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Low-earth orbit (LEO) satellite networks are an important part of the sixth-generation mobile communication system (6G) network, which overcomes the blind spots in ground-based station coverage. However, the limited onboard computing capability and battery capacity cause the problems of extended mission duration and high-energy consumption; therefore, edge computing is introduced in LEO satellite networks, and its key technology is computational offloading. To address the problems of dynamic changes in the intersatellite environment and high-dimensional action space during computational offloading, we propose a genetic algorithm (GA) and deep deterministic policy gradient (DDPG)-based offloading algorithm for edge computing in space-based LEO satellite networks—the GA-DDPG algorithm. The constant change in a satellite edge computing environment will result in sparse rewards (system overhead) and a lack of DDPG exploration. In this study, a GA is introduced into the DDPG algorithm. First, the selection operator of the GA is used to enable the DDPG algorithm to adapt to a changing satellite environment. Second, to address the problem of unstable convergence of the DDPG algorithm owing to the increasing dimension of the action space, the diversity exploration and redundancy of the GA population are used to improve the stability of the convergence of the DDPG algorithm. In this study, a system model, including a space-based LEO satellite constellation structure, mission model, computational model, and load model, is constructed; in addition, a system overhead, weighted by the residual rate of battery energy of edge satellites, is designed to model the problems of minimization of mission delay, minimization of mission energy consumption, and optimization of computational resource allocation as a Markov process. First, the offloading algorithm obtains edge satellites that are visible to the local satellites by analyzing the constraints for establishing links between the satellites. Second, the channel is modeled, and the intersatellite path loss and Doppler shift are modeled, following which the intersatellite transmission rate is obtained. Subsequently, information on the computational capacity and battery energy remaining of each satellite is obtained through the intersatellite link, and a monitoring cycle is set to timely correct the satellite network topology structure. Third, the intersatellite link parameters and mission information are transmitted to the GA-DDPG computational offloading algorithm; various strategies are generated using the GA; elite strategies are input into the replay buffer of the DDPG algorithm; the less adapted strategies are input into the actor network of the DDPG algorithm; the strategies in the replay buffer are used to train their strategies, and the strategies with improved adaptation after training are inserted into the GA population, following which the optimal strategy is determined from the strategy population, and the strategy is updated using the GA to generate the next generation strategy population. The simulation results demonstrate that the GA-DDPG unloading algorithm reduces the computational load of space-based low-orbit satellite networks, and the algorithm confirms its stability (low volatility) through the variance of the computational load. The delay and energy consumption are lower than those of the DDPG and GA unloading algorithms, respectively, increasing the convergence speed and stability of the algorithm compared with the DDPG unloading algorithm.

Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly in complex backgrounds and with diverse lesion morphologies. To address these challenges, we proposed Unet with Vision Mamba and ConvNeXt (VMC-Unet), an asymmetric segmentation model for quantitative analysis of tomato blight. Built on the Unet framework, VMC-Unet integrated a parallel feature-aware backbone combining ConvNeXt, Vision Mamba, and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance spatial feature focusing and multi-scale information processing. During decoding, Vision Mamba was hierarchically embedded to accurately recover complex lesion morphologies through refined feature processing and efficient up-sampling. A joint loss function was designed to optimize the model’s performance. Extensive experiments on both tomato epidemic and public datasets demonstrated VMC-Unet superior performance, achieving 97.82% pixel accuracy, 87.94% F1 score, and 86.75% mIoU. These results surpassed those of classical segmentation models, underscoring the effectiveness of VMC-Unet in mitigating over-segmentation and under-segmentation while maintaining high segmentation accuracy in complex backgrounds. The consistent performance of the model across various datasets further validated its robustness and generalization potential, highlighting its applicability in broader agricultural settings.

