In this letter, we propose a novel algorithm for sparse L-shaped array that addresses gain/phase uncertainties and off-grid direction of arrival estimation using compressed sensing theory. Firstly, the receiving signal of the sparse array is structured into an Errors in Variables model. Then, a novel algorithm termed IOMP-TLS is proposed for off-grid signal reconstruction and gain/phase uncertainties estimation based on an enhanced greedy algorithm. Finally, the simulations show that our proposed algorithm is superior to many other methods in estimation performance.

Phased array antennas, with high directivity and low sidelobe levels, are extensively employed in radar and communication systems. As the array scale increases, the complexity and cost of controlling each element individually are increased significantly. To overcome this challenge and improve the efficiency, one of the effective engineering solutions is subarray partitioning. Traditional clustering algorithms, such as K-means, require a predefined number of subarrays, and limits the flexibility, especially in scenarios with complex geometries or irregular distributions. A subarray partitioning approach is presented in this paper using the Iterative Self-Organizing Data Analysis (ISODATA) algorithm, and the proposed scheme dynamically adjusts the number of subarrays based on the geometric characteristics of the array. According to numerical experiments results, good performance of sidelobe suppression, beamforming and partitioning flexibility have been achieved, and shows better characteristic compared with the K-means. This algorithm has potential application in large-scale phased arrays.

Abstract: Regional short-term power load forecasting is essential for developing regional energy-saving optimization strategies and establishing smart buildings. The popularity of smart meters can collect a large number of effective load data, and meteorological factors also have an important impact on regional power load. This paper presents a CNN-GRU short term power load forecasting approach incorporating an attention mechanism. The goal is to identify the characteristic relationships between regional power load, time, and meteorological data by building a deep neural network model, thereby enabling short term predictions of regional power load. The integration of the attention mechanism strengthens network model’s ability to filter data features, helping to improve prediction accuracy. Ultimately, It is determined whether the suggested approach is effective through a dataset of regional power load and meteorological data.

The Global-Local Attention Transformer (GLAT) model is utilized for the effective integration of global and local features in the classification and detection of defects within Geographic Information System (GIS) imagery. Comprised of a Vision Transformer (ViT) backbone and a Global-Local Attention (GGLA) module, GLAT employs localized pooling and convolutions to enhance channel interactions. Input features are segmented, followed by the application of pooling and convolutions, culminating in the generation of attention weights via a sigmoid function. This process refines the features by highlighting pivotal information and diminishing irrelevant details. Achieved through experimentation, a classification accuracy of 83.4% is reported for GLAT, outpacing traditional Convolutional Neural Networks (CNNs). Additionally, the incorporation of the GGLA module across various ViT configurations results in an average accuracy enhancement of 0.9 percentage points. Such outcomes underscore GLAT’s capability in bolstering the identification of both global structures and local details, signifying its potential as a novel solution in the realm of GIS defect detection and the advancement of technology in this domain.

In this letter, a beam downtilt reconfigurable linear antenna array is presented for the demand of the electrical downtilt of 5G/6G macro base station antennas. The antenna is capable of fine-angle beam direction reconfiguration, allowing for improved adaptability in various environmental conditions. A hierarchical network design is employed, which incorporates essential elements such as phase shifter modules, power dividers, and couplers. These fundamental components are used to construct a five-element reconfigurable feeding network for the antenna array. The designed network ensures stable phase shifts and excellent impedance matching performance. By integrating the feeding network with the antenna elements, the system achieves electronic beam tilt adjustment across four states, enabling the linear array to tilt downward within a range of 6 - 12° around 3.5 GHz. This design provides enhanced beam control, making it suitable for next-generation macro base station applications in 5G and 6G networks.

