Advancing Lunar Communication Through Interdomain Space Networks and Dynamic Orchestration
Bibliographic record
Abstract
The resurgent era of lunar exploration is defined by a strategic shift from temporary visits to a sustained international and commercial presence, resulting in an unprecedented demand for a robust and continuously available communication infrastructure. The conventional direct-to-Earth communication architecture relies on limited and oversubscribed deep space networks, which are further challenged by the radiative environment and insufficient visibility in certain areas of the cislunar domain. We address these issues by proposing a foundational move toward inter-domain space network cooperation by introducing architectures based on near space networks. They can directly service lunar surface users or, via cislunar relays, by forming a resilient and multi-layered communication backbone. First, we establish a unified link analysis framework incorporating frequently disregarded environmental factors, such as the Moon's variable illumination, to provide a high-fidelity performance evaluation. Second, we assess architectures' reliability based on the outage risk, essential for quantifying the operational robustness of communication links. Finally, to manage the inherent dynamism of architectures, we propose an inter-domain space digital twin: a dynamic decision-making engine that performs real-time analysis to autonomously select the best communication path, ensuring high and stable reliability while simultaneously optimizing power consumption. Overall, our paper provides a holistic architectural and conceptual management framework, emphasizing the necessity of lunar communications to support a permanent human and economic foothold on the Moon.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".