Abstract The development of cyber‐physical power systems raises concerns about the data quality issue of phasor measurement units (PMUs). Low signal‐to‐noise ratios (SNRs) and data losses caused by malicious electromagnetic interference, false data injections, and equipment malfunctioning may jeopardize the data integrity and availability necessary for power system monitoring, protection, and control. To ensure grid resiliency, this paper proposes a robust fast PMU measurement recovery (RFMR) algorithm based on improved singular spectrum analysis (SSA) of Hankel structures. It utilizes single or multiple channels of PMU time‐series to restore the problematic phasor measurements with low‐SNR noises and data losses. Additionally, the traditional singular value decomposition (SVD) and Tucker decomposition (TD) in RFMR are replaced by randomized SVD (RSVD) and sequential TD (STD) to reduce the computational complexity in single‐channel and multi‐channel RFMR, respectively. Numerical case studies demonstrate that the proposed algorithm can recover the noise‐contaminated measurements with higher accuracy than existing methods, such as matrix/tensor decomposition approaches and robust principal component analysis (RPCA), and effectively complement the missing data with the observed measurements corrupted by low SNRs. Moreover, the latency margins of various power system synchrophasor application scenarios can be satisfied with the reduced computational complexity.

Safe and reliable generator circuit breaker (GCB) failure protection can ensure effective isolation between generator and system under different fault conditions to avoid generator damage. In this paper, aiming at the existing problems of the GCB failure protection criterions based on current quantities, the voltage quantities are combined to constitute the improved GCB failure protection. Through fault sequence network analysis, it is concluded that there are voltage phasor differences between the two sides of GCB for the normally separated phases, while the values are zero for the rejected phases. On this basis, a GCB failure protection criterion based on the voltage phasor difference is proposed for the generators equipped with PTs at the two sides of GCB. After a generator fault occurs, in the process of de-excitation, the terminal voltage drops quickly except GCB three-phase rejection because of the voltage support from the system side. Moreover, under the GCB three-phase rejection situation, the loads cannot be effectively shed and no terminal voltage increase is caused compared with other GCB operation situations. Thus, the protection criterions based on the voltage variation are proposed to distinguish GCB three-phase rejection situation, including the voltage changing rate criterion and the increased voltage mutation criterion. They are used for generators equipped with only one terminal PT. Simulation results of PSCAD/EMTDC verify the effectiveness of the proposed methods.

Abstract Extensive adoption of Information and Communication Technologies makes power systems and communication systems more tightly coupled to form cyber‐physical power systems. It causes power systems to be growingly susceptible to cyber contingencies. Cyber contingencies may sabotage measurement availability, further disrupt the observability analysis in state estimation, and thus threaten the stable operation of power systems. This paper investigates the impact of cyber contingencies on measurement availability. A workflow is presented to achieve accurate and meticulous impact identification under intricate communication architectures with various cyber contingencies. And a set of indicators are proposed to quantitatively evaluate measurement availability. Case studies demonstrate that the proposed method is effective in identifying and assessing the impact of cyber contingencies on measurement availability. Meanwhile, the proposed workflow and indicators can also be employed to dynamically evaluate the system's resilience in protecting measurement transmission against hypothetical cyber contingencies, which could benefit the offline planning and online operation of cyber‐physical power systems

