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Quantitative Study of a Full-scale Pedaling Mannequin for Wind Tunnel Testing
No full textInternational audienceHuman subjects introduce variability in cycling wind tunnel testing due to slight inconsistencies in position and fatigue. To address these limitations, this study introduces a dynamic, full-scale pedaling mannequin, capable of generating repeatable and controlled pedaling movements.The study focuses on three main objectives: (1) validating the mannequin's kinematics by comparing its pedaling motion to an idealized mechanical model, (2) quantifying the dynamic forces generated by the mannequin during operation and assessing their impact on measurement stability, and (3) evaluating the variability of aerodynamic measurements across different pedaling cadences and wind speeds. Experiments were conducted in a low-turbulence wind tunnel, with kinematic analysis performed using computer vision and force measurements recorded at multiple cadences and wind speeds.Results demonstrate that the mannequin produces highly repeatable pedaling motions with a minimal and maximal standard deviation of 0.24 % and 0.88 % across all tested conditions. The smallest detectable meaningful power change in optimal conditions was 1.61 W at 50 km/h and at a pedaling frequency of 67 rotations per minute. Pedaling-induced forces were shown to influence the aerodynamic drag measurements, highlighting the importance of distinguishing dynamic and aerodynamic components of the force signal. These findings establish the mannequin as a reliable tool for controlled aerodynamic analysis, with applications in both scientific research and performance optimization in competitive cycling.</p
Aerodynamic drag between two cyclists: OverPressure push and effect of wheel rotation
No full textInternational audienceBased on previous scientific findings about Computational Fluid Dynamics (CFD) modeling of rotating wheels and the aerodynamic interactions between two cyclists riding one behind another, this article aims to compare and combine the effect of both parameters. The aerodynamic drag of two identical models of female track cyclists was simulated in static and with simulated wheel rotations through a validated CFD approach. Two sets of eleven simulations with and without wheel rotation at a wheel-towheel distance between the two bikes ranging from 5 centimeters to 5 meters were carried out. Results show that wheel rotation on an isolated cyclist model with his bike and equipment has a 3.6% larger drag than the same model without wheel rotation. Furthermore, at the usual wheel-to-wheel distance of 0.15m, two cyclists, one behind the other show a 4.3% drag reduction for the leading cyclist and 27.9% drag reduction for the trailing cyclist compared to an isolated one. The overall drag is higher with wheel rotation than without it but the gain from OverPressure Push (OPP) and drafting stays similar. These results suggest the implementation of wheel rotation and accurate 3D geometry in future CFD cycling models to make them more realistic
Neural Space-Filling Curves for Traffic Event Retrieval in Automotive Perception Systems: A Research Vision
No full textInternational audienceEvent retrieval is essential for Advanced Driver Assistance Systems (ADAS), enabling real-time identification of critical traffic scenarios, including pedestrian movements, vehicle interactions, and dynamic lane maneuvers. Traditional retrieval methods rely on predefined Space-Filling Curves (SFCs) with fixed scan orders, limiting adaptability to diverse driving conditions. In this vision paper, we propose Neural Space-Filling Curves (Neural SFCs), a graph-based learning framework that dynamically optimizes scan orders using Graph Neural Networks to enhance retrieval efficiency and robustness. We discuss their application in ADAS event retrieval, outline a proposed evaluation framework, and highlight key research challenges, including scalability, real-time deployment, and integration with ADAS perception pipelines. By merging structured spatial indexing with deep learning, Neural SFCs offer a promising path toward more efficient real-time event retrieval in automotive systems
Automotive applications augmented with Large Language Models: a research vision
No full textInternational audienceAs autonomous vehicles become more complex, ensuring safety, collaboration, and security is increasingly challenging. Large language models (LLMs) are gaining traction in automotive research, and their automotive applications are expanding rapidly. LLMs are however prone to hallucinations that may lead risks in safety-critical scenarios, such as failing to detect pedestrians. To maximise their potential, we must find ways to identify and mitigate these inaccuracies. By discussing the findings of a number of selected articles, the present position paper presents a vision on the potential future role of LLMs within the automotive domain, for better or for worse
Convergence rates for polynomial optimization on set products
No full text22 pagesInternational audienceWe consider polynomial optimization problems on Cartesian products of basic compact semialgebraic sets. The solution of such problems can be approximated as closely as desired by hierarchies of semidefinite programming relaxations, based on classical sums of squares certificates due to Putinar and Schm\"udgen. When the feasible set is the bi-sphere, i.e., the Cartesian product of two unit spheres, we show that the hierarchies based on the Schm\"udgen-type certificates converge to the global minimum of the objective polynomial at a rate in , where is the relaxation order. Our proof is based on the polynomial kernel method. We extend this result to arbitrary sphere products and give a general recipe to obtain convergence rates for polynomial optimization over products of distinct sets. Eventually, we rely on our results for the bi-sphere to analyze the speed of convergence of a semidefinite programming hierarchy approximating the order quantum Wasserstein distance
