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Low Earth Orbit (LEO) satellite signals of opportunity (SOP) provide a viable positioning alternative in GNSS (Global Navigation Satellite System)-denied environments, yet their accuracy is fundamentally constrained by the low-quality orbital data typically available, such as SGP4 (Simplified General Perturbations model 4) predictions derived from Two-Line Elements (TLEs). To address this limitation, this paper proposes a dynamics-informed non-causal deep learning framework that enhances low-quality orbital data into high-fidelity trajectories for accurate SOP positioning. The proposed Non-Causal Dynamics-Informed Representation Temporal Convolutional Network (Non-Causal DIR-TCN) integrates phase space reconstruction and a Temporal Convolutional Network to explicitly model the chaotic dynamics inherent in LEO orbits, while relaxing the causality constraints of standard temporal convolutions to utilize both past and future context from the available SGP4 stream. Experimental results demonstrate that the framework significantly reduces orbit estimation errors and accelerates model convergence. When applied to LEO-SOP positioning, it achieves approximately 20% improvement in 2D positioning accuracy compared to conventional SGP4-based methods. This work effectively bridges the gap between accessible low-precision orbital data and high-accuracy state estimation, advancing the practical deployment of opportunistic signals for resilient positioning in challenging environments. Full article

Global Navigation Satellite Systems (GNSS), benefiting from global coverage, all-weather operation, high precision, and high temporal resolution, have progressively become a key technology in natural hazard monitoring and early warning systems. This paper adopts a hybrid review strategy that integrates scientometric analysis with a systematic review to examine the development trajectory, research hotspots, and technological evolution of GNSS applications in natural hazard studies based on the existing literature. From a technological perspective, three core capabilities of GNSS in hazard monitoring are identified: high-precision, multi-scale deformation sensing; multi-sphere environmental sensing based on signals of opportunity; and real-time monitoring supporting rapid early warning and emergency response. The paper further reviews the development of GNSS in conjunction with multi-sensor collaborative observation and its integration with data-driven methods such as machine learning. Representative applications of GNSS and its integrated techniques are summarized across major hazard types, including earthquakes, tsunamis, landslides, land subsidence, hydrometeorological hazards, and volcanic activity, and further discussions are provided on methodological considerations, the commonalities and differences in GNSS applications across different hazards, and future development directions. The review demonstrates that GNSS applications in natural hazard research are evolving from single-source deformation monitoring toward multi-source integration, intelligent sensing, and operational early warning support systems. This work provides a reference for the further development of GNSS technologies in natural hazard monitoring and risk mitigation. Full article

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems. Full article

Percutaneous computed tomography (CT) -guided cryoablation is an effective curative treatment for renal cell carcinoma. Improvements in treatment efficacy reflect not only the learning curve but also the integration of multiple adjunctive techniques that can be implemented at different stages of the procedure. Tumour targeting can be enhanced by intravenous contrast administration, or by intra-arterial delivery of contrast medium or iodized oil. Fusion imaging is another option to improve tumour delineation by registering intraprocedural CT with prior cross-sectional imaging. Probe placement for difficult-to-access lesions may be facilitated by alternative access routes, while electromagnetic navigation and robotic systems are being developed as alternatives to manual advancement. To mitigate the cold-sink effect and reduce bleeding risk, transarterial techniques such as embolization or temporary arterial occlusion can be added. Finally, thermoprotective manoeuvres are increasingly used to displace adjacent organs, thereby improving the feasibility, safety, and efficacy of renal cryoablation. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article

