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Record W2091118550 · doi:10.1002/jnm.521

A novel approach to propagation prediction in confined and diffracting rough surfaces

2003· article· en· W2091118550 on OpenAlexaff
M. Ndoh, G.Y. Delisle, René Le

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2003
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité du QuébecUniversité du Québec en Abitibi-TémiscamingueUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsMultipath propagationFadingSurface finishSurface roughnessComputer scienceDiffractionStatisticWirelessBendingCascadeAcousticsExtremely high frequencyElectronic engineeringEngineeringTelecommunicationsOpticsMaterials scienceStructural engineeringPhysicsMechanical engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Modern wireless systems operating in the millimetre waves bands are now to be used in complex confined media such as mining environment. System designs require that multipath, fading and diffraction effects be accounted for in a suitable model. This paper presents a new propagation prediction method that can be used in mine corridors, buildings, underground roads, galleries with rough surfaces and others complex sub‐surface installations. A method named cascade impedance method (CIM) is used in combination with the segmental statistic method (SSM) in mine tunnels having considerable wall roughness and bending forms. Two (2D)‐ and three (3D)‐dimensional profiles of the radio waves are simulated and compared with available measurements at 2.45 and 18 GHz. Copyright © 2003 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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