Eiffage (France)

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while machine learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C-/N0-based crowdsourcing metric. Experiments show detection precision up to 88% for jamming and 97% for spoofing, with false positive rates around 1–2% and an average detection time of 10 s.

Abstract Construction is one of the sectors most at risk in terms of health and safety. The social security system attributes more than 14% of the accidents recorded in 2018 to the construction sector. In addition to safety risks, construction workers are exposed to occupational diseases. Musculoskeletal disorders (MSD) are at the top list of the most frequent occupational diseases. In order to address these issues, construction companies have implemented health and safety policy to protect their employees. This policy provides, among other things, for an improvement in the organizational aspect of work, the adaptation of work to people and training. Training is carried out according to a classic methodology: theoretical and practical training. The training represents constraints of cost, duration and organization. The content must be adapted to specific profiles, be repeatable and allow the trainers to assess the level of the trainees. For training on the right actions and posture to avoid the risk of MSDs, the impact of virtual reality training is less relevant due to the lack of haptic feedback. In this article, we propose to evaluate with the construction workers a new training methodology based on visual immersion and a robotic platform for haptic interaction with motion tracking. This methodology is evaluated for a particular task: the formwork panel adjustment, a highly risky operation on a highly sensitive tool. We believe that this approach would allow to better engage the construction workers during training on the formwork, to ensure their safety and awareness of MSD risks. The objective through this method is to enable the construction workers to understand the risks linked to their job, to train them to avoid risks, and to enable the trainer to benefit from a tool for monitoring and supporting the construction workers. In order to validate the methodology, experiments were conducted and questionnaires were submitted to two profiles of people: novices and professionals. This experimentation shows an acceptance of the solution by the construction workers.

This study investigates the influence of surface roughness on contact mechanics, addressing the limitations of existing models that often rely on idealized and symmetric asperity shapes. We introduce a generalized representation of asperity geometries, including non-symmetric profiles, to better capture the diversity of surface characteristics encountered in real-world applications. By applying this parametric asperity model, we perform numerical simulations to analyze the impact of different parameters on contact behavior, effectively identifying various interaction regimes. The analysis is based on the assumption of elastic contact and focuses on two-dimensional roughness profiles characterized by surfaces that remain invariant along the axis orthogonal to the rolling direction. This approach effectively simulates geometries that display sufficient invariance along this axis, thereby representing realistic asperities in the form of streaks. Our theoretical framework quantifies the resulting analytical pressure distribution as a function of both geometric and mechanical parameters of the generalized asperities. By accommodating non-symmetric asperity geometries, our approach enhances model accuracy while significantly reducing computational time and resource requirements compared to traditional numerical methods.

This study investigates EEG correlates of car sickness during real-world slalom driving. Seventeen participants were exposed to low-frequency lateral accelerations while EEG was recorded using dry electrodes. The signal was preprocessed with ICA-based artifact removal and segmented into task-specific epochs. Spectral relative power was extracted across four brain regions and frequency bands. Analysis were conducted within and between groups according to their sensitivity. Results revealed that participants sensitive to car sickness exhibited distinct spatial and spectral patterns, including higher Gamma activity in parietal regions and reduced Alpha power in occipital areas. These findings highlight EEG as a viable tool to characterize neural responses to car sickness in ecological conditions.

Abstract In creativity using digital tools, traditional design guidelines promote abundant, inexpensive, disposable ways to create which are not compatible with sustainability concerns. We explore alternatives to this paradigm by comparing the main Virtual Reality sketch method with a new paradigm inspired by traditional Japanese concept named Wabi-Sabi. We observe that removing the possibility to erase and limiting the quantity of virtual paint are changing participants’ creative posture and process and bring them satisfaction. Crossing the Wabi-Sabi paradigm with digital technologies is a concrete opportunity to “go forward” incorporating sustainable values in practice and tools for digital artists and creators.

Human activity recognition (HAR) using Wi-Fi signals has gained significant attention due to its non-invasive nature, ubiquity, and respect for privacy, in contrast to camera-based systems. This study aims to develop a robust deep learning model for real-time HAR using Channel State Information (CSI) from Wi-Fi signals. We propose a deep Convolutional Neural Network (CNN) architecture incorporating an attention mechanism to process CSI data and classify various human activities. Our model was trained and tested using a comprehensive dataset of Wi-Fi CSI, employing advanced data augmentation techniques to enhance model generalization. The proposed model achieved an impressive accuracy of 99.69%, significantly outperforming existing approaches in terms of precision, recall, and F1 score. These findings demonstrate the potential of using Wi-Fi CSI with deep CNNs and attention mechanisms for accurate and real-time HAR, paving the way for applications in smart homes, healthcare monitoring, and other domains requiring efficient and privacy-preserving activity recognition. Future work will explore adapting this methodology to a variety of datasets and integrating an emotion recognition system to further expand its capabilities.

Despite the advantage of being quieter than traditional internal combustion engine vehicles, electric vehicles are often distinguished by high-frequency tonal components, which can be perceived as unpleasant to the occupants and the environment. To ensure optimal acoustic comfort in electric vehicles, it is important to analyze the NVH behavior of e-powertrains during the early stages of the design process which poses inherent uncertainties, such as varying operating conditions, partial knowledge of design parameters, dispersion in measurement-based data, etc. To effectively address these uncertainties, it is necessary to use fast and comprehensive stochastic models during the design phase. In this work, a probabilistic framework is presented to estimate the electric powertrain’s interior whining noises considering the structure-borne contribution. Hence, two different stochastic metamodels are developed for efficient quantification and propagation of uncertainties from electric motor stage to powertrain mounting system. Multivariate Bayesian regression models help to incorporate prior knowledge on the uncertain parameters and generate the respective posterior distributions using Markov chains Monte Carlo (MCMC) techniques. For this particular application, the data is generated through weakly-coupled multi-physical domains estimated using semi-analytical approaches and combined with measured vehicle transfer functions. Importantly, the validation of each domain is conducted separately to ensure accurate representation. The results obtained from the developed probabilistic framework will aid in the early design stages by guiding engineers in making informed decisions to optimize NVH performance.

The purpose of this article is to present a new method for measuring the shielding effectiveness (SE) of enclosures without the use of an internal sensor. This technique uses a reverberation chamber (RC) and relies on the ability to accurately estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -factor as a function of frequency under different load conditions using an electromagnetic absorbing material. This material is placed successively in the RC, outside and inside the shielded enclosure under test (EUT). Based on the contrast of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -factor estimates, it is possible, in a noninvasive way, to characterize the SE of the EUT, using an energy conservation approach. The dynamic range of measurements is assessed. Experimental results are provided. It validates the principle and confirms the estimated dynamic range.