Search Results (656)

This work presents a spatial multi-feed beam steering reflectarray concept intended for compact small satellite payloads with adapted field of view (FoV) coverage. Unlike conventional reflectarray beam steering approaches based on tunable unit cells or mechanical reconfiguration, the proposed approach exploits the controlled displacement of the phase center of a digital planar feed array to illuminate a passive reflectarray surface from different spatial positions, enabling beam steering without tunable unit cells or mechanical reconfiguration. First, the beam steering mechanism is analyzed through the phase gradients induced by feed displacement, including the impact of amplitude illumination and incidence angle-dependent unit cell response. Then, the concept is experimentally validated at 30 GHz using a developed reflectarray and a 2 × 2 patch array feed repositioned over a multi-position interface to emulate overlapping subarrays of a virtual 4 × 4 feed array. The measured radiation patterns show good agreement with simulations, confirming the predicted beam pointing trends with measured gains between 26.57 and 27.91 dBi for the evaluated subgroups. Finally, a mission-oriented architecture for the UPMSat-4 scenario is analyzed, considering a reflectarray surface up to 600 mm × 400 mm integrated into solar panels and a 16-element linear array feed. The results demonstrate the feasibility of generating a linear multibeam FoV, achieving beam overlap and a minimum carrier-to-noise ratio (C/N) of approximately 32 dB in the considered link budget scenario. The proposed architecture provides a scalable and low-complexity alternative for flexible smallsat antenna payloads. Full article

Generative artificial intelligence (GenAI) has diffused through higher education faster than institutions have been able to govern it, reshaping the conditions under which universities produce knowledge, judgement, credentials, and public trust. Current responses (prohibition, detection, and accommodation) fall short of a settled governance posture, and existing frameworks, from AI ethics principles to standard Responsible Research and Innovation (RRI) models, are not calibrated to higher education’s distinctive epistemic, formative, and public-good missions. This article addresses that gap through a disciplined conceptual synthesis drawing on RRI, reflexive governance, and higher education theory. The synthesis develops a Reflexive and Responsible Innovation Paradigm (RRIP): a six-dimensional framework that re-specifies RRI’s canonical dimensions (anticipation, reflexivity, inclusion, responsiveness) for the university context and adds two higher-education-specific dimensions: epistemic stewardship and distributive justice. Epistemic stewardship, the article’s central theoretical contribution, names the institutional obligation to protect the conditions under which knowledge claims are formed, warranted, assessed, and trusted under AI mediation. RRIP is operationalized through a multi-level architecture of institutional mechanisms (deliberative AI councils, transparency registers, and reflexive assessment redesign) with a tiered implementation pathway calibrated to institutions of varying capacity. Institutional leaders, program directors, policymakers, and accreditation bodies will find in RRIP a theoretically grounded and practically applicable guide for assessment redesign, curriculum decisions, procurement governance, and sectoral coordination. Full article

As unmanned aerial vehicle (UAV) missions expand from aerial inspection and environmental sensing to physical interaction and autonomous manipulation, rotary-wing UAVs equipped with flexible manipulators offer a promising platform for contact-rich operations in complex environments. Among these tasks, stable near-ground hovering and the grasping of ground targets are particularly challenging because they involve ground-effect aerodynamics, rigid–flexible coupling, and mode transitions caused by contact and load transfer. This review provides a structured, task-oriented critical synthesis of advances in this interdisciplinary field. First, system configurations are classified by aerial-platform architecture, manipulator type, mounting arrangement, and end-effector design, and the suitability of rigid-link, compliant, continuum, and soft manipulation mechanisms for near-ground grasping is assessed. Next, modeling approaches for rotor ground effect, coupled rigid–flexible dynamics, hybrid contact and load-transfer dynamics, model identification, and model reduction are reviewed. Trajectory planning, coordinated stabilization, impedance control, hybrid force/position control, and switching control are then compared across free flight, contact establishment, and payload-carrying hover. Although the reviewed literature provides a substantial theoretical foundation for aerial manipulation, continuum robotics, and multirotor ground effect, direct evidence remains limited for methods that jointly address near-ground aerodynamics, large flexible deformation, and contact-induced load transfer across the complete near-ground grasping sequence with integrated experimental validation. Based on these evidence gaps, this review identifies multiphysics reduced-order modeling and event-driven hybrid control as author-synthesized directions for future investigation. Full article

