Design and Development of Swimming Pool Water pH Level Monitoring System and Automatic Selenoid Valve Control Based on the Internet of Things
Bibliographic record
Abstract
A device for monitoring the pH level of swimming pool water and an IoT-based automatic solenoid valve control has been specially designed. This system consists of several main components, including a pH sensor that continuously monitors the pH level in swimming pool water. This sensor will be connected to the Esp32 Board microcontroller which has been programmed to retrieve pH data periodically. The collected data will be sent via an internet connection to the cloud platform and can then be accessed via a mobile application to display real-time pH level messages. In addition, this system is also equipped with automatic solenoid valve control. Based on the measured pH data, the system will be able to make a decision to open or close the solenoid valve. If the pH level is outside the set limit, the system will automatically activate the solenoid valve to open the floodgates for filling water into the swimming pool, thus maintaining the pH balance automatically. In the design system for monitoring the pH level of swimming pool water and controlling this automatic solenoid valve using a pH-014 sensor which functions to detect the pH level in swimming pool water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".