Search Results (482)

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

Avocado (Persea americana Mill.) is one of the most widely consumed fruit tree crops worldwide, with cultivation expanding rapidly beyond its Mesoamerican and northwest South America center of origin. In emerging secondary diversity centers such as East Africa, farmers have long propagated seedling naturalized populations that may hold valuable reservoirs of genetic diversity, yet these resources remain underexplored. To help fill this gap, this study developed the first genomic resources for avocados in Tanzania, where avocado has a long history of introduction and diversification dating to the first Arab incursions and Catholic missionary missions. Low-coverage whole-genome resequencing (lcWGS) data were obtained from 95 trees sampled in Tanzania across the low- to mid-altitude Morogoro region (n = 25) and the Southern Highlands—i.e., the Iringa (n = 20), Mbeya (n = 30) and Ruvuma (n = 20) regions. In order to guide racial assignation, sequences were merged with NCBI-available lcWGS data from 205 avocado trees, including 42 commercial varieties, with reported ancestry. Population stratification as inferred via maximum likelihood phylogenetic inference, genetic principal component analysis, and ADMIXTURE unsupervised clustering suggested that the sampled Tanzanian avocado trees were genetically closer to the West Indian race and more distant from the northwest South American Caribbean and Andean groups. Additionally, while the trees from the low- to mid-altitude region of Morogoro were almost exclusively West Indian type, some trees from the Southern Highlands aligned more closely with West Indian × Guatemalan and West Indian × Mexican hybrids. These trends were equally supported by a subset of 10,460 high-coverage (10×) SNP markers. Together these findings clarify the dynamics of avocado diversification in a secondary center in East Africa, spanning recent introductions from a single Mesoamerican race, adaptation to a wide range of locally geographic conditions, and farmer-driven selection matching local tribal preferences. Characterizing these locally adapted resources is key for identifying underrepresented yet promising provenances, developing resilient and sustainable horticultural production systems, and safeguarding the species’ global genetic heritage. Full article

With the growing applications of maritime unmanned systems in environmental monitoring, ocean patrol, and emergency response, achieving efficient multi-platform cooperation in complex and dynamic marine environments remains a critical challenge. Unmanned Aerial Vehicles (UAVs) provide flexible and high-coverage sensing capabilities but are constrained by limited energy capacity, whereas Unmanned Surface Vehicles (USVs) offer long endurance and can serve as mobile platforms and energy supply nodes. Existing studies mostly focus on single-factor optimization, lacking a systematic analysis of the coupled relationships among energy, information (communication and positioning), and task decision making. To address this problem, this paper proposes an Energy–Information–Decision Coupling Optimization Method for Cooperative Maritime Unmanned Systems. A unified coupling model is established to integrate task completion, energy consumption, communication delay, and replenishment scheduling into a multi-objective optimization framework. A bi-level optimization algorithm is designed: the upper layer optimizes USV trajectories and energy supply strategies, while the lower layer optimizes UAV path planning and task allocation. A closed-loop adaptive mechanism is incorporated to achieve optimal cooperation under dynamic tasks and energy constraints. Extensive simulations combined with real-world experimental data are conducted to evaluate the method in terms of mission efficiency, energy balance, communication latency, and system robustness, with ablation studies quantifying the contribution of the coupling module. Results demonstrate that the proposed method significantly outperforms non-coupled or single-factor optimization strategies across multiple performance metrics: it achieves a task completion rate exceeding 93%, reduces total energy consumption by approximately 6% and replenishes waiting latency by over 28% compared with the decoupled baseline method. This effectively enhances the cooperative efficiency and robustness of maritime unmanned systems, and provides theoretical and methodological guidance for large-scale, complex ocean missions. Full article

This paper presents the H-CoRE (Heterogeneous Cooperative Multi-Robot Execution) framework designed to enable autonomous multi-robot operations in GNSS-denied environments. Built on an ROS 2-based architecture, H-CoRE enables collaborative, structured task execution through standardized software stacks. Each robot’s stack combines a high-level executive system with an agent-specific motion layer and leverages multi-sensor fusion for localization and mapping. The framework is inherently reconfigurable, allowing individual agents to operate autonomously or as part of a multi-robot team for collaborative missions. In the considered scenario, the system integrates aerial and ground vehicles, a fixed pan–tilt–zoom camera, and a human supervisory interface within a unified, modular infrastructure. The proposed system has been deployed in indoor, GNSS-denied environments, demonstrating autonomous navigation, cooperative area coverage, and real-time information sharing across multiple agents. Experimental results confirm the effectiveness of H-CoRE in maintaining general awareness and mission continuity, paving the way for future applications in search-and-rescue, inspection, and exploration tasks. Full article

