MétaCan
Menu
Back to cohort
Record W4389164429 · doi:10.1109/access.2023.3338150

3D Metal-Only Phoenix Cell and its Application for Transmit-Reflect-Array

2023· article· en· W4389164429 on OpenAlexfundno aff
Zhihang An, Tony Makdissy, Maria García‐Vigueras, Sébastien Vaudreuil, Raphaël Gillard

Bibliographic record

VenueIEEE Access · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsnot available
FundersNational Council for Scientific ResearchRégion BretagneConseil National de la Recherche ScientifiqueCentre National de la Recherche ScientifiqueAgence Universitaire de la FrancophonieEuropean Commission
KeywordsPhoenixOpticsTransmission (telecommunications)RadiationPolarization (electrochemistry)Side lobeMaterials scienceComputer scienceOptoelectronicsPhysicsTelecommunicationsAntenna (radio)

Abstract

fetched live from OpenAlex

This paper presents a 3D metal-only waveguide-based phoenix cell. The proposed cell uses open-ended waveguides, which allow a portion of the incident wave to pass through the phoenix cell. It thus has the ability to control both reflection and transmission phases. Its principle is analyzed in detail. Two metal-only transmit-reflect-array antennas are then designed. The proposed transmit-reflect-array antennas are able to produce both transmitted and reflected beams at 16GHz in the target directions simultaneously. One of the transmit-reflect-array antennas is fabricated using selective laser melting 3D printing technology. The measured results show that a good agreement between the simulated and measured radiation patterns is achieved. The side lobe and cross polarization levels at 16 GHz are -15.3 dB and -23.1 dB respectively. The measured gain of transmitted and reflected beams at 16GHz are 25.7 dBi and 24.1 dBi respectively. Both the simulation and measurement results fully demonstrate the capabilities of the proposed 3D metal-only phoenix cell.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.470

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 head0.030
GPT teacher head0.297
Teacher spread0.267 · 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 designBench or experimental
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIEEE AccessSame topicAdvanced Antenna and Metasurface TechnologiesFrench-language works237,207