A software-in-the-loop simulation of an intelligent microsatellite within a virtual environment
Bibliographic record
Abstract
Rapid growth in space missions necessitates the onboard intelligence, which creates autonomous space systems by providing high level decision making, robust execution of decisions, and automatic fault repairing. Mostly, autonomous space systems are implemented as hybrid architectures with a few conceptual layers. Validating the stability and evaluating the performance of an autonomous architecture is critical for space missions. Software-in-the-loop simulation is a suitable approach for addressing this demand. However, the data acquired from simulation is represented as alphanumeric values or diagrams, which needs to be interpreted. In this paper, we propose an intelligent architecture to provide onboard autonomy for an observation micro-satellite. The architecture integrates the low level physical actions with conceptual decision making ability in a hierarchical manner. To evaluate the proposed architecture, we have implemented a distributed software-in-the-loop simulation to simulate the space, satellite, ground stations, and intelligent onboard software. Moreover, for the first time, we have used virtual reality to visualize the satellite's autonomous behavior in the orbit. It lets the users have a high level feedback from integrated simulation. Scenario-based evaluations have shown the stability and efficiency of the proposed architecture.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".