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Record W1512614264 · doi:10.1109/milcom.2002.1180411

Bandwidth management of MilSatCom links for multimedia network traffic

2003· article· en· W1512614264 on OpenAlexafffund
Claude Bélisle, Charles Auger, Jing Peng, Martin Phisel, M. Delorey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsDepartment of National DefenceCommunications Research Centre Canada
FundersMinistère de la Défense NationaleDefence Research and Development Canada
KeywordsComputer scienceComputer networkBandwidth (computing)Dynamic bandwidth allocationNetwork traffic controlDigitizationMultimediaBandwidth allocationBattlefieldTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

With the digitization of the battlefield, there is a need to establish an efficient worldwide wireless network infrastructure that offers timely and accurate command, control and intelligence information. A terrestrial infrastructure is frequently not a viable option due to the mobility requirements of deployed units and the hostile terrain in which they must often operate. In contrast, satellites can potentially provide an essential component of this global grid. Unfortunately, terrestrial network protocols do not behave well over satellite links, and military satellites have not yet been designed to support multimedia network traffic efficiently, both of which result in poor response times and low bandwidth usage. The expected performance of multimedia traffic supported over military satellites is analyzed. Application response time and bandwidth usage are used as a measure of performance in a simulation environment. Simulation results are given, and a number of improvements, such as enhancements to the TCP stack and dynamic bandwidth management techniques, are proposed.

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)0.004
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations2
Published2003
Admission routes2
Has abstractyes

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