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Record W2802738918 · doi:10.1117/12.2305238

Optical design of space cameras for automated rendezvous and docking systems

2018· article· en· W2802738918 on OpenAlexaff
Xiang Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsNeptec Design Group (Canada)
Fundersnot available
KeywordsField of viewLens (geology)OpticsComputer scienceRendezvousNight visionStray lightComputer visionComputer graphics (images)Artificial intelligenceSpacecraftPhysicsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.225
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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