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Record W4360611047 · doi:10.37285/bsp.sasat2023.50

The Radar Cross Section of Marine Vessels

2023· article· en· W4360611047 on OpenAlexaboutno aff
G S Unil Sriharsha, P Bhuvana, N Harini, Mallikarjun Bangi, M, Gundapaka Naresh, Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsnot available
Fundersnot available
KeywordsRadar cross-sectionSection (typography)GeologyRadarRadar imagingComputer scienceRemote sensingTelecommunications

Abstract

fetched live from OpenAlex

This paper focuses on the estimation of ship RCS using High-Frequency Structure Simulator (HFSS).The results produced here have been compared to the estimates available in the open literature of a Canadian cargo ship named Teleost and also measured values.In this paper, the RCS of a Canadian marine ship named "Teleost" is presented.RCS depends on various parameters like shape, size, orientation, operating frequency and aspect angle.Simulations using Finite Element Method (FEM) in Ansys HFSS software for monostatic Radar Cross Section were made through 0.1MHz to 8MHz frequency.The results of the proposed method are compared with the existing measured values and simulated values available in open literature.The work is carried out using PEC and steel as the dielectric material to analyse the monostatic RCS values, and a broad comparison is made among previous values and the obtained values.The results of this proposed method are more accurate when compared to the previous works, and are very near to the measured values.The work is carried out using Perfectly Electrical Conductor (PEC) and steel as the materials.The simulations of these two materials are carried out individually, to analyze the Monostatic RCS values.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.100

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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