An Orbit Design Method to Support Small Body Interior Radar Studies
Bibliographic record
Abstract
Consider a spacecraft equipped with a radar system that can generate signal returns from both the front and back ends of, as well as any significant voids or composition transitions in, a small body. For the purpose of determining the internal structure and inferring the composition of the body, it is necessary to collect returns from directions that encompass the whole body. Operational constraints include minimization of the survey duration and costs, and an unfamiliarity of the target shape, spin period and direction before arrival. This paper describes an approach that is robust for a variety of small body shapes and spin directions. The first part of this strategy uses orbits that are stable with respect to solar radiation pressure, at distances where the irregular shape is not a significant consideration. The second part uses orbit orientations that provide coverage at high latitudes not reached in the first part. One forces the eccentricity to evolve from an initial value through zero and back up, typically stretching out the useful time period over a couple of weeks, allowing for safe polar observations. This secular orbit evolution in the second part of the strategy is shown through averaging of the perturbing potential due to the solar radiation pressure force, as well as through numerical simulations. This paper shows orbit selections and coverage metrics for various small bodies, with masses, spin directions and rates that are representative of the observed subset of the total population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".