To advance our understanding of atmospheric processes and climate dynamics, improved knowledge of outgoing long-wave radiation (OLR) spectral emission is essential. The FORUM mission, selected for the ninth cycle of the European Space Agency’s Earth Explorer programme, is specifically designed to address the long-standing observational gap in the far-infrared (FIR) spectral region. When combined with measurements from the IASI-NG instrument, FORUM will provide complete spectral coverage of Earth’s OLR emission (spanning 100 to 2760 cm⁻¹ wavenumber, or 3.62 to 100 μm wavelength), thereby enabling robust climate model validation and enhanced understanding of climate change processes. While IASI-NG’s primary mission is to support numerical weather prediction, FORUM is designed to measure key climate variables, which also enable the retrieval of atmospheric parameters in the troposphere and lower stratosphere. In this study, we assess the information content of FORUM and IASI-NG measurements for atmospheric profiling through a simulation-based approach. Synthetic retrieval products are generated using a linearized formulation of the retrieval transfer function, allowing an efficient and physically consistent evaluation of the sensitivity of the two instruments to atmospheric temperature and water vapor profiles. The analysis reveals a non-negligible sensitivity of FORUM to atmospheric temperature extending into the stratosphere, resulting in significant information content at altitudes higher than previously reported. This finding highlights the potential of far-infrared observations to contribute to atmospheric temperature profiling beyond the lower troposphere. The complementary capabilities of FORUM and IASI-NG suggest that their combined use can enhance the characterization of the atmospheric thermal structure. These results represent a first step toward evaluating the potential role of FORUM Level-2 products in future numerical weather prediction applications. Full article

The Atomic Clock Ensemble in Space (ACES) mission, currently operating aboard the International Space Station (ISS), is designed to provide high-precision time and frequency measurements and to test fundamental aspects of relativistic physics. Gravitational redshift (GRS), a fundamental prediction of General Relativity (GR), implies that clocks positioned at different gravitational potentials experience relative time dilation. Previous GRS experiments have focused primarily on microwave technologies, with negligible experimental coverage in the optical domain, particularly for ground-to-space links. Motivated by the European Laser Timing (ELT) experiment and the high-precision laser-cooled cesium clock aboard ACES, we introduce and evaluate an optical time-transfer method designed to achieve high-accuracy measurements of GRS. In the absence of actual ELT/ACES optical data, a high-fidelity numerical simulation framework was developed to assess the performance of this method. The framework incorporates representative ELT/ACES mission parameters, including the space-based cesium clock and the H-MASER clock located at the reference ground station, both providing frequency stability at the level of ${10}^{-15}$ for 1000 s averaging time. Applying a $\pm 1\sigma$ filtering criterion, we obtain a simulated dataset comprising 33 ELT/ACES passes, representing a total observation time of 4.38 h over a single week. Analysis of this high-fidelity dataset reveals a GRS deviation from GR of $(-7.19\pm 0.63)\times {10}^{-5}$, achieving a 3.4 orders of magnitude improvement over the best previous laser-ranging experiment conducted at the University of Maryland (UMD), USA, 51 years ago. These simulation results demonstrate that the optical time-transfer link constitutes a powerful tool for testing fundamental physics and, when combined with next-generation optical atomic clocks, enables unprecedented capabilities in space-based timekeeping and geoscience applications. Full article

In the framework of Harmony, the 10th ESA Earth Explorer mission, this paper presents a general methodology to optimize the formation parameters relevant to the single-pass, cross-track interferometry (XTI) configuration. The proposed method considers the requested height sensitivity and the maximum allowable temporal lag and derives the formation parameters for an optimal coverage over different ranges of latitudes by leveraging the relative eccentricity and inclination vector formalism. Our approach addresses the problem of interferometric coherence through the wavenumber support alignment method which is able to take into account the specific geometry of XTI in Harmony, which is a long-baseline multistatic configuration with large squint angles. The analysis is completed by an estimate of the propellant budget, required to maintain the optimized formation, which can be used as a further trade-off parameter within the mission design process. The results indicate that the passively stable helix configuration (with relative eccentricity and inclination phase angles set to 90°) provides a robust solution at equatorial and mid-latitude regions with perpendicular baselines up to the order of 1 km and temporal lag below 10 ms. Conversely, for high-latitude and polar regions, two alternative strategies are identified, revealing a trade-off between enhanced interferometric performance and increased formation maintenance requirements. For polar regions, a first strategy adopts relative eccentric and phase angles of 10°, achieving satisfactory performance across most latitudes, whereas an alternative approach retains the value of 90° and optimizes the formation specifically for high latitudes. These two options result in distinct station-keeping demands since the former strategy requires a $\Delta \mathrm{V}$ budget about two orders of magnitude higher, while the latter remains within a $\Delta \mathrm{V}$ range that is typical for missions of the considered class. Full article

