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Record W4385585757 · doi:10.5515/kjkiees.2023.34.2.138

A Study on the Suppression of Secondary Reflected Signals during Naval Gun Fire Using Naval Surveillance Radar

2023· article· en· W4385585757 on OpenAlexaff
In-Cheol Cho, Choung-Hyun Lee, Jae-Yup Shin, Hyun-Wook Moon, Sung-Hwan Sohn

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsArtilleryRadarScope (computer science)NavySecondary surveillance radarClutterTracking (education)AeronauticsFire-control radarLow probability of intercept radarSIGNAL (programming language)EngineeringComputer scienceRemote sensingContinuous-wave radarAerospace engineeringRadar imagingArtificial intelligenceGeologyGeography

Abstract

fetched live from OpenAlex

Ship radar systems extract the distance, bearing, and altitude of a target and deliver three-dimensional tracking information to the combat system of the ship. In addition, high-resolution information about the target and B-Scope can be obtained using TWS (track while scan) tracking, and the information is used for naval gun firing. However, the normal B-scope is not formed if strong clutter signals from secondary reflection signals from the ground, coastal islands, or mountains are introduced during tracking. In such cases, it could be difficult to adjust the zero point and check the impact using the water column when firing the artillery. Therefore, in this study, a method is proposed to acquire a normal B-Scope by removing the secondary reflected signal, and the proposed method is verified by applying it to the actual Navy ships.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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