MétaCan
Menu
Back to cohort

IoT-based Transceiver Antenna System for 5G Future High-Speed Train Communications

2023· article· en· W4378841916 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsTransceiverComputer scienceAntenna (radio)Internet of ThingsTelecommunicationsElectronic engineeringElectrical engineeringWirelessEmbedded systemEngineering

Abstract

fetched live from OpenAlex

The serious problems with rail radio systems across the world mainly originated from poor train antenna design and deployment. Hence designing antennas for reliable radio access is so essential in public advanced railway systems. On the other hand, the mm-wave frequency spectrums are recognized sources of valuable frequencies to supply the high-rate 5G and beyond 5G Internet services with a speed of over than 10 GB per second. In this article, an mm-wave 5G transceiver antenna which includes a tapered-slot antenna and an EM signal distribution network has been introduced to overcome the challenges of harmful interaction between the transmitting and receiving high-speed train antennas and reduce their technical conflicts in the installation.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.429

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)0.0000.000

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicIoT Networks and ProtocolsFrench-language works237,207