bluuebunny/crossref_metadata_2025 huggingface dataset:
Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance
Authors: Eraslan Yüksel
Published: December 2024

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, and a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively.
A Smart Contract-Based Algorithm for Offline UAV Task Collaboration: A New Solution for Managing Communication Interruptions
Authors: Zhang Linchao , Hang Lei , Zu Keke , Wang Yi
Published: December 2024

Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task-coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios.
Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information
Authors: Wang Junqiao , Yu Zhongliang , Zhou Dong , Shi Jiaqi , Deng Runran
Published: December 2024

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.
Improving Satellite-Based Retrieval of Maize Leaf Chlorophyll Content by Joint Observation with UAV Hyperspectral Data
Authors: Yang Siqi , Kang Ran , Xu Tianhe , Guo Jian , Deng Caiyun , Zhang Li , Si Lulu , Kaufmann Hermann Josef
Published: December 2024

While satellite-based remote sensing offers a promising avenue for large-scale LCC estimations, the accuracy of evaluations is often decreased by mixed pixels, attributable to distinct farming practices and diverse soil conditions. To overcome these challenges and to account for maize intercropping with soybeans at different growth stages combined with varying soil backgrounds, a hyperspectral database for maize was set up using a random linear mixed model applied to hyperspectral data recorded by an unmanned aerial vehicle (UAV). Four methods, namely, Euclidean distance, Minkowski distance, Manhattan distance, and Cosine similarity, were used to compare vegetation spectra from Sentinel-2A with the newly constructed database. In a next step, widely used vegetation indices such as NDVI, NAOC, and CAI were tested to find the optimum method for LCC retrieval, validated by field measurements. The results show that the NAOC had the strongest correlation with ground sampling information (R2 = 0.83, RMSE = 0.94 μg/cm2, and MAE = 0.67 μg/cm2). Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. Here too, validation results showed a highly precise LCC estimation (R2 = 0.93, RMSE = 1.10 μg/cm2, and MAE = 1.09 μg/cm2), demonstrating that integrating UAV hyperspectral data with a random linear mixed model significantly improves satellite-based LCC retrievals.
A Long-Range and Low-Cost Emergency Radio Beacon for Small Drones
Authors: Martínez-Heredia Juana M. , Olivera Jorge , Colodro Francisco , Bravo Manuel , Arahal Manuel R.
Published: December 2024

The increasing use of unmanned aerial vehicles (UAVs) in the commercial and recreational sectors has led to a heightened demand for effective recovery solutions after a crash, particularly for lightweight drones. This paper presents the development of a long-range and low-cost emergency radio beacon designed specifically for small UAVs. Unlike traditional emergency locator transmitters (ELTs), our proposed beacon addresses the unique needs of UAVs by reducing size, weight, and cost, while maximizing range and power efficiency. The device utilizes a global system for mobile (GSM)-based communication module to transmit location data via short message service (SMS), eliminating the need for specialized receivers and expanding the operational range even in obstacle-rich environments. Additionally, a built-in global navigation satellite system (GNSS) receiver provides precise coordinates, activated only upon impact detection through an accelerometer, thereby saving power during normal operations. Experimental tests confirm the extended range, high precision, and compatibility of the prototype with common mobile networks. Cost-effective and easy to use, this beacon improves UAV recovery efforts by providing reliable localization data to users in real time, thus safeguarding the UAV investment.
Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning
Authors: Li Mingfei , Liu Haibin , Xie Feng
Published: December 2024

The performance of unmanned ground vehicle (UGV) formation is crucial for large-scale material transport. In a non-communicative environment, visual perception plays a central role in formation control. However, due to unstable lighting conditions, dust, fog, and visual occlusions, developing a high-precision visual formation control technology that does not rely on external markers remains a significant challenge in UGVs. This study developed a new UGV formation controller that relies solely on onboard visual sensors and proposed a teacher–student training method, TSTMIPI, combining the PPO algorithm with imitation learning, which significantly improves the control precision and convergence speed of the vision-based reinforcement learning formation controller. To further enhance formation control stability, we constructed a belief state encoder (BSE) based on convolutional neural networks, which effectively integrates visual perception and proprioceptive information. Simulation results show that the control strategy combining TSTMIPI and BSE not only eliminates the reliance on external markers but also significantly improves control precision under different noise levels and visual occlusion conditions, surpassing existing visual formation control methods in maintaining the desired distance and angular precision.
A DNA Data Storage Method Using Spatial Encoding Based Lossless Compression
Authors: Şatır Esra
Published: December 2024