In this paper a method of Laser surface smoothing of 3D printed polymer material based on a nanosecond Laser is introduced. The adequate Laser enabled reducing surface roughness by 65% on a 25x25 mm² surface and therefore improving the printing quality of metal layer deposited on top to fabricate HF passive devices. This paper also examines the impact of Infrared (IR) sintering combined with thermal treatment in order to improve the electrical conductivity of printed silver layers by micro dispensing technique (nScrypt technology) up to 30.5 S/µm. A patch antenna was printed on smoothed PEEK substrate and gave promising results (gain of 5.4 dBi @ 6.8 GHz).

Cyber-physical threats to unmanned aerial vehicles (UAVs) involve manipulating control, communication, and sensor data through evil actions that an intruder can carry out. Drone cyber-physical systems rely on wireless connections, which can be attacked in many ways. The intruder can take advantage of the inherent vulnerabilities in the Global Positioning System (GPS) to spoof it and generate a fake signal that is transmitted to the receiver. The routes of UAV dynamic movements that are predicted, hijacked, and mitigated are presented in this work. The dynamic and adaptive GPS spoofing threat detection and mitigation system for networked UAVs has been proposed in this research. The suggested design technique finds the deviation in the flying path at different altitudes that cyber threats could cause. Drone route design based on multi-logit regression has been suggested to consider the spoofing errors between the waypoints of the expected and spoofed (hijacked) path to predict elevations and angles. By generating the appropriate thrusts of the drone’s rotor and then responding with a new rectified path to a spoof position detection, a proportional and derivative (PD) control has been developed for the attitude and position control of drones. For the swarm of drones, blockchain-based delegated proof of location (DPoL) as a consensus mechanism with GPS spoofing mitigation capability, at various waypoints and intervals, has been proposed. The effectiveness of the proposed work has been tested with simulation work supported by UAV testbed.

To address the challenges posed by frequent source and load fluctuations in existing loop closing current calculation methods, this paper proposes an online loop closing current calculation method that considers source and load uncertainties. First, a dual-stack dynamic monitoring system is utilized to obtain real-time voltage and current variations before and after disturbances. Second, Thevenin's theorem is employed to build an equivalent model of the distribution network, simplifying the complex network into a combination of an independent voltage source and a series impedance. Then, the steady-state loop closing current is calculated based on the open-circuit voltage and equivalent impedance at both sides of the loop closing point. Next, the optimal frequency method is applied to determine the equivalent impedance and attenuation time constant at a specific frequency, achieving accurate calculation of the transient loop closing current. Finally, simulations are conducted to model the fluctuations in distributed generation and load, analyzing the steady-state and transient loop closing currents. The simulation results demonstrate that the proposed method accurately captures the effects of source and load fluctuations on the loop closing current in dynamic environments, with minimal calculation error, indicating its high practicality.

The inherent intermittency and fluctuation of renewable energy generation can lead to uncertainty in electricity carbon emission intensity. This uncertainty poses challenges to the low-carbon scheduling of integrated energy systems and impacts the economic benefits of system operation. To this end, this paper proposes an optimization model for integrated electricity-heat-gas energy system operation considering uncertain indirect carbon emission intensity. First, a regional integrated electricity-heat-gas energy system model considering the tiered carbon trading mechanism is formulated. Then, polyhedral uncertainty sets are introduced to analyze and qualify the uncertainties in renewable energy and indirect carbon emission intensity. Finally, a stochastic optimization method is introduced to deal with the uncertainty in the integrated energy scheduling model. Extensive case studies are conducted to verify the competence of the proposed models in enhancing the environmentally friendly and economic benefits of integrated energy systems.

This paper presents the development of Electromagnetic Transient (EMT) and EMT-(transient stability)TS co-simulation models of power grid with photovoltaic (PV) plants. The EMT models are used to study the impact of fault disturbance events on PV plants in a bulk grid. The step-by-step approach for developing the EMT-model from the TS-model of a grid is discussed, followed by an exhaustive comparison among different grid and PV plant models to understand the effectiveness of those for replicating the event accurately. The TS-model of the Western Electricity Coordinating Council (WECC) grid, and information about the Angeles forest fault event have been utilized in this case study. The objective is to simulate the event near the fault location as well as in one of the affected large plants where partial generation loss occurred. First, a region of the grid is developed in the EMT domain to replicate the events sequentially near the fault location, and match the voltage near affected PV plant. Next, various models of the PV plant are compared in the EMT-model to demonstrate the necessity for a high-fidelity model of the PV plant that accurately replicates the partial generation loss observed during the fault event.

