## 4 EWM-TOPSIS-Gray Relational Degree Model
### Comprehensive Evaluation and Analysis of the Level of Situational Awareness  
The two-way ANOVA indicates that under remote tower settings, varying qualifications and tiredness levels of air traffic controllers lead to substantial differences in the parameters of each indicator. We thoroughly investigate and assess the situational awareness levels of air traffic controllers in remote tower settings using an entropy-TOPSIS-Gray relational degree model. We have selected nineteen assessment indicators and processed the experimental data using the aforementioned processes. Initially, we determine the weights of each indicator using the entropy approach, as shown in Table 6:  
Subsequently, the TOPSIS method is employed to calculate the Euclidean distances \(D_{i}^{+}\) and \(D_{i}^{-}\), while the Gray relational analysis method is utilized to compute the Gray relational degrees \(R_{i}^{+}\) and \(R_{i}^{-}\). Finally, the comprehensive proximity degree, \(\mu_{i}\), for each treatment scheme is derived by integrating \(D_{i}^{+}\), \(D_{i}^{-}\), \(R_{i}^{+}\), and \(R_{i}^{-}\). The results are presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Entropy** & **Coefficient of Variation** & **Weight** \\ \hline A1 & 0.9412 & 0.0588 & 0.0653 \\ A2 & 0.9128 & 0.0872 & 0.0969 \\ A3 & 0.9541 & 0.0459 & 0.0509 \\ A4 & 0.9546 & 0.0454 & 0.0504 \\ A5 & 0.9203 & 0.0797 & 0.0885 \\ A6 & 0.9493 & 0.0507 & 0.0563 \\ A7 & 0.9442 & 0.0558 & 0.0620 \\ A8 & 0.9621 & 0.0379 & 0.0421 \\ A9 & 0.9630 & 0.0370 & 0.0411 \\ B1 & 0.9784 & 0.0216 & 0.0240 \\ B2 & 0.9522 & 0.0478 & 0.0531 \\ B3 & 0.9560 & 0.0440 & 0.0489 \\ B4 & 0.9755 & 0.0245 & 0.0272 \\ B5 & 0.9757 & 0.0243 & 0.0270 \\ C1 & 0.9755 & 0.0245 & 0.0272 \\ C2 & 0.9566 & 0.0434 & 0.0482 \\ C3 & 0.9609 & 0.0391 & 0.0434 \\ C4 & 0.9521 & 0.0479 & 0.0532 \\ C5 & 0.9625 & 0.0375 & 0.0416 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Entropy value method to find the weight of each indicator.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & \multicolumn{2}{c}{**Euclidean Distance**} & \multicolumn{2}{c}{**Gray Relational Degree**} & \multicolumn{2}{c}{**Coupling**} & \multirow{2}{*}{\(\mu_{i}\)} & \multirow{2}{*}{**R**} \\ \cline{2-3} \cline{5-9}  & \(D_{i}^{\bullet}\) & \(D_{i}^{-}\) & \(R_{i}^{\bullet}\) & \(R_{i}^{-}\) & \(f_{i}^{\bullet}\) & \(f_{i}^{\bullet}\) & \\ \hline
001  
The total proximity degree represents the situational awareness levels of air traffic controllers in distant tower settings. Table 4 shows that a higher proximity degree correlates with an enhanced level of situational awareness for each controller, as determined by their comprehensive proximity degrees.  
Figure 7 displays the ranking of the indicators, derived from the weights determined in Equation (2). The top five indications are average blink frequency, length of eye saccades, average fixation duration, fixation time, and average pupil diameter. Figure 8 depicts the complete scores for the assessment items as per Equation (12). Evaluation items 1 to 12 denote data gathered from air traffic controllers during non-fatigue intervals, and evaluation objects 13 to 24 pertain to data acquired during fatigue intervals. The data unequivocally demonstrates that the situational awareness of air traffic controllers operating under remote tower settings is much superior during non-fatigue intervals in contrast to fatigue intervals.