Timestamp Error Detection and Estimation for PMU Data based on Linear Correlation between Relative Phase Angle and Frequency

Time synchronization is essential to synchro-phasor-based applications. However, Timestamp Error (TE) in synchrophasor data can result in application failures. This paper proposes a method for TE detection based on the linear correlation between frequency and relative phase angle. The TE converts the short-term relative phase angle from noise-like signal to one that linear with the frequency. Pearson Correlation Coefficient (PCC) is applied to measure the linear correlation and then detect the timestamp error. The time error is estimated based on the variation of frequency and relative phase angle. Case studies with actual synchrophasor data demonstrate the effectiveness of TE detection and excellent accuracy of TE estimation

Non-intrusive Load Monitoring based on Self-supervised Learning

Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems, self-supervised learning (SSL) is proposed in this paper, where labeled appliance-level data from the target data set or house is not required. Initially, only the aggregate power readings from target data set are required to pre-train a general network via a self-supervised pretext task to map aggregate power sequences to derived representatives. Then, supervised downstream tasks are carried out for each appliance category to fine-tune the pre-trained network, where the features learned in the pretext task are transferred. Utilizing labeled source data sets enables the downstream tasks to learn how each load is disaggregated, by mapping the aggregate to labels. Finally, the fine-tuned network is applied to load disaggregation for the target sites. For validation, multiple experimental cases are designed based on three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides, state-of-the-art neural networks are employed to perform NILM task in the experiments. Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.Comment: 12 pages,10 figure

Generation of Ultra-intense Gamma-ray Train by QED Harmonics

When laser intensity exceeds 10^22W/cm^2, photons with energy above MeV can be generated from high-order harmonics process in the laser-plasma interaction. We find that under such laser intensity, QED effect plays a dominating role in the radiation pattern. Contrast to the gas and relativistic HHG processes, both the occurrence and energy of gamma-ray emission produced by QED harmonics are random and QED harmonics are usually not coherent, while the property of high intensity and ultra-short duration is conserved. Our simulation shows that the period of gamma-ray train is half of the laser period and the peak intensity is 1.4e22W/cm^2. This new harmonic production with QED effects are crucial to light-matter interaction in strong field and can be verified in experiments by 10PW laser facilities in the near future.Comment: 12 pages, 4 figure

Effects of hypoxia on serum hepatic chemistries of Tibet chicken and Shouguang chicken

Hypoxia is a major factor that affects the subsistence and development of multicellular organisms. Tibet chicken, as a unique native chicken breed in altiplano, shows genetic adaptation to hypoxia comparing with the breeds at the low altitude. In the present study, to explore effects of hypoxia on chicken fetal livers, eggs of Tibet chicken and Shouguang chicken were collected and the samples from each breed were divided into two groups, incubated in hypoxia and in normoxia respectively. The blood of embryos on the 16th day of incubation was collected and the serum chemistry  parameters indicating liver metabolism were determined, which included glutamic-pyruvic transaminase (GPT), aspartate aminotransferase (GOT), total bilirubin (TB), direct bilirubin (DB), total bile acid (TBA), gamma glutamyltransferase (GGT), alkaline phosphatease (ALP), lactate dehydrogenase (LDH), creatine kinase (CK), glucose and creatinine. The results show that biochemical indices varied significantly between hypoxia and normoxia except for GPT and glucose. Moreover, the concentration of ALP and LDH showed significant differences between the breeds and the incubations. The results suggest that the livers of both Shouguang chicken and Tibet chicken suffered damages in hypoxia, but the former was more serious. The results of this study support the opinion that Tibet chicken had better genetic adaptability on hypoxia, and made a good basis for further study of the genetic mechanism of adaptation to hypoxia.Key words: Hypoxia adaptation, liver metabolism, serum chemistry, Tibet chicken, chicken embryo

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results