# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Simulation Scenario and Dataset Details  
This scenario simulates the task of remote sensing data decomposition and allocation in a dynamic satellite network, with a duration from 04:00:00.00 UTC on 3 September 2024 to 04:00:00.00 UTC on 5 September 2024. When constructing remote sensing tasks, we thoroughly considered the satellites' operational status and the target area's geographical characteristics. Each remote sensing task is represented as a structured data unit designed to facilitate efficient scheduling and resource allocation in a dynamic satellite network. The dataset captures both the satellites' operational status and the target areas' geographical characteristics, enabling accurate imaging and timely task execution. The main fields include the task ID, satellite ID assigned to the task, task time window, target area, expected data volume, task priority, and task dependencies. Regarding the target area, we describe it in the form of latitude and longitude ranges, clearly specifying the minimum and maximum latitude and longitude of the imaging area to ensure accurate coverage of the predetermined area during task execution. The \"task dependency relationship\" field clearly describes the sequential dependencies between the current task and other tasks. Specifically, in each task, the observable angle and altitude requirements between the satellite and the target area are determined through precise calculations based on the satellite's orbit information and ground station position. We use a formula to calculate the observation angle that meets the minimum elevation angle requirement, in order to derive the most suitable imaging time window. Subsequently, we set different priorities for each task based on its importance and resource consumption. The resource requirements of the task are mainly reflected in the working status of the imaging payload and the data transmission requirements. During the task execution process, the satellite needs to complete multi-stage operations such as data acquisition, compression, and transmission in real time, and each stage has strict time and resource limitations. To further illustrate the structure of the task dataset used in this study, Table 1 provides a more detailed breakdown of actual task instances, including task IDs, target observation regions, discrete time ranges, and resource requirements. The interface for adding remote sensing tasks is shown in Figure 2.  
We combined precise satellite data and using the CSTK simulation platform to construct a real multi-satellite network environment. The actual dataset was constructed using precise satellite data from multiple authoritative platforms, including Celestrak (CELESTRAK same as below. satellite database: [[https://celestrak.org/](https://celestrak.org/)]([https://celestrak.org/](https://celestrak.org/))) and SatNOGS (SatNOGS open-source ground station network: [[https://satnogs.org/](https://satnogs.org/)]([https://satnogs.org/](https://satnogs.org/))). Celestrak provides precise orbit parameters (comprising the semi-major axis, eccentricity, inclination, ascending node longitude, and perigee angle) that meet the standards of the North American Aerospace Defense Command, ensuring reliable orbit information. SatNOGS provides detailed ground station observation data, such as coordinates and communication link status. Figure 2: A three-dimensional global simulation depicting satellite orbital paths. This dataset covers various key parameters in satellite networks, including the computing, storage, and payload resources of each satellite, as well as task identification, subtask details, task time windows, task priorities, and resource requirements in task data. Specifically, the satellite data section provides a detailed record of the resource distribution of each satellite.