0YYUfm1JaeE3T h3rw402sg3t4R0+dJ42rl5Kp44bLPFOwkDYiQkZO+VhqFM8db3s3H/hdv//x2ympV76QDOrTRTb+ eNKynIE9O1mmf6PdS3L54gVZ//0xyZb90QT51qhZG2nwYnO9nNHv95Vnn2/q1jpsWzhLPVNR9mzf LC/UKCNZH35EatRtKJ179ZdMWR6SEe/2km9XfS6zlq2WqNu3pXOrRhIcEiIjJ3+cYHmebFtU1B1Z 8+VnEhQcLMMmzJAW9SvLWtN6Bo+bKqGhYaaArZ+sWDpf3hk2Tpq0ai9j3u8XL937//w3wbY8+sRT uoV4x6b1OphOlz6DDnbVsCdy5rG7/eblKNbzZn/0cR3s1qzfSC/XNsC1ndd2n3CVVmfTW+fTyEFv y2cL58iK776X8PBwna/933xVvtj0g8M0udpvXKWn4UutpF7jl/W63nq9law1BcsNmrSQ2qXz6f1M jXOnvB2tx519ytG+fT+PLR4HvF4cW5wFvLpeKvikfj/+0wm709oO6zLuVenfcIgOeNM8kEbC04ZL 5zEdpHuVvnanV/XFIzkflm7jX9Pjrl64poerH20tP4KZhiVmXP5SkXJo91Hp32ioZMqWUUrWLC6N u9SX9JnTy/zhS2WPqZ5UdVJUVLSM7ThJL6Pz6PYJtnHK9rH6/fyZi9K7zrsye+ACmbRltGW6mQPm JdhGZ/noannzD03zaP0EvAS8AFKY+1WRu1MZO6sc7VUg7kxvDgitKzEqfP+o8D0NeFWrdbAHfVDH 3SOccAWZHsoq74+dKj3av6xbvSbO/Vi3mFqn69qVyzLtgxGya9t38teZuGDh4vm/4y1vgylgUC1y aliatHGBzOvNG0qp8pWkRfsu8mSue4Fbtkces3w2L8edddh+HjtjkXw85QPZtO5rOXXsqCydO123 Ds5fuVHWf/2lnq5UuUr6nltFDYudZEzUtn2z4lOJjo6SspWqydNFS0iZClV00K2GP9e0pXz9+Sd6 nqZtXtP39fYzBb6uLkVWw1q+2lUHrSuXLZJ0GTLq4e269HK6/WZqOjXv8sUfS848+e4O6xlvmp/+ uOFyOa7GuZreOp9WfrpID2tc7V6L7umTx52mydV+4yo91vuV7XcVSHuzn1kPc2efsrdv3/+A17Mr obw5tji6isY8PP6xPNbhdGYZMqeTDkNbyYRu0+TWv7dM9WtnyfhQ+gTTq1twpvWZI7/+cEIGL+sn EenTxFuW7XITM67bhNfk61lr5cfv9stfJ8/J+sWb5NTB3+S9xX1k79q4H3Lyl4m0tNSrYZ1GvRJv GeqH4S+mfi0Hdx6Ri3/F3RZx7eL1eOubuGWkZMySwW56bPPRneVZf3Znelf7Bgh4ASR3wOvhQ6u8 qsjtLF+1cHYY1lomdP3obmXcRTJmzRAvXd0+7GiqHL+VHzfaVI5L3k6wDYo708879FGC+SwV/vfH TRX+OxKRIW28NNumPzHjnKXRUuGXzRu/wh/dLt4ydIU7RVW4h+NXuFbrm7h1VFyFbyc9tsPcWZ71 Z3emd7XPJc1JafwusdzR8P2VojrUMtr8b3av8y3TZ4PB7vLVsCq16suPZ27EG2b9efywd+WLT+bL 6I8WSNU69aVMrswJpsv8UDbL92mLV8mCGRPlh93b5ZO5M2TzutWyetdhu8v3ZB22n9NneFC6vzNE v86cPikNKxXRAa/tdAZD3FUd6j73xG7bV5/HXc68e+t3Uuzxe/fwq+ENmrR0WZ6OhpWvUkueyh0p +3Zu1a3H6nOpcpWdbr+Zmk5N//O+3XL8yEF9SfrTRUo6nN5ZelzN42x663wy23fqqm4FdWf5nu43 rpbni/3M0Xqc7VOuytvZMSAl1JNeHVscLN88/MQvp/V7gbL57E5rr74pVq2wzD04xWH9dPrg7/JR n4/l6vmr8tb0rvLQ41ks02R6+EG5+Oclibr7LALzMDXe23Gqbm3a63n9+vv3C9K37vs6DQnqmrsX kQUFGRKMWzruc9n6+U7p+kEHfRXVq8XeTLBt6vzCUd1sm4/uLM/6szvT34/6DwS8AFwFpMYkrsgd XH5VrKqqjKfGG2b9OSK9qXLs2Ui/4irHQXGVo83yzN/dmd523rgKf87dCr9bXIVvtKnw79hU3Eaj 1+PcTqPBqsK3Gbd0rKpwd5gq3FfvVrjdE2xbhszpHebTiQNWFb6by7P+7M70rva5pKAvOfbwHt5F /eq5PW2rUav1Ouyt11W6/rke1+pfvEx5+evMGbvzWn9+7Imc0m/YeLl88bzUKplbjLGxDqf1dh22 yzj315/6veQzFfW4mvUbyzcrlsq2jWstT1CuVOPZBMvwZL1XLl3U9zaHhITKd/vPSMQDD8i/N/6R 6kWf1MNVi555vepy3hdatJOPxg2Vrn0G6fs81f29f575TR557Am7ZfBiyw4ybkg/iY2JkZfavm43 nxwtp9krnWXkwF66lb5Nxx5257WmWvEvXfhbzpw+pR8I52odzqa397luo5fky08XyJwpH0i7rm/Z fYq1bZoc7Tfu5J2j/cqT/czVetzdpzz5+/J2Wo+OLV7c+uPxscXJbThXTPXTgqGfSFh4qDzftZ7H Vw44Wu6QZmMkLG2YvDmlk+QpkTvePCVrFZNv526Qfet/1vW4UqJGUT2Nt+OsXf477n7+fKXz6HGl axeXnV/tlZ+3HtT1nlK0SqEEVw7c/CfuIWiRJfPoZ4HY215H+WMvH50tT91O9O/1m3LBVJ+rp14r 7qzfVb6DgBdAsge8nj+0ytOK3J3Lr1yNv/L3ZUvl6OwyKlfT2047pNloS4UfWSJXvPGlTBX3GlPF /f36n+JV3Goab8c5S6O5wt+/9RfdRYS5wrdN/3//xD30Jq/phPbS3RZW221zlOdxFf5SXeE3MlX4 rpZnrvAv/nnRUuG7s35PytZ3J6VJfzLhzommvfFtOvWUgz9/r4OQ8lVruTxJa1S5iFw4d1aiY6Kl YNGS8tagUS4vo/V0Hepzi7oV5PdTx+X2rf/0vccNX2ot3foN1uPeen+0KYCIkbdea66nV/dS9n5/ TIJleLLeM7+f0oFcvoJFJY1pfWp42gfSSae33pVTx4/Imd9O6fWGp00rE4b3l1HvvSUly1bU0/UZ PFYm/m+gNCgf94CpbUf+TrD8hi+3lSMH4+6Bf/7lNnbzydFyylevIzJQJEfO3FK5dn2XAUSPAcNl zKDe0uzZctLqtTdcrsPZ9PY+vz10nKQ3BZCfzJsu08YPl5DgEHkiVx5ZsmbnvaDkyXuXqO49fd3h fuNO3rkTNLkqb1frcXef8jZAiE2qFl4vfhj2Zh32vFqku4SGh5iOt3nk7YFvypMFcrh1D6+r9Opb NGJj9RVWo9tPjDdu9oFJ8nznunLjyg2Z1X++Hlbh+bJ36wyj1+PebzJS/j59Xu7cjjL9jYdJxcbP SJOez+txLd5pon+wmNQ97sGSZeuV0sNsf3it276mnPrlN+lR5R0pXPFphz+Yu5uPzpbXon9TWfbB F9Kn9nu6zvxo3wdurf9+/OAL1wxGch9IUe5EG6X30mMytlmkhIUYkmQdt6NvS9Np9aRh5YYSHOR+ v7qfL3tCFvat61HA+0LT3+MNa1/4DZljqlDtUeMUNX6QTeVY5tkSunJMnymdZTozT6c3z2NvuHnc 7Zu3ZeGIZbJ7ddxDqMrWLSmtTBVgeES41+OcpVEFlouGL5N9pkBZB9w1i+kKP92DD8TLF1XZTu01 Wy6dvaIr3APbDiXYHuv8NQ9TLSrmCl+dgOQsFHcvr7Pl7fp6n3xqqvCvnr+mK/xppgrf2fTuiDGd 8K7cslJG15kimTNmlnTp0klERESi/16alcsuIR50ET30400er2tgu6ocoPzUhGH9ZcnsKdJnyDhp 0vpVMiQVUg/5X7rznHQtGyKZMqZL9LHFXE82qNTAo3ryi8+e8nhdjZqcpgADgKr/vtr6lc/qv/t5 juqrvzMCXoCAN8kD3ucqP+dRRb5i2ZMer6tx098oVMSr8FdtWeXzgPflsg97FPAOm7fZ4x9v3m1b hQL0Q3+f/VOa1S4tEQ+kly+2HtBPi0bqDHg/2f23zwPe+hXre1RPfrk8p8fHludfPEUBBkj99/W2 rwl47wMuaQYCmDeXanlakfObGmz3uaTgcbdEXq4D/idr9kdlw/4/KedUzh8vaQb1Hwh4AfjgBN5w H+57RGDvcykh4M2TK4v+QcaT6dmXgcALeGP1QxjdX7hXxxY5QQEGwj7KU5oJeAEkP09/ufamIjca T5LRiLfPpYSA94XyBUTKp4xgHUDKDXid9ZNrT76iu0wvT9ZwTDi0UP+BgBdAkh18PXtKc/6iu00v T9ZwnIocCfa5JDspjSV/gcA9tiRlwEtFBgJeAl4AqfPgK3SEjuTf55LqRCKWXRkg4CXgBQEvCHgB UJHD3yr86NhYU8BrIIOBABWTRFd4qL6DqSfhm32Uy5AIeAEkOypy+EuFHxUdLGt/uUQGA/Cpf678 K3uO7SIjAAJeAKldxpAHAAAgAElEQVTRqT9Pyok/eKgUUr8aT2eS2oUykxFAgFL9g/b/7LjPl1sy dxkpnaccGYxEi46JlulrJ5ERBLwAklPtQg0kqHAwGYFUX+EHBxkkLIRLmgH4VpAhWEKCOV0GCHgB UJEDAAAAKe18lywAAAAAABDwAgAAAABAwAsAAAAAAAEvAAAAAAAEvAAAAAAAEPACAAAAAAh4AQAA AADwF3TACQBI9WJijXInmnwAAtWdaGOSLDfWGCPRMeQvEi86hkqKgBdAsqMih79U+OsPXpZ1phcA +NL3J/bInmO7yAiAgBcAFTlw/1TLn1FqPv0gGQEEKNXCO3DFKZ8vt2TuMlI6TzkyGImmfvCdvnYS GUHACyA5UZHDXyp8gxglhKdSAAErNihpLmkOMgRLSDCnywABL4BUiYoc/sJoNJpe5AMQyMcAACDg BQD4pdjYWNPLQEYAAXsMIOAFQMALAPBTtPACHAMAgIAXAOCXaOEFAv0YQMALgIAXAOCnaOEFOAYA AAEvAMBvT3Zp4QEC+RhAHgAg4AUA+HHAywkvQMALAAS8AAC/c+LUaQnmFl4gYEXHqv/T+ny56a9k ktCgMDIYiXYn5jaZQMALAIB3Fh1OSyYA8LmRu94jEwACXgCpFb9cI7n5+hfusBCDjG0WKfv37ydz AUhocHqfLCc8JFyWdfrGb48t6jaQ478elzx584jBwOUxySksmPMuAl4AyYZfruEXJw+moLdUiaJy 8uRJMgOAz6igt3SJ0n55bImOjpZjvx6TUiVLSUgI4QAIeAH4YSXuz79cu4tfuO9jkJoEv3DnypVL zp8/T+YC4NjiRsCrpEuXjoAXBLwA/Dfo9ddfrj2p8PmF279ky5ZNbt68SUYA4NjiRFRUlH5/4IEH JDQ0lAIGAS8A/xXIrWL8wu2fIiIiyAQAHFvcCHjVNhHwgoAXgN8L1FYxfuEGAAAg4AUQAAKxVYxf uAEAAPxbEFkAAAAAACDgBQAAAACAgBcAAAAAAAJeAAAAAAAIeAEAAAAAIOAFAAAAABDwAgAAAABA wAsAAAAAAAEvAAAAAAAEvAAAAAAAEPACAAAAAEDACwAAAAAg4AUAAAAAgIAXAAAAAAACXgAAAAAA CHgBAAAAACDgBQAAAACAgBcAAAAAQMALAAAAAAABLwAAAAAABLwAAAAAACSNELIASJliY2NNLwMZ kcR5fC+vY8kQAAD1H+BwvzES8ALwHaPRaHqRD0mdx/fymswGAFD/Aa72GwJeAD5BC2/y5PG9vOYX bgAA9R/geL8h4AXgQ7TwJk8e38trMhsAQP0HuNpvCHgBEPBS4QMAQP0HAl4CXgCOcElz8uTxvbzm ki4AAPUf4Hi/IeAF4EO08CZPHt/LazIbAED9B7jabwh4ARDwUuEDAED9BwJeAl4Azg4qqfXSkdRY 4XNJFwCA+g9wtt+kznQT8AIpuDLiR9fkq/D5hRsAQP0HEPACIOClwgcAgPoPBLwEvAC8d+LUaQnm Ic1JKiYmRr8fP/mbBAcHkyEAAOo/wIFoffV7WgJeAL6x6HBaMiGJGYzRktn0vvhIGjEaOBwCAKj/ AH/DHg6kMGEhBhnbLFL2799PZiQx9Qv3mpMiLfLf4hduAAD1H+CG0OD0BLwAEh/0lipRVM6fP09m JKHo6Gj9nuOx7BISwuEQAED9B/gb9nAgBcuWLZvcvHmTjEgiUVFR+v2BBx6Q0NBQMgQAQP0HEPAC SE4RERFkQhJX+CqPqfABANR/gP8JIgsAAAAAAAS8AAAAAAAQ8AIAAAAAQMALAAAAAAABLwAAAAAA BLwAAAAAAAJeAAAAAAAIeAEAAAAAIOAFAAAAAICAFwAAAAAAAl4AAAAAAAh4AQAAAAAEvAAAAAAA EPACAAAAAEDACwAAAAAAAS8AAAAAAAS8AAAAAAAQ8AIAAAAACHgBAAAAACDgBQAAAACAgBcAAAAA gKQR4s5Ed6KN5BQAvxN199imjnFGQ+Ad58JCDOwEAAAgsANedSLYe+kxcgqA3zEYoyWz6b3/Z8dN AW9IwG3/2GaRBL0AACCwA16zrjUelZAgTowA+I/o6ChZelKkS3XT8S0kNHC2O9YoUzb8xQ4AAAAI eC0TmoLd0GACXgD+w2CMO6apY1sIxzcAAAC/w0OrAAAAAAAEvAAAAAAAEPACAAAAAEDACwAAAAAA AS8AAAAAAAS8AAAAAAACXgAAAAAACHgBAAAAAEjJQtyd8FZUrETFGMgxAH4jJjpWv/93J1aCY2MD ZrujY4z6/U60kZ0AAAJQVPS9esBooC5A6hAW4l0s6jLgNZ8QTd90llwG4FcMxmjJbHqfsfmsqcIP Cbjt7//ZcXYCAAjg+k/VA4FY/yF1Gtss0qug1+Uebl7o4Ma5JDSYK6AB+I+oqCiZ+IHI+41yS2ho aMBtv7e/lAIAUn/9N3a0yIgmeQKy/kMq219jYuWdZSe8nt/tn3QeCA/m5AiAfx1A7/6Ily5NkKnC 5wc9AEBgMBjjzunVuX0o5/dI8eLO0U6dOiX5InN5OTcAAAAAACnY+fPnCXgBAAAAAP7p5s2bBLwA AAAAAPBYNgBAohkMcfeAGY2J695i79690qFDBzly5IiEhIR4/CsufFcWpB8AQMALAHAoZ+d5Dsed +qhtQAZgroKZbt26yYEDB2Tfvn1SsmRJAjR4LDY2Vh5++GG5ePGiZMuWTc6ePStBQUEel6G75erJ /g0AIOAFAL9iL7B1Fgj7I09O/r///nv9nlqCXaQ8a9eu1cGuoh5usn79eqldu3aS7bcEtwBAwAsA ActRcOtJ6++lS5ekc+fOsnLlSv29YcOGMn36dMmUKZOlRWnOnDl6mlu3bukWrVatWsnWrVslS5Ys cu7cObsn5p4uV11e3K9fP5k7d67+XqRIEfnoo4+kdOnS8ZY7b948eeWVV6Rnz57ywQcfeNR6FhMT YxmvxnmaxitXrug0Llq0SKKjo6VChQqyYcMGp2nfuXOn9O7d2xJsq9bB3377LUG5ZM2aVf755x/d gpg3b14ZOXKkNGjQwO42mT8PGTJExo0bJ+Hh4TJ58mT59ddfZeLEiboPzEmTJknLli2dbqOjtJmX r/J46tSp8thjj8ny5culWLFilvQMHjxYxo8fr1s3p0yZIs2bN3eaD56WuTv55mgaZ9vsbr7bS6+i yl4pUKCAHD58WBYsWGAJeB3tH87K8MSJE5I7d25JmzatXL16VefnQw89JNeuXZOjR49Kvnz5HO7f tvOqy/TNrc9qXK5cuThIAgABLwCkXl+818ij6RsN+SLBsO7du8uyZctk48aNcvv2balbt66EhoZa TuyV9u3bWz736tVLT6sCCDVcTWuPp8tVwdWMGTPk4MGDkiZNGn0i37ZtWzl06JBlmp9++kkHH889 95wOdh1x1SpmHu9pGlVwpQIgFUx27NhR54WrtDdq1Ei3BP7xxx86cHTkwoUL+v3kyZN6/tdee03/ uOBsm1Tgr4JaNf1LL70kv//+u7Rp00aeeOIJnTY1ztk2ukpb37595dlnn5U6derIm2++KZs3b46X L61bt9brVutSAa87ZehumbuTb46mcadc3c136/TeuXNHPvnkEwkODtY/vJQpU0avZ/bs2RIWFuZw /3BWhiooVfn77bff6vRlzJhRB7tqmArAXe3f1vPmyJFDB7svvvgiwS4AEPACgH/Ilj5jouZXLXdK tWrVdCuXeZh1cKBacVXLkbJixQr9/uqrrzq9d9HT5aoAQilYsKBlvGqxtPb888/rIGDx4sU+yTtP 02ge3qVLF73tqlXVVdojIiIsgUnVqlV1MGYbyKgWSdViqi6PPX36tGW9rqgAx9F3c1+CzrbRVdrU dptbL9UDvxyty5xWd8rQ3TJ3J98cTeNOubqb79bpXbJkiW49r1mzpm6Frl69ug6q1XAVqDvaP1zp 0aOHDlrnz5+vA17zjw3uUNOpeWfNmiX58+f3aF4AAAEvAAQUcwBrG8iaT/jtTevr5aqAQl2aaY9q jVOXkpoDnfu17Y7YS/u6det0a/SWLVv0pb+rVq1KcGnu22+/rVsGP/30Ux3Uq0uUk6N83UmbOWD0 hLMydLfM3UmbO9M4Kld38906vQsXLtTvKkA2X1ZsHq4CXm+pVnR16fKmTZt067H6rIJ1d6jp1PQ7 duzQD2QrX758gtsAAABJeO5EFgBA0goPdv9lT9OmTfX7mjVr9AN5FOt7R22VK1fOctJvfemnooIA cyDg6XLV5bfKqFGjHAZZqmVXtWY5Ci7M92haBz3WaUrstpunV5fhqjQOGDDAZdrz5Mmj74M138tp Hm+dLnUJq1K5cmV9WbKrbfKEs210lDbrYNfcatm4cWOX63KnDN2d3p18czSNu+XqLN/t/djy3Xff 6Uuj1T2/6rJiNb/6rob//fffDvcPd8qwU6dO+l3dZ66eJu7u/q2olm1FpatPnz4cFAEgGdHCCwBJ LNiQuPnVQ47Uybm6H1Jp0aKFHuaIaklr1qyZbpVSDwPSQbedljFvlqtO6NVloIMGDdItfury1P37 91umUcsaM2aMPqlX92yqBwxZU/dOqla7p556Sj/Ix1U/u56mUY1TrcvqXk0VlFSpUsVl2tV9oX/+ +ad+iJG653PChAkJlqsuQVWXDGfPnl3fb5qYbfJkG12lrUSJEvp+WnUJr710e1OG7k7vTr45msbd cnWW77bUPb4qQC9evLikS5dOD8uQIYMMHTpU55Ea72j/cKcM1S0CP/74o+WzPY6WU79+fenatatE RkZathkAkDwMRhdPDrkTbZTeS4/J2GaREhZiIMcA+A11meaIESOkf//+Dh/slBjqSczqoVW5s7h/ D2/hN+f5tI9e9aRm1Tqm7p9UrVzwk8qb/l5TFfVwLPXEbPUDgrp/GPD3+g/wJXM82rVsiGTKmE7/ qOnJrVO08AJAErL31OXk8OCDD+puWtR9keqBQTNnzqQw/EhS3UMM3ztz5oy+hFq1UjtqGQYAJB0C XgBIIr5sqfWU6vMT/svc5yxSPvW07Bs3bpARAHCf8NAqAAAAAAABLwAAAAAABLwAAAAAABDwAgAA AABAwAsAAAAAAAEvAAAAAICAFwAAAAAAv+FVP7wxsUbTi8wDkLpFRRv1+x3Tu9FgJEMAANR/QAoR HKRehvsT8K795bKs3n+JUgBwX93574Ys6FNHOkze7tX8BmO0ZDa99//suKnCDyFDAQABgfoPqUHd Iln0674EvLULZZYaT2emFADcV+GhcXdljG0W6dX8UVFRMna0yIgmeSQ0NJQMBQAEBOo/pAbBPrr5 NsS7lRt8lgAA8JbRaBSDwSBhId5d7mIwxs2n5g8NMZChAICAQP2HQELYCgAAAAAg4AUAAAAAgIAX AAAAAAACXgAAAAAACHgBQFMPrLJ+BwAAAKzR8RaAVEs9pRkAAABwhBZeAAAAAAABLwAAAAAABLwA AAAAABDwAkDS27p1q1y9etXpNGq8mg4AAAAEvACQargTzLoTFAMAAICAFwBSlEqVKsmBAwccBrRq uBqvpgMAAAABLwCkGg8++KAULlzYYSuvGq7Gq+kAAABAwAsAqYqjVl5adwEAAAh4ASBVc9TKS+su AAAAAS8ApHrmVt5r167p7+qd1l0AAAACXgBI9cytvNu3b9ff1TutuwAAAAS8AOAXVGvuwYMH9Wf1 TusuAACA/wkhCwD/cjv6NpnghrTp0kr+AvnlwP4D+l19J+9Sp/CQcDIBAAAQ8AKBEOw2nVaPjHA3 UIpNK5FBhWTJmTkyd9oUMiSVWtbpG4JeAABAwAsEima1m0tIUCgZ4aZiUoBMSIWiY6Nk6dolZAQA ACDgBQJJsCFEgoO4RR/+zWiMq8JOnTol+SPzkyEAAICAFwiIQODuP8Df93Oz8+fPS7Zs2cgUAABA wAv4fSBgNOoX4O/7ubWbN29KREQEGQMAACy45hEAAAAA4Jdo4QX8EC28ye+nLQdk6tuz5L9/b8mC A9PJkGTazwEAAJyhhRfwx0CAfw7/vRzZLlHjHf1bNPpTeeujN2T+gWnkcjL+AwAAcIYWXsAvI15j 3AsJLP11tuu88SLv/jxxVvIWy0W+J/d+DgAA4AQtvIA/xgG89OvO7SiZPmCe5V0Na5a3g+V92cQv pV3xrtKxXE/ZuXqfpb1QjTNPd+3yPzKq44fSutDr0r1GPznyw3HL/Deu3ZRuVfvo7zHRMfHmU68u lXtLy6c76uVvXLZVD7t49rIMaDJMDx/SerTTdVinXb1TpglfAAAAztDCC/hjwMs9vHL+zAWZ3Hum