# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 4 Results and Discussion  
Since the end goal is to create a model that can be uploaded to an on-board astrionics system in the iDSS, it is necessary to optimise the network complexity, parameter count, and inference processing time. The use of a small chip reduced the capability to execute the wildfire detection task, which necessitated the development of an accurate model as a result of the hardware's constraint. In order to assess the viability of the suggested methodology, a prototype for carrying out the study was developed. To get started, the model was altered so that it is compatible with the hardware that was selected and so that it can detect wildfires on-board the iDSS. The model is trained with the assistance of the sophisticated computer on the ground, and the trained model is evaluated in astrionics, i.e., the hardware accelerators for the on-board data processing that are detailed in Table 3.  
From the reported results for the CubeSats or other small satellites, the Movidius and the Jetson Nano appear to be the most promising possibilities since they are in line with the entire power budget of the spacecraft platform. All of the findings that have been reported in Table 3 are in line with this budget. In this particular instance, we are taking into consideration the presence of Jetson Nano and Intel Movidius on-board the proposed constellation for the detection of wildfires. When it comes to CubeSats or small satellites, the Movidius, which has an inference time (inference time is the amount of time it takes for a model to process data and make a prediction) of 5.8 ms and a power consumption of 1.4 W, and the Jetson Nano, which has an inference time of 3.4 ms and a power consumption of 2.6 W (2.0 GPU only), appear to be the most promising options. These findings come from our previous work [56; 57; 58]. All of the results obtained are in line with the spacecraft platform's total power budget. The introduction of reactive features into the DSS architecture enables the TASO, as demonstrated by the results presented above. An analysis of the AOI on the Earth and conical sensors on-board a heterogeneous constellation of satellites is presented as part of the simulation. If a ground station is located in the Field of View (FOV) of a satellite's conical sensor and in the Elevation Angle (EA) of the conical sensor with regard to the AOI, then it is said that the AOI and the conical sensor have access to the ground station. For the purpose of simulation, the altitude of the KANYINI mission in considered, as presented before the simulation makes use of a constellation of 40 low-Earth orbiting satellites located at an altitude of 500 km. To generalise the results of the analysis, the likelihood of wildfires occurring on the four continents, i.e., four different AOI, was carefully selected. Every satellite has a camera with a FOV of thirty degrees, and the mission of the entire satellite network is to acquire images of the AOI whenever there is adequate illumination from the sun. In order to gather high-quality pictures with minimal effects of atmospheric distortion, the Earth-orbiting satellite's EA should be at least 30 degrees with respect to the AOI. It is important to compute the windows of time during a predetermined period of six hours during which each satellite can obtain an image of the location in consideration. It is also important to compute the percentage of time that a camera on at least one satellite could see the location throughout this duration. This percentage is given by SWAP and be calculated based on Equation (6). The fact that the AOI may be found inside the contour provides evidence that it is visible in the FOV of the payload camera. The FOV of the satellite's visualisation is depicted in Figure 9a for the nadir-pointing configuration. In addition to calculating the times when each camera can capture the AOI, it is necessary to determine the system-wide access percentage, which corresponds to the percentage of time from the simulation start