diff --git a/paper/sections/05-control.tex b/paper/sections/05-control.tex
index ce91f3ebf4663cb90d2a4a3e6cd239ce4f70039f..3eb9709c5ae7c343cf4ed011a2daeeafbfeb47f6 100644
--- a/paper/sections/05-control.tex
+++ b/paper/sections/05-control.tex
@@ -29,7 +29,7 @@ font=\footnotesize]
 \draw [arr, bend left]  (gp)  to node [near start, left, yshift=4mm, align=right] {\texttt{sensor in}\\\texttt{position\_available} \\ \textcolor{red}{\texttt{turn\_off}}} (wst);
 \draw [arr, bend right] (gp)  to node [below, align=center, yshift=-1mm] {\texttt{lost\_visibility} \textbf{\texttt{or}} \\ \texttt{ephemeris\_expiring}} (re);
 %arrows from 4
-\draw [arr, bend left]  (wst) to node [right, align=left, xshift=2mm, yshift=-3mm, at start] {\texttt{$\sum(\Tr(P)>th)$} \\ \texttt{\textcolor{red}{\texttt{turn\_on}}}} (gp); 
+\draw [arr, bend left]  (wst) to node [right, align=left, xshift=2mm, yshift=-3mm, at start] {\texttt{$\Tr(P)>th$} \\ \texttt{\textcolor{red}{\texttt{turn\_on}}}} (gp); 
 \draw [arr, bend right] (wst) to node [left, at start, yshift=4mm, align=right] {\texttt{ephemeris\_expiring} \\ \textcolor{red}{\texttt{turn\_on}}} (re);
 \end{tikzpicture}
 \caption{State Machine of the Sampling Strategy Controller.}
@@ -61,7 +61,7 @@ milliseconds to be acquired. This could be critical for real-time
 applications. The data validity is instantaneous, since they are used
 as soon as they are received to compute the current position (and
 moving will invalidate them). The time scale allows us to derive a
-bound in the sensor sampling period. Sampling as frequently as the
+bound in the sensor sampling period. Sampling as frequently as
 this (lower) bound is equivalent to keeping the sensor always on.
 
 Another important consequence of the sampling policy is the
diff --git a/paper/sections/06-results.tex b/paper/sections/06-results.tex
index c7d26b4e7eaeed29cb05d86ef8452c65ceef47f2..0a8a1319de5e6876c59f6fc28ccab8bb8d6ac9f8 100644
--- a/paper/sections/06-results.tex
+++ b/paper/sections/06-results.tex
@@ -296,7 +296,7 @@ synchronized in the antenna's state.
  legend style={at={(1.3,1.1)},anchor=south},
  legend columns=2,
  xlabel = {Time [s]},
- ylabel = {Sum of Trace \\ Turn On Signal},
+ ylabel = {Trace \\ Turn On Signal},
  xmin=500,xmax=3500,
 ]
 \pgfplotsset{filter discard warning=false}
@@ -312,11 +312,11 @@ synchronized in the antenna's state.
 \addplot[thick, red, dashed]
  table[x index = {0}, y index = {2}, col sep=comma]
  {data/exp_biketraceCtl.csv};
-\addlegendentry{Sensor Fusion $th = 0.1$, $\sum Tr(P)$}
+\addlegendentry{Sensor Fusion $th = 0.1$, $Tr(P)$}
 \addplot[thick, black!70!green, dashed]
  table[x index = {0}, y index = {4}, col sep=comma]
  {data/exp_biketraceCtl.csv};
-\addlegendentry{Sensor Fusion $th = 4.1$, $\sum Tr(P)$}
+\addlegendentry{Sensor Fusion $th = 4.1$, $Tr(P)$}
 \end{axis}
 \end{tikzpicture}
 \caption{Control Signal and Sampling when cycling.}
@@ -335,7 +335,7 @@ synchronized in the antenna's state.
  scaled y ticks = false,
  ylabel style = {align=center},
  xlabel = {Time [s]},
- ylabel = {Sum of Trace \\ Turn On Signal},
+ ylabel = {Trace \\ Turn On Signal},
  xmin=500,xmax=3500,
 ]
 \pgfplotsset{filter discard warning=false}
@@ -389,7 +389,7 @@ algorithm is very basic and more advanced versions could improve the
 tracking performance also in the biking case. For the same
 simulations, we also show in Figures~\ref{fig:bike-trace-ctl}
 and~\ref{fig:car-trace-ctl} the signal used for the GPS triggering
-(i.e., the sum of the trace of $P$) and the state of the GPS antenna.
+(i.e., the trace of $P$) and the state of the GPS antenna.
 The figures show how the sampling strategy is able to keep the
 variance of the estimation bounded, while reducing the on time.