Bibliographic record
Abstract
WirelessHART is a novel standardized wireless sensor network protocol for industrial process automation. The WirelessHART protocol is designed with the aim to complement the HART protocol by providing wireless extension to it. However, due to the different physical and data-link layers the two protocols are not directly interoperable. WirelessHART is based on IEEE 802.15.4 mesh networks whereas HART is a 4-20mA analog wired protocol. Keeping in view the huge installations of HART networks throughout the world we feel the need to integrate HART and WirelessHART networks as the WirelessHART standard does not specify the means to securely connect the two networks. In this paper we provide different options to integrate Wire-lessHART and legacy HART networks. We start integrating the two networks using a gateway. However, gateway based integrations are sometimes not feasible and are insecure. The main contribution of this paper is that we provide a novel and comparatively secure solution to interconnect WirelessHART networks with HART networks. We specify and design a new WirelessHART Integrator that extends the capabilities of the WirelessHART adapter and provides integration at the network level rather than at the device level only. We also analyze and compare our solution with gateway and adapter based solutions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".