lKhaVAeWNV6qYskT83vWxx+SWfsmyfGfT8r4blOl7LMl9fDFR2dZpps/fInUalFN+kzvLicOnJZp fWfLmG+G6vHHfj4hE78bbVme9XzK5M1jLGnp+9z7UrVJRZkzeKGUqFZUhi4b4HId+Urm0WkvXqWI DGszRjqP6iAPP0nXO9b7OQAAAAEvEHihQMDf3zjUFCC27NtUFgz/RFoPeNkUzJay5In5vVLjcvo9 T/FccvXitQTjlT3ffi/bV+22fA8KDrKML1KpYLxprT9fv/yPfD55lRzYdlAu/31F7tyK0uPV915T urq1DpW+4NBgWTxqmbQwbYvapkmbR7N7W+U4AAAAAS8QiIFAgLd+vbewj0x5a5bUaFZFvpmzTqLv REuF58rezR5j/HdLttkfvvDQdDEEGVxOZ/196djl8tTTT0iTNxrKAxkipNXTr8efz2ZWe+vY9uUu 2bB0s1R/qZKsW7hR3p3/FvetEvACAAAPcA8v4KdhQKC/sjyaRfqbAsSLZy/JANP7oT1HLeGR0U4+ mb+HhoXo+2zNw0tULyrrlmyS2Fij/HP1X5n93kKn85tfN//5TwpXeFoiTMHu7rU/WMZHlsgjq2Z/ G29aR+s4vO9XvQ1Xzl/T79meyEbZCvfwAgAA99HCC/hjwMs9vFpwSLB0GNJaf1bvtvfw2uaR+v72 rDflgy5T5Nzp8zLrh4nSdmBzmTt0iSwatUxP0+LtJk7nN2vwah35X7vxcu3SdanXrpZlvErHxDen yWcffimRxXLJgAW9Ha7DnPZ2g1vaXR/7OfkBAACcMxhdnDHciTZK76XHZGyzSAkLMZBjQAp2O/q2 NJ1WT16o/qIEBwWTIfBrMbEx8vnG5TK6zhTJnDGzpEuXTiIiIsgYAHAhKipKRowYIf3795fQ0FAy BCmaOR7tWjZEMmVM53F9Twsv4I/ohxeBsp8DAAAQ8AIBFgeIZ/c3/rinsJz+47rpk0HinjVsiPtk iBtiG1vkfDy9FC9zgIzGfd/PAQAACHiBQAsEPLyH99SZ6zKxY2W3p39jxmYpVppwA/d/PwcAACDg BQIvFBBP2r9iY2IlPCzunt9dR36XZ/I/Ic+884ll/K7/vWwZHjeD756R+0rBrvp97sEpFJufSfqy JeAFAAAEvEDghbtePKU5JDhIth08LT3m7ZR9o58yvZpLqbeX6HdFDZ/QVqRiwacs67B24+q/svzD lbLrm+8l6naU5CmWU7I+/pB0GNbK7TR7ta2m4Lt7lXfkxpUbkiFzepmwaUT8/mztaFeom37/+JfJ Toe5mtfdeWznDQoKktDwEMltyqNGXepLZPFcbs/r6boU9bRqVR4t+zeVHHkf8+m+5k66kqollhZe AABAwAsEYsArnrd9hQQbdFD707gWCYYranixtxab3nNa1mFt6luz5fDuX+WtGV2lYPkC8dLibpq9 cWDHYR3sKtcv/yMHdx2Jt3531znnbsDmKh32pvM07bP2T5TL567I5O4zZVS7D6XfvB6Su2jOJMkn ld6Dpjwa13GKTO01R0Z8NdCn+5o7+WZMwv0cAACAgBcItIDXixbe0OAgmdC2nA5qD05oFW+4UrDH Qj3e/N12+Uf3HtfvGbKkt9v6u2DoJ/LTprgHXRWrWljaDmomERki4qVZebPyO3Lr31u65fbhp7JJ kx4NpWiVQtKh8Bt6fLuhLfWypn8/Xn/f9dVe/f5Iruxy9uQ52bFyjzxdLr8edvP6Tfls/ErZ9fVe iYmJ1S2pvWe9YVln+7utk7MPTLIsf+TqQdKv7mAJCw+VSTvHSFCQQbpX7Cf/3fhPB4v9Gwy1zGO7 HNt5g0151aNqfx2Qq3HW25rp4Qel5YCmMrzlOPl84ipLuhxtv700uzO9Wpc5Py7+dSmuf187eemo jP415aG7+fGPaTtn918gh3YdlcfzPhovDXdu3dFlsf3LXRJ1O1qPbz3wZclZ6EmHZevOfg4AAEDA CwReyCsetX+ZAofQYIPUK5XnbpBrkLxvLLAEur9Oaq2DXfP4WDv38OYrnUe38A56caQ8mC2jlKhR RBp2rivpM6WTxf/7TPat/VH6zH5Dou9Ey/jOH+nLbDuOamOTZpEPt4zQ7xf+uKQDrXnvL5EPvhtm merjgYss00dHxcieNT/oS4Q7DG8lw5qPlb2m9bwypIWEhAbLJ2NXyLYVu6Rl/yZSuUkF+WTM5/HS PfvAxHjrVrI+nkUKVSggv2w/bAqU90hEurQ6uFPDHn4ya4L0xl+OxJs3c/ZMOtgtWauYXq7tvDkL xd0TfeKnU25vv22aXU2vpvvpu7ggNn/pyHjpts5LZ2Xkbn4sGblcDmw7JG1MgXKlxs/Ia8V6WMYt HbVcNn+2Q4Z+0V9CTcGzSuvsAQtk2JcD7KbH/f0cAACAgBcIrHDXwxZeozFWgu/e9/pcmci4IGxK m3jTmIc7Wn7nce1k9ZwNuoXw7Mm/ZeOSrXL64Bnpv7CnfL/+J0tQbIyNm08NMxpbx1umamVcNW2N biG89OdlPfzaxevx1qWCOXMr8u5v9klMdIw8/Uw+eapgDslfJq8c2fOrHl6+YRlTgLZPz1P1pYr6 vt4W7zSJtyx7eaSG1WxVRQd4qrU4rSnAU55tV8PhvNaf1XRq3i3Ld8ojOR/Ww+o6mNd2mDvbb/3Z nek7FO4uIWEhUqp2Md2i7CgvnZWRu/lhXoYKdq3vo1bjtpvmVQY2GmEZ/vfpCw7T4+5+DgAAQMAL BFrAK563fTV8f6WoXneNNv+b3euh1/TZYEiw/LQZIuSFHs/p1/nfL8qABkPlt0NnEt7rejcQsl2G +qwueVUtsq+PbSfFqhWWziV73ZvvrvQqILr7eedXcQGtCvheLfKmZRo1vJwp4I23Xgf5ZG9YwQoF JPtT2fRl2qr1WH3OZwr4Hd23a/1ZTaemV622fx77Sz+Y6slCT9id/tTB3/V7/rJ53d5+68/uTD9z /4cOt9k6L52Vkaf5oeZ3NG76j+Ml6O5l8e6kx9V+DgAAQMALBFzEa4x7eWBRv3puT9tq1Gqny7/6 9xX9nrdUbj1dqVrFdGvrL9sOWVr+ilYpGH8Zps83//kvbr4SueTyX5fjb4/NZ3W/6NE9x/Rltx9u HSHhEeHy37+3pGflAXr49YvXLevd8tl2qfxiefliyjfS+I36+t5hdX/vpb8uSZZHMifMO5MqTdUl 0CskNjZWqjWrlHB7Td8dLadGi8qyaMRncuvf21KnbXW78149f00WDV+mL+9t2PlZl9tvb12e5JfD /eQuV2XkTn6oJ0EfMeX94Z1H5dE82eONe6ZeSdn2xW5ZPXu91OtQ0/6TtD1tsaWFFwAAEPACARjv 3v2X1OuwNuSlMfoSVdUlUXjaMKnQqKy80KOBnq5Zv8YSa4yVKT1m62nL1Cshzfu+EG8Z6vOz7avL 6YO/y1vVB0qhigXsrsv8+cIfF6Vs/ZKSI/9jEhYRpoeneSD8/+ydB1wUV9fGn92lCEGJYE0sATVW VFQsICqIIjbsLWo0drDEgjWKaESxxYbdqGgsr4kVRXoUBOy9RKNEY/sAEVCRsuXbe8musAV2FQty /v7Gmblz65mZZZ89t8Dd0413qWbXWblGJobYt+wQdvn+wQU4i9d/eg/88csRTO8wjwtO/zOL1fJv 2aMZHtx6+N9xc7X25pdPvVZ1AF+gXJWyaOhcTy3tKNtJfFmiGo2rweunsahSp1KB7ddUlj72Kuge FnSPdLHHgJm9sFaefvmotahrXytv/eXXTEqZIGLPSRxeGwShgZCPAfb+fapOddXlGSQIgiAIglBF ICtgEFSWWIYpe+5gab8aMDIQkMUI4hMmU5yJ3us7ws3BDSKhSOd0R/ZX07usLj3uksG1sG/pIYTt PCEXeT3Rpo8DGeQ9IZFKEHQqCItd/WFhbgEzMzOYmpqSYQiCIAogOzsbvr6+mDlzJgwNDckgxCeN Qo96NjNAaXMzvf/ek4eXID5D3mYM785pbjrHZV2aybemmedPUxD1RyxKlSkJh+7NyU7v+TknCIIg CIIgwUsQxU4JyN7/+EYaP6mR0uXNsTJmIdnpQz3nBEEQBEEQJHgJopjpAOg3vrG6tWXORFR6xJfh Dhma+OjPOUEQBEEQBAlegihuQkDPdXi/rR8r3/Qtg+xMfPznnCAIgiAIggQvQRQ3IQDyfhHF4zkn CIIgCIIgwUsQxU4JyMgFSxSP55wgCIIgCIIEL0EUL6QyCWRSEgPE541EKiUj6ElaWhpq1qyJJ0+e kDEIgiAIErwEQRRN4h/F497De2QIgiDyYG5uTkYgCIIgSPASBFG0ca3XGUIbERmC+KwRS8TYELKa DKEHbKIvgUBAhiAIgiBI8BIEUXQRCkQwENHrTRAEQRAEQZDgfe9kijPJ0sQnhbGBMRmBIAiCIAiC IEjwvrvY7b2+I1ma+KTYN/oYiV6CIAiCIAiCIMFbOPRr3x8GQkOyOPFREUuzsSdkNxmCIAiCIAiC IIoBwg9WkEAEoVBAG20fdxPkTOQUHx9Pbz9BEMUOxYRVNHEVQRAEUVz4YB5eNjMk2wjiY5L7GUxI SEC5cuXIKARBFMvPQIIgCIIgwfsRBW/sqRr452Ga/Ij9Ci1PK9/zI0FOSE6eb+JbVSqJFg536I4S en3ZS09Ph7Xxju0AACAASURBVKmpKRmGIAiCIAiCIEjwfjjBG/9vGlaNbKVz/HEbT6A5/XJN6Cl4 CYIgCIIgCIIgwfvuQuO/f7oilUhhbCTSvQCpfvkXF/p/O4zvd9/eQsb47zkkCIIgCIIgCIIEb+EK jbcYw2sgEupdRm6ePUnGtvm7cPvC30h/8RoiAxEqflMeCw95F3r7BtQczve7/tr8XvJ6mfISe5bv R0zgGWRnZqOGbTWUq1wWoxcOfSvb6GxTqQyj7SfixfOXKGVZEuuil0MgFOhdf13tkzteYdr0Xe1A EARBEARBEAQJ3nyQysWGfrNCGoj0iy+TSfOc71y0F+fDL2HYvIFo0bEpTMxKaIxXuMJe+l7yWjlh Pa7H3cL0LT/CxqGO3uW9bb0uR1/nYpeR9uwFrsZcz1O+rmX+dmujTvXQFK9w75e0WLzYUpkEYgl9 wBGfN2KJmIxAEARBEMSnIXilMhkEenrXDPX08EpV8r8cdZ3vq9apAuMvjPNcf5nyClt9fsOFiMv8 vJFzA/zgMxBflDLFoNqjeFiPsV1wfHsYX77h+zkD0KKTHf6+dA+7Fv+O+Ov3eRxzy1JYEbFQme93 tUby/Y6bG/jew34yXr/K4J7SCt+UR9/J3WHbpn6+ZWjK6+bZ2/yYeVlV25lfW1RtU1B9Riz4nue1 9bI/Pz91JI7vv6pWEY/vPkHUoVjUta/Nw16lpWPvsv2IOXIGEokE3zaqjhlbJ2qsvyL/ZSELMLn9 LBiVMMSGMysgEArh0WIS98AvCZoHL7c5eeyXOx/VtCKRCJ4tp3BBzq6Vq1xG72fkc+X83TM4cyeO PuEIQk5KSgqioqLw5ZdfwtHRkQxCEARBECR4Cx+9uzTL4xrq4eGVStXzz8rI4vtv5IJX9VrAgj04 c/w8F2jZ2WIsHbmad3kes/gHZZxWPexh36UZprj+hN8W/Q/NOzbBL55rkZb8AisjF6F0+S+VbVPm e2N9njD/U0v5PuHfJJ7Pltk7sPrk4nzL0JRXrSY1cOP0X5jZbT5KlzNHYxdbdPfohJIWJXVqi671 2TRruzK+WJ5X3LFzEIqEGLnwe8ztswin5eUMmz8IBoYG2OW3Dyf3x2DQrH5w6tNSfv57vrZglK1k CZuWdXA1+oZcTJ+GqZkJF7ssrHzVcmr1zZ0PI3daywqludi1a2/L89Xl+SouXZobV2sKu+ot6BOO KHKUemWhc9xsaTaWRs8rUOhevXoVNjY2fCMIgijuSKVSREdHo02bNtx5QBAkeAtL8Oo5aRXrxioS CvSIrz1/TeFnQy7wfa1m33JvpyJs9OI3Y2JLV/hSeZyalMbzMTIx4ueLR6xE7abfot1AJ+4p1VQW 87we9D+K67E3kfT4WZ588itDU15jV47E0c3BuBhxBY/vPUXYrj+5l3nO7qk6tYXlpUt9VkX5ca81 C4s9ehYSsQR1W9SCVb2qqNOsJhfdLLxlt+Z8PDGjbf9WfFzvoJ/6aq1/7jDXwc5ctEYfiuOCl9Fp mKvWtLmPWTyW9sTv0ahoVSEnbLirzs9WUZ60Kj4+HlZWVjrFFQpEMBDp9nr7bRchNCwQHZy7YsrQ 7CJjj91r9+Jh/CN4LZmUJ3yJ13I4ujmguXMz+oQvghgKDVH6qy/lz3DBPXwyxBk6CV0PDw/u3SUI giCAx48f49q1axg3bhwZgyDBW6iC9y0mreo69zDYqrsylf8VvFmhF7xLsGr+RiWMkPEqA//efoRK Nb7SWi/Fwr5CYd48VPNj516bxyN4ezj+OndHLjpP4GLkVSwL+1ljmj1L9yNqfww8lw+HrXN9DG84 Xi2OpjI0HZuWNEHvid349n8PEjHNzRv/XH+gHj+ftuhSn1IWJZXnMUdO8/312Fv4vs4YZRwW7uDe LG+5Mi221RBWz6EOKliVx60zt7n3mB3XalpDa9tzH7N4LP6di/fk9/Uxqje0xjd1q+j8bBVlD29A QAAqV64MFxcXVKlS5Z3yWhQgQFhYKMICXBAaHoio1RPhONZPLnjd4TLwODq0k4vf77MKre4uVm4I iw/KE8a6wXdv0Jt3a99/ca9evzK/SH2Jo7uDsCl4ndq1sT5j4NFlPBo0rw8TUxP6lC+CMLFrIih4 fewsQd5nNDU1FeHh4SR0CYIg8uHEiRNo3LgxGYIgwVv4glf/Sat+m95R57gD/YLUJjeq27wmzodf lovT27Co8KVy0ioG6wYbG3gWV6KuKWcdbtC6Xr6TJbHzcpUtMeinPnwCpwmtZ/BuISycjZdlY1qT HiXB8qucLnmvX6Tz/beNq+HZfx5V1Xw1laEpr9w8/79kvq9pV53H17Ut+tSHdRW+efo27xrtH7MY xqbGeP0yA+NaTuPhqUmpynIj90WhTS8H7F8diJ4TuuRbf0X+rAv0br8/+PJTLv1b6WWHdt+1xo6f /8d/zHAb6qLXpFbvc8KyD0Hp0qWxbds2VKxYEa6urm8lfJdsEyEs4jAilk6A88A12DZ7ODKyga3e 4+AyaBMilo+H86QVkMi6Y9qQzPfWltORZ9GopS0XvHHhZ+DQXvcu2OGHIuDs7qRR0LIwR7eWiAmJ Q9tuTvQp/xnAeq6wzzbFXpWXL1/i8oXL+Pvvv1GvXj2MHDkS5ubm/Fp2djYZkCCIYg/7vvrs2TMu drOystC8eXMyCkGCt/AFr+y9e9dU8+89yZ2Pad2/KpALJAMjA1SqXhFz9k7FgBm9+JenVeNzZgVm Y2d5WAHe12luPniekAKpWAorm6oYMK0nD/9uZm/8b/lBTGk/B0bGhlh/bjncfnBB/LX7XBizsaea 8tVUhqa8vHstwv/9k4CszGwYmxjBsXtz9JrozuPr2hZ96pPwIBHNOzdB1VqVeDduFl7iC2N0H9cJ j+8+RcK/ibwM5kXfu2Q/dsrtmyPANddfNf9WPVrg/s1/+bGj/FhXOzDqO9bl+/JVy8LW2Uav56qo j+Ht3r07n3SH/cFiHt+vvvpKb48v8+gGLpqA9CwZjvqNhlRmiIzXYlQwMUDQwnF4lSXBET9PdJm+ Vi543TTmkZqcCr/JS3Eh+hKsa1vh7xt3EfL3UR6+2Gu5PPwiylSwxPRlXqjbJOdZY15ehsLTeyo4 Bg3tG/BnNzY8Til4WbzeI3ri4PbDsCxnAZ8Nc1CtjnWe8i/HXUWPoe5a22jXujGO7goiwfsZwHoC pCSl/DccQYDSZb5U6w1wIuIEF72MS5cu8Y0gCILIi4mJCRe69vb2EAqFZBCi2CCQFaAAssQyTNlz B0v71YCRgUDvAjLFmei9viO6tOoCkVD3LosH9lXVXwz0vk93tBiwd8kBhAREYuCs3nDqp9+MqxKp XMydPILFrv6wMLeAmZkZTE1Ni0S7fXx84O39Zg3ppKQk5ThFJnzbtm2Lryp/xd83T7eJ+Y7hbT8o CHu9xwMyCYLPXZLLCDGkUsC1WRO+cNMAH3+E7HDTmp6JXSZGh3kNRUpyCnrbDeBC1vfHxWjr7oRm Tnb468ptHu/X0I1qXZqZyO3RqA9W7FvGJ0ebMmAa9l/4H/fesbjzNnnD3qU577Ycuj9cHm9pnvL7 Nh+IPTE7tK4JzUQS69a84Zg/vTBFDPOXlrD82oJ3aVaIXfbDZWZGJkqUMIbI0EApelPFKfDaMxZz HPxw9/ZdpYfXwcFB6eElCIIgcmCfmyR0iaKIQo96NpN/BzA30/v7+yft4d05zU3nuDldmmX0RHzm JD9NwYl9p2BephRaavAM6/Icfi6UKVOGe3xbt26NyMhI/Pbbbyhfvjy+EJfUmmbJNkMEhx+Gr8dQ pGWIEXvtIhe9m46dx0g3O4gM0uFsWwuPDi3Co8QklCr1JR5knlLL58/AkzhybT8XnKXLlFaGRwVF I+JQpPJcqGVpsatnr/E/ulVr5Him2Szrl09fQcMWDfg5E7uMjn07YM1c9XG6qc9StYpdxR/1h/EP 6YUpwijErlgs4bPCW5a3QGpyGp9I77k8nIleBewPn5OTE1q1aoVz585h48aNfAwv6w1BY3gJgiAI onjzSa/D+zZlEJ83X5Y3x+q4xW99zxXxI8IikJiQyI+rVq2KIUOG8GM2Pvb+/fufZLg2LCws0LNn T/z111/Ys2cPasEWj64+QdWGldXiBocHwneEPG8JkJScKReaYmwPuoQTvj0QdPM5GtesAr/YDVhx Jhi1y5SHq3VDuNaqqf7DgVygaptkKvTusXzFKCM6OAapz9OU3ZwZMaFxSsH75n5J+ThyVcwtzbWO 51SIpUpWleiFKaKwe6sQu+z+W5QtzX8gYSKXiV2F6DUonfcHFebV7dKlCxe6rPfD2rVrSfgSBEEQ BAneD/QFRk8Pb3VrS+611Se+THaP7ihR4HPIcHZx1tilWSE0VfkUwlmXZk08f/4cgYGBuHfvHkqW Kokr0nNobqN59kW3dp0xc3MARvbojdIlRBDIzTG0UyMcv5GC0d0cMC7MFxbGAhzo3gsPX75A7MOH WBn9Lya0dMmTT62GNXF8Xwjc+rrixoWbynDmmT208wjcB3bBi9QX2LJkGyb6joehkSESnySibMWy SsE7edGPPD0j5I8wbF26HR5zRvHz5IRk7jnesngb7NupT2ZVp1FtXq5ifLAq7JpVzW/ogS+isB8y jE2MIXudqRS7DPYji0L0lpBflwjFGtMzcUvClyAIgiCIDyx49ZuluVaD0/JNnxL+Bjl4CV2ew8+F tLQ0HDhwAP/88w9KlSqFvn37wqq6FR/Dq43Jg7PlmytcBu3F0F7dICxRGbKsxxjVzR4rL26DiYEE nWvUxmuJGJYmJvLj6th36xaO3ryBTrXfiEsmYueOno9fZq3iY3YNDHI+SsbP98Sq2f7YsGAzPx81 azjf+wUswOwRPnzd3JX7liHhcQJsmtZT5mdjVw+JT5P4uF/GT8Pn4t7NeHmcuvhp9Qy1dtSXpz39 51mtgvfsifOwa92EHvgijFkpM3xh9oWaF5+JXsuyFjycjeHND1Xhy8a7s2OCIAiCIEjwFr7QAP6b ZZMgPqLg/QzakMejW7IkunXrhgYNcn4dYpPEFYTL4GB07dgJKS8lgCATQlk5iCHCqjOh8Ha0x1/P E99ElmuNemym5KjQPIKXjb3dGr6JH7PxuEycMswtzDHbf6a6QG1mg/WBa5TnqmvyVqxSIU/Y2sOr 8m9D97YY1dET/Ub3hqlZ3kkLXqe/RnTwKfT36EMPfBFHW5f1grrMaxO+BEEQBEGQ4H1/QuMDLEtE ELo8h0WZ7du38zG9TOj26tULdevW1TsPx9bOOHzsKJyc2uFEZDAcWrlAIgW+MiuFf54lw9zYKNfP AwKkZmbxa6r0bzEIKcmpXKxO8ZtYaG1k3Z8LoqS5Gbp81xFr529QK3vdvI0YNXOExjV6iaIBG7v9 Guk6xKO/KQRBEARBfCKCVyqVkOAlPjoSadHu0swWje/Tpw9q1ar11nl4DzPEzAw2s3M4wnZ0gsug IEg8WsHVugHuJNyGm5U117rMh8ZGIVx4nMCvqbI7dsd7aWPQX4d1itffo6/G8EmLJtCDXsR5/jhF p3jZ0mwyFkEQBEEQn4bgjX90D3cf0qRSBPG2DB48GFZWVoWSl6+nKeCZM2FUh7bdERAYg6ldx2HM sZk4+eAhulhXQ2LGa8Q9fgwDQzP0s21EN4AgCIIgCIIgwauN9vU6Q2gjIosTHxWxRIwNIauLZN0L S+yqMmVIhvx/EV9vd0bbTjhy5SrGR0fyZYmcKtVBl/o29OAQH5TSX30JoUBYYLwMcQYZiyAIgiCI T0PwCgUiGIgMyOIE8YnDBC6JXOJjwsSuicC0wHhZgiwyFkEQBEEQ+X+vIBMQBEEQBEEQBEEQJHgJ gvjs8Nsu4hNXLd1qWGTqvHvtXizxWq4WzsLiIk7TTSUIgiAIgiBI8BJEcWVRgAAug8P4cWh4IKJW T8Tx8N/5ucvA41i63ahQy3OxctN6/teV29i5epfOeb1IfYmju4Mw1meM2jUWtmHBZr4WL0EQBEEQ BEGQ4CWIYsaSbSKEhQUiYuloLm63zRqOjGxgq/c4uAwKRsTy8TgeehB+24zfe13Snqfh8umrGDhu gM5pwg9FwNndSeM6uyzM0a0lYkLi6EYTBEEQBEEQoFmkCKKYwTy6gYsmID1LhqN+oyGVGSLjtRgV TAwQtHAcXmVJcMTPE12mr8W0IW4a80hNToXf5KW4EH0J1rWt8PeNuwj5+ygPX+y1XB5+EWUqWGL6 