The gravitational wave (GW) event S250206dm, as the first well-localized neutron star (NS) merger candidate potentially located in the mass gap, presented a unique opportunity to probe the electromagnetic signatures from such a system. Here we report a deep, multiband search with the new 2.5 m Wide Field Survey Telescope (WFST), covering similar to 64% of the localization region up to a 5 sigma limiting magnitude of 23 mag. In total, 12 potential candidates have been identified, but none of them are likely related to S250206dm. This nondetection provides the most stringent constraint to date on any associated kilonova. Crucially, an AT 2017gfo-like event at 269 Mpc can be excluded only by WFST observations. Based on ejecta mass limits, a NS-black hole with a large mass ratio (Q greater than or similar to 3.2) is disfavored. This optically derived constraint on the mass ratio reaches, for the first time, a precision comparable to that inferred from the GW signal. This work presents the best observation of this type of event until now, and demonstrates the power of rapid, deep follow-up observations to constrain the properties of compact binary progenitors, offering key insights into the constituents of the mass gap.

We propose the Transformer-based Tidal disruption events (TDE) Classifier (TTC), specifically designed to operate effectively with both real-time alert streams and archival data of the Wide Field Survey Telescope (WFST). It aims to minimize the reliance on external catalogs and find TDE candidates from pure light curves, which is more suitable for finding TDEs in faint and distant galaxies. TTC consists of two key modules that can work independently: (1) A light-curve parametric fitting module and (2) a Transformer (Mgformer) based classification network. The training of the latter module and the evaluations for each of the modules utilize a light-curve dataset of 7413 spectroscopically classified transients from the Zwicky Transient Facility (ZTF). The Mgformer-based module is superior in performance and flexibility. Its representative recall and precision values are 0.79 and 0.76, respectively, and can be modified by adjusting the threshold. It can also efficiently find TDE candidates within 30 days from the first detection. For comparison, the parametric fitting module yields values of 0.72 and 0.40, respectively, while it is >10 times faster in average speed. Hence, the setup of modules allows a trade-off between performance and time, as well as precision and recall. TTC has successfully picked out all spectroscopically identified TDEs among ZTF transients in a real-time classification test, and selected similar to 20 TDE candidates in the deep field survey data of WFST. The discovery rate will greatly increase once the differential database for the wide-field survey is ready.

Microwave SQUID multiplexer (& micro;MUX) for transition-edge sensors achieves high multiplexing ratio, significantly reducing wiring complexity. However, the micro-nano fabrication technologies can lead to quality defects and non-functional channels during & micro;MUX production. Therefore, comprehensive performance characterization is essential to identify defective channels prior to deployment and ensure reliable scientific data acquisition. This work proposes the design and implementation of a novel system for parameter characterization to address this need. The system employs an innovative critical polyphase filter bank architecture implemented on FPGA hardware to overcome computational bottleneck in broadband signal processing. By systematically combining results measured at different local-oscillator frequencies, this approach addresses transition band attenuation inherent in critically sampled filter banks. The use of combined synthesis-analysis filter banks enables broadband signal generation and channelization. The developed prototype system achieves a phase noise of -101 dBc/Hz at 10 kHz for the probe tone. This system provides an optional solution for per-channel screening of & micro;MUX chips, enabling the identification and rejection of nonfunctional or substandard chips.

Wide-field surveys have markedly enhanced the discovery and study of solar system objects. The 2.5 m Wide Field Survey Telescope (WFST) represents the foremost facility dedicated to optical time-domain surveys in the Northern Hemisphere. To fully exploit WFST’s capabilities for solar system object detection, we have developed a heliocentric-orbiting objects processing system (HOPS) tailored for identifying these objects. HOPS integrates HelioLinC3D, an algorithm well suited for the WFST survey cadence, characterized by revisiting the same sky field twice on the majority of nights. In this paper, we outline the architecture and processing flow of HOPS. The application of HOPS to the WFST pilot survey data collected between 2024 March and May demonstrates exceptional performance in terms of both temporal efficiency and completeness. A total of 658,489 observations encompassing 38,520 known asteroids have been documented, and 241 newly discovered asteroids have been assigned provisional designations. In particular, 27% of these new discoveries were achieved using merely two observations per night on three nights. The preliminary results not only illuminate the effectiveness of integrating HelioLinC3D within HOPS, but also emphasize the considerable potential contributions of WFST to the field of solar system science.

