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Record W3173981194 · doi:10.1109/tap.2021.3091637

A High-Performance Standard Dipole Antenna Suitable for Antenna Calibration

2021· article· en· W3173981194 on OpenAlexaff
Zhanghua Cai, Zibin Weng, Yihong Qi, Jun Fan, Weihua Zhuang

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDipole antennaAntenna gainOpticsAntenna measurementPhysicsRadiation patternCoaxial antennaAntenna (radio)Loop antennaDipoleAcousticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Ideal dipole antennas are desirable for antenna calibration. However, real-world implementation issues introduce inevitable nonideal effects that can significantly affect the quality of calibration. In this communication, a 2 GHz standard dipole (SD) antenna is presented with an excellent overall performance. This result is achieved by an innovative design with an optimized element shape and an embedded (shielded) balun. The designed feeding structure is a balanced structure that transitions from parallel strip lines to coaxial lines. The feeding method avoids the impact of electromagnetic environment asymmetry while achieving an appropriate balance. The measured relative bandwidth of the dipole exceeds 15%, and the antenna gain is approximately 2 dBi. The cross-polarization ratio is greater than 27 dB in the horizontal plane, and the horizontal gain variation is less than 0.2 dB. The dipole has a symmetrical vertical plane pattern, and the maximum gain point does not deviate from the horizontal plane. The high performance of this SD makes it suitable for antenna calibration.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.012
GPT teacher head0.210
Teacher spread0.199 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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