Fix some compound word

043_EndToEndRobustSystem/EndToEndRobustSystem.tex

@@ -186,13 +186,13 @@ However, different network topologies with different number of neurons in each l
 \section{Experimental evaluation}
 \label{Section6:ExperimentalResults}
 
-\def\NumCases{910\xspace}
+\def\NumCases{845\xspace}
 
 In this section the experimental results obtained with the proposed method are presented and discussed.
 The error resilience techniques used in the proposed method are used in the context of the \ac{hevc} standard and compared with the reference implementation (\REF). 
 %The experimental results were obtained with the methodology described in Section~\ref{ExperimentalTestSetup}.
 To demonstrate the effectiveness of the proposed optimal error robustness scheme several test cases were used.
-In this experimental evaluation 14 test sequences were used, encoded using at 5 different bitrates. 
+In this experimental evaluation 13 test sequences were used, encoded using at 5 different bitrates. 
 Moreover, the following \acs{plr} were used to cover a wide range of loss rations giving more detail to low \acs{plr}: 0\%, 0.5\%, 1.0\%, 1.2\%, 1.6\%, 2.0\%, 2.2\%, 2.4\%, 2.6\%, 2.8\%, 3.0\%, 5.0\% and 10.0\%. 
 This results in a total of \NumCases test conditions. 
 
@@ -231,7 +231,7 @@ In the latter, the quality obtained with the proposed optimal error robustness s
 
 %As mentioned before a total of \NumCases test cases are used for the evaluation of the proposed method.
 To effectively train and validate the \ac{nn} used in the proposed method the data set is randomly divided based on the following: 70\% of the test cases are used for training and 30\% for validation.
-The \ac{nn} supervised training uses 637 test conditions and a scaled conjugate gradient algorithm~\cite{NNScaledConjugateTraining_1993}.
+The \ac{nn} supervised training uses 591 test conditions and a scaled conjugate gradient algorithm~\cite{NNScaledConjugateTraining_1993}.
 The remaining 273 conditions are used to evaluate the accuracy of the proposed method after training using different conditions.