## 4 Results
Then, the harmonic S-spline according to Formula (3) was used to normalize the input data. Fragments of the normalized data for Aircraft 1 are given in Table 4[26], and for Aircraft 2 in Table 5[27]. This approach provides convenience for comparing different parameters; however, it is important to note that this method does not change the physical meaning of parameters such as sound pressure or sharpness. The model for predicting the individual psychophysiological workload of the flight instructor in the working environment is referred to as \(M_{\text{\it{ave}}}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Aircraft** & **Flight Phase** & \(\mathbf{ve_{1}}\) & \(\mathbf{ve_{2}}\) & \(\mathbf{ve_{3}}\) & \(\mathbf{ve_{4}}\) & \(\mathbf{ve_{5}}\) \\ \hline \multirow{4}{*}{Aircraft 1} & \(F_{1}\) & 51,900 & 15,873 & 511,015 & 103,800 & 51,901 \\  & \(F_{2}\) & 12,338 & 3773 & 121,476 & 24,675 & 12,339 \\  & \(F_{3}\) & 24,975 & 7638 & 245,907 & 49,950 & 24,976 \\ \hline \multirow{4}{*}{Aircraft 2} & \(F_{1}\) & 29,363 & 8980 & 289,107 & 58,725 & 29,364 \\  & \(F_{2}\) & 16,463 & 5035 & 162,092 & 32,925 & 16,464 \\  & \(F_{3}\) & 19,050 & 5826 & 187,569 & 38,100 & 19,051 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of noise level data obtained. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Flight Phase** & **Time (s)** & \(\tilde{\mathbf{ve_{1}}}\) & \(\tilde{\mathbf{ve_{2}}}\) & \(\tilde{\mathbf{ve_{3}}}\) & \(\tilde{\mathbf{ve_{4}}}\) & \(\tilde{\mathbf{ve_{5}}}\) \\ \hline \multirow{4}{*}{\(F_{1}\)} & \(t_{1}\) & 99.43844791 & 0.049407323 & 0.090870225 & 1.820649273 & 38.90455337 \\  & \(t_{2}\) & 98.92557213 & 0.393186246 & 0.297258102 & 1.881493262 & 47.30783701 \\  & \(t_{3}\) & 98.37738021 & 0.184487022 & 0.240906663 & 1.878014839 & 52.00322967 \\  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\  & \(t_{276}\) & 95.89348756 & 0.001414645 & 0.053960675 & 1.641768102 & 31.59757744 \\ \hline \multirow{4}{*}{\(F_{2}\)} & \(t_{1}\) & 96.26082911 & 0.044468346 & 0.073482189 & 1.595405282 & 26.34302168 \\  & \(t_{2}\) & 96.36349313 & 0.359881713 & 0.052506191 & 1.625766713 & 32.43032776 \\ \cline{1-1}  & \(t_{3}\) & 96.30693495 & 0.170487214 & 0.065447515 & 1.625473513 & 30.34985811 \\ \cline{1-1}  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\  & \(t_{65}\) & 96.25085571 & 0.001119628 & 0.052483539 & 1.629409028 & 31.99962864 \\ \hline \multirow{4}{*}{\(F_{3}\)} & \(t_{1}\) & 94.47751299 & 0.031812957 & 0.096674962 & 4.10159268 & 16.39833676 \\  & \(t_{2}\) & 94.17346367 & 0.332458617 & 0.0888424 & 4.144692964 & 19.63654232 \\ \cline{1-1}  & \(t_{3}\) & 94.12463797 & 0.265889948 & 0.082689402 & 4.134540352 & 19.76975005 \\ \cline{1-1}  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \cline{1-1}  & \(t_{133}\) & 85.76090952 & 0.014108723 & 0.078172879 & 2.094322104 & 1.65973182 \\ \hline \end{tabular}
\end{table} 
Table 2: Fragments of the input data of the average value of the noise level for Aircraft 1. Next, the level of individual psychophysiological workload in the working environment for the following period was predicted. For this, paired linear regression (4) was used. Although modern methods such as ARIMA (autoregressive integrated moving average), support vector regression, or neural networks can indeed provide higher accuracy, their use was postponed for reasons of validity at the basic modeling stage. The regressions for \(we_{1}\)-\(we_{5}\) were analyzed separately, after which their results were reconciled by calculating the weighted average of the loadings. First, the coefficients a and b were calculated using the least squares method, Formulas (5) and (6). The calculation results are presented in Table 6, which characterizes the calculation of regression coefficients for constructing the equation on which the forecasting is based. The table also shows the calculated predicted estimates of the individual psychophysiological load on a flight instructor in future.