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Record W2121648074 · doi:10.1109/tap.2010.2052568

Design of Broadband, Single Layer Dual-Band Large Reflectarray Using Multi Open Loop Elements

2010· article· en· W2121648074 on OpenAlexaff
M. R. Chaharmir, J. Shaker, N. Gagnon, David Lee

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMulti-band deviceBroadbandLoop (graph theory)Bandwidth (computing)OpticsFrequency bandCenter frequencySquare (algebra)Ku bandPhysicsComputer scienceAntenna (radio)TelecommunicationsMathematicsBand-pass filterGeometry

Abstract

fetched live from OpenAlex

A novel method is introduced to design a single layer, dual-band large printed reflectarray with open loop elements of variable size for both bands. The reflectarray is designed for two frequency bands: 11.4-12.8 GHz for receive and 13.7-14.5 GHz for transmit. Different classes of open cross loop elements were used in the design of the receive band elements. Noting the larger relative bandwidth at the lower band as compared to the upper band, the dimensions of these cross loops are adjusted, using an optimization technique to achieve required phase distribution at the center frequency and minimize frequency dispersion at extreme frequencies of the lower band. Double square open loop elements with variable loop length were used for the transmit band elements. The reflectarray consists of 3 × 3 panels of 40 cm × 40 cm, that are arranged side by side to construct the large 120 cm × 120 cm reflectarray. The flat configuration and modular nature of this reflectarray gives it an advantage from the installation point of view as compared to conventional dish antennas.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.064
GPT teacher head0.300
Teacher spread0.236 · 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

Citations150
Published2010
Admission routes1
Has abstractyes

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