Design of Multi-functional Remote-control Terminal for IoT
Bibliographic record
Abstract
The quality of smart home control applications and software is currently uneven. Faced with redundant functions such as open-screen advertising, manual control is more convenient. How to obtain a more convenient device control experience has become an urgent problem. Integrated with various standard communication control protocols, the multi-functional remote-control terminal of IoT focuses on the remote control of equipment. It simplifies the remote-control operation and has no redundant operation. Communicating with the smart car through the 2.4G wireless communication module, the terminal can remotely control the movement of the smart car and the on/off of the LED lamp on it by the LCD. With the built-in ESP-NOW function of the ESP32 microcomputer, the intelligent device equipped with an ESP chip can be controlled. The fan, light, and temperature and humidity display on the ESP intelligent device can be controlled by LCD. With the set temperature threshold of the ESP intelligent device, the start/stop of the fan can be automatically controlled. With the Wi-Fi function built-in ESP32 microcomputer, the system can be connected to the Wi-Fi, and the lights or windows of IoT devices can be controlled over the Internet.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".