Towards Agile Oceanographic Target Tracking with a DGSPCMG Equipped CubeSat<sup>*</sup>
Bibliographic record
Abstract
The accelerated growth and development of CubeSat projects has proven the capability for small scale autonomous satellites to accomplish significant remote sensing objectives. CubeSats, however, have not yet seen widespread use on oceano-graphic mission objectives. To achieve modern oceanographic remote sensing objectives it is typically required that the satel-lite have the ability to rapidly maneuver towards targets of opportunity. Recent developments in Double-Gimbal Scissored-Pair Control Moment Gyroscope (DGSPCMG) control laws have shown promising results for this actuator to achieve high CubeSat maneuverability. Building on these recent developments, this paper aims to demonstrate high CubeSat agility for oceanographic target tracking and quantify the pointing performance for a complete Attitude Determination and Control System (ADCS) equipped with a DGSPCMG. The expected performance of the proposed ADCS is demonstrated by high fidelity numerical simulations which subject the system to a detailed hypothetical target tracking campaign. For the simulation conditions used in this research, the proposed system is shown to be capable of sub 3 km average mapping errors for a CubeSat in Low-Earth Orbit.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".