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Search Results (929)

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Keywords = 5G base station

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12 pages, 5365 KiB  
Article
A 100 MHz 3 dB Bandwidth, 30 V Rail-to-Rail Class-AB Buffer Amplifier for Base Station ET-PA Hybrid Supply Modulator
by Min-Ju Kim, Donghwi Kang, Gyujin Choi, Seong-Jun Youn and Ji-Seon Paek
Electronics 2025, 14(15), 3036; https://doi.org/10.3390/electronics14153036 - 30 Jul 2025
Viewed by 174
Abstract
This paper presents the first hybrid supply modulator (HSM) designed for envelope tracking power amplifiers (ET-PAs) in base station applications. The focus is on a rail-to-rail Class-AB linear amplifier (LA) optimized for high-voltage and wide-bandwidth operation. The LA is designed using 130 nm [...] Read more.
This paper presents the first hybrid supply modulator (HSM) designed for envelope tracking power amplifiers (ET-PAs) in base station applications. The focus is on a rail-to-rail Class-AB linear amplifier (LA) optimized for high-voltage and wide-bandwidth operation. The LA is designed using 130 nm BCD technology, utilizing Laterally Diffused Metal-Oxide Semiconductor (LDMOS) transistors for high-voltage operation and incorporating shielding MOSFETs to protect the low-voltage devices. The circuit utilizes dual power supply domains (5 V and 30 V) to improve power efficiency. The proposed LA achieves a bandwidth of 100 MHz and a slew rate of +1003/−852 V/μs, with a quiescent power consumption of 0.89 W. Transient simulations using a 50 MHz bandwidth 5G NR envelope input demonstrate that the proposed HSM achieves a power efficiency of 83%. Consequently, the proposed HSM supports high-output (100 W) wideband 5G NR transmission with enhanced efficiency. Full article
(This article belongs to the Special Issue Analog/Mixed Signal Integrated Circuit Design)
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20 pages, 2352 KiB  
Article
Three-Dimensional Physics-Based Channel Modeling for Fluid Antenna System-Assisted Air–Ground Communications by Reconfigurable Intelligent Surfaces
by Yuran Jiang and Xiao Chen
Electronics 2025, 14(15), 2990; https://doi.org/10.3390/electronics14152990 - 27 Jul 2025
Viewed by 205
Abstract
Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base [...] Read more.
Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article
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30 pages, 435 KiB  
Article
Dombi Aggregation of Trapezoidal Neutrosophic Number for Charging Station Decision-Making
by Mohammed Alqahtani
Symmetry 2025, 17(8), 1195; https://doi.org/10.3390/sym17081195 - 26 Jul 2025
Viewed by 182
Abstract
In engineering and decision sciences, trapezoidal-valued neutrosophic fuzzy numbers (TzVNFNs) have become effective tools for managing imprecision and uncertainty in multi-attribute group decision-making (MAGDM) problems. This work introduces accumulation operators based on the Dombi t-norm [...] Read more.
In engineering and decision sciences, trapezoidal-valued neutrosophic fuzzy numbers (TzVNFNs) have become effective tools for managing imprecision and uncertainty in multi-attribute group decision-making (MAGDM) problems. This work introduces accumulation operators based on the Dombi t-norm (DTn) and Dombi t-conorm (DTcn) specifically designed for TzVNFNs. These operators enhance the flexibility, consistency, and fairness of the aggregation process. To demonstrate their practical applicability, we propose three novel geometric aggregation operator’s namely, the trapezoidal-valued neutrosophic fuzzy Dombi weighted geometric (TzVNFDWG), the trapezoidal-valued neutrosophic fuzzy Dombi ordered weighted geometric (TzVNFDOWG), and the trapezoidal-valued neutrosophic fuzzy Dombi hybrid Geometric (TzVNFDHG) operators. These are incorporated into a systematic MAGDM framework to support the selection of optimal locations for charging stations. Comparative analysis with current decision-making methodologies highlights the efficacy and benefits of the suggested method. The suggested method provides a flexible and mathematically based choice framework designed for uncertain condition. Full article
(This article belongs to the Section Mathematics)
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12 pages, 249 KiB  
Data Descriptor
Time Series Dataset of Phenology, Biomass, and Chemical Composition of Cassava (Manihot esculenta Crantz) as Affected by Time of Planting and Variety Interactions in Field Trials at Koronivia, Fiji
by Poasa Nauluvula, Bruce L. Webber, Roslyn M. Gleadow, William Aalbersberg, John N. G. Hargreaves, Bianca T. Das, Diogenes L. Antille and Steven J. Crimp
Data 2025, 10(8), 120; https://doi.org/10.3390/data10080120 - 23 Jul 2025
Viewed by 594
Abstract
Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen [...] Read more.
