Search Results (7,652)

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

This study seeks to examine the attractiveness of higher education institutions as community spaces for students and the significance of the services they provide. It aims to explore students’ perspectives on planning their long-term futures, particularly in assessing whether the university environment serves as a stronger influence than their place of origin or family background. The role of the university is particularly significant if it is located outside the student’s town of origin, so the student’s decision to attend a particular institution is not necessarily based on the specific undergraduate program or the prestige of the university. The study combines a review of the national and international literature with an empirical investigation, utilizing a questionnaire survey to analyze students’ decision-making processes. Many students perceived the university as a transitional “island”, offering a temporary space to inhabit before embarking on their future careers. The degree obtained serves as a “passport” to professional opportunities, while the university experience provides a unique community environment and represents a significant step toward independence and separation from familial influence. These findings hold particular relevance for universities, which are continually redefining their roles in response to changing student expectations. Many students view the university not merely as a site of learning but as a precursor to adulthood and a foundational space for personal growth. This study addresses a gap in the existing literature by focusing on the appeal of universities as local hubs and comparing their influence to the retaining power of family ties, offering insights for student development. Full article

Traditional journalists now utilize social media platforms such as Facebook, Instagram, Twitter, and TikTok to disseminate information. With the emergence of TikTok as a prominent social network for entertainment and information, many journalists worldwide, including in Israel, have begun leveraging it to create and share short video content. This study presents a qualitative case study of journalists operating within the Israeli media system, examining why and how journalists use TikTok, the professional challenges they face on the platform, and how they address these challenges. Specifically, it focuses on how journalists perceive TikTok as a journalistic space and their professional role within it. Focusing on the Israeli context, which is both digitally advanced and characterized by a democratic and pluralistic media environment, semi-structured in-depth interviews were conducted with 15 prominent journalists from traditional Israeli media outlets who are extensively active and considered at least micro-influencers on TikTok. The findings reveal several key themes regarding journalists’ use of TikTok. These include the platform’s role as a tool for reaching younger audiences and maintaining relevance; and the journalists’ self-perception as gatekeepers combating fake news. However it was found that they face ethical dilemmas and an absence of the structural and ethical foundations necessary for serious investigative journalism. This is the result of adapting their work to the platform’s light, fast-paced, and visually engaging format, favoring content that is entertaining and often sensational, to meet the expectations of TikTok audiences. While grounded in the Israeli case, the findings contribute to broader discussions on the platformization of journalism and the transformation of professional norms in media environments. Full article

Wireless sensor networks (WSNs) are fundamental building blocks of modern ubiquitous sensing systems. In many practical WSN deployments, sensing devices are tightly constrained in buffer capacity, while device mobility leads to topology decentralization. These characteristics pose significant challenges for reliable and timely data delivery across WSNs. In this paper, we propose a general multi-probing opportunistic routing strategy tailored for buffer-constrained WSNs, aiming to enhance transmission opportunity utilization under realistic sensing device limitations. With the help of Queueing Theory and Markov Chain Theory, we capture the sophisticated queueing processes for the buffer space of sensors, which enables the limiting distribution of the buffer occupation state to be determined. On this basis, we develop a theoretical performance modeling framework to evaluate the fundamental performance metrics of the WSN with the multi-probing opportunistic routing, including the per-flow throughput and the expected end-to-end delay. The validity of the performance modeling framework is verified by network simulations. Moreover, extensive numerical results demonstrate the network performance behaviors comprehensively and reveal some insightful findings that can serve as important guidelines for the configuration and operation of WSNs. Full article

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

This survey article offers a snapshot view of the present state of fixed point theory within modular spaces, highlighting fundamental principles and their applications. The discussion primarily revolves around operators and their semigroups that satisfy pointwise asymptotic nonexpansive and contractive conditions in the modular sense, and the results can also be applied directly to Banach spaces. Utilizing the framework of regular and super-regular modular spaces, our research generalizes several established results concerning fixed points of nonlinear operators, applicable to both Banach spaces and modular function spaces. The study seeks to identify and discuss current challenges, knowledge gaps, and unresolved questions, providing insights into the potential of future research opportunities. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

