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Record W2017018092 · doi:10.1002/sat.860

Cross‐layer design for call admission control of a high‐capacity GEO satellite network with on‐board cross‐connect

2006· article· en· W2017018092 on OpenAlexaff
Rose Qingyang Hu, Yi Qian, Yu Cheng, Sastri Kota

Bibliographic record

VenueInternational Journal of Satellite Communications and Networking · 2006
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProvisioningComputer scienceSatelliteComputer networkTelecommunicationsAdmission controlQuality of serviceAerospace engineering

Abstract

fetched live from OpenAlex

Abstract This paper addresses cross‐layer design of end‐to‐end call admission control and beam‐to‐beam connectivity provisioning for a high‐capacity multi‐beam GEO satellite network with on‐board cross‐connectivity. On‐board satellite switching is a technology designed to offer heterogeneous services, especially in demographically dispersed areas. Nevertheless, full on‐board switching techniques are far from maturity. Their implementations have been proven expensive and difficult. There are also high risks involved in launching satellites for the stationary orbit surrounding the Earth. As a substitute, a satellite network with on‐board cross‐connect is devised in this paper. Connectivity provisioning and call admission control mechanisms associated with such a network are also presented. Simulation studies are conducted to show the effectiveness of the proposed mechanisms. Copyright © 2006 John Wiley & Sons, Ltd.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.290
Teacher spread0.243 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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