Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Algorithm Design of Short-Circuit Current Grid Based on High-Speed Breaking Technology

Distribution network optimization is a multi-objective, multi-stage, discrete, nonlinear and constrained mixed integer programming problem. For a long time, scholars from all over the world have done a lot of research on this problem and formed many algorithms. Conventional mathematical methods are difficult to deal with such complex problems, so in recent years, domestic and foreign scholars have introduced intelligent algorithms into distribution network planning. It is an important way to improve the breaking ability, reliability and service life of the circuit breaker by controlling the circuit breaker to realize micro-arc energy breaking near the zero point of short-circuit current(SCC) through the fault current phase-controlled breaking technology. In traditional transmission network planning, the SCC check is carried out after the grid is formed, which does not reflect the guiding effect of SCC on the grid planning process, and the SCC in the planned grid often exceeds the standard. By analyzing the influence of broken lines on each element of impedance matrix, this paper derives the sensitivity relationship between the broken lines and the self impedance change of stations beyond the standard, and proposes a grid adjustment optimization algorithm to limit 500 kV short circuit current based on this sensitivity. The algorithm can quickly find the optimal combination of broken wires limiting the SCC at the points beyond the limit by obtaining the maximum value of the weighted sum of the self impedance sensitivity of the stations beyond the limit, avoiding the tedious process of checking all the broken wire combinations one by one.
Passive Positioning Model and Calibration Study of Least Squares UAV Based on Improved Wolfe Linear Search

UAV flies using a pure azimuth passive positioning method. To accurately carry out passive positioning and calibration, this paper establishes a passive positioning and calibration model of a UA V swarm based on optimization theory in the case of circular formation. Specifically, when the UA V at the center of the known circle and two other UA Vs with known numbers and no deviation transmit signals, the positioning models of the remaining host machines(U A V s that receive signals) are established; When the receiving positions of the two UAVs are known to be unbiased, determine the minimum number of unnumbered UAVs required to achieve effective positioning of UAVs; When the position of the drone deviated at the initial moment, an adjustment scheme is given so that the nine drones are evenly distributed on a certain circumference.
Cyclic Timing Path Evaluation Using Commercial Static Timing Analysis Algorithms

Many circuit design methodologies employ combinational logic with feedback to realize logic functions. Obtaining accurate delay values for cyclic timing paths is an impediment to the adoption of these technologies. Static timing analysis (STA) algorithms are the standard for digital timing validation but require that timing arcs form directed acyclic graphs. STA algorithms can be used to evaluate cyclic timing paths with a loss in accuracy by cutting cycles into acyclic segments and summing each segment in a cyclic timing path. This paper reports on sources of error in using static timing analysis to evaluate acyclic timing segments of cyclic timing paths, and shows that using timing segments to evaluate cyclic timing paths with STA can lead to both overly pessimistic and incorrect delay calculations. A method is presented to accurately evaluate segmented cyclic timing paths and detect drafting conditions using STA tools. The algorithm is executed on a number of common cyclic circuits and has been shown to accurately evaluate segmented cyclic path delays compared to fully unrolled circuit paths for different operating conditions, fanout loads, and timing path cuts.
Object Detection and Tracking Based on OFDM Communication Waveform

In this paper, the application of the orthogonal frequency division multiplexing (OFDM) waveform in integrated radar and communication (ISAC) scenario is considered. With realization of precise tracking with less bandwidth, it is regarded as significant application value. For an object moving in a 2-dimension plane, a detecting and tracking model with minimized position error is designed. By analyzing OFDM waveforms and their reflected waveforms by fast Fourier transform (FFT), the position of the measured object can be measured. In linear 1-demision systems, Kalman filter (KF) is used to eliminate the errors in OFDM measurement. In nonlinear 2-dimension scenario, the measured results are optimized by extended Kalman filter (EKF) after the system is linearized by Taylor expansion. Simulation results verify the reliability of this approach combined by OFDM waveform and EKF in a certain range of signal to noise ratio.
Design and Application of Image Recognition Framework Based on Cloud Edge Collaboration in Distribution Stations

Targeted on the intelligent identification and cloud load reduction requirements of power system in video monitoring, a vision analysis system platform and implementation scheme for distribution station based on cloud edge system are proposed. Through multi-source data fusion analysis, optimization of defect fault location, image recognition algorithm, cloud edge threshold classification collaboration, image processing and feature extraction technologies such as YOLOv3, Mask-CRNN, etc., the high-definition real-time image is analyzed, and the main equipment status recognition and security abnormal behavior recognition functions of the distribution station are realized. The real-time and accuracy of each analysis function is verified by the on-site distribution station, and the relevant experimental results of some functions in the power grid are compared to show the effectiveness of the system scheme.
EE-GCN: A Graph Convolutional Network based Intrusion Detection Method for IIoT