A 0.6-V low-power VCO by using TSMC 0.18-μm CMOS process has been proposed. For lowering phase noise, injection-locked enhancement and bulk-driven technologies are proposed. The injection-locked enhancement technology reuses second order harmonic signals of the oscillation signals through the injection-locked capacitors with the enhancement circuit. The buck-driven technology provides extra transconductance for the cross-coupled pair. The proposed technologies can enlarge the fundamental signal power of the oscillation signals for lowering the phase noise. The measured results show the phase noise at 1 MHz offset frequency from the carrier signal is -118.16 dBc/Hz with 1/f3 corner frequency of 23 kHz while the control voltage is 0.6 V. The frequency tuning range is from 2.33 to 2.37 GHz under power consumption 1.3 mW. The figure of merit at 1 MHz offset frequency from the carrier signal is -184.43 dBc/Hz.

not-yet-known not-yet-known not-yet-known unknown Abstract text. Recently, the rotating target detector has been widely used in remote sensing images. However, the existing methods often use a large number of preset rotating anchor to cover the target, without taking into account the interference caused by the shape change of the aerial target with high aspect ratio during the training, which is mainly reflected in the following two points: 1) missing high-quality positive samples that cover the critical information of slender targets 2) the gradient with the sharply changing causes training instability. In order to meet these challenges, we have designed a sample assignment strategy that can adapt to targets with different aspect ratios, and a training strategy that can more stably and accurately regression the bounding box with high aspect ratio. Specifically, first of all, the designed Shape Adaptive Label Assignment strategy introduces a weight function based on the IoU. Secondly, Gradient Equalization Regression Loss function is proposed to effectively alleviate the gradient instability of large aspect ratio targets during regression and make the model have better convergence. A series of experiments on DOTA and HRSC2016 datasets have confirmed the effectiveness of the proposed strategy.

Efficient ventilation is a crucial aspect in ensuring the efficient and secure operation of mines. Central to this ventilation system are axial fans, whose maintenance directly impacts their operational efficiency, longevity, and safety. Despite the integral role these fans play, a comprehensive understanding of how ventilation design influences their maintenance remains to be fully explored in existing literature. This study, therefore, seeks to delve deeper into this issue. The paper aims to highlight the significant role ventilation design plays in the maintenance and overall performance of axial fans. We hypothesize that optimized ventilation design can not only reduce the frequency of maintenance interventions but also enhance the fan's lifespan and performance. The research will encompass in-depth examination of current ventilation designs and their respective maintenance challenges. It will identify the key parameters that influence fan efficiency, maintenance, and life span. The research will explore strategies for enhancing ventilation design to improve maintenance outcomes. By bridging this gap in knowledge, the study's findings can offer valuable insights for mining industries, informing the development of more effective maintenance strategies. This can lead to more efficient mining operations, reduced downtime, and enhanced safety protocols, which is most important in the mining sector.

In the modern power system, the intermittency of distributed renewable energy and user-side load fluctuations challenge grid stability. Using IoT data, the distribution regulation center directs user resources to engage in demand response through the network. However, deep coupling between the power and communication networks leads to issues like communication delays and packet loss, increasing regulation costs and affecting robustness. To address these issues, this paper proposes a demand response autonomous decision-making method considering regulation command communication delay and reliability. Initially, we develop a cloud-edge-end collaborative framework for autonomous demand response, considering the coupling of power and communication networks. We then define an optimization problem to minimize the weighted sum of regulation costs and grid losses, considering user-side resource outputs and grid constraints. Utilizing Information Gap Decision Theory (IGDT), we address uncertainties in renewable outputs like wind and PV, forming a robust optimization model. We introduce a deep actor-critic (DAC)-based method that incorporates communication delay and packet loss impacts, building a multi-objective Markov decision process (MDP) to optimize and adjust power outputs. This dual cooperative DAC algorithm iteratively refines decisions, enhancing algorithm convergence. Simulation results confirm the method significantly reduces regulation costs and grid losses, improving economic efficiency and robustness.

