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Record W2944400630 · doi:10.1029/2018rs006643

Observations of 5.9‐GHz Radio Propagation and 802.11p Network Performance at Road Junctions

2019· article· en· W2944400630 on OpenAlexaff
Connor Clayton, A. J. Stocker, Saddam K. Alwane, Dina S. M. Hassan

Bibliographic record

VenueRadio Science · 2019
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsSignal strengthField strengthRadio propagationSIGNAL (programming language)Network packetRange (aeronautics)Computer scienceTransient (computer programming)Density ratioRadio signalTelecommunicationsAcousticsComputer networkEnvironmental sciencePhysicsRadio frequencyMagnetic fieldWirelessEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The propagation of 5.9‐GHz radio signals and performance of an 802.11p network were measured at three road junctions each having a different density of buildings. The maximum range for which acceptable performance (defined as where the packet delivery ratio was greater than 90%) was dependent on the junction but lies in the range of 45–70 m. While reflections from transient vehicles were often found to have a small positive impact on network performance, this could not be relied upon to provide a reliable improvement in communications. The received signal strength was dependent on the junction type with the strong reflections from buildings located on the opposite side of a T‐junction leading to higher signal strength. Finally, an empirical relationship between the packet delivery ratio and the received signal strength has been established that will allow modelers to link signal strength to network performance for field conditions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.185
Teacher spread0.177 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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