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Record W3149741447 · doi:10.1002/mmce.22674

<scp>High‐gain</scp> cavity antenna combining <scp>AMC‐reflector</scp> and <scp>FSS</scp> superstrate technique

2021· article· en· W3149741447 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

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsReflector (photography)Antenna (radio)Radiation patternSide lobePeriscope antennaAntenna measurementAntenna gainAntenna efficiencyOpticsMaterials scienceComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

In this paper, a Fabry-Perot cavity antenna with an improved gain using AMC-reflector and FSS superstrate approaches is proposed. The antenna is designed at 5.8 GHz for IoT applications, it constitutes of I-shaped slot antenna, an artificial magnetic conductor (AMC) reflector and a superstrate of frequency selective surfaces (FSS). Three antenna configurations are provided to improve the gain and reduce the side lobe levels (SLL). First, an I-shaped slot antenna operates at 5.8 GHz, is proposed. The second configuration provide an AMC reflector layer, placed under the antenna, to degrade SLL in the desired band. Finally, three FSS superstrate layers located above the I-shaped slot antenna in order to maximize the level of the main lobe. An experimental prototype is fabricated, tested and presented to demonstrate the proposed antenna design. Simulated and experimental results, in terms of reflection coefficient, radiation pattern and gain, are presented and discussed to assess the proposed antenna design.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.220
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