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Record W2059269676 · doi:10.1117/12.921668

Overview of the Sapphire payload for space surveillance

2012· article· en· W2059269676 on OpenAlexaffabout
John P. Hackett, R. Brisby, Kenneth W. Smith

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsCOM DEV International
Fundersnot available
KeywordsPayload (computing)NASA Deep Space NetworkComputer scienceTelescopeCardinal pointRemote sensingReal-time computingComputer hardwareOpticsPhysicsEngineeringAerospace engineeringSpacecraftGeology

Abstract

fetched live from OpenAlex

This paper provides an overview of the satellite based Sapphire Payload developed by COM DEV to be used for observing Resident Space Objects (RSOs) from low earth orbit by the Canadian Department of National Defence. The data from this operational mission will be provided to the US Space Surveillance Network as an international contribution to assist with RSO precision positional determination. The payload consists of two modules; an all reflective visible-band telescope housed with a low noise preamplifier/focal plane, and an electronics module that contains primary and redundant electronics. The telescope forms a low distortion image on two CCDs adjacent to each other in the focal plane, creating a primary image and a redundant image that are offset spatially. This combination of high-efficiency low-noise CCDs with well-proven high-throughput optics provides a very sensitive system with low risk and cost. Stray light is well controlled to allow for observations of very faint objects within the vicinity of the bright Earth limb. Thermally induced aberrations are minimized through the use of an all aluminum construction and the strategic use of thermal coatings. The payload will acquire a series of images for each target and perform onboard image pre-processing to minimize the downlink requirements. Internal calibration sources will be used periodically to check for health of the payload and to identify, and possibly correct, any pixels with an aberrant response. This paper also provides a summary of the testing that was performed and the results achieved.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.008

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 head0.017
GPT teacher head0.233
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2012
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpacecraft Design and TechnologyFrench-language works237,207