User Manual 4.0 Environment Models : Différence entre versions

De Wiki
Aller à : navigation, rechercher
m (1 révision importée)
Ligne 6 : Ligne 6 :
  
 
=== Javadoc ===
 
=== Javadoc ===
All the classes related to physical models are in the <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
All the classes related to physical models are in the <code>fr.cnes.sirius.patrius.forces</code> and <code>fr.cnes.sirius.patrius.math.parameter</code>packages. The classes related to reading potential files are in the package <code>fr.cnes.sirius.patrius.forces.gravity.potential</code>.   
</syntaxhighlight> and <code>fr.cnes.sirius.patrius.math.parameter</code>packages. The classes related to reading potential files are in the package <code>fr.cnes.sirius.patrius.forces.gravity.potential</code>.   
+
  
 
|=(% colspan="3" %)Library|=(% colspan="6" %)Javadoc
 
|=(% colspan="3" %)Library|=(% colspan="6" %)Javadoc
Ligne 23 : Ligne 22 :
 
Some useful links are given hereunder.
 
Some useful links are given hereunder.
  
**IERS Page**
+
'''IERS Page'''
(% style="margin-left:30px;list-style-type:square;" %)
+
 
[http://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn36.html IERS Conventions (2010), Technical Note No.36]
 
[http://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn36.html IERS Conventions (2010), Technical Note No.36]
  
**Project Pages**
+
'''Project Pages'''
  
(% style="margin-left:30px;list-style-type:square;" %)
 
 
* [http://cddis.nasa.gov/ NASA - Crustal Dynamics Data Information System (CDDIS)]
 
* [http://cddis.nasa.gov/ NASA - Crustal Dynamics Data Information System (CDDIS)]
 
* [http://op.gfz-potsdam.de/grace/ GFZ - Grace (Gravity Recovery and Climate Experiment) Mission Homepage]
 
* [http://op.gfz-potsdam.de/grace/ GFZ - Grace (Gravity Recovery and Climate Experiment) Mission Homepage]
Ligne 37 : Ligne 34 :
 
'''Results Pages'''
 
'''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 [http://cddis.nasa.gov/926/egm96/egm96.html here].
 
* ''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 [http://cddis.nasa.gov/926/egm96/egm96.html here].
 
* ''GFZ : Global Gravity Field Models'', Available [http://icgem.gfz-potsdam.de/ICGEM/modelstab.html here].
 
* ''GFZ : Global Gravity Field Models'', Available [http://icgem.gfz-potsdam.de/ICGEM/modelstab.html here].
Ligne 50 : Ligne 46 :
 
=== Earth Potential Models ===
 
=== Earth Potential Models ===
  
The <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
The <code>fr.cnes.sirius.patrius.forces.gravity.potential</code> package provides tools allowing the user to read external gravity potential data files. The following file formats are supported :
.gravity.potential</syntaxhighlight> 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
 
* EGM96 ASCII format data
 
* EIGEN-GRACE format
 
* EIGEN-GRACE format
Ligne 59 : Ligne 53 :
 
* GRGS format
 
* GRGS format
  
Below is a diagram showing the architecture of the <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
Below is a diagram showing the architecture of the <code>fr.cnes.sirius.patrius.forces.gravity.potential</code> package.
.gravity.potential</syntaxhighlight> package.
+
  
 
[[File:physicalModels.PNG|center]]
 
[[File:physicalModels.PNG|center]]
Ligne 66 : Ligne 59 :
 
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.
 
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 <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
The <code>fr.cnes.sirius.patrius.forces.gravity.variations</code> 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 :
.gravity.variations</syntaxhighlight> 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
 
* GRGS RL02
  
Below is a diagram showing the architecture of the <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
Below is a diagram showing the architecture of the <code>fr.cnes.sirius.patrius.forces.gravity.variations</code> package.
.gravity.variations</syntaxhighlight> package.
+
  
 
[[File:varPOT.PNG|center]]
 
[[File:varPOT.PNG|center]]
 
  
 
=== Atmosphere models and solar activity model ===
 
=== 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
 
eAEAgAv58uVTcHCw15bvzz''tILyli1bav78+Tp''rzy5s2bZOXx9fXVsWPHVL9+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+''dZ6b04fdnWDqGrVqmn''v3O8X79+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 ====
 
==== 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 :
 
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**
+
*'''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 [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/DTM2000InputParameters.html DTM2000InputParamters]. Atmosphere models available include US76 (for low altitudes in range 0 to 1000km), DTM2000 and MSISE2000.
 