Insulators play a vital role in transmission lines, and their defect detection using drone inspection technology can be adversely affected by complex environments, including influence of complex lighting, complex background and complex defects. To address the issue of complex lighting first, this paper innovatively designs an adversarial network based on the decomposition and fusion of structure and brightness layers. This network is further enhanced with a brightness balance loss strategy to improve imaging of insulators and their defects in overexposed and underexposed scenarios, resulting in an average PSNR improvement of 0.4 and an SSIM increase of 0.29.Next, to tackle the problem of complex backgrounds, an axial attention feature extraction backbone is designed, considering the distribution characteristics of insulators and their defects, to extract targets from complex backgrounds. Finally, for the multi-scale defect target in complex defects, we propose the Res-PANet, a feature fusion structure based on multi-scale residual connections, which enhances the network’s detection accuracy in scenarios with multiple targets. Experimental results demonstrate that the detection model, after preprocessing for complex lighting conditions, achieves higher detection accuracy, with an average precision of 89.93%. This underscores the model’s strong practical value for engineering applications.

In this paper, a Packaged Tapered Balun (PTB) with enclosed metal shield is proposed to achieve multi-octave bandwidth operation for power combining applications such as push-pull power amplifiers and harmonics suppressed combiners. By establishing a parasitic transmission line equivalent circuit model of double-sided Shielded Tapered Lines, a suspended balance compensating branch and a common mode suppressed Balanced-to-Balanced transformer is introduced to improve the balance of PTB. Based on the proposed technique, a 0.6-6.5GHz PTB is demonstrated. According to the measured results, the designed balun exhibits better than -10dB return loss, -0.5dB insertion loss, ±0.15dB amplitude balance and ±0.4° phase balance.

This research proposes a novel spatio-temporal approach that integrates ConvLSTM (Convolutional Long Short-Term Memory) networks and GNNs(Graph Neural Networks) to model and predict wind power generation and its impact on power flow. By using ConvLSTM models, the work achieves an R^2 value of 0.977 indicating high accuracy in forecasting wind generation dynamics across various temporal and spatial scales. Meanwhile, the GNN model, achieving an R^2 of 0.771, shows a viable approach to modeling power grid performance without the need for traditional iterative Newton-Raphson load flow methods. While the GNN model could be further optimized, it does mark a substantial improvement in scaling machine learning for real-time grid management. By harmonizing these models, this research addresses critical gaps in current approaches to the integration of renewable energy sources into power grids, aligning with ongoing efforts to enhance grid reliability and efficiency in the face of increasing renewable penetration.

This work aims to analyze the impact of usage patterns of multiple and different types of Electric Vehicles (EVs) and schedulable home (Smart Homes) on system reliability and suggest measures to improve system reliability. Distributed Generation (DG) using renewables is a salient feature of a smart grid. However, Renewable Energy Sources (RES) are highly intermittent. During times when energy available from RESs and EVs is lower than the demand, energy may be purchased from the grid at Real Time Price (RTP). Demand Response (DR) is performed on two scales: Incentive-Based Demand Response (IBDR) on the consumer side and Price Based DR (PBDR) on the grid side. Controllable loads are shifted based on RTP of grid at that hour to ensure energy balance in the system. Part of the curtailable load can be shed based on the availability of power depending on the usage patterns of individual customers to ensure reliability. The novelty of this work is analysing the Impact of Usage Patterns with multiple and different types of EVs and implementing DR incorporating individual customer usage pattern for ensuring reliability of the system