diff --git a/tex/chapters/experiments.tex b/tex/chapters/experiments.tex
index bbe33fe..e9ad1f5 100644
--- a/tex/chapters/experiments.tex
+++ b/tex/chapters/experiments.tex
@@ -52,8 +52,8 @@ However, both cases do not give a deeper insight of the error behavior of our me
 \subsection{Performance}
 In the following, we underpin the promising theoretical linear time complexity of our method with empirical time measurements compared to other methods.
 All tests are performed on a Intel Core \mbox{i5-7600K} CPU with a frequency of \SI{4.2}{\giga\hertz}, and \SI{16}{\giga\byte} main memory.
-We compare our C++ implementation of the extended box filter based KDE approximation to the KernSmooth R package and the \qq{FastKDE} Python implementation \cite{oBrien2016fast}.
-The KernSmooth packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
+We compare our C++ implementation of the box filter based KDE approximation to the \texttt{ks} R package and the fastKDE Python implementation \cite{oBrien2016fast}.
+The \texttt{ks} packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
 % Vergleich zu weighted average (in c++) um unseren großen Geschwindigkeitsvorteil zu zeigen.
 With state estimation problems in mind, we additionally provide a C++ implementation of a weighted average estimator.
 \commentByToni{Vielleicht sollten wir hier noch paar Worte über die Implementierung verlieren. Ist das alles std c++? nehmen wir iwas mega kraßes? usw. vielleicht im camera ready sogar nen link zum coder oder sowas.}
diff --git a/tex/chapters/realworld.tex b/tex/chapters/realworld.tex
index 47e135e..0845afd 100644
--- a/tex/chapters/realworld.tex
+++ b/tex/chapters/realworld.tex
@@ -46,6 +46,7 @@ Additionally, in most real world scenarios many particles share the same weight
 \end{figure}
 
 Further investigating fig. \ref{fig:realWorldTime}, the boxKDE performs slightly better then the weighted-average, however after deploying \SI{100} MC runs, the difference becomes insignificant.
+\commentByMarkus{Was sind MC Runs? Die Abkürzung kommt das erste mal vor.} 
 The main reason for this are again multimodalities caused by faulty or delayed measurements, especially when entering or leaving rooms. 
 Within our experiments the problem occurred due to slow and attenuated Wi-Fi signals inside thick-walled rooms. 
 While the system's dynamics are moving the particles outside, the faulty Wi-Fi readings are holding back a majority by assigning corresponding weights.