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 [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/DTM2000InputParameters.html DTM2000InputParamters]. Atmosphere models available include US76 (for low altitudes in range 0 to 1000km), DTM2000 and MSISE2000.
***Reading and storing solar data**
+
*'''Reading and storing solar data'''
 
The way to store the solar data is enclosed in the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataProvider.html 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 [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/package-summary.html 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.
 
The way to store the solar data is enclosed in the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataProvider.html 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 [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/package-summary.html 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**
+
*'''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 [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataProvider.html SolarActivityDataProvider]) and answers to the interfaces that define the ways in which the atmosphere models use this data (e.g. implementing  [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/DTM2000InputParameters.html DTM2000InputParameters]). These classes are contained in the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/specialized/package-summary.html fr.cnes.sirius.patrius.forces.atmospheres.solarActivity.specialized package]). So far, one class for each of the DTM2000 and MSISE2000 models have been implemented.
 
Making effective use of the solar data for specific atmospheres requires an object that provides solar data (implementing the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataProvider.html SolarActivityDataProvider]) and answers to the interfaces that define the ways in which the atmosphere models use this data (e.g. implementing  [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/DTM2000InputParameters.html DTM2000InputParameters]). These classes are contained in the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/specialized/package-summary.html 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 <code>fr.cnes.sirius.patrius.forces.atmospheres</code> package. Please note that the <code>fr.cnes.sirius.patrius.forces.atmospheres.solarActivity</code> package follows the same architecture as the <code>fr.cnes.sirius.patrius.forces.gravity.potential</code> package. The user must use the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataFactory.html SolarActivityDataFactory class].
 
Below is a diagram showing the architecture of the <code>fr.cnes.sirius.patrius.forces.atmospheres</code> package. Please note that the <code>fr.cnes.sirius.patrius.forces.atmospheres.solarActivity</code> package follows the same architecture as the <code>fr.cnes.sirius.patrius.forces.gravity.potential</code> package. The user must use the [{{PathCurrentJavaDoc}}/fr/cnes/sirius/patrius/forces/atmospheres/solarActivity/SolarActivityDataFactory.html SolarActivityDataFactory class].
 
  
 
[[File:atmosphere.png|center]]
 
[[File:atmosphere.png|center]]
Ligne 1 658 : Ligne 130 :
 
Solar and geomagnetic activity can be provided in various ways:
 
Solar and geomagnetic activity can be provided in various ways:
 
* Constant solar activity using:
 
* Constant solar activity using:
{{code language="java" title=""}}
+
<syntaxhighlight lang="java">final SolarActivityDataProvider constantSolarActivity = new ConstantSolarActivity(140, 15);</syntaxhighlight>
final SolarActivityDataProvider constantSolarActivity = new ConstantSolarActivity(140, 15);
+
{{/code}}
+
 
* Variable solar activity using (for instance):
 
* Variable solar activity using (for instance):
{{code language="java" title=""}}
+
<syntaxhighlight lang="java">
 
final SolarActivityDataProvider variableSolarActivity = new NOAAFormatReader(...);
 
final SolarActivityDataProvider variableSolarActivity = new NOAAFormatReader(...);
 
final SolarActivityDataProvider otherVariableSolarActivity = new ACSOLFormatReader(...);
 
final SolarActivityDataProvider otherVariableSolarActivity = new ACSOLFormatReader(...);
{{/code}}
+
</syntaxhighlight>
  
Solar and geomagnetic activity data often having a limited timespan, the class {{code language="java" title=""}}ExtendedSolarActivityWrapper{{/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:
+
Solar and geomagnetic activity data often having a limited timespan, the class <syntaxhighlight lang="java">ExtendedSolarActivityWrapper</syntaxhighlight> 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:
  
 
<syntaxhighlight lang="java">
 
<syntaxhighlight lang="java">
Ligne 1 687 : Ligne 157 :
  
 
The following file formats are supported by PATRIUS:
 
The following file formats are supported by PATRIUS:
(% style="margin-left:30px;list-style-type:square;" %)
 
 
* ACSOL format
 
* ACSOL format
 
* NOAA format
 
* NOAA format
Ligne 1 693 : Ligne 162 :
 
The user access point is the <code>SolarActivityDataFactory</code> 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.
 
