The shift from fossil fuel to electric based propulsion in the waterborne transport sector has been sped up by recent policies aiming to reduce the sector emissions. This trend creates highly electrified vessels, with needs for energy storage systems (ESS) to satisfy the power demand affordably and to increase the on-board grid reliability and efficiency. Initial industry efforts have been put in the study and integration of high energy density ESS solutions, mainly electrochemical batteries. However, other innovative ESS, with different capabilities, have not been yet fully addressed. It is the case of Fast Response Energy Storage Systems (FRESS), such as Supercapacitors, Flywheels, or Superconducting Magnetic Energy Storage (SMES) devices. The EU granted project, POwer StoragE IN D OceaN (POSEIDON) will undertake the necessary activities for the marinization of the three mentioned FRESS. This study presents the design process followed in the POSEIDON project for the definition of an SMES suitable for maritime operation. First, the boundary conditions imposed by the marine environment, and the potential on-board applications of the SMES will be established. Next, the technological options: superconducting material, cooling system, coil fabrication and magnet topology which have been selected for this specific system will be presented.

In M-ary Aggregate Spread Pulse Modulation (M-ASPM) [Nikitin, A. V. and Davidchack, R. L. (2022) IEEE Access, 10 96652-96671], while an entire transmitted packet can consist of constant-envelope pulses with the same magnitude, different segments of the packet can employ considerably distinct processing gains, and have significantly different sensitivities to the carrier frequency offset (CFO) between the transmitter and the receiver. Thus a relatively short, low-gain front portion of the packet that is insensitive to a large CFO can be used for robust asynchronous detection of the packet at low computational cost. Further, this detection can be combined with measuring the CFO within the desired range and with desired precision. Then the subsequent costly processing of the high-gain, CFO-sensitive segments of the packet (e.g., those allocated to the synchronization and the payload) needs to be implemented only after the detection, and can be performed without undue deterioration in the quality of the received signal due to the CFO. However, since the contrast in the processing gains between different portions of the packet can exceed two orders of magnitude, matching the detection sensitivity with that of the synchronization and decoding of the payload, while maintaining the portion of the packet dedicated to the detection relatively small, represents a significant challenge. The main objective of this paper is to provide a comprehensive description of the detection algorithm that meets this challenge, together with detailed explanation of the procedures and the tools employed in implementation of its steps.

This paper proposes a conceptual design for a novel antenna system operating at 5.8 GHz, featuring a vertical three-layer configuration. The system utilizes Rogers 3003™ material (ϵr ≈ 3.0, tan δ ≈ 0.0013) with a thickness of 1.52 mm. The design includes a non-metallic bottom layer, a middle layer housing the main antenna and ground, and a top layer incorporating a triangular structure for beam control. This triangular structure rotates from −180 • to +180 • in steps of 20 • relative to its initial position, enabling directional control of the beam from right to left or vice versa. This innovative approach leverages Rogers 3003™ for its highfrequency capabilities and low signal loss. The proposed system integrates probe feed technology and mechanical rotation to achieve dynamic beam steering and optimize antenna performance. Simulation using Ansys HFSS has been employed to validate functionality and optimize both the antenna's surface position and the triangular layer's relative position for enhanced signal reception and transmission efficiency across the range of triangular rotations. This process identifies the optimal placement of the triangular layer relative to the antenna's layers to maximize performance under specified conditions.

Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaIoT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.

This study presents a systematic review of 89 blockchain-related publications from 2003 to 2024. Utilizing Latent Dirichlet Allocation (LDA) topic modeling, we identified seven primary research themes: blockchain platforms, smart contracts, decentralized applications (DApps), consensus mechanisms, security and privacy, scalability, and blockchain governance. We explore the relationships among these themes and propose a conceptual framework for the blockchain ecosystem. The analysis reveals that blockchain technology is rapidly evolving but faces scalability, security, and regulatory challenges. We suggest future research directions, including cross-chain technology, integration with emerging technologies, and user experience optimization.

This study investigates the advanced machine learning models' regression, XGBoost, and RNNs, for predicting the retailer's behavior concerning economic features to enhance pattern discovery. The two-year dataset from a major dairy company shows that the combination of economic indicators such as inflation rates and seasonal variations with machine learning models significantly improves prediction accuracy. The XGBoost model, RNNblended in particular, reveals higher-level intelligence in relating complicated retail patterns and, therefore, helps to manage inventory and optimize the supply chain better. Our results convey the power of adopting machine learning models with economic features, statistical models, purchasing patterns, and considering seasons and holidays in periods of the year actionable insights to organizations regarding customer demand prediction, inventory overstocking, and stockout reduction, thus, supporting marketing and operational efficiency improvement.

