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Record W4380629069 · doi:10.1063/5.0152269

Broadband bowtie antenna for short range impulse radar applications

2023· article· en· W4380629069 on OpenAlexfundno aff
Doojin Lee

Bibliographic record

VenueAIP Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsnot available
FundersUniversity of WaterlooChangwon National University
KeywordsImpulse (physics)BroadbandRadarAcousticsPhysicsImpulse responseOpticsComputer scienceElectronic engineeringTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, the resistively loaded bowtie antenna (RBA) is characterized in both frequency and time domains for short pulse radiating and sensing impulse radio ultra-wide band applications. The exclusive design procedures of RBA are implemented, aiming to optimize RBA to be suitable for short range target sensing applications. The figure of merit is introduced to reflect the short pulse radiating characteristic into the design procedures. The developed RBA is comprehensively evaluated by additional proposed standard criteria such as evaluation factor (EF) and pulse distortion factor (DF). Those EF and DF for RBA are calculated as 149 and 0.12, respectively. The performances of RBA are remarkably investigated in terms of short-range impulse radar characteristic perspective. The proposed RBA has been experimentally demonstrated by intensive characteristic investigations, which are to be commensurate with short range impulse radar applications.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.263
Teacher spread0.250 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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