This paper investigates the passive state estimation issue for two-time-scale Markov jump complex networks subject to sensors nonlinearities, in which the Markov chain with the general probability information is considered. The singular perturbation parameter in Markov jump complex networks characterises the system's two-time-scale behaviour. A state estimator is devised that utilises nonlinear measurement output to estimate the states of two-time-scale Markov jump complex networks with general probability information. The estimation error system is guaranteed to be stochastic stable under the passive performance criterion by establishing sufficient conditions using Lyapunov stability theory. For the purpose of reducing conservatism in the process of obtaining estimator gains, an improved decoupling approach is presented. Finally, the superiority of the proposed decoupling approach and the efficiency of the designed estimator are showcased through simulations.

We carry out an imaging survey of six globular clusters (GCs) with a limit magnitude to 22 mag at the 5 sigma level, down to the main sequence stars of the respective cluster, as one of the pilot observing program of the Wide Field Survey Telescope (WFST). This paper present the early results of this survey, where we investigate the tidal characters at the periphery of the clusters NGC 4147, NGC 5024, NGC 5053, NGC 5272, NGC 5904 and NGC 6341. We present the estimated number density of cluster candidates and their spatial distribution. We confirm the presence of tidal arms in NGC 4147 and NGC 5904 and identify several intriguing potential tidal structures in NGC 4147, NGC 5024, NGC 5272, corroborated the elliptical morphology of the periphery of NGC 6341. Our findings underscore the WFST's capability for probing faint structural features in GCs, paving the way for future in-depth studies, especially for the search of the large scale tidal streams associated with the clusters with the future wide field survey.

The correlation between neutron energy and neutron-induced soft error rate (SER) is crucial for estimating the impact on electronic devices fabricated using complementary metal-oxide-semiconductor (CMOS) technology in diverse neutron environments, including those found in high-energy physics experiments, aviation settings, and so on. This article presents an investigation conducted independently at China spallation neutron source (CSNS) using the Back-n white neutron source and atmospheric neutron irradiation spectrometer (ANIS) neutron source to directly measure single-event upset (SEU) effects of the configuration random access memory (CRAM) and block random access memory (BRAM) in 20-nm CMOS technology-based UltraScale Kintex FPGA. By recording the frequency of SEU events and the time of flight (TOF), SEU cross sections can be calculated for different energies. Experiment in situ indicates a coherent alignment of different outcomes under neutron irradiation from Back-n and ANIS, despite their different neutron energy spectra. The SEU threshold energy is estimated to be 0.69 +/- 0.076 MeV for CRAM and 0.80 +/- 0.013 MeV for BRAM. The measured cross sections can reach saturation of 6.9 x 10(-15) cm(2)/bit and 1.6 x 10(-14) cm(2)/bit for CRAM and BRAM, respectively, while the neutron energy is about 20 MeV.

WFST telescope is a large-aperture sky survey telescope jointly built by the University of Science and Technology of China and Purple Mountain Observatory. It is currently the most powerful optical time-domain sky survey telescope in the northern hemisphere. Its scientific goals are diverse, and the urgency and observation window period of different scientific tasks are different. During the observation period, the control of equipment requires accuracy, real-time, efficiency and safety. Therefore, we design and implement the OCS (Observatory Control System) of WFST which performs multi-layer abstraction on each hardware device and puts more attention on the operation of the observation process and the scheduling of observation tasks. In order to ensure the safety of equipment during the observation process, the OCS introduces a weather alarm system and fault diagnosis system. At the same time, we design an observation strategy system in the OCS to adjust the observation plan based on task priority, equipment information and meteorological information to maximize the efficiency of sky survey.

Ultra-narrow pulses serve as critical components in numerous applications. These pulses have ultra-fast leading edges that typically function as precision trigger signals to synchronize various instruments. Ultra-narrow pulses inherently exhibit an ultra-wide bandwidth, gaining significant attention in diverse electronic systems encompassing communications, radar imaging, electronic warfare, and others. Although several techniques have been explored for generating ultra-narrow pulses, field programmable gate arrays (FPGAs) offer a promising alternative in terms of flexibility and integration. This study introduces a scalable delay pulse synchronizer method with a resolution of 23 ps. A programmable, successive, narrow pulse sequence operating at a 1-GHz repetition frequency is implemented within a monolithic FPGA. The performance of the proposed method is evaluated using an existing board with a general commercial FPGA in the laboratory. This new method presents a convenient and efficient approach of achieving ultra-narrow pulse synchronization, being applicable across various fields.

