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Record W2163012425 · doi:10.1109/aps.2007.4395461

Analysis of different defect configurations in CEBG structures for directive patterns

2007· preprint· en· W2163012425 on OpenAlexafffund
Arezou Edalati, Tayeb A. Denidni, Halim Boutayeb

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBandwidth (computing)MetamaterialHorn antennaElectromagnetic radiationAcousticsPhysicsAntenna (radio)Electric power transmissionTransmission coefficientOpticsRadiation patternTransmission (telecommunications)Electrical engineeringTelecommunicationsComputer scienceEngineeringSlot antenna

Abstract

fetched live from OpenAlex

Electromagnetic bang gap (EBG) materials are periodic structures characterized by forbidden propagation of electromagnetic waves in their band-gap and by the ability to open localized modes inside the band-gap by introducing defects. Cylindrical electromagnetic band gap (CEBG) structures are radially and circularly periodic, and they present pass-band and stop-band to cylindrical electromagnetic waves. By applying a horn-shaped defect in these structures, it has been shown that a re-configurable directive pattern can be obtained. In (P. Ratasjack et al., 2004), experimental results for a reconfigurable CEBG-based antenna have been presented, whereas in (H. Boutayeb et al., 2006), an analysis of the transmission coefficient of CEBG structures and experimental results for a CEBG directive antenna have been proposed. In (H. Boutayeb et al., 2006), a new defect configuration has been proposed for reducing the power supply, but this solution leads to a narrower band. In this paper, new defect configurations are analyzed in order to reduce the number of active elements and the required power supply in CEBG-based agile antennas. The solution with the widest bandwidth is indicated.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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 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

Citations3
Published2007
Admission routes2
Has abstractyes

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