Collaborative Space and Ground Interactions with Varying Space Vehicle Autonomy
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-4389.vid A typical satellite control scheme centers on the ground applications managing the tasking and commanding. While generating a command load for the vehicle, the ground software manages the constraints and controls the state of the space vehicle. When considering autonomous vehicle actions, the system must still manage the vehicle state and constraint set during this time. Operational options typically lengthen the time to return to the mission after autonomous action or severely limit the satellite's capabilities. They are due to the recovery time necessary to bring the satellite back into a nominal mission or the limited set of behaviors that the satellite can execute on its own. At Lockheed Martin, we developed a space system architecture across the space vehicle and ground system that minimizes the drawbacks of autonomous vehicle actions. Through intentional constraint design and implementation in CONOPS, the entire architecture works together to enable remote-sensing capabilities. It enables interweaving different space vehicle actions for mission purposes, whether ground directed or fully autonomous. These hybrid operational use cases empower new collection CONOPS for remote-sensing data gathering and enterprise decision-making.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".