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Record W2043906256 · doi:10.1109/tap.2015.2389838

Design of a Multilayer X-/Ka-Band Frequency-Selective Surface-Backed Reflectarray for Satellite Applications

2015· article· en· W2043906256 on OpenAlexaff
M. R. Chaharmir, J. Shaker

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsKa bandGround planeMulti-band deviceConductorOpticsCascadePolarization (electrochemistry)Materials sciencePerfect conductorRadio spectrumAperture (computer memory)SatelliteFrequency bandWidebandOptoelectronicsPhysicsBandwidth (computing)TelecommunicationsEngineeringAntenna (radio)AcousticsChemistry

Abstract

fetched live from OpenAlex

A dual-band X-/Military Ka-band (MKa-band) single-aperture reflectarray structure that transmits and receives at both MKaand X-bands is introduced in this paper. Frequency selective surface (FSS) is used as the ground plane for the MKaband reflectarray. A cascade configuration of FSS-backed reflectarrays is designed for each of the receive and transmit bands of MKa-band to reduce the coupling between the elements of these two bands when they are etched on the same substrate. Additional FSS's are implemented to further enhance the isolation between the bands. The MKa-band elements are located on top of the X-band reflectarray that is composed of elements etched on a perfect electric conductor (PEC)-backed ground plane. The reflectarray elements also convert circular to linear polarization which results in a simplified feed structure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
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 head0.047
GPT teacher head0.270
Teacher spread0.223 · 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 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

Citations106
Published2015
Admission routes1
Has abstractyes

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