## 5 Concluding Remarks  
In summary, the current study attempted to integrate the virtual counterparts of different applications of simulation technology in aviation for collaborative decision-making training and research. Previously, standalone simulators were well developed with high fidelity, but there was a lack of an integrated platform to unleash the potential of a well-developed virtual counterparts for aviation team training applications. This research contributes by proposing such an integrated system to benefit aviation training and further human performance research in aviation. For aviation training, the platform can be deployed in a collaborative exercise between pilots and ATCOs. In addition to the present curriculum, training on shared situational awareness and collaborative decision making can be included in the standardised vacation educational procedures. In terms of human-centred research, this platform provides a solid foundation to support such research. Experiments via offline simulated settings can be conducted through this platform with the aid of psychophysiological measurement devices and the feasibility of using user-defined scenarios. This facilitates researchers in setting up different scenarios and compares the human performance between them accordingly. Therefore, as aviation remains highly dependent on human intelligence, this platform further contributes to the world of human factor research in aviation. Eventually, with a comprehensive understanding of interacting human factor contribution to air transport operations, more intelligent automation aids \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency of** **Request**} & \multicolumn{3}{c}{**Day**} & \multicolumn{3}{c}{**Night**} \\ \cline{2-9}  & \multirow{2}{*}{**Heavy Traffic**} & \multirow{2}{*}{**Medium** **Traffic**} & \multirow{2}{*}{**Light Traffic**} & \multirow{2}{*}{**Heavy Traffic**} & \multirow{2}{*}{**Medium** **Traffic**} & \multirow{2}{*}{**Light Traffic**} \\  & & & & & & & \\ \hline \multicolumn{9}{c}{Take-off and climbing phase} \\ \hline
1 Hz & *** (0.97\%) & * (6.27\%) & ** (2.71\%) & ** (2.82\%) & ** (2.04\%) & ** (1.44\%) \\
6 Hz & * (5.45\%) & * (5.63\%) & * (5.20\%) & * (5.07\%) & * (5.64\%) & * (5.39\%) \\ \hline \multicolumn{9}{c}{En route cruising phase} \\ \hline
1 Hz & *** (0.499\%) & * (9.37\%) & ** (1.07\%) & ** (2.99\%) & ** (1.04\%) & ** (1.27\%) \\
6 Hz & * (5.23\%) & ** (4.17\%) & * (5.11\%) & ** (4.32\%) & * (5.31\%) & ** (4.15\%) \\ \hline \multicolumn{9}{c}{Descent and landing phase} \\ \hline
1 Hz & ** (1.05\%) & ** (1.46\%) & ** (1.62\%) & ** (1.62\%) & ** (1.47\%) & * (5.07\%) & *** (0.420\%) \\
6 Hz & * (6.12\%) & ** (3.13\%) & ** (3.61\%) & ** (3.59\%) & * (5.25\%) & ** (4.98\%) \\ \hline \hline \end{tabular}  
* Rounded to three significant figures. The notations ***, **, and
* denote values less than 1%, 5%, and 10% respectively.  
\end{table} 
Table 5: Percentage of data loss resulted over the specified flight task period 1.  
that facilitate decision making can be built using models trained through physiological and behavioural responses. Regarding the performance and the robustness of the platform, in Section 4, the two main dimensions, i.e., latency and integrity, were measured through a flight task experiment repeated for different settings. Different variables were utilised as independent variables to form matrices to present the results. For latency, three KPIs in terms of the time elapsed were presented in the form of their 95% confidence interval. The average values of all the KPIs were less than the threshold specified. Additionally, the upper bound of the 95% confidence interval of the system's latency did not exceed said threshold. For integrity, one KPI in terms of data loss percentage was measured during the experiment. The results demonstrated all the test scenarios resulted in less than 10% data loss, and over 60% of the test scenarios resulted in less than 5% data loss.