Here, we develop a digital community management application (APP) and an energy prediction and analysis system for smart communities. The system integrates the internet of things (IoT) technology and multiple prediction models to improve the intelligence and automation of community energy management. The developed APP has the following functions: user classification, announcement notification, express delivery management, GPS positioning navigation, calendar, and energy forecast. The hardware architecture of the system consists of a voltage/current sensing module, a Wireless Fidelity (Wi-Fi) module, and an Arduino platform, allowing real-time feedback and display of power consumption data. The energy forecasting part proposes a two-layer hybrid model architecture. This architecture combines Seasonal Trend decomposition using Loess (STL) time series decomposition, extreme gradient boosting (XGBoost), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to predict residential electricity consumption trends over the next 3 years. The results of the model prediction are verified using the data on Taiwan’s electricity consumption. The model accurately predicts the average monthly residential electricity consumption with a relative error of 5.8%, an acceptable energy management accuracy. This system integrates APP applications and efficient prediction models, demonstrating its great potential in smart community energy management and enhanced resident interaction. Full article

As the maritime industry transitions towards green shipping, operational sustainability and energy efficiency are increasingly crucial for long-endurance Unmanned Surface Vehicle (USV) missions. To this end, proactively adjusting driving strategies based on the prediction of other USVs’ motion is essential. This proactive approach directly minimizes carbon emissions and reduces high-energy driving behaviors resulting from passive sudden braking or sharp turns in unexpected situations. However, existing trajectory prediction methods are trained based on low-frequency automatic identification system data of large merchant vessels, which cannot be directly used on the highly dynamic USV data. To address this limitation, this study constructs a large-scale simulated USV scenario dataset grounded in nonlinear ship hydrodynamics, which contains complicated interactive scenarios with multiple USV agents. To effectively model the interaction among agents for accurate prediction, we further propose USV-Former, a hierarchical encoder-decoder architecture designed for proactive navigation. The framework integrates a symmetric encoding structure with a dual-stage pipeline: a Local Attention Module captures high-frequency dynamics, while a Global Graph Attention Module enforces COLREGs-compliant topological constraints. Experimental results demonstrate that the proposed model outperforms established baselines in prediction accuracy. Qualitative analysis further reveals that by accurately anticipating target intentions, the model minimizes unnecessary avoidance maneuvers, enabling more stable and momentum-conserving velocity profiles. Ultimately, this architecture exhibits high computational efficiency, reduces operational energy waste, and provides a robust, measurable algorithmic foundation for green autonomous shipping and marine environmental protection. Full article

This paper presents the design, optimization, and performance evaluation of a Satellite-Based Augmentation System (SBAS) ground segment tailored to Colombia’s air navigation infrastructure, with emphasis on ionospheric anomalies in equatorial latitudes. The configuration comprises six Reference Stations (RIMS), strategically sited via geometric dilution of precision (GDOP) minimization and airspace demand models from ADS-B data. A simulation suite—integrating STK^®, Radio Mobile™, and Stanford-ESA certified monitors—quantifies service volume, link margins, and protection level compliance. Ionospheric threat characterization uses regional scintillation datasets (σln ≈ 0.36, ROTI₉₅ ≈ 85 mm/km), informing GIVE inflation and dual-frequency pseudorange integrity validation. Simulations confirm the system sustains ≥ 99.8% APV-I availability over the CAR/SAM FIR, with Horizontal and Vertical Protection Levels (HPL/VPL) bounded below 28 m and 46 m. Uplink integrity and GEO broadcast continuity are modelled under worst-case masking and multipath, confirming ICAO Annex 10 SARPs compliance. The architecture achieves a high performance-to-cost ratio, enabling nationwide SBAS coverage with a 65% cost reduction versus legacy navaids. The system is forward-compatible with dual-frequency multi-constellation SBAS (DFMC), supporting future APV-II scalability. These results position Colombia as a regional node for GNSS augmentation, fostering safety, efficiency, and procedural harmonization. Full article