Cassava is the sixth most important food crop and is cultivated in more than 100 countries. The crop tolerates low soil fertility and drought, enabling it to play a role in climate adaptation strategies. Cassava generally requires careful preparation to remove toxic hydrogen cyanide (HCN) before its consumption, but HCN concentrations can vary considerably between varieties. Climate change and low inputs, particularly carbon and nutrients, affect agriculture in Pacific Island countries where cassava is commonly grown alongside traditional crops (e.g., taro). Despite increasing popularity in this region, there is limited experimental data about cassava crop management for different local varieties, their relative toxicity and nutritional value for human consumption, and their interaction with changing climate conditions. To help address this knowledge gap, three field experiments were conducted at the Koronivia Research Station of the Fiji Ministry of Agriculture. Two varieties of cassava with contrasting HCN content were planted at three different times coinciding with the start of the wet (September-October) or dry (April) seasons. A time series of measurements was conducted during the full 18-month or differing 6-month durations of each crop, based on destructive harvests and phenological observations. The former included determination of total biomass, HCN potential, carbon isotopes (δ13C), and elemental composition. Yield and nutritional value were significantly affected by variety and time of planting, and there were interactions between the two factors. Findings from this work will improve cassava management locally and will provide a valuable dataset for agronomic and biophysical model testing. Full article
20 pages, 13715 KiB  
Article
Dynamic Reconfiguration for Energy Management in EV and RES-Based Grids Using IWOA
by Hossein Lotfi, Mohammad Hassan Nikkhah and Mohammad Ebrahim Hajiabadi
World Electr. Veh. J. 2025, 16(8), 412; https://doi.org/10.3390/wevj16080412 - 23 Jul 2025
Viewed by 200
Abstract
Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations [...] Read more.
Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article
(This article belongs to the Special Issue Power and Energy Systems for E-Mobility, 2nd Edition)
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20 pages, 8592 KiB  
Article
Spatial Differentiation in the Contribution of Innovation Influencing Factors: An Empirical Study in Nanjing from the Perspective of Nonlinear Relationships
by Chengyu Wang, Renchao Luo and Lingchao Zhou
Buildings 2025, 15(14), 2565; https://doi.org/10.3390/buildings15142565 - 21 Jul 2025
Viewed by 264
Abstract
The agglomeration characteristics of innovation spaces reflect the intrinsic mechanisms of regional resource integration and collaborative innovation. Investigating the contributions of influencing factors to innovation space agglomeration and their spatial differentiation has significant implications for improving urban innovation quality. Taking the Nanjing central [...] Read more.
The agglomeration characteristics of innovation spaces reflect the intrinsic mechanisms of regional resource integration and collaborative innovation. Investigating the contributions of influencing factors to innovation space agglomeration and their spatial differentiation has significant implications for improving urban innovation quality. Taking the Nanjing central urban area as a case study, this research applied gradient boosting regression trees (GBRT) and multiscale geographically weighted regression (MGWR) models to explore the contributions of influencing factors to innovation space agglomeration and its spatial differentiation. Findings demonstrated that (1) Innovation platforms and patents emerged as the most significant driving factors, collectively accounting for 54.8% of the relative contributions; (2) The contributions of influencing factors to innovation space agglomeration exhibited marked nonlinear characteristics, specifically categorized into five distinct patterns: Sustained Growth Pattern, Growth-Stabilization Pattern, Growth-Decline Pattern, Global Stabilization Pattern, and Global Decline Pattern. The inflection thresholds of marginal effects across factors ranged from approximately 12% to 55% (e.g., 40% for metro stations, 13% for integrated commercial hubs); (3) Each influence factor’s contribution mechanism showed pronounced spatial heterogeneity across different regions. Based on these discoveries, governments should optimize innovation resource allocation according to regional characteristics and enhance spatial quality to promote efficient resource integration and transformation. This research provides a novel perspective for understanding innovation space agglomeration mechanisms and offers actionable references for urban policymakers to implement context-specific innovation economic development strategies. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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18 pages, 2884 KiB  
Review
Advances in Solidification Technologies of Lunar Regolith-Based Building Materials Under Extreme Lunar Environments
by Jun Chen and Ruilin Li
Buildings 2025, 15(14), 2543; https://doi.org/10.3390/buildings15142543 - 19 Jul 2025
Viewed by 449
Abstract
With the launch of the Artemis program and the International Lunar Research Station project, the construction of lunar bases has emerged as a global research focus. In situ manufacturing technologies for robust lunar regolith-based building materials are critical to ensuring building safety under [...] Read more.
