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Record W2323679649 · doi:10.1115/jrc2015-5629

Radio Propagation Prediction for CBTC Data Communication Subsystem Design

2015· article· en· W2323679649 on OpenAlexaboutno aff
Arash Aziminejad, Andrew W. Lee, Gabriel Epelbaum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsUltra high frequencyRadio propagation modelRadio propagationRay tracing (physics)TrainRadio frequencyComputer scienceProcess (computing)Electronic engineeringRadio Link ProtocolEngineeringSimulationTelecommunicationsWirelessOpticsPhysics

Abstract

fetched live from OpenAlex

The radio-based Data Communication Subsystem (DCS) between trains and wayside is a key factor for safe and efficient operation of a Communication-Based Train control (CBTC) system. To provide means for a reliable and optimized design of the DCS RF segment, this article presents an in-depth discussion of the UHF/SHF radio propagation process in tunnels. To present a flexible and realistic radio propagation prediction model inside a tunnel environment the framework of modal analysis has been combined with the ray tracing technique and a heuristic approach has been adopted to assimilate horizontal curvatures in the tunnel geometry. The theoretical radio propagation loss predictions from the proposed model are compared to field measurements collected in Toronto subway tunnels and satisfactory agreement is observed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.124
GPT teacher head0.252
Teacher spread0.128 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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