Bibliographic record
Abstract
The development of smart platforms in the Internet-of-Things (IoT) paradigm requires a number of technological advances to go hand in hand. IoT devices and entities have very diverse set of capabilities in terms of memory, power, and processing and are connected via a wide range of links with various qualities and capacities. This work focuses on the design of state-of-the-art data transmission methodology with arguably the highest level of flexibility and adaptability. We present a novel fountain-based encoding technique using overlapped generations of Luby-Transform (LT) codes. The proposed overlapped LT (OLT) codes achieve significant gains in BER and/or code rate. They are highly energy-efficient, scalable, and robust. Our analysis shows in particular that by using OLT codes, we can modify the fountain degree distribution such that it does not contain degree-one packets starting from the second generation. Thus, we introduce new degree distributions to improve the performance of OLT codes; the new scheme is referred to as smart OLT (SOLT). Simulation results are provided to show the improvements of OLT and SOLT codes in AWGN channel in terms of error rate. For example, SOLT codes can achieve the same error performance as LT codes, but they require smaller transmission overhead, for instance, at SNR of 9 dB and error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , SOLT codes require a code rate of 0.476 while LT codes require a code rate of 0.417.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".