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2026 EN
Qingpeng Wu · Wei Cheng · V. Chandrasekar
+4 more
Understanding global precipitation microphysics is essential for quantitative precipitation estimation (QPE) and numerical weather prediction (NWP). Using nine years (2015-2023) of the Global Precipitation Measurement (GPM) mission Dual-frequency Precipitation Radar (DPR) observations over the quasi-global domain (67°S-67°N), we characterize precipitation particle size distribution (PSD) properties for rainfall and snowfall. We analyse the mass-weighted mean diameter ( D m ), normalized intercept parameter ( N w ), radar reflectivity ( Z ), and dual-frequency ratio ( DFR ), stratified by land/ocean, convective/stratiform type, altitude, and phase. Rainfall rates are grouped into six classes and snowfall rates into five classes to quantify intensity dependence. To interpret DPR dual-frequency signatures, we develop an observation-constrained forward-scattering framework that adopts DPR-consistent PSD and particle-shape assumptions and performs T-matrix simulations for rain, dry snow, and wet snow with varying liquid-water volume fraction ( F vw ), evaluated against nine-year near-surface DFR-Z statistics. Quasi-global PSD peaks ( D m ∼1.0 mm; log 10 N w ∼3.4) closely resemble oceanic precipitation, and convective and stratiform regimes substantially overlap in D m (0.9-1.8 mm) and log 10 N w (3.0-4.0). Vertically, D m skewness and kurtosis decrease from 2-6 km, with distinct features near ∼10 km and ∼15 km. Rainfall DSDs become more variable at high intensities (>5 mm h -1 ), whereas snowfall PSDs remain comparatively stable ( D m mainly 1.0-1.5 mm; log 10 N w ∼3.4). Forward-scattering comparisons show that dry-snow assumptions cannot reproduce the observed DFR-Z space, while increasing F vw yields close agreement, indicating that near-surface snowfall is consistent with wetted melted particles. These results provide observation-based constraints for improving solid-hydrometeor scattering representations in DPR snowfall retrievals and microphysical parameterizations in NWP.
Resource
2026 EN
Rong Wang · Yongjiu Feng · Yuhao Wang
+10 more
Mars landing site selection requires a multi-criteria suitability assessment that balances scientific potential and engineering constraints. However, conventional methods often employ uniform global weighting, which may oversimplify spatial heterogeneity in terrain risks and mission priorities across diverse Martian environments. This study proposes the SH TOPSIS method, a spatially heterogeneous extension of the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) framework. By implementing engineering safety zoning and differentiated weighting schemes, SH-TOPSIS captures local environmental variations that often obscured by global averaging methods. Application to Jezero Crater identifies two high-suitability areas, including a delta area aligned with the Mars 2020 landing ellipse. While maintaining high structural similarity (SSIM = 0.935) with conventional models, SH-TOPSIS better preserves boundary complexity and local suitability gradients—features that are critical for landing assessments. The proposed SH-TOPSIS provides a robust quantitative tool for early-stage regional screening, facilitating subsequent task specific engineering optimization and landing ellipse refinement. Furthermore, its flexible weighting configuration enables scenario-based sensitivity analysis, supporting the exploration of diverse science–engineering trade-offs in preliminary site selection.
Resource
2026 EN
V. Dehaye · K. Dassas · P. Fanise
+8 more
Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated strong potential for retrieving geophysical parameters related to surface properties, as confirmed by numerous in situ, airborne and satellite experiments. In particular, the recently launched ESA HydroGNSS mission aims to exploit GNSS-R observations for the monitoring of soil moisture, inland water bodies, and vegetation parameters at the global scale. In this context, this study introduces a modified version of the French airborne GNSS-R instrument GLORI (GLObal Navigation Satellite System Reflectometry Instrument), originally proposed in 2015. While the first GLORI instrument provided polarimetric measurements limited to the GPS L1 frequency band, the modified-GLORI (M-GLORI) extends these capabilities to multiple GNSS configurations, including GPS L1 and L5 as well as Galileo E1 and E5 frequency bands, fully aligned with the frequency bands used by HydroGNSS. Following a series of laboratory qualification tests, an airborne measurement campaign was conducted in June 2024, encompassing a total of five flights over southwestern France. The campaign covered a variety of land cover types, including agricultural landscapes and forested aereas, but only data acquired over agricultural plots are used for band intercomparison. These flights were conducted in parallel to in situ ground-truth measurements over more than ten agricultural test fields. This paper presents the instrument design, calibration strategy, reflectivity statistics, and band-to-band consistency analysis of the M-GLORI instrument. Preliminary analysis of the collected data indicates that the M-GLORI instrument operates within its specified performance parameters. An analysis of the performance of each configuration (frequency band, polarization) for GPS and Galileo acquisitions is presented. An inter-comparison of measurements of all proposed bands, after normalization to one incidence angle and inter-calibration, is discussed. These results highlight the strong potential of combining GPS L1 and L5 bands and Galileo E1 and E5 bands for the accurate retrieval of land surface geophysical parameters.
