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Record W1504326126 · doi:10.1109/ccece.2015.7129432

Performance evaluation of an ITS system integrated with complementary LEO mobile satellite system

2015· article· en· W1504326126 on OpenAlexaff
Tianning Liang, Zhipeng Wang, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceLow earth orbitPacket lossNetwork packetCommunications satelliteFocus (optics)Computer networkSatelliteWireless ad hoc networkMobile telephonyReal-time computingMobile radioTelecommunicationsWirelessEngineering

Abstract

fetched live from OpenAlex

A novel 2-tier integrated communication system is proposed to address the issue of unavailable Road Side Unit (RSU) in Intelligent Transport System (ITS). Compared to some other solutions proposed in the previous research works, which mostly focus on improving the system performance by adjusting parameters of vehicular ad-hoc network among vehicles, the proposed 2-tier communication network, called ITS-LEO Integrated System (ILIS), is composed of conventional ITS system and Low Earth Orbit (LEO) mobile satellite system (MSS), where the LEO MSS is utilized as the complementary network when RSU is unavailable. A real-time simulation program in C++ is developed to evaluate the performance of ILIS in terms of Packet Loss Rate (PLR). The simulation results show that adding LEO MSS as a complementary network to ITS is an effective way to solve the problem of unavailable RSU.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.239
Teacher spread0.210 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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