Mi/UbVKHp1F4dcPig5T5nIu6gD4jeirPWZze8vOD2w/DspwFfDbMQbU61nnKvhx3FT2Gumttn13r xji6KwhtuznRzSYIgiAIgijmkIeXIIoZITvc0HX6arzOECI9XYhDUedx+NQZ/H7iHF5kiPEqQwZ3 udgN2+GmNY/1CzbBupYVgv46jAW/+kAqkfJw/3kb0HVgZx7+0+oZWDZjhTINE7q5xe6pkFg4d22j lrdN03o8fX+PvljtvVbt+o0LN1GvcV2tdavTqDbi//qHbjRBEARBEARBHl6CKE4s2WaI4PDD8PUY ijS5uI29dhGQSbDp2HmMdLODyCAdzra18OjQIjxKTEKpUl/y9XlV+TPwJI5c2w+BUIDSZUorw6OC ohFxKFJ5LhRp/03tzJ9nYWBogGZOdnnC7V2a833Hvh2wZu46tXSpz1J5udoQiUR4GP+QbjZBEARB EARBgpcgihPB4YHwHTEEkABJyZmQSsXYHnQJJ3x7IOjmczSuWQV+sRuw4kwwapcpD1frhnCtVVMt H5lUxoWlJkLvHstXkCqY6Dsey6evhHWtb1C2Ylm161KZVC7A1cswtzTn5WsrQyKRoJJVJbrZBEEQ BEEQxIcTvFKZBGIJGZz4uIgl4mLdfrd2nTFzcwBG9uiN0iVEEMiAoZ0a4fiNFIzu5oBxYb6wMBbg QPdeePjyBWIfPsTK6H8xoaVLnnxqNayJ4/tC4NbXlXcxVsC8s4d2HoH7wC54kfoCW5Zs48LW0MgQ iU8S1YTtmDkjsdTrF8xYMZV7exnJCcnca7xl8TbYt2uh1gbWZZmVqRgbrAq7ZlXzG3rYiWJPpjiT jEAQHxhjA2MyAkEUV8F7/u4ZnLlDM6cSxMdk8uBs+eYKl0F7MbRXNwhLVIYs6zFGdbPHyovbYGIg QecatfFaIoaliYn8uDr23bqFozdvoFPtNwKTidi5o+fjl1mr0NbdCQYGOR8l4+d7YtVsf740EGPU rOF87xewALNH+OBh/CMEXj+gzIfNqvzduP7wn7ceE+aP5WE/DZ+LezfjYdO0Lh8HrEr9pvVw+s+z WgXv2RPnYde6Cd1sotiL3d7rO5IhCOIDs2/0MRK9BFFcBW/jak1hV70FWZz4qDAP74aQ1cXaBi6D g9G1YyekvJQAgkwIZeUghgirzoTC29Eefz1PfBNZANRjsyVHheYRvFVrVMHW8E38+OrZa1ygMswt zDHbf6a6SG1mg/WBa5TnuSevYpNfKcQuY+3hVfnXv3tbjOroiX6je8PUzDTPNbb+bnTwKfT36EMP O0HI6de+PwyEhmQIgnjf3y+k2dgTspsMQRDFWfAKBSIYiGjIMEF8bBxbO+PwsaNwcmqHE5HBcGjl AjbJ8ldmpfDPs2SYGxv9F1PGFW9qZha/pkr/FoOQkpyKilUqYIrfxEKpG+v6XBAlzc3Q5buOWDt/ g1q56+ZtxKiZIzSu0UsQxRH2t1eow5h6giDe8V2T5cw5ER8fj1o1apFBCKI4Cl6CID4NvIcZYmZG a0RGhiNsRye4DAqCxKMVXK0b4E7CbbhZWXOty74iy+T/XXicwK+psjt2R6HXjS1HpAtsySJNTFo0 gW4wQeRCJpPxjSCI9/+uKUhISEC5cuXIKARBgpcgiI+Fr6cp4OnKjzu07Y6AwBhM7ToOY47NxMkH D9HFuhoSM14j7vFjGBiaoZ9tIzIaQZDgfe/EnqqBfx6m4b+f3OT/BMof3wTKNr2Jb1WpJFo43KEb TXxSgpeRnp4OU1NTMgxBkOAlCOJjM2VIhvx/EV9vd0bbTjhy5SrGR0fyZYmcKtVBl/o2ZCSCIMH7 QYj/Nw2rRrbSOf64jSfQnDzYxCcoeAmCIMFLEMQnChO4JHIJ4jP5Ev7fv6KCVCKFsZFIjwSF177+ 3w7j+923t9CDQ7zVu0YQBAlegiAIgiA+5JfwIjiG10Ak1LuNuXmZ8hJ7lu9HTOAZZGdmo4ZtNZSr XBajFw59q/x0rodcfI+2n4gXz1+ilGVJrIteDkEBE4YNqJmzdNuuvzbnG1ZQWl3TqKbNja5pdWlD cX3XCIIgwUsQxCeI33YRQsMC0cG5K6YMzSaD/IeLlVue5ZPelt1r9/L1h72WTMoTvsRrORzdHNDc uRkZm3iPSOVfxIvWLM0GIv3qK5NJ85yvnLAe1+NuYfqWH2HjUEdrPF3z05XL0de52GWkPXuBqzHX 85Sva5m/3dqoUz00xdO37oo83qXdhZX+c3jXCIIgwUsQxCfCogABwsJCERbggtDwQEStngjHsX5y wesOl4HH0aGdXPx+n/XexCM7V6CvqHxXIZpfXd62Ttp4kfoSR3cHYVPwOrVrY33GwKPLeDRoXp+W UVL92ij/4vwa6TrEI4+KLjYSFDE7Gerp4VV9Dm6evc33zMsqVfP+vsJWn99wIeIyP2/k3AA/+AzE F6VM1fLzsJ+M168yuOe2wjfl0Xdyd9i2qY9BtUfx6yMWfM/z2nrZn5+fOhLH919Vq4jHd58g6lAs 6trX5mGv0tKxd9l+xBw5A4lEgm8bVceMrW+WVfuu1ki+33FzgzL/ZSELMLn9LBiVMMSGMysgEArh 0WIS0l+8xpKgefBym6NMo5qPalqRSATPllO4IGfX8nuHtLVbnzbkZ2dV+/kF+uRb13KVyxSZd40g CBK8BEF8AizZJkJYxGFELJ0A54FrsG32cGRkA1u9x8Fl0CZELB8P50krIJF1x7Qhme+lDkxQFpYH tbDq8z4IPxQBZ3cnjYKWhTm6tURMSBzadnOiB1PxR0ligBf/vtQpbraUeiQURJHr0iyvq6EeHl6p VL19tZrUwI3Tf2Fmt/koXc4cjV1s0d2jE0palETAgj04c/w8F2rZ2WIsHbkaIgMRxiz+IY/NGP6n lvJ9wr9JmOL6E7bM3oHVJxcr422atV0ZXyzPK+7YOQjlYn3kwu8xt88inJaXM2z+IBgYGmCX3z6c 3B+DQbP6walPS/n573nqHXBjfZ6yGWUrWcKmZR1cjb4hF9OnYWpmwsUuCytftZxafXPnw8id1rJC aS4g7drb8nwVKMRn7rTa2q1PG3Sxs8J+qu1UrWtReX6pSzNBkOBFyeelYSg0IosTH5UsSWaxtwHz 6AYumoD0LBmO+o2GVGaIjNdiVDAxQNDCcXiVJcERP090mb5WLnjdNOaRmpwKv8lLcSH6EqxrW+Hv G3cR8vdRHr7Ya7k8/CLKVLDE9GVeqNskp0ufwpOqSVyya4PGD8AfWw7AqIQxxs3zQOuOjgg/GIkN vpu4p7Slqz1mrZquMa/+LQbheVIKzMzNMMxrCNz6umrNs6C6aKqbqkeYnfv+uBht5WK2mZMdYsLi 8GfgScxcMTVP2stxV9FjqLvWvO1aN8bRXUEkeHMhFolRuuKXEAoK9vJliDPIYAV9CS9ik1axbrEi oUCP+OrtG7tyJI5uDsbFiCt4fO8pwnb9ifjr9zFn91ScDbmQI4qbfcs9mAwWNnrx0Dw2Yx7Kg/5H cT32JpIeP8v53EtKy1PWqig/mFuW4mGxR89CIpagbotasKpXFXWa1eSim4W37NacjydmtO3fio/r HfRT3zx5abpHLMx1sDMXgtGH4rjgZXQa5qo1be5jFo+lPfF7NCpaVcgJG5437fYb63Rutz5t0MXO ue2nS12LwrtGEEQxF7yL4uaQtQniEyBkhxvaD1qNvd7juTcl+Nx5CCCGVAq4NmvCRyEN8FmLsB1u WvNYv2ATrGtZYcGWeUhJTkFvuwE83H/eBnQd2Fke7oO/rtzmovjX0I06icuKVSri0JU/cPPSLcwd PZ+L019mrcLWsI0oU74MDgYcVsZVzWt37A6+f/LgKUa4jeGCV1uemtKrdmvWRQiP8/HAlP7TUMn6 a2xduh3Ldvupxblx4SbmrJmpNY86jWpjjfc6eihVEAqFMBEUvH5lljCLjKWLICxinqeuc9m7LuDy Iff/Ct6s0Cs/FgjU2mda0gS9J3bj2/89SMQ0N2/8c/1Bnnj8WKB43gRq1/Ys3Y+o/THwXD4cts71 Mbzh+Dfp/qOURUnlecyR03x/PfYWvq8zRhmHhTu4N8tbrkzzfdIUVs+hDipYlcetM7e595gd12pa Q70tGo5ZPBb/zsV7+Pf2Y1RvaI1v6lbRGp+hS7v1bYM2O+e2ny51LQrvGkEQxVTwGhsYY9/oY7hy 5QpZm/hkMBIVz94GS7YZIjj8MHw9hiItQ4zYaxflf6Ul2HTsPEa62UFkkA5n21p4dGgRHiUmoVSp L/n6vKowb+aRa/v5r/yly5RWhkcFRSPiUOQb4aLHWLz2PV2UIjA58Tk/rlKtMtbO38iFaoc+7TWm Y17lgJW/4dzJC0h6mojMjKx889TE23RpLmluhqFTBmOs+wRMWTwJpUqXUq/bs9R8Z2llY9Uexj+k F1LbF0ipTKP9tIUTmr6EF71Jq36b3lHnuAP9gvKdLOn5/yXzfU276jwe6yYbG3gWV6KuKZ+hBq3r qU389PpFzhjybxtXw7P/PJ2Ka6rHrPvtzdO3eZdd/5jFMDY1xuuXGRjXchoPT01KVZYbuS8KbXo5 YP/qQPSc0IWPaWVjY5MeJcHyKwu1e8dg3Yd3+/3Bl2xy6d9Krb3sXFs+7b5rjR0//w8ZrzLgNtRF Y9rc5Ndufdqgq51zU1Bdi8K7RhBEMRW8CtFr18gO9+7dI4sTxEckODwQviOGABIgKTkTUqkY24Mu 4YRv433FZAAAIABJREFUDwTdfI7GNavAL3YDVpwJRu0y5eFq3RCutWpqFBxMrGki9O6xQhMjy/b4 4XTEWZwKieGzHW845q8WZ5Pfr6hetxq+/3GgXISWRLtqHT+YPcVZ4v/sofmLjrmleb7ijE38Usmq Ej2YGnj14hVep2egdJkv8zxrzGYpSSkwNjEGTMlOBX8Jl332nifV9nn3WoT/+ycBWZnZ8ufECI7d m6PXRHceb8CMXvydXDU+p+dJ845NcsJUPJhuP7gg/tp9TGg9g48v1VSW4jjhQSKad26CqrUqwUhe Hgsv8YUxuo/rhMd3nyLh30RehlEJI+xdsh875aIuR4DL8N3M3vjf8oOY0n4OjIwNsf7ccrX8W/Vo gfs3/+XHjvJj1fbml099x7p8X75qWdg622hMm5v82q1PG3S1c24KqmtRew4JgihmgleBtbU1EhIS yOoE8ZFwa9cZMzcHYGSP3ihdQgSB/O/z0E6NcPxGCkZ3c8C4MF9YGAtwoHsvPHz5ArEPH2Jl9L+Y 0NIlTz61GtbE8X0hvOsw67arwN6lOQ7tPAL3gV3wIvUFtizZhom+42FoZIjEJ4koW7GsXvXdt2k/ eo/ogfpN62Gw0zAepprXqxfpaOLYmIvdk8ejC8xT37pYlC3N21i7YS0E/xGqDE97noZtv+zAmkMr MW/MAtRvZqPm5WWeZZZWMY5ZFXbNquY39GBq+EGFiV02JpKNzVaIXoXYFcvDZa8zYVBCSMYqyJZF bAwvg3lt9W1jbub+Pk1rPNNSJhjhNxgjMFjt2parq5TH39SrgsUhPhrzyB2PYVW/KobJN9W6MPGY m8HeffmWO69mnRrzLb/8jUyNMGzBQLX25o6nKR9G6G9/8n27gW3y1E21DAX5tZvZTtc26Grn3Gir a1F61wiCIMHLKVeuHNLT08nyBPERmDw4W765wmXQXgzt1Q3CEpUhy3qMUd3ssfLiNpgYSNC5Rm28 lohhaWIiP66Ofbdu4ejNG+hU+41oYyKWjYllY2zZxE0GBjkfJePne2LVbH9sWLCZn4+aNZzv/QIW YPYIH74ebeD1A8oxswXN1PzkwRP0sO2LL0p+gYkLxmvMq9/o3pg6cKZcGD2Xi+OeBdpANX1BeMwZ jTkj5yErIxN9R/dRhq+dtwFDJ3+Pr6t+hR+8hmC191rlpFoKmFA//edZrYL37InzsGvdhB5MFXK6 yn/Jxa5C9LIfE148T+Nil3UdZT9EvJCmkbEK+hJeBD28O6e56SWOybOmmeSnKTix7xTMy5RCSw2e Yapr4b9rBEF8ot8rZAW8oVliGabsuYOl/WrAyIDGTBHEp0ymOBO913eEp9tEGIg0/57lMjgYXd3c IJIK5Z8AYghlhvAb1gQ1N/SDt6N93tlxBWzZDyl8omIQ/P0EjfldPXuNT7ykqbtxcYfNLj2qoyc2 B6+DqVne/rev01/D030C/A+tpHV4c2H+0hKWX1vwSauYR5eJXbbkS2ZGJkqUMIbI0ICLXTaxVao4 BV57xmKxqz8szC1gZmYGU1Pq55z7s6Bjy04QCUVFpt6H/7DSW/B27RlPN5z46EikEhyLPkqfRwTx HlDoUc9mBihtbqb3+0Xr8BJEMcOxtTMOHzsKJ6d2OBEZDIdWLvI/1MBXZqXwz7NkmBsrJvTKmR01 NTOLX1OFLQWUkpyKilUqYIrfRDKsBtjEVl2+64i18zeo2WjdvI0YNXMEid18YN2YFZ5ekYEQAoFQ KXYJ3ShqHt7q1pZ6dWlm8WUymh+E+DTeNYIgPk1I8BJEMcN7mCFmZrRGZGQ4wnZ0gsugIEg8WsHV ugHuJNyGm5U117p86Q/5fxceJ/BrqiiWAiLyp79HX43hkxZNIOPoKHoty1rwbs40O/PbfAkvWrM0 12pwWr7pk+JvkM4gPpV3jSAIErwEQXwi+HqaAp45a9V2aNsdAYExmNp1HMYcm4mTDx6ii3U1JGa8 RtzjxzAwNEM/20ZkNOKjoRC5JHbf4ks4aDIdgvhQ7xpBECR4CYL4QJR8XhqGQt3WGp7Xlf3/BVKe 3MJC27wzanao8N9BMtmU+HBfGpPik3V66LIkmWSwguxZDJYlIohP5V0jCIIEL0EQH4hFcXM++zZW SK+Cp6YPqM4EkQ9SqYS+iBPEB0AipS7NBEGClyCI946xgTH2jT6GK1eufNbtFMn/HTlyBJ1rd0fF rytSnYs5RiIjMoIW4h/dw92HNKkTQRAEQYKXIIjPSPTaNbLDvXuf75fc8+fP8/2d23dQr249qjNB aKF9vc4Q2ojIEATxnhFLxNgQspoMQRAkeAmC+FBYW1sjISHhs2vXy5cvuZjv378/9uzZg8TERFhZ WVGdCUIDQoFI65rcBEEQBEGClyCIIk25cuWQnp7+WbUpKiqKi/lvv/0WDRs2xKlTp2BjY0N1JgiC IAiCIEjwEkRxw9TU9LNpC/OM3rp1CyNGjODnbdq0waVLl/DgwQPUqlWL6kwQBEEQBEHkQUgmIAii qMA8pdWrV+eea0apUqXQoEEDHDt2jOpMEARBEARBkOAlCKJo8vTpU9y8eROtW7fOE962bVs+RpZ5 UanOBEEQBEEQBAlegiCKHHFxcXwMbIUKFfKEm5mZoX79+ggKCqI6EwRBEARBECR4CYIoWjBP6fXr 19U8pQqcnZ0/OY9pUawzQRAEQRAECV6CIIgPTExMDJ/gSTEOVhU2LrZevXqflMe0KNaZIAiCIAji c4NmaSYI4pMmPj4eV69e5cfXrl3TKf7HXuO2KNaZIAiCIAiCBC9BEMQHhglBb29vtfBt27ZhyJAh VGeCyAepTAKxhOxAEO8bsURMRiAIErwEQRCFx/3796nOBFEA5++ewZk7cWQIgiAIggQvQRAEQRCf F42rNYVd9RZkCIJ4zzAP74aQ1WQIgiDBSxAEUThUrVqV6kwQBSAUiGAgoj/1BEEQRDH+W0gmIAii KFIUx8LS+F3iU8Zvuwgug4KwdKthoeftYuWmV/jb5ke8X97V7rvX7sUSr+Vq4SwsLuI0GZggCBK8 BEEQBEEUHosCBHAZHMaPQ8MDEbV6Io6H/54jbgYex9LtRh+kHvfvPMCEXpPhVrMrOtV2x6iOnoVe hkQiQdd6PeBevxc/1kfYFST0FNd1FYQsHttYW3/sPQVnT5wrNLGpyLtDjS7cjjcu3PwknrUXqS9x dHcQxvqMUbvGwjYs2IzX6a/ppSQIotChfk4EQRRJiuKMxzRLM/EpsWSbCGERhxGxdAKcB67BttnD kZENbPUeB5dBmxCxfDycJ62ARNYd04Zkvte6+Hj8jGFeQ+HQ/v2NNz4deRaNWtpCIBQiLvyMXmWF xQe903VtaWRSGWIjTmPptBWY8YsXGrZoUChtVeR94lgUFk5cjB0ntn705y38UASc3Z1gYmqido2F Obq1RExIHNp2c6KXkyAIErwEQRA0SzNBvBvMoxu4aALSs2Q46jcaUpkhMl6LUcHEAEELx+FVlgRH /DzRZfpaueDV7l3s32IQnielwMzcTC5ah8CtrytSk1PlQmsJLsVchnXtN2tMawt/8uAp6jezUcs7 8UkifMYswN/X76J63WrwXjcLZSuWLbB85uE8cGkf93DuOrWdxzsVHIOG9g1yRGZ4nFLw5i6jbpM6 WLbbj4crPKpMPLJjtu/TdADWB66BRTkLJCckY6Q8/31ndqFdtY5K0atId/jqH+jXfCD2nd0NYxNj ZGVmobfdAOyOCVDWXSAUwN6lOYyMjRCwYqdS8Gpqk2qd8ounyLt1R0f4TV6ax/6LvZbjQvRFlKlg ienLvHib3+Y+aspHk90VXI67ih5D3bU+R3atG+PoriASvARBkOAlCIIgCOLdCdnhhvaDVmOv93hA JkPwufMQQAypFHBt1gTyHQb4rEXYjvy70u6O3aEUrSPcxnCh5D9vA6rXscbCrfORlpKGno378Tja wkfOGAaPruPg2KGlvCoyDJ08mIvAX2auglPXNlhzcAX++PUglk1fiUXbfy6wfMatS7eUoouJ3JjQ WPQZ2QvibDEXl5MX/shF4arZ/mjetikvIzeavLasLpGBJ9Hzh27ce9quR1ueh7Z0bTq3RtjBCHTq 74bwg5Fw7dUOpmamavk2drCF90ifAtukWidt8RRt/uPXA2jpaq8MY/bvOrAzFmzxwV9XbnMx/Gvo xre6j9ryyW333LCu1XPWzNT6HNVpVBtrvNfRi0kQBAlegiAIBs3STBBvz5JthggOPwxfj6FIyxAj 9tpFuUKSYNOx8xjpZgeRQTqcbWvh0aFFeJSYhFKlvsSDzFNq+TBPX8DK33Du5AUkPU1EZkYWD48K ikbgjQNcDJpbmCvjawvvPsQd9Zva4OzJ83KhJsXKn9bAa8kkXIy5LBdV83icHvI4m/1+1al8hl3r Jsrjq2evQSgUomqNKvxcKheDl09f4R7Vc1EXMG+jt052a9/DBavm+HPB+6dc+I6fl/9YY7d+HeA/ dx0XvMG/h2LK4okFlpFfm3SNpxxT3M2Z2zG3/SMORSrPhSLhW99HTfmo2j1PfZ+lqv04kBuRSISH 8Q/p5SQIggQvQRAEg2ZpJoi3Jzg8EL4j5M+jBEhKzpQLQDG2B13CCd8eCLr5HI1rVoFf7AasOBOM 2mXKw9W6IVxr1VTLZ5NcgLKuxt//OBAlzUvyrr1KESTQPC+mtvBqdaz5xjyTnet2yyPUtJFf+bmJ Do5B6vO0PBM/xYTG6T1mltUv/WU6F9Bsz87zo3bDmsjOysapkFiUMC2BSlZfa4x3/tRF7uHUp035 xWOe4Cunr2Ljwi3co8285QpC7x5TE55vcx815ZMf5pbm/N5qS8MmEqtkVYleToIgCh2apZkgCIIg ihlu7Tpj5uYA/JOUjuT01xDIgKGdGuH4jRSMdnfAuHBfJLx4iAPde8HDtiE/XhkdppbPqxfpaOLY mIukk8ejleH1mtRB4O5j/Dj3cjPawnNzKiQGlatV5se29g14t1zGvi370aB5fZ3K1yR4Jy/6kQtB tk1dOpl7KRV12rvx9zzxDY0M+dheTbTr6cK7WrPuyapoSse8u0unLscAj75q8ZkAZJNpsWV5BsvF Zn5tUs27oLazMdFjfhqJZdPedNVm44UP7TzCy017nsbb8Tb3UVs++cEEfX4zRrNrVjW/oZeTIIhC hzy8BEEUSWiWZoJ4eyYPzpZvrnAZtBdDe3WDsERlyLIeY1Q3e6y8uA0mBhJ0rlEbryViWJqYyI+r Y9+tWzh68wY61a6jzKff6N6YOnAmnic9R+8RPZXhY+d68JmX/eeuR9dBnQsMn9TXi48Fzc4Wo3qd apjxy1QePtF3POZ5+mKz31bUsKmOOf55x4BqKz83d2/cQ8LjBNg0racMs7Grh8SnSbzMSQt/5HXa tixALspqYfneJfALWIDZI3zwMP4RAq8fyJOfi7sT71qtaXIlTenYuN/DOwPVJuVi3mYmYr+1qcG9 2axO+bVJNW9d2s4mkhIaiLBV3jY2Lnr8fE8+ZpktAcQYNWv4W91HbfnkR325/U//eVY5SZYqZ0+c 19odmiAI4l0QyNjsEPmQJZZhyp47WNqvBowMBGQxgiA+CXx8fODt7U11JggNZIoz0Xt9R3i6TYSB SPNv2y6Dg9HVzQ0iqVD+bUAMocwQfsOaoOaGfvB2tM/blVXAxr1K4RMVg+DvJ5CB9eDwjkDejbfL d52KtR3YOrxs9ubNwevUJu5i6+96uk+A/6GVGpctKgqIJWL4B/2Cxa7+sDC3gJmZGUxNTekFIIhC QKFHPZsZoLS5md7vF3l4CYIgCKIY4tjaGYePHYWTUzuciAyGQysXSKTAV2al8M+zZJgrx33KuOJN zczi13LDZjtmkx0RBcMm4iKArjY9tV7rUreH8ph18R484TsyGEEQ7wwJXoIgiiQ0SzNBvBvewwwx M6M1IiPDEbajE1wGBUHi0Qqu1g1wJ+E23KysudZlfbtk8v8uPE7g13LDRcl/Y08JgiAI4lOEJq0i CKJIQrM0E8S74+tpKhe7OWu3dmjbHQGBMZjafBwMDEri5IOHMDc0RqZEyo8NDM3Qz7YRGY0gCIIo