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2026 EN
Riccardo Longari · Francesca Scala · Gabriella Gaias
+2 more
Single-pass across-track synthetic aperture radar (SAR) interferometry using formation flying is a well-established technique for the generation of high-quality digital elevation models (DEMs), as it avoids temporal decorrelation and allows for long baselines, thus leading to high height accuracy. A successful implementation, employed in the TanDEM-X mission, foresees the use of a Helix formation, which entails a reduced control effort to be maintained in the presence of external perturbations. However, an intrinsic limitation of the Helix concept lies in the inherently time-varying baseline and height of ambiguity (HoA), which results in non-homogeneous DEM performance. This letter proposes an analytical method to minimize the HoA variability at the global level by optimizing the range of incidence angles used at each latitude. The approach is primarily aimed at systems, such as TanDEM-X, for which the area to be imaged becomes significantly smaller than the ground access range as the latitude increases. A two-step approach is presented: An initial approximate solution is derived in closed form and subsequently refined. The method is validated against numerical results and compared with simpler strategies involving fixed incidence angles, showing reduced variation of the HoA for different formation parameters. The procedure presented in this work could enable a better operation of current interferometric SAR systems and foster an improved design of future ones.
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2026 EN
Manuel Boldrer · Vit Kratky · Viktor Walter
+1 more
In this letter, we present a distributed algorithm for flocking in complex environments that operates at constant altitude, without explicit communication, no a priori information about the environment, and by using only on-board sensing and computation capabilities. We provide sufficient conditions to guarantee collision avoidance with obstacles and other robots without exceeding a desired maximum distance from a predefined set of neighbors (flocking or proximity maintenance constraint) during the mission. The proposed approach allows to operate in crowded scenarios and to explicitly deal with tracking errors and on-board sensing errors. The algorithm was verified through simulations with varying number of UAVs and also through numerous real-world experiments in a dense forest involving up to four UAVs.
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2026 EN
Juan Bravo-Arrabal · Ricardo Vazquez-Martin · J. J. Fernandez-Lozano
+1 more
This paper presents field-validated Search and Rescue (SAR) use cases that demonstrate how the co-design of mobile robots and communication systems can support an edge–cloud architecture built on 5G Standalone (SA). The main goal is to enable effective cooperation among multiple robots and professional first responders in realistic, infrastructure-challenged environments. Our deployments include Hybrid Wireless Sensor Networks (H-WSNs) for risk and victim detection, smartphones integrated into the Robot Operating System (ROS) as edge devices for mission requests and path planning, real-time Simultaneous Localization and Mapping (SLAM) offloaded to Multi-access Edge Computing (MEC), and Uncrewed Ground Vehicles (UGVs) for casualty evacuation under different navigation modes. These experiments, conducted in collaboration with professional first responders, highlight the need for intelligent network resource management to balance low-latency and high-bandwidth traffic. Network slicing emerges as a key enabler for ensuring that critical emergency services remain available under adverse communication conditions. The paper distills architectural requirements, lessons learned, and open challenges that future 5G-Advanced and 6G technologies must address to strengthen emergency response capabilities.
Resource
2026 EN
Carlos Alberto Silva · Amy Neuenschwander · Caio Hamamura
+22 more
The extension of NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission beyond the cryosphere to include the study of vegetation and the land surface has significantly advanced global Earth observation. This review synthesizes findings from 293 peer-reviewed articles and provides a comprehensive assessment of the mission’s contributions to terrestrial ecosystem science. We begin by outlining the mission’s objectives, instrumentation, and core data products—particularly ATL03 (geolocated photons) and ATL08 (terrain and canopy heights)—which have supported a wide array of applications in forestry, terrain analysis, and environmental monitoring. Our analysis shows that most studies focus on temperate and tropical broadleaf forests, with a strong emphasis on estimating canopy height, terrain elevation, and forest structure. Terrain metrics derived from ICESat-2 products typically achieve high accuracy, while vegetation variables, such as canopy height, cover, leaf area index (LAI), and aboveground biomass, demonstrate moderate to strong accuracies across biomes. While parametric models remain the most used approach, machine learning methods are expanding, and more than one third of the reviewed literature incorporates synergistic analyses with other satellite missions. Despite its versatility, ICESat-2 remains underutilized in several key domains—including nonforest vegetation, fire ecology, wildlife habitat assessment, urban monitoring, and disturbance detection—mainly due to the scarcity of standardized algorithms and validated reference datasets. However, the growing availability of open source processing tools presents a significant opportunity for expanding the user base and fostering innovation in these emerging areas. Future advancements, such as the forthcoming ATL18 gridded canopy height and terrain products, alongside synergies with missions like the European Space Agency (ESA)’s BIOMASS and the NASA–Indian Space Research Organisation (ISRO) Synthetic Aperture Radar (NISAR), promise to enhance multisensor integration and ecosystem monitoring. Overall, ICESat-2 continues to evolve as a powerful resource for characterizing vegetation structure, ecosystem dynamics, and land surface processes on a global scale.