With the launch of the Artemis program and the International Lunar Research Station project, the construction of lunar bases has emerged as a global research focus. In situ manufacturing technologies for robust lunar regolith-based building materials are critical to ensuring building safety under the Moon’s extreme environmental conditions. This paper reviews the relevant advancements in two areas: solidification technologies for lunar regolith-based construction materials and simulation techniques of extreme lunar environments. This review reveals that, although significant advancements have been made in solidification technologies, the development of lunar environment simulation technologies, particularly for 1/6 g gravity, has lagged, thereby hindering the assessment of the in situ applicability of these solidification methods. To address these limitations, this paper introduces a newly developed comprehensive lunar extreme environment simulation system based on superconducting magnetic suspension technology and its potential applications in lunar regolith-based construction material solidification. This review highlights the current progress and challenges in solidification techniques for lunar regolith-based building materials, aiming to enhance researchers’ attention to the extreme environmental conditions on the lunar surface. Full article
(This article belongs to the Special Issue Research on Sustainable Materials in Building and Construction)
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23 pages, 6991 KiB  
Article
Comparing the Accuracy of Soil Moisture Estimates Derived from Bulk and Energy-Resolved Gamma Radiation Measurements
by Sonia Akter, Johan Alexander Huisman and Heye Reemt Bogena
Sensors 2025, 25(14), 4453; https://doi.org/10.3390/s25144453 - 17 Jul 2025
Viewed by 310
Abstract
Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost [...] Read more.
Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference 40K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGRG–M and spectrometry-based 40K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from 40K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGRG–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGRG–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGRG–M than on 40K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGRG–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article
(This article belongs to the Special Issue Sensors in Smart Irrigation Systems)
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20 pages, 2995 KiB  
Article
Standardized Workflow and Analytical Validation of Cell-Free DNA Extraction for Liquid Biopsy Using a Magnetic Bead-Based Cartridge System
by Shivaprasad H. Sathyanarayana, Sarah B. Spracklin, Sophie J. Deharvengt, Donald C. Green, Margery D. Instasi, Torrey L. Gallagher, Parth S. Shah and Gregory J. Tsongalis
Cells 2025, 14(14), 1062; https://doi.org/10.3390/cells14141062 - 11 Jul 2025
Viewed by 760
Abstract
Circulating cell-free DNA (cfDNA) is an important biomarker for various cancer types, enabling a non-invasive testing approach. However, pre-analytical variables, including sample collection, tube type, processing conditions, and extraction methods, can significantly impact the yield, integrity, and overall quality of cfDNA. This study [...] Read more.
Circulating cell-free DNA (cfDNA) is an important biomarker for various cancer types, enabling a non-invasive testing approach. However, pre-analytical variables, including sample collection, tube type, processing conditions, and extraction methods, can significantly impact the yield, integrity, and overall quality of cfDNA. This study presents a comprehensive analytical validation of a magnetic bead-based, high-throughput cfDNA extraction system, with a focus on assessing its efficiency, reproducibility, and compatibility with downstream molecular applications. The validation was performed using a range of sample types: synthetic cfDNA spiked into DNA-free plasma, multi-analyte ctDNA plasma controls, Seraseq ctDNA reference material in a plasma-like matrix, extraction specificity controls, residual clinical specimen from patients, and samples from healthy individuals stored at room temperature or 4 °C for up to 48 h to assess stability. Extracted cfDNA was analyzed for concentration, percentage, and fragment size, using the Agilent TapeStation. Variant detection was evaluated using a next-generation sequencing (NGS) assay on the Seraseq ctDNA reference material. The results demonstrated high cfDNA recovery rates, consistent fragment size distribution (predominantly mononucleosomal and dinucleosomal), minimal genomic DNA (gDNA) contamination, and strong concordance between detected and expected variants in reference materials. The workflow also showed robust performance under different study parameters, variable sample conditions, including sample stability and integrity. Together, these findings confirm the efficiency and reliability of the evaluated cfDNA extraction system and underscore the importance of standardized pre-analytical workflows for the successful implementation of liquid biopsy for early cancer detection, therapeutic monitoring, and improved patient outcomes. Full article
(This article belongs to the Special Issue Current Status and Future Challenges of Liquid Biopsy)
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16 pages, 419 KiB  
Article
Energy-Efficient Resource Allocation for Near-Field MIMO Communication Networks
by Tong Lin, Jianyue Zhu, Junfan Zhu, Yaqin Xie, Yao Xu and Xiao Chen
Sensors 2025, 25(14), 4293; https://doi.org/10.3390/s25144293 - 10 Jul 2025
Viewed by 318
Abstract
With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is [...] Read more.
