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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 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: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
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