UpCHlyAIgiAITBmSIf9fxNfbndG2E45cuYrx0ZF8WSKnSnXQpb4NGYkgCIIgwUsQBPEhoFmaCeL9 wgQuiVyCIAiiqENdmgmCKJLcv3+f6kwQBEEQBEF8moKXTS9NG220ad8IgiA+JH7bRXziqqVbDQs9 b7bmrD7hunD2xDn0atIfnWq7F2qdPlY+xMe3++61e7HEa7laOAuLizhNRieIIspH6dKsWEuJIAjt 0NrX+UOzNBPEu7MoQICwsFCEBbggNDwQUasnwnGsH6YMdYfLwOPo0K4rpnyf9cHEi3EJI9SoVwPf je0Hu9ZN8k2zceEWzNs4B3Ua1X7nssPig5Tn9+88wPIZK3H76h0IhQJUsqqEDcf8C7W9EokE3Rv0 hkAoxP6LeyESiXSun+q5tvgFxXsX2+ubt4GBAarWqIIJP4995/v1vmDrBB/dHYRNwevUro31GQOP LuPRoHn9IrtOMEGQ4P1IjO1gDQMR9aomiNyIJVKsOX6PDFEANEszQbwbS7aJEBZxGBFLJ8B54Bps mz0cGdnAVu9xcBm0CRHLx8N50gpIZN0xbUjme68PE1AyqQyxEaexdNoKzPjFCw1bNNAa/9+7D9+L ePLx+BnDvIbCoX2L99bW05Fn0ailLRe8ceFn9CqrIKGpixB9V9u/Td4njkVh4cTF2HFi6yf5PoQf ioCzu5NGQcvCHN1aIiYkDm27OdGHB0GQ4NUdoUAAETmwCCIPUkHOSxEfH4+aNazJIARBvBeYRzdw 0QSkZ8lw1G80pDJDZLwWo4KJAYIWjsOrLAmO+Hmiy/S1csGrvfto/xaD8DwpBWbmZnKhOARufV2R mpwqFzdLcCnmMqxrWynjagtXIBAKYO/SHEbGRghYsZOLLpZmsddyXIi+iDIVLDF9mRfqNqkDsVis 9CAqRJ6muujiIc2dz5MHT1G/mfpkXYlPEuEzZgH+vn4X1etWg/e6WShbsWyBtmB5H7i0D6M6emLX qe083qngGDS0b5AjMsPjlII3dxmsjct2+6nVT1H/Pk0HYH3gGliUs0ByQjJGyvPfd2YX2lXrqGyf It3hq3+gX/OB2Hd2N4xNjJGVmYXedgOwOyYgX9tra5NqnfKLp8i7dUdH+E1emudZ0HRf3+aZ0pSP qt3DD0Zig+8m7slt6WqPWaum57l3l+OuosdQ7d3j7Vo3xtFdQSR4CYIEr55f7GUy+UY3gSBU3wsF CQkJKFeuHBlFAzRLM0G8GyE73NB+0Grs9R4PyD93gs+dhwBiSKWAa7MmkO8wwGctwnbkP1Zyd+wO vmdCcYTbGC5O/OdtQPU61li4dT7SUtLQs3E/HkdbuCqNHWzhPdJHmabrwM5YsMUHf125zUXTr6Eb 84it/OqiC7nzGTljGDy6joNjh5Zys8gwdPJgLgJ/mbkKTl3bYM3BFfjj14NYNn0lFm3/Wafyb126 pRS7TOTGhMaiz8heEGeLubicvPBHLgpXzfZH87ZNeRna6qeA1SUy8CR6/tCNe0/b9WjL89CWrk3n 1gg7GIFO/d24+HPt1Q6mZqb52j6/Nulje9bmP349wIWmgvzuq77PlLZ8ctv9l1mrsDVsI8qUL4OD AYfV2n3jwk3MWTNT6zPCehOs8V5HHxwEQYJXP9gfEpkegnf1kdP452Ga/Ih9oMvTyvf8SJATkpPn m/hWlUpiXJdmdJeJIoVM5aVIT0+HqakpGUYFmqWZIN6eJdsMERx+GL4eQ5GWIUbstYvyDx8JNh07 j5FudhAZpMPZthYeHVqER4lJKFXqS74+ryrMuxaw8jecO3kBSU8TkZmRM943KigagTcOcAFmbmGu jK8tPD9YmohDkcpzoZahUNrqoi/dh7ijflMbnD15Xi7UpFj50xp4LZmEizGX5aJqHo/TQx5ns9+v Opefe0zs1bPXIBQK+ZhWhlQuBi+fvsI9queiLmDeRm+d6tm+hwtWzfHngvdPufAdP88z3/hu/TrA f+46LniDfw/FlMUTCyxDV5vmF0/hCXbp5sztWNB9fZtnStvzkdvuVapVxtr5G7mnuUOf9upteJaq 9oNBbtg464fxD+nDgyBI8OqHvh7e+H/TsGpkK53jj9t4Io+3jCCKAvTMEgTxvgkOD4TviCGABEhK zpSLLjG2B13CCd8eCLr5HI1rVoFf7AasOBOM2mXKw9W6IVxr1VTLZ5Nc9LHuvd//OBAlzUvy7rRK 4SHQLEy1hefm/KmLecbnht49lq8YKagu+lKtjjXfmGeyc91ueYTau5YfHRyD1OdpeWYZjgmN03vM LKtf+st0LqDZnp3nR+2GNZGdlY1TIbEoYVoClay+LtD2urYpv3jME3zl9FU+yRjzaDNveX739W2e KV2ej2V7/HA64qy8/TF8NmbVicjMLc35/daWD5tojE1gRhBE0eOjzhjFftXUa5NIYWwk0nmDvvnT RtsnshEFQ7M0E8Tb49auM2ZuDsA/SelITn8NgfxjZ2inRjh+IwWj3R0wLtwXCS8e4kD3XvCwbciP V0aHqeXz6kU6mjg25sLk5PFoZXi9JnUQuPsYP869nIu2cAVMcLAJndgyMIPlgofBxpUe2nmEX0uT C0XWtVgT2upiUbY0767K0h/fF6KWztDIkI+d1QQTR5WrVebHtvYNeLdcxr4t+/mMvbqUr0nwTl70 IxeCbJu6dDL3Uirss3fj7zrXr11PF24P1j1Zl3Yx7+7SqcsxwKOvTrbX1ibVvAtqOxsTPeankVg2 7U1XbW33Vd9nStfnY9+m/WjmbMfr8ej+Y7XrTOSz50Qb7JpVzW/ow4MgiiAf1cMLPbs08wrrOauz jLxlRFGDnlmdoFmaCeLtmTw4W765wmXQXgzt1Q3CEpUhy3qMUd3ssfLiNpgYSNC5Rm28lohhaWIi P66Ofbdu4ejNG+hUu44yn36je2PqwJl4nvQcvUf0VIaPnevBZzv2n7seXQd1LjCcwTyeTEh9a1OD e1Rt7Orx8PHzPfnY1g0LNvPzUbOGa2yTtrp4zBmNOSPnISsjE31H91FL5xewALNH+OBh/CMEXj+A SX29+FjQ7Gwxqtephhm/TOXxJvqOxzxPX2z224oaNtUxx3+mTuXn5u6Ne0h4nACbpvWUYaydiU+T eJmTFv7I7bNtWYBcgNXC8r1L1OqXx2buTrxrtaaJlDSlY+N+D+8MVJuUS5vttbVJNW9d2s4mkhIa iLBV3jY2LlrbfdX3mdL1+Xjy4Al62PbFFyW/wMQF49VFufyenP7zrHLiLFXOnjhf4HJNBEF8mghk BShCxZq5hbkmqCLPEc6V5QJW9zynrQ/HDq/2OscfvDQEi0a1zRNWp4L6dPM3nr7Wmoef91RERYQi MOpioRldUYf8ys0vzb/34+E7azJOR/8JE1NTDB87BUM9fiz0MpV2H/sDjvy+G07tO8E/4Pd846ra q2e75nz/R2ic1jS54+hi75TnybCv/TVq1q2PA+E5v/J2b9sMd2/fxKX7KXxs1Pgf+iLs2GGcuByP suUrFKmXUiyRYVPEv/BsZoDS5mYwMzOjMbwEQehFpjgTvdd3hKfbRPnfWc2/bbsMDkZXNzeIpEL5 twExhDJD+A1rgpob+sHb0T5v91EB65UlhU9UDIK/n0AGLqIc3hHIu+x2+a4TGUMFNnszm9F5c/A6 tcm8Xqe/hqf7BPgfWql1HV6xRAz/oF+w2NUfFuYW9LebIAoRhXZ82+/GH3kML/SepdlQTw+vtvyv PXldYBzG9g2rC4zzLu1/mzRjBvbAvTu3sONwBOo1bIyVvnN0zkvfMiViMUICD6J8xa8RFRmCly9f wvQLM53ttS8krsByc8fRxd6lvrRA7XoNcOevGzxexut0/HX9Cr92/kwsGjdzwNWL57kgtixXocjN BE49mnWDZmkmiHfDsbUzDh87CiendjgRGQyHVi6QSIGvzErhn2fJMFeOtZRxxZuamcWv5YbNMMwm GCKKFmwiLkIzXW16ar3WpW4P5THr9j14wndkMIIoAhSpWZpZZEM9PMJsLKQ2B3bu8PRXL9GmgRW+ MDND+IW76NfBAf8+iEf4+b+VcepVNMHVx+ncuzj7x5GI/jMUlapYYd7ydbC1awGbr3J+Zeje73sc 3b+H5zVn8Rq4dHTHs8QETB41EJfPn0aNmnXy1EFbfvmleXg/nh+XKVseBgaGmDxnIQ9PS02B3xwv BB/Zz2eWbOPaCd7yOpQy/1Kt3Z0d6uPRw/v4srQF5i5Zi9bt3JRtqGpVHYGncgRk2LFDyMx4jU49 +uFX/2Vy8XsA7n1yxvawus+ZNBpREcEwNDTCmb8T1eylyHNfaBx6/+fJPXs3CXbVyqiFs/iq6d1a 1EXCk8c4/89zDOnRHq/TX2Hv8VNo0doFN69dxj93b+Pe37dRplx57o24dDYWNrZN8H9PHvE6s/Zq ald+7e/YvS/Cjh5Exa8rY/G67ahT3zbfawU9E7nL1fW9IAqGZmkmiHfDe5ghZma0RmRkOMJ2dILL oCBIPFrB1boB7iTchpuVNde6itUQLjxO4Ndyw7/0/zfekyAIgiA+RYrUpFUymRQioUDnjQkHbZMB MTGi2EqYfIG+349AUsL/Ydn8mVxIfT9yAg9XcPnhK55+sfdU/Bl6DPvDz3KxxQRf7nyZyNp+MBzP k59h0ewp/NoiuQg9HxeNn3/ZiOnzl+Vpv7b88kszc8EvEIpE6NWuGf63Y4uybay8w/t+w9L1O7Bq 6z6EyIXv/OkT8tRPEfdw1GWEnLnN26yop4IVW/Yq4x098D8u3sd6zYFxCRMcP/S78hqre2RwIHyW rkPc7YQ8eSjspeDb2jb4qnLOhD2RwUf5vopcCLLw3HVTTd+gcTNkZWXi7u2/cPncaXTs1oeHN2sp 5B0OAAAgAElEQVSZM17pzq2buHD6FJrat0ZDuchknt24qD/5NRZHW7vyaz8Ttet2HcL9+L+x8KfJ BV4r6JnIXS5NWkUQxKeEr6epXOzmrJfaoW13BATGYGrzcTAwKImTDx7C3NAYmRIpPzYwNEM/20Zk NIIgCKJIUaSWJWJ0ncsWCxf818Hqzf8K3qzQKz8WCLQu8XLx35d56tF3yGgEbFyNgA2ruMDrP8wj T1rFcfDhP3Lq0aoh3z/4516eeI1bOCqPExOe8msRQTkLnLfr0pOPL2VeWbE4m1/Tll9+adz7DUaF rytj6phBmD9tHKQSCXoNGq7My97pzThnlo9qO5Lk9Tqwe5tcRN7iYY8fPcgT55saNfk56yp8MiwI 7Tp3h0AusNu074TQoweQkvKce40V5XVgIlTFzprOnTt0wc5NaxAblbNenqu8bZpsnPu4pbMr95gf +WOXPEyKDnLBya41sW/FbRIvF5hMCPM6SKVYseAnODi14+KcxdHUrvzaz35IcMhlv2uXzhd4raBn QlGuPu8FUTA0SzNBFC5ThmTI/xfx9XZntO2EI1euYnx0JF+WyKlSHXSpb0NGIgiCIEjw6oPsLWZp /m267uvqDfQL0qlLM4MtKM66xEohQWZGJu/mnHusqmr8s/eeQ2RgoHYt9zETojleZmmOGBf853WW SdXiquZXUJqmLdtg9/FT6NSiLpbMnYaeA4dprIPIQKRWvzHfuePvW9cx03cFF7CKeqqmDwncz0V2 0MH/8U1B+LFD6CYX3Xnvoyxf+7JzZzd3Lngf/3v/PzHfQ6vtFMf1GubMiBj4+y7usbWwLMuvMbu0 dG6P65fP4/KFMxg3Yx6fmIV1v967bSPs27gobaeaZ37tZ89Abvuze6LLNV2fCV3fC6JgaJZmgni/ MIFLIpcgCIIo6nzcLs0yhZdXt+3tytCch2r4b1vWcnHHuqyy/Z5tG3i4ZdnyPD7roszOm7dy5uc7 t/jz852b/TXmm/u8QZOcMapsrGvMiXAusBTXtOWXX5o1i32QmZWJZ4k5Y2YrVqrCw106dePnp/4M 5WkYrl17q9Xn0YOcMcB2Dm3y5KvaBua5ZF7NiCsPcO5+GkIv3OPXjx/+XellZbB4ijSq9sqdZ/3G zfj1m1cvoqp1DVh/W1stjmr6ipWromz5inxMrtt/nmTFxsbxxsjbyoRoPVs71Kpvy72+t29dQ3NH Z633PL/2s3t/LjaKi31G6/adCrym6zOh+0YfTARBEARBEATxGQhemd6Cl3ltdd3yE7xNqpZSbmzm 4X0Bm7jYmr1kLUpblsXuX9fy8AmzfuZL/7Rt+A1PP2vxGji6uGHdkvlwqFkep6MjCxS80xYsxzfV a2Ly8P7Yu31Dnmva8ssvTQnTL9DVwQbDe7nycasL1mzl4ZPnLoare294jfwOU0b0R9c+gzDhpwXq 9fn5FxgZGWPFz7PyhOc+Tk15jrioCNRv1BRmpcx5mLmFJReW52JPckH6k7zurPzZE0fAxdaKx1G1 l6pdnN268sm1mHdXk61U07PNppEdr2/bzt3z3MvGcsH9Oj0ddRo0Yq5wvvFuzHIRa+/sqlXwFtT+ lb6zMefHkbxtk+YsLPCars/E+/5xp7jBZjymOhNE4eG3XcQnrlq61fCd8mFrun4MPla5xZ33Yffd a/diiddytXAWFhdxmoxOEIRefNR1ePs1rwADke7p5m/7Ezun6f7BykTv7CFt6C4XYbKzs9C3nR2a O7bF1PnL32tZzazN+f70vVS9rhU2YgmwJ+4prcNbAD4+PvD29qY6E4QGdFmHl7EoQICwsFCEBbjA ZXAQolZNhONYP4TtdIfLwOPo0K4rpnyfpbcACosPeuc23L/zAMtnrMTtq3cgFApQyaoSNhzzL9Ry JRIJujfoDYFQiP0X9/LhTbrmX1B5iuu61kshHI1LGKFGvRr4bmw/2LVuUii2VuRtYGCAqjWqYMLP Y1GnUe1CE7yFcb8VsPVwx3Qei03B69TWvGXr4Xp0GY+1R1ZpXQ/3Y0Hr8BLE+6OIr8P7/rtvkres aONkUwnVvq2N4T/OeO/30tDIWOszk9+1wn9m6b4TBPH+WbJNhLCIw4hYOgHOA9dg2+zhyMgGtnqP g8ugTYhYPh7Ok1ZAIuuOaUMyP3j9fDx+xjCvoXBo3+K9lXE68iwatbTlgjcu/IxeZRUk8t5GBLI0 MvkfgdiI01g6bQVm/OKFhi0aFEpbFXmfOBaFhRMXY8eJrZ/kcxl+KALO7k4aBS0Lc3RriZiQOLTt 5kQvMUEQn5/grW5tqeyqrGt8ErxFmz9vPP1gP14oytJUTn7XSPB+HGiWZoJ4N0LDAxG4aALSs2Q4 6jda/tljiIzXYlQwMUDQwnF4lSXBET9PdJm+Vi54NfeuSnySCJ8xC/D39buo26QOlu32U15LTU7F Yq/luBB9EWUqWGL6Mi8eh9G/xSA8T0qBmbmZXNQOgVtfV+4pPHBpH0Z19MSuU9vx5MFT1G9mk2+Z 1etWg/e6WShbsWyeOLrkzzgVHIOG9g1yRGZ4nFLwamuXwlOa23Pbp+kArA9cA4tyFkhOSMZIef77 zuxCu2odlaJXke7w1T/Qr/lA7Du7G8YmxsjKzEJvuwHYHROgrLtAKIC9S3MYGRshYMVOpeDV1CbV OuUXT5F3646O8Ju89K3vE4u/cOISXIq5DOvaVgXmo2r38IOR2OC7iXtyW7raY9aq6Xnu3eW4q+gx 1F3rc2vXujGO7goiwUsQRNEQvPrO0tzLQf4B7KB/GQRRlKBHVjdolmaCeDdCdrih/aDV2Os9nn/w BJ87DwHEYBPRuzZrAjYf/QCftQjboX0o0arZ/mjetinWHFyhds1/3gZ0HdgZC7b44K8rt7nI+jV0 I7+2O3YH3zNRO8JtjFKU3bp0SylGR84YBo+u4+DYoSX/Wz508mAuAn+ZuQpOXdvwMv/49SCWTV+J Rdt/zlO2LvkzkRsTGos+I3tBnC3m4nLywh+5KNTWLk1eW1aXyMCT6PlDN+49bdejLc9DW7o2nVsj 7GAEOvV34+LPtVc7mJqpd81r7GAL75E+BbZJtU7a4ina/MevB7jQfNv7xOJXr2ONhVvnIy0lDT0b 9yswn9x2/2XWKmwN24gy5cvgYMBhtXbfuHATc9bM1PrMsa7Ya7zX0QtMEETRELxS+QevVEA3gSDy vhdkA4Ig3i9LthkiOPwwfD2GIi1DjNhrF+VqSIJNx85jpJsdRAbpcLathUeHFuFRYhJKlfqSr8+r yrmoC5i3UfO49KigaEQcilSeC0U582QyT2DAyt9w7uQFJD1NRGbGmzHCucesdh/ijvpNbXD25Hm5 UJNi5U9r4LVkEi7GXJaLqnk8Tg95nM1+v+YpV9f8r569xte5Z2NaFd9JLp++wj2q+bVLlfY9XLBq jj8XvH/Khe/4eZ75xnfr1wH+c9dxwRv8eyimLJ5YYBn5tUnXeApPsEs3Z27Ht71PLH7gjQNc1Jtb mBeYj6rd/5+984Br6nr7+C8QRCxK1VZtFVtR/46quHCjFVDAgbvu9bpFwD1Qi9iKojiQOtEqaotW bSsyZVjFgVqcrbOKA1DRCo4iIyHvPQcTk5BAoqiM5+sn5t5zz33OuDchv/s855yatc2x/rvN3NPs 8E3XvG3492meBwbKsHHWiQmJ9CEmCKJ4CF4ZZOTNIog8nwtCF9iMx8XNY1oc60yUTCKig+E1TrgX pcDjJ5mC2JMgIOw8jnj1RdiVVLSoVxPeJzdhzekINPikKuwtmsK+fj29y4m8GZpHvPgLApWFIo+c Ogzlzcrz0F9t1G5owV/MM9njq94qQk0buto/FnECT1OfqcwyfCIyTu8xs6x+6S/SuYBm72w/Pxo0 rYfsrGwcP3QSZcuVRY1a1TXmiz9+TjGxlK5tyi8f8wRfPHUJm5du5R5t5i1/0+vElgPU9Xqrs3K3 N07FnBHaf4LPxqw+EZlZZTN+vbXZYRONsQnMCIIgdOWDLkskzclBtlRGL3rRS+klkZLk1YU7d+5Q nQniDXHs0gPuW3bg9uN0PEl/CZHwtTO6e3OEX07DxF7t4RLthZTnifitT39MbtaUb/sei8pjp1HL htizeZ/GMtg41AO7DnLx8kwQliwUmfHf83S0tG7BRdTR8GM61ZeJI/Pa5ny7WTtLHpbL2Lv1V1i2 aaKSV1f7TPDOWDaVC0H2mu0zg3sptbXLqIwRH9uriS797Hj7WHiyOprOY95dn9mrMGTywLwPPYX+ YpNpsSV4RghiM782qdsuqO1sTPSkBeOxcs6aN75OrG+CA0P5tvISQdrsqLPX/1e0trHi9Ui6k5zn OBP5LKxZG+xYrXpf0oeYIAid+aAe3msPMnD9/ku6CgRBEATxHpkxIlt42cNu+B6M7t8bBmXNIctK xoTe7eB7bjtMxFL0qNsAL6USVDYxEbbrYO/Vqwi5chndGzRU2Jm+dCqfTXn7yh2CUKmPVXtWKI65 fufMx8JuWrKF70+YP5a/D5o4ALOHuSP1cSoGjOuntY7TB87iY0GzsyWo07A25q2ezdOneblisbMX tnhvQ93GdfDtOtXxnrrYv3n5FlKSU9C4VSNFWmOrRnj04DEvU1O7vHcswcJxnkhMSELw37+p2LPr 1ZmHVmuaSEnTeWzcb9Cu4DyTcjFvMxOx/2tcl3uzWZ3ya5O6bV3aziaSMhAbYpvQNjYuWt/rNGXR ZN436xZthNPwHgVeb3Xu372Pvs0G4qPyH2HaEte8oly4Jqf+OKOYOEudM0fiC1yuiSAIQpkPug6v 9zd1YGhAg3gJQv3z4b7vH1qHtwAopJkgtKPLOrx2IyLg5OgIwxwD4deABAYyI3iPaYl6mwbBw7qd atiqiI1xzYFn7AlEjHSjDn5LgnYG85DdnkO7U2eowWZvZjM6b4nYkGcyL7YOr3MvN6w74Evr8BJE KfttXGzX4WVit7BENEEQpQuapZkg3g7rTjYICg1B585dcORwBNp3tIM0B/jctAJu//sEZooxnjKu eJ9mZvFjyrCZjdnERsSbwSbiIjTj1Fi797/nV30V2yzse4TbUOowgiCKpuAlCIIgCOLD4DHGCO4Z nXD4cDSidnaH3fAwSCd3hL2FJW6kXIdjLQuuddljaZnw39nkFH5MGS42Xo0zJQiCIIiiiAF1AUEQ xREWHkx1Joi3w8u5nCB2c9dpdbDtgx3BJzC7jQvE4vI4ejcRZkbGyJTm8G2xkSkGNWtOnUYQBEEU K8jDSxBEsYRmaSaIwmXmqAzhf0O+3u482+44ePESXI8d5ssSda7RED2bNKZOIgiCIEjwEgRBEARR /GECl0QuQRAEUdyhkGaCIIolX3zxBdWZIAiCIAiCIMFLEETJg2ZpJojCxTvAkE9c5bPNqMjWka1T S5SMaxG4fg9WzFqVJ52lxcWcog4mCIIEL0EQpZPk5GTqBIIoJJbtEMFuRBTfjowORqzfNIRH78sV NMPC4RNQ5p2KpDs37sKt/ww41nNC9wa9+PqrhY1UKoVTo77o1aQ/39anfgWJOvlxXcUfy8derK1T B8zEmSN/FpqwlNtmfTl94CwkJiQV2YcMbK3dkMAwTPGclOcYS9u0ZAtfc5cgCKIwoDG8BEEUG0JC QhAfH4/hw4ejVq1axa7+bJZm8vISRYUV2w0RFROEGB832Az7AdsXjkVGNrDNwwV2w/0Rs8oVNtPX QCrrgzmjMt9JHTwnf48xs0ajfde276ydpw6fQfMOzSAyMEBc9Gm9yopKCHur49rOkeXIcDLmFHzm rMG81bPQtK1lobRVbjt0Tzi+d1mKjcE/FIrNwib6QAxsenWGSTmTPMdYmrVjB5w4FAfb3p3pg0oQ RPEWvFLhSzlLQheBIJTJksj4e0zUIaSkPOTbbOynXCgx0SSf7be0pYtEItja2hZLscugWZqJogTz 6AYvc0N6lgwh3hORIzNCxksJqpmIEbbUBf9lSXHQ2xk9564XBK9mL9/TJ0/hPcMHZ4+dh0WDWvjn 