Resource
2026 EN
Gustavo Muller Hauck · Cleverson Bringhenti · Mauricio Andres Varela Morales
+4 more
Aviation plays a fundamental and valuable role in the modern world, yet it faces significant challenges, including high fuel costs and substantial environmental pollution. These issues have prompted the aviation industry to establish emission standards and develop less-polluting propulsion systems, such as those utilizing synthetic fuels, fuel cells, and electrification. Among these, electrification holds promise as a potential solution for reducing emissions in commuter aircraft, despite the mass limitations posed by batteries. In this study, a methodology was developed and implemented within in-house software to simulate the performance of a commuter aircraft with a hybrid-electric propulsion system. The analysis focused on key metrics like fuel economy and climb time to cruise altitude. The EMB-120 Brasilia was chosen as the base aircraft for this research. Its long-standing use by the Brazilian Air Force (FAB), the authors' extensive familiarity with its performance, and the availability of experimental data for lift and drag coefficients made it an ideal model for our simulations. To evaluate the performance of the hybrid propulsion system and compare it with the standard case, a gas turbine was utilized as the primary engine. Mathematical models were developed for the PW118 gas turbine, which powers the real aircraft that was considered the standard case, and for the PT6A-68C, which was suggested as a substitute for the hybrid system. To evaluate the aircraft's performance, a standard mission was simulated on a short-range route of 926 km (500 NM), flying at an altitude of 7,620 m (25,000 ft), a common mission that is used by Brazilian Air Force. For hybrid simulations, battery packs were tested with specific energies ranging from 0.125 kWh/kg to 0.750 kWh/kg, in multiples of the initial value. Batteries with 0.250 kWh/kg were considered the current state-of-the-art, while the 0.750 kWh/kg packs represent an extreme upper bound associated with far-future technological developments. The results demonstrated significant performance gains depending on the chosen battery technology and the power split, the hybrid system achieved fuel savings of up to 18% to 19% and reduced climb time by 17% to 25%.
Resource
2026 EN
Ruben Schnitzler · Tobias Fink · Dominik Koch
+2 more
Silicon carbide (SiC) MOSFETs are increasingly used in automotive applications due to their ability to operate at high frequencies and power levels, with high partial-load efficiency. However, gate-switching instability (GSI), which describes cycle-dependent degradation of the threshold voltage due to bipolar gate-switching, poses a major reliability concern. While extensive research exists on this phenomenon’s influence on static characteristics, such as on -resistance and threshold voltage, the impact of GSI on switching losses remains largely unexplored. This work provides a detailed analysis of this degradation mechanism on hysteresis, gate charge, capacitances, and switching losses. Key parameters, including drain current, drain voltage, and both positive and negative gate-to-source voltages, are examined to develop an aging-dependent switching loss model. The results show that after ${10 12}}$ switching cycles at 18/–9 V gate–source voltage stress, turn-off losses slightly decrease in a planar 1.2 kV, 63m $\Omega $ SiC MOSFET. Furthermore, a current-dependent increase in turn-on losses is observed. Further analysis reveals that the degradation in threshold voltage is directly proportional to the increase in switching losses and can be effectively modeled by a decrease in the overdrive voltage for turn-on and an increase in the overdrive voltage for turn-off. Finally, the influence of degradation on switching and conduction losses is evaluated for a typical mission profile, yielding 2.2% degradation at 15V and 1% at 18V positive gate–source voltage.
Resource
2026 EN
Aleksi Karhunen · Teemu Hakala · Vaino Karjalainen
+1 more
Interest in utilizing autonomous uncrewed aerial vehicles (UAVs) for under-canopy forest remote sensing has increased in recent years, resulting in the publication of numerous autonomous flight algorithms in the scientific literature. To support the selection and development of such algorithms, a reliable comparison of existing approaches based on published studies is essential. However, reliable comparisons are currently challenging due to widely varying experimental setups and incomplete reporting practices. This study proposes a standardized experimental setup for evaluating autonomous under-canopy UAV systems to fill this gap. The proposed setup emphasizes quantitative reporting of forest complexity, visual representation of test environments, execution of multiple repeated flights, and reporting of flight success rates alongside qualitative flight results. In addition, flights at multiple target speeds are encouraged, with reporting of realized flight speed, mission completion time, and point-to-point flight distance. The proposed setup is demonstrated using a lightweight lidar-based quadrotor employing state-of-the-art open-source algorithms, evaluated through extensive experiments in two natural boreal forest environments. Based on a systematic evaluation of the original system, several improvements were introduced. The same experimental protocol was then repeated with the optimized system, resulting in a total of 93 real-world flights. The optimized system achieved success rates of 12/15 and 15/15 at target flight speeds of 1 m/s and 2 m/s, respectively, in a medium-difficulty forest, and 12/15 and 5/15 in a difficult forest. Adoption of the proposed experimental setup would facilitate the literature-based comparison of autonomous under-canopy flight systems and support systematic performance improvement of future UAV-based forest robotics solutions.
