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A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: Atomic polarizabilities
No full textInternational audienc
Ellipsoid uncertainty tether model for collision avoidance in a fleet of Remotely Operated Vehicles
No full textInternational audienceDuring collision avoidance, the tether of Remote Operated Vehicle (ROV) is subject to entanglement with obstacles or other ROVs' tether. This specificity renders traditional multi-robot obstacle avoidance approaches inadequate for tethered multi-robot scenarios. This paper proposes a guaranteed ellipsoid model for representing the ROV's tether and its nearby obstacles, enabling an efficient, low-computation collision avoidance method for a fleet of ROVs. The model ensures that if the ellipsoid encompassing the tether remains entirely outside the ellipsoid encompassing an obstacle, there is no risk that the tether collides with it. The approach requires only the two attachment points of the tether and its length, without needing any information about the tether's shape, dynamics, or external disturbances such as underwater currents. A collision avoidance strategy is developed based on potential field methods combined with tether length management. When multiple ROVs are involved, personalities are added to ROV to obtain different behaviors, reducing the likelihood of deadlocks during avoidance maneuvers. Simulations demonstrate the method's effectiveness across various scenarios, and its limitations are also discussed
I/O patterns modeling of HPC applications with call stacks for predictive prefetch
No full textInternational audienceModern high-performance computing (HPC) storage systems use heterogeneous storage technologies organized in tiers to find a compromise between capacity, performance, and cost. In these systems, prefetching is a common technique used to move the right data at the right moment from a slow to a fast tier to improve overall performance while using the costly high-performance tier only when needed. Effective prefetching requires precise knowledge of the application I/O patterns. This knowledge can be extracted through the source code, I/O tracing tools or I/O functions call stacks. State-of-the-art solutions based on the latter approach mainly focus on applications with regular I/O profiles to avoid scalability issues due to the grammar-based techniques used. In this paper, we present an approach based on I/O call stacks that models POSIX and STDIO I/O patterns for both regular and irregular applications, thanks to the use of directed graphs. We present different models usable for prefetching. Our models were used to predict the next I/O call stack on five real HPC applications with a prediction accuracy of up to 98%. Compared to the state-of-the-art Omnisc'IO, they incurred up to 120x lower model overhead (334 ns vs. 45 μs on LAMMPS) and had a model size 10x to 15x smaller (463 B vs. 7 kB on LQCD)
Detecting GNSS spoofing in a tightly coupled INS using directionnal statistics
No full textInternational audienceThis study presents a novel theoretical framework for the detection of GNSS spoofing, addressing the growing need for secure navigation solutions in critical applications such as aeronautics. We specifically target highly challenging scenarios, including so-called "perfect spoofing," which are defined and constrained by rigorous aeronautical standards. Unlike many existing approaches, our methodology does not rely on assumptions about the spoofing attack profile, nor does it attempt to estimate the spoofed trajectory. Instead, we classify spoofing scenarios solely to construct detector performance tables.Our detection protocol is evaluated through extensive Monte Carlo simulations, comprising 10,000 runs for each spoofing scenario, involving straight-line trajectories under varying latitude, longitude, and heading conditions. Each simulation consists of an initial nominal phase, allowing filter convergence, followed by the introduction of a spoofing attack.Results indicate that the proposed detector achieves excellent performance for specific amplitude thresholds, notably for velocity and acceleration steps, while maintaining a low contamination rate in favorable scenarios. Interestingly, very small spoofing amplitudes do not necessarily evade detection, contrary to intuition; there appears to be an optimal spoofing level that maximizes detection difficulty. However, performance is limited for position offset attacks affecting a single iteration, suggesting avenues for future improvement, particularly with shorter detection windows or alternative statistical tests.In conclusion, our framework advances GNSS spoofing detection capabilities, demonstrating robustness under strict regulatory constraints, and lays the groundwork for further research, including three-dimensional contexts and post-detection mitigation strategies.</div
Using virtual reality for enhancing neuroanatomy learning by optimizing cognitive load and intrinsic motivation.
