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Record W2144697227 · doi:10.1109/aps.2010.5561061

Electric fields from RF tag interrogators underneath an urban rail train

2010· article· en· W2144697227 on OpenAlexaff
Andrea Lüttgen, C.C. Bantin, K.G. Balmain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsThales (Canada)University of Toronto
Fundersnot available
KeywordsBallastBogieTrainGround planeMultiphysicsElectrical engineeringAntenna (radio)EngineeringElectrical conductorTrack (disk drive)Finite element methodComputer scienceMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

RF tag interrogation systems are used for position determination in the automatic control of urban rail trains. Such systems consist of a Tag Interrogator Unit (TIU) which is attached to an antenna mounted underneath the rail car and transmits interrogation pulses to tags installed between the rails. The TIU operates in a complex electromagnetic environment that contains some components which are highly conductive, such as the rail car itself, the wheel bogie sets, the rails and rail spikes into the ground. Other components have lower but still significant conductivities and permittivities different from that of free space, such as the ground, the ballast, and the sleepers (ties). In the design of a positioning system it is important to gain an understanding of the influence of these components on the RF field distribution underneath the rail car, especially in the plane where the tags are located. This paper discusses the use of a computer simulation, using the RF module of the COMSOL Multiphysics finite element software package, to model the situation and to predict the electric field distribution, most importantly in the plane of the tag. The results of the simulation can be used to better understand the response of the TIU and the train position can be better determined.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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
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

Citations1
Published2010
Admission routes1
Has abstractyes

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