The climatic parameters within greenhouse facilities, such as temperature, humidity, and light, exert significant influence on the growth and yield of crops, particularly seedlings. Therefore, it is crucial to establish an accurate predictive model to monitor and adjust the greenhouse microclimate for optimizing the greenhouse environment to the fullest extent. To precisely forecast the greenhouse microclimate and assess the suitability of nursery environments, this study focuses on greenhouse environmental factors. This study leveraged open-source APIs to acquire meteorological data, integrated a model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), and utilized the sparrow search algorithm to optimize model parameters, consequently developing a time series greenhouse microclimate prediction model. Furthermore, Squeeze-and-Excitation (SE) Networks were employed to enhance the model’s attention mechanism, enabling more accurate predictions of environmental factors within the greenhouse. The predictive results indicated that the optimized model achieved high precision in forecasting the greenhouse microclimate, with average errors of 0.540 °C, 0.936%, and 1.586 W/m2 for temperature, humidity, and solar radiation, respectively. The coefficients of determination (R2) reached 0.940, 0.951, and 0.936 for temperature, humidity, and solar radiation, respectively. In comparison to individual CNN or LSTM models, as well as the back-propagation (BP) neural network, the proposed model demonstrates a significant improvement in predictive accuracy. Moreover, this research was applied to the greenhouse nursery environment, demonstrating that the proposed model significantly enhanced the efficiency of greenhouse seedling cultivation and the quality of seedlings. Our study provided an effective approach for optimizing greenhouse environmental control and nursery environment suitability, contributing significantly to achieving sustainable and efficient agricultural production.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural networks (CNNs) and LSTMs to capture spatial–temporal patterns from the data and improve the model’s ability to recognize dynamic features of the disease. In addition, we introduced a Squeeze-and-Excitation (SE) Network attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epoch) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the occurrence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers make accurate management decisions and reduce disease impacts through optimal fungicide application timing.

Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model based on transfer learning with convolutional neural networks (CNNs). In the naturally growing vineyards, 195,120,135 samples of Grade I, Grade II, and Grade III red grapes were collected using a Canon EOS 550D camera, and a data set of 1800 samples was obtained using data enhancement technology. Then, the CNN transfer learning method was used to transfer the pre-trained AlexNet, VGG16, GoogleNet, InceptionV3, and ResNet50 network models on the ImageNet image dataset to the red grape image grading task. By comparing the classification performance of the CNN models of these five different network depths with fine-tuning, ResNet50 with a learning rate of 0.001 and a loop number of 10 was determined to be the best feature extractor for red grape images. Moreover, given the small number of red grape image samples in this study, different convolutional layer features output by the ResNet50 feature extractor were analyzed layer by layer to determine the effect of deep features extracted by each convolutional layer on Support Vector Machine (SVM) classification performance. This analysis helped to obtain a ResNet50 + SVM red grape external appearance grading model based on the optimal ResNet50 feature extraction strategy. Experimental data showed that the classification model constructed using the feature parameters extracted from the 10th node of the ResNet50 network achieved an accuracy rate of 95.08% for red grape grading. These research results provide a reference for the online grading of red grape clusters based on external appearance quality and have certain guiding significance for the quality and efficiency of grape industry circulation and production.

Abstract Shutting the window is usually the last resort in mitigating environmental noise, at the expense of natural ventilation. We describe an active sound control system fitted onto the opening of the domestic window that attenuates the incident sound, achieving a global reduction in the room interior while maintaining natural ventilation. The incident sound is actively attenuated by an array of control modules (a small loudspeaker) distributed optimally across the aperture. A single reference microphone provides advance information for the controller to compute the anti-noise signal input to the loudspeakers in real-time. A numerical analysis revealed that the maximum active attenuation potential outperforms the perfect acoustic insulation provided by a fully shut single-glazed window in ideal conditions. To determine the real-world performance of such an active control system, an experimental system is realized in the aperture of a full-sized window installed on a mockup room. Up to 10-dB reduction in energy-averaged sound pressure level was achieved by the active control system in the presence of a recorded real-world broadband noise. However, attenuation in the low-frequency range and its maximum power output is limited by the size of the loudspeakers.

By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems, cyber attackers can mislead the control center into taking wrong actions. From the viewpoint of machine learning, normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space. Hence, a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data. However, malicious synchrophasors occur infrequently in practice. It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training. For most existing detectors, if they are directly trained with highly imbalanced datasets, their performances may severely deteriorate. In this paper, a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing, Bagging-based ensemble learning, and the widely recognized eXtreme Gradient Boosting (XGBoost) classifier. Experiments show that although fewer malicious instances are provided, the proposed detector is still capable of detecting malicious synchrophasors.

Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.