The user access point is the <code>SolarActivityDataFactory</code> 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.
  
{{code language="java" title=""}}
+
<syntaxhighlight lang="java">
 
//Directory containing the file ACSOL.act
 
//Directory containing the file ACSOL.act
 
final File potdir = new File("/my/data/solar");
 
final File potdir = new File("/my/data/solar");
Ligne 1 708 : Ligne 177 :
 
final double kp = provider.getKp( userDate );
 
final double kp = provider.getKp( userDate );
 
final double f = provider.getInstantFluxValue( userDate );
 
final double f = provider.getInstantFluxValue( userDate );
{{/code}}
+
</syntaxhighlight>
  
  
Ligne 1 714 : Ligne 183 :
 
==== Tides model for force computation =====
 
==== Tides model for force computation =====
  
The PATRIUS <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
The PATRIUS <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces.gravity.tides</syntaxhighlight> package provides tools allowing the user to use Terestrial and Ocean tides. The <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces.gravity.tides.coefficients</syntaxhighlight> package also allows reading external ocean tides coefficients data files. The following file formats are supported by PATRIUS:
.gravity.tides</syntaxhighlight> package provides tools allowing the user to use Terestrial and Ocean tides. The <syntaxhighlight lang="java">fr.cnes.sirius.patrius.forces
+
 
.gravity.tides.coefficients</syntaxhighlight> 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
 
* FES2004 format
  
 
==== Reference point displacement ====
 
==== Reference point displacement ====
  
The PATRIUS <syntaxhighlight lang="java">fr.cnes.sirius.patrius.utils
+
The PATRIUS <code>fr.cnes.sirius.patrius.utils.ReferencePointsDisplacement</code> 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 method '''solidEarthTidesCorrections(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:
.ReferencePointsDisplacement</syntaxhighlight> 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 method'''solidEarthTidesCorrections(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:
+
  
 
<syntaxhighlight lang="java">
 
<syntaxhighlight lang="java">
'' Test from source ftp:''tai.bipm.org/iers/convupdt/chapter7/dehanttideinel/DEHANTTIDEINEL.F
+
// Test from source ftp://tai.bipm.org/iers/convupdt/chapter7/dehanttideinel/DEHANTTIDEINEL.F
  
 
// date : 13/04/2009
 
// date : 13/04/2009
Ligne 1 749 : Ligne 215 :
 
==== Design ====
 
==== Design ====
  
The <syntaxhighlight lang="java">fr.cnes.sirius.patrius.models.earth
+
The <code>fr.cnes.sirius.patrius.models.earth</code> package provides tools allowing the user to use different geomagnetic models. For the moment, there are only the two following models available :
</syntaxhighlight> 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
 
* IGRF 11  : International Geomagnetic Reference Field eleventh generation
 
* WMM 2010 : World Magnetic Model published in december 2009
 
* WMM 2010 : World Magnetic Model published in december 2009

Version du 20 février 2018 à 09:38


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 IERS Conventions (2010), Technical Note No.36

Project Pages

Results Pages

  • 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 :

  • 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.

PhysicalModels.PNG

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.

VarPOT.PNG

Atmosphere models and solar activity model

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.

Atmosphere.png

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 :

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:
final SolarActivityDataProvider constantSolarActivity = new ConstantSolarActivity(140, 15);
  • Variable solar activity using (for instance):
final SolarActivityDataProvider variableSolarActivity = new NOAAFormatReader(...);
final SolarActivityDataProvider otherVariableSolarActivity = new ACSOLFormatReader(...);
Solar and geomagnetic activity data often having a limited timespan, the class
ExtendedSolarActivityWrapper
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:

  • 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.

//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 );


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:


  • 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 method solidEarthTidesCorrections(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 :


  • 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 :

Geomaguml.png

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" %)...