Intrusion detection in the Industrial Internet of Things (IIoT) is a challenge for the network security protection. Graph neural network (GNN) is employed to improve the network security by virtue of efficiently constructing a message passing function. However, existing intrusion detection methods based on GNN do not fully exploit the information of original data which results in the poor intrusion detection performance. In this paper, we propose an Exploiting Edge feature based on Graph Convolutional Network (EE-GCN), which can capture both the edge features of the network traffic link as well as the relationship between device nodes. In addition, we construct a two-layer GCN network to extract the edge features. Finally, two benchmark datasets (NF-BoT-IoT and NF-ToN-IoT) in Network Intrusion Detection System (NIDS) are used to evaluate the performance of the proposed method. The results show that the method proposed in this paper outperforms other methods.
Exploiting Routing Asymmetry for APUF Implementation in FPGA: A Proof-of-Concept

Implementing Arbiter PUF in an FPGA requires identical logic and symmetrical routing to ensure the delay differences are due to process variations. As the FPGA routing tools optimise for performance and not for symmetry, the FPGA CAD flow requires interventions like manual routing and the use of hard macros. These measures require a designer to work at a lower level of abstraction than RTL which can be tedious and error prone. Furthermore, they require an extensive knowledge of the FPGA fabric which may not be available owing to their proprietary nature. Considering these challenges, we investigate the possibility of an arbiter PUF implementation within the FPGA CAD flow by leveraging the routing asymmetry instead of eliminating it. Preliminary characterisation of a proof of concept APUF model demonstrated uniformity of 49.4 % and reliability of 96.3 %.
TL-PVCNN: A Point Cloud Semantic Segmentation Framework on Transmission Line Scene

The transmission line is an important part of the power system. The point cloud semantic segmentation in transmission line scene can automatically segment the important equipment such as towers and transmission conductors, so that it has an understanding of the power scene. We propose a Transmission Line Point-Voxel CNN (TL-PVCNN) neural network framework for point cloud semantic segmentation in the transmission line scene. TL-PVCNN aims to solve three difficulties, and the three difficulties are information loss caused by uneven point cloud density, segmentation difficulty caused by the complex and changeable geographical environment, and difficulty in network convergence due to large gaps between foreground and background point clouds. To solve these problems, we propose our solutions in three processes, which are point clouds data processing, neural network structure construction, and loss function design. In point clouds data processing, we propose a method named Block Density Importance Sampling (BDIS) to solve the problem of information loss caused by uneven point cloud density. In neural network structure construction, we proposed a module named Attention PV-Conv to solve the problem of segmentation difficulty caused by a complex and changeable geographical environment. In loss function design, we proposed Improved Point Dice Loss to solve the problem of difficulty in network convergence due to large gaps between foreground and background point clouds. TL-PVCNN achieves mIoU of 0.81 on the transmission line point cloud dataset, specifically IoU of 0.83 on towers and IoU of 0.84 on transmission conductors. The experiment result is better than most the point cloud semantic segmentation neural network models.
Fast Routing Algorithm Based on Topology Pruning in Mega Satellite Optical Networks

Satellite laser communication is gaining more and more attention, leading to rapid expansion of the scale of optical satellite networks. However, when the network scales up significantly, routing calculation time will become unmanageable, due to the increased time complexity of the routing algorithm resulting from the growing number of nodes and links. To address this issue, a topology-pruning based fast routing (TPFR) scheme for mega satellite optical networks is proposed in this paper. By establishing a representation of satellite optical networks and implementing topology pruning using regression tree method, the routing process is completed with reduced calculation time in small sub-graph. Simulation results show that TPFR can reduce routing calculation time by more than 45% compared to traditional routing algorithms.
Flexible Channel Access Against Unknown Dynamic Jamming Attack: A Reinforcement Learning Approach

This paper investigates multi-domain anti-jamming communication in a malicious jamming and channel fading environment using deep reinforcement learning (DRL). Unlike existing research, which mainly focuses on single-domain anti-jamming methods such as frequency selection and power control, multi-domain anti-jamming is considered to cope with dynamic jamming and channel fading in the cognitive radio communication scene. We consider whether to transmit at the current time slot in the time domain and joint channel-bandwidth selection in the frequency domain. To represent the environment state, A two-dimensional time-frequency spatial-spectral matrix is designed. The channel switching and bandwidth adjustment overhead are considered in the instant reward to avoid frequent channel switching and bandwidth adjustment. We propose an intelligent anti-jamming algorithm based on DRL and long short term memory network (LSTM) layer is introduced into the DRL network. Simulation results demonstrate that our proposed algorithm is adaptive in dynamic jamming environments and outperforms other anti-jamming algorithms.
A review on Performance of Various Types of Brain-Computer Interface