As the core infrastructure for providing all-weather, full-coverage, high-speed, and diversified information services, spatial information networks (SINs) possess significant social, economic, and military value. However, due to the inherent characteristics of their network architecture, SINs are susceptible to core service paralysis and functional failure under large-scale targeted attacks or random disturbances, posing a critical bottleneck that constrains their stable operation. Current research on SIN vulnerability is predominantly confined to a single network topology perspective, lacking an integrated consideration of the task execution perspective. Consequently, it fails to accommodate the dual requirements of “network topology stability” and “task execution effectiveness”. To address the aforementioned research needs and challenges, this study adopts a “topology-task” dual-perspective fusion approach and proposes a vulnerability analysis framework for SINs that integrates multi-layer networks and hypernetworks. First, a two-layer SIN topology model encompassing the user layer and the satellite layer is constructed. Leveraging hypernetwork theory, information tasks involving multiple network entities are formally defined, and an integrated multi-layer hypernetwork model is established. Second, based on distinct task types, three categories of task efficiency evaluation metrics are defined, and corresponding quantitative methods for calculating SIN vulnerability are derived. Third, during the vulnerability analysis phase, a novel strategy for identifying and removing overlapping nodes in hypernetworks is introduced to enable precise localization of critical nodes within the network. Concurrently, a pre-attack node hardening strategy is designed to minimize the impact of attacks on network performance. Finally, through systematic analysis of vulnerability performance and critical node characteristics under different node removal strategies, the results demonstrate enhanced network performance. The effectiveness of the proposed method is validated by comparing the defense performance of the hardening strategy across various attack scenarios. To verify the feasibility and superiority of the proposed method, this study designs 5 × 5 groups of simulation experiments with varying network parameters. The results indicate that, compared with traditional methods, the proposed strategy can more accurately identify core nodes affecting the stable operation of SINs, significantly reducing network vulnerability and improving network survivability. In addition, a comprehensive sensitivity analysis of SIN vulnerability is conducted from three key influencing dimensions—mission scale, satellite count, and constellation configuration—clarifying the impact of each dimension on network invulnerability. Thus, this paper provides a reliable theoretical foundation and technical support for the planning, design, optimal deployment, and operation and maintenance management of SINs. Full article

Unmanned vehicles are becoming increasingly essential in executing high-risk missions in unknown environments such as search and rescue. As the complexity of operational environments escalates, carrying out unmanned tasks becomes cumbersome or even infeasible for a single vehicle, hampered by limited perception and operational constraints. Aiming at enhancing the flexibility of unmanned operations under complicated scenarios, this study introduces AGC-NeRF, an innovative air–ground collaborative exploration framework that harnesses the functional complementarity of UAVs and UGVs—enabling a UGV to navigate through a complex scenario with the assistance of a UAV via referencing a neural radiance map. First, a UAV is employed to collect aerial images for reconstructing the environment to be explored by a UGV, leveraging its aerial perspective to achieve wide-area coverage and global environmental perception that is unattainable for a single UGV. Concurrently, an innovative image saliency evaluation approach is introduced to meticulously select landmarks that are contributive to the UGV’s navigation system, yielding a pre-trained NeRF model of the operation scene. Then, a landmark-aware 6-DOF ego-motion estimator and collision-free trajectory optimizer are designed for the UGV based on the NeRF map. Finally, an online replanning architecture is established which relies on a ground station for NeRF training and state optimization by synergizing the trajectory planner and the state estimator, which forms a dual-agent vision-only navigation pipeline. Simulations and experiments validate that AGC-NeRF enables reliable UGV trajectory planning and state estimation in unknown environments, demonstrating superior efficacy and robustness of the air–ground collaborative paradigm. Full article

