Open Source Data Logging and Data Visualization for an Isolated PV System
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.
Bibliographic record
Abstract
Monitoring the operation of an isolated photovoltaic (PV) system needs both data loggers and web transfer to collect the sensor data. The data includes the measurement of the voltage and current of the PV system and for local weather. The PV system in Memorial University of Newfoundland (MUN) is 5 m away from the window, where the weather data is collected. In reality, PV systems are approximately 25–50 m away from the weather sensors. It is, therefore, more meaningful to realize the sensor communications by wireless transfer than long cables, which can significantly reduce the cables of a large PV system with long distances among sensors. The PC receives all the sensor data and transfers hem to a web server (Thingspeak). A web server is applied to monitor the operation of the system instead of a local server when its users are far away from the location, even though the local server allows more frequent data logging (once per second). The data transformation between the PC and the web server must guarantee the stability and robustness of the program. The system alarm that reports the disconnection failure is also necessary to notify the users. This paper will first introduce the general system setup, then present each part of the system in detail, and finally, analyze the collected data.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it