The interface between external systems and the brain with a direct communication path is called the brain-computer interface (BCI). BCIs serve the purpose of assisting, enhancing, or restoring human cognitive or sensorimotor functions. Control signals are generated based on specific characteristics of brain activity in BCI systems, and they can be used to direct different outputs. There are many neurological disorders that can be caused by a malfunction in the sensorimotor integration system. In numerous situations, such disorders are not treatable with traditional drugs or currently available therapeutic technologies. A (BCI) is a tool that allows the reintegration of the sensorimotor loop, providing direct access to brain information. In this article, we tried to explain about BCI users and the basic principles of BCI and a complete explanation of what BCI is and how it can make, ALS and LIS patients communicate with their surroundings. In the following, we discuss the types of BCI and the most important received signal, which is P300. And finally, three common BCI methods have been explained regarding signal acquisition and signal analysis, which is done in order to translate the signal.
Impact of Device Specifications on VLC Path Loss and System Performance: An Empirical Study

Visible light communication (VLC) is a promising technology for sixth-generation (6G) networks due to its unlicensed bands and high transmission rates. In recent years, several studies have explored the physical propagation characteristics of VLC channels. Nevertheless, the existing models are limited by reliance on customized optical devices. In addition, only sporadic studies have investigated the influence of device specifications, yet neglecting the joint effects of the transmitter and receiver specifications. In this paper, we experimentally measure the optical channel taking into account the specifications of light-emitting diode (LED) and photo-diode (PD). Measurement results show that the path loss exponent (PLE) is 2.47 and 1.78 when the angle of departure (AOD) is larger and smaller than the half-power angle (HPA), respectively. Moreover, changing the PD size from 9.7 mm to 2 mm results in a 15.05 dB difference in path loss (PL). We further derive a PL model that can capture the joint effects of LED and PD specifications based on empirical data. The root mean square error (RMSE) of the measured and simulated data is 2.52, thereby demonstrating the accuracy and practicality of the proposed model. Based on the model, we particularly investigate the impact of the device specifications on the bit error rate (BER) performance. Results indicate that the specifications of the device need to be carefully considered for developing VLC systems. Overall, the proposed model accurately characterizes PL under different device specifications and provides a feasible and universal device selection paradigm for the design and optimization of VLC systems.
EMC Hybrid Loop/Monopole LDS Antenna With Three-Sided Ground Walls for 2.4/5/6 GHz WLAN Operation

A multiresonant, electromagnetic-compatible, hybrid laser-direct-structuring (LDS) antenna capable of operating in the 2.4, 5, and new 6 GHz wireless local area network bands is presented. The LDS metal patterns are formed on a holder with the volume of 8 × 10 × 30 mm3 and comprise an inner loop, a parasitic strip, and three-sided, vertical ground walls. The fundamental and the first higher-order resonance of the parasitic strip acting as a shorted monopole generate the 2460 and 6460 MHz modes. In addition, two loop modes formed by the inner loop with the parasitic strip and the inner loop only, respectively, contribute to the 5420 and 7020 MHz resonance. All of these together generate a tri-WLAN-band, 2.4/5/6 GHz operation. The vertical ground also functions as the shielding walls that assorted conductive components can be placed in the close vicinity of the design for compact integration. The proposed antenna applied to laptop computers for the most recent Wi-Fi 6E operation is demonstrated in this article.
Grain-boundary-limited carrier mobility in polycrystalline silicon with negative temperature dependence: modeling carrier conduction through grain-boundary traps based on trap-assisted tunneling

Seto’s model based on thermionic emission (TE) theory explains that the grain-boundary (GB)-limited carrier mobility in polycrystalline silicon (poly-Si) has a positive temperature dependence. Although this model is widely accepted as a standard model for GB-limited carrier mobility in poly-Si, many experimental measurements under steady-state conditions support extremely weak or negative temperature dependence. In this report, we formulate carrier conduction through GB traps by utilizing the trap-assisted tunneling (TAT) model based on non-radiative multi-phonon transition theory. Self-consistent calculation of poly-Si under steady-state conditions reveals that TAT current exceeds TE current in regions with high GB potential barriers. In contrast to Seto’s model, our novel model reproduces the negative dependence of GB-limited carrier mobility on temperature. From the viewpoint of consistency with experimental measurements, our model seems to be suitable for describing carrier conduction in poly-Si under steady-state conditions.
Comparison Study of Different FSCWs with Flux Barrier Stator

