Comparative Numerical Analysis of Torpedo-Shaped and Cubic Symmetrical Autonomous Underwater Vehicles in the Context of Indonesian Marine Environments
Bibliographic record
Abstract
This study presents a comparative analysis of the performance dynamics of torpedoshaped and cubic symmetrical autonomous underwater vehicles (AUVs) within the unique context of Indonesian marine environments.The dynamics of these AUV models were assessed in MATLAB's ode45 solver, incorporating real-world sea wave data from Indonesian waters as external disturbance variables.As the AUVs were subjected to these disturbances, the performance of each model's positional capability was critically evaluated to determine their respective hydrodynamic attributes.Notably, the cubic symmetrical AUV model demonstrated superior trajectory following and differential actuation capabilities, indicating its aptness for missions necessitating meticulous path-tracking.Conversely, the torpedo-shaped AUV showcased an enhanced robustness in managing external disturbances, particularly when viewed from a bi-dimensional standpoint.The selection between these AUV models is contingent upon the specific mission requirements, including environmental factors, mission objectives, and design capabilities.This comparative investigation offers valuable insights into the design and operation of AUVs in the Indonesian marine environment, thus informing the development of optimized AUV models tailored to tackle the unique challenges of this maritime context.The findings from this study contribute significantly to the progression of underwater exploration, environmental surveillance, and offshore industries operating within Indonesian waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".