Search Results (94)

Fish protection and guidance are critical factors in the design and operation of water intakes at hydropower plants. In this study, the hydraulic performance of the angled Oppermann fine screen has been investigated in a hybrid model with and without a guidance wall. The experiments were conducted under two different angles of 30° and 45°, and a bar spacing of 10 mm at a large-scale flume with a width of 2 m. Just up- and downstream of the screen, three-dimensional velocities were measured with Acoustic Doppler Velocimetry (ADV). In the computational fluid dynamics (CFD) model, the Large Eddy Simulation (LES) coupled with the Darcy–Forchheimer law, in which screens were modeled as homogeneous porous media, was employed. The experimental results revealed that velocities less than 0.5 m/s just upstream of the Oppermann fine screen and tangential velocity gradients over the entire cross-section of the screen were found to be 0.04–0.338 m/s/m and 0.04–0.856 m/s/m for α = 30° and α = 45°, respectively, creating favorable hydraulic conditions for effective downstream fish guidance. The CFD model was validated against the experimental data within an acceptable error range, both for the velocity and the turbulent kinetic energy. Numerical simulations showed that implementing a curved guidance wall creates a symmetrical and homogeneous downstream flow field without the formation of recirculation zones behind the angled screen. Full article

A Digital Twin (DT) is not just a collection of static digital models at the component level of a physical system, but a dynamic entity that evolves in parallel with the physical system it mirrors. This evolution starts with physics-based or data-driven physics models representing the physical system and advances to Authoritative Virtualization or DT through continuous data assimilation, and ongoing Digital Engineering (DE) Test and Evaluation (T&E) processes. This paper presents a generalizable mathematical framework for the DE Test and Evaluation Process that incorporates data assimilation, uncertainty quantification, propagation, and DT calibration, applicable to diverse physical–digital systems. This framework will enable the DT to perform operations, control, decision-making, and predictions at scale. The framework will be implemented for two cases: (i) the DT of the CubeSat to analyze the CubeSat’s structural deformation during its deployment in space and (ii) the DT of the CROME engine. The DT of the CubeSat will be capable of predicting and monitoring structural health during its space operations. The DT of the CROME engine will be able to predict the thrust at various conditions. Full article

There is a growing pressure on industry to secure environments and demonstrate their commitment in taking right steps to secure their products. This is because of the growing number of security compromises in the IT industry, Operational Technology environment, Internet of Things environment and smart home devices. These compromises are not just about data breaches or data exfiltration, but also about unauthorised access to devices that are not configured correctly and vulnerabilities in software components, which usually lead to insecure authentication and authorisation. Incorrect configurations are usually in the form of devices being made available on the Internet (public domain), reusable credentials, access granted without verifying the requestor, and easily available credentials like default credentials. Organisations seeking to address the dual pressure of demonstrating steps in the right direction and addressing unauthorised access to resources can find a viable approach in the form of the zero trust concept. Zero trust principles are about moving security controls closer to the data, applications, assets and services and are based on the principle of “never trust, always verify”. As it stands today, zero trust research has advanced far beyond the concept of “never trust, always verify”. This paper provides the culmination of a literature review of research conducted in the space of smart home devices and IoT and the applicability of the zero trust five-step implementation process to secure them. We discuss the history of zero trust, the tenets of zero trust, the five-step implementation process for zero trust, and its adoption for smart home devices and Internet of Things, and we provide suggestions for future research. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article

Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation. However, traditional deep learning approaches often suffer from challenges such as overfitting, limited generalization capability, and suboptimal prediction accuracy. To address these issues, this paper proposes a novel framework that combines a Long Short-Term Memory (LSTM) network with a Physics-Informed Neural Network (PINN), referred to as LSTM-PINN, for high-precision SOH estimation. The proposed framework models battery degradation using state-space equations and extracts latent temporal features. These features are further integrated into a Deep Hidden Temporal Physical Module (DeepHTPM), which incorporates physical prior knowledge into the learning process. This integration significantly enhances the model’s ability to accurately capture the complex dynamics of battery degradation. The effectiveness of LSTM-PINN is validated using two publicly available datasets based on graphite cathode materials (NASA and CACLE). Extensive experimental results demonstrate the superior predictive performance of the proposed model, achieving Mean Absolute Errors (MAEs) of just 0.594% and 0.746% and Root Mean Square Errors (RMSEs) of 0.791% and 0.897% on the respective datasets. Our proposed LSTM-PINN framework enables accurate battery aging modeling, advancing lithium-ion battery SOH prediction for industrial applications. Full article

