## 3 Results  
In this section, we present the achieved results concerning the tasks of movement direction determination and classification accuracy. Please note that a correct classification of an unknown feature set implicitly goes hand in hand with a correct identification and a correct speed estimation, as described in Section 2. For the first experiments, the overall condition were chosen in a way that the LED of the light source was the only active light source in the laboratory room and that the shades of the windows were completely closed in order to block any sunlight, identical to the experiments performed in [15]. In order to determine the data for the results generation, the following workflow was executed after the initial powering up of the VLS unit. As the first step, the chosen foil (for example, 3M green) is placed on the train at the defined position in the chosen size configuration (exemplarily shown in Figure 3). Please note that the ME was placed on the train at the discussed position and remained unchanged throughout all the performed experiments. Then, in the second step, the train is set to the desired speed setting and the desired movement direction and the movement along the given tracks is initiated. As the train moves through the detection area of the VLS unit, the oscilloscope detects the triggering event from the ME and stores the acquired output values of the VLS unit with the aforementioned resolution in a binary file. After completing a round, the train triggers the acquisition of the oscilloscope again, resulting in the next binary file and so \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Description** \\ \hline Min\_Green & Minimum value of the green color channel in the defined sample number range \\ \hline Min\_Red & Minimum value of the red color channel in the defined sample number range \\ \hline Min\_Blue & Minimum value of the blue color channel in the defined sample number range \\ \hline Mean\_Green & Calculated mean value for the green channel in the defined sample number range \\ \hline Mean\_Red & Calculated mean value for the red channel in the defined sample number range \\ \hline Mean\_Blue & Calculated mean value for the blue channel in the defined sample number range \\ \hline Diff\_Flanks & Duration (in sample numbers) of the triggering event “caused” by the ME \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the features used in the random forest model generation. forth. As explained earlier, each of these binary files is considered as one run. For each of the chosen foils, size configurations, speed settings and movement directions, 20 runs were performed. Please note that we also stored the metadata for each run, giving the \"ground truth\" necessary for determining the correct classifications of the scenarios and movement direction determinations. After finishing the 20 runs, we started over with the first step until all the runs for all the defined combinations of foils, size configurations, speed settings and movement directions were completed. In total, since we performed 20 runs for each of the 8 different foils in 4 different size configurations, performed in 2 different movement directions, this yielded 5120 binary files that were used for the feature generation and online testing. Identical to [15], we split the available data in half, resulting in 2560 binary files used for the model generation and the other half for the online test of the generated random forest. The subsequent processing and feature generation steps in GNU/Octave were applied to all of the binary files, regardless of whether they were later on used for training or for online testing. After importing the binary file, the data for all the three channels were immediately resampled to emulate the sampling rate of 100 kHz. Then, a moving average over 50 samples was used for data smoothing. In the next step, the positions of the two flanks \"caused\" by the ME were determined. Then, the described Mean_Flank1 and Mean_Flank2 values were calculated.