With the rapid increase in global data and rapid development of information technology, DNA sequences have been collected and manipulated on computers. This has yielded a new and attractive field of bioinformatics, DNA storage, where DNA has been considered as a great potential storage medium. It is known that one gram of DNA can store 215 GB of data, and the data stored in the DNA can be preserved for tens of thousands of years. In this study, a lossless and reversible DNA data storage method was proposed. The proposed approach employs a vector representation of each DNA base in a two-dimensional (2D) spatial domain for both encoding and decoding. The structure of the proposed method is reversible, rendering the decompression procedure possible. Experiments were performed to investigate the capacity, compression ratio, stability, and reliability. The obtained results show that the proposed method is much more efficient in terms of capacity than other known algorithms in the literature.
Machine Learning Advances in High-Entropy Alloys: A Mini-Review
Authors: Sun Yibo , Ni Jun
Published: December 2024

The efficacy of machine learning has increased exponentially over the past decade. The utilization of machine learning to predict and design materials has become a pivotal tool for accelerating materials development. High-entropy alloys are particularly intriguing candidates for exemplifying the potency of machine learning due to their superior mechanical properties, vast compositional space, and intricate chemical interactions. This review examines the general process of developing machine learning models. The advances and new algorithms of machine learning in the field of high-entropy alloys are presented in each part of the process. These advances are based on both improvements in computer algorithms and physical representations that focus on the unique ordering properties of high-entropy alloys. We also show the results of generative models, data augmentation, and transfer learning in high-entropy alloys and conclude with a summary of the challenges still faced in machine learning high-entropy alloys today.
Revisions of the Phenomenological and Statistical Statements of the Second Law of Thermodynamics
Authors: Koczan Grzegorz Marcin , Zivieri Roberto
Published: December 2024

The status of the Second Law of Thermodynamics, even in the 21st century, is not as certain as when Arthur Eddington wrote about it a hundred years ago. It is not only about the truth of this law, but rather about its strict and exhaustive formulation. In the previous article, it was shown that two of the three most famous thermodynamic formulations of the Second Law of Thermodynamics are non-exhaustive. However, the status of the statistical approach, contrary to common and unfounded opinions, is even more difficult. It is known that Boltzmann did not manage to completely and correctly derive the Second Law of Thermodynamics from statistical mechanics, even though he probably did everything he could in this regard. In particular, he introduced molecular chaos into the extension of the Liouville equation, obtaining the Boltzmann equation. By using the H theorem, Boltzmann transferred the Second Law of Thermodynamics thesis to the molecular chaos hypothesis, which is not considered to be fully true. Therefore, the authors present a detailed and critical review of the issue of the Second Law of Thermodynamics and entropy from the perspective of phenomenological thermodynamics and statistical mechanics, as well as kinetic theory. On this basis, Propositions 1–3 for the statements of the Second Law of Thermodynamics are formulated in the original part of the article. Proposition 1 is based on resolving the misunderstanding of the Perpetuum Mobile of the Second Kind by introducing the Perpetuum Mobile of the Third Kind. Proposition 2 specifies the structure of allowed thermodynamic processes by using the Inequality of Heat and Temperature Proportions inspired by Eudoxus of Cnidus’s inequalities defining real numbers. Proposition 3 is a Probabilistic Scheme of the Second Law of Thermodynamics that, like a game, shows the statistical tendency for entropy to increase, even though the possibility of it decreasing cannot be completely ruled out. Proposition 3 is, in some sense, free from Loschmidt’s irreversibility paradox.
A Multi-Source Circular Geodesic Voting Model for Image Segmentation
Authors: Zhou Shuwang , Shu Minglei , Di Chong
Published: December 2024

Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters. To overcome these challenges, we propose a novel segmentation approach, named PolarVoting, which combines the minimal path encoding rich geometric features and CNNs which can provide efficient initialization. The introduced model involves two main steps: firstly, we leverage the PolarMask model to extract multiple source points for initialization, and secondly, we construct a voting score map which implicitly contains the segmentation mask via a modified circular geometric voting (CGV) scheme. This map embeds global geometric information for finding accurate segmentation. By integrating neural network representation with geometric priors, the PolarVoting model enhances segmentation accuracy and robustness. Extensive experiments on various datasets demonstrate that the proposed PolarVoting method outperforms both PolarMask and traditional single-source CGV models. It excels in challenging imaging scenarios characterized by intensity inhomogeneity, noise, and complex backgrounds, accurately delineating object boundaries and advancing the state of image segmentation.
Shannon Entropy Analysis of a Nuclear Fuel Pin Under Deep Burnup
Authors: Kubiński Wojciech R. , Ostrowski Jan K. , Fornalski Krzysztof W.
Published: December 2024

