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Record W2317508849 · doi:10.1109/tap.2016.2535125

A Hybrid Ray-Tracing/Vector Parabolic Equation Method for Propagation Modeling in Train Communication Channels

2016· article· en· W2317508849 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsThales (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRay tracing (physics)Computer scienceSoftware deploymentChannel (broadcasting)TracingWave propagationSimulationTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

In recent years, various techniques have been applied to modeling radio-wave propagation in railway networks, each one presenting its own advantages and limitations. This paper presents a hybrid channel modeling technique, which combines two of these methods, the ray-tracing (RT) and vector parabolic equation (VPE) methods, to enable the modeling of realistic railway scenarios including stations and long guideways within a unified simulation framework. The general-purpose RT method is applied to analyze propagation in complex areas, whereas the VPE method is reserved for long and uniform tunnel as well as open-air sections. By using the advantages of VPE to compensate for the limitations of RT and vice versa, this hybrid model ensures improved accuracy and computational savings. Numerical results are validated with experimental measurements in various railway scenarios, including an actual deployment site of communication-based train control (CBTC) systems.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.856

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 communication0.0000.001
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 head0.054
GPT teacher head0.274
Teacher spread0.220 · 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