With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article
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27 pages, 5427 KiB  
Article
Beyond Traditional Public Transport: A Cost–Benefit Analysis of First and Last-Mile AV Solutions in Periurban Environment
by Félix Carreyre, Tarek Chouaki, Nicolas Coulombel, Jaâfar Berrada, Laurent Bouillaut and Sebastian Hörl
Sustainability 2025, 17(14), 6282; https://doi.org/10.3390/su17146282 - 9 Jul 2025
Viewed by 349
Abstract
With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. [...] Read more.
With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article
(This article belongs to the Section Sustainable Transportation)
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15 pages, 1816 KiB  
Article
A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas and Christos Bouras
Appl. Sci. 2025, 15(13), 7603; https://doi.org/10.3390/app15137603 - 7 Jul 2025
Viewed by 446
Abstract
Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) [...] Read more.
Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) in (5G) environments. To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over 99%. Overall, our framework is capable of capturing long-term temporal features and spatial features from 5G user data, taking a significant step towards a holistic approach in data-driven resource allocation and traffic prediction in 5G networks. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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28 pages, 2982 KiB  
Article
Site Selection Evaluation of Pumped Storage Power Station Based on Multi-Energy Complementary Perspective: A Case Study in China
by Hui Zhao and Yanqi Xu
Energies 2025, 18(13), 3549; https://doi.org/10.3390/en18133549 - 4 Jul 2025
Viewed by 275
Abstract
Pumped storage power stations (PSPSs, hereafter) have garnered significant attention due to their critical roles in peak regulation and frequency modulation, contributing to the advancement of global new energy and power systems. Site selection of power stations is the key to successful operation. [...] Read more.
Pumped storage power stations (PSPSs, hereafter) have garnered significant attention due to their critical roles in peak regulation and frequency modulation, contributing to the advancement of global new energy and power systems. Site selection of power stations is the key to successful operation. In this paper, a new site selection index system and evaluation model covering hydrogeology, construction, social economy, and energy grid are proposed to meet the multi-energy complementary needs of new energy sources. The index system was constructed by the literature review and Delphi method, the subjective and objective weights were calculated by the G1 method and Gini weighting method, and the combined weights were obtained by modifying the G1 method based on the Gini coefficient. The VIKOR method was used to evaluate the pre-selected sites, determine the best scheme, and verify the stability of the results. The results of the case study show that the Centian station site in Guangdong Province is the most promising. This study provides decision support for the construction of pumped storage power plants and has important significance for the development of clean energy and new power systems. Full article
(This article belongs to the Section A: Sustainable Energy)
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23 pages, 3869 KiB  
Article
Fault Diagnosis Method for Pumped Storage Units Based on VMD-BILSTM
by Hui Li, Qinglin Li, Hua Li and Liang Bai
Symmetry 2025, 17(7), 1067; https://doi.org/10.3390/sym17071067 - 4 Jul 2025
Viewed by 273
Abstract
The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. [...] Read more.
The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article
(This article belongs to the Section Engineering and Materials)
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29 pages, 3101 KiB  
Article
Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS
by Ural Mutlu and Yasin Kabalci
Sensors 2025, 25(13), 4140; https://doi.org/10.3390/s25134140 - 2 Jul 2025
Viewed by 420
Abstract
Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation [...] Read more.
Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article
(This article belongs to the Special Issue Communication, Sensing and Localization in 6G Systems)
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