This paper analyzes the behavior of the entropy of a nuclear fuel rod under deep burnup conditions, beyond standard operational ranges, reaching up to 60 years. The evolution of the neutron source distribution in a pressurized water reactor (PWR) fuel pin was analyzed using the Monte Carlo method and Shannon information entropy. To maintain proper statistics, a novel scaling method was developed, adjusting the neutron population based on the fission rate. By integrating reactor physics with information theory, this work aimed at the deeper understanding of nuclear fuel behavior under extreme burnup conditions. The results show a “U-shaped” entropy evolution: an initial decrease due to self-organization, followed by stabilization and eventual increase due to degradation. A minimum entropy state is reached after approximately 45 years of pin operation, showing a steady-state condition with no entropy change. This point may indicate a physical limit for fuel utilization. Beyond this point, entropy rises, reflecting system degradation and lower energy efficiency. The results show that entropy analysis can provide valuable insights into fuel behavior and operational limits. The proposed scaling method may also serve to control a Monte Carlo simulation, especially for the analysis of long-life reactors.
Ornstein–Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines
Authors: García Fernández Jesús , Ahmad Nasir , van Gerven Marcel
Published: December 2024

Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex information flow makes its implementation in biological and neuromorphic systems challenging. This has motivated the exploration of alternative learning mechanisms that can operate locally and do not rely on exact gradients. In this work, we introduce a novel approach that leverages noise in the parameters of the system and global reinforcement signals. Using an Ornstein–Uhlenbeck process with adaptive dynamics, our method balances exploration and exploitation during learning, driven by deviations from error predictions, akin to reward prediction error. Operating in continuous time, Ornstein–Uhlenbeck adaptation (OUA) is proposed as a general mechanism for learning in dynamic, time-evolving environments. We validate our approach across a range of different tasks, including supervised learning and reinforcement learning in feedforward and recurrent systems. Additionally, we demonstrate that it can perform meta-learning, adjusting hyper-parameters autonomously. Our results indicate that OUA provides a promising alternative to traditional gradient-based methods, with potential applications in neuromorphic computing. It also hints at a possible mechanism for noise-driven learning in the brain, where stochastic neurotransmitter release may guide synaptic adjustments.
Leveraging a Candidate Assessment System to Develop an Equity-Centered School Leadership Pipeline Through a University–District Partnership
Authors: Thessin Rebecca A. , Tekleselassie Abebayehu A. , Trimmer Leslie B. , Shepard Shaun D. , Clayton Jennifer K.
Published: December 2024

The role of the school principal has garnered international significance. When it comes to student learning outcomes, the effectiveness of the principal has been recognized as being more important than the effectiveness of a single teacher. Studies also highlight the role school leadership plays in fostering equity and social justice practices in schools and communities. Yet only a small body of research exists on how to prepare leaders to lead for equity. In this paper, we will describe, analyze, and reflect on the components of one school leadership preparation program’s (SLLP’s) candidate assessment system (CAS), which guided the selection of equity-centered leadership candidates for a cohort program in a university–district partnership. We applied a qualitative content analysis to the documents we utilized to select aspiring equity-centered leaders through the program’s redesigned CAS. Our findings revealed that the content and process of the program’s CAS was aligned with many tenets of equity-centered leadership, specifically in CAS documents and in the involvement of a broad group of constituencies and partners during the design and implementation of CAS. We also uncovered a new finding outside of our framework: an emphasis on instructional leadership in our CAS documents as a key component of leading for equity. This study is likely to inform other SLPPs intending to select leadership candidates who will have the capacity to lead for equity.
A Filtered Differential Phase Shifter with High Selectivity
Authors: Fan Xiangsuo , Li Tianfeng , Feng Linping , Ma Yanfang , Xu Wenhao , Zhang Tao
Published: December 2024