The push for greater urban sustainability has increased the urgency of the search for noise mitigation solutions that allow for natural ventilation into buildings. Although a viable active noise control (ANC) solution with up to 10 dB of global attenuation between 100 Hz and 1000 Hz was previously developed for an open window, it had limited low-frequency performance below 300 Hz, owing to the small loudspeakers used. To improve the low-frequency attenuation, four passive radiator-based speakers were affixed around the opening of a top-hung ventilation window. The active control performance between 100 Hz and 700 Hz on a single top-hung window in a full-sized mock-up apartment room was examined. Active attenuation came close to the performance of the passive insulation provided by fully closing the window for expressway traffic and motorbike passing noise types. For a jet aircraft flyby, the performance of active attenuation with the window fully opened was similar to that of passive insulation with fully closed windows. In the case of low-frequency compressor noise, active attenuation’s performance was significantly better than the passive insulation. Overall, between 8 dB and 12 dB of active attenuation was achieved directly in front of the window opening, and up to 10.5 dB of attenuation was achieved across the entire room.

Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

Active noise control (ANC) is a re-emerging technique to mitigate noise pollution. To reduce the noise power in large spaces, multiple channels are usually required, which complicates the implementation of ANC systems. In this paper, we separate the multichannel ANC problem into two subproblems, where the subproblem of computing the control filter is usually an underdetermined problem. Therefore, we could leverage the underdetermined system to simplify the ANC system without degrading the noise reduction performance. For a single incidence, we compare the conventional fully-coupled (pseudoinverse) multichannel control with the colocated (diagonal) control method and find that they can achieve equivalent performance, but the colocated control method is less computationally intensive. Furthermore, the underdetermined system presents an opportunity to control noise from multiple incidences with one common fixed filter. Both the full-rank and the overdetermined optimal control filters are realized. The performance of these control methods was analyzed numerically with the Finite Element Method (FEM) and the results validate the feasibility of the full-rank and overdetermined optimal control methods, where the latter could even offer more robust performance in more complex noise scenarios.

Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.

The rapid growth of Internet applications has made communication anonymity an increasingly important or even indispensable security requirement. Onion routing has been employed as an infrastructure for anonymous communication over a public network, which provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. However, existing onion routing protocols usually exhibit poor performance due to repeated encryption operations. In this paper, we first present an improved anonymous multi-receiver identity-based encryption (AMRIBE) scheme, and an improved identity-based one-way anonymous key agreement (IBOWAKE) protocol. We then propose an efficient onion routing protocol named AIB-OR that provides provable security and strong anonymity. Our main approach is to use our improved AMRIBE scheme and improved IBOWAKE protocol in onion routing circuit construction. Compared with other onion routing protocols, AIB-OR provides high efficiency, scalability, strong anonymity and fault tolerance. Performance measurements from a prototype implementation show that our proposed AIB-OR can achieve high bandwidths and low latencies when deployed over the Internet.

The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping controllers is rarely involved. A simultaneous robust coordinated multiple damping controller design strategy for a power system incorporating power system stabilizer (PSS), static var compensator (SVC) POD and DFIG POD is presented in this paper. This coordinated damping control design strategy is addressed as an eigenvalue-based optimization problem to increase the damping ratios of oscillation modes. Both local and inter-area electromechanical oscillation modes are intended in the optimization design process. Wide-area phasor measurement unit (PMU) signals, selected by the joint modal controllability/ observability index, are utilized as SVC and DFIG POD feedback modulation signals to suppress inter-area oscillation modes. The robustness of the proposed coordinated design strategy is achieved by simultaneously considering multiple power flow situations and operating conditions. The recently proposed Grey Wolf optimizer (GWO) algorithm is adopted to efficiently optimize the parameter values of multiple damping controllers. The feasibility and effectiveness of the proposed coordinated design strategy are demonstrated through frequency-domain eigenvalue analysis and nonlinear time-domain simulation studies in two modified benchmark test systems. Moreover, the dynamic response simulation results also validate the robustness of the recommended coordinated multiple damping controllers under various system operating conditions.