State estimation for systems with unobservable packet losses: Approximate estimation, stability, and performance analysis
Bibliographic record
Abstract
Abstract For a system with packet losses, if the estimator can observe the status of packet losses, it is called a system with observable packet losses (an OPL system); otherwise, it is called a system with unobservable packet losses (a UPL system). We obtain the optimal estimator (OE) for UPL systems, which consists of an exponentially increasing number of items, and thus is computationally intractable. To address the computation issue, we design an approximate optimal estimator (AOE), which can be computed recursively. The proposed AOE features a theoretically‐proven stability condition and a theoretically‐guaranteed superiority to the optimal linear estimator (OLE). Specifically, for stability, we prove that for a stable UPL system, both the OE and the proposed AOE are stable; for performance, we show that both the OE and the proposed AOE are superior to the OLE in the mean sense. Then, we obtain a tight upper bound of the performance deviation between the OE and the proposed AOE. Finally, numerical examples are presented to illustrate the obtained results and the effectiveness of the proposed AOE in estimating system states when the packet‐loss status, that is, the private information of packet losses, cannot be observed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".