MétaCan
Menu
Back to cohort
Record W3091840905 · doi:10.1109/mce.2020.3029772

Free Space Optics for Next-Generation Satellite Networks

2020· preprint· en· W3091840905 on OpenAlexafffund
Aizaz U. Chaudhry, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Consumer Electronics Magazine · 2020
Typepreprint
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsCarleton University
FundersNational Research Council Canada
KeywordsSatelliteFree-space optical communicationSatellite constellationOptical wirelessConstellationComputer scienceWirelessLow earth orbitLaserTelecommunicationsOptical communicationRadio frequencyElectronic engineeringAerospace engineeringEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Free space optics (FSO) refers to optical wireless communications in outdoor environments. The aim of this article is to analyze the role that FSO is envisaged to play in the creation of next-generation satellite networks. To begin with, the reader is introduced to the types of FSO links and functionalities of a basic FSO system. Next, a comparison of FSO and radio frequency (RF) technologies for intersatellite links (ISLs) is provided, including a comparison between FSO and RF links when employed between low Earth orbit satellites. After that, the types of FSO or laser ISLs are considered, and the challenges in establishing laser ISLs, the properties of laser ISLs, and the capabilities of terminals for laser ISLs are discussed. Then, the parameters of a satellite constellation are highlighted, and different aspects of SpaceX's upcoming megaconstellation Starlink are explored. In addition, the optical wireless satellite network that is created by utilizing laser ISLs is examined. Finally, a use case is investigated for next-generation optical wireless satellite networks that are envisioned by the mid to late 2020s.

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)0.001
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.005

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 head0.051
GPT teacher head0.258
Teacher spread0.207 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Consumer Electronics MagazineSame topicOptical Wireless Communication TechnologiesFrench-language works237,207