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Record W3216984858 · doi:10.1109/5gwf52925.2021.00093

Phasing Parameter Analysis for Satellite Collision Avoidance in Starlink and Kuiper Constellations

2021· article· en· W3216984858 on OpenAlexafffund
Jintao Liang, Aizaz U. Chaudhry, Halim Yanıkömeroğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsCarleton University
FundersNational Research Council Canada
KeywordsConstellationPhysicsPosition (finance)SatelliteSatellite constellationValue (mathematics)Phase (matter)Computer scienceSpacecraftPhaserAlgorithmMathematicsStatisticsAstronomyOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The phasing parameter F determines the relative phasing between satellites in different orbital planes and thereby affects the relative position of the satellites in a constellation. The collisions between satellites within the constellation can be avoided if the minimum distance among them is large. From among the possible values of F in a constellation, a value of F is desired that leads to the maximum value of the minimum distance between satellites. We investigate F for two biggest upcoming satellite constellations including Starlink Phase 1 Version 3 and Kuiper Shell 2. No existing work or FCC filing provides a value of F that is suitable for these two constellations. We look for the best value of F in these constellations that provides the maximum value of the minimum distance to ensure intra-constellation avoidance of collisions between satellites. To this end, we simulate each constellation for each value of F to find its best value based on ranking. Out of the 22 and 36 possible values of F for Starlink Phase 1 Version 3 and Kuiper Shell 2, respectively, it is observed that the best value of F with highest ranking is 17 and 11 that leads to the largest minimum distance between satellites of 61.83 km and 55.89 km in these constellations, respectively.

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: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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

Citations36
Published2021
Admission routes2
Has abstractyes

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