Military confined spaces face poor ventilation and severe airborne hazards (toxic gases/particulates), while conventional air purification systems with separate filtration–adsorption units are bulky and hard to miniaturize. Activated carbon fiber paper (ACFP) is a promising integrated filtration–adsorption material, but existing studies lack systematic comparisons of different ACF precursors and rational balancing of adsorption, filtration, and mechanical properties. Herein, ACFPs were fabricated via wet papermaking technology, using two ACFs (rayon-based, RACF, and phenolic-based, PACF) as the adsorptive component, glass wool as a filtration enhancer, and dual-melting-point polyethylene terephthalate (PET) fibers as a mechanical reinforcer. Dynamic adsorption was evaluated via DMMP (a Sarin simulant). Results showed that PACF had a micropore ratio twice that of RACF. Under the optimal formulation (20% glass wool, 30% PET, and 50% ACF), both types of ACFP showed FE_0.3 μm ≥ 90%. PACFP outperformed RACFP in comprehensive performance, showing higher adsorption capacity, tensile strength, and filtration quality factor. Both ACFPs exhibited superior bed utilization efficiency (RACFP: 91.3%; PACFP: 86.0%) to granular activated carbon (AC: 82.7%), confirming better dynamic adsorption kinetics. This work provides a rational optimization strategy for ACFPs, offering a lightweight, integrated material for air purification in military confined spaces. Full article

Superhydrophobic surfaces are typically achieved through the synergistic integration of appropriate nanostructures and low-surface-energy chemical compositions. This study presents a novel and facile method for constructing a superhydrophobic hierarchical structure directly on a pristine selective laser melting (SLM) titanium surface. The intrinsic partially melted Ti particles, which are inherent to the SLM fabrication process, were strategically utilized as a natural microscale template for the in situ growth of TiO₂ nanotubes via electrochemical anodization. Three distinct micro/nano-topographies were successfully fabricated, integrating the spherical microparticles with either conventional TiO₂ nanotube arrays or separated nanotube arrays. The results demonstrate that the resulting superhydrophobic behavior can be effectively regulated by two key factors: the liquid–solid contact mode at the microscale and the strength of capillary action within the nanostructures. Notably, these characteristics can be tailored by controlling the nanotube diameter and intertubular spacing. These findings contribute to a deeper understanding of the role of micro–nano hierarchical structures in engineering superhydrophobic surfaces, thereby opening new avenues for advanced applications. Full article

Urbanization of cities demands efficient spatial management. The construction of utility lines significantly alters the spatial landscape. The subsurface space is often neglected, resulting in outdated or absent records of underground utility infrastructure. This clearly underscores the need and importance of maintaining accurate utility records. Modern non-destructive techniques for underground utility detection, such as ground penetrating radar (GPR), can enhance the documentation and mapping of subsurface infrastructure. The subject of this paper is the optimization of GPR survey and processing workflows to improve the accuracy of underground utility detection when using the Leica DS2000. The research comprises both theoretical and experimental analyses, including the application of various GPR data collection methods on test sites. The experimental component of the research was conducted using the Leica DS2000 GPR system. The geospatial data were processed using several software applications, including uNext Advanced, IQMaps, and Geolitix. Based on the multicriteria analysis of these results and an assessment of detection accuracy, an optimal workflow (decision diagram) was defined for the detection of underground utility infrastructure using Leica DS2000 under favorable soil conditions. This study explored the feasibility of efficiently updating the cadastral database of public utility infrastructure through non-invasive technologies, thereby contributing to the improvement of subsurface utility infrastructure management. Full article

To meet the need for global coverage, space–air–ground integrated networks (SAGINs) are crucial, but the openness of wireless links makes communications vulnerable to eavesdropping. This paper investigates the physical layer security (PLS) of uplink transmissions in a cooperative dual-hop SAGIN. The system comprises a ground source with a directional antenna, an unmanned aerial vehicle (UAV) relay cluster, and a low Earth orbit (LEO) satellite. Utilizing stochastic geometry, we model the spatial randomness of terrestrial eavesdroppers and the multi-layered dual-orbital LEO destination. To combat mixed radio-frequency (RF) and free-space optical (FSO) fading, multiple relay selection and maximum ratio combining (MRC) are integrated into the UAV cluster. We analytically derive the piecewise probability density function for the FSO link distance, obtaining exact closed-form expressions for the end-to-end secrecy outage probability (SOP). Monte Carlo simulations strictly validate the derivations. The results demonstrate that while increasing available relays and antennas enhances PLS via spatial diversity, a security bottleneck restricts the RF-FSO architecture under high-transmit power regimes, generating asymptotic secrecy floors. These findings provide explicit theoretical guidelines for the secure design and parameter optimization of future SAGINs. Full article

