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Record W2144759558 · doi:10.1109/aps.2007.4396007

Parametric study of dispersion and filtering capabilities of SRR-type FSS loaded rectangular waveguides

2007· article· en· W2144759558 on OpenAlexaff
Malcolm Ng Mou Kehn, Eva Rajo‐Iglesias, Óscar Quevedo-Teruel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPassbandMaterials scienceOpticsWaveguideCutoff frequencyResonatorSlabBand-pass filterWaveguide filterParametric statisticsDispersion (optics)Split-ring resonatorDielectricMicrowaveStopbandFilter (signal processing)OptoelectronicsPhysicsLow-pass filterPrototype filterEngineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

An array of metallic patches printed over the surface of a grounded dielectric slab is known as a frequency selective surface (FSS). When such an FSS loaded slab is placed within a waveguide, special features such as bandgaps and TEM behavior can be achieved. Just like other metallic inclusions, split ring resonators (SRR) can also be used to realize such FSS-loaded waveguides. Due to its inherent ability to create backward waves, SRR-type FSS loaded waveguides can support backward traveling modes. This characteristic has been experimentally shown. Since these modes exist only within a finite band, such waveguides can potentially exhibit bandpass filtering capabilities. This is achieved when the backward passband is below the cutoff frequency of the dominant ordinary waveguide mode.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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