In the complex problem of air-to-ground mission planning, multi-UAV systems face significant challenges such as system complexity and heterogeneity, insufficient target observability, and difficulties in collaborating information sharing. To address these issues, this paper proposes a novel learnable sparse attention graph architecture (SAGA). This architecture deeply integrates graph reasoning and policy optimization within the MAPPO framework and includes three innovative mechanisms: (i) a GATv2-based graph neural network encoder that performs multi-round distributed consensus on the communication graph among UAVs via a multi-head attention mechanism, enabling selective aggregation of tactical information; (ii) an edge predictor that learns to prune low-value communication links, generating a sparse and mission-adaptive communication topology; and (iii) an L1 sparsity penalty term that further enhances communication efficiency. In a self-developed simulation environment for heterogeneous multi-UAV mission planning, comprehensive comparative experiments were conducted against the following baseline reinforcement learning algorithms: MADDPG, MATD3, QMIX, MAPPO, TarMAC, DGN, and G2ANet. The experimental results show that SAGA achieves reward values of 390 and 1100 in small-scale and large-scale scenarios, and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics. Generalization experiments validate the model’s robust transfer capability under unknown defense deployment modes. Ablation experiments further confirmed the individual contributions of the three components. This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments. Full article

The maritime sector is undergoing rapid change due to stricter environmental regulations, shifting policies, and growing concerns about energy security and operational resilience. In this landscape, hybrid and all-electric ships (AES) are gaining recognition as effective solutions for reducing emissions, boosting efficiency, and harnessing alternative energy sources. Nonetheless, many electrification initiatives mainly target propulsion, while the entire onboard energy infrastructure, including generation, storage, distribution, power conversion, and energy management, is often developed separately. This paper investigates hybrid and AES systems linking maritime decarbonisation goals to the development of integrated onboard energy architectures. It starts with an overview of key international, European, and Portuguese policies that impact maritime decarbonisation, emphasising their influence on both the commercial and military sectors. The literature review then charts the evolution of onboard power systems, showing the shift from mechanical propulsion to integrated power solutions. Finally, it analyses different architectural configurations, energy storage technologies, alternative energy sources, and energy management strategies found in current research. Our analysis shows that adopting hybrid and AES systems requires a comprehensive system-level strategy that links power generation, storage, distribution, operational profiles, and energy management. By combining policy initiatives, technological progress, and current research, our review highlights the crucial role of integrated energy system architectures in the development of future hybrid and AES systems. We also identify key research gaps, such as multi-domain modelling, consistent performance metrics, mission-oriented energy management, and assessments of long-term robustness and operational sustainability. Full article

While there are evident advantages to deploying UAVs for structural inspection applications within reinforced-concrete structures where human access may be hazardous or restricted, UAV deployment is still inhibited within structures due to a lack of GPS, payload and power limitations, signal attenuation, limited transmission opportunities, and low bandwidth for communication. Within the confines of the present project, a low-cost two-layer six-rotor architecture was developed; a single Pixhawk PX4 manages stabilized flight, while a Raspberry Pi 4 manages mission-level operations. The complete prototype integrated a relative-pose/nearby-object estimator, hybrid 433 MHz and Wi-Fi/MQTT communications, avionics-side energy management, mission continuity via SQLite, and human-in-the-loop fail-safe support. Prototype experiments revealed reduced horizontal drift compared to both tested comparison configurations, continued operation under constrained communication conditions, and a 62.0% reduction in avionics-side power consumption, excluding propulsion. This was followed by a complementary PX4–Gazebo evaluation probing horizontal and vertical proximity responses, six-sector LiDAR processing, and stale-data watchdog functionality. Across 45 repeated simulation runs and 3000 retained sector-level observations, no simulated collisions occurred during the horizontal-approach, vertical-proximity, or watchdog tests. No false sector assignments were observed, and the overall mean absolute error was 0.0285 m. From this limited demonstration, the architecture appears satisfactory at the prototype and simulated-subsystem levels. Further physical testing will be needed prior to operational deployment. Full article

