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Record W1518618855 · doi:10.1109/gsmm.2015.7175455

Flip-flop low profile wideband reflectarray antenna for ka-band

2015· article· en· W1518618855 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
KeywordsWidebandBroadbandBandwidth (computing)OpticsKa bandMaterials sciencePolarization (electrochemistry)PhysicsOptoelectronicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A novel broadband flip-flop low profile Reflectarray (RA) is designed for Ka-Band based on the principle of dual side printed substrate. First, the reflected wave phase curve is obtained by varying the patch size on top layer while energy is coupled through a bottom slot of equal size to the patch. Such a cell provides 360° degress reflected phase with almost linear behavior. Second, the element is flipped and analysis for reflected phase when square slot is varied on top layer while the complementary patch, is varied in the bottom layer. Both methods provide full 360 degrees phase range. In both methods, a small air gap is introduced below substrate to add GND plane on bottom. The proposed methods provide broadband using the thinnest available substrate. Both designs achieve good performance in term of Half Power Beam width (HPBW), Side Love Level (SLL), cross polarization and gain bandwidth (at 30 GHz). The first 15*15 RA design provides, HPBW of 6.6 degrees, SLL -20 dB, cross polarization -25 dB down than copolar component, 1dB gain bandwidth of 14.5 % and 3-dB bandwidth of 23.2 % centered. Similarly, the second flipped 15*15 RA design provides, SLL of -17 dB, cross polarization of -25 dB down than copolar component, 1-dB gain bandwidth of 11.5 % and 3-dB bandwidth of 21 %.

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 score0.685
Threshold uncertainty score0.543

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 head0.033
GPT teacher head0.265
Teacher spread0.231 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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