# Offloading of Atomic Tasks in Satellite Networks:
## 5 Numerical Experiments
### Experimental Fundamentals  
To study the key problems of mission scheduling, link optimization, collaborative computing, and network topology discovery in spatial information networks. We independently developed a Chinese Satellite Tool Kit (CSTK) based on actual satellites and common mission data in spatial information networks, which is based on the Java Agent DEvelopment Framework (JADE) (accessed on 20 October 2021 for link [[https://jade-project.gitlab.io/](https://jade-project.gitlab.io/)]([https://jade-project.gitlab.io/](https://jade-project.gitlab.io/))) framework, using the two-line element (TLE) set for the satellite and calculates satellite positions based on the Sgp4 and sdp4 packages [44]. The system simulates satellite data, including satellite payload data, satellite orbit data, inter-satellite visible time window and satellite ground visible time window data, satellite resource capacity, and satellite resource quantity. In addition, we simulated data for common Earth observation application scenarios, including Earth observation area, mission execution time demand, mission resource type, and mission resource demand. In addition, we developed various computational libraries required for the CSTK system. The experiments in this paper are done based on the CSTK experimental simulation platform, coded in Python. CSTK calculates the orbits of subsatellite points in the two-dimensional plane using the satellite orbit parameter data provided by celestrak ([[https://celestrak.com/](https://celestrak.com/)]([https://celestrak.com/](https://celestrak.com/)), accessed on 20 October 2021). The main software interface of CSTK is shown in the Figure 5.  
We complete the performance validation of the numerical experiment based on the simulation capabilities of satellite and mission data provided by the CSTK platform. In this experiment, we use multiple datasets to validate the performance of different algorithms. Each dataset contains satellite and task data of varying sizes. We observe the effectiveness of our algorithm and baseline model approach by the running results on each dataset. The experimental data details are shown in Table 1.  
As shown in Table 1, where 'ID' denotes the dataset number, 'Scale' denotes the satellite and task scales. From the above table, it can be observed that our experiment uses six datasets with increasing size of the dataset. The minimum number of missions is 50,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Scale**} \\ \cline{2-3}  & **Tasks** & **Satellites** \\ \hline D1 & 50 & 10 \\ D2 & 100 & 20 \\ D3 & 500 & 50 \\ D4 & 1000 & 100 \\ D5 & 1500 & 200 \\ D6 & 5000 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-scale experimental datasets.  
Figure 5: Spatial information network computing environment simulation platform.  
the maximum is 5000, the minimum number of satellites is 10, the maximum number is 500. With the increase of data set size, the satellite network needs to deal with more tasks, and the interaction between satellites is more complex, which requires higher timeliness of task processing algorithm. And this is also closer to the explosive growth trend of multimedia services and the continuous emergence of new space tasks.