As Global Navigation Satellite Systems (GNSSs) underpin safety-critical infrastructure, their vulnerability to sophisticated spoofing attacks poses severe physical layer security risks. To address the limitations of existing single-antenna defense mechanisms, this paper proposes a rigorous instantaneous spoofing detection framework utilizing a novel “one-primary-two-auxiliary” three-antenna configuration. By embedding the rigid baseline length as a hard geometric constraint into the Integer Least Squares (ILS) model, we derive a specialized constrained LAMBDA algorithm that significantly shrinks the ambiguity search space. A rigorous hypothesis testing mechanism is established based on the Sum of Squared Residuals (SSR), analytically deriving the detection threshold from the central Chi-square distribution and analyzing the sensitivity via the non-central parameter. Through conducting field experiments using commercial receivers and professional GNSS signal simulators, the proposed method was validated using both single-satellite spoofing and full-constellation spoofing scenarios. Results demonstrate that the system achieves a Minimum Detectable Deviation (MDD) of spatial direction as low as $0.33^{\circ }$ and maintains an empirical detection rate of >99% with a negligible false alarm rate. Notably, the method exhibits instantaneous response capabilities, effectively identifying both single-satellite and full-constellation spoofing attacks within a single epoch without requiring prior attitude information or external aiding. Full article

A reliable and well-planned charging infrastructure is an essential pillar for enabling the widespread adoption of electric vehicles (EVs) and realizing their environmental and economic benefits. Car rental companies are increasingly transitioning towards EV fleets to support sustainability objectives, reduce emissions, and lower operational costs. However, EV charging management in rental car facilities presents unique challenges, including limited parking space, strict vehicle availability requirements, and unpredictable charging demand patterns. This study introduces a data-driven and probabilistic framework to estimate EV charging demand in rental car fleets. The proposed model integrates rental mobility data, vehicle technical specifications, and charging standards and employs Monte Carlo simulation to capture uncertainties in user behavior and charging processes. In addition, a priority-based charging management framework is developed to minimize technical disruptions in the power system, reduce infrastructure costs, and ensure efficient load distribution. The results demonstrate that the proposed framework supports sustainable charging infrastructure planning by improving charger utilization, enhancing grid compatibility, and enabling cost-effective EV fleet operations. Full article

The study reconsiders the architectural production associated with Russian Suprematism (which was speaking of “the supremacy of pure artistic sensation” rather than the veritable figurative depiction of real-life subjects) in the early Soviet period as a coherent and conceptually rigorous mode of speculative world-making rather than as a marginal or unrealized appendix to avant-garde art history and theory. By examining the architectural propositions articulated by Kazimir Malevich and then elaborated by his younger colleagues Lazar Khidekel, Ilya Chashnik, and Nikolai Suetin, the study advances the claim that Russian Suprematist architecture constituted an epistemic experiment aimed at redefining the very ontological premises of architecture. Far from functioning as a mere transposition of abstract pictorial language into three-dimensional form, Suprematist planits, architectons, and aerocentric projects operated as instruments for thinking spatiality beyond terrestrial gravity, anthropocentric utility, and historical typology. Situating these projects within the intellectual horizon of Russian cosmism and early aerospace thought, the article demonstrates how Suprematist architecture intersected with contemporary philosophical, scientific, and technological discourses that envisioned humanity’s active participation in the reorganization of cosmic space. The architectural imagination of Suprematism emerges here as inseparable from broader debates on excitation, non-objectivity, transformation of matter, and the reconfiguration of human corporeality. Through close analysis of formal strategies, pedagogical frameworks, and theoretical writings, the paper reveals the internal plurality of avant-garde Suprematist architectural inquiry, ranging from ecological proto-urbanism and hovering settlements to magnetic and cruciform spatial systems. Ultimately, the paper argues that the historical non-realization of these projects should not be interpreted as a failure but as an intrinsic feature of their speculative methodology. Suprematist architecture is thus redefined as an anticipatory practice whose unresolved propositions continue to resonate with contemporary discussions on space habitation, planetary design, ecological responsibility, and post-human architectural thought, challenging inherited assumptions about the scope and function of architecture as such. Full article