In this paper, a filtered differential phase shifter with good selectivity is proposed and constructed. This study proposes and builds a filtered differential phase shifter with good selectivity. The main and reference lines in the suggested filtered differential phase shifter are structurally identical. The main line’s bandpass filtering function is achieved by a new coupled feeder, a parallel coupled line loaded with shorted branches, and a coupled resonator at the load end. It has impedance matching and additional transmission zeros/poles. By loading the coupled resonator at the load end, the selectivity of the phase shifter can be improved and the passband and transmission zeros/poles of the phase shifter can be equitably distributed by varying the coupling coefficients and the electrical lengths of the loaded branches. The reference line is a coupled resonator with the same structure and its electrical length is related to the number of degrees of phase shift. Finally, a differential phase shifter is designed and measured. The fractional bandwidth of the proposed phase shifter is about 38%, the return loss reaches −11 dB, the insertion loss is about 1.02 dB, the deviation of the simulated results in terms of the number of degrees of phase shift is about 2.5 degrees, and the deviation of the tested results is about 6 degrees. The trend of the simulation results and the actual measurement results are in good agreement.
A Systematic Study of Adversarial Attacks Against Network Intrusion Detection Systems
Authors: Sharma Sanidhya , Chen Zesheng
Published: December 2024

Network Intrusion Detection Systems (NIDSs) are vital for safeguarding Internet of Things (IoT) networks from malicious attacks. Modern NIDSs utilize Machine Learning (ML) techniques to combat evolving threats. This study systematically examined adversarial attacks originating from the image domain against ML-based NIDSs, while incorporating a diverse selection of ML models. Specifically, we evaluated both white-box and black-box attacks on nine commonly used ML-based NIDS models. We analyzed the Projected Gradient Descent (PGD) attack, which uses gradient descent on input features, transfer attacks, the score-based Zeroth-Order Optimization (ZOO) attack, and two decision-based attacks: Boundary and HopSkipJump. Using the NSL-KDD dataset, we assessed the accuracy of the ML models under attack and the success rate of the adversarial attacks. Our findings revealed that the black-box decision-based attacks were highly effective against most of the ML models, achieving an attack success rate exceeding 86% across eight models. Additionally, while the Logistic Regression and Multilayer Perceptron models were highly susceptible to all the attacks studied, the instance-based ML models, such as KNN and Label Spreading, exhibited resistance to these attacks. These insights will contribute to the development of more robust NIDSs against adversarial attacks in IoT environments.
BATG: A Backdoor Attack Method Based on Trigger Generation
Authors: Tang Weixuan , Xie Haoke , Rao Yuan , Long Min , Qi Tao , Zhou Zhili
Published: December 2024

Backdoor attacks aim to implant hidden backdoors into Deep Neural Networks (DNNs) so that the victim models perform well on clean images, whereas their predictions would be maliciously changed on poisoned images. However, most existing backdoor attacks lack the invisibility and robustness required for real-world applications, especially when it comes to resisting image compression techniques, such as JPEG and WEBP. To address these issues, in this paper, we propose a Backdoor Attack Method based on Trigger Generation (BATG). Specifically, a deep convolutional generative network is utilized as the trigger generation model to generate effective trigger images and an Invertible Neural Network (INN) is utilized as the trigger injection model to embed the generated trigger images into clean images to create poisoned images. Furthermore, a noise layer is used to simulate image compression attacks for adversarial training, enhancing the robustness against real-world image compression. Comprehensive experiments on benchmark datasets demonstrate the effectiveness, invisibility, and robustness of the proposed BATG.
Improving Art Style Classification Through Data Augmentation Using Diffusion Models
Authors: Moyano Miguel Ángel Martín , García-Aguilar Iván , López-Rubio Ezequiel , Luque-Baena Rafael M.
Published: December 2024

Classifying pictorial styles in artworks is a complex challenge due to the diversity and lack of available datasets, which often limit the performance of machine learning models. To address this issue, we propose a novel data augmentation approach using Diffusion models in contrast to traditional augmentation techniques. Our method generates new samples based on the existing data, expanding the available dataset and enhancing the generalization capability of classification models. We evaluate the effectiveness of this data augmentation technique by training deep learning models with varying proportions of augmented and real data and assessing their performance in pictorial style classification. Our results demonstrate that the proposed Diffusion model-based augmentation significantly improves classification accuracy, suggesting that it can be a viable solution for overcoming data limitations in similar applications.
The Hybrid AC-DC Dynamic Order-Reduced Modeling Method and Damping Enhancement Strategy Based on Improved ERAG
Authors: Zhou Bo , Sun Xinwei , Wei Wei , Xu Yunyang
Published: December 2024

