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Paneled center-fed reflectarray for bandwidth enhancement

2017· article· en· W2768951313 on OpenAlexaff
Muhammad M. Tahseen, Ahmed A. Kishk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsBandwidth (computing)Center frequencyWidebandOpticsPhase centerPhysicsPath lengthPlanarAperture (computer memory)Axial ratioAntenna apertureTelecommunicationsAcousticsRadiation patternEngineeringAntenna (radio)Circular polarizationComputer scienceMicrostripBand-pass filter

Abstract

fetched live from OpenAlex

Regardless of the bandwidth of reflectarray (RA) elements, the RA bandwidth is narrower for many possible reasons. For a RA with small f/D, the ray path length varies as we move away from the center, which is compensated by the elements that are designed at the center frequency. However, as the frequency changes, the path length phase errors grows, as we move away from the center, at a more rapid rate than the element frequency phase variation. Therefore, the phase errors introduced as the frequency changes are so significant deteriorating the aperture phase distribution that causes very low aperture efficiency and in return limiting the gain bandwidth. In order to reduce the path length as we move away from the center, the RA is divided into annular planar panels centered with a small square sub-RA. The annular panels are displaced towards the feed position reducing the path length within each panel. A circularly polarized refectarray designed at 30 GHz with wideband cross Bowtie elements is used as an example. The RA size is 25.25λ × 25.25λ, which is corresponding to 101 × 101 elements. The performance of the antenna is compared with the original RA of the same diameter. The proposed method exhibits the maximum simulated aperture efficiency of 48 %, a 1-dB gain bandwidth of 16.9 %, and the 0.5-dB axial ratio bandwidth of 25.6 %.

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: none
Teacher disagreement score0.606
Threshold uncertainty score0.353

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 head0.034
GPT teacher head0.293
Teacher spread0.259 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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