High-Resolution and Secure IoT-Based Weather Station Design
Bibliographic record
Abstract
The pivotal role of weather information in domains such as workplace safety, economic activities, and natural disaster prevention is undeniable. This study introduces an advanced weather monitoring system designed to observe weather conditions at specific locations and make the data accessible globally. Utilizing Internet of Things (IoT) technology, this system connects a network of devices, including electronic gadgets and sensors, to the internet, thereby creating an interconnected web of information. The system focuses on monitoring environmental parameters such as temperature, relative humidity, pressure, and rainfall. It employs sensors to gather data, which is then transmitted to a web page and graphically represented as statistical information. The data, accessible from any location via the internet, enhances the resolution and accuracy of weather monitoring. The implementation of high-precision sensors in this system ensures the delivery of detailed and accurate meteorological data, facilitating a comprehensive understanding of local weather conditions. Additionally, the system's design allows for the easy deployment of multiple weather stations, broadening the scope of weather monitoring and providing valuable insights into microclimates and localized weather patterns. A significant feature of this system is the incorporation of a robust security framework, safeguarding the integrity and confidentiality of meteorological data. This aspect is crucial for ensuring the reliability of the data for various applications, including disaster preparedness and response. Timely evacuation warnings, efficient resource allocation, and accurate damage assessment are facilitated by the real-time weather monitoring provided by this system.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".