diff --git a/text/evaluation/details.tex b/text/evaluation/details.tex
index fae9afe..e27fea9 100644
--- a/text/evaluation/details.tex
+++ b/text/evaluation/details.tex
@@ -1,4 +1,4 @@
-\section{Experimental setting and data sets}
+\section{Setting, configurations, and data sets}
 \label{sec:eval-dtl}
 In this section we list all the relevant details regarding the evaluation setting (Section~\ref{subsec:eval-setup}), and we present the real and synthetic data sets that we used (Section~\ref{subsec:eval-dat}), along with the corresponding configurations (Section~\ref{subsec:eval-conf}).
 
@@ -123,8 +123,7 @@ For this reason, and in order to create a more controlled environment for our ex
 We model the temporal correlation in the synthetic data as a \emph{stochastic matrix} $P$, using a \emph{Markov Chain}~\cite{gagniuc2017markov}.
 $P$ is an $n \times n$ matrix, where the element $P_{ij}$
 %at the $i$th row of the $j$th column that 
-represents the transition probability from a state $i$ to another state $j$.
-%, $\forall i, j \leq n$.
+represents the transition probability from a state $i$ to another state $j$, $\forall$ $i$, $j$ $\leq$ $n$.
 It holds that the elements of every row $j$ of $P$ sum up to $1$.
 We follow the \emph{Laplacian smoothing} technique~\cite{sorkine2004laplacian}, as utilized in~\cite{cao2018quantifying}, to generate the matrix $P$ with a degree of temporal correlation $s > 0$ equal to
 % and generate a stochastic matrix $P$ with a degree of temporal correlation $s$ by calculating each element $P_{ij}$ as follows
diff --git a/text/evaluation/theotherthing.tex b/text/evaluation/theotherthing.tex
index 90cb20c..4ba8f9c 100644
--- a/text/evaluation/theotherthing.tex
+++ b/text/evaluation/theotherthing.tex
@@ -1,4 +1,4 @@
-\section{Selection of landmarks}
+\section{Selection of {\thethings}}
 \label{sec:eval-lmdk-sel}
 In this section, we present the experiments on the methodology for the {\thething} selection presented in Section~\ref{subsec:lmdk-sel-sol}, on the real and synthetic data sets.
 With the experiments on the synthetic data sets (Section~\ref{subsec:sel-utl}) we show the normalized Euclidean and Wasserstein distance metrics (not to be confused with the temporal distances in Figure~\ref{fig:avg-dist})