# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 4 Results
### Results of the NASA-TLX and the Significance Test  
The average results of the TLX responses are shown in Figure 6 on a scale from 1 to 10 (1 very low and 10 very high). We can observe that the scenarios from (b) to (f) are more demanding than (a), with an increase in mental, physical, and temporal demand. Additionally, a slight decrease in performance from (b) to (f) can be observed. The trends align with the expected results, in which an ATCO would suffer an increase in workload once an RPAS operates in their sector and higher if the RPAS suffers a contingency. Scenarios (d) and (f), with intent, have a higher demand for ATCO than those without intent but also achieve higher performance.  
Figure 5: Emergency airfield selected for the RQ-4 A engine failure contingency.  
The responses of the NASA-TLX have been used to test our hypothesis about the statistically significant differences between scenarios using the \(t\)-test statistic with a \(p\) value = 0.05. First, we have analysed scenarios (a) and (b) to check if adding the RPAS affects the ATCO's workload. The results can be seen in Table 9, first row (HORPAS). Then, a second statistical analysis compares scenario (b) with the rest of the scenarios (c, d, e, and f) to check the effect of contingencies on the ATCO's workload. The results are shown in Table 9 on the second row (H0 contingency).  
As a result of the statistical analysis, we can assume that the null hypothesis is true for the HDRPAS. This means that the workload of the ATCOs has no significant difference when introducing one RPAS mission integrated with the air traffic compared with the same traffic without the RPAS.  
The second null hypothesis, H0 contingency, can be rejected for almost all the TLX categories (shown in red in Table 9). The only exception is the physical demand, which does not show significant differences. Overall, the results show that when an RPAS is introduced in the ATC sector and suffers a contingency (engine or lost-link failures), the mental demand, the temporal demand, the effort, and the frustration show a significant increase and the performance of the ATCOs decreases.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**TLX Category** & **Null Hypothesis** & **Statistic** & \(p\)**-Value** \\ \hline Mental demand & H0 RPAS & \(-1.911015\) & 0.0637736 \\ \hline  & H0 contingency & \(-2.359384\) & 0.02035207 \\ \hline Physical demand & H0 RPAS & \(-0.802929\) & 0.42700899 \\ \hline  & H0 contingency & \(-1.931829\) & 0.05632923 \\ \hline Temporal demand & H0 RPAS & \(-1.705852\) & 0.09619784 \\ \hline  & H0 contingency & \(-2.466407\) & 0.01542096 \\ \hline Performance & H0 RPAS & 0.7513499 & 0.45707032 \\ \hline  & H0 contingency & 2.7804155 & 0.00652202 \\ \hline Effort & H0 RPAS & \(-1.431552\) & 0.16044591 \\ \hline  & H0 contingency & \(-2.613374\) & 0.01039295 \\ \hline Frustration & H0 RPAS & \(-0.218248\) & 0.82840352 \\ \hline  & H0 contingency & \(-2.961887\) & 0.00384499 \\ \hline \hline \end{tabular}
\end{table}
Table 9: \(t\)-test statistical analysis for each of the six TLX categories.  
Figure 6: Average and standard deviation of TLX results per scenario and category.