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Record W2809769497 · doi:10.1109/aero.2018.8396523

Planning and optimization for a multiple space debris removal mission

2018· article· en· W2809769497 on OpenAlexaff
Mikkel K. Jorgensen, Inna Sharf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpace debrisDebrisRendezvousCollision avoidanceAerospace engineeringOrbital maneuverOrbit (dynamics)ThrustGeocentric orbitComputer sciencePlan (archaeology)Low earth orbitSpace explorationEnvironmental scienceSimulationCollisionGeologyEngineeringSpacecraftMeteorologyPhysics

Abstract

fetched live from OpenAlex

The purpose of this research is to formulate and evaluate a mission plan for de-orbiting multiple pieces of large space debris, with the use of a flexible chaser-debris connection. Within this mission plan, continuous low thrust maneuvers are used to achieve the necessary orbital transfers by considering the trade-offs between fuel mass and mission time. Two sets of five debris have been selected for demonstrating the proposed methodology - both situated in lower Earth orbit. The orbital characteristics of these debris act as inputs in the analysis, together with certain chaser specifications. For each transfer, a modified Edelbaum methodology is used to account for the changes in semi-major axis and inclination. A drift orbit is optimized as part of the rendezvous phase in order to naturally change the RAAN of the chaser to match that of the debris. The outcome is the best possible trade-off between time and fuel for the mission and the transfer characteristics required to achieve it. Finally the results are compared and contrasted with a similar scenario where the de-orbiting method using impulsive transfers is considered.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.220

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 head0.228
Teacher spread0.215 · 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
GenreMethods

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

Citations1
Published2018
Admission routes1
Has abstractyes

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