Since 1957, the near-Earth population of trackable space objects has grown in number to over 36,000. Of these 36,000+ trackable objects now in low Earth orbit, just a few thousand are working spacecraft. The rest are Earth-orbiting objects which are no longer operational and are considered to be space junk. Because this junk can no longer receive maneuvering commands from its Earth-based owners, the survivability of other spacecraft traveling through or operating in Earth orbit can be jeopardized by the impacts of any number of pieces of this space junk, whose origins can usually be traced back to defunct satellites. As a result, a major design parameter for Earth-orbiting spacecraft is the possibility of such high-speed impacts and the damage they can cause. Furthermore, several private companies are now launching several thousand spacecraft into Earth orbit, many of which are satellites built for communication purposes. Other satellites have been launched to expand the reach of the World Wide Web and to provide better tools for disaster management. Two questions quickly become evident, namely, what is the beneficial purpose of these large satellite constellations, and what are some of the deleterious consequences of their proliferation? Numerous topics related to space debris will be discussed in this paper, including issues in space law that concern the growing problem of orbital debris. In the end, several areas of concern will be noted that are vital to the continuing presence of humans in near-Earth space and must be addressed as the near-Earth orbital environment becomes more congested and space traffic management becomes more difficult. Full article

This research introduces a novel, high-performance multiple-input–multiple-output (MIMO) antenna designed to operate in allocated millimeter-wave (mmWave) 5G wireless communications. Operating in the tri-band, 28, 35, and 38 GHz, the four-port MIMO antenna possesses a compact size—measuring just 50 × 50 × 0.787 mm³ (4.67λ_o × 4.67λ_o × 0.73λ_o). The antenna delivers a remarkable performance, achieving peak gains of 9.6, 7.8, and 13.7 dBi in the tri-band, respectively. The realized bandwidths are 1.1, 2.2, and 3.7 GHz, at the tri-band frequencies. The antenna’s performance was significantly improved by carefully spacing the elements and employing a decoupling technique using metamaterial cells. This minimized interference between the antenna elements, resulting in efficient MIMO operation with a low envelope correlation coefficient of 0.00015 and a high diversity gain approaching 10 dB, and high isolation of 34.5, 22, and 30 dB, in the tri-band. This proposed design is confirmed with experimental measurements and offers a promising candidate for multi-band use of mmWave communication systems. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. Full article

A novel single-layer multi-coupling 3D frequency-selective surface featuring high selectivity and a 3D dual-band frequency-selective surface (3D-DBFSS) is presented in this paper. By incorporating three different coupling gaps within each unit cell, the design achieves two distinct passbands with wide bandwidths and steep transition edges. For the first passband, the −3 dB bandwidth ranges from the lower cutoff frequency of 9.9 GHz to the upper cutoff frequency of 15.2 GHz, yielding a bandwidth (BWL3dB) of 5.3 GHz. For the second passband, the −3 dB bandwidth extends from 22 GHz to 26 GHz, providing a bandwidth (BWU3dB) of 4 GHz. The structure exhibits eight transmission poles and four transmission zeros within a single layer, enhancing its selectivity. The simulation results indicate that the dual passbands are centered at 12 GHz and 24 GHz, respectively, with bandwidths sufficient for practical applications. The proposed frequency-selective surface demonstrates a low insertion loss of just 0.8 dB, which is significantly lower compared to most reported dual-band FSS designs. Furthermore, the thickness of the 3D-DBFSS is only one-third of the wavelength in free space, making it considerably thinner than other 3D-FSS structures operating in the same frequency range. The proposed design also ensures stable performance over a wide range of incident angles, which is crucial for practical deployment. Additionally, the overall size of the unit cell of the frequency-selective surface is 4 × 4 × 10.8 m³. The structure is easy to fabricate, which contributes to its potential for cost-effective mass production. Overall, the 3D-DBFSS offers high frequency selectivity, effective bandpass performance, and strong suppression in the stopband region. Full article