Near-infrared imagery is essential for vegetation monitoring, precision agriculture, and environmental remote sensing, but multispectral UAV systems remain significantly more expensive and less accessible than conventional RGB imaging platforms. This study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV. The proposed methodology formulates the reconstruction task as a pixel-wise nonlinear regression problem and employs a compact multilayer perceptron (MLP) containing only 609 trainable parameters, without exploiting spatial neighborhood information. The framework was developed and evaluated using 280 synchronized multispectral UAV image sets acquired with a DJI Phantom 4 Multispectral platform over a heterogeneous agricultural landscape in the Republic of Moldova. Of these, 252 image sets were used for model development, and 28 were reserved as a held-out within-mission test subset. Quantitative evaluation on a held-out test dataset from the same acquisition mission yielded a mean squared error of 0.010329, a root mean squared error of 0.101632, a mean absolute error of 0.079883, a coefficient of determination of 0.253383, and a Pearson correlation coefficient of 0.683637 between measured and reconstructed normalized NIR digital intensities. The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations. An illustrative NDVI-based assessment showed that broad spatial vegetation patterns remained identifiable. Rather than introducing a new neural network architecture, this work establishes a compact empirical baseline to investigate the practical performance and limitations of pixel-wise NIR reconstruction from a single red-band value with minimal model complexity. Full article

Modular satellite architectures can shorten development cycles and support flexible mission configurations; however, their thermal management is complicated by inter-module coupling, variable operating conditions, and stringent temperature limits. This study proposes a Thermal Digital Datasheet (TDD) framework that provides a standardized representation of module-level thermal information for data exchange, rapid system analysis, and consistent characterization across suppliers. The TDD organizes the passive thermal properties, active control capabilities, and operational constraints of each module in a unified format. To populate the datasheet with physically consistent parameters, a physics-constrained algebraic identification method is developed using excitation-based thermal response data and a structured least-squares formulation derived from the energy-balance equations. The resulting TDD representation is then integrated with a graph neural network (GNN), in which the modular interconnection topology is represented explicitly as a graph for network-wide temperature prediction. On the simulated modular satellite dataset, TDD-GNN achieves a mean absolute error of 0.096 °C and an $R^2$ of 0.962 for one-step temperature-increment prediction, maintaining high accuracy over autoregressive horizons of up to 2 h. In an end-to-end evaluation in which the thermal parameters of each test configuration are independently identified before GNN inference, the model retains $R^2=0.959$, demonstrating robustness to realistic parameter-identification errors. Perturbation-based sensitivity analysis further shows that the learned parameter ranking is consistent with the expected thermal behavior. These simulation results indicate that the proposed framework can support computationally efficient thermal-state prediction for modular satellite systems. Full article

Moon and Mars mission architectures are shifting from short stays to longer surface stays in isolated, confined and extreme (ICE) conditions, where small crews live almost entirely inside pressurized habitats. As transit durations increase and lunar outposts evolve into more permanent bases, crews are exposed for longer periods to environmental hazards and non-terrestrial gravity that disrupt usual sensorimotor patterns. In this context, the habitat becomes the primary interface between human bodies and extreme environments, shaping how inhabitants perceive, move, orient themselves and sustain everyday routines away from Earth. This article develops a neuroarchitecture integrative model for indoor living in lunar and Martian habitats, treating space habitat resilience as a cognitive and experiential property of the human–habitat system. The model connects advances in space architecture and planetary science research with person–environment theories to show how interior form and indoor environmental quality (IEQ) influence attention, emotional regulation and social functioning under confinement. It distinguishes a macro scale, where planetary constraints compress human experience into Built Environments in Extreme Environments (BEXEs), from a micro scale, where habitability is organized into four functional clusters (somatic, operational, psychosocial and ludic-recreational). Conventional IEQ assessment addresses a small set of generic dimensions applicable to any building; here, these are reorganized into twelve cluster-specific dimensions, three per cluster, calibrated for confinement and for the absence of an accessible exterior. Focusing on room shape and proportions, degrees of enclosure and visual order as key interior variables, the model positions the habitat as an active co-regulator of cognition and argues for design agendas that move beyond minimum safety and volume standards toward evidence-informed cognitive habitability in emerging off-Earth settlements. Full article

