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Record W4391743766 · doi:10.21203/rs.3.rs-3937868/v1

Achieving High Efficiency, Super Broadband, Ultra-Thin, and Advanced Electromagnetic Absorption for S, C, X, Ku, K, Ka, V, W, and Millimeter Band Radar Applications

2024· preprint· en· W4391743766 on OpenAlexaff
Aykut Coşkun, Ahmet Özmen, Fahad Ahmed, Mehmet Ertuğrul

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversité du QuébecUniversité du Québec à Montréal
Fundersnot available
KeywordsMillimeterBroadbandExtremely high frequencyRadarAbsorption (acoustics)Ka bandPhysicsMaterials scienceOptoelectronicsTelecommunicationsOpticsComputer science

Abstract

fetched live from OpenAlex

<title>Abstract</title> The aim of this study is focused on an excellent electromagnetic absorption response for radar applications by proposing an insensitive to incidence angle, ultra-thin, super broadband, and high efficiency metasurface. Achieving qualified absorption from low to high frequency is challenging due to impedance matching requirements in the structure. Herein, the proposed structure achieves a wide absorption bandwidth (over 90% absorptivity) from 2.4 GHz to 300 GHz. Moreover , the absorption efficiency of electromagnetic waves reaches (99%) from 4 GHz to 300 GHz. For transverse electric (TE) and transverse magnetic (TM) modes, the proposed structure behaves the same up to 45° when exposed to oblique incidences. The total thickness of the designed absorber is 3.6 mm, corresponding to 0.03λ0 at the lowest operating frequency. In addition, the proposed absorber is 1 compared with previous reports of radar bands. It is observed that it offers superior practical feasibility and is a promising candidate for S, C, X, Ku, K, Ka, V, W, and millimeter band 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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