# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 4 Simulation Results
### Scenario 1 Insufficient Electric Energy Scenario  
Scenario 1 represented an insufficient electric energy scenario where the satellites had limited initial power. There were four satellites with sun-synchronous orbits. The orbital parameters of the satellites are shown in Table 2. The subsystem parameters of the four satellites were the same, as shown in Table 3.  
In this scenario, five conditions are generated by randomly generating target points. The improvement effect of the method in this article is explained by the statistical results comparison of each method in multiple conditions and the results comparison of each method in a single condition. The target number was set to 100 in this scenario; the longitude ranges were [100\({}^{\circ}\), 180\({}^{\circ}\)] and [\(-\)180\({}^{\circ}\), \(-\)160\({}^{\circ}\)], and the latitude range was [40\({}^{\circ}\), 60\({}^{\circ}\)]. The scenario included 3 ground stations with the following coordinates: (105\({}^{\circ}\), 20\({}^{\circ}\)), (90\({}^{\circ}\), 30\({}^{\circ}\)), and (100\({}^{\circ}\), 10\({}^{\circ}\)). The profit from the target points was randomly assigned between 0.9 and 1. The distribution of targets and ground stations is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite No** &
\begin{tabular}{c} **Semi-Major** \\ **Axis (m)** \\ \end{tabular} & **Eccentricity** & **Inclination (\({}^{\circ}\))** & **RAAN (\({}^{\circ}\))** & **Argument of** & **True** \\  & & & & **Perigee (\({}^{\circ}\))** & **Anomaly (\({}^{\circ}\))** \\ \hline
1 & 7,028,140 & 0 & 97.9908 & 40.348 & 0 & 0 \\
2 & 7,028,140 & 0 & 97.9908 & 80.348 & 0 & 30 \\
3 & 7,028,140 & 0 & 97.9908 & 120.348 & 0 & 60 \\
4 & 7,028,140 & 0 & 97.9908 & 160.348 & 0 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial satellites orbit parameters in scenario 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Attitude Maneuver Calculate Method & Trapezoidal Method \\ Max Attitude Maneuver Angular Velocity & 1\({}^{\circ}\)/s \\ Max Attitude Maneuver Angular Acceleration & 0.5\({}^{\circ}\)/s\({}^{2}\) \\ Max Maneuvering Range Half-Cone Angle & 45\({}^{\circ}\) \\ Antenna Coverage Half-Cone Angle & 70\({}^{\circ}\) \\ Storage Capacity & 1000 Gbit \\ Camera’s Data Generation Rate & 2 Gbps \\ Satellite-Satellite Data Transmission Rate & 1 Gbps \\ Satellite-Ground Station Data Transmission Rate & 1 Gbps \\ Battery Electrical Energy Capacity & \(5\times 10^{6}\) J \\ Battery Initial Electrical Energy & \(1\times 10^{6}\) J \\ Max Charging Electrical Power of Solar Arrays & 1 kW \\ Camera Electrical Power & 1 kW \\ Satellite-Satellite Data Transmission Devices Electrical Power & 0.5 kW \\ Satellite-Ground Station Data Transmission Devices Electrical Power & 0.5 kW \\ Satellite Attitude Maneuver Electrical Power & 0.2 kW \\ Satellite Normal Electrical Power & 0.55 kW \\ Observation time of a single target & 20 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Satellite subsystem parameters in scenario 1. The statistical results of the comparison of all six methods are shown in Table 4. The average optimal solution objective function values and the average optimal solution iteration time of each method are compared as follows: The optimal solution objective function values of CTSRD and CTSND are nearly the same. The optimal solution objective function value of CTSRD is 23.9% higher than that of the initial solution of CTSTD. The optimal solution iteration times of CTSND, CTSTD, and CTSRD are nearly the same. The optimal solution objective function value of ITSRD is 3.8% higher than that of CTSRD, and the time required for ITSRD to reach the optimal solution is 31.8% less than that of CTSRD. The optimal solution objective function values of SASRD and GASRD are slightly lower than those of CTSRD, and it takes longer to reach the optimal solution. ITSRD performs better than SASRD and GASRD in terms of the optimal solution objective function value and the optimal solution iteration time.