The muon detector (MUD), serving as the outermost detector of the high-precision spectrometer in STCF, is used to provide muon identification in the presence of a significant pion background. The accuracy of muon identification relies heavily on excellent momentum resolution, which can be determined by their flight trajectory positions. Physical simulations of the measurement precision for reconstructed muons indicate that a spatial resolution of 2 cm is required. In the barrel MUD, a double-ended readout is required to determine the hit position, with a time resolution requirement of approximately 500 ps. To meet the readout requirements of the MUD, a comprehensive scheme for the front-end readout electronics of the STCF MUD is proposed, and a prototype of MUD readout electronics is developed. To validate the final 8-channel application-specific integrated circuit (ASIC) design, an 8-channel time-to-digital converter (TDC) is implemented using a field programmable gate array (FPGA). The results of the electronics tests demonstrate that the average root mean square (RMS) precision ranges from 14 to 16 ps for each channel within a 1∼20 ns time interval. In the joint test with the detector, the system achieves a single-channel RMS precision of 297 ps, with a detection efficiency exceeding 95.5%. All indicators meet project requirements. This validates that the prototype is capable of preliminary evaluation and parameter optimization of the STCF MUD.

In this paper, we consider a linear time-invariant discrete system with quantized state feedback. In the networked control system we consider, the next sampling time is given by a self-triggering mechanism based on feedback packets received. For unstable or marginally stable systems, we develop a quantization strategy and self-triggered sampling mechanism to guarantee system stability. And the bit rate we consume can arbitrarily approximate the bit rate lower bound in periodic sampling case. Simulation results are presented to validate the effectiveness of our approach.

In this paper, the periodic event-triggered system (PETS) under random deception attacks is studied. In the concerned system, a detector with system state and predicted state as input is used to detect deception attacks. The detectors have different detection results in the presence and absence of noise. To address these differences, state estimator update strategies are designed and the mean-square stability of the system states is ensured, respectively. With the design of the controller and state estimator, we can guarantee exponential convergence of the system state once the communication network is attacked. Finally, the paper proposes sufficient conditions for deception attacks and minimum bit rates that ensure the stability of the system.

The increase in luminosity, and consequent higher backgrounds, of the LHC upgrades require improved rejection of fake tracks in the forward region of the ATLAS Muon Spectrometer. The New Small Wheel upgrade of the Muon Spectrometer aims to reduce the large background of fake triggers from track segments that are not originated from the interaction point. The New Small Wheel employs two detector technologies, the resistive strip Micromegas detectors and the "small" Thin Gap Chambers, with a total of 2.45 Million electrodes to be sensed. The two technologies require the design of a complex electronics system given that it consists of two different detector technologies and is required to provide both precision readout and a fast trigger. It will operate in a high background radiation region up to about 20 kHz/cm$^{2}$ at the expected HL-LHC luminosity of $\mathcal{L}$=7.5$\times10^{34}$cm$^{-2}$s$^{-1}$. The architecture of the system is strongly defined by the GBTx data aggregation ASIC, the newly-introduced FELIX data router and the software based data handler of the ATLAS detector. The electronics complex of this new detector was designed and developed in the last ten years and consists of multiple radiation tolerant Application Specific Integrated Circuits, multiple front-end boards, dense boards with FPGA's and purpose-built Trigger Processor boards within the ATCA standard. The New Small Wheel has been installed in 2021 and is undergoing integration within ATLAS for LHC Run 3. It should operate through the end of Run 4 (December 2032). In this manuscript, the overall design of the New Small Wheel electronics is presented.

WSN is one of the most efficient technologies in intelligent communication and because of its advantages, this technology has been utilized in various applications. By using WSNs, different types of data can be collected and analyzed in wide environments. The high variety of applications and types of data available in this network can cause several challenges about heterogeneous data routing. This research, presents a Fuzzy Model for Content-Centric Routing (FMCCR) in WSN to solve these challenges. The performance of FMCCR is based on two basic steps: "topology control", and "data transmission through content-centric and fuzzy logic-based routing algorithm". In the first step of FMCCR, the network topology is constructed. In the second step of the proposed method, data transmission paths are determined based on network topology and content type, and data transmission is performed. The performance of FMCCR has been evaluated in a simulation environment and the results have been compared with previous algorithms. The results show that FMCCR reduce energy consumption and improve the traffic load distribution in the network in addition to increasing the network lifetime. According to the results, FMCCR can increase network lifetime at least 10.74% and at the same time, deliver at least 88.1% more packets through the network, compared to previous methods. These results, prove the efficiency of the proposed method for using in real-world scenarios.

Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient strategies to deal with this problem is strongly felt. This problem is even more important in applications such as Intelligent Transport Systems (ITSs). This article introduces a new method for congestion control in ITSs based on WSN-IoT infrastructure, namely, the Congestion Avoidance by Reinforcement Learning algorithm (CARLA). The purpose of the research was to improve the performance of the Zigbee protocol in congestion control through more efficient routing and also the intelligent adjustment of the data rate of the nodes. For this purpose, a topology control and routing strategy based on the multiple Bloom filter (MBF) is proposed in this research. Further, learning automata (LA) was used as a reinforcement learning model to adjust the data rate of network nodes in a distributed manner. These strategies distinguish the current research from previous efforts and can be effective in reducing the probability of congestion in the network. The performance evaluation results of the proposed algorithm in a simulated ITS environment were compared with conventional Zigbee and state of the art methods. According to the results, CARLA can improve PDR by 4.64%, and at the same time, reduce energy consumption and end-to-end delay by 11.44% and 25.26%, respectively. The results confirm that by using CARLA, in addition to congestion control in the ITS, energy consumption and the end-to-end delay can also be reduced.

Retrieval-based Question Answering (RQA) is a critical research subject in the field of Natural Language Processing (NLP). Its goal is to obtain the most relevant answer candidate from a knowledge base. Existing methods often overlook the emotional and structural information contained in questions, leading to a decrease in accuracy. This paper proposes an RQA method based on an Emotion-Aware Graph Attention Network (EAGAN). The method initially screens candidate answers using the BM25 algorithm, followed by extracting emotional features of the question using a pre-trained language model. Subsequently, it uses a Graph Attention Network to transform the question into a graph structure and compute the attention weights for each node in the graph. Lastly, it multiplies the graph attention weights with the emotional features to obtain semantic features of the question, and calculates the similarity between semantic features to screen out the optimal answer. Experimental results show that this method achieves high EM and F1 scores on multiple datasets.

This paper mainly investigates the consensus problem of multi-agent Markovian jump systems (MJSs) under event triggering strategies. To reduce the frequency of system information transmission, information transmission will only occur if the trigger conditions are met. Due to the information transfer between MJSs and overall performance considerations involved in multi-agent systems, it poses difficulties and challenges to the design of event triggering strategies. Our control strategy ensures that the multi-agent system is consensus for a limited period of time when the corresponding conditions are satisfied. Finally, the effectiveness of these consensus conditions is proved by simulation experimental results.

This paper presents a multi-channel readout electronic system with a high event rate. Its primary purpose is to meet the increasing collision luminosity in large particle physics collision experiments in recent years, thus bringing about the requirement of high event rates for readout electronics systems. This system is designed for Micromegas detectors and consists of front-end readout boards (FEB) and a data acquisition board (DAQ). It realizes that at 128 channels simultaneous readout, each channel can work at the maximum 2.2 Mhits/s event rate. Each event data contains amplitude and time information, which is read out by FEB. The event data will be sent to DAQ by the GTX transceiver at 3.2 Gbps and then transferred to the data acquisition software. In the data acquisition software, each particle hit position and track will be analyzed and reconstructed. The specific characteristics and implementations of this readout electronic system are described in detail.

As one of the most clean energy sources, solar energy is playing an increasingly important role in energy generation, thus driving the rapid development of photovoltaic (PV) power plants. In one PV power plant, there may be hundreds of thousands PV modules. Fault diagnosis of such a huge number of PV modules is critical and challenging. Recently Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras are taken to execute this fault diagnosis by taking photos and analyzing these photos for faults. To improve the fault diagnosis accuracy and efficiency of PV modules, this paper proposes an automatic PV module fault diagnosis algorithm based on deep learning for infrared images, which is made up of two steps, including localization and classification. In the localization step, we first design a lightweight convolution neural network (CNN) to detect edges of PV modules in infrared images; then a region extraction method is proposed to segment PV modules. In the classification step, a lightweight classifier is used to detect faulty PV modules. Moreover, we introduce an Out-Of-Distribution (OOD) detection algorithm to identify and eliminate false PV modules, which actually come from the background and are wrongly segmented in the localization step. The effectiveness of the proposed PV module fault diagnosis algorithm has been successfully verified on real images.

This paper investigates the stabilization of switched linear systems under denial-of-service (DoS) attacks with event-triggered strategies and finite bit rate quantization. Unlike previous research that only considers switching when DoS attacks are inactive, we investigate a general case where unknown switches are allowed during DoS activation, as well as asynchronous communication between controller modes and subsystem modes. We assume that switching instants follow the average dwell time and that DoS attacks with limited energy are described by restricted frequency and duration. Firstly, event-triggered strategies and finite bit rate quantized policy are designed to estimate the bounds of the state estimation error under unknown switches and DoS attacks, and achieve the quantized state feedback under finite bandwidth. Additionally, by using multiple Lyapunov functions, we establish a joint constraint of switching signals and DoS attacks energy, which implicates the influence of multiple switches in DoS attacks and quantization error on system stability. Based on the above discussions, the global asymptotic stability of the closed-loop system is established. Finally, simulations are conducted to confirm the obtained results.