École Nationale de l’Aviation Civile

Despite being considered a hard-to-abate sector, aviation’s emissions will play an important role in long-term climate mitigation of transportation. The introduction of low-carbon energy carriers and the deployment of new aircraft in the current fleet are modeled as technology-centered decarbonization policies, while supply constraints in targeted market segments are modeled as demand-side policies. Shared Socioeconomic Pathways (SSPs) are used to estimate trend-mitigation traffic demand and to limit the sectoral consumption of electricity and biomass. Mitigation scenarios are formulated as optimization problems, and three applications are demonstrated: no-policy baselines, single-policy optimization, and scenario-robust policies. Results show that the choice of energy carrier is highly dependent on assumptions regarding aircraft technology and the background energy system. Across all SSP-based scenarios, emissions peak around 2040, but achieving alignment with the Paris Agreement requires either targeted demand management or additional low-carbon energy supply. The use of gradient-based optimization within a multidisciplinary framework enables the efficient resolution of these nonlinear, high-dimensional problems while reducing implementation effort. • Optimization framework links SSP scenarios with detailed aircraft technology. • Fleet replacement and demand saturation leads to emissions peak around 2040. • Impact of alternative aircraft requires extending analysis beyond 2050. • Respecting +2 ∘ C carbon budgets requires demand caps or additional energy availability. • Policy optimization was prohibitive without speedups from numerical methodology.

This article analyzes the main elements of the participation of indigenous peasant women in the Guerrilla Army of the Poor and in the Community of Population in Resistance–Ixcan, in Guatemala. The theoretical framework guiding this work is that the counterinsurgency war in that country involved a dialectical and complex process of de-subjectification and simultaneous autonomy of the indigenous female subject, through which she overcame the condition of absolute victimhood and positioned herself as a survivor in a political sense, that is, with the hope of achieving a truly just future. The article begins with a brief introduction, followed by a discussion of the fundamental methodological aspects, then addresses the background, causes, and subjective factors, and finally presents the concrete life trajectories that determined the forms of participation of these women.

This paper introduces a data-driven model for generating synthetic satellite users based on population and geographical factors. The model applies the logarithmic opinion pool to combine complementary perspectives on user distribution, such as population density and rural predominance. This principled aggregation captures realistic spatial disparities in connectivity and improves the representativeness of satellite constellation simulations. The approach supports the development of adaptive non-terrestrial network (NTN) strategies aligned with both orbital topology and terrestrial demand.

In order to learn distributed port-Hamiltonian systems (dpHs) using Gaussian processes (GPs), the partitioned finite element method (PFEM) is combined with the Gp-dpHs method. By following a late lumping approach, the discretization of the functional hyperparameters of the GP prior over the Hamiltonian functional is chosen independently from the discretization of the dpHs, thus reducing the numerical complexity of our method. We next model the mean of the GP prior of the Hamiltonian as a quadratic form, enabling the GP kernel to focus on the nonlinear part of a given dpHs. We illustrate our method on a nonlinear one dimensional wave equation with unknown physical parameters (tension and linear mass).

Effective inspection of offshore wind farms is essential to maintain their operational reliability. UAVs provide a promising means to reduce inspection costs and duration while minimizing risks to human operators, making them valuable tools for infrastructure health monitoring. When organized as swarms, UAVs can exploit collective intelligence and coordination to improve inspection coverage and efficiency. However, offshore environments impose unique challenges, including limited accessibility and harsh weather conditions, which require the swarm to operate with high fault tolerance. In this work, we introduce FOCUS, a novel mission-level approach for offshore wind farm inspection, that leverages mobile ad hoc connectivity and distributed consensus within a UAV swarm to enable fault-tolerant operations. Emulation-based evaluations