Design of a Low Cost Switching Board Enabling a Reconfigurable Remote Experiment
Bibliographic record
Abstract
Most of currently remote laboratories implementations include interactive experimentation. In this case, students use real devices and equipment to perform real experiments, which need some flexibility of interaction with the hardware platform. The hardware platform is composed of a Raspberry Pi as a lab server, a switching board (SB), a practical work circuit board and some measurement instruments. The SB is used to make configuration of experimentation by establishing connection between the practical work circuit and measurement instruments. During the experimentation process, students change the setup using a web page. In the background, the hardware configuration is realized using SB, which is controlled by the lab server. The purpose of this work is to develop a new SB in order to provide more possibilities, interaction flexibility with the hardware platform, ease of use, improve performance in response time and finally reduce the cost of the hardware. The SB is based on switches instead of relays. This board can be plugged directly on a Raspberry Pi to facilitate the assembly. It extends the “SPI” bus in order to control some electronic components such as digital potentiometers. Its use is illustrated with a circuit with multiple combinations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".