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Record W2053725267 · doi:10.1115/jrc2014-3816

Secure Communication-Based Train Control: Performance Evaluation of a Design Framework

2014· article· en· W2053725267 on OpenAlexaff
Arash Aziminejad, Mustafa Seçkin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsThales (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkDefault gatewayEmbedded systemIPsecCommunications protocolEthernetAuthentication (law)Network architectureOperating systemComputer securityThe Internet

Abstract

fetched live from OpenAlex

Railroads are increasingly using Communication-Based Train Control (CBTC) technology to improve service capacity and operating efficiency. CBTC is a mission-critical system under which train monitoring and train control are integrated into a single unified system through data links between vehicles, central processors, and wayside equipment. Radio over fiber technology provides a flexible and efficient solution for the Data Communication System (DCS) which needs to ensure integrity and reliability of message delivery in a transparent manner for the train control functions. A Security Device (SD) is defined as a network entity located between the railroad administration’s (the customer) trusted wired network and the non-trusted portion of the DCS network including the radio-based segment, which runs on a customized piece of hardware with a secure operating system and provides secure gateway functionality. This paper puts forward a network architecture and SD software platform design which meets the requirements of a typical CBTC system. The IPSEC protocol used by the SD for data protection renders authentication service through X.509 certificates. A network setup is put together as the proof-of-concept for the presented design proposal and performance assessment is conducted through experimental studies.

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)0.003
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 score0.776
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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