This study explores the factors contributing to educational stakeholders’ resistance to pedagogical and policy changes within a rural school in the Bojanala District, South Africa. Utilizing a qualitative approach, fifteen participants comprising five members of the School Governing Body (SGB), five members of the School Management Team (SMT), and five Grade 12 learners were purposively sampled to provide a multi-perspective analysis of the institutional environment. The findings reveal that resistance is driven by a complex interplay of limited policy awareness, deep-seated cultural and traditional beliefs, systemic socioeconomic challenges, and significant psychological barriers. These factors collectively undermine the quality of teaching and learning by inhibiting curriculum innovation, fostering learner disengagement, and eroding school morale. To address these systemic hurdles, the study advocates for a multi-tiered integration strategy that prioritizes transparent stakeholder communication frameworks to align national policy with local rural realities, the institutionalization of sustained, context-specific professional development, and the cultivation of transformational leadership capable of navigating the unique socio-economic constraints inherent in rural educational landscapes. Full article

Global Navigation Satellite Systems (GNSS) are highly affected in equatorial regions, especially due to the formation of Equatorial Plasma Bubbles (EPBs), which cause disturbances in the ionosphere resulting in different forms of signal degradation. Despite Colombia’s privileged geographic position, its limited monitoring infrastructure hinders the detection and mitigation of these effects. This study proposes the development of a Low-Cost Scintillation Laboratory (LCSL) using a cognitive radio–based approach for real-time scintillation monitoring, aimed at improving GNSS reliability. The system was designed following a Systems Engineering methodology, defining functional architectures and constraints. A communication system model was developed to account for EPBs’ effects on GNSS signals, while cognitive radio algorithms within a Software-Defined Radio (SDR) framework enabled real-time detection, monitoring, and alert generation. To implement this approach, monitoring stations were deployed in Bogotá, Cartagena, and Santa Marta utilized low-cost GNSS receivers integrated with Machine Learning (ML) algorithms for the automatic classification of scintillation events. Additionally, the system’s accuracy was validated by comparing experimental data with historical records from the Geophysical Institute of Peru (IGP). The results demonstrated that the integration of cognitive radio and ML-based detection enhanced precision and adaptability compared to traditional methods. The network of monitoring stations effectively validated the system’s performance, providing valuable insights into equatorial ionospheric dynamics. This study contributes to the advancement of monitoring methodologies and highlights the importance of accessible infrastructure for mitigating EPB effects on GNSS, ultimately fostering more resilient navigation and communication systems. Full article

Virtual museums delivered through immersive virtual reality (VR) function as information environments where users access interpretive content while navigating spatially. With the integration of generative artificial intelligence (AI), conversational assistants can dynamically mediate information interaction; however, evidence remains limited regarding how different AI interface representations affect user experience. This study compares three generative AI interface modalities in a VR virtual museum: voice only, voice with synchronized text, and voice with an embodied AI avatar. A controlled experiment with 75 participants examined their effects on user engagement, perceived information quality, and subjective cognitive workload while holding informational content constant. The results indicate that the voice-and-text modality produced the highest perceived information quality, whereas the embodied AI avatar modality yielded the highest user engagement. No significant differences were observed in cognitive workload across modalities. These findings suggest that AI interface modalities play complementary roles in VR-based information interaction and provide design guidance for selecting appropriate AI representations in immersive information systems. Full article

The digital transformation of the tourism industry faces a dual challenge: the fragmentation of data across platforms and the lack of immersive “try-before-you-buy” experiences. While Large Language Models (LLMs) have revolutionized information synthesis, they typically lack real-time visual verification capabilities. This paper proposes a novel, multimodal AI Agent architecture that integrates advanced natural language planning with photorealistic 3D visualization. We present a system where a conversational agent, powered by Gemini 2.5 Flash, orchestrates a suite of dynamic tools to build structured travel itineraries (flights, hotels, activities) while simultaneously deploying a neural rendering engine. This engine utilizes a modular Structure-from-Motion (SfM) pipeline feeding into 3D Gaussian Splatting (3DGS) to render navigable, high-fidelity digital twins of hotel facilities directly within the chat interface. Positioned as a Technology Readiness Level 4 (TRL 4) proof of concept (PoC), this work demonstrates the technical feasibility of the multimodal integration between conversational logic and automated visual synthesis. The results demonstrate the technical feasibility of a pipeline that dynamically binds LLM inference to 3D spatial data, providing a foundation for high-fidelity, interactive travel consultancy. Full article

