Unmanned Surface Vehicle for Water Quality Monitoring
Bibliographic record
Abstract
This paper proposed an Unmanned Surface Vehicle (USV) for water quality monitoring purposes. The USV offered high maneuverability and accurate monitoring result with IOT implementation. The paper discussed the design and development of a USV, identifying the functionality of USV sensory system, and evaluating performance of USV based on stability, velocity, and acceleration. The USV is designed based on hemisphere shape and is equipped with two brushed DC motor propellers for maneuvering purposes. The buoyancy of USV is set at 89.1% positive buoyancy for stability purposes. Dabble Gamepad controller is implemented for USV to move remotely. Temperature, pH and turbidity sensors are embedded into the USV system for monitoring purposes. Internet of Things (IoT) system is coupled with the vehicle for data monitoring via internet as it offers versatility and efficiency. The developed USV shows outstanding results in terms of maneuverability and sensors functionality. The embedded sensors reading shows stable and accurate. This developed USV will have impact in maintaining the sustainability, and wellbeing of ecosystems and health of water resources.
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 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".