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Record W3147278562 · doi:10.1109/mvt.2021.3063706

Laser Intersatellite Links in a Starlink Constellation: A Classification and Analysis

2021· article· en· W3147278562 on OpenAlexaff
Aizaz U. Chaudhry, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Vehicular Technology Magazine · 2021
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsConstellationSatellite constellationSatelliteComputer scienceCommunications satelliteRemote sensingTelecommunicationsAerospace engineeringEngineeringPhysicsGeographyAstronomy

Abstract

fetched live from OpenAlex

Laser intersatellite links (LISLs) are envisioned between satellites in upcoming satellite constellations, such as the one in phase I of SpaceX's Starlink. Within a constellation, satellites can establish LISLs with other satellites in the same orbital plane (OP) or in different OPs. We present a classification of LISLs based on the location of satellites within a constellation and of LISLs' duration. Then, using the satellite constellation for phase I of Starlink, we study the effect that varying a satellite's LISL range has on the number of different types of LISLs it can establish with other satellites. In addition to permanent LISLs, we observe a significant number of temporary LISLs between satellites in crossing OPs. Such LISLs can play a vital role in achieving low-latency paths within next-generation optical wireless satellite networks.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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 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

Citations278
Published2021
Admission routes1
Has abstractyes

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