Decentralized control offers a more effective way to avoid the complexities and coordination challenges encountered in centralized control. In the decentralized design of additional controllers for multi-infeed DC transmission systems, it is crucial to focus on the interactions among the controllers. To solve this problem, this paper primarily discusses an enhanced damping characteristic method for hybrid AC-DC systems based on an improved Effective Relative Gain Array (ERGA) index. Initially, feedback signals with better control effects on existing oscillation modes are pre-screened. Subsequently, the ERGA-based interaction index is utilized to pair these feedback signals with control locations, aiming to identify the optimal pairing scheme with the minimum index value, indicating the least interaction effect. This approach minimizes the mutual influence between loops and reduces adverse interactions among controllers. Simulations of multi-DC additional damping controllers designed using the multi-stage Linear Quadratic Regulator (LQR) method in a multi-DC system demonstrate that the optimal pairing scheme significantly outperforms both uncontrolled and poorly paired schemes in controlling low-frequency oscillations, thereby validating the optimality of the proposed method. Furthermore, various disturbances are introduced to verify the effectiveness and robustness of the proposed control strategy against low-frequency oscillations.
A Novel Architecture for Virtual Network Twin Deployment
Authors: Banisadr Amir Hossein , Hesselbach Xavier
Published: December 2024

In this paper, a novel architecture is proposed that enhances network connectivity by combining network virtualization and the digital twin approach. A virtual network twin (VNT) framework is designed to emulate the behavior of the original network within a virtualized environment. This framework provides an enhanced connection experience for users, mirroring the performance of the original network while avoiding the limitations of previous methods such as Telnet, SSH, and VPN. By integrating the network virtualization approach and the concept of digital twins, this framework can improve network visibility, security, and robustness in real-time connectivity through appropriate communication protocols and artificial intelligence (AI) methods. The application of this proposal can significantly impact key areas such as medical applications, autonomous driving, and space communications. This paper introduces the VNT architecture; its core components; requirements; and evaluation metrics, such as the accuracy of the VNT.
A Two-Stage Distributionally Robust Optimization Model for Managing Electricity Consumption of Energy-Intensive Enterprises Considering Multiple Uncertainties
Authors: Li Jiale , Du Zhaobin , Yuan Liao , Huang Yuanping , Liu Juan
Published: December 2024

Energy-intensive enterprises (EIEs), as vital demand-side flexibility resources, can significantly enhance the power system’s ability to regulate demand by participating in demand response (DR). This helps alleviate supply pressures during tight demand–supply conditions, ensuring the system’s safe and stable operation. However, due to the current level of electricity management in EIEs, their participation in demand response has disrupted the continuity of production to some extent, which may hinder the sustainability of demand-side management mechanisms. To address this issue, this paper proposes a two-stage distributionally robust optimization (DRO) model for managing production electricity in EIEs, considering multiple uncertainties. First, a production electricity load model based on the state task network (STN) is developed, reflecting the characteristics of industrial production lines. Next, a two-stage DRO model for day-ahead and intra-day electricity management is formulated, integrating an uncertainty set for distributed generation output based on the Wasserstein distance and probabilistic constraints for the day-ahead DR capacity. Finally, a cement plant in western China is used as a case study to validate the effectiveness of the proposed model. The results show that the proposed model effectively guides EIE in participating in DR while optimizing electricity costs, enabling cost savings of up to 27.7%.
Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
Authors: Choi Yunsung , Kim Dongwoo
Published: December 2024

This study explores the potential of heterogeneous hybrid Free Space Optical (FSO) and Radio Frequency (RF) cognitive networks, which feature both cooperative and economic systems. The cooperative system is defined as a heterogeneous network where the hybrid FSO/RF node possesses dedicated RF resources and shares these resources to create additional transmission opportunities. In contrast, the low-cost economic system consists of a heterogeneous network where only an RF node has RF resources, and the hybrid node shares these resources. We provide a comprehensive analysis for each system, employing stay-and-switch (SAS) and simultaneous multipacket transmission (SMT) methods to ensure a thorough understanding of its performance. As a performance measure, we investigate the stability region of the proposed cognitive and economic systems and devise a reference system without cognitive capability for comparison. Numerical evaluations indicate that the cooperative system using SMT typically outperforms the reference system, increasing stability throughput by up to 52%. However, this advantage diminishes when SAS is used or in rainy conditions. The economic model shows performance levels comparable to the reference model, particularly when incoming traffic is low and when SAS is implemented in clear or hazy environments.
Qualitative and Quantitative Impact of the National Regulation on the Business Case for Energy Communities and Collective Actions
Authors: Neumann Camilla , Türk Andreas
Published: December 2024

