# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 1 Introduction
Activities required to meet local objectives (i.e., those specific to each module) or small bits of a global objective's functioning (i.e., particular to the infrastructure) may be included in modules performing activities in a distributed infrastructure, whether it be in independent satellite systems or distributed spacecraft. The main classification of the satellite system is shown in Figure 1. Table 1 provides a detailed description of different types of DSS architecture. The level of the operational independence of a satellite or a fraction of distributed spacecraft is characterised as _operational/functional independence_. Individualspacecraft or fractions of a distributed spacecraft's _homogeneity_ is defined as the degree of similarity between them [9; 23; 24; 25]. A DSS has several advantages: (i) simultaneous multipoint data collection, (ii) increased availability, (iii) the ability to look at different things at once, and (iv) reduced  
Figure 1: Classification of satellite systems. downtime and graceful degradation. A DSS that includes these enabling technologies can provide four major benefits [3]:  
1. _Distributed Coordination:_ can share data and change what they prioritize. 2. _Autonomous Re-tasking:_ can respond to environmental stimuli autonomously, without requiring intervention from a ground operator. 3. _Increased Availability:_ when only a single spacecraft can be reached, it can relay commands to the others. 4. _Workload Balancing:_ can re-task satellites based on available computation, power, and communications resources. The purpose of this research is to demonstrate the Trusted Autonomous Satellite Operations (TASO) of a DSS for mission management, such as wildfire detections. The findings of such analyses could be useful for future time-critical missions, i.e., disasters and rare events, and the following contributions were made from an intelligent DSS (iDSS) perspective:  
* _Mission Asstrictions:_ A reactive element, such as Artificial Intelligence (AI), is integrated with the DSS to achieve TASO for on-board data processing to provide real-time/near-real-time alerts. To accomplish the same, deep learning is developed and demonstrated for detecting wildfires on-board the satellite using optical payload, i.e., hyperspectral imagery. * _Service Astrictions:_ For the TASO, the iDSS will reconfigure either based on the detection of a disaster event (wildfire) or based on the requirements of the owner/operator for the requested duration. Due to the Inter-Satellite Link (ISL) between the satellites in the architectures, iDSS can execute TASO. It is essential to mention that the detection of wildfires should be treated as an example test case and that the suggested methodology (or ones similar to it) can be successfully applied to other scenarios or activities, as has already been explored and shown in other publications [26]. The article is organised as follows: following the introduction, Section 2 discusses a DSS and its operation. Section 3 discusses the reconfiguration and reactive element in the DSS, followed by more in-depth information about the study area's description, and finally, information about the PRISMA data and the dataset's definition as well as the on-boardimplementation are covered. Section 4 delves deeply into the results and findings, as well as their applicability, and is followed by a conclusion in Section 5. This is an extended version of an article [27] that was presented at the 2022 IEEE MetroXRAINE Conference.