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Record W2043615895 · doi:10.1109/aero.2013.6497172

Optical trades for evolving a small arcsecond star tracker

2013· article· en· W2043615895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStar trackerComputer scienceBitTorrent trackerSpacecraftBaseline (sea)Star (game theory)Interplanetary spaceflightReal-time computingAerospace engineeringArtificial intelligenceEye trackingEngineeringPhysics

Abstract

fetched live from OpenAlex

We present a series of system performance models for nanosatellite star trackers. Many Earth-observing missions rely on spacecraft body motion to track ground targets. These operational scenarios lead to requirements for arc-second-accuracy attitude estimates during body motion at rates of up to 1 deg/s. Achieving these performance targets with a small sensor presents a challenge. We develop models to predict sensor availability and accuracy in terms of a number of optical design parameters. Starting from the baseline optical design of the Sinclair Interplanetary ST-16, we explore strategies for improving the sensor accuracy. We highlight distinctive features of the trade-space relative to more conventional star tracker design. Our discussions include an overview of system-level trends and an analysis of promising point designs. Results from these trades are valuable for prioritizing further development.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.828

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)0.0010.000

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 head0.196
Teacher spread0.183 · 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

Quick stats

Citations19
Published2013
Admission routes1
Has abstractyes

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