Energy communities (ECs) have emerged across the EU, with the Clean Energy Package providing the legal foundation for the establishment of energy communities. The actual transposition is up to the member states, leading to heterogeneous regulatory approaches. The national frameworks often provide financial support mechanisms like local grid tariffs, along with regulatory limitations on size and membership, which significantly influence the business case for ECs. This paper analyzes the economic feasibility of ECs under different national frameworks in Spain, Portugal, France, Italy, and Austria. Two scenarios were evaluated by means of a case study, consisting of households and two public entities. The first scenario compares national incentives under uniform conditions, while the second considers country-specific factors like PV irradiation and electricity costs. Results show that energy communities can only partially decouple electricity prices from market dynamics. The study concludes that financial incentives lead to adequate financial savings for the members in most countries; however, the level of incentive does not necessarily relate to the number of established energy communities. Electricity grid benefits could be created by promoting demand response measures in the EC.
Engine and Emission Performance of Renewable Fuels in a Small Displacement Turbocharged Diesel Engine
Authors: Chiavola Ornella , Matijošius Jonas , Palmieri Fulvio , Recco Erasmo
Published: December 2024

A reduction in emissions in transportation is paramount to achieve full compliance with the European Union’s 2050 targets. In this framework, a great boost to the carbon dioxide (CO2) emission of internal combustion engines fueled by petroleum-derived fuels can be obtained through the adoption of biomass-derived fuels that can be employed in conventional series production engine vehicles. This paper presents the results of an experimental activity on a two-cylinder turbocharged common rail diesel engine, whose main application is for urban mobility, fueled with renewable fuels: Neste MY Renewable Diesel and Eni HVOlution. Aimed at analyzing the potential employment of renewable fuels as drop-in alternative fuels, the engine performance and emissions were investigated under fixed settings of the injection parameters, in the complete range of the engine speed, at the full pedal position. The comparison with the data from tests in which the engine was fueled with fossil diesel highlighted minimal differences in the performance outcomes, while significant differences were observed in the emissions results. In more detail, there were reduced carbon monoxide (CO) emissions (HVO produced using Ecofining™ technology retained better behavior in relation to HVO produced using the NEXBTL™ technology), advantages in hydrocarbon (HC) and nitrogen oxide (NOx) emissions (HVO from NESTE Oil performed better than HVO from ENI), a decrease in the particle mass and number emissions (HVO from Ecofining™ technology was characterized by a lower particle number and court mean diameter in relation to HVO from the NEXBTL™ technology). The results highlight that an optimization of the engine settings based on the specific properties of each fuel could allow us to take full advantage of these fuels in reducing the environmental impact of cars.
Analysis of Greenhouse Gas Emissions and Energy Consumption Depending on the Material and Construction Solutions and the Energy Carrier Used—A Case Study
Authors: Nawalany Grzegorz , Sokołowski Paweł , Jakubowski Tomasz , Atilgan Atilgan
Published: December 2024

The article presents the results of research on the impact of material and construction solutions on energy demand and greenhouse gas emissions. Field research was conducted in an existing free-standing greenhouse located in southern Poland. The research period covered the entire calendar year. The measurement data were used in the next step to validate the computational model using the numerical method of elementary balances. The data distribution was also analyzed in terms of basic statistics. The validated and verified model was used in the further part of the analysis to conduct computer simulations for three variants, differing in terms of material and construction solutions. The variants included: no foundation insulation, extruded polystyrene (XPS) insulation and the use of single-chamber polycarbonate panels with thermal insulation of the foundations. The obtained results showed a high agreement between theoretical and real data (85–89% for the coefficient of determination (R2) and 84–88% for the GOF method). In the case of variant 1, which in terms of material and construction solutions corresponded to the actual construction of the greenhouse, it was found that the annual energy demand for heating purposes amounted to 153,234 kWh/year. In variant 2, in which additional thermal insulation relative to the zero state was used, the energy demand for heating purposes was lower and amounted to 147,704 kWh/year. The lowest heat load was characteristic of variant 3, in which 116,294 kWh/year was required to cover heating needs. The variant with polycarbonate and foundation insulation brought energy savings of 24% and a reduction of CO2 emissions by 24%. In addition, replacing fuel from hard coal with natural gas brought significant benefits, reducing pollutant emissions by 51%. The paper is a new approach to the use of the mentioned numerical method for the assessment of gaseous pollutant emissions in this type of building based on numerical simulations of energy consumption.
Research on the Internal Flow Mechanisms and Pressure Fluctuation Characteristics of Seawater Reverse Osmosis Energy Recovery Turbines
Authors: Bai Xiaobang , Yang Junhu , Yuan Xueping , Chen Xiaozhuang , Qi Bing
Published: December 2024

