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Record W2170924295 · doi:10.1109/aps.2011.5997075

Reconfigurable dual-band frequency selective surfaces using a new hybrid element

2011· article· en· W2170924295 on OpenAlexaff
Mahmoud Niroo‐Jazi, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPIN diodeReflection coefficientCapacitanceFrequency bandMulti-band deviceReflection (computer programming)OpticsMaterials scienceElectromagnetic shieldingTransmission coefficientDiodeReconfigurable antennaOptoelectronicsTunable metamaterialsSelective surfaceCutoff frequencyDipole antennaTransmission lineTransmission (telecommunications)Radio spectrumAntenna (radio)PhysicsElectrical engineeringComputer scienceMetamaterialEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a new active frequency selective surface (AFSS) is proposed as an electromagnetic (EM) window for TE-polarized incident waves. A unit cell of this AFSS includes of a strip dipole and an elliptical loop, deliberately combined in a single layer configuration to create a dual-band transmission/reflection coefficient response. High frequency PIN-diodes are also integrated into the surface to reconfigure the transmission/reflection coefficients of the EM window across two close frequency bands of 1.8GHz and 2.45GHz. In this configuration, the effect of the diode parasitic capacitance is used as a potential feature in creating the first operating band. The achieved results show that the proposed surface can be a good candidate for reconfigurable reflectors or it can be used in electromagnetic shielding applications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.045
GPT teacher head0.234
Teacher spread0.190 · 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 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

Citations7
Published2011
Admission routes1
Has abstractyes

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