8k0c+ieEpy+ftUpIP4dPqlXG3JWz8FXLhvwcuceQCan7dx+gSeu8E2M9uv8InpOW4J+/b6LOV7Xh sWE+Pv3sU5U8g9sOR+rjNJiamQqieRQcB9pz27+d38s9xT8fD+D5jkecQNN2lrkiMzpOIXiVy2B1 Wxnonad+bJu9f9NqCBePlapUwpOUJxgv2N97+md0qd1NIQjl5wVd2o9BbYZh75lAGJsYIyszCwOs hiDwxA5F3UUGIrSza4MyxmWwY80uheDV1Cb1OuWXT267+2BH+H27XnGucp9o69uC2siu6dJpK3D+ xAV+rZXvAU3XWtO1kHMh7hL6ju6l9d606tQCIT+HkeAlCKL4C97oy6k49Ne/dBUIQgM2dl1RUfgx Y2pqinLlyinStXkIS1s6QRBvx6Gdjug63A97PFwBmQwRf8ZDBAlycgD71i0hvGGI53pE7dQe0rpx iT8s6tfCkq2LkfYkjQs7xrrFm+A0rIeQ7olrF69zUfxj5GYV0cYYP28MJju5wNqhg1AFGUbPGMFF 4Gr3tejs9DV++H0N9v/4O1bO9cWygO9Vyg48uZO/M9E8znGSQvRdPX9VIbCYyD0ReRLfjO8PSbaE i8sZS6dyUbh24Tq0sW3Fy1BGk0eT1eVw8FH0+7/eOBIaiy59bbkNbed93aMTon6P4cIz+vfDsO/f BeVMy+Wx26J9M3iM9yywTep10pZP3uaf1gVykS9HuU+09W1BbWTXtE5DCyzd9h2epT1DvxaDCrzW yuUqc/nsFXz7g7vW+6ph8wb4wWMDfUgJgij+gte2YUV0bVSJrgJBKME8vO77/qGOKIHQLM1EUWHF diNERAfBa/JoPMuQ4ORf5wSlJIV/aDzGO1rBUJwOm2b1kXRgGZIePUaFCh/z9XnV+UMQSAf/+pUL o4qfVFSkx4YdQ8yBw4p9A0PNU4b0GdULTVo1xpmj8YJQy4Hvgh8wa8V0nDtxgYtoRl8hzxbvH1XO Y17FHb4/4c+jZ/H4wSNkZmQpjll1aqnYvnTmLxgYGOCLujX5fo4gBi+cusg9qn/GnsXizR469VfX vnZY++06LgZZm10X5z/W2HGQA9Yt2sAFb8S+SMxcPq3AMvJrk675mFfVqIwR9x6zUGlNfaKtbwtq I7umwZd/49farJKZTtdauVyVNvz7NM8DA2UMDQ2RmJBIH1SCIIq/4DUUvuzKiEV0FQiCKBWQt5oo KkREB8NrnHA/SoHHTzIFIShBQNh5HPHqi7ArqWhRrya8T27CmtMRaPBJVdhbNIV9/Xp57DBvIhMn moi8GZqvqJFTu6EFfzFbPb7qzQVvQfgLIo2F446cOgzlzcrzsFtNHIs4gaepz1QmXjoRGaf3mFlW v/QX6VxAs3e2nx8NmtZDdlY2jh86ibLlyqJGreoa88UfP8e9mfq0Kb98bzPeVpc2GogM3upayzGr bMavt7Zz2ORiNWrVoA8qQRCFQrGZpbmrZyRE1m5w9Iimq0YQBEEQb4Fjlx5w37IDtx+n40n6S4hk wOjuzRF+OQ0Te7WHS7QXUp4n4rc+/TG5WVO+7XssKo+d+oKwC997iG+zMFU5zMN4YNdBLmqeCYKT hdEymPeRjSHVxPFDJ2Be25xvN2tnif0//sa39279FZZtmqjk/e95Olpat+CC72j4Ma3tZIJ3xrKp XAiy12yfGdwjyWjUsiH2bN6nkj+/+nXpZ8fbwcKT1dF0HvPu+sxehSGTB2p8UMAm02JL8IwQhGt+ bVK3rWvbtZFf3+bXRtZfwYGhfFt52SBt1zo/mMhXvl/UYcdq1fuSPqgEQRQKRXqW5i6LIxAVFQnZ UR/FcgnWU7wBT1uIOkyFQxcnhHnY0FUkCKJYQLM0E0WFGSOyhZc97Ibvwej+vWFQ1hyyrGRM6N0O vue2w0QsRY+6DfBSKkFlExNhuw72Xr2KkCuX0b1BQ4WdaV6uWDTxO6yevxa2vTpDLM79WeH6nTMf I8uWl2FMmD+Wv3vvWIKF4zz5kjnBf//Gl89h4z6zsyWo07A25q2erbC72NkLW7y3oW7jOvh2nep4 z0ETB2D2MHekPk7FgHH9NLbx5uVbSElOQeNWjRRpja0a4dGDx7zM6Uun8lmit6/cIQiw+li1Z0We +iljJ7SPhf9qmkhJ03lsTGzQruA8k3LJw47/17gu92azOuXXJnXburQ9P/Lr2/zaOGXRZN5f6xZt hNPwHop0bdc6P5oI1+TUH2cUE5mpc+ZIvNZwaIIgCH0RydgsEfnAxhPO3H0DPoPqFlr4sS42HRZF IkK+XMJ0tlzCGFSvVB6JaekYvdgfMStd+HIJXez64NCiwp3Fr2nTpjh//vzrThKJEBYWBgcHB5V8 a9aswbRp0yDvQvXztHa6KLfNRkZGaNiwIbcxcuRIveulCyz/pEmTcPLkSUVa+/btsW7dOm6PKHrI Px/OrcUaJ60iii+enp7w8PCgjiDeOZmSTAzY2A3OjtMgNtT8bNtuRAScHB1hmGMg/GGSwEBmBO8x LVFv0yB4WLdTDV8VsfGvOfCMPYGIkW4a7bFQWDbR0KbQdXQBBIJ2BvOQ3Z5Du1NnqMHW4WWzN2+J 2JBnMi+2/q5zLzesO+CrcdmioopEKsG6sNVYbr8Olcwq0d9ugihCv42LrIdXn+USUMiC98KFCyr7 xsbG2LhxYx7BGxgYCBMTExVxqStMJLMfD2fOnMGQIUMgkUgwZswYveqlC7t27cLAgarhVGz/559/ JsFLEARRirHuZIOg0BB07twFRw5HoH1HO0hzgM9NK+D2v09gZlxG/heLK96nmVn8mDJs1mM2gZKK kK7lSJ2rBJuIi9CMU2PtHuqeX/VVbLOw7xFuQ6nDCIJ4I4qs4JXG+sLQ2q3A5RJkQr78SEpKQt++ fXHp0iXMnz8fCxYsUHhkb926hW7duiE5OZkLQycnJ4X3Vf7O8mZmZqJx48aIj49HixYteHp4eDjs 7Oxw+vRpRVnsHLlttv3LL79gypQpfH/Tpk3o3bu3St3YzJGtW7fmZTMvrFzwtm3blovbMmXKwM/P D8OHD9dYL031V+fcuXPo06ePShprw4EDB+juJ4j3DM3STBQlPMYYwT2jEw4fjkbUzu6wGx4G6eSO sLewxI2U63CsZcG1LvurIxP+O5ucwo8pw4XIqzGoBEEQBFEUKZKTVjksiuYTVH3/armEiPizwl/b bPiH/AlD4U8vWy7BzqomXy4h/to93Lj/XKutmTNnonPnznjx4gU+/VR10XpXV1cEBARwMTx79myF kJS/K0d7jx8/HkuXLlXss3BmlpYfV69exf3797Fq1SoetqwNJnovX76s2Gfhx+np6YiLi+P111Yv TfVXh3mQraysVNLYPksnCOL9QuN3iaKGl3M5QezmruHqYNsHO4JPYHYbF4jF5XH0biLMjIyRKc3h 22IjUwxq1pw6jSAIgihWFEkP75ssl7D8M81jipgnMy0tjXtTmUCdMGGC4lhUVBRCQkL4trZlFeSY m5vzEOTr16/ndpxYXKC3hnmUWbmDBw8ucIyufKIPueBduXIlYmJikJqaqvUcXerPhDPzFCvD9rOy sujuJwiCKOGUT60II4MyOuVdzIOEPkLa/atY2myEyjGHaq82nlCfEoQmsqSZ1AkEQYJXd+TLJYzv OwAVyxqqLpfQuz1corxQyVjEl0tIfPEcJxMT4bRrJoKG+eSxxcbGysUkE6zqZGdnq4jN/GBeWubZ ZYJx1qxZBeZnYlf+ztaU00ZQUBCfSErOoEGD4OXlxcfZsvHD+aFP/ZXR1BcEQbxbaJZm4n2zLO5b 6gSCIAiCBG9RI/RbW0B4sbBmfZZLmB62CqscVResZ+HCLBR58uTJmD5d9RgbK8uOLVy4UCWdiUw2 PtbCQnXRdWtra7i5ufGJqpjwfVuY6AwNDeU2f/31V0X6w4cPYWNjw8cJ51cvbfVXpmzZstybq+zl ZfssnSCI98udO3eoE4j3grHYGHsnhuLixYvUGQTxHiljWIY6gSBI8OqGqOM0OHXrjrQXUmEnEway KpDAEGtPR/LlEq6lKi3wLgIaVakEz9jIPIKXTfrUr18/eHt786V42MzKcjZs2MAntGJLhTRp0gRn z57l6WxcLBv3y8KJnz17pmKPhUUzr/Fbt08k4gKWeXb37dunmAxLXj6bQVndi6xeL231V4bZZZNt MXEsR3nyLYIgCKLkil6r5lb8QSlBEARBlFaK7Dq8HRceROwfUSrLJWxx/hp2gePRpebnGpdLiLyb jLsz9mgt9/fff+chhey9tMC82swjLJ8tmrF27VrcvXsXPj4+9AkogtA6vCUXCmkmPgQpKSnUCQTx nqG/3QRRdH4bF1kP79HveqL1rGy+XIIs1o+HN+u7XAKDeUrZRFMshJeFCfv7+5eqG2To0KFc9CoL 3v3798PX15c+PQTxniGxS3wIqlSpwicwJAiCIIjSiLgoV+7UCrboeO7C44rlEpxcMCnUnS+R0NOi Nh5lvERccjJfLsG/j3seG+fPny/VF5iFLh85ckQlTX2fIAiCKNmQp4kgCIIorRgUl4qGLfoaSyd0 R93PyiNqjB/qVqkL12NHsO/2Hb7N0giCIIoyLKSZIAiCIAiCeH+Ii2vF1zjN4i+CIIjiAs3STBAE QRAE8X4xoC4gCIIgCIIgCIIgSiLFxsPb1TMSkVHBcLBxQpinLV05giCKHV988QV1AkEQBEEQxHuk SHt4uyyOgKjjTL4dGR2MWL9pCI/ex/dFHabC0TOGriBBEMUGmqWZIAiCIAiCBC/HYVEkoqKCEeMz kYvb7fPHIiMb2ObhApH1NMSsckV45O/ouuhwoZfNljJSRiQSITw8PE++NWvW8GNvAjuPvcqUKcPL CwgIeKO66QKbqbpt27Yqae3bty/1M1gTBEEQBEEQBEGC94PAPLrBS92QniVDiPdEVP6oHLvThFgA ACAASURBVDJeSlDNRIywpS74L0uKg97OQr7fC73sCxcuqOwbGxtj48aNefIFBgbCxMTkjcuRyWTI yMjApk2bsHjxYmzdulXvuunCrl27MHDgQJU0tv/zzz/TJ4Ag3iM0SzNBEARBEMT7pciO4ZXG+sLQ 2g17PFyZMkTEn/EQQYKcHMC+dUsIbxjiuR4yIV9+JCUloW/fvrh06RLmz5+PBQsWcKHJuHXrFrp1 64bk5GQuCp2cnBQeW/k7y5uZmYnGjRsjPj6er2vLYB5fOzs7nD59WlEW86IyQcq8tn5+fhg+fDjG jRsHS0tLTJkyBVFRUdi8eTN++eWX108cDAzQunVrXv6kSZMwZswYrbY01U1TG9Q5d+4c+vTpo5LG 2nHgwAH6BBDEe4RmaSYIgiAIgiDBC4dF0YiIDoLX5NF4liHByb/OCepOCv/QeIx3tIKhOB02zeoj 6cAyxF+7hwoVPubr82pi5syZ6Ny5M06ePIktW7aoHHN1deWhxNWqVYO9vT0Xi0xEMkEpF8Vyxo8f j2nTpmHfvtwxxCycmXlmvby8FHlYGYyrV6+iU6dOXKQuW7aMhw+PGDECS5YsUZyvDhO9ly9fzteW prppaoM6Z86cgZWVlUoa22fpBCEnU5JJnUD9XKIxFhtTJxAEQRAECd4PT0R0MLzGjQKkwOMnmcjJ kSAg7DyOePVF2JVUtKhXE94nN2HN6Qg0+KQq7C2aYvlnbhptMS9mWloa96Qy0TphwgTFMeZxDQkJ 4duGhob51snc3FyoRw6uX7+e23FicZ4ZV5lIXblyJWJiYpCamsrTKleuzMvt0KEDli9fzve1Xgyx OF9bmtClDenp6dxTrAzbz8rKok8AoRBhAzZ2o454x/xP3IT6+QOyd2IoiV6CIAiCIMH74XHs0gPu W3ZgfN8BqFjWECIZMLp7c4RfTsPE3u3hEuWFSsYi/NanPxJfPMfJxEQ47ZqJoGE+eWxJJBKFkGSC VZ3s7GwVoZkfzMPLPLtMLM6aNSvP8UGDBnGPLxsby8b9yjEzM8Pz58/zHe8bFBTEPcEF2dKEPm1Q RlN/EKWbQV0HQ2xgRB3xDmmFptQJ7xlJTjZ2HwqkjiAIgiCIUkiRnLQq9FtbyI6uxub9e5CSmQ6U NYdMJsKE3u3ge247TMRS9KhbFy+lElQWRGSPunV42vSwVXlssVDhpUuXci+pfHysHDZOlh1ThwlM NjZWHWtra8TFxfFQYBZmrM7Dhw9hY2OjMqPzo0ePsHr1an7e3Llz8ezZszyiMzg4GG5ubjz8OT9b muqmrQ3KlC1bNo83l+2zdIJQ+UIQGcLAQEQvepWslyg3+iUhIYE+5ARBEARBgrdoIOo4DU7duiPt hRRPMl8iLacKJDDE2tORaFSlEq6lPnr9SnvM09gxddiETz/++CMPP2YCUtlbumHDBj7TMvOONm/e XJHOxsSycb8VKlTIY4+FJw8ePFhjndl5bNkgedgzw9nZGfPmzUPVqlX5eFu2r2ijSIRy5cpxQczG 9sonxNJmS1PdtLVBGWaXTbiljPIEXAQhh40Ppxe9SuJLTkpKCn3QCYIgCKIUIZKpz86kRpZEhpm7 b8BnUF2UEYsKpVBdbHZceBCxf0QJ4q4LjhyOQPuOdtji/DXsAsejS83PYWYsH5PKqi/C08wsRN5N xt0Ze7SW+/vvv/NlQdh7aWL69OmwsLDgM0XLWbt2Le7evQsfHx/6FBQx5J8P59ZiVDQzhampKX8w 8i6Rj+Ed1GUIxAWMZ1fm5PG6uJ34jH8G2WdRJrzzLVFuSq6Ifp2/Vo3yaNv+Bl1k4q34ps4oxfYv /2wvML9EKsXuyJ+x3H4dKplVei+fKYIgCIIgisZv4yK7LNHR73qi9axsHD4cDVmsH0TWbpBO7gh7 C0vcSLkOx1oWXOvKf1yfTU7hx9SRe0lZ+C7z8Pr7+5e6m2To0KFc9CoL3v3798PX15c+QYQK6t6w gki49wxrx3fUOb/L5iNoo4d9gtDlni2MPARBEARBlEzERblyp1b0Ff7vy7cdbPtgR/AJzHZywaRQ dxy9m4ieFrXxKOMl4pKTITYyhX8f9zw2zp8/X+ovMgtdPnLkiEqa+j5BcGHw6p+u5EhzYFxGd48w cvSzryvSX9dj2Nx4YJw3Amd9QheyGPGpkSFCR47CqqMtMP3Y97Cqkqz3PVsYeQiCIAiCIMH7QQlb 9LViO2qMH6YGrYDrscN8WaLONRpijdMsupoE8baCV08PL/8SMTTQuwxtVCkjfiV+lFP7YWXCWHyW laT9nFA2Rl0QTCObCPaT9G63arn5l6fQ7r9t4CLbatl2TOsjEWz8i0215mAvcoVby0+11Vc1X/Nj C1TsFMR9n7GYoRaoMiAwDH2aF9xu9Tq/SVno6IZdAQ4wyEoqnJsu6RxiWb937IDm1YX7I0tWaPeT PnkIgiAIgiDBW6RgApdELkEUNjl8RnT9BK9++WUyzcthVX38N+Z18MUZJqgSHCDKvPf6oLAt01Uw Zeq/3NaDPX5qIlt7PXldjcsoRLZ1h6pC3nt4mFkRva9uRm+e4x60aSzlfFWNH+axkx9VjVPxm/9r UW6Ji7niefACmAviucUn9/Sqs35lPXz1UOAYziY5CGUVzrJmD44dwxnh3apbM37N9ZWm+V0n5fua IAiCIAgSvARBlHa5K6g0kZ7eMCM9Pbw5WuxfCPDlwmfAJAfIMu7mET6iA5tzw5ZfwbyaTk3vqgim aknhuaJZ6Tx5vs/OH4Ld4P087DnK5jTsNtTgnkp+Dg+H7ocB/vuxV62e6uVinBumX/N9JZDjsaqD I/d6rux2DDO499Qb1qFzFCG6zSorla0xn7IdoQ4Q6qDl3F2TEnPrN64Vqgh9dB8fo804YK9/PO4J +rUb/tbY/gltHip5sF/XeZcX4KulvxB3WqUslE3DPX7+l6heXeifDNnruin6xhs7p3+Mz/69rPU6 MFJWTVDzHOeK8BzZXe02la7fkCGzIPKuj9thY7TeT7rcc0TJhk3GRxD6Yiw2pk4gCBK8H4aunpGI jAqGg40Twjxt6coRxDtA75BmIa+RHh7enBzN9j83KYtz13K39w52fCU61T2LTASGCaIuV9Tu3RCO XgHNcFbJa5lcqQFcLm/MtfnkSm6+mIvoaWmGCzGvRJT/HNgJYouF9VZjHlIuzISy3Fshzv+10Pqs rLFCJDKx1tPyDj6/EIlNcEDz2seAo7mhwS5OGbz+oSN9FfVAKJTspGGTXLCy+iryNUFz4T2vnf1a znXAQy/HXPFow0K37yj1WwuYmyPf9jfvploWvx7Cq6D+kpd1wWsOvy4DAsfi05d3kLJ6Iob7s2sU xvcfCfsz/H/BuZHfQ1ZJptHuhDZVVcbryq/lmVfe+YdeE7TaTFG6fiLh+jlsuY0vhN2jFNJMaBG7 bOZ5gtCXvRNDSfQSBAne90eXxRGIioqE7KgPIqODEes3DdZTvAFB8Io6TIVDF0H8etjQVSSIwhK8 ek5axcJJDQ1EeuTXbD/p5Ut0C9gOqIzf3Y8ZI2twjyhLs1rmAstKt4EnyieqhjNXi4tU9Q6+Emyf m1zEQaXw3E9eCnbwUkXEsfDgOH7GbSQlAZb3DubWZZw3elje5rVOsrTDBBNBgG9QCg0W8qvX46xS +bllCALPK9eb/DrfQxWxzuywfqhej6k4DecK9Tvwqg1t2gh9+VKmR/vVQqdZnQWqX4gqoL+UHkC8 CjWXCn33uXDeCP9X16iW0vkF1OPBngUq1/JBkJJ3Pm4Lhmm1+brt7PpN/9JB5Z7V5b4mSieDug6G 2MCIOoIoEElONnYfCqSOIAgSvO8Ph0WRiIoJRoyPGxe32xeORUY2sM3DBSLraYhZ5Qqb6WvQVSbC oUWdC7VstpSR8uzOIpEoz492efqbeg7YuWFhYXBwcFBJX7NmDaZNm6awq16X/OwxjIyM0LBhQ25j 5MiRerdVF1j+SZMm4eTJk4q09u3bY926ddweUYwF7xtMWuW0KIjdga9WxH79v+LehHyF3vw/M4np 6WiyYT0E2YsaF6NzBZMg/pJUxp0mqIQw45ifYpt5P4e98twuHYhXnsRccagcnls5PYHXj5UxUl3U KWCidj9eT4SVoFVkS9JlKnWSCO1QiNZjW3jo7oDA79GkolD3g8rh13ntqKB2rqY2KIeB59t+DWX9 u2Yyb3/+/fUq/Jtdi1djd5tUlL32/gaGoXuTBNWe01oPQeArPSioribCLwRot1ktLlql7Zq+jwu6 r4nSiYHIEAYGIuoIouB7RZa74kBCQgLq161PHUIQJHjfPcyjG7zMDelZMoR4T0SOzAgZLyWoZiJG 2FIX/JclxUFvZ/Scux4oZMF74cKFd/6DydjYGBs3bswjeAMDA2FiYqIiLvURKzk5OThz5gyGDBkC iUSCMWPG6N3Wgti1axcGDhyoksb2f/75ZxK8xV7w6j9p1U9zdQ8bHOYdVuAkQ+YfmSqJTUHUKq02 ViP1Wu54245ucBNuwbMj5YLpIeLmshz9MGhgVUHgvfKkCgKp0n831cJzb8I87QbmcQ9krseQ5TH/ 6L9Xsycrkzs2tvHHOTC/dBibMBa97mxViNZsfp6p2mRQNxXnrnq1VFK3xjdRo5xqPmWRzOxArdw8 56oI8Jt44jvllSBmY3QvYtNg7e1XL4u1NcRft/5Cm6qY3nG/IFqFOgVcRJR7VVx8FX5++85DyBrn KF077XZZaHqc0hhgVREuiGEtNvn3lNr1U79ndbmvCXqIRxAF3StyUlJSUKVKFeoUgiDB+26RxvrC 0NoNezxc+TjBiD/jIYJEEHSAfeuWfOzZEM/1kMX65msnKSkJffv2xaVLlzB//nwsWLBA8aV269Yt dOvWDcnJyVzEOTk5KTyl8ndd/1Cqe67k++PGjYOlpSWmTJmCqKgobN68Gb/88gsyMzPRuHFjxMfH 83VyGeHh4bCzs8Pp06c12mXb7Fxmi7Fp0yb07t1bpR4GBgZo3bo1bw/zwsoFb9u2bbm4LVOmDPz8 /DB8+HCNbdXUJ+qcO3cOffr0UUljbThw4AB9oujHoV4/KhhlQrarTgrFEITej+vKCRvPUXNSP0Fs 7ecTLa2SH3Mtp+YhFcRVR2H7qGo4LBNI5h9dRKhSKLA5BPE4+ZVnNHAsKr74R0PAa1V083JDLBt7 qjKmGKgmCDYmSM/MHYXRcwUBGthBxXua9UKGqrVb8Dw8DNi9iZD2T97w4y/U7Bz7Ho3M/sn/XPmE U4r65Naf1ddcS/tlsn80lOWiNb96f9198Vwx/hf+vyB05PfoFuCNe+zhALf36uRXY5S12b0r1DN3 gi3VY0yEZwllaLf5OeKUQ7lfyPR+GEmCh77TdOXk8bq4nfgMyrEpfEuUm5Jr83X+WjXKo237G9TR JUzwMtLT01GuXDnqGIIgwftucFgUjYjoIHhNHo1nGRKc/Ouc8E0khX9oPMY7WsFQnA6bZvWRdGAZ 4q/dQ4UKH6PuZ+U12po5cyY6d+7Mw2+3bNmicszV1RUBAQGoVq0a7O3tubhjX3iawi6Vw5r1+eO5 bNkyHu47YsQILFmyBPv27VMcGz9+PA89lqexcGYmYr28vLTau3r1Ku7fv889wexcdcErh4ney5cv v/4j/ir8mJ3fqVMnLng1tVVTn6jDPMhWVlYqaWyfpRPF/A++nmN4Gcxrq28ZymR2H4mt3UdqzXen USdsvdQpz7E7ZrUx8dJaTGQ2XjzDV+vWYmseKzdw54UJHIR8PJbihbAvvL3Oe0NRG5V87Dy8tq+A na+hPl8p6nEjT5vkaar11WTnBmqavsSmua/WFPZy0Hgu1Ooj43XX3n6Zxj4UftDr2F+yPNdILY/y