Office National d'Études et de Recherches Aérospatiales

We present a framework for adapting a large pretrained latent diffusion model to very high-resolution Synthetic Aperture Radar (SAR) image generation. The approach enables controllable synthesis of rare or unseen scenes beyond the training set. Rather than training a task-specific small model from scratch, we adapt an open-source text-to-image foundation model to the SAR modality, using its semantic prior to align prompts with SAR imaging physics (side-looking geometry, slant-range projection, and coherent speckle with heavy-tailed statistics). Using a 100k-image SAR dataset at 40 cm resolution, we compare full fine-tuning and parameter-efficient Low-Rank Adaptation (LoRA) across the UNet diffusion backbone, the Variational Autoencoder (VAE), and the Text Encoders. Evaluation combines (i) statistical distances to real SAR amplitude distributions, (ii) GLCM-based textural similarity, (iii) semantic alignment with a SAR-specialized CLIP model, and (iv) SAR-specific analyses, including layover–shadow geometry on ∼ 150 buildings and Point Spread Function (PSF) fidelity via spectral main-lobe widths. Our results show that a hybrid strategy — full U-Net tuning with LoRA on the text encoders and a learned token embedding — best preserves SAR geometry and texture while maintaining prompt fidelity reducing the KL divergence. The framework supports text-based control and multimodal conditioning (e.g., segmentation maps, TerraSAR-X, or optical guidance), opening new paths for large-scale SAR scene data augmentation and unseen scenario simulation in Earth observation.

Recent advances in Neural Ordinary Differential Equations (Neural ODE) have shown that high-fidelity aircraft dynamics can be learned from flight recorder data, but such proprietary datasets remain largely inaccessible. In this study, we extend these principles to open trajectory data by training a Neural ODE model on Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode~S Enhanced Surveillance (EHS) information retrieved from the OpenSky Network. The model focuses on reconstructing the vertical dynamics of transport aircraft using only openly available surveillance variables. The resulting framework learns continuous-time dynamics that remain physically consistent through embedded kinematic relations, demonstrating that realistic vertical profiles can be generated solely from open surveillance data. This work contributes to reproducible, data-driven performance modelling and supports the broader adoption of open, physics-guided learning methods in aviation research.

Understanding and controlling the dynamic interactions between fluid flows and solid materials and structures-a field known as fluid-structure interaction-is central not only to established disciplines such as aerospace and naval engineering, but also to emerging technologies such as energy harvesting, soft robotics, and biomedical devices. In recent years, the advent of metamaterials has provided exciting opportunities to rethink and redesign fluid-structure interactions. The idea of engineering the internal structure of materials that interface with fluid flows opens a new horizon for the precise and effective manipulation and control of coupled fluidic, acoustic, and elastodynamic responses. This review focuses on this relatively unexplored interdisciplinary theme with broad technological significance. Salient potential applications, such as reduction of fuel consumption in transport systems, efficiency of renewable energy extraction, noise mitigation, and resilience against structural fatigue, depend on controlling interactions among flow, acoustic, and vibration mechanisms. Flow control, for example, which spans a wealth of regimes such as laminar, transitional, turbulent, and unsteady separated flows, is strongly influenced by fluid-structure interaction. This review surveys and discusses conceptual frameworks that describe the interplay between fluids and elastic solids, with a focus on contemporary and emerging concepts. The paper is organised into three main sections: fluid-structure and flow-phonon interactions, flow-induced acoustic interactions with metamaterials, and exotic metamaterial concepts with potential impact on fluid-structure interaction. It concludes with perspectives on current challenges and future directions in this rapidly expanding area of research.

Measurements of electric potentials and currents in the Earth’s ionosphere are primarily influenced by the thermal plasma properties and the intensity of photoelectron emission from sunlit spacecraft surfaces. These measurements can be significantly affected by a plasma sheath that forms around the satellite to neutralize surface electric charging in contact with the ambient plasma. In the ionosphere, this sheath typically ranges from a few to several tens of centimeters in thickness and contains plasma that slightly differ from the surrounding undisturbed environment. Therefore, accurately characterizing the electrical properties of the sheath is essential for reliable data interpretation. In this study, we analyze ion plasma and electric field measurements taken at an altitude of 700 km by the Centre National d’Etudes Spatiales (CNES) detection of electro-magnetic emissions transmitted from earthquake region (DEMETER) microsatellite. The data were collected during specific orbital passes when onboard instruments operated in calibration mode. During these sequences, electric currents were intentionally injected from the electric field sensors into the surrounding plasma, enabling estimation of the sensor-to-plasma coupling resistance across the sheath. Plasma parameters obtained by fitting the resistance–current curve with a standard orbital-motion-limited (OML) model are compared with those derived from the ion instrument on the same spacecraft. The dataset confirms the consistency of plasma measurements performed by the two instruments.

Abstract As artificial intelligence (AI) increasingly mediates human decision-making, there is growing concern about its impact on the human sense of agency (SoA)—the subjective experience of controlling one’s actions and their outcomes. Opacity and automation may undermine this experience, raising both ethical and cognitive challenges. In this study, we systematically examined how varying levels of automation, explanatory transparency, user engagement, and decision conflict shape explicit (Feeling of Control) and implicit (Intentional Binding) measures of agency. Participants interacted with an AI system in a simulated autonomous driving scenario, where we manipulated the degree of automation ( Motor-control vs AI-assisted condition ), the presence and type of explanation ( Proximal , Distal , Combined , No-Explanation ), and the level of conflict between user intentions and AI choices. Automation significantly reduced explicit agency, but explanatory cues—especially when combining Proximal and Distal rationales—partially restored it. Critically, explanation effectiveness depended on user engagement and goal-outcome conflict: transparency lost some of its restorative effect when participants declared their intentions but outcomes mismatched, yet reinforced predictive integration under high cognitive involvement. These findings highlight the need for adaptive, user-sensitive transparency strategies to preserve agency in automated environments.

The operating range of compressors is limited by the outbreak of flow instabilities with a highly negative impact on performance. Active control such as air tip injection delays the onset of stall. Previous research [1] indicates that optimal performance is achieved when the air injection is tangential to the casing with a relative blowing yaw angle closely matching the blade stagger angle. As the relative blowing yaw angle changes with the operating point, the study focuses on the development of a device which can adjust its blowing angle using two co-flow jets and the Coandă effect. An extensive numerical parametric study is conducted involving various geometries. To fasten and ease the meshing process, a mesh adaptation technique is employed. A first bi-dimensional case is examined to validate the method on a Coandă-based co-flow jet vectorization. Subsequently, a three dimensional actuator geometry is analysed, accounting the constraints of a compressor. A validation of the method on the bi-dimensional case is presented and the effect of massflow ratios and radius of curvature are examined. Numerical results on the actuator geometry demonstrate the ability of the actuator to deflect a jet with an angle up to 15 degrees, while staying attached to the casing.