Resource
2026 EN
Rafal Sienicki · Manuel A. Cervera · Philip H. W. Leong
Backscatter sounder systems co-located with Over-the-Horizon Radar (OTHR) can be leveraged to provide a near real-time characterisation of the ionospheric propagation conditions to aid the OTHR surveillance mission. Ionogram inversion can be performed using features derived from a backscatter ionogram to infer the optimum OTHR operational parameters and to enable the geolocation of the detected targets. In this paper, we demonstrate the application of a deep learning segmentation model on a backscatter ionogram dataset obtained from the Jindalee Operational Radar Network (JORN) frequency management system to extract the F2 leading edge feature. Our results show that deep learning is a viable method for leading edge feature identification and extraction in backscatter ionogram imagery.
Resource
2026 EN
Richard de Jeu · Susan Steele-Dunne · Timothy Lang
+4 more
The derivation of geophysical parameters from passive microwave observations over land has always been challenging. Soil conditions, land cover, and the atmosphere affect the measurements to varying degrees, and it is difficult to isolate these individual contributions. In this study, we assess whether multi-angle observations provide additional information that can strengthen existing retrieval algorithms. Between October and November 2024, a series of airborne flights carrying the Advanced Microwave Precipitation Radiometer (AMPR) were conducted over the United States. Three land-based flights with multi-angle observations from 0–45° and dual-polarized measurements at 10.7, 19.35, and 37.1 GHz were analyzed. The data showed a strong linear relationship between the microwave polarization ratio and the incidence angles within the 25–45° range (e.g. R 2 > 0.9 for 71.2% of all flight scans analyzed at 10.7 GHz). The linear model for the polarization ratio showed a similar performance in terms of Root Mean Square Error (RMSE) to simulations based on a τ −ω radiative transfer model and commonly used assumptions. The observed linearity was further evaluated with satellite observations from the Advanced Microwave Scanning Radiometer (AMSR2). This evaluation confirmed the observed linearity across all three frequencies. The slope of the relationship between the polarization ratio and the incidence angle was calculated for each multi-angle flight scan, which was sensitive to both soil moisture and vegetation. This new parameter, which was derived from multiple observations, appeared to be consistent in time and space, revealing similar patterns along flight lines acquired at different times. The slope was used as input in regression models to derive soil moisture. A model solely based on 10.7 GHz data revealed a strong correlation ( R 2 = 0.81) with Level-3 soil moisture from the Soil Moisture Active Passive (SMAP) mission, demonstrating the potential of multi-angle retrievals with established soil moisture products.
Resource
2026 EN
Yi Xiao · Taoyong Jin · Jiancheng Li
+3 more
The deflection of the vertical (DOV) carries abundant gravitational information and serves as a critical input for gravity anomaly derivation, rendering it a core topic in satellite altimetry research. The Surface Water and Ocean Topography (SWOT) mission, a new-generation satellite altimetry program, delivers two-dimensional (2-D) wide-swath sea surface height (SSH) data via its unique interferometric measurements. While DOV retrieval from Along-track and Cross-track observations is well established, incorporating additional directional observations into the inversion model remains optional. To optimize multi-directional DOV estimation, we propose a novel Multi-Directional Least Squares Adjustment (MD-LSA) method, and conduct case studies in a part of the Philippine Sea. Unlike conventional LSA, which employs a fixed set of observation equations, MD-LSA selects an optimal equation subset for each grid point based on predefined criteria, thereby enhancing DOV retrieval accuracy across heterogeneous regions. Simulation results demonstrate that MD-LSA outperforms traditional LSA across varying prior information accuracy levels. Specifically, with random-error-only observations and high-quality prior data, it achieves 64.5% and 54.0% accuracy gains for the north and east DOV components, respectively, relative to the two-direction solution. Real-data experiments verify that MD-LSA achieves the global optimum across different survey lines, with accuracy improved by 27.9% over two-direction LSA and 55.9% over least squares collocation (LSC). Furthermore, in the nearshore zone, MD-LSA residuals exhibit a near-zero concentrated distribution, reflecting its superior capability to retrieve nearshore gravitational signals. These findings confirm that MD-LSA well leverages the 2-D observational capability of SWOT SSH data, mitigates random errors, and accurately recovers coastal marine gravity field signals.
