On the Role of Communications for Space Domain Awareness
Bibliographic record
Abstract
Space domain awareness (SDA) has become increasingly important due to the rapid growth of commercial space activities and the expansion of New Space. This paper examines the need for transitioning from centralized to distributed SDA architectures, highlighting the limitations of traditional centralized systems in scaling with increasing satellite nodes. The analysis demonstrates that centralized architectures, which rely on individual downhaul, struggle to maintain efficiency as the number of satellites grows. In contrast, distributed architectures offer enhanced scalability, coverage, and resilience by processing data on orbit. Specifically, a low Earth orbit constellation capable of performing data analysis and response formulation on orbit can reduce routing times from 60 to 9 ms, providing an order-of-magnitude improvement in performance. The study applies this analysis to Starlink, OneWeb, Planet Labs, and Jilin constellations, demonstrating the advantages of distributed approaches across diverse satellite systems. This paper also discusses the tradeoffs between centralized and distributed architectures and provides key considerations for selecting the most appropriate approach for scalable and resilient SDA systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".