Management of Sensor-Related Data Based on Virtual TEDS in Sensing RFID 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
Integration of multiple sensors into active radio frequency identification (RFID) system is a technology trend. Currently, sensing RFID (SRFID) systems face a number of challenges including description of characteristics of nonsmart sensors, management of sensor-related data, and operational mechanism of sensor sampling. Based on transducer electronic data sheet (TEDS), this paper presents a virtual description method of nonsmart sensors integrated into the active RFID tag. The objective is to improve interoperability and compatibility of the sensing RFID system with off-the-shelf active RFID readers or novel sensing RFID open-loop systems in the future. This paper also proposes a data table storage mode for management of sensor-related data, in the system based on virtual TEDS. The paper analyses the operational mechanism and procedure of the sensor-sampling system that is equipped with these two proposed techniques. Several experiments were carried out with a newly developed container supply chain monitoring application system based on SRFID to examine the performance of the 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.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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