The multi-beam tracking, telemetry, and command (TT&C) scheduling problem for large-scale low Earth orbit (LEO) constellations carrying thousands of satellites brings formidable challenges. Strict resource limitations and visibility constraints trigger combinatorial explosion of feasible scheduling solutions. This paper proposes a three-phase progressive multi-beam scheduler (3PMS), which disassembles the complex integrated scheduling problem into hierarchically tractable subproblems. Phase I adopts a priority-aware first-come-first-served (FCFS) strategy combined with a dual-heap preemption mechanism to guarantee the execution of emergency tasks. Phase II implements load-balanced beam allocation based on a load-balance scoring function. Phase III introduces optimal execution window selection within visible arcs. Experiments are performed on two LEO constellation scenarios: the first is a single-shell orbital configuration with 1500 satellites, and the second is a three-shell architecture comprising 6080 satellites. Under extremely limited beam resources (1 beam), the worst-case single-beam capacity is 708 tasks per day, assuming every task consumes the maximum duration of 120 s, corresponding to 47.2% coverage for 1500 satellites and 11.6% for 6080 satellites. As the number of beams increases to five, all algorithms achieve 100% coverage for the 1500-satellite constellation, while the 6080-satellite constellation requires twenty beams for near-complete coverage. 3PMS demonstrates significant advantages in load balancing (Gini coefficient reduced from 0.0993 to 0.0003) and link quality (C/N improved by 5.5 dB). This paper verifies the feasibility of multi-beam scheduling algorithms for TT&C missions of large-scale satellite mega-constellations. Full article

Radio-spectrum prediction can support proactive channel verification, sensing scheduling, and access decisions in unmanned aerial vehicle (UAV) systems. However, existing evidence remains fragmented across UAV-oriented prediction, label-efficient learning, and deployment-oriented efficiency. This article presents a structured critical review of studies identified in IEEE Xplore, Scopus, and the Web of Science Core Collection from database inception to 24 August 2026. Studies were included when they evaluated the prediction of a future spectrum-related condition and were excluded when they addressed only current-state sensing, static spectrum mapping, UAV detection or classification, localization, or superseded study versions. The final corpus comprised 76 retained publications, including 63 original studies and 13 background references. The original studies included 14 direct UAV-related prediction studies, 38 transferable radio-spectrum studies, and 11 UAV-scenario studies. The review shows that transfer learning currently provides the most consistent support for reducing target-domain data requirements when related source bands, sensing stations, or radio environments are available. Self-supervised and other unlabeled-data methods are particularly relevant to UAV missions that can continuously collect spectrum traces but cannot obtain extensive labels, whereas meta-learning remains promising but lacks a standardized support–query evaluation protocol for UAV spectrum prediction. Generative augmentation can expand limited training data, but its effectiveness depends on whether the generated samples preserve the temporal, spectral, spatial, and propagation characteristics of the target environment. Lightweight architectures, online learning, model compression, knowledge distillation, FPGA implementation, and embedded execution provide complementary efficiency mechanisms, but their benefits should be distinguished from one another. Embedded prediction-related processing has been demonstrated on Raspberry Pi and software-defined-radio platforms; however, no end-to-end validation of a UAV-mounted predictor during flight was identified. Overall, the evidence suggests a conditional trade-off among target-domain data requirements, prediction generalization, adaptation cost, and deployment efficiency rather than a universal conflict between few-shot learning and lightweight models. The principal research gap is the limited joint validation of UAV-acquired data, target-domain adaptation, efficient inference, uncertainty-aware decision making, and onboard hardware under representative flight conditions. Full article