Resource
2026 EN
Marianna Turra · Silvia Proia · Alessandro Bonetti
+1 more
Effective path planning for multi-vehicle autonomous systems is essential to guarantee safe and efficient operation in industrial environments composed of both structured and unstructured settings. In the proposed framework, the overall environment is explicitly modeled as the integration of static and dynamic areas, each exhibiting distinct spatial and temporal properties. Static areas correspond to structured zones, such as corridors or production lines, and are represented by roadmaps with fixed topology. Dynamic areas, instead, represent unstructured portions of the environment and are modeled as gridmaps to enable adaptive navigation under changing occupancy conditions. Building upon this representation, a hybrid roadmap unifying static and dynamic areas is proposed, with a hierarchical control architecture for coordinated multi-vehicle navigation. The high-layer planner determines the optimal sequence of areas to traverse by solving a multi-objective optimization (MOO) problem that balances efficiency, safety, and traffic-related factors. On the other hand, the low-layer planner generates feasible paths within each area according to its structural constraints. The MOO formulation is embedded in a bi-level optimization framework, where the outer-level employs a quasi-adaptive self-tuning process for objective weights, addressing the limitations of subjective or suboptimal weight selection. Experimental validation across two realistic industrial layouts demonstrates improved mission efficiency, reduced travel-time variability, and scalability with fleet size, confirming the effectiveness of the proposed method for Industry 4.0 applications.
Resource
2026 EN
I. Sandberg · C. Papadimitriou · S. Aminalragia-Giamini
+8 more
The first Norwegian Radiation Monitor (NORM) sensor unit, flying aboard the Arctic Satellite Broadband Mission (ASBM), provides critical information on the space radiation environment along its three-apogee (TAP), 16-hour highly elliptical orbit (HEO). This work reviews the first year of NORM measurements, presenting the first detailed evaluation, analysis, and validation of radiation environment measurements along the TAP orbit, specifically focusing on trapped electrons and solar particle radiation. A series of validation studies demonstrate the inter-consistency of NORM measurements relative to the unit’s on-ground and numerical calibration, as well as with measurements from other radiation monitors. In addition, comparisons between NORM flux measurements and electron radiation belt specification models are presented. The results show that NORM provides high-quality and high-resolution measurements of varying electron differential fluxes within the 0.7–5.3 MeV energy range. Furthermore, NORM provides high-quality differential flux measurements of solar energetic protons within the 12–88 MeV energy range. ASBM/NORM datasets are available to EU users and collaborators, providing an invaluable asset for developing and validating space radiation environment models.
Resource
2026 EN
Jacob Schaffner · Michael Caplinger · Michael Ravine
+11 more
Juno’s JunoCam instrument has been exposed to the Jovian radiation environment for over nine years. It survived the Juno primary mission with little degradation, but during the extended mission has suffered two different radiation-induced problems, the first involving a start-up issue with a linear regulator and the second involving CCD image sensor performance. Both anomalies were overcome with high-temperature annealing. Here, we describe the sequence of events and our subsequent responses to maintain instrument functionality, through orbit 57 and beyond. The JunoCam experience may inform the design of heating systems for future spacecraft headed to high-radiation environments, as well as the operations flows needed to call upon them quickly and flexibly for annealing in a spacecraft emergency or for periodic preventative annealing cycles.
Resource
2026 EN
Alba Jano · Serkut Ayvasik · Yash Deshpande
+1 more
Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduce QUEST , a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment, QUEST supports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show that QUEST consistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows that QUEST achieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.