tSygHh+7Csdc1Y89e9UGbTaRpz753U+63HMECV5tJNx7hrXjO+qc32XzEbShByolUvASBEGC950S ER0Mr3GjACnw+EkmcnIkCAg7jyNefRF2JRUt6tWE98lNWHM6Ag0+qQp7i6ZY/pmbRlvM65iWlsY9 n0xgTpgwQXGMeVxDQkL4tqGh4Tv5IqxcuTIvt0OHDli+fDnfl2Nubs5DkK9fv557McRifPHFF/na Y15q1pbBgwcXOEaX2VMWvCtXrkRMTAxSU1O1nqNLn7CnnsxTrAzbz8rKok9UKftxyNg1x1EvcUw/ KjTzZfkMRUg1G7fbsMJ1UFcVzo9UuudK8f2h50O8HGkOjMsY6l5AjqzQHqgM/l9uRFbg9a2l7jop t/1D9QM9GCMIErzvFccuPeC+ZQfG9x2AimUNIRK+g0Z3b47wy2mY2Ls9XKK8UMlYhN/69Efii+c4 mZgIp10zETTMJ48tNo5VLvyYuFQnOztbRRi+KcriUL0cMzMzPH/+XGVsrhzmpWWeXSYYZ82aVWA5 TOzK36VSqdZ8QUFB3LMsZ9CgQdxzzMbZsvHD+fGmfaKpf4nixZusw/smZRB5ufXMGF0urEEXvneN /Y4mCul+onuudD8Q0feBh1jPtcXV7b9Ie4Hdq37FieDTyM7MRt1mtVHF/FNMXDr6jezpwpB6Y3N/ Gwh1NypjxMvs7+KE/zWvo/O5P1/boldZ6uh6vq5tf98PqujBGEGQ4H2vhH5rCwgvkbUbRvfvDYOy 5pBlJWNC73bwPbcdJmIpetRtgJdSCSoLIrJH3TrYe/UqpoetwirH6Sq2WGjv0qVLMXnyZEyfrnqM jWtlxxYuXKiSzgQhG8tqYWGhc52ZZ5aFAvfr1w+zZ89WpD969AirV69GXFwcDz+OiIhAhQoVFMet ra3h5ubGxTATvm/9408QnaGhodzmr7/+qkh/+PAhbGxs+Djh/NqqrU+UKVu2LPfmKnt52T5LJ0rX j8M6FpX1Cmlm+WWyW9TRxFux+cIave9rotQ+EtF7Ij6xoUjP+0v1Ya+v20b8HXcVc7dOReP2DbXm 09WePuz8eyP+vf8Eq6dswHfDV2Dhrlmo29SiUMv96epm/j60/niV/cKYHE7ZxvufbI4e2hMECd73 jKjjNDh16460F1JhJxMGsiqQwBBrT0fCw7odrqU+UsoMNKpSCZ6xkXkEL5ugiYlQb29vvmwOG/sq Z8OGDXxCK09PTzRp0gRnz+aunsmEKxv3y0J/nz17plN9fX19ebi0i4sLL4fZZjg7O2PevHmoWrUq Hx/L9nfu3KlyLgt5Zp7ot+4zkYgLWObZZeOC5ZNhydvEZlBW9yKrt1VbnyjD7LLJtpg4lqM8+RZR nAWvfj8O61ueEl76lPAPhekSH+S+Jkqp3H2DqBUjPT286hEEV87kDlOqULl8nmMv0v7DNs+fcDYm d4WE5jaW+D/PYfioQrk89ia3m4GX/2VAliNDtS+rYuCMPmj2dRMMb5A7NGvckpHc1rYL61TOrVit IkZ+OxiLBi7DL2t+x7xt0/K1J0cuYHde2aRTfk3tz699+rSdEeizH+E7ovHxp2aY+sMkfFHfXKuN v09cgd+0zWjl0AIuq3Pb4euyEX9GnYPzqnFo49hS7+tIEAQJ3neOdScbBIWGCGKsC44cjkD7jnaQ Cr9ZPjetgNv/PoGZsdy7mLvu59PMLH5MHSbybt7MXcbi999/V1kGqH79+ioTO8lhS+woL7ujzTug nN6jRw8+I7QcNikUg82qLIeNu2Uv9XMnTpyo1W5+4T36hP4ot4lN5KWtrdr6pCDByyasIsFbAoQB aBwTUTLva6K0PuzQM6RZyGukh4c3Jyev/fot6+LyqWtw7/0dKlYxQwu7ZugzuTvKVyqPHUt243R4 PBeh2dkS+Iz3g6HYEJOW/1+ev+frjucO00q59xgz7Rdg68Kd8Du6XJHPf36A1t8Ctb7KnQ/kn/M3 dba34/JGvcrX9Psjv/bp03aG/UhbfNW2PrzH+GLHd7uxYNfMfG04CPnDA6JxoF4odwAwsWs/3Aat BRFMY/0JggRvkeTodz3RelY2Dh+OhizWj4c3Syd3hL2FJW6kXIdjLQv+K0a+XMDZ5BR+TJPgZZNC sXBbFtLr7+9PV/0tGTp0KA8Ply+PxNi/fz/3chOl7MchQRST+5oorQ879JtUikUDGBqI9Lq31O1P 8R2PkC0ROBdzEcm3HiDq5z+Q8PcdfBs4G2cO5UZN1W/9P+45ZbC0ictHq9SZeTJ/XxeCv09ewePk f3n608fPVMpaG+sNs8oVVNLk2+pputhT3tYlv6bz8mufrm2XwzzkDQXBy7h9+Q4/lp+NQbP6Cf18 F/t8c5dIrNeyLobMG6Dz9aeHvQRBgveDcGpFX+H/vnzbwbYPdgSfwGwnF0wKdcfRu4noaVEbjzJe Ii45GWIjU/j3cc9j4/z583SVCxnmyT1y5IhKmvo+UTzJyZGSOCBKHFKaUK9UP+zQ9zvNaVEQ2ON0 mdr/cl6v0AuNyxiWK2+CAdN689fDu48wx9EDtwUhlscT+0pXGxiI8hzb7fMrYn89AedVY9HMpgnG NnXN8/CmQqXyWiO/bl66zd8btK6nsz3lbV3y5/dAqaD25XtM+drl5BPxpmZDvtSiuIxYIWHZrNsi HR9g0N8+giDB+8EJW/S1YjtqjB+mBq2A67HDfFmizjUaYo3TLLqaBPGWJCTdws1EmlSKIIiSInj1 n7Tqp7nddM6bu9Sa9gcqqQ+f8Pd6VnV4PquuzXAy+Awuxv6lEGKWnRrlmazp5fN0vv2/FrXx7ysP q/yYpm3ltNSUNOz8bjfKGBuht3O3Au2xMbT/PUvH46THqPx5JZ6uS/ma0vJrn65tl5MjleJkyBm+ 3dzWskAbgSt+xdUz1zHi20E8zHnbtz/hZ++9GDK3v873CkEQJHiLFEzgksgliMKla6MeMGhsSB1B lCgkUgk2HfKjjiiVgvfdD9NQt+/Rfxke3k5BVmY2jE3KwLpPG/Sf1ovnGzKvP/darnXNndm4TbeW uWlq3kvH/7NDwl934NZpHhp3aKixLE3tGtPEFUbGYkGo1sGchW74ooF5gfaGug/AL6t+x8yu33KR vPHPVTqVryktv/bp2nYGW3pxlv23SH34FA3b1MPgOf3ytfFn5Hkc2hHD978ekLsk45W4a4jc9Qdq W9ZCK4fmel9HgiBKDiJZAZ/wLIkMM3ffgM+guigjFhVKoe/CJkGUFOSfD+fWYlQ0M4WpqSnKlSv3 TsvMlGRiwMZucHacBrGhmC4CUeIE77qw1Vhuvw6VzCq9l88U8WGRf6f17NgThga6P8T7be8XepfV Z8Ad6vASgDRHioNHD9L3BEGUwN/GxeaXbVfPSERGBcPBxglhnrZ05QmCIAiCyJc38fDumuOoc97c kGbyDJaUe4UgiJKJQVGuXJfFERB1zF1CJzI6GLF+0xAevY/vizpMhaNnDF1BgiAIgiA0wtZW1ef1 PsqgV9F9EQRRMvmgHt6cnBzhpTmkudviaETFBCPGx42L2+0LxyIjG9jm4QKR9TTErHKFzfQ16CoT IfzbToVar+bNm+Ps2bOKfUND1XAoqVSqSJdv6ws7NzQ0FPb29irpbGkftuTPm9iV19PIyAgNGzbE 1KlTMWLECL3bqwts9mtnZ2ccP35ckWZtbQ0/Pz++FBTxNp8L+qNLEARRGOjr4a1jUZl7bfXJL5PR RH8l5V4hCIIE7zv6Q6T5GPPoBi9zQ3qWDCHeE5EjM0LGSwmqmYgRttQF/2VJcdDbGT3nrodsYcdC rdeFCxfyfPFJJBKNX4pv+gVpbGyMjRs3omvXrirpgYGBMDExeWO7rJ7sQcKff/6JYcOGITs7G//3 f/+nd3sL4qeffsKAAQNUzmP7rP6Wlpb0yaI/ugRBEEXg+1S/WZrrW54SXvqU8A/oK7vk3CsEQZDg LXTy8/BmxPigrM1M7PFwZd9CiPgzHiIwMQfYt24J9rU0xHM9soR8OfmssZicnMyF2F9//YW5c+fC w8MDWVlZ/FhCQgJ69uyJ+/fvIyAgAD169ECZMmVyO0ac2zXyvNrKkKez8+R5lfcnTpyIJk2aYPLk yYiOjsaWLVu4KMzMzESjRo24MGUeVkZERARsbW1x5swZhV3mNb148SK3t2bNGgwdOlSrTeX6tGzZ Etu3b8eUKVMwatQorbY0tVdTv6hz7tw5ODk5qfRLs2bNEBQUlO/1IHT5XNCvJ4IgiEIRMchdM5cg dLlXCIIgwVv4Xy5aPLxOXrGIiA6C1+TReJYhwcm/zgmZpfAPjcd4RysYitNh06w+kg4sw8VbD1Ch wsf4orKxxjJmz56NTp064ejRo/jxxx8V5TJYyO/WrVtRrVo1dO/enb+YEGXeV/aunFcuDBnyY8rH 1bfl+99//z2+/vprLi6XLl2K3bt3K/KNGTMGM2fO5GmMtWvXYv369Vi2bJkiD6s349q1a1wMDxky JF+bynWwsrLClStX8rWlqb2a+kUdJtRbtGihUh7bZ+nkoXz7zwVBEARRWL8z6DuVoL+9BEGCt4gJ 3ojoYHiNGwVIgcdPMpGTI0FA2Hkc8eqLsCupaFGvJrxPbsKa0xFo8ElV2Fs0xXedJ2os4+DBg3j4 8CFEIhEXmGzcqfxLLSYmBmFhuWN12PjX/MRrRkZGgSJXk+CtVKkSL7dz587w8vLi+/J81atX597Q 69ev514MsRjm5uYqtk6dOsW9sX/88QdSU1MLtKleH2YzP1ua6p5fv8hJT0/nY4WVj7F95iGmPxr0 R5cgCKIokJMjpe9UQiekFJ1GECR4380fIs0hzY5desB9yw6M7zsAFcsaQiT8rRrdvTnCL6dhYu/2 cIkSRJ6xCL/16Y/EF89xMjERA/bOx55+3+Wxxca0sgXMc8vKUZQr59mzZ4pwXuV09bDcgkKamTBU ty9/L1++PJ4/f849qeplsJBj5tllHmTmWVU/l43D9fT0xLZt2/Dxxx/rZFNZ7Ldt27ZAW5rap61f CuoT5X4m3vRzQT/OCIIgCoOEpFu4mUiTShEEQZDg/UBo8/Dum9kWEF4f2c/D6P698xfuLgAAIABJ REFUYVDWHLKsZEzo3Q6+57bDRCxFj7oN8FIqQWUTE2G7DvZevYr5f2zA951UPb0srHfFihUYP348 5syZoyiX0apVK36Mje1VhonIW7duoVatWip11dYGRs2aNbFr1y706tULCxYsUBx7/Pgxn7mYeVUH DhzIx7gysSo/3q5dOx7WzCaqWr58eR5PbUpKCg/JPnTokM42meBk44GZXTa2Nz9bmtqrrV+UKVu2 LA+DVg71Zt5dZouepr/95+KDiW2ZFBIpXQOiZCGRSqgTSildG/WAQWND6ghCp++JTYf8qCMIggTv +xG8DFPHBXDq1h1pL4Rf36JMGMiqQAJDrD0dCQ/rdriW+uh1ZhHQqEoleMZG4ruOE1Ts+Pj48LGu K1euxOrVq7F3716FoGBLAA0ePJiHBbMJpORL7GzevBmOjo487PfBgwc6CV4mVl1dXfmSQqwcf39/ xXhYJjw//fRTTJo0ie+zSaaUzx09ejRfhkhTiDGrS5s2bfh58vT8bH700UdcdLJzmABnSwTlZ0tT e7X1izLMLlvKqHXr1oo0ts8mriLBW3wFb/zN0zh9I44uAkEQJQIDkSHEhmLqCIIgiFKMSFbAr+ss iQwzd9+Az6C6KCMWFUqhcpuLenwGIy0PXrsv/wOxf0Shc+cuOHI4Au072mGL89ewCxyPLjU/h5mx 3LMo44r3aWYWIu8m4+/xW7SWGxwczJfTkc9oTLw58+bN4x5h5jmXw5ZZunfvHpYsWUId9BZkS4FF wffh3FqMimamMDU1Rbly5d5pmZmSTAzY2A2THFz5D8TSwtdfdOHvf9yJpBuvBCP33Cy3X4dKZpXe y2eK+LDIv9OcHaeR4CV0/p5YF7aavicIoggi145v+tu4yHp4g2d1QpeMbBw+HI2nIUtg1n0hpJM7 wt7CEjdSrsOxlgXXukyCsyX2zian8GPq+r19+/a4efMm93p27NiRj5clD+Tb880338Dd3R3jxo1T pB04cADe3t7Uv2/9ufhwZZc2b4ixOHd2d/pBTBAEQRAEUTIpsoKXcWiBrfC/Lc/nYNsHO4JPYLaT CyaFuuPo3UT0tKiNRxkvEZecDLGRKXxsnPOIrWPHjmksl3g7LC0tERISotKXbJ/6t3gLXn3xDjBE ZFQwHGycMHN0tn4nJ4VjnjvgFuCAam9TibgtsItphSj3JhoOXsSmWnOwd5z36+Os3A6JGJQwFrZ0 uxEEQRAEQZDgfVfcTLgNQx2jpL9zqij8XxFP79/CsmZjVY45vvq1fOPmbbqiRLFHwie5Nimy9Vu2 Q4SoqEhE7bBDZHQwYv2mwXqKtyB4e8FuWDgcugjid2RWgXYesIdR3VzeTuwKXIjZjwE2Y7WI3V9g fiwMUdWVkqs3g3VHX8TFCed8SfcbQRAEQRAECd53xE9XTOgKEEQxYsV2Q0TFBCHGxw02w37A9oVj kZENbPNwgd1wf8SscoXN9DWQyvpgzqjMfCw9xNlQwNqr6lvW6CLi/PuhjXte+6Ej5wCBYehWXf1Y VVSvB8TeeUgXlCAIgiAIggRv4cMmv2KTYF28eJGuAEFowciwfJGrE/PoBi9zQ3qWDCHeE5EjM0LG SwmqmYgRttQF/2VJcdDbGT3nrhcEr6N2Q0nnEIsOcGNilIUkD96fm97RDbtehThf8HLEDH/5Cf2w MmEsLKGa36pjC5wZ9w0mqNuPO4hV9bwR1YbuI4IgCIIgCBK8H0j0tmxuydd/JQiieHBopyO6DvfD Hg9XPtg44s94iCBBTg5g37olWDT2EM/1iNrpmK8dRTgzE68bamBXQpggcnPH2x6Ic8CENg9RdWQY orjnlnlrR/EQZEso588VxV/afK9mXbAz+DamHxurpfSHSLoG4byqdEEJgiAIgiBI8L5bLCwskJKS QleCIIo4K7YbISI6CF6TR+NZhgQn/zoniF4p/EPjMd7RCobidNg0q4+kA8uQ9OgxKlT4GHczj2uw dBEH5n6JQQkPBYGbK0xfj+NtAXPzXFF6oMMo7FWk98PKACaIgZUJ8kmutIQzx53G3nHfqI7bVRO8 944K5wXQNSUIgiAIgiDB+x6oUqUK0tPT6WoQRBEmIjoYXuNGAVLg8ZNM5ORIEBB2Hke8+iLsSipa 1KsJ75ObsOZ0BBp8UhX2Fk1hX79eXkNckLbCBOF9RscO2FVdKZ3t39sCuw5M2Iblhiq/moXZkuVn 56nbUTOvfRKrXB7s+QW3l7nkDYMmCKLEkSOTQiKlfiAKhq3DSxAECd53Ci3uTRBFG8cuPeC+ZQfG 9x2AimUNIZIBo7s3R/jlNEzs3R4uUV6oZCzCb336I/HFc5xMTITvsXtw62CnRZCeVkp9HYb8MMAR AwLDcsfrJgnpG4T8k8biwZ1fhIRWudnZ0kKDhfRAdWGbG64MG22tkHuXKZyZIEoD8TdP4/SNOOoI giAIErwEQRD5M2NEtvCyh93wPRjdvzcMyppDlpWMCb3bwffcdpiIpehRtwFeSiWobGIibNfB3qtX EXLlMro3aKiw8zoMuRUGDJ6DYbV8wUKZpx/7ns+o/KB2C+wd7JgbztyxBayO9sOgAKCaeQdYzZ0D O/9X6RDs6Dkp1QWvObi9bDt5dwmilNCiditY1WlLHUEUCPPwbjrkRx1BECR4CYIozdiNiIBTt+5I eyEFRJkwkFWBBIZYezoSHtbtcC310evMIqBRlUrwjI1UEbwTEuRe2SbCdlge8Vlt4PeIGqih8OoO WJrgUEANlZYcaqPqxX2wZwFmwFuwTd5dgigtGIgMITaknzoEQRCl+m8BdQFBELpi3ckGQaEhSMvM wIGwEKSkv4A0B/jctAJu//sE/z5/9ur1FP8+e8bT2LH3iaVNP5yZexAXFClssitHDLv5DaLcm9BF JAhCK94BhrAbHgafbUalps12tRwLxU7g+j1YMWtVnnSWFhdzim4ugiBI8BIEUfTxGGOEVm064fDh aBza2R2xR2MgleXA3sISN5+komrZj1DV+CNUMzbl2zf/TePH3ittxiJKvmYvJ9eTTGKXIAhNLNsh gt2IKL7N1hqP9ZuG8Oh9uWJwWDh8Asq8U4HJ9r8dv1i1TtNWFJoQVS5H/VVYPH/6AiGBYZjiOSnP MZa2ackWvEx/STcbQRAfBIrzIQhCL7ycywHO9nzbwbYPdgSfwGwnF0wKdcfRu4noaVEbjzJeIi45 GWIjUwxq1rzItiX6djhdUIIoxazYboiomCDE+LjBZtgP2L5wLDKygW0eLrAb7o+YVa6wmb4GUlkf zBmV+c7q8fjBY9y7eQ/mtc3x6P4j3BW23wVRCWHv5rv0QAxsenWGSTmTPMdYmrVjB5w4FAfb3p3p piMIggQvQRAfjvKpFWFkoLs3Y7ET+/8jpN2/iqXNRqgcc5AvrvuE+pX4sGRJM6kTCI0wj27wMjek Z8kQ4j0ROTIjZLyUoJqJGGFLXfBflhQHvZ3Rc+56QfBq9og+ffIU3jN8cPbYeVg0qIV/Lt/EoX9C ePryWauE9HP4pFplzF05C1+1zJ3PQO5dlQvQIZMH4qcfdmPu6ln4bXsQ31806XuFfW12BrcdjtTH aTA1M8WYWaPgONCe2x7uOgT7t/6GMmWN4bJ4Mjp1sy6wL9h5yoJYvu81dTlsBTHburMVTkTF4Y/g o3BfM1vl3Atxl9B3dC+ttq06tUDIz2EkeAmCIMFLEMSHZVnctyW6fdXSa+JBubt0oak/CYJzaKcj ug73wx4PV0AmQ8Sf8RBBgpwcwL51S+QwMeq5HlE7tYf/blziD4v6tbBk62KkPUnDAKshPH3d4k1w GtZDSPfEtYvXuSj+MXKzitCV08GhPbau2I7b12/j7PFzGD9vjOJYfnYCT+7k7/fvPsA4x0lc8DI+ q/kZDlzcjyvnr2LRxO8Uglc9jFkXj6+L52TMHDwHNSyqY5tPAFYGeufJc/nsFXz7g7tWGw2bN8AP HhvohiMIggQvQRAfBmOxMfZODMXFixcL1W6O8KvxdNxptGrTCgYGH3bKAAPhX/DBYHSr3wvVa1Sn i14K+7OMYRm6cISCFduNEBEdBK/Jo/EsQ4KTf50TRK8U/qHxGO9oBUNxOmya1UfSgWVIevQYFSp8 jLuZx/PYYR7Pg3/9CpGBCBU/qahIjw07hpgDh19/Zgzz/w7sM6oX5oxYgKHOg1TStdlhnt8dvj/h z6Nn8fjBI2RmZCnydO1npxCaTx6l6iVw1SlvZorRM0dgSi83zFw+HRUq5p2I8Om/T3n7tWFoaIjE hES66QiCIMFLEMSHFb1Wza1w69atQrOZkZGBJ/8+Qfly5VG2bNkP2r74+Hj+/s/1f9CkEU1gRf1J lHYiooPhNW4UIAUeP8lETo4EAWHnccSrL8KupKJFvZrwPrkJa05HoMEnVWFv0RT29evlsSPLkXFB p4nIm6H5CkFlug9xxLmTF9BzaHed7Ph7/4g6X9XGyKnDBFFaHl1qd3tnfSXJkrxqa47G42aVzXg/ aGurVCpFjVo16KYjCIIEL0EQHx4LCwukpKQUzheMOPcrxtTU9IMK3hcvXnAhP3jwYOzevRuPHj1C rVq16GJTfxKlGMcuPeC+ZQfG9x2AimUNIZIBo7s3R/jlNEzs3R4uUV6oZCzCb336I/HFc5xMTITv sXtw62CnYqd+03oI33uIhxOz0F457eza4MCug+g1rCeeP33OQ5anebnCqIwRn5jq088+VbHDRLPH +vl56qnNzn/P09HSugUXu0fDj711f1T6tCKvf4Om9RGxP1KR/iz1Gbav3okfDvhi8aQlaNK6cR4v L/Mks3PlY4vVYcdq1fuSbjqCIEjwEgRRNKhSpQrS09Pf2o7c6/HRRx/BxMTkg7UnNjaWC/n//e9/ aNq0KY4fP47GjRvThab+JEoxM0ZkCy972A3fg9H9e8OgrDlkWcmY0LsdfM9th4lYih51G+ClVILK wvdXj7p1sPfqVYRcuYzuDV4LOyY+2TjZ1fPX8smd5A/6XL9zxtqF6/iSPIwJ88fyd+8dS7BwnCcS E5IQ/PdvBdZTm51BEwdg9jB3pD5OxYBx/d66PyZ/O5Evj5SVkYmBE79RpK9fvAmjZ4xE9S8+x//N GgU/j/WYv3auyrlNWjXCqT/OaBW8Z47Ew6pTS7rpCIL4IIhkAvllyJLIMHP3DfgMqosyYhH1GPFu bkSRCAXcikQxhInmFStWYNasWShXrtwHqQPzPm7evBnjxo3jQv7Zs2dYs2YNvvnmG9SvX58uEvUn UQLJlGRiwMZucHacBrGh9mf7diMi4OToCMMcA+EPkQQGMiN4j2mJepsGwcO6HQxESuNuRbnzEnjG nkDESDeN9i6d+YtPzrQpdF2p6m+2Du+Ebs7YErEB5UxVv+vZ+rvOvdyw7oCvxmWLigoSqQTrwlZj uf06VDKrxCOTPtTfLYIgNOtR59ZiVDQz1fvzSR5e4oNSvXp1DB06lAve2bNnIzAwEPfu3aOOIQoN 5o2sU6cOF2eMChUqwNLSEqGhoSTQqD+JUo51JxsEhYagc+cuOHI4Au072kGaA3xuWgG3/30CM2P5 RGcyrnifZmbxY8rsWLOLTx6lIqRrOZbK/nRqrN3T3POrvortEVOHYYTbULoBCYJ4L5DgJT4oAwcO 5B5ABnt3c3OjTiEKjQcPHuDKlSsYM2aMSrqtrS0uXLiAq1evkkij/iRKMR5jjOCe0QmHD0cjamd3 2A0Pg3RyR9hbWOJGynU41rLgWpfFt8mE/84mp/BjynDxJrwIgiCIookBdQHxIdm7dy/mzp3LPbws 