## 4 Experimental Results
### Experimental Setup  
As shown in Table 2, the remote sensing tasks were randomly generated in the region between 114\({}^{\circ}\)-124\({}^{\circ}\)E and 30\({}^{\circ}\)-45\({}^{\circ}\)N, which is located in the northeast and eastern coastal areas of China. Scenarios involving different numbers of tasks were established. One training scenario and five test scenarios were used for each task.  
The scheduling horizon was set to 24 h from 1 September 2022 00:00 to 1 September 2022 24:00. The observation duration for each target was randomly generated between [15 s and 30 s]. To standardize the description of profit, we randomly assigned a profit value, an integer in the range of 1-10, to each task. The VTWs between the AEOS and the targets were obtained directly from the System Tool Kit (STK), and the orbit parameters of the AEOS are listed in Table 3. The satellite orbits the Earth 14.42 times within the scheduling horizon.  
The attitude transition time \(tr_{ij}\) was calculated as follows:  
\[tr_{ij}=\max\left\{\frac{\left|\theta_{pi}-\theta_{pj}\right|}{v_{p}},\frac{ \left|\theta_{ri}-\theta_{rj}\right|}{v_{r}}\right\}, \tag{20}\]  
where \(\theta_{pi}\) is the pitch attitude angle observed by the satellite to task \(i\), \(\theta_{ri}\) is the roll attitude angle observed by the satellite to task \(i\), and \(v_{p}\) and \(v_{r}\) are the angular velocities of the satellite pitch maneuver and roll maneuver, respectively. \(v_{p}\) and \(v_{r}\) were both set to 5\({}^{\circ}\)/s, and the attitude maneuvering range was set to \(\pm\)30\({}^{\circ}\).  
AEOS observation scheduling is typically an oversubscribed problem that requires an AEOS to observe tasks beyond its time resource capacity. Therefore, in addition to the total profit, the profit acquisition rate was calculated to evaluate the performance of the algorithms, which could serve as a reflection of the degree of task overload. The profit acquisition rate is calculated as follows:  
\[PAR=P_{schedulat}/P_{total}, \tag{21}\]  
where \(P_{schedulat}\) is the profit obtained, and \(P_{total}\) is the total profit of all tasks waiting for scheduling.  
The PyTorch (1.13.1+cu116) deep learning framework was employed to build the scheduling networks. Table 4 lists the parameters of the training process.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Maximum number of episodes M\({}_{e}\) & 100 \\ Memory capacity & 500 \\ Batch size & 64 \\ Learning size & 200 \\ Discount factor & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Number of Tasks** & **Training Scenarios** & **Test Scenarios** \\ \hline
114\({}^{\circ}\)-124\({}^{\circ}\)E, & 100, 200, 300, 400, 500, & 10 & 50 \\
30\({}^{\circ}\)-45\({}^{\circ}\)N & 600, 700, 800, 900, 1000 & 10 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random generated simulation scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Semi-major axis & 7129 km \\ Orbital inclination & 98.41\({}^{\circ}\) \\ Right ascension of the ascending node & 129.9734\({}^{\circ}\) \\ Orbital eccentricity & 0.0001996 \\ Argument of perigee & 89.5171\({}^{\circ}\) \\ Mean anomaly & 188.83\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Orbit parameters.