Vehicular communication systems can be used to improve the safety level of road users by exchanging safety/warning messages. In this paper, we propose an antenna for a pedestrian-to-vehicle (P2V) device that provides a safety service to road workers on the highway or the road environment. A metasurface antenna system was fabricated and tested first in a controlled laboratory environment in an anechoic chamber. The device is designed to be worn close to the body of the road worker and in this framework the specific absorption rate (SAR) has to be evaluated and compared to the threshold values recommended by standards. Field tests results in an environment representative of the highway are conducted at the Transpolis test tracks are also provided to validate the performances of our system. It is shown that the proposed antenna system consisting of two monopoles backed by a metasurface reflector is an excellent candidate for P2V communications based on IEEE 802.11p systems with desirable radiation pattern, polarization, and safety limits.

The paper presents relatively simple formulations of the problem of acoustic scattering by elastic shells immersed in fluids, which can serve as a basis for efficient numerical models. The full rigorous formulation of the problem, which involves the Helmholtz equations for acoustic pressures in the fluids and the Navier equation for 3D displacements in the elastic material, is reduced to a boundary value problem only for the Helmholtz equations with effective boundary conditions relating the boundary pressures and normal displacements on both sides of the shell. To that end, the thin elastic shell is regarded as a neighborhood of its midsurface and the boundary values of the elastic quantities (displacements and stresses) are expressed via their values at the midsurface. The shell thickness is considered a small parameter. Special consideration is given to the cases of flooded and hollow shells. Under certain conditions, the problem is reduced to a system of hypersingular integral equations, appropriate for solving by the Boundary Element Method (BEM). The resulting numerical models are validated by the comparison with the exact solutions for the case of spherical elastic shells. In particular, the BEM numerical solutions reproduce the resonant peaks and dips of the exact solutions.

Vehicular Ad-Hoc Networks (VANETs) has drawn great attention due to their potential role in enabling Intelligent Transportation System (ITS). VANETs using a connected-car technology forms an important part of ITS as it provides communication between different neighboring vehicles and infrastructure. The architecture of VANETs includes vehicles and road side units (RSU) to provide secure communication and preventing its architecture from dangerous attacks various algorithms are discussed. A comparison between impacts of attacks on security and privacy of VANET is discussed. Therefore, we comprehensively presented the classification of attacks on the basis of their behavior, trust, architecture and infrastructure. Then their possible cryptographic solutions and analysis between them will be demonstrated. To ensure safety and security in VANETs different type of cryptographic schemes are introduced and their working are discussed. Three main solutions are discussed that are further applied with new technologies. As we have noticed from recent studies the growth in research and the application of these schemes in different projects and real time applications.

Vehicular Ad-Hoc Networks (VANETs) has drawn great attention due to their potential role in enabling Intelligent Transportation System (ITS). VANETs using a connected-car technology forms an important part of ITS as it provides communication between different neighboring vehicles known as Vehicle-to-Vehicle Communication (V2V). In this paper, we have proposed an integrated simulation tool for modeling and testing traffic scenarios and V2V based applications. This integrated simulated environment is designed by combining two software packages, i.e., Simulation of Urban Mobility (SUMO) as traffic simulator and Network Simulator (NS3) for building dynamic network of nodes. It also enables us to implement and verify if a traffic management algorithm generates the desired effect. For evaluating our simulator, a vehicle crash prediction algorithm has been designed and tested which predicts the risk of a crash and alerts the vehicles to prevent collisions.

Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys in this field focus on individual methods, lacking a comprehensive overview of the entire generative process. This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output. Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation. Our survey reviews the latest developments and technological trends in human motion video generation across three primary modalities: vision, text, and audio. By covering over two hundred papers, we offer a thorough overview of the field and highlight milestone works that have driven significant technological breakthroughs. Our goal for this survey is to unveil the prospects of human motion video generation and serve as a valuable resource for advancing the comprehensive applications of digital humans. A complete list of the models examined in this survey is available in Our Repository.

With the growing concern about climate change and the rapid advancement of technology, integrating electric vehicles (EVs) into power systems, particularly within low voltage (LV) distribution networks, is becoming increasingly vital to ensure reliable and sustainable grid operation. This significant paradigm shift necessitates careful consideration of the impact of home charging on the grid, including potential issues such as power quality degradation, voltage deviation, and the overloading of distribution transformers. Under-voltages, especially at the far end of the feeder, can significantly undermine the network's hosting capacity. An active voltage constraint management approach for EVs has been implemented to effectively manage the clustering of EVs within residential power distribution networks, aimed at enhancing the grid's hosting capacity while accounting for realistic household electricity demand. Additionally, the substantial impact on transformer health and power losses is carefully analyzed. The results indicate that the hosting capacity of the studied network could be notably increased through the proposed EV charging demand-shifting strategy. Moreover, the negative impacts on transformer health could be significantly mitigated, leading to a 34.57% improvement in life expectancy.

Magneto-mechanical transmitters (MMTs) are of active interest for ultra-low-frequency (ULF) communication, as they can significantly reduce the power consumption and size requirements compared to conventional ULF antennas. Typical MMTs produce ULF signals through either oscillatory or continuous rotational motion of permanent magnets. In this paper, we present an adaptive driving circuit that uses a negative impedance converter (NIC) to autonomously sustain motion for both resonant and continuous rotation MMTs without the need for sensors or active tuning. The driving circuit adapts seamlessly to a wide range of resonant frequencies and rotational speeds, ensuring consistent performance across different MMT configurations without altering the system's inertia or stiffness. Additionally, it features a saturable gain mechanism that regulates the oscillation amplitude and rotational speed using DC voltage control, which can be used for amplitude and frequency modulation.

