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Record W3007911081 · doi:10.2514/1.g004751

Achievable Halo Phasing with Short-Range Trajectories

2020· article· en· W3007911081 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

VenueJournal of Guidance Control and Dynamics · 2020
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhaserHaloHalo orbitControl theory (sociology)ComputationOrbit (dynamics)Impulse (physics)Computer sciencePhysicsMathematicsAerospace engineeringAlgorithmEngineeringOpticsClassical mechanicsAstrophysics

Abstract

fetched live from OpenAlex

Short-range halo phasing orbits, which are a type of phasing orbit remaining around the halo orbit, are investigated in the Earth–moon system. The influence of the start and end points on the halo phasing problem is investigated by numerical computation. Under the limitation of the thrust engine, an optimization problem for two-impulse phasing orbits is proposed to achieve the maximum phase change. Numerical computations show that, for the given maximum possible impulsive burn, there exist four types of locally optimal preceding phasing orbits and four types of locally optimal receding phasing orbits. The results of the eight types of optimal phasing orbits are discussed in detail. The “leapfrogging” strategy, composed of several two-impulse phasing arcs, is proposed for phasing missions requiring a large phase difference. Furthermore, halo phasing orbits with continuous thrusts are constructed and investigated. By first using the backstepping method to transform impulsive maneuvers into continuous thrusts and then using the backstepping results as initial guesses, optimal continuous phasing orbits are further constructed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.563

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 head0.005
GPT teacher head0.185
Teacher spread0.179 · 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