## 3 System Architecture and Methodology
### Operational Description of the Digital Twin-Based Platform  
To illustrate the operations of the digital twin-based platform, a conceptualised framework for training on collaborative decision making is presented in Figure 6.  
The training based on this digital twin-based platform comprises at least one pilot and one ATCO. The pilot is required to operate a flight task in the region that the ATCO is managing. As with BlueSky's universality, both a macro view for tower/en route cruising operations and a micro view for ground operations can be supported. Therefore, multiple ATC tasks can be assigned to different ATCOs if more W-ATCOs are connected to the system architecture. The training begins with executing Prepar3D and BlueSky on W-Visual and W-ATCOs, respectively. A predefined air traffic scenario using text-based scenario files that can be read by BlueSky is also generated. Past flight trajectory data can also be adopted to formulate these scenarios, or self-designed scenarios can be utilised. Figure 5: The modified user interface of BlueSky. After loading an appropriate scenario, the button \"Initialise\" is first clicked. As previously described, the function allows moving the radar map in BlueSky to a predefined airport. The data request application is called, and the required data are sent through wireless media. The aircraft is then created accordingly. This can be demonstrated by the flight shown in Figure 5, which is at 2163 feet with a speed of 149 knots. The heading is shown via an arrowhead. By using the flight simulator hardware located in the laboratory, pilots can then operate the aircraft according to the flight task assigned to them. During the flight task, the ATCO-in-charge is assigned to monitor the operations of the concerned flights and other pre-set surrounding flights in their area of control. To train for different abnormal situations in the corresponding flight information region (FIR), a tailor-made scenario can be adopted. For instance, heavy traffic scenarios would usually cause a disturbance and raise the stress faced by the ATCOs. More human errors are expected in heavy traffic scenarios. Therefore, the real flight data with dense air traffic can be converted into training materials, or an imaginary heavy traffic scenario can be defined. To imitate the air-ground communication, a wireless microphone is placed in the cockpit for the pilots to communicate with the ATCOs. The ATCOs are required to listen to the pilots' requests/reports with an earphone and provide instantaneous verbal instructions to the pilots through a microphone. Their transmission is done through a video conferencing tool. The communication history, their behavioural responses, and the psychophysiological responses of the ATCOs can be recorded and analysed, such that possible intelligent decision or communication aids can be proposed to relieve their workloads. These decision aids can utilise the recorded behavioural reaction time in responding to the pilot's requests/reports and provide a real-time alerting mechanism to ATCOs regarding their potential lack of situational awareness. Furthermore, online electroencephalography (EEG) data-based classification of mental workload state can aid in improving communication performance between ATCOs and pilots. When the threshold of automatic decisions is reached, the ideal mechanism is to entertain the pilots' request via computational intelligence until the ATCOs are free to entertain the request. Most importantly, an experiential learning approach is always useful as ATCOs could understand the constraints of the pilots more deeply via discussions on the communication history and vice versa. In addition to abnormal airspace situations, various abnormal flight operations, such as engine fire and landing gear failure, can be triggered through the instructor's panel next to the captain's seat. To simulate such situations, the scenarios in the flight simulator can be modified via the instructor's panel.