Low Earth Orbit (LEO) satellite networks have attracted considerable attention for their ability to deliver global, low-latency broadband Internet services. In this paper, we present a large-scale measurement study of the Starlink network, the largest LEO satellite constellation to date. We first propose an efficient method for discovering active Starlink user routers, identifying approximately 5.98 million IPv6 addresses across 208 regions in 165 countries. Compared to general-purpose IPv6 target generation algorithms, our router-centric approach achieves near-complete coverage and, to the best of our knowledge, yields the most comprehensive known set of active IPv6 addresses for Starlink user routers. Based on the discovered user routers, we further propose an efficient method for mapping the Starlink backbone network and uncover a topology consisting of 49 Points of Presence (PoPs) interconnected by 98 links. We conduct a detailed statistical analysis of active Starlink user routers and PoPs, and further characterize the IPv6 address assignment strategy adopted by the Starlink network. Finally, we analyze the latency of Starlink user routers, propose a method to distinguish different types of users within the same region using outside-in measurement, and identify the ongoing V2 Mini satellite deployment as a potential driver of the performance improvements. The dataset of the Starlink backbone network is publicly available at https://ki3.org.cn/#/starlink-network.

Underwater thrusters, vital components in various underwater vehicles, have been extensively studied for fault identification and classification. However, the automatic and unsupervised assessment of fault levels remains largely unexplored. This article presents a novel approach integrating physical information into a data-driven architecture and training process, enabling automated and unsupervised fault identification and evaluation. The process begins with constructing a physical model of the thruster and estimating its low-fidelity current based on the vehicle's velocity and the thruster's rotational speed. An improved SimGAN is then utilized, in combination with the vehicle's motion state, to map this low-fidelity current to its high-fidelity counterpart in the physical space. Statistical features are extracted from the absolute errors between the high-fidelity current and the measured current as conditions for the discriminator. The framework achieves thruster malfunction identification and assessment by analyzing the discriminator's output. Ocean trial data validate the effectiveness of this framework, with experimental results showing its satisfactory performance in fault identification, level evaluation, computational complexity, and robustness when compared with advanced methods.

Due to the advantages of low energy consumption, high robustness and fast inference speed, Spiking Neural Networks (SNNs), with good biological interpretability and the potential to be applied on neuromorphic hardware, are regarded as the third generation of Artificial Neural Networks (ANNs). Despite having so many advantages, the biggest challenge encountered by spiking neural networks is training difficulty caused by the non-differentiability of spike signals. ANN-SNN conversion is an effective method that solves the training difficulty by converting parameters in ANNs to those in SNNs through a specific algorithm. However, the ANN-SNN conversion method also suffers from accuracy degradation and long inference time. In this paper, we reanalyzed the relationship between Integrate-and-Fire (IF) neuron model and ReLU activation function, proposed a StepReLU activation function more suitable for SNNs under membrane potential encoding, and used it to train ANNs. Then we converted the ANNs to SNNs with extremely small conversion error and introduced leakage mechanism to the SNNs and get the final models, which have high accuracy, low latency and good robustness, and have achieved the state-of-the-art performance on various datasets such as CIFAR and ImageNet.

Deep learning methods of fault diagnostics require the availability of a large amount of labeled data for training, i.e., signal values corresponding to known degradation and fault states. Furthermore, the distribution of the training data should be similar to that of the (test) data collected in the field. Since these conditions are typically not satisfied in most industrial applications, this article develops a deep multiadversarial conditional domain adaptation network. The main original contribution lies in a novel method to align, class by class, the weighted marginal data distributions using multiple domain discriminators. The network allows overtaking the classification underperformance caused by the problem of negative transfer, which is typically encountered when only few training data of some of the classes are available. The proposed method is shown to outperform other state-of-the-art methods on two cross-domain fault diagnostic case studies, verified by applying Friedman and Holm post-hoc tests.

This paper presents a technique for improving the performance of inductor at high-frequency through mitigating the effects caused by the parasitic capacitance. This technique adds a small capacitor to the coupled windings of the inductor to cancel the parasitic capacitance of the inductor. This technique can also be used to improve the coupled windings with leakage inductance. The relationship between the parasitic capacitance, magnetic coupling coefficient, and the small capacitor is introduced. The method to determine the value of the small capacitor is described in this paper. The results of applying this technique with different value of magnetic coupling coefficient k show that an improvement of the inductor impedance by around 20-45 dB depending on k. MATLAB software is used to verify the technique.

This article presents a switched-capacitor (SC) voltage boost converter and its control methods for implementing dc-ac and ac-dc power conversion. The SC converter employs a switched-capacitor circuit augmented with the main converter circuit to the power source, thus providing unique features that cannot be attained by the traditional voltage-source inverter (VSI) or boost VSI. The additional features include doubling the area of the linear modulation region and eliminating both the large inductor in the boost dc-dc stage and the large filtering capacitor, which leads to a higher energy density and lower cost. The SC converter concept can be applied to all dc-ac, ac-dc, ac-ac, and dc-dc power conversions. To describe the operating principle and the control, we focus on one example: a bidirectional SC converter for dc-ac and ac-dc power conversion in electric and hybrid electric vehicles. This paper is accompanied by a video demonstrating the change of the modulation functions with the value of A.

This paper is focused on conditioning the voltage and current waveform quality of a hybrid matrix converter that consists of a conventional nine-switch matrix converter and an auxiliary back-to-back voltage source converter. Upon critical evaluation of the existing methods for shunt and series compensation, the fundamental limitations for achieving superior results have been identified. A new strategy based on power averaging for obtaining the reference compensating current and voltage has been proposed. The effectiveness of the proposed method has been verified by the simulation results for both the shunt and series compensation with concurrent presence of the harmonic components in voltages and currents.

A matrix converter is a single-stage direct AC/AC converter. By employing only nine bidirectional switches, the matrix converter is able to generate variable amplitude and frequency output voltages. Matrix converters would inject a significant amount of current harmonics and voltage harmonics back in to the power source and the load, respectively, if filtering measures are not properly implemented. These harmonics cause distortion and adversely impact the operation of the whole system. The increasing demand for high power makes the conventional solution of passive filters no longer sufficient to solve the power quality problem in matrix converters, beside the other drawbacks of using bulky passive components such as, short life time, big size, and high cost. This paper presents a hybrid matrix converter for medium voltage high power applications. By utilizing a low-frequency modulation techniques, combined with a shunt active filter implemented to the input side of the matrix converter to eliminate current harmonics and a series active filter is applied to the output side of the matrix converter to eliminate the voltage harmonics. This approach is more efficient in terms of reducing the total number of passive components used in the system, reducing the switching power losses, and improving voltage transfer ratio. The explanation of predicting the compensation current and the compensation voltage waveforms is based on the instantaneous reactive power theory. The feasibility and effectiveness proposed topology have been verified by the simulation results.

This paper proposes a boost configuration of wind turbine generation system based on matrix converters. The proposed boost configuration features i) the limitation of the voltage transfer ratio of 0.866 will not limit the terminal voltage at the stator winding under normal operating mode; and ii) low voltage ride through capability is enhanced when grid disturbances occur. The main attention of this paper is focused on the dynamics and stability issue of the matrix-converter based wind turbine generation system, which very much distinguishes itself from the systems that consist of voltage source inverters or current source inverters, where the dynamics of input and output state variables are naturally decoupled by the large-enough passive components in the dc link. Stability criterion that will guide proper design of the system is proposed and validated through numerical simulation.