Resource
2026 EN
Luis Eduardo Arenas-Deseano · Juan Manuel Ramirez-Cortes · Pilar Gomez-Gil
Learning by doing is a cornerstone of technical education and plays a vital role across all engineering disciplines, particularly in instrumentation and measurement applications [1]. Furthermore, when the topics addressed in coursework and laboratory activities have a direct application impacting the community, students become highly motivated to apply the theoretical concepts and, consequently, to further explore and deepen their technical skills learned in the classroom [2],[3]. This concept is in full correspondence with the IEEE's mission to advance technology for humanity. The student project presented in this paper uses a sensor-based system which monitors air quality through its operation in a hybrid wireless system. The system provides users with uninterrupted data access through its combination of cloud resources and smartphone connectivity features. To support preventive decision making, an Autoregressive Integrated Moving Average (ARIMA) model is implemented to forecast short-term air quality trends based on historical data [4]. The system uses a naive Bayes classifier to determine risk levels for people who have been exposed to different situations. A cloud-based database stores cumulative daily exposure metrics and facilitates long-term trend analysis. The prediction and classification system allows for modular de sign which enables users to test different methods including AI-based models for system improvement purposes.
Resource
2026 EN
Fabio Maresca · Francesco Devoti · Guillermo Encinas-Lago
+3 more
Search and Rescue (SAR) operations in complex indoor environments require efficient robotic coordination to ensure timely victim detection while optimizing area coverage and operation time. However, relying solely on exploration may be insufficient, as early victim localization can enhance path planning and prioritize victim access over the exploration of empty areas. In this work, we integrate Radio Frequency (RF)-based victim localization into State-of-Art multi-robot exploration approaches to guide decision-making and prioritize areas with higher likelihood of victim presence. Specifically, we perform probabilistic victim localization using Angle of Arrival (AoA) triangulation of RF signals, enhanced by Received Signal Strength Indicator (RSSI) filtering. This information is then used to dynamically update exploration priorities and replan robot paths toward potential victim locations. We evaluate the proposed method using the Cramér-Rao Lower Bound (CRLB) and Root Mean Square Error (RMSE). Extensive simulations confirm that the proposed approach significantly increases SAR efficiency, allowing up to ~34% of victims to be reached earlier in typical indoor environments and reducing the total mission time by up to 50%.
Resource
2026 EN
Muhammad Morshed Alam · Md. Alomgir Kabir · MD. Fahim Abrar Omey
+4 more
In air–ground collaborative networks, low-altitude unmanned aerial vehicle (UAV) swarms and high-altitude platform cooperate to ensure reliable and efficient data collection for ground users (GUs) in dynamic and mission-critical environments. Maintaining data freshness, quantified by the age of information (AoI), while minimizing energy consumption is highly challenging due to UAV mobility, heterogeneous agents, and limited resources. This paper formulates a joint optimization problem that simultaneously minimizes the long-term average AoI and energy consumption of UAVs and GUs under realistic constraints, including quality of service (QoS), queue size, and resource limitations. To tackle this complex mixed discrete–continuous problem, we propose a swarming behavior-integrated multi-agent multi-expert soft actor-critic (MA-MESAC) framework that synergizes swarm intelligence with multi-agent deep reinforcement learning. The actor network employs self-attention to model intra-agent dependencies, while the critic network uses cross-attention to capture inter-agent spatial–temporal correlations. We design a composite actor loss function by integrating the SAC-based policy objective with regularization terms that enforce fair resource allocation and physics-informed swarming behavior, ensuring stable and efficient learning. Moreover, we design a multi-objective reward function that explicitly accounts for stringent mobility, QoS, and resource constraints. Simulation results demonstrate that the proposed framework significantly enhances fairness-aware data freshness, energy efficiency, and convergence compared with existing baselines.