No full textInternational audienceThis research investigates the effectiveness of virtual reality (VR) in enhancing neuroanatomy learning among medical students, focusing on optimizing cognitive load, intrinsic motivation, and user experience. A total of 77 s-year medical students participated in the study, which compared traditional video-based instruction with three VR conditions: active, guided, and passive. The results demonstrate that VR significantly improved anatomical learning performance compared to traditional methods, particularly in the passive and active conditions. VR also enhanced intrinsic motivation, reduced extraneous cognitive load, and increased germane cognitive load. Interestingly, the guided VR condition yielded the poorest learning performance, although differences between the VR conditions were not statistically significant. These findings suggest that higher interactivity is not inherently linked to better learning outcomes in VR-based education. The study highlights the importance of balancing interactivity and cognitive load in the design of effective VR learning environments. Overall, VR holds strong potential as an educational tool, but its instructional design must be carefully tailored to support both motivation and cognitive efficiency. Future research should further examine the role of interaction modes and learner expertise in shaping the instructional effectiveness of VR
A Proof-Assisted Conversion of Signal Temporal Logic Formulas into Negative Normal Form
No full textInternational audienc
Resolved DEM-CFD coupling for wave-armour blocks interactions
No full textInternational audienceThe present work aims to tackle breakwater stability challenges through an innovative numerical deterministic method using a resolved DEM-CFD (Discrete Element Method—Computational Fluid Dynamics) strategy, which simulates the individual motions of armour units within a fluid solver. To achieve this, a coupling between a DEM code and a CFD code is implemented and validated. The fluids (air and water) are solved using a Eulerian–Eulerian CFD solver, and the contacts between blocks are solved using a DEM code. The solids are defined within the fluid solver using a discrete forcing approach and are therefore fully resolved. In this way, the fluid solver enables the prediction of object motions with complex shapes such as tetrapods. To couple the codes, forces exerted on the solids are calculated in the fluid solver and sent to the DEM solver. Then, contact and gravity forces are computed and added to the fluid forces. The DEM solver then computes the new positions and velocities of the bodies, which are retrieved by the fluid solver. An experimental study is performed on a fixed and instrumented idealized breakwater to evaluate the wave forces acting on a coastal structure. The experiments are then numerically reproduced to validate the numerical model. Simulations of the impact of solitary waves on a row of mobile isolated tetrapods laid on a horizontal berm are then performed using the DEM-CFD coupling. The importance of initial placement and friction parameters is investigated to show the sensitivity to these parameters
Assessment of the PPP-AR Strategy for ZTD and IWV in Africa: A One-Year GNSS Study
No full textInternational audienceWith the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. PRIDE PPP-AR is compared with established PPP-AR and PPP solutions, including CSRS-PPP, IGN-PPP, and NGL and using GipsyX, ERA5, and IGS products as references. A robust methodology combining time series processing and statistical evaluation was adopted. Multiple tools were leveraged to ensure a comprehensive performance analysis of GNSS data from seven stations in Africa, where such studies remain scarce. The results show that PRIDE PPP-AR achieves ZTD accuracy comparable to GipsyX (RMSE < 6 mm, R2 ≈ 0.99) and performs at a similar level to NGL and CSRS-PPP. Compared to the other solutions, PRIDE PPP-AR has an accuracy similar to CSRS-PPP and NGL, but slightly better than IGN-PPP, in line with ERA5 and IGS references. For IWV retrieval, comparisons with ERA5 indicate RMSE values of about 1.5 to 2.7 kg/m2, depending on station location and climatic conditions. IWV variability tends to increase towards the equator, where the recorded fluctuations are higher than in subtropical zones. In addition, collocated radiosonde (RS) measurements in Abidjan confirm good agreement, further validating the reliability of the software. This study highlights the potential of GNSS meteorology, in providing reliable spatiotemporal IWV monitoring and indicates that the PRIDE PPP-AR is ready for the high precision meteorological applications in African regions. These results offer promising prospects for spatiotemporal studies through African multi-GNSS networks and the PRIDE PPP-AR approach
Dual Photoredox/Cu‐Catalysis Enables Diastereoselective Synthesis of 3‐ β ‐Ethynyltropanes via Decarboxylative Alkynylation
No full textInternational audienceAn efficient decarboxylative alkynylation of a tropane redox‐active ester with terminal alkynes is described, yielding ethynyltropanes with exclusive β ‐configuration. This cost‐effective process utilizes mild, nontoxic copper‐catalyzed, photoinduced reaction conditions. Beyond introducing a novel pathway for the catalytic alkynylation of nortropanes, this method offers a versatile and convergent approach for synthesizing complex nortropane derivatives through further functionalization of the triple bond, including reductions, and inverse demand Diels–Alder reactions
3D Scanning of Celtic Coins and Deep Learning to Identify Monetary Dies
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