# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 3 Reactive Features and Reconfiguration in iDSS  
In order to process the data on-board the satellite, a reactive element, such as on-board processing with AI, is required [52; 53; 54; 55]. In our earlier works [55; 56; 57; 58; 59; 60], we demonstrated the viability of AI-on-the-edge paradigms. A one-dimensional (1D) convolutional neural network (CNN) was explored for observing bushfires on-board the satellite employing optical sensors, and encouraging results for the edge implementation on three different hardware accelerators were demonstrated. There are various optical sensors that have been deployed and used for a variety of purposes in Earth's orbit, and the main important optical characteristics are detailed in Table 2. PRISMA hyperspectral imagery data from the listed optical sensor was used to demonstrate the on-board processing capabilities. The VNIR channel, in particular, is used for wildfire management. The simulation made use of the PRISMA optical sensor, i.e., hyperspectral imagery, and it was shown that AI-on-the-edge paradigms for futuristic mission proposals are feasible by leveraging proper CNN architectures and established technology to execute time- and power-efficient inferences. The analysis was carried out using Level 2D data. It is important to point out that we directly employed high-level products in this instance, specifically L2D, and assumed that the pre-processing is carried out on board prior to the analysis due to the fact that the pre-processing demands a significant amount of time for the fine band-to-band alignment, the fine georeferencing, the radiometric calibration, and the radiance to reflectance conversion. We were able to gather optical imagery of wildfires over Australia and used the same for analysis. Figure 7 presents the segmentation image of the study region with three different fires over Australia. Nevertheless, by focusing on individual bands, one can extract specific information. The strategy of using AI is utilised in order to put into action automatic segmentation from the image that was collected using the optical payload. The requirements of the owner or operator can be used to customise the iDSS operations. In the event that real-time mission management is required, the iDSS will reconfigure autonomously. The proposed iDSS makes use of a constellation of forty satellites, all of which are continuously connected to one another through ISL. The proposed iDSS is assumed to be in near-circular orbit (i.e., eccentricity is 0.001), 500 km altitude, and inclination 55, with 40 satellites evenly spaced (plane spacing 36) in 4 orbital planes. We can disregard the values of the Right Ascension of the Ascending Node (RAAN) and mean anomaly because we are considering a continuous coverage problem. The proposed constellation's participants are assumed to be similar and to carry the same optical payload [35]. In general, every satellite will be in periodic planning, and the nadir orientation of the camera will be maintained at all times. If one of the satellites in the constellation detects the event, such as a wildfire, then that satellite will communicate with the other satellites in the constellation.