## 5 Results
### Algorithm Performance  
In this section, the effectiveness of the algorithm is first verified. In the above experimental scenario, six sets of experiments are set up, each with task sizes of 100, 200, 300, 400, 500, and 600, and the tasks are distributed in a mixed distribution. In the experiments, the running time of the DMP-TA algorithm, the number of aggregated tasks obtained after aggregation, and the sum of the energy required to perform the aggregated tasks after aggregation is verified. The sum of the energy consumption for the execution of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite Parameters** & **Satellite \(\boldsymbol{S_{1}}\)** & **Satellite \(\boldsymbol{S_{2}}\)** & **Satellite \(\boldsymbol{S_{3}}\)** \\ \hline Semi-major axis (km) & 7201 & 7052 & 7480 \\ Orbital eccentricity & 0.0134947 & 0.0008895 & 0.0015076 \\ Orbit inclination (\({}^{\circ}\)) & 98.2597 & 98.15 & 98.0036 \\ True anomaly (\({}^{\circ}\)) & 191.1477 & 246.0537 & 7480 \\ Ascending node equinox (\({}^{\circ}\)) & 7.0557 & 73.3937 & 7480 \\ Argument of periapsis (\({}^{\circ}\)) & 169.2664 & 114.1612 & 7480 \\ Field of view (\({}^{\circ}\)) & 2.1 & 0.931 & 7480 \\ Lateral swing rate/(s) & 1 & 1 & 7480 \\ Maximum swing angle (\({}^{\circ}\)) & \(\pm 45\) & \(\pm 45\) & \(\pm 45\) \\ Maximum operating time (s) & 80 & 80 & 80 \\ Observation of energy consumption (km/s) & 200 & 200 & 200 \\ Side pendulum energy consumption (km/\({}^{\circ}\)) & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellite orbit parameters. aggregated tasks solved by the algorithm is compared with the sum of the energy consumption for the performance of the tasks without aggregation and the sum of the energy consumption for the execution of the aggregated tasks solved by the CP-TM algorithm. To improve the reliability of the results, the two algorithms were run 15 times, and the average of the relevant results was taken. The obtained experimental results are shown in Figures 4-6. Figure 4 indicates that the execution time of the DMP-TA and CP-TM algorithms increases with the increase of task size. The execution time of the DMP-TA algorithm is slightly higher than that of the CP-TM algorithm for the same task size, and the difference in the execution time of the algorithms increases with the increase of the task size. This phenomenon is because the merging priority used by the DMP-TA algorithm in considering task merging needs to calculate the minimum energy consumption of the aggregated tasks, and this part of the calculation consumes some time. However, from the proof in Section 4.2, the time complexity of the DMP-TA algorithm is the same as that of the CP-TM algorithm, so it can be theoretically speculated that the difference in execution time between the DMP-TA algorithm and the CP-TM algorithm will not be significant regardless of the increase in task size. Figure 5 shows that both the DMP-TA and CP-TM algorithms have relatively significant aggregation effects. The task size can be reduced by about 25% after aggregation using the CP-TM algorithm. The task size can be reduced by about 50% after aggregation using the DMP-TA algorithm, about 25% higher than that of the CP-TM algorithm. Overall, the aggregation capability of the DMP-TA algorithm is significantly better than that of the CP-TM algorithm because the DMP-TA algorithm takes into account the extended start-up time of the remote sensor in the process of aggregation. Tasks with observation time windows at the maximum start-up time of the remote sensor have the opportunity to be aggregated. In contrast, the CP-TM algorithm only considers tasks with common time windows, and the length of the common time window is more significant than that required for task execution. The aggregation condition of the CP-TM algorithm is more severe than that of the DMP-TA algorithm. Figure 6 shows that both the DMP-TA algorithm and the CP-TM algorithm effectively reduce the energy consumption required for task execution.