7HXfvn3UKUShERcXx8eZVqtWTSWdhcI0adIEYWFh1EnUn0Qpx8u5nCB2c9evdbDtgx3BJzC7jQvE 4vI4ejcRZkbGyJTm8G2xkSkGNWtOnUYQBEGClyB0g4UvL126lI/fXb58ORITaZ0+onBg3si///4b nTp10njcxsaGzzbMvJIE9SdBMGaOyoCdvSFfb3eebXfUrVIXrseOYN/tO3ybpREEQRDFCwppJooE NGEVUdicOHGCh9fKx5qqw8aeNmrUiHslKQyX+pMgNNGzSWP+IgiCIEjwEgRBFBkSEhJw6dIlvv3X X3/plJ/WkaX+JAiCIAiCBC9BEESRh4ktDw8PlbSisEQS9SdBFG28AwwRGRUMBxsnzBydrdM5bEbm qISiO349cP0evubvrBXTVdJXzFoFa8f2aGPTmi48QRAkeAmCIAiCIEoiy3aIEBUViagddoiMDkas 3zRYT/EWBG8v2A0Lh0MXQfyOzCqUsjQtV1SQWJZKpehjOQAiAwP8em4PDA0NdS6PrY8bEhgG/4gN eY5N8ZyEyT1dYdmmSZFeH5cgCOJtoUmrCIIgCIIolazYbiiI3WDE+Ezk4nb7/LHIyAa2ebjAbngE Yla5Ijzyd3hvNy6U8pi4lQtc5e38OHX4DJp3aMZfcdGn9Sov+kAMbHp11ihoWZq1YwecOBRHNwJB ECUa8vASBEEQBFEqYR7d4GVuSM+SIcR7InJkRsh4KUE1EzHClrrgvywpDno7o+fc9ZgzyrFAe34e 6/GZeTVsXOKPAeP64feAIFSuUgmem75F7YYWWs97dP8RPCctwT9/30Sdr2rDY8N8fPrZp/zY8YgT aNrOErIcGU5Gx6F917Y8nXmLCyrjQtwl9B3dS2u5Vp1aIOTnMNj27kw3A0EQJRby8BIEQRAEUSo5 tNMRTnP98DLDAOnpBjgQG4+g46ex78ifeJ4hwX8ZMvQSxG7UzoLFrvcMH9RtVAf9x/bl+41bNULY tSAMnjyQC+H8WO2+Fp2dvkb4jYP8feVcX57ORO6JyJNo1rYpmrRqzMUvS5NTUBmXz15BoxZfaS23 YfMGSLh2m24EgiBI8BIEQRAEQZQkVmw3gt3wMHw/eTSeCeI2Iv6soDCz4R/yJwwhgqE4HXZWNZF0 YBk+zvwKNY3ba7W1bNoKVKtRFQ4DuirS2tm14e/dBjrg2sXr+dbl3IkL6Dsq1xPL3i/EXeTbl878 BQMDA3xRtyb33uYIYvfCqYs6l/H036cQGYi0lsvGAycmJNLNQBBEiYZCmgmCIAiCKHVERAfDa9wo QAo8fpIpiEkJAsLO44hXX4RdSUWLejXhfXIT1pyOQINPqsLeoins69fTaMtpeA/4LvgB3QY5KEKR 5eTIcgTxbPhGdTwWcQJPU5+pTHZ1IjIOTdta6lSGWWUz7hHWJnrZhFg1atWgm4EgiBINeXgJgiAI gih1OHbpAfctO3D7cTqepL+ESAaM7t4c4ZfTMLFXe7hEeyHleSJ+69Mfk5s15du+x6I02mKhwUu3 f4d1nhu5iGQ8SXnCxebW5dvRrkvbfOvSrJ0l9v/4G9/eu/VXPnOyXPDOWDZVMcHVbJ8ZiA07pjiv oDJYvVhYszbYsVr1vqSbgSCIEg15eAmCeCdIJBLs2bMHX375JcqUKUMdQhBEkWLGiGzhZQ+74Xsw un9vGJQ1hywrGRN6t4Pvue0wEUvRo24DvJRKUNnERNiug71XryLkymV0b9Awj71KVSrBdbEzfvph N99fMHYRbl1JQONWX2GB37x86zLNyxWLnb2wxXsb6jaug2/XuePm5VtISU7h43TlNLZqhEcPHivC lwsqo4lw7qk/zuCrlg01lnvmSDysOrWkm4EgCBK8BEEQ+nLx4kUkJSVhwYIF1BkEQRRJ7EZEwKlb d6S9kAKiTBjIqkACQ6w9HQkP63a4lvrodWYR0IjNhhwbqSJ4lZcWYqJ3hNtQ7FizC+uD1motV305 IhYG7ffr6jxp6vk+q1lNJS2/Mnj7+thiQjdnDJo4AOVMy6kce5n+EscijmPw5G/oRiAIggQvQRCE vkRHR8PCwoI6giCIIot1JxsEhYagc+cuOHI4Au072kGaA3xuWgG3/30CM2N5dIqMK96nmVn8mDJM 3O7w/Smv2Kzl+M7rr2sZTo37aT3W86u+iu0RU4dxwU4QBEGClyDeAywkNisrizqimF2z27dv49Ch Q3wc2zffkOeAIIiii8cYI7hndMLhw9GI2tmdz9osndwR9haWuJFyHY61LLjWZVM+yYT/zian8GPK cJEovAiCIAgSvARRIBcuXEB4eDiqV6/OxdOdO3eoU4oZIpGIj9tlYlcspq8YgiCKNl7O5QBne77t YNsHO4JPYLaTCyaFuuPo3UT0tKiNRxkvEZecDLGRKQY1a06dRhAEQYKXIPQnISEBBw4cgK2tLdq3 b08e3mIKm6CKhC5BEMWRmaMyhP8NcTfzOObZdsfBi5fgeuwwX5aoc42G6NmkMXUSQRAECV6CeDOC g4Nhbm7OxS6/OQXRRMKJIAiC+FAwgUsilyAIonhD6/ASRYa0tDQ0b06hYgRBEARBEARBkOAlShAs fNnIyAi1a9emziAIgiA+CN4BhnziKp9tRiWmTYHr92DFrFV50llaXMwpuugEQZDgJYj3ARurm5mZ CQMDuiUJgiCI98eyHSLYjYji25HRwYj1m4bw6H18325YOHwCyhRqeWwpIfVXQfnlXLt4Hbv8fta5 rOdPXyAkMAxTPCflOcbSNi3ZwtfjJQiCIMFLEARBEARRwlix3RBRUcGI8ZnIxe32+WORkQ1s83CB 3fAIxKxyRXjk7//f3p3AyVz/cRz/7M4ebI6/oyV/iWX/bnKsnPmTyE0R/m6pRFI6hHJVrIrIEUXK mfRHjkj5y7mOKMqVHAlpscvKunZm/vP9Mtvs7ly7dmdmZ1/PHtP85nd8f7/5znt++/v4/fa3Mv6T 0Exb57fH1+hH6mFXEuITZO+On6TbwP+4va71X/5PGrdtJLnDcqeZpsY1aF5ftq3bThAA+DXuCAQA AHIkdUZ3VfQgSbxhltXj+4nJHCzXriZJ0dxBsmbcQLlywygrxw+Q1q9OlyG9HJ+J7VKnu8Sfvyh5 8ueRJ17uJc07NdNnZjs++Zgs/3SFFAovKKNnjpDSFSIctnHuj3My+pm35Nf9R6VMxdIy8oPhcvc9 dydP/37zHnnc0p6VO+3v3f6TPNq7rcN1RjWsIasXrpGH2jUiDAD8Fmd4AQBAjrRuXnNp8+oUuXot UBITA+XLzbtlxdad8sXG7+XytSS5cs0sbS3F7rfznF92vChmnqw9slKmLH1Ppo2ZkTy+cq1Ksubw CunSv5NMGTndaRvvDXtfGrX5t25HPU94dXLytK3rYqSxZVxqrto/sOegVKpR0eE6K1QvL8cPnyAI APwaZ3gBAECO884nwfL1+hUytn9vSbAUtzE//yBiNspHX+2Wp5pHiSEoURpXKyenv4yW0+fOS758 /9B/nze1S3GXZO7kBfL9pj1y/uw5uX7t778fX7dJbf3cotMjMnXUB06354dte+Wt2WP08KO92sqs 8R8nT9v53S4JCg6SBxpFpVjGVfuXLlySgMAAh+s0GAxy6vgpwgCAghcAAMCffL1+lYx9speIUeR8 3HUxmZLk0zU/ysaxj8qag/FSo2wJGR8zUybt/FrKFy4izSLul2blyqZp5yNLYaouQe75fDfJmz+v PFy6RZp5TGaTpYA2ZHhbXxj7nEx8dbJElCuZ4jJnV+3nL5RfzCazw6LXaDRK8VLFCQMAv8YlzQAA IMdp/nArGTZrrpw4nyhxiVclwCzSu2V1WXvgovRrW08Grh8rsZdPybL2HaR/tfv18OQt36Zp58rl RKnZoIYudjet3ZJiWlxsnC44Z7/9idR9uI7T7alWt6r89+NlenjJ7KVStXaVFNOfGfGUzHjzI0m6 meR2++qSZXVZsyNqWqmyJQkDAL/GGV4AAJDjvNjjpuXRTJp0Xyy9O7STwFz3ivnGGXm6XV2Z/MMn kjvIKK0iy8tVY5IUyp3bMlxGlhw6JKsPHpCW5Sskt9O5X0d5pdswiT8fr28iZeu1vqPk2MHjUrlW RXltylCn26PO4o4ZMFZmjZ8jkZXLyIhpw1JMV3dV7jqwi/4d4UFvPOtW+1VqVZId3+2SijUr2F3n ro27JaphTcIAgIIXAADA3zTp8bW0adFSLv5lFAm4LoHmcEkSg7y/8xsZ2aCuHI4/9/fMASKV1N2Q N3+TouAtW+VfsnDrp8mv+7zUM3l4+or3Ha479Z8jUpcqq5teOZsvolyp5GLXVfv6/bV/SJ5uMUAX 5WF5wlJMU39/d8vXW6VL/8cJAgAKXiCzzZs3Txo2bCglSpRwOM/Jkydl48aN0r17dzoMAJDpGjRs LCu+Wi2NGj0sGzd8LfUebCJGk0ixPPnkxIU4yR8acntOs654L12/oafZmjtpvr5pld2Cs1TzrC3Y 3Wy/TeXHHE5rXfHR5OEez3eTHoO6EgwAFLzAnUpISJCVK1fKgAEDHM6jpgMAkFVGPhEsw641lA0b 1su381pKk+5rxNj/QWkWUVWOxP4izUtF6FpX3fLJbPnfnjOxepotXSRaHgAA38RNq+AVrVu3lvPn z+uzuPao8Wq6mg8AgKwydkCYpdhtpocfeai9zF21TV6pPVCCgvLKppOnJH9wqFw3mvRwUHAe6Vyt Op0GANkIZ3jhFepS5sKFCzs8y6vGq+nOLnkGAMCZvPEFJDgwxO35x7RR/79LLv5xSMZV65Fi2iNF bw/E0a/+6IbxOp0AUPACmUudvZ0zZ44+m6uKWyvr2d3evXvTSQCADIvePoJOAAAKXsA7bM/y2ha3 nN0FANyJ0KBQWdLvK9m3bx+dgXQJMYTQCQAFL5B5rGd5T58+rV+rZ87uAgAyo+iNqh4lx44dozMA gIIX8A7rWd61a9fq1+qZs7sAgMwSEREhsbGxdAQAUPAC3mE9y6vExcVxdhcAkKnCw8MlMTGRjgAA Ct6scz2Ju9/BviLFikiBAgUkPj5eP6vXmZkXdVkbuYX1s1TPhiSDz29venLrjZxmt/6Ed/elviAs LIwPEQAoeLPuwKjjjBb0NhzKayogkVJJdpg2ybczvszUttWNSzJyoEZu/UusLD/LAAAQaElEQVSg ZXcXGVxRes/tLCZJ8vntdTe33sppdutPeG9fCgCA3xe8Vp2bdpGgwGB6HQ5VkFKZ1laS6aZ8tm4R uUUKUVLVp7cvo7n1Vk59vT/hO/tSAAD8vuANDDBIYGAAvQ7P5M186zLL48ePS7nIcuQWfp1bcgpf 35cCAOD3Ba/ZbNYPwFN5s1J36FQ3LSG38NfcklP4+r4UAAAKXhsxWyPlxKkEy5A6Y2FZzvKshwJu jbnV3t/zlyqeV+rUO8InCrsHaYq6Q2dGblqS3kKC7MIbuSWn8PV9KQAAFLw2jv+eIO8/9aDbbQ/8 cKPU5uwGnBykeSK3ZBfeyi05ha/vSwEAyAEFr8nycO93zExGk4SGpONPXZjMuv3M0KVsX/286PAs EpKtD9JMHs+tt7OLrJfV+4eMZoGckklfyyQAADmv4LX8Zzkkc3/jDIHpaj91239d/EsWT1wm21bt lJvXb0pktdISfm9heXpc7wy15/b7tBwYPlN3sFyO/0vyFcor07dMkAAXN5PpWvZJ/bzg8EdOx7la 1t1lUi8baOnr4JBgSx9FyGMD28i/qpdxe1l315XRzyO967HNmzdymxnZdZWjC3/EyadvLJJf9vwq iZeviiEoUIqWLCrjvhzhtJ8vnrski97+Qg7sPCyXzidIvoJ5pWLtctLllQ7yj7vzO+xr63hb6f08 MpOz74tiCDLIP8sUk16vd5GyNSOzZBtMYs6y/WR2yanq8+CQIBk6Z3ByP6tx6nNxlTV39pXOppNJ 388kAAA5suBNz6VRQYb03XE0dduTB82UA9sPyZDZz0vlehUczudue+7at2W/PkBTEi5clp+2HUix fnfXOf/Qh25th7350rvtc/fP0EXUpGc/kDe7vyuvzX9ZIu+PyNR+8vTnkZkFb3rXfafZdZWjBdGf y+71P0qfMd2kTotakjtPruR2nPWzWk4VwgMm9JVaj9SUbSt3yMxX54jJZJL+7/Z12dfWrN3J9yNT 9yl2tkFt49G9x2Vkp3Ey7aVZMnlDtMfW7e2C1xs5vXkjSSY8M1VGLBxiKejuSVfWXO0r3dmXkknf zSQAADmv4E3n75gFp/PsQ+q2D+36RT/nL5Q3zbS/Ll6ROaMXyJ7/7dWvqzeuKn1Gd5O78oWlaa9/ 3Rfl6pVr+mxD0ZJFpNOL7aXav6tI9/JP6+lPvtVTtzVn7zT9euvK7fq5WOl75MzRP2TLlzFSqW55 Pe5KQqIsnrDUcvC3U4xGoz6TOnTOC8nr7FbuKf087+DM5PYnrHtLXmw6XEJyBcvMnZMkIDBQ+tcZ rM/svbNmjLzcfETyMqnbSb2swWCQAfVf0geRaprtey1YtID0HNFFRnWKliWTlidvl6P3b2+bnc3v 7PO4ce2G7pdNy2L0WckSZYtLr5H/kYjKJR2ux1MHfxm5++2dZtdVjvZu3q+fS1YoIbnuCk2xvLN+ 3rvpZ/1c5v4I/SdsrGfy1Xh3/sEkPd8je98P67jHnmsja+Z8I0EhQdLztS7yx4k/Zd38/4nxplF6 vNZZ6rZ+IEPZs25jRJVbubl0ISF5m11lzNG6LsddlplDP5Gftx3Uy9iux1mbjvYPWZVbb+XUms/o Pu/JmC+GJc/nTtacZdyd6WTStzMJAIAvCPTkyqwHZO481O1Bgw0Bbj9MprRtlLt92diwdm/Icw1f 0ZeAJtw+2Jj71meyc+1ueXnmQHl+6jN6WE1PfdCvHtO2visf/zhVxq8eLad/PSOzX5+XYr6Phn8q STeS9LibN27K9q++15cIPzWup56+w9K2Gq+mLxy/RDZ8vlkeH9xePtw1SYpFFE3R1twDM/TDdtzd xQtJ5foVLAcyNy0HgDtkz/ofdbGrxhW5L9zuQYm1ndTL7t9+UBe7UU2r6Wmp32upivfp17/+eNTt 92+7zc7md/Z5zB/3ueXgcoOM/GyIXu74/t/02SBn63ErRx7ObWZl11WO1EGtteBNT+7VwbNS6J6C +rU1A9cSr9vNfup+VAfM1oe73yPb74dVg3Z1LMXRcH3WbsoLH0r9trVlzJJh+h+EFkQvyVD2bLd9 9+1ip26rWsltOcuYs3XNsyynijRV9IywLGu7Hndya/v+szK33sipMmTWIH25cnzsRRnXa2LytrjK mquMu5pOJn0/kwAA+AKfvWmVmtcQGJCOttPeUOXZSU/K6lnr5IcN++TMsbPy7cLv9A//EQtfll3r 9uh5ytWK1P96rqhx/cb3SrEN6mzB8umrZX/MITl/5oIer34fzXZd72+KlvyF8ulxMat3ijHJKBXr lLMUjyWkwgNl5cCOw3q8OoBSl/gpD3VuoH8Xrfvwx1O0Ze/mIGpcs+6N5actB2TL8u0SlvfW5ast n2jqcFnbYTWfWnbjki1yj6XAdrZsyoM0996/7bCz+Z19HpuXx+h5h7YendzW2d9iXfaNqwx5OreZ lV1XOXL2Hp31s6PlVBbd6etP909PMY873yPb74dVgSL5U7Rr+zoh7nKGsmfVo0I//awufe0zumvy PM4y5mxd1vfY8LG6ln5K+Tm7k1t77z8rcuuNnCr/CM8nL84YION6T9J5c7Yvss2aq4y7+x0gk76b SQAAcl7Be/s/d7UZtUIdHuklbP+ffOAk1r8eaRkOCEjTdli+3NJxcFv9+PPkORnSfJSc2H8yxXx6 +PYBg7rsLvW0zyYslc1LY2TAxCekWuMq0vf+QX8vd5u6mYr1tbpUWVEHKT0r9k+eR42v1/aBlOt1 0Ef2xlWqX16KliqiL1dVZzzUsD6gS/1e7Ayr+dT8R348Jr8fOaMvMSxZqYTd+Y/+fEI/l7ccXLr7 /m2Hnc3vzucxe98UMdhcZuloPe7mzRu5zYzsuspRSK4QuXblmuXzPC3FI4u5nfvcd+XSZ6wunI3T l7DH/3lRLxNqac+dvnY63sH3yPb74agde6/Tmz2rsStek+hek+Wg5bty6uiZNP1jL2PurEu9P0fr dpZbe+8/K3LrjZxa13tfxXvl2Ul9ZWK/6fruz+5kzVXGM2VfSia9mkkAAHJcwWsyWw6c0nFp1IJX W7g9b7fxa3T7jsT9Ga+fy0aV0fOpS3pjVu3SvwsZePssR9WGlVK0oYbVpcNKZI0ycv5MXIppqYfV pcIHd/yi78Y5ddvbEhoWKlf/uibP1R+ix188n5C83g1LtkjDDvVk2ZRV8uig1vr3y9TB4bnTF6RQ sYJp+k1p9Hh9WTT+v/qA8qEuD6Z5v+q1o3aadG0o89/8XBdJj/RuYndZdUnivDcWS0hosLQd0MLl +7e3Lnf6y97nUadllGxeFqPPTLbs2zTFnVqd9Y2rvHkjt3eaXXdyVKF2Wdmzfq8c+v6IFLAUE9ab VrnKfZUHK+r8/bztkC4a9m05oKer8fYy7apP3f0euWrH3uv0Zs+qaERRGb7wRRnX4z2ZNni2jFoy RIItmXaWMWfrUncuV/2u+uyfZYqmmOaszYzmMKO59XROU29vhTrlpM+b3WTW0Lkus3Yp7rLTjJ/6 9Q+X3wEy6fuZBAAgxxW8Wf27QKnbHtUhWv48ESs3rt+U0NwhUr99benwQls933+GdtC/kzbluVt3 +HygRU09LvXvXTXv00SO//ybPN9wqP5dWHvrsg7HnjwntVvVlBLlikuIZX1qvLqhUPuBLeXM0bMS +/s5vQ51dm7xO0t1AaoKEb09wzrKkonL5eWmI3TB+cH3E9O03+DROvLbwd+Th1O/X2ftVGlQUT8X ue9uqda4cppl+1Z5znIAFiT/shxcvfL6ILmv/L0u37+9dTmb39nn0XV4R31mcv3CjbJ86moJVH9m 575wGb10qNO+SU8efDW3qbfVnRyps7dJN5Nk6furdI7UjXbUHXJHLH7FaT93HNxOr+O/k1fIx6/P l/yF8+nsq/G229Cn0rPJw7N/muKwT939Hrn6bOy9Tm/2bKcX/mcheW3RSxLd/T1ZMHaJ9BzVxWnG nK2r6/DHZarl/b375BSpVK98qmmO28xoDj1506o73cemHlenVZTEn413mTVXGVf3EHD1HSCTvp9J AAB8QYDZxU+yG0lmeemzI/Ju50gJCQrI0EquJ12XjjNaSKsGrcQQaHBrmeVflEz3etp1OMEn6sDi d5bJN/O+0wdDjTrVzxHv2WgyyqrNq+TtZtOkYP6CkidPHgkLC8vS3JJdeDq35BS+vi8FAOBOWOvR AQ8ESYH8edL9c8inz/DOH9Lc7XnV5Xb8K7R9cWcvyqYvtukzLOpsX07pJ2+e4SW78HRuySl8fV8K AIA3+PUlzbhF3W10Ssz4HNdP2fWSZlBckFNQ8AIAkA0LXpO+26d7PzjLRBTSZxTcpeY3yVE+UaTI m6dzS3bhrdySU/j6vhQAAL8veB39HUd7ylbdbnmkp/Ujwj9CI3XePJ1bsgtv5Zacwtf3pQAA5JCC lx+cyI4FL7lFdih4ySkoeAEAoOAFB2nkFhS8AAUvAICCNzMlGY384ITHGE2mTGnHZCK38P3cklP4 +r4UAAC/L3g37FpPjyPbuRx/RXYe2U5HgJwCAABQ8Dr2dNOBEmQIotfhEUnGJJm5bsodt1OjdC2J KlOHDoVP55acwtf3pQAA+H3Bq4pdCl5kN4EBBnILcgoAAJAdj5HoAgAAAAAABS8AAAAAABS8AAAA AABQ8AIAAAAAQMELAAAAAAAFLwAAAACAghcAAAAAAH/h0T/aqP54PZDd8mYyGy1t0Z/w7dySU/Cz GwAALxe8M9dNoceR7ew+ulN2HtlOR4CcAgAAUPA69nTTgRJkCKLX4RHqrERm/CNLjdK1JKpMHToU Pp1bcgpf35cCAOD3Ba8qdil4kd0EBhjILcgpAABAdjxGogsAAAAAABS8AAAAAABQ8AIAAAAAQMEL AAAAAAAFLwAAAAAAFLwAAAAAAApeAAAAAAAoeAEAAAAA8GFBnlxZ3vgCEhwYQq/DI24Yr5Nb5Jjc klP4+r4UAAC/L3ijt4+gx5HtkFuQUwAAAApeh0KDQuX1utH0NrwixBCS4dwu6feV7Nu3j06Ez+aW nMLX96UAAPh9watEVY+SY8eO0ePIVlQxQXZBTgEAACh4XYqIiJDY2Fh6HdkO2QU5BQAAoOB1KTw8 XBITE+l5ZDtkF+QUAADATwtek8lkeQRkykpz5cpFz8MrVI7JLvw9t+QUvr4vBQDA/Z85Zs8UvGaz 2fKgwwEAAAAAnmE2e6jgzcwzvAAAAAAAuK5DOcMLAAAAAPBDHjvDS8ELAAAAAPDLgpdLmgEAAAAA nsQlzQAAAAAAv8QlzQAAAAAACt47LXjv9HQyAAAAAADu16F3tjxneAEAAAAAFLwUvAAAAAAAvyt4 jx4/IQZu0gwAAAAA8JAkk/p/7qwveBcczE1vAwAAAACyDZcFb0hQgLzbOVL27dtHbwEAAAAAPC7Y kDdrCl5r0VuzelU5duwYPQ0AAAAAyBaC0jNzRESExMbG0msAAAAAAP8qeJXw8HBJTEyk5wAAAAAA /lXwKmFhYfQcAAAAAMCnBdIFAAAAAAAKXgAAAAAAKHgBAAAAAKDgBQAAAACAghcAAAAAAApeAAAA AAAFLwAAAAAAFLwAAAAAAFDwAgAAAABAwQsAAAAAwB37P+gvMs1Lic9gAAAAAElFTkSuQmCC