Energy recovery turbines (ERTs) play an important role in reducing the energy consumption of seawater reverse osmosis (SWRO). The internal flow loss mechanisms and hydraulic vibration characteristics of a centrifugal ERT model used in SWRO were studied under multiple conditions. The results show that the high-efficiency range of the ERT tends toward high-flow conditions. The energy loss is mainly concentrated at the tongue in the volute, which is due to the generation of flow separation vortices. There is a flow separation phenomenon on the suction surface side of the blade, trailing under the 0.8Qd condition. The energy loss of the ERT is the lowest under the 1.0Qd condition, because the interaction between the fluid and the blades is the weakest. There are vortices formed by flow impact on the blade pressure surface under the 1.2Qd condition. There are vortices on the blade suction face, which are generated by fluid separation. The closer the relative liquid flow angle is to the blade settle angle, the smaller the energy loss is in the impeller. Therefore, the ERT should first reduce rated flow appropriately before hydraulic design, as this is beneficial for efficient operation under multiple conditions.
Performance Analysis of a Micro-Photovoltaic Concentrator Designed for Automotive Applications
Authors: El Ayane Salima , Ahaitouf Ali
Published: December 2024

This research paper delves into the potential use of solar energy as an alternative energy source for future vehicles. The study introduces a system that overcomes the limitations of traditional solar panels by achieving a reduced thickness of less than 35 mm, while acknowledging the challenges faced by vehicles, such as the inability to maintain a fixed orientation towards the sun and frequent shading from surrounding objects. To tackle these challenges, our system incorporates the design of an asymmetrical and extended polynomial lens and optimizes it to widen the acceptance angle of incident sunlight, enabling the solar panels to capture a wider range of solar radiation, even when the vehicle is not ideally aligned with the sun. The goal of this innovative design is not only to maximize energy output in urban conditions, ensuring efficient solar utilization despite shading challenges, but also to maintain a compact, lightweight structure suitable for installation on vehicle rooftops and competitive with ordinary PV panels. Additionally, our system is a tracking and heat spreader-free structure. This simple structure enables cheaper mass production and the lightweight nature of the structure results in affordable manufacturing and assembly processes. Through collaboration with micro-fabrication, macro-electronic industries, and micro-LED technologies, our system is a strong candidate for a low-cost, high-efficiency system. The results show an optical efficiency of around 52.53% for incident rays at a 45° angle, with the remaining rays captured by adjacent lenses resulting in a total optical efficiency around 76%.
Study on the Influence of an Internal Stiffening System on the Structural Strength of the Semi-Submersible Structures for a Floating Offshore Wind Turbine
Authors: Dou Hao Yu , Jeong Han Koo , Jiang Jian Lun
Published: December 2024

This study presents the development and comparative analysis of a new Y-type floating offshore wind turbine platform based on the existing T-type model. Utilizing advanced simulation tools, such as MSC, Patran and Nastran 2022.3, FEGate For Ship 5.0, and Ansys AQWA 2021 R2, extensive evaluations are conducted on the structural strength, stability, and dynamic response of both the T-type and the newly proposed Y-type platforms. In this research, the structural optimization algorithm based on the above simulation tools is adopted, and its results are compared with preoptimization results to demonstrate the improvements made in design precision and reliability. Results indicate that the Y-type model achieves a maximum reduction in von Mises stress by 30.21 MPa compared to the T-type model, and its heave and pitch motion amplitudes are reduced by 4.3412 m and 4.9362°, respectively, under extreme sea state conditions. Through structural optimization using the Nastran SOL200 module, the column structure weight is reduced by 11.31%, meeting the strength requirements while enhancing efficiency. These findings highlight the Y-type platform’s improved performance and provide robust design strategies for floating offshore wind turbines in deep-water regions, crucial for advancing global renewable energy solutions. Future research should focus on the impacts of different marine conditions on platform performance and consider integrating new materials or innovative design enhancements to further optimize platform functionality. Additionally, due to potential limitations from model simplification, emphasis on real-world testing and validation under operational conditions is recommended. Overall, this research clarifies the differences in structural performance between the T-type and Y-type floating platforms and introduces an improved platform design approach, offering valuable insights and guidance for the future development of floating offshore wind turbine technology.
Impact of Multi-Valve Exhaust Gas Recirculation (EGR) System on Nitrogen Oxides Emissions in a Multi-Cylinder Engine
Authors: Krakowian Konrad
Published: December 2024

