The Radar Cross Section of Marine Vessels
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".