Remotely operated vehicles (ROVs) equipped with multibeam forward-looking sonar are widely used for underwater object search in environments where visibility is limited. Ensuring complete three-dimensional (3D) scan coverage within a bounded mission duration remains a challenging planning problem due to sonar beam geometry and vehicle motion constraints. This study presents a deterministic, geometry-driven framework for volumetric path planning of ROV-based forward-looking sonar scanning in predefined circular and rectangular underwater volumes. The proposed approach constructs layered planar scan trajectories by explicitly incorporating sonar detection range, horizontal and vertical beamwidths, and scan volume geometry. Mission duration is analytically estimated from path length and vehicle kinematic parameters, enabling systematic comparison among multiple planning strategies. To support qualitative interpretation of scan effectiveness, a distance-based target position certainty metric is introduced and combined with the active sonar equation to estimate likely target locations within the scanned volume. Simulation results under idealized sensing and motion assumptions demonstrate that the corrected zigzag pattern for rectangular scan areas, as well as the corrected zigzag-II and corrected arithmetic spiral-III patterns for circular scan areas, achieve complete volumetric coverage with bounded mission duration and consistent localization performance. The proposed framework provides a transparent analytical baseline for evaluating volumetric scan path planning strategies for forward-looking sonar–equipped ROVs. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

Civil registration of births and deaths underpins people’s legal identity, access to essential services, and evidence-based policy. Over the last two decades, the expansion of the National Health Mission (NHM) and the dramatic increase in institutional deliveries have created new opportunities to link maternal healthcare with critical event documentation. Primary health centres (PHCs) and community health centres (CHCs), which are frequently the initial point of contact for rural households, are emerging as important places for birthing and registration. Despite their expanding importance, the particular role of these grassroots facilities in birth registration results has not been thoroughly investigated. This study addresses that gap by assessing their role in increasing registration coverage among children under the age of five. We analyzed nationally representative data from the National Family Health Survey rounds 4 (2015–2016) and 5 (2019–2021). This study focused on children under five, examining the association between place of delivery and registration status. Descriptive analysis and multivariable logistic regression estimated the odds of registration across delivery settings. Pooled data from both survey rounds captured temporal shifts, and predicted probabilities were calculated for institutional deliveries, adjusting for socio-demographic covariates. The proportion of institutional births occurring in PHCs and CHCs rose from 30.5% to 34.7% between the two survey rounds. Registration among children delivered in these facilities increased from 80.8% to 90.2%, the highest gain among all delivery settings. Regression analysis showed that births in PHCs/CHCs were associated with 38% higher odds of being registered compared to private facilities. States designating PHCs and CHCs as official registrars, such as Delhi, Rajasthan, and Uttar Pradesh, reported the greatest improvements. Lower-level government health facilities are not only advancing safe delivery but also acting as pivotal nodes for civil registration. Their dual function creates a scalable model for integrating healthcare with legal identity creation, supporting equity and accelerating progress toward Sustainable Development Goal 16.9. Full article

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions. Full article

This study addresses the challenge of anticipatory resource allocation in field-deployed communication networks under dynamic unmanned aerial vehicle jamming. In such scenarios, energy supply is severely constrained. It cannot be replenished in real time, necessitating a one-time resource pre-allocation that must remain effective throughout the mission. To overcome these limitations, we propose a novel optimization framework consisting of four integrated components: (1) independent threat assessment via trajectory-coverage spatial mapping using digital elevation models and ray-tracing algorithms, (2) evidence-theoretic fusion of heterogeneous information sources—including objective intelligence data and subjective expert knowledge, (3) jamming distribution modeling through dedicated probability transformation algorithms for fixed-interval and continuous random jamming modes, and (4) decoupled resource-confidence optimization solved via convex programming. By employing evidence discount factors and Dempster’s combination rule, the framework quantifies reliability disparities. It integrates multiple heterogeneous sources and uses theoretically derived, forward-computable models—combining Binomial distributions, piecewise cubic Hermite interpolation, and uniform distribution assumptions—to efficiently convert threat basic probability assignments into jamming duration probability density functions. Extensive Monte Carlo simulations demonstrate significant improvement in mission assurance metrics, with consistent performance under diverse uncertainties. The approach is also validated in cross-domain applications using Bohai rescue data, confirming its utility in resource-limited, highly uncertain environments. Full article

This paper presents a multi-layer architecture designed to enhance the reliable autonomous flight of single and multiple quadcopters in simulation. The architecture leverages concepts inspired by the resilient spacecraft executive to hierarchically organize trajectory planning and flight control and integrates an extended Simplex framework that employs multiple candidate algorithms to provide safety assurance at each layer, with a supervisory program that adapts Simplex behavior based on system states and environmental conditions to enable high-level mission management. The approach is evaluated in bridge-inspection simulations under environmental uncertainties, including varying wind conditions and obstacles. Across multiple operating configurations and Monte Carlo simulation runs, the architecture achieves high coverage rates; notably, under high-wind conditions, it reduces average trajectory deviation by 66.2%. The results demonstrate proactive safety through graceful degradation in both trajectory planning and flight control under stress and off-nominal conditions. Full article