## 5 Results
The aggregation using the CP-TM algorithm reduces the time required for task execution by about 20%, while the aggregation using the DMP-TA algorithm reduces the time required for task execution by about 50%, which is about 30% higher than that of the CP-TM algorithm. The reason is that the algorithm calculates the overall energy reduction before and after each edge aggregation and prioritizes the merging of the two clustering tasks corresponding to the edges with the most considerable energy reduction. In contrast, the CP-TM algorithm considers the  
Figure 4: Comparison of algorithm running time. number of remaining observation opportunities in each round of task merging. In addition, the aggregation condition of the CP-TM algorithm is more stringent than that of the DMP-TA algorithm, such that the number of tasks that are not aggregated after aggregation using the CP-TM algorithm will be greater compared to that using the DMP-TA algorithm, resulting in the CP-TM algorithm being inferior to the DMP-TA algorithm in terms of reducing the sum of energy consumption required for task execution. ### Algorithm Performance  
Considering the possible large influence of the distribution density of tasks on the algorithm, this paper generates 18 sets of observation tasks in the range of latitude [\(-30^{\circ}\), \(60^{\circ}\)] and longitude [\(0^{\circ}\), \(150^{\circ}\)], respectively (groups 1-6 are observation tasks with task sizes of 100, 200, 300, 400, 500, 600 under dense distribution, groups 7-12 are observation tasks with task sizes of 100, 200, 300, 400, 500, 600 under the uniform distribution, respectively 100, 200, 300, 400, 500, 600, and 13-18 groups of 100, 200, 300, 400, 500, 600 under mixed distribution), and the DMP-TA algorithm is experimentally validated under these 18 groups of data. The experimentally obtained results are shown in Figures 7 and 8. Figure 5: Comparison of the number of aggregation tasks. Figure 6: Comparison of the sum of energy requirements for aggregation task execution. From Figure 7, it can be seen that the DMP-TA algorithm has a strong task aggregation capability in all three distributions: dense, uniform, and mixed distributions. Among them, the DMP-TA algorithm obtains the lowest number of aggregated missions when they are densely distributed and the highest number of aggregated missions when they are uniformly distributed. When the tasks are densely distributed, the number of tasks that can be covered simultaneously by a single satellite transit is higher. Thus the chance of task aggregation is greater. From Figure 8, it can be seen that when the missions are densely distributed, the satellite resource consumption can be reduced the most by using the DMP-TA algorithm for aggregation, and the satellite resource consumption can be reduced the least by using the DMP-TA algorithm for aggregation when the missions are uniformly distributed. This phenomenon may be due to the higher overlap rate of observation time windows between missions when missions are densely distributed and the lower energy consumption of aggregated missions resulting from mission aggregation when the overlap rate of mission observation time windows is higher. Also, fewer task aggregations are generated in the case of dense distribution, which leads to the minor overall energy consumption of the observation tasks. Figure 8: Comparison of reduced task execution energy consumption at different distribution densities. Figure 7: Comparison of the number of aggregated tasks under different distribution densities.