diff --git a/matlab/cycling_data.mat b/matlab/cycling_data.mat
index 139b5c4b02acab4bc3b5e4f81ecec798dc0e41c6..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 100644
Binary files a/matlab/cycling_data.mat and b/matlab/cycling_data.mat differ
diff --git a/matlab/cycling_data_raw.mat b/matlab/cycling_data_raw.mat
new file mode 100644
index 0000000000000000000000000000000000000000..139b5c4b02acab4bc3b5e4f81ecec798dc0e41c6
Binary files /dev/null and b/matlab/cycling_data_raw.mat differ
diff --git a/matlab/manipulate_cycling_data.m b/matlab/manipulate_cycling_data.m
index b214551b5b6c9571cd214ed9458f986a1ade5199..c98e0617783e5257c338c7ecd25d1c5fcbea66fb 100644
--- a/matlab/manipulate_cycling_data.m
+++ b/matlab/manipulate_cycling_data.m
@@ -56,34 +56,36 @@
 % in_data.GNSS.t=gps(5:length(gps),1)
 
 %synchronize accelerometer and gyro data
-Gyr=gyr(1,2:4);
-Acc=acc(1,2:4);
-t=0;
-j=1; % to cycle over the gyro
-for i=1:length(acc) %cycle over the accelerometer
-    while gyr(j,1)<=acc(i,1)
-        %if the data are available and synchr then store them (with time)
-        if gyr(j,1)==acc(i,1)
-            Gyr=[Gyr;
-                 gyr(j,2:4)];
-            Acc=[Acc;
-                 acc(i,2:4)];
-            t=[t;gyr(j,1)];
-        end
-        j=j+1;
-    end
-    
-    %checkpoint for verifying progress
-    if ~mod(i,100000)
-        %display('sono arrivato al tempo:')
-        %t(j-1)
-        display('sono alla seguente riga delle accelerazioni(totale di 1901808):')
-        i
-    end
-end
-%in_data.IMU.acc
-%in_data.IMU.gyro
-%in_data.IMU.t
+% Gyr=gyr(1,2:4);
+% Acc=acc(1,2:4);
+% t=0;
+% j=1; % to cycle over the gyro
+% for i=1:length(acc) %cycle over the accelerometer
+%     while gyr(j,1)<=acc(i,1)
+%         %if the data are available and synchr then store them (with time)
+%         if gyr(j,1)==acc(i,1)
+%             Gyr=[Gyr;
+%                  gyr(j,2:4)];
+%             Acc=[Acc;
+%                  acc(i,2:4)];
+%             t=[t;gyr(j,1)];
+%         end
+%         j=j+1;
+%     end
+%     
+%     %checkpoint for verifying progress
+%     if ~mod(i,100000)
+%         %display('sono arrivato al tempo:')
+%         %t(j-1)
+%         display('sono alla seguente riga delle accelerazioni(totale di 1901808):')
+%         i
+%     end
+% end
+
+
+in_data.IMU.acc = Acc;
+in_data.IMU.gyro = Gyr;
+in_data.IMU.t = t
 
 
 
diff --git a/paper/sections/03-model.tex b/paper/sections/03-model.tex
index 232d4da714e55676497c6b9e9e84549019b8d207..e4af43a3e3ebadb938971c0b46689077468b259d 100644
--- a/paper/sections/03-model.tex
+++ b/paper/sections/03-model.tex
@@ -328,14 +328,13 @@ properties that define it. For instance, if the antenna is off (states
 No Info \textcircled{\scriptsize 1} and Warm Start Available
 \textcircled{\scriptsize 6}), the automaton can accept the transition
 \texttt{turn\_on} and move to a different state, but cannot accept the
-transition \texttt{turn\_off}. There are three exceptions,
+transition \texttt{turn\_off}. \textcolor{red}{There are three exceptions,
 corresponding to the self-loops in Figure~\ref{fig:cyberDynamics}. In
 state Cold Start \textcircled{\scriptsize 2} and Warm Start
 \textcircled{\scriptsize 5}, the ranging data are not available. The
 transition \texttt{lose\_visibility}, that models the loss of ranging
 data, can be fired to capture the interruption of the ongoing
-acquisition process, that has to be restarted. In state Position
-Available \textcircled{\scriptsize 4} the transition
+acquisition process, that has to be restarted.}\todo{remove alro transitions from figure.} In state Position Available \textcircled{\scriptsize 4} the transition
 \texttt{get\_ephemeris} can be fired to indicate an update of the
 ephemeris data before their expiration.
 
diff --git a/paper/sections/06-results.tex b/paper/sections/06-results.tex
index 3ed9fd495ba5c2808bf47d4763f2d9679c3fa331..97a6cc2d4bbb87a16566f6c943ea2f3d72160c88 100644
--- a/paper/sections/06-results.tex
+++ b/paper/sections/06-results.tex
@@ -111,7 +111,7 @@ from time to time on one of the two traces above.
 \subsection{Simulation Results}
 \label{sec:res:sim}
 
-Montecarlo simulations. Characteristics:
+Power-accuracy trade-off: Montecarlo simulations. Characteristics:
 \begin{itemize}
 \item We generate 10000 traces, 60 minutes long.
 \item For each point in each trace, we randomly extract from
@@ -121,3 +121,11 @@ Montecarlo simulations. Characteristics:
   the axis \emph{accuracy} (sum of distances from the ideal GPS trace)
   and \emph{power consumption} (due to antenna).
 \end{itemize}
+
+To show how the proposed model joined with the sensor fusion algorithm allows to capture the trade-off betwen accuracy and power consumption we run simulations for different treshold values for triggering the sampling of the GPS. Coherently with the intuition, we can see that higher values for the treshold allow to save power at the cost of lower precision in the positioning. The accuracy is measured as the distance from the positioning that uses the GPS continuously.
+
+In these simulations the GPS model exposes a random acquisition time for the fetching of the ephemeris data, drawed for a uniform distribution between \textcolor{red}{2 and 12 milliseconds}. Also the number of satellites is randomized as in the previous simulations.
+
+
+
+