Fuel cells, propulsion systems, and reaction control systems (RCSs) are just a few of the space applications that depend on pressure vessels (PVs) to safely hold high-pressure fluids while enduring extreme environmental conditions both during launch and in orbit. Under these challenging circumstances, PVs must be lightweight while retaining structural integrity in order to increase the efficiency and lower the launch costs. PVs have significant challenges in space conditions, such as extreme vibrations during launch, the complete vacuum of space, and sudden temperature changes based on their location within the satellite and orbit types. Determining the operational temperature limits and endurance of PVs in space applications requires assessing the combined effects of these factors. As the main propellant for satellites and rockets, hydrogen has great promise for use in future space missions. This study aimed to assess the structural integrity and determine the thermal operating limits of different types of hydrogen pressure vessels using finite element analysis (FEA) with Ansys 2019 R3 Workbench. The impact of extreme space conditions on the performances of various kinds of hydrogen pressure vessels was analyzed numerically in this work. This study determined the safe operating temperature ranges for Type 4, Type 3, and Type 1 PVs at an operating hydrogen storage pressure of 35 MPa in an absolute vacuum. Additionally, the dynamic performance was assessed through modal and random vibration analyses. Various aspects of Ansys Workbench were explored, including the influence of the mesh element size, composite modeling methods, and their combined impact on the result accuracy. In terms of the survival temperature limits, the Type 4 PVs, which consisted of a Nylon 6 liner and a carbon fiber-reinforced epoxy (CFRE) prepreg composite shell, offered the optimal balance between the weight (56.2 kg) and a relatively narrow operating temperature range of 10–100 °C. The Type 3 PVs, which featured an Aluminum 6061-T6 liner, provided a broader operational temperature range of 0–145 °C but at a higher weight of 63.7 kg. Meanwhile, the Type 1 PVs demonstrated a superior cryogenic performance, with an operating range of −55–54 °C, though they were nearly twice as heavy as the Type 4 PVs, with a weight of 106 kg. The absolute vacuum environment had a negligible effect on the mechanical performance of all the PVs. Additionally, all the analyzed PV types maintained structural integrity and safety under launch-induced vibration loads. This study provided critical insights for selecting the most suitable pressure vessel type for space applications by considering operational temperature constraints and weight limitations, thereby ensuring an optimal mechanical–thermal performance and structural efficiency. Full article

Deep learning has revolutionized image compressed sensing (CS) by enabling lightweight models that achieve high-quality reconstruction with low latency. However, most deep neural network-based CS models are pre-trained for specific compression ratios (CS ratios), limiting their flexibility compared to traditional iterative algorithms. To address this limitation, we propose the Dual-Ascent-Inspired Transformer (DAT), a novel architecture that maintains stable performance across different compression ratios with minimal training costs. DAT’s design incorporates the mathematical properties of the dual ascent method (DAM), leading to accelerated training convergence. The architecture features an innovative asymmetric primal–dual space at each iteration layer, enabling dimension-specific operations that balance reconstruction quality with computational efficiency. We also optimize the Cross Attention module through parameter sharing, effectively reducing its training complexity. Experimental results demonstrate DAT’s superior performance in two key aspects: First, during early-stage training (within 10 epochs), DAT consistently outperforms existing methods across multiple CS ratios (10%, 30%, and 50%). Notably, DAT achieves comparable PSNR to the ISTA-Net+ baseline within just one epoch, while competing methods require significantly more training time. Second, DAT exhibits enhanced robustness to variations in initial learning rates, as evidenced by loss function analysis during training. Full article

Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; they offer real opportunities for energy sustainability, using technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). In this context, this study focuses on improving energy efficiency in smart spaces within the context of Industry 4.0 by utilizing the SmartParcels framework. This framework creates a detailed and cost-effective plan for equipping specific areas of smart communities, commonly referred to as parcels. By adapting this framework, we propose an integrated model for planning and implementing IoT applications that optimizes service utilization while adhering to operational and deployment cost constraints. The model considers multiple layers, including sensing, communication, computation, and application, and adopts an optimization approach to meet the needs related to IoT deployment. In simulated industrial environments, it demonstrated scalability and economic viability, achieving high service utility and ensuring broad geographic coverage with minimal redundancy. Furthermore, the use of heuristics for device reuse and geophysical mapping selection promotes cost-effectiveness and energy sustainability, highlighting the framework’s potential for large-scale applications in diverse industrial contexts. Full article