Exhaust gas recirculation (EGR) systems, in addition to catalytic reactors, are now widely used in reciprocating internal combustion engines to reduce oxides of nitrogen (NOx) in the exhaust gases. They are characterized by the fact that part of the exhaust gas from the exhaust manifold is recycled and directed to the intake manifold through a special valve. This valve, depending on the current engine load and velocity, doses an appropriate amount of exhaust gas which, with each new charge, is fed to the individual engine cylinders. In addition, the positioning of the valve has a significant effect on the formation of nitrogen oxides in the exhaust gas from individual engine cylinders, which is due to the uneven distribution of exhaust gas into the intake manifold channels. Tests were carried out on a power unit equipped with a symmetrical intake manifold with a centrally located EGR valve. The article presents the results of tests on a system in which each cylinder was supplied with a separate EGR valve. This solution made it possible to charge each cylinder with the same mass of recirculated exhaust gas, which was dependent on engine velocity and load. The exhaust nitrogen oxides emissions were measured for the originally manufactured system and compared with the multi-valve system. The results confirmed the need for individual selection of the dose of recirculated exhaust gas for particular cylinders, as the multi-valve system equalized the levels of nitrogen oxides emissions in the exhaust gases coming from individual cylinders of the internal combustion engine.
Integration of Plant Pomace into Extruded Products: Analysis of Process Conditions, Post-Production Waste Properties and Biogas Potential
Authors: Soja Jakub , Oniszczuk Tomasz , Vaskina Iryna , Combrzyński Maciej , Wójtowicz Agnieszka
Published: December 2024

Waste streams from cereal-based food production processes, rich in organic matter and carbohydrates, have untapped potential for biogas production. This study uniquely investigated the extrusion-cooking process conditions, physical properties and biogas efficiency of snack pellets enriched with plant pomace (apple, chokeberry, pumpkin, flaxseed and nigella seeds) at different levels (10, 20 and 30%), produced using a single-screw extruder-cooker. The highest efficiency obtained in the extrusion-cooking process (18.20 kg/h) was observed for pellets with the addition of 30% flaxseed pomace. The SME value during the entire process was in the range of 0.015–0.072 kWh/kg. New insights into the interaction between the inclusion of pomace, the physical properties of the extrudate and the anaerobic fermentation efficiency were obtained. The results show that 30% chokeberry extrudate maximized methane production (51.39% gas), demonstrating a double innovation: improving snack pellet quality and converting food waste into renewable energy.
A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
Authors: Dang Xuan-Toan , Vu Binh-Minh , Nguyen Quynh-Suong , Tran Thi-Thuy-Minh , Eom Joon-Soo , Shin Oh-Soon
Published: December 2024

Federated learning (FL) has emerged as a decentralized, cutting-edge framework for training models across distributed devices, such as smartphones, IoT devices, and local servers while preserving data privacy and security. FL allows devices to collaboratively learn from shared models without exchanging sensitive data, significantly reducing privacy risks. With these benefits, the deployment of FL over wireless communication systems has gained substantial attention in recent years. However, implementing FL in wireless environments poses significant challenges due to the unpredictable and fluctuating nature of wireless channels. In particular, the limited energy resources of mobile and IoT devices, many of which operate on constrained battery power, make energy management a critical concern. Optimizing energy efficiency is therefore crucial for the successful deployment of FL in wireless networks. However, existing reviews on FL predominantly focus on framework design, wireless communication, and security/privacy concerns, while paying limited attention to the system’s energy consumption. To bridge this gap, this article delves into the foundational principles of FL and highlights energy-efficient strategies tailored for various wireless architectures. It provides a comprehensive overview of FL principles and introduces energy-efficient designs, including resource allocation techniques and communication architectures, tailored to address the unique challenges of wireless communications. Furthermore, we explore emerging technologies aimed at enhancing energy efficiency and discuss future challenges and opportunities for continued research in this field.