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Fuel selection is a strong driver of the mass fraction for many proposed lunar and Mars missions, but fuel technology trends have not been comprehensively evaluated for their impact on the system in literature. We evaluate the impact of fuel selection on overall lunar architectures. Our analysis shows that although hydrogen architectures have a higher wet mass cost, they provide more payload capacity to the lunar surface than nonhydrogen architectures given the same number of campaign launches. The Moon has been viewed as a stepping stone for future planetary exploration, so we evaluate both Mars and lunar architectures. We functionally decompose architectural decisions and compare key campaign decisions across 18 notable Mars architectural studies. The 18 landers are classified into four groups depending on which of the four the functional capabilities the lander performs, namely outbound transit, mars descent, mars ascent, and inbound transit. We find that there is no strong relationship between the Martian landers' wet mass and the length of crewed Martian surface. Furthermore, fuel type selection did not have a clear trend with the aforementioned capabilities. The lack of similarities across Mars architectures suggests the reference studies had a wide range of depths of analysis along with an array of different methods. Furthermore, they were completed at various points in history, some with high political pressure.

The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions. Artificial Intelligence (AI) and learning-based methods are expected to become key players in the industry, given the poor scalability and slow reaction time of current resource allocation mechanisms. While AI frameworks have been validated for isolated communication tasks or subproblems, there is still not a clear path to achieve fully-autonomous satellite systems. Part of this issue results from the focus on subproblems when designing models, instead of the necessary system-level perspective. In this paper we try to bridge this gap by characterizing the systemlevel needs that must be met to increase satellite autonomy, and introduce three AI-based components (Demand Estimator, Offline Planner, and Real Time Engine) that jointly address them. We first do a broad literature review on the different subproblems and identify the missing links to the systemlevel goals. In response to these gaps, we outline the three necessary components and highlight their interactions. We also discuss how current models can be incorporated into the framework and possible directions of future work.