Telecommunications infrastructures are increasingly cloud-native, multi-vendor, and mission-critical environments, integrating 5G core networks, virtualized network functions, and software-defined infrastructure that expand the operational attack surface and make robust security architecture a core engineering requirement. Zero-Trust Architecture (ZTA) has emerged as the leading technical paradigm for securing these environments through continuous authentication and policy-based access control; however, technical Zero-Trust controls alone do not guarantee successful deployment, and large-scale deployment introduces significant socio-technical and governance challenges that existing engineering-focused frameworks only partially address. This study makes two contributions: it provides longitudinal evidence on the impact of ZTA on organizational trust using an extended Technology Acceptance Model (TAM) that incorporates Perceived Trust, and it proposes a Proactive Trust Management Playbook (PTMP) for telecommunications infrastructure organizations that complements technical Zero-Trust deployments through organizational governance. The study draws on a five-wave repeated cross-sectional longitudinal case study conducted between 2020 and 2023 in a multinational telecommunications infrastructure organization. The five waves span three organizational phases, enabling an assessment of employee perceptions of usefulness, ease of use, and trust before and after ZTA deployment and following a structured governance intervention. The findings reveal a substantial decline in the composite TAM index following ZTA implementation (−24%, Cohen’s d = 1.12), with no meaningful spontaneous recovery over time (d = 0.08). A structured Communication Campaign was associated with a partial but incomplete recovery (d approximately 0.47), indicating that trust erosion under ZTA is measurable and suggesting that trust recovery is shaped more by governance interventions than by technological adaptation alone. The proposed PTMP complements technical Zero-Trust architectures by strengthening organizational trust and governance in telecommunications infrastructure environments. Full article

Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capabilities for semantic perception, task reasoning, and language-conditioned control. However, these capabilities do not by themselves produce flight-ready autonomy. We structure our analysis around a Perception–Cognition–Action (P–C–A) framework. At each layer, we identify the capabilities contributed by LLM-based components and examine how they connect to existing flight modules through input specifications, output representations, architectural coupling patterns, and safety mechanisms. Across the surveyed systems, LLMs extend UAV autonomy beyond fixed perception categories, scripted task plans, and pre-programmed controllers. However, field deployment depends on whether model outputs can be transformed into representations that downstream modules can parse, verify, and safely execute. Without adequate validation, captions, task plans, code, waypoints, and control commands may become failure points that propagate across the P–C–A loop. Our analysis highlights structured output contracts, independent safety barriers, and deterministic fallback mechanisms as key design elements for the reliable integration of LLM capabilities into UAV platforms. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

In long-range sequential orbital pursuit-evasion game scenarios, traditional Nash equilibrium algorithms rely on symmetric simultaneous decision-making assumptions. Such algorithms fail to fully exploit the sequential advantages of the leader-follower paradigm in practical space missions, and often yield conservative strategies with excessive fuel consumption. This paper proposes a Stackelberg equilibrium-based control algorithm for sequential pursuit-evasion games involving satellites with impulsive orbital maneuvers. A multi-stage impulsive maneuver game model incorporating orbit determination delays is established, and a bi-level nested optimization architecture is designed. The outer layer employs the Pattern Search algorithm to derive the optimal maneuver strategy of the pursuer, while the inner layer uses the Sequential Quadratic Programming (SQP) algorithm to obtain the optimal response strategy of the evader. Simulation results demonstrate that, compared with the traditional Nash-equilibrium Action-Reaction Search (ARS) algorithm and the greedy algorithm neglecting evader maneuvers, the proposed algorithm achieves a shorter terminal relative distance with lower fuel consumption. Full article