The new stator configuration with magnetic flux-barriers in the stator tooth region represents an efficient method for reducing the sub-harmonics of electric machines with FSCW. Moreover, at the same time the flux-barriers in the stator increase the amplitude of the working harmonic and produce higher torque compared with conventional stators. In this work, this technique has been investigated for different single-layer FSCW topologies. The analysis of numerous permanent magnet machines with FSCW and flux barrier stator shows higher performances compared to the common stator designs. Moreover, the newly proposed FSCW flux barrier machine also provides better torque ripple characteristics compared to the previous designs.
Vibrational Analysis of an Unbalanced Brushless Doubly Fed Induction Machine

The recent increase in both the adoption and size of wind energy conversion systems (WECS) has led to the investigation of new generator topologies. The doubly fed induction generator (DFIG) remains the most popular generator to be employed in WECS due to its variable speed and partially rated converter. Frequent maintenance requirements to their slip ring and brush assemblies leads to additional downtime, resulting in financial losses. The brushless doubly fed (induction) machine (BDFM) is an alternative to the DFIG, with both its power and control windings on the stator whilst using a special rotor winding. It offers higher reliability at the expense of power density. The BDFM is not a commercialized machine, and several design aspects are still under investigation. In this paper, the magnetic forces induced in the air-gap by the two sets of windings are investigated and a 2/3 (power/control winding) BDFM is analyzed with simulation results on the vibrations and deflections within the machine are presented. Experimental vibration testing is then performed on a custom built 5.5kW BDFM to validate the analytical results. It is shown that the main vibration components are due to the unbalanced flux in the machine, causing a dynamic rotor eccentricity. The results obtained from experimental tests closely matches with the presented vibrational model.
Revolutionizing Cyber Security: Exploring the Synergy of Machine Learning and Logical Reasoning for Cyber Threats and Mitigation

The integration of machine learning (ML) and logical reasoning (LR) in cyber security is an emerging field that shows great potential for improving the efficiency and effectiveness of security systems. While ML can detect anomalies and patterns in large amounts of data, LR can provide a higher-level understanding of threats and enable better decision-making. This paper explores the future of ML and LR in cyber security and highlights how the integration of these two approaches can lead to more robust security systems. We discuss several use cases that demonstrate the effectiveness of the integrated approach, such as threat detection and response, vulnerability assessment, and security policy enforcement. Finally, we identify several research directions that will help advance the field, including the development of more explainable ML models and the integration of human-in-the-loop approaches.
Combined star-delta windings in small induction machines for reducing no-load and additional losses

This paper presents a combined star-delta winding design for reduced core losses in squirrel cage induction machines. The study focusses on small machines with single layer integer slot distributed windings with relatively high stator resistance. Theoretical considerations show that machines with combined star-delta windings can be operated bidirectionally by swapping two phases of the supply voltage. Combined windings typically increase the fundamental winding factor and decrease the harmonic content in the air-gap field. Therefore, new formula are derived for the harmonic leakage coefficient and the increase in the fundamental winding factor due to a combined winding. A 4 kW motor is investigated analytically using the well-known perphase equivalent circuit. Measurements are presented to prove that the combined winding reduces core losses, magnetizing current and additional losses effectively. Finally, comments are given on the generalization of the results.
High-gain extended L-band EDFA with pre-amplified double-pass configuration

An extended L-band EDFA with a pre-amplified double-pass configuration is demonstrated. The main amplifier contains an Er/Yb/P co-doped fiber pumped by two 1480 nm laser diodes. And the pre-amplifier is formed by a conventional erbium-doped fiber pumped by a 980 nm laser diode. The 20- and 30-dB gain bandwidths reach 65 nm (1556-1621 nm) and 58 nm (1558-1616 nm), respectively, with a maximum gain of 48.8 dB at 1566 nm and a gain of 20.1 dB at 1621 nm. The noise figure is 5.61±1.75 dB in the range of 1565-1610 nm. Besides, the saturated output power is up to 20.58 dBm tested at 1605 nm.
Compass Augmented Regional Constellation Optimization by a Multi-objective Algorithm Based on Decomposition and PSO

With the recent launching of the 8th Compass navigation satellite, the Compass navigation satellite system has been completed. To augment the performance of this system for navigation on the Chinese mainland, it is necessary to optimize its current regional constellation. This paper analyzes the inadequacies of the current regional constellation and presents a method for optimizing the augmented regional constellation in which a new evolutionary algorithm and a Compass constellation optimization problem model are proposed. Three objective functions (the Weighted geometric dilution of precision (WGDOP), the number of visible satellites and constellation costs) are then introduced. Three optimal solutions were obtained through the optimization process. Based on a comparison of the performance of the three optimal solutions, this paper suggests that one GEO satellite and one IGSO satellite be added to complete the Compass augmented regional constellation.