Optical design of space cameras for automated rendezvous and docking systems
Bibliographic record
Abstract
Visible cameras are essential components of a space automated rendezvous and docking (AR and D) system, which is utilized in many space missions including crewed or robotic spaceship docking, on-orbit satellite servicing, autonomous landing and hazard avoidance. Cameras are ubiquitous devices in modern time with countless lens designs that focus on high resolution and color rendition. In comparison, space AR and D cameras, while are not required to have extreme high resolution and color rendition, impose some unique requirements on lenses. Fixed lenses with no moving parts and separated lenses for narrow and wide field-of-view (FOV) are normally used in order to meet high reliability requirement. Cemented lens elements are usually avoided due to wide temperature swing and outgassing requirement in space environment. The lenses should be designed with exceptional straylight performance and minimum lens flare given intense sun light and lacking of atmosphere scattering in space. Furthermore radiation resistant glasses should be considered to prevent glass darkening from space radiation. Neptec has designed and built a narrow FOV (NFOV) lens and a wide FOV (WFOV) lens for an AR and D visible camera system. The lenses are designed by using ZEMAX program; the straylight performance and the lens baffles are simulated by using TracePro program. This paper discusses general requirements for space AR and D camera lenses and the specific measures for lenses to meet the space environmental requirements.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".