In recent years, ecofriendly and renewable energy solutions have gained relevance mainly to lessen the effects of climate change. Governments and companies across the world have commitments to reduce fuel consumption and emissions as part of the 2030 Sustainable Development Goals. Solar energy systems have great importance as a renewable energy source; however, they often have large space requirements to be effective, e.g., large areas covered by solar panels, as well as low efficiency and strong dependance on the weather. On the other hand, origami, the art of folding paper, can be a source of inspiration for new technologies and solutions for modern problems. In this paper, origami-inspired solar panels are presented as a potential solution for naval and mining operations. Prototype panels are manufactured based on the Miura-Ori pattern. Using this pattern, the photovoltaic modules can be folded by just one movement, thus reducing their footprint by up to 90%. The prototype photovoltaic modules are then tested on land and on board a vessel, where their efficiency and resistance can be tested. It is shown that naval and mining operations, where fuel consumption can be extremely high and available space is a major constraint, benefit greatly from this kind of development. Full article

In electric vehicles (EVs), the batteries are arranged in the battery pack (BP), which has a small layout space and difficulty in dissipating heat. Therefore, in EVs, the battery thermal management systems (BTMSs) are critical to managing heat to ensure safety and performance, particularly under higher operating temperatures and longer discharge conditions. To solve this problem, in this article, the thermal analysis models of a 3-battery-cell BP were created, including scenarios (1) natural air cooling without a BTMS; (2) natural air cooling with water cooling hybrid BTMS; and (3) forced air cooling plus water cooling composite BTMS. The thermal performances of the pack-level BPs were simulated and analyzed based on computational fluid dynamics (CFD). A variety of boundary conditions and working parameters, such as ambient temperature, inlet coolant flow rate and initial temperature, discharge rate, air flow rate, and initial temperature, were considered. The results show that without a BTMS (Scenario 1), the maximum temperature in the BP rises rapidly and continuously to reach 63.8 °C, much higher than the upper bound of the recommended operating temperature range (ROTR between +20 °C to +35 °C) under the extreme discharge rate of 3 C and even if the discharge rate is 2 C. With a hybrid BTMS (Scenario 2), the maximum temperature in BP rises to about 38.7 °C, slightly above the upper bound of the ROTR. Lowering the coolant (water) initial temperature can effectively lower the temperature up to 5.7 °C in BP, but the water flow rate cannot since the turbulence model. While with a composite BTMS (Scenario 3), the temperature can be further lowered up to 1.5 °C under the extreme discharge rate of 3C, just reaching the upper bound of the ROTR. In addition, lowering the initial coolant temperature or air temperature can effectively decrease the temperatures up to 5.1 and 1.0 °C, respectively, in BP, but the coolant flow rate (due to the turbulence model) and the air flow rate cannot. Finally, the thermal performances of the different battery cells in the BP with different cooling systems and at the different positions of the BP were compared and analyzed. The present work may contribute to the design of BTMSs in the EV industry. Full article

The conversion of open space to residential development increasingly continues across the United States, impacting both humans and nature. Residential development requires public input to generate meaningful places and understand contextually relevant priorities. Most municipal policies do not guarantee the provision of open spaces when residential development occurs, missing opportunities for benefits to those communities and reducing both environmental and spatial justice. This study operated a seven-state verbal questionnaire to collect and analyze a small-sample population perceptions concerning open space conservation and green space preferences towards future residential development priorities. Statistical analytical results indicated patterns, trends, and relationships within data. Although 46% of United States residents living in rural, suburban, and urban community types believe the amount of open space required in new developments should be determined on a case-by-case basis, just under half believe that requiring at least 50% open space in new developments is appropriate. More than half of Americans in the states targeted, particularly Colorado and liberal-leaning respondents, believe a lack of coherent planning will prevent open space conservation and that open space planning and conservation should be a priority for city governments. Beyond the United States, this study provides research and insight into conservation strategies that foster healthier landscapes and living environments globally. Full article