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Record W3047524225 · doi:10.1002/mop.32536

Compact dual band circularly polarized meta‐structured antenna for <scp>GPS</scp> application

2020· article· en· W3047524225 on OpenAlexaff
Chan Ham, Chang‐Hyun Lee, Jeong‐Hae Lee

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsNexen (Canada)
FundersNational Research Foundation of KoreaMinistry of Education
KeywordsPhysicsAntenna (radio)OpticsCoupling (piping)Aperture (computer memory)Resonance (particle physics)Multi-band deviceAntenna apertureRadiation patternTelecommunicationsAcousticsAtomic physicsEngineering

Abstract

fetched live from OpenAlex

Abstract In this letter, a dual band circularly polarized (CP) meta‐structured antenna is designed by −1 mode for L1 band and mu‐zero resonance mode for L2 band, respectively. The antenna consists of two kinds of unit cells which have four unit elements for −1 mode and mu‐zero resonance mode, respectively, in order to utilize the limited space efficiently, and to maximize the gain. Since two modes of −1 mode and mu‐zero resonance mode operate like as loop antenna, these are fed by magnetic coupling of an aperture, simultaneously. In order to reduce mutual coupling between two modes, the orientation of −1 mode antennas are tilted by 45° from that of MZR mode antennas. In this arrangement, magnetic fields with two directions are needed to excite all unit elements, simultaneously. Thus, a cross shaped asymmetric aperture is employed. The RHCP gain of the antenna is measured to be 0.59 dBic at L1 band and −0.02 dBic at L2 band, respectively. The size of the antenna is only 0.14 λ0 by 0.14 λ0 (krground = 1.28) at L2 band. The measured results show a good agreement with those of simulations.

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: Empirical
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 science0.0000.000
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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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