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Record W1986001256 · doi:10.1109/milcom.2006.302487

Path Loss Measurements With Low Antennas For Segmented Wideband Communications at VHF

2006· article· en· W1986001256 on OpenAlexaff
Jeffrey A. Pugh, Robert Bultitude, P. J. Vigneron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsPath lossLog-distance path loss modelAntenna height considerationsWidebandRange (aeronautics)Antenna (radio)Sensitivity (control systems)Standard deviationComputer scienceAcousticsElectronic engineeringTopology (electrical circuits)MathematicsStatisticsTelecommunicationsPhysicsEngineeringWirelessElectrical engineering

Abstract

fetched live from OpenAlex

This paper reports path loss modelling results based on quasi-simultaneous wideband channel measurements at the VHF frequencies 37.8, 57.0, and 77.5 MHz. The measurements were conducted with low antenna heights (2 m) for both the fixed and mobile terminals and are therefore considered applicable to tactical network communication scenarios. For the area surveyed, the least squares fit to the basic log-distance model yielded path loss exponents in the range of 3.0 to 3.6 and shadowing standard deviations between 3.3 and 4.6 dB. In addition, shadowing cross-correlation coefficients among the three bands were found to be in the range of 0.61 to 0.72, the inclusion of which permits a simple, multiband joint statistical path loss model. The sensitivity of model parameter estimates to the choice of spatial averaging interval is also examined

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.230
Teacher spread0.196 · 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 designObservational
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

Citations30
Published2006
Admission routes1
Has abstractyes

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