Generalities about Solar Activity and Atmospheres

The atmospheres make use of solar activity in order to compute the density at the given user location excepted US76 model that is only based on altitude parameters. The PATRIUS architecture of atmospheres and solar activity is divided into three layers :

• • • Atmospheres use solar data in specific ways**

Each atmosphere model uses the solar data in a specific way (more simply, US76 doesn't use solar data). These representations are enclosed in the atmosphere model specific interfaces, such as DTM2000InputParamters. Atmosphere models available include US76 (for low altitudes in range 0 to 1000km), DTM2000 and MSISE2000.

• • • Reading and storing solar data**

The way to store the solar data is enclosed in the SolarActivityDataProvider interface. It defines the basic coefficients (Ap, Kp and F10.7 cm) that any solar activity data provider class should be able to return, in order to be compatible with the atmosphere specific implementations (see next point). The solarActivity package contains classes that can read different file formats and can return the solar activity data. So far, one class for each of the ACSOL and NOAA formats has been implemented. Additionally, one class representing constant solar activity (that requires no external file) has been implemented.

• • • Using the solar data in an adequate fashion**

Making effective use of the solar data for specific atmospheres requires an object that provides solar data (implementing the SolarActivityDataProvider) and answers to the interfaces that define the ways in which the atmosphere models use this data (e.g. implementing DTM2000InputParameters). These classes are contained in the fr.cnes.sirius.patrius.forces.atmospheres.solarActivity.specialized package). So far, one class for each of the DTM2000 and MSISE2000 models have been implemented.

Below is a diagram showing the architecture of the fr.cnes.sirius.patrius.forces.atmospheres package. Please note that the fr.cnes.sirius.patrius.forces.atmospheres.solarActivity package follows the same architecture as the fr.cnes.sirius.patrius.forces.gravity.potential package. The user must use the SolarActivityDataFactory class.

For a detailed explanation of the Data Management System, please refer to the [SUP_DMS_Home Data Management System section] of the Support User Manual.

Atmospheric models

Various models are available in PATRIUS: DTM-2000, MSIS-00, US76, etc. all models inherit the Atmosphere interface providing total density information. DTM and MSIS models also implement the ExtendedAtmosphere interface which provides more detailed data such as temperature and partial densities of atmosphere constituents. Some models require solar and geomagnetic information (see below for how to provide solar and geomagnetic data).

MSIS2000 Atmosphere model

The NRLMSISE-00 empirical atmosphere model was developed by Mike Picone, Alan Hedin, and Doug Drob. It describes the neutral temperature and densities in Earth's atmosphere from ground to thermospheric heights. (quoted from [1])

More information can be found at the Naval Research Laboratory website.

In order to use this atmosphere model, the user must proceed by giving the following arguments as inputs to the MSISE2000 class :

• Solar activity data (as a MSISE2000InputParameters)
• the Earth model (as a BodyShape)
• the Sun model (as a CelestialBody)

The following code snippet creates an instance of MSISE2000 :

 // Create an instance of the BodyShape "EARTH", with user chosen
 // equatorial radius, flattening and body frame
 Frame frame = FramesFactory.getITRF();
 double f = 0.29825765000000E+03;
 double ae = 6378136.46;
 BodyShape earth = new OneAxisEllipsoid(ae, 1 / f, frame);
 
 // Get the instance of the CelestialBody "SUN"
 CelestialBody sun = CelestialBodyFactory.getSun();
 
 // Create the solar activity data to be used
 SolarActivityDataProvider solarActivity = SolarActivityDataFactory.getSolarActivityDataProvider();
 final MSISE2000InputParameters msiseData = new ClassicalMSISE2000SolarData(solarActivity);
 
 // Create an instance of the atmosphere model
 Atmosphere atmosModel = new MSISE2000(msiseData , earth, sun);

Warning: this model is not continuous. There is a discontinuity every day (at 0h in UTC time scale). Discontinuities are however very small (1E-3 on a relative scale).

Solar and geomagnetic activity

Solar and geomagnetic activity can be provided in various ways:

• Constant solar activity using:

Modèle:Code language="java" title="" final SolarActivityDataProvider constantSolarActivity = new ConstantSolarActivity(140, 15); Modèle:/code

• Variable solar activity using (for instance):

Modèle:Code language="java" title="" final SolarActivityDataProvider variableSolarActivity = new NOAAFormatReader(...); final SolarActivityDataProvider otherVariableSolarActivity = new ACSOLFormatReader(...); Modèle:/code

Solar and geomagnetic activity data often having a limited timespan, the class Modèle:Code language="java" title=""ExtendedSolarActivityWrapperModèle:/code allows data extension with constant values. Solar and geomagnetic data returned before timespan are equals to an average of first available data (the average duration being user-chosen). Solar and geomagnetic data returned after timespan are equals to an average of last available data (the average duration being user-chosen). Example:

final SolarActivityDataProvider innerProvider = new NOAAFormatReader() // Variable solar activity over a given timespan
final double duration = 86400; // Duration on which average solar activity will be computed if date out of innerProvider timespan
final ExtendedSolarActivityWrapper solarActivity = ExtendedSolarActivityWrapper(innerProvider, duration)// Extended solar activity

The above code will create an solar activity whose value will be:

• Value of innerProvider if date is within innerProvider timespan
• Average value on [lower boundary, lower boundary + duration] if date is before innerProvider lower boundary
• Average value on [upper boundary- duration, upper boundary] if date is after innerProvider upperboundary

These providers are used as inputs of atmospheric models.

Reading Solar Activity Data files

The data is read through the DataLoader infrastructure; it provides several ways to load solar activity data. Please see the [SUP_DMS_Home Data Management System section] for more information.

The following file formats are supported by PATRIUS: (% style="margin-left:30px;list-style-type:square;" %)

• ACSOL format
• NOAA format

The user access point is the SolarActivityDataFactory which automatically detects available files and uses the adequate solar file reader. If no file is specified by the user, this factory uses the first available file.

Modèle:Code language="java" title="" //Directory containing the file ACSOL.act final File potdir = new File("/my/data/solar"); //The directory is given to the data loader DataProvidersManager.getInstance().addProvider(new DirectoryCrawler(potdir)); //The ACSOL file is registered in the SolarActivityDataFactory //If it is the only solar activity file of the directory, this step is not necessary SolarActivityDataFactory.addSolarActivityDataReader(new ACSOLFormatReader("ACSOL.act")); //A provider for the data is created final SolarActivityDataProvider provider = SolarActivityDataFactory.getSolarActivityDataProvider(); //Get the ap, kp and instant flux at date final AbsoluteDate userDate = new AbsoluteDate(); final double ap = provider.getAp( userDate ); final double kp = provider.getKp( userDate ); final double f = provider.getInstantFluxValue( userDate ); Modèle:/code

Tides models

Tides model for force computation =

The PATRIUS

fr.cnes.sirius.patrius.forces
.gravity.tides

package provides tools allowing the user to use Terestrial and Ocean tides. The

fr.cnes.sirius.patrius.forces
.gravity.tides.coefficients

package also allows reading external ocean tides coefficients data files. The following file formats are supported by PATRIUS:

(% style="margin-left:30px;list-style-type:square;" %)

• FES2004 format

Reference point displacement

The PATRIUS

fr.cnes.sirius.patrius.utils
.ReferencePointsDisplacement

class provides a model describing the displacement of reference points due to the effect of the solid Earth tides. The computation is performed by the static methodsolidEarthTidesCorrections(AbsoluteDate, Vector3D, Vector3D, Vector3D). The implemented model has been validated by comparison with tests available in the IERS website. The example below shows the user how to compute displacements of reference points:

'' Test from source ftp:''tai.bipm.org/iers/convupdt/chapter7/dehanttideinel/DEHANTTIDEINEL.F
 
// date : 13/04/2009
final AbsoluteDate date = new AbsoluteDate(2009, 4, 13, 0, 0, 0., TimeScalesFactory.getUTC());
 
// entries : moon position, sun position, station location
final Vector3D moon = new Vector3D(-179996231.920342,-312468450.131567, -169288918.592160);
final Vector3D sun = new Vector3D(137859926952.015, 54228127881.4350, 23509422341.6960);
final Vector3D point = new Vector3D(4075578.385, 931852.890, 4801570.154);
 
// compute the displacement
final Vector3D disp = ReferencePointsDisplacement.solidEarthTidesCorrections(date, point, sun, moon);
 
// comparison with reference results (IERS)
Assert.assertEquals(0.07700420357108125891, disp.getX(), Precision.EPSILON);
Assert.assertEquals(0.06304056321824967613, disp.getY(), Precision.EPSILON);
Assert.assertEquals(0.05516568152597246810, disp.getZ(), Precision.EPSILON);

Geomagnetic models

Design

The

fr.cnes.sirius.patrius.models.earth

package provides tools allowing the user to use different geomagnetic models. For the moment, there are only the two following models available :

(% style="margin-left:30px;list-style-type:square;" %)

• IGRF 11  : International Geomagnetic Reference Field eleventh generation
• WMM 2010 : World Magnetic Model published in december 2009

A class diagram is given hereunder to show how geomagnetic is read and used in the library :

The user can create its own GeoMagneticModelReader in order to provide GeoMagneticField from any file format.

IGRF 11 geomagnetic model

The International Geomagnetic Reference Field (IGRF) was introduced by the International Association of Geomagnetism and Aeronomy (IAGA) in 1968 in response to the demand for a standard spherical harmonic representation of the Earth's main field. The model is updated at 5-yearly intervals, the latest being the 11th generation, produced and released by IAGA Working Group V-MOD (formerly V-8) December 2009.

More information can be found at the IAGA Division V-Mod.

WMM 2010 geomagnetic model

The World Magnetic Model is a joint product of the United States’ National Geospatial-Intelligence Agency (NGA) and the United Kingdom’s Defence Geographic Centre (DGC). The WMM was developed jointly by the National Geophysical Data Center (NGDC, Boulder CO, USA) and the British Geological Survey (BGS, Edinburgh, Scotland).

The World Magnetic Model is the standard model used by the U.S. Department of Defense, the U.K. Ministry of Defence, the North Atlantic Treaty Organization (NATO) and the International Hydrographic Organization (IHO), for navigation, attitude and heading referencing systems using the geomagnetic field. It is also used widely in civilian navigation and heading systems. The model, associated software, and documentation are distributed by NGDC on behalf of NGA. The model is produced at 5-year intervals, with the current model expiring on December 31, 2014.

The current model, WMM2010 (published 12/2009)

More information can be found at the National Oceanic and Atmospheric Administration.

Details, limitation and precautions

In the geomagnetic field's computation from the different models, to convert geodetic coordinates (defined by the WGS-84 reference ellipsoid) to Earth Centered spherical coordinates, the following constants are used :

• Semi major-axis of WGS-84 ellipsoid : 6378.137 km
• The first eccentricity squared : 0.0066943799901413169961

and to compute the spherical harmonic variables for a given spherical coordinate uses the mean radius of IAU-66 ellipsoid 6371.2 km is used.

The different models are used to compute gemoagnetic field near earth surface. In the model file, the given altitude range of validity is-1 to 600 km even if we can compute field outside of this range.

The method GeoMagneticField.calculateField(final Vector3D point,final Frame frame, final AbsoluteDate date) has been added to Patrius and allows to compute the field from a position vector in a specific frame and at a specific date. This method is based on tranformModel method which recomputes the field at a date. This method doesn't work if the model doesn't support time transform. In this way, this method added to Patrius throws a Patrius exception about model not supporting time transform.

Here is the list of all possible actual models and the time transform support (please note that only dates prior to 2010 won't support time transformation) :

|Model and associated data file |Model Name|Validity Period|Time transform support |=(% rowspan="23" %)IGRF (% style="font-weight: normal;" %)(GeoMagneticFieldFactory.getIGRF(..) uses IGRF.COF)|IGRF00 | 1900.0 - 1905.0 | false |IGRF05 | 1905.0 - 1910.0 | false |IGRF10 | 1910.0 - 1915.0 | false |IGRF15 | 1915.0 - 1920.0 | false |IGRF20 | 1920.0 - 1925.0 | false |IGRF25 | 1925.0 - 1930.0 | false |IGRF30 | 1930.0 - 1935.0 | false |IGRF35 | 1935.0 - 1940.0 | false |IGRF40 | 1940.0 - 1945.0 | false |DGRF45 | 1945.0 - 1950.0 | false |DGRF50 | 1950.0 - 1955.0 | false |DGRF55 | 1955.0 - 1960.0 | false |DGRF60 | 1960.0 - 1965.0 | false |DGRF65 | 1965.0 - 1970.0 | false |DGRF70 | 1970.0 - 1975.0 | false |DGRF75 | 1975.0 - 1980.0 | false |DGRF80 | 1980.0 - 1985.0 | false |DGRF85 | 1985.0 - 1990.0 | false |DGRF90 | 1990.0 - 1995.0 | false |DGRF95 | 1995.0 - 2000.0 | false |DGRF2000 | 2000.0 - 2005.0 | false |DGRF2005 | 2005.0 - 2010.0 | false |IGRF2010 | 2010.0 - 2015.0 | true |=(% rowspan="1" %)WMM (% style="font-weight: normal;" %)(GeoMagneticFieldFactory.getWMM(..) uses WMM.COF)(%%) | WMM2010 | 2010.0 - 2015.0 | true

Precautions : The method GeoMagneticField.calculateField (final double latitude, final double longitude, final double height) doesn't use SI units. Latitude and longitude are given in degrees, and height is given in kilometers.

Getting Started

Code Example

The following code sample computes geomagnetic field elements for four (date, position) of a fake trajectory :

    public void codeExemple() throws PatriusException {
 
        Utils.setDataRoot("earth");
        FramesFactory.setConfiguration(Utils.getIERS2003ConfigurationWOEOP(true));
 
        //Fake trajectory : list of date and list of position
        List<AbsoluteDate> dateList = new ArrayList<AbsoluteDate>();
        AbsoluteDate initDate = new AbsoluteDate(2010, 1, 1, 12, 0, 0.0, TimeScalesFactory.getTT());
 
        dateList.add(initDate);
        dateList.add(new AbsoluteDate(initDate, 600));
        dateList.add(new AbsoluteDate(initDate, 1200));
        dateList.add(new AbsoluteDate(initDate, 1800));
 
        List<Vector3D> positionList = new ArrayList<Vector3D>();
        positionList.add(new Vector3D(6.46885878304673824e+06,-1.88050918456274318e+06, -1.32931592294715829e+04));
        positionList.add(new Vector3D(6.58239141552595049e+06,-1.43349476017528563e+06, -1.39460373997706010e+04));
        positionList.add(new Vector3D(6.66499609614125639e+06,-9.79745192516532145e+05, -1.45334684008149434e+04));
        positionList.add(new Vector3D(6.71628402448997274e+06,-5.21392324304617418e+05, -1.50526405214286660e+04));
 
        // Get the model to the initial date
        final GeoMagneticField model = GeoMagneticFieldFactory.getIGRF(dateList.get(0));
 
        // For each date and position, compute the GeoMagneticElement and add it to a list
        List<GeoMagneticElements> geoMagList = new ArrayList<GeoMagneticElements>();
        int i = 0;
        for (AbsoluteDate date : dateList){
            geoMagList.add(model.calculateField(positionList.get(i), FramesFactory.getEME2000(), date));
        }
 
        // Print each field vector B 
        for (GeoMagneticElements geoMagElement : geoMagList){
            System.out.println(geoMagElement.toString());
        }
    }

This code produces the following standard output :

MagneticField[B={29817,109;-2303,065; -9138,097},H=29905,92,F=31270,895,I=-16,991,D=-4,417]
MagneticField[B={29442,018;-2166,283; -8949,299},H=29521,606,F=30848,261,I=-16,864,D=-4,208]
MagneticField[B={29067,408;-1996,393; -8794,324},H=29135,885,F=30434,19,I=-16,796,D=-3,929]
MagneticField[B={28695,713;-1796,821; -8680,411},H=28751,913,F=30033,681,I=-16,799,D=-3,583]

Contents

Interfaces

|=(% colspan="3" %)Interface|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)Atmosphere|(% colspan="6" %)Interface for atmospheric models.|(% colspan="1" %)... |(% colspan="3" %)ExtendedAtmosphere|(% colspan="6" %)Interface for atmospheric models with detailed data.|(% colspan="1" %)... |(% colspan="3" %)SolarActivityDataProvider|(% colspan="6" %)Interface for solar activity data providers, to be used for atmosphere models|(% colspan="1" %)... |(% colspan="3" %)DTM2000InputParameters|(% colspan="6" %)Container for solar activity data, compatible with DTM2000 Atmosphere model.|(% colspan="1" %)... |(% colspan="3" %)MSISE2000InputParameters|(% colspan="6" %)Container for solar activity data, compatible with MSISE2000 Atmosphere model.|(% colspan="1" %)... |(% colspan="3" %)OceanTidesCoefficientsProvider|(% colspan="6" %)This interface is used to provide ocean tides coefficients.|(% colspan="1" %)... |(% colspan="3" %)PotentialCoefficientsProvider|(% colspan="6" %)This interface is used to provide gravity field coefficients.|(% colspan="1" %)... |(% colspan="3" %)VariablePotentialCoefficientsProvider|(% colspan="6" %)This interface is used to provide variable gravity field coefficients.|(% colspan="1" %)... |(% colspan="3" %)**RadiationSensitive|(% colspan="6" %)This interface is used to provide an direct solar radiative pressure model.|(% colspan="1" %)... |(% colspan="3" %)**RediffusedRadiationSensitive|(% colspan="6" %)This interface is used to provide an rediffused radiative pressure model.|(% colspan="1" %)... |(% colspan="3" %)GeoMagneticDataProvider|(% colspan="6" %)This interface is a generic geomagnetic data provider.|(% colspan="1" %)...

Classes

• • Earth potential**

|=(% colspan="3" %)Class|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)EGMFormatReader|(% colspan="6" %)This reader is adapted to the EGM Format.|(% colspan="1" %)... ||(% colspan="6" %)Factory used to read gravity field files in several supported formats. Main user access point : the simple way of reading a potential file is by using this factory.|(% colspan="1" %)... ||(% colspan="6" %)Factory used to read variable gravity field files in several supported formats. Main user access point : the simple way of reading a variable potential file is by using this factory.|(% colspan="1" %)... |(% colspan="3" %)GRGSFormatReader|(% colspan="6" %)Reader for the GRGS gravity field format.|(% colspan="1" %)... |(% colspan="3" %)GRGSRL02FormatReader|(% colspan="6" %)Reader for the GRGS RL02 variable gravity field format.|(% colspan="1" %)... |(% colspan="3" %)ICGEMFormatReader|(% colspan="6" %)Reader for the ICGEM gravity field format.|(% colspan="1" %)... |(% colspan="3" %)PotentialCoefficientsReader|(% colspan="6" %)This abstract class represents a Gravitational Potential Coefficients file reader.|(% colspan="1" %)... |(% colspan="3" %)SHMFormatReader|(% colspan="6" %)Reader for the SHM gravity field format.|(% colspan="1" %)...

• • Atmosphere Models**

|=(% colspan="3" %)Class|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)DTM2000|(% colspan="6" %)This class implements the DTM2000 atmospheric model.|(% colspan="1" %)... |(% colspan="3" %)JB2006|(% colspan="6" %)This class implements the JB2006 atmospheric model.|(% colspan="1" %)... |(% colspan="3" %)MSISE2000|(% colspan="6" %)This class implements the MSIS00 atmospheric model.|(% colspan="1" %)... |(% colspan="3" %)US76|(% colspan="6" %)This class implements the US76 atmospheric model.|(% colspan="1" %)...

• • Solar Activity**

|=(% colspan="3" %)Class|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)DTM2000SolarData|(% colspan="6" %)This class represents a solar data container adapted for the DTM2000 atmosphere model|(% colspan="1" %)... |(% colspan="3" %)ClassicalMSISE2000SolarData|(% colspan="6" %)This class represents a solar data container adapted for the MSISE2000 atmosphere model. The average ap values are computed arithmetically.|(% colspan="1" %)... |(% colspan="3" %)ContinuousMSISE2000SolarData|(% colspan="6" %)This class represents a solar data container adapted for the MSISE2000 atmosphere model. The mean ap values are computed by trapezoidal integration.|(% colspan="1" %)... |(% colspan="3" %)ACSOLFormatReader|(% colspan="6" %)This class reads ACSOL format solar activity data|(% colspan="1" %)... |(% colspan="3" %)ConstantSolarActivity|(% colspan="6" %)This class represents constant solar activity|(% colspan="1" %)... |(% colspan="3" %)NOAAFormatReader|(% colspan="6" %)This class reads NOAA format solar activity data|(% colspan="1" %)... |(% colspan="3" %)SolarActivityDataFactory|(% colspan="6" %)Factory used to read solar activity files and return SolarActivityDataProvider|(% colspan="1" %)... |(% colspan="3" %)SolarActivityToolbox|(% colspan="6" %)Solar activity toolbox. Has methods to compute mean flux values, to convert from ap to kp.|(% colspan="1" %)... |(% colspan="3" %)MarshallSolarActivityFutureEstimation|(% colspan="6" %)This class reads and provides solar activity data needed by atmospheric models: F107 solar flux and Kp indexes.|(% colspan="1" %)... |(% colspan="3" %)ExtendedSolarActivityWrapper|(% colspan="6" %)This class extends a solar activity provider out of its timespan with constant values.|(% colspan="1" %)...

• • Ocean Tides Coefficients**

|=(% colspan="3" %)Class|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)FES2004FormatReader|(% colspan="6" %)Reader for FES2004 format coefficients files.|(% colspan="1" %)... |(% colspan="3" %)OceanTidesCoefficientsFactory|(% colspan="6" %)Factory used to read ocean tides coefficients files in different formats and return an OceanTidesCoefficientsProvider.|(% colspan="1" %)... |(% colspan="3" %)OceanTidesCoefficientsReader|(% colspan="6" %)This abstract class represents a Ocean Tides Coefficients file reader.|(% colspan="1" %)... |(% colspan="3" %)OceanTidesCoefficientsSet|(% colspan="6" %)Represents a line from the ocean tides data file.|(% colspan="1" %)...

• • Geomagnetic Field**

|=(% colspan="3" %)Class|=(% colspan="6" %)Summary|=(% colspan="1" %)Javadoc |(% colspan="3" %)GeoMagneticElements|(% colspan="6" %)This class contains all the elements about a magnetic field : the magnetic field vector and associated caracteristics Inclination, Declination, Total Intensity, Horizontal Intensity.|(% colspan="1" %)... |(% colspan="3" %)GeoMagneticField|(% colspan="6" %)These objects are produced by the factory and are based on a model for a decimal year date and allows to compute GeomagneticElements |(% colspan="1" %)... |(% colspan="3" %)GeoMagneticFieldFactory|(% colspan="6" %)Factory to produce GeoMagneticField.|(% colspan="1" %)... |(% colspan="3" %)GeoMagneticModelReader|(% colspan="6" %)To load the model from an input file|(% colspan="1" %)... |(% colspan="3" %)COFFileFormatReader|(% colspan="6" %)Class loading the geomagnetic data from COF files.|(% colspan="1" %)...