This paper addresses the challenge of modeling distributed tendon tensions and friction losses in tendon-driven continuum manipulators, with a particular focus on cardiac ablation catheters. Tendon compliance and friction between the tendons and the inner channels of the catheter's sheath introduce several undesired effects, such as motion history-dependent frictional dynamics (friction hysteresis) and non-uniform tension profiles along the catheter's backbone. These factors adversely affect model prediction accuracy, motion precision, and control performance. To tackle these issues, we employ Cosserat theory to model the dynamics of tendon-driven ablation catheters, and incorporate the LuGre friction model and tendon compliance into the nonlinear partial differential equations (PDEs) that govern catheter behavior. We present a comprehensive framework for simulating catheter dynamics, including geometrically exact calculations of dynamic friction, along with associated tendon tensions as functions of arc length, without relying on prior assumptions about the model states. In actuation-based experiments, our friction model achieved a 38.89% improvement in distal end positioning prediction over the frictionless model. Validation through simulation and empirical experiments demonstrates that our model effectively replicates the complex, motion history-dependent frictional dynamics observed during the loading and unloading actuation phases that are inherent to the class of tendon-driven robotic systems.

This research explores the application of possibility theory in the stock market, aiming to improve trading outcomes by integrating various technical indicators. The study focuses on the Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), and Moving Average Crossover (MAC) to generate actionable buy, sell, or hold signals. By using possibility theory to combine these indicators, the research seeks to provide a more accurate assessment than individual indicators alone. Results indicate that the possibility theory-based approach significantly enhances returns while mitigating risk compared to traditional methods.

This study examines the training needs in digital technologies of companies in the Valencian Community, in the context of the growing importance of digital transformation. Using a mixed methodology combining in-depth interviews and surveys, the perceptions, obstacles and training demands of more than 100 innovation-intensive companies with a track record of more than 5 years were analysed. The results reveal a significant gap between awareness of the importance of digitalisation and the eKective implementation of digital strategies. Participating companies demand practical, contextualised and management-led training, with a preference for blended learning and short durations. The study provides recommendations to adapt the training oKer to the specific needs of the Valencian business fabric, thus contributing to the literature on digital transformation in regional contexts.

The global climate crisis has accelerated the search for sustainable energy alternatives, positioning green hydrogen as a promising solution for the decarbonisation of various industrial sectors. This article takes a comprehensive look at the role of green hydrogen in Chile's energy transition, analysing its potential to reduce greenhouse gas emissions, promote sustainable economic development and position the country as a world leader in clean energy. It assesses the World Bank's recent investment of US$150 million in the 'Green Hydrogen Facility in Chile' programme, exploring its implications for the Chilean energy industry and the Latin American and Caribbean region. The study addresses the technical, economic, political and social aspects of green hydrogen development in Chile, including technological challenges, market opportunities, necessary public policies and expected socioeconomic impacts. It analyses the current energy context in Chile, characterised by an increasing share of renewable energies, and projects the country's potential to become a leading exporter of green hydrogen globally. It also examines the role of green hydrogen in the decarbonisation of key sectors such as mining, transport and heavy industry, as well as its potential to drive the creation of green jobs and the development of new value chains. The article concludes with a critical assessment of the prospects for green hydrogen in Chile, offering recommendations to maximise its benefits and mitigate the risks associated with its large-scale implementation.

The integration of deep learning into signal processing has changed how we decode & classify signals. This applies to areas, including neuroscience and wireless communication systems. In this paper, we together three new research studies that use deep learning techniques to improve signal processing. First, we look at convolutional neural networks (s). These are for decoding electrooculogram (EEG and local field potential (L) signals. The results show much better accuracy and efficiency in brain-computer interface (BCI) systems. Also, using these networks on IBM's TrueNorth neuromorphic chip shows it is possible to do real-time, exceptionally low power signal processing for neurobionics. Next, the paper reviews deep neural networks (DNNs) for channel decoding in communication systems. This study shows DNNs can reach near-optimal decoding performance, especially when using structured codes like polar codes. This study shows DNNs can reach near-optimal decoding performance, especially when using structured codes like polar codes. The networks can even do well with data they have not seen before. But there are some issues too. The curse of dimensionality and the difficulty of scaling these networks for longer codewords are important challenges discussed. Finally, we examine an adaptive ensemble deep learning model made for signal detection in Orthogonal Frequency Division Multiplexing (OFDM) systems. This model tackles big challenges in channel estimation and compensation by adjusting to changing conditions of multi-path channels. Because of this adjustment, there are major improvements in signal reliability & fewer symbol error rates (SER). Through these advanced methods, this paper highlights how deep learning impacts signal processing. It opens doors for future advancements in both neuroscience and wireless communication fields.