## 9 Conclusions
As this constellation is aimed for the Asian region though it has a global coverage, the area of interest was chosen to be South Korea by creating a specific region-based procedure to highlight the points on that region in 3D/2D. The reason for this is whenever satellites pass through this highlighted region, the colors of the points change and indicate the coverage over this area of interest is ensured and covered by the optimizer. There can be many such regions as per the end user locations and ground stations. In addition to this, perturbation analysis was carried out to re-configure the satellite with atmospheric drag (J2) and Solar Radiation Pressure (SRP) with an individual and combined effort insofar as that satellite propagation in the aligned orbit is concerned. This contributes towards the better understanding of how these satellites can be influenced by these disturbances the orbit in long run and helps in preparation of the risk management on-orbit and on the ground. Not only on the orbit scenario of a constellation, but the ground-based operations were detailed with six ground stations located globally and a specific analysis was carried out to determine the impact of varying inclination, position, and altitude of each satellite with aligning elevation angles from the ground. This will give a thorough overview instantly to the operator of the orientation of satellites to ground station with the given constellation propagation scenario and the health of the reception during a pass as given in Section 5. Except, few deviations, the overall propagation of the satellites over the ground stations remained under the common proportions, satisfying the designed mission to be effective in operation under the aligned real-time harsh environmental conditions. After due consideration to all these aspects, the integrated optimizer was scripted along with the given mission configuration of the set-up presented in Section 3. It was then run for finding a best possible solution and even checking if the solution is feasible with the given scenario or not. The solution was feasible and was obtained with about 150 iterations and 250 evaluation count as given in Table 1. The computation was quick and took about 30 s to give out the final solution, which was competitive with 48 satellites on a traditional desktop with nominal configuration but can be fairly improvised with additional computing abilities provided. The semi-major axis was improvised by 4.19% and the inclination was optimized by 15.91%, which is a significantly sufficient scenario for this mission configuration. The time and space complexity with or without optimizer were about negligible (1-2 s) and executed instantly without any anomalies with storage consumption of about 50-60 KB on memory. With the basic assumptions made in the form of set-up shown in the Section 3 with real-time disturbances such as J2 perturbations and solar radiation pressure, there were a few limitations with this approach. This optimization technique is specific only with large-scale non-linear systems and environment. It provides limited iteration-based mathematical solutions to the optimization for the coverage analysis for this proposed constellation. Though the accuracy of the optimization was optimal and continuous, the computation and number of the iterations can be further improvised as small satellites have limited computation abilities on-board. Overall, the method of executing this optimization yielded satisfying results as desired and is very well suited for complex constellations. This research also aimed to support Artemis missions with the solutions experimented and obtained for LEO, which can indeed facilitate the operations (both crewed and uncrewed) on the lunar surface. Simulations were presented in this paper to highlight a scenario in which a small satellite constellation will be crucial for the communication and data transfer among the satellites and to the ground station based both on the lunar surface and on Earth, respectively.