Towards programming languages free of injection-based vulnerabilities by design
No full textInternational audienceMany systems are controlled via commands built upon user inputs. For systems that deal with structured commands, such as SQL queries, XML documents, or network messages, such commands are generally constructed in a "fill-inthe-blank" fashion: the user input is concatenated with a fixed part written by the developer (the template). However, the user input can be crafted to modify the command's semantics intended by the developer and lead to the system's malicious usages. Such an attack, called an injection-based attack, is considered one of the most severe threat to web applications. Solutions to prevent such vulnerabilities exist but are generally ad hoc and rely on the developer's expertise and diligence. Our approach addresses these vulnerabilities from the formal language theory's point of view. We formally define two new security properties. The first one, "intent-equivalence", guarantees that a developer's template cannot lead to malicious injections. The second one, "intentsecurity", guarantees that every possible template is intentequivalent, and therefore that the programming language itself is secure. From these definitions, we show that new design patterns can help create programming languages that are secure by design
Collision Avoidance in Model Predictive Control using Velocity Damper
No full textWe propose an advanced method for controlling the motion of a manipulator robot with strict collision avoidance in dynamic environments, leveraging a velocity damper constraint. Unlike conventional distance-based constraints, which tend to saturate near obstacles to reach optimality, the velocity damper constraint considers both distance and relative velocity, ensuring a safer separation. This constraint is incorporated into a model predictive control framework and enforced as a hard constraint through analytical derivatives supplied to the numerical solver. The approach has been fully implemented on a Franka Emika Panda robot and validated through experimental trials, demonstrating effective collision avoidance during dynamic tasks and robustness to unmodeled disturbances. An efficient open-source implementation along examples are provided here: https://gepettoweb.laas.fr/articles/ haffemayer2025.html
A new methodology for sub‐femtomolar detection of organic molecules through the combination of surface‐enhanced Raman spectroscopy and a superhydrophobic fluidic concentrator
No full textInternational audienceA specific device that combines (1) surface‐enhanced Raman spectroscopy (SERS) and (2) superhydrophobic surfaces is developed to detect traces of analytes diluted at sub‐femtomolar concentration in water solutions. The first step of the analysis consists in the evaporation of a drop of the solution on the device, designed to concentrate all the analytes on a central functionalized small area (80 µm diameter). This analytical zone is covered with Ag nanoparticles dedicated to enhance Raman signals. In a second step, this zone is scanned pixel by pixel to accumulate around 2200 Raman spectra. The third step is an algorithmic analysis of the pile of spectra to identify Raman peaks that are specific to the targeted molecules. We detail an original analysis method that allows (1) to select spectra that are significantly different from those obtained when a pure solvent is evaporated (control experiment), (2) to classify the spectra by a criterion of similarity and, finally, (3) to select the SERS spectra of the analytes. This method uses hierarchical correlation clustering techniques, the originality being to classify the different spectra on the basis of their peak positions, with all peaks being normalized at the same intensity and bandwidth. The method leads to a convincing identification of spectra of the targeted molecules (i.e. rhodamine B), down to atto‐molar concentrations
Le rôle de l'auto-interaction dans la détection des défauts dans les semi-conducteurs irradiés
No full textInternational audienc
Une classification des techniques de manipulation utilisées dans les attaques d’ingénierie sociale et les biais cognitifs, les besoins, les normes et les émotions sous-jacents
No full textInternational audienceThis survey addresses the critical challenge of social engineering attacks in cybersecurity, offering a detailed classification of manipulation techniques and an exploration of the cognitive processes they exploit. We identify 40 distinct manipulation techniques by integrating insights from social and cognitive psychology literature, focusing on cognitive biases, social norms, individual needs, and emotions. Our analysis reveals that techniques frequently employ cognitive biases such as the halo effect and individual needs like the need for affiliation to deceive users. We provide practical examples of how these techniques manifest in phishing and spear-phishing campaigns, underscoring their relevance to current cybersecurity threats. Our findings underscore the necessity for cybersecurity defenses that incorporate psychological insights, aiming to enhance user awareness and resilience against these sophisticated attacks. This survey lays the groundwork for future research and practical applications in combating social engineering, ultimately contributing to the fortification of information systems against human-centric vulnerabilities