High-precision localization is essential for autonomous navigation and environment perception of unmanned aerial vehicles (UAVs) in complex power inspection scenarios. To overcome the limited accuracy and accumulated drift of conventional GPS-based single-sensor localization, this paper proposes a LiDAR–IMU–GPS-aided navigation method that combines a tightly coupled front-end and a loosely coupled back-end. The front-end employs an improved Lie-group-based UKF-SLAM framework to explicitly handle the nonlinearities of rotational motion, thereby improving the stability of local pose estimation. The back-end integrates GPS absolute constraints, loop closure detection, and point cloud registration via pose graph optimization, which effectively suppresses long-term accumulated drift. The framework achieves accurate and robust localization for UAV power inspection. Experiments on public benchmark datasets and real-world power inspection scenarios demonstrate the effectiveness of the proposed method. On the MH_02_easy sequence, the absolute trajectory error is reduced from 0.521 m to 0.170 m compared with ROVIO, while in a real inspection sequence the cumulative error is reduced by more than 99% after back-end optimization. Moreover, the system maintains stable navigation under GPS-degraded conditions, indicating strong robustness and practical applicability. Full article

Geographical exclaves face distinctive development challenges as spatial separation creates cross-border dependencies and institutional vulnerabilities. Musandam Governorate, Oman’s exclave separated from the mainland by United Arab Emirates (UAE) territory, exemplifies how exclave status shapes development trajectories, cross-border interactions, and population resilience. This study examines Musandam’s socio-economic dynamics, development patterns, and cross-border relationships, addressing gaps in understanding how exclave residents navigate spatial discontinuity while maintaining mainland and cross-border connections. Mixed methods combined quantitative assessment using the adapted Vera Carstairs Index (VCI) across seven domains (education, skills, employment, housing, living environment, household facilities, health) with qualitative fieldwork spanning four campaigns (2019–2023). Semi-structured interviews with 47 residents across all four wilayaat (provinces), complemented by citizen science approaches engaging twelve community participants, documented mobility patterns and cross-border transactions. Secondary data from the 2010 Population Census and national statistics provided contextual depth. Findings reveal two of four Musandam wilayaat (Daba and Khasab) ranking in the lower half nationally, with low health scores (ranks 1 and 9) and education institution deficits reflecting structural integration into transnational economic and services systems. COVID-19 border closures amplified pre-existing dependencies, converting eight-month isolation into a humanitarian crisis with food shortages, medicine unavailability, and social fragmentation. Residents maintain stronger functional connections with UAE cities than with mainland Oman despite preserving national identity. Policy implications emphasize six strategic priorities: higher education institutions, transportation infrastructure, marine fisheries development, tourism enhancement, small-medium enterprise facilitation, and residential land provision. Full article

Autonomous navigation in complex environments requires efficient and reliable road-network representations for fast path planning. However, traditional grid and skeleton-based approaches often suffer from high computational cost and limited path quality. This paper proposes a Hierarchical Topology-Metric Road Graph (HTMRG) framework for autonomous navigation of unmanned ground vehicles (UGVs). The method automatically constructs a hierarchical road graph from grid maps by identifying key intersection structures and generating smooth corridor and intersection connections. In addition, a dedicated start–goal insertion strategy is developed to enable efficient graph-based path planning in previously unexplored scenarios. Extensive simulations and real-world experiments demonstrate that the proposed method can automatically construct hierarchical road graphs and generate smooth, high-quality paths with improved planning efficiency and robustness. The HTMRG framework has also been successfully integrated into a UGV system, validating its effectiveness and practicality in real-world navigation scenarios. Full article