## 5 Data Analysis  
### Preliminary Analysis and Input Variable Selection  
The time series of the LHD operation parameters and the corresponding NOx emission amounts are represented in Figures 5 and 6. Both graphs have grid lines of time equal to 60 s. To make the graphs more suitable for comparison, time series are re-sampled by a linear moving average filter (5 points or 5 s). In this way, the sharp changes of operational parameters are equalized to the NOx sensor output and the intake pressure signal INTAKEP (kPa), which is originally sampled with a 5 s period by the onboard monitoring system. Looking at the graphs in Figure 5, we can conclude that engine rotations expectedly follow the engine acceleration (operator's pedal), and both signals are well-correlated with NOx sensor output and a certain delay in its reaction, i.e., such a natural logical sequence of signals exists: \"acceleration-rotations-emission\". During every period of idle engine rotations (pointed out by the arrows in the graph) at about 855 rpm without acceleration (stable value of 2%), the NOx emission signal gradually falls to a minimum value (about 68 ppm) and then rises. The maximum value of the NOx signal observed in the given whole dataset is equal to 1650 ppm. Hence, the NOx sensor output is non-linearly dependent on the idle mode duration. This effect is difficult  
Figure 4: The structure of a Multi-Layer Perceptron (MLP) network. Figure 5: The time series of recorded signals: NOx emission ENGNOX (ppm); engine rotations ENGPRM (rpm); and engine acceleration ENGTPS (%). to account for in the ANN-based model. These periods of time can be excluded from NOD training by the logical condition: (ENGTPS = 2% and ENGRPM < 860 rpm). By comparison, the logical condition (ENGTPS = 2% and ENGRPM = 0 rpm) corresponds to a fully stopped engine. In this case, engine acceleration remains non-zero (2%), too, which is probably related to signal bias in the monitoring system. The graphs in Figure 6 have the same time axis as in Figure 5 but show parallel to the NOx sensor output other signals potentially suitable for ANN-based model training. The value of the selected gear SELGEAR (\(-4\ldots 0\ldots 4\)) reflects the operator's control of the engine load and its rotations (output torque), including reverse motion (negative values). As previously, the same periods of idle rotation of the engine are presented in this graph when SELGEAR = 0. Therefore, this logical condition can be added to exclude data points in the ANN-based model training of NOx prediction. Hydroraulic oil pressure HYDOILP (MPa) corresponds to the \"load-haul-dump\" cycles of the LHD vehicle and reaches maximum values at the beginning of every cycle when the operator fills the bucket by digging into the hill of blasted bulk rock. These peaks in hydraulic pressure are well-correlated with the maximum engine acceleration rate (100%) at a rotation speed of 1700 rpm and the lowest selected gear (1). However, during the transportation period, the NOx emission signal is almost uncorrelated with this parameter, demonstrating more frequent oscillations of small amplitude around a certain value until the moment of unloading. The intake air pressure signal INTAKEP (kPa) is responsible for the fuel combustion process and is well-correlated with NOx emission. Even having a lower sampling frequency (5 s), this signal can be a good candidate for inclusion into input parameters for ANN-based model training and further NOx prediction. The correlations of the most-dependable parameters are shown in Figure 7 and are constructed from the data subset without idle modes. The ENGNOX (ppm) non-linear relation with ENGTPS (%) has R\({}^{2}\) = 0.2298, and the linear relation with INTAKEP (kPa) has R\({}^{2}\) = 0.5278, which are not satisfactory for NOx prediction in practice. Figure 6: The time series of recorded signals: NOx emission ENGNOX (ppm); intake air pressure INTAKEP (kPa); selected gear (\(-4\ldots 0\ldots 4\)); and hydraulic oil pressure HYDOILP (MPa).