Temporal Spectrum Analysis for Multi-Constellation Space Domain Awareness
Bibliographic record
Abstract
Space Domain Awareness (SDA) system has different major aspects including continuous and robust awareness from the network that is crucial for efficient control over all actors in space. The observability of the space assets, on the other hand, requires efficient analysis of when and how observed space objects can be controlled. This becomes crucial when real-world spatial dynamics are taken into account, as it introduces complexities into the system. The real-world dynamics can reveal the structure of the network, including isolated and dominant stations. We propose a Temporal Spectrum Analysis (TSA) scheme that takes into account a set of real-world parameters, including actual dynamics of the objects in space, to analyze the structure of a ground-space network that inherits temporal spectrum as the key element of design. We study the potential interactions between multiple constellations using TSA and conduct comprehensive real-world simulations to quantify the structure of the network. Numerical results show how the temporal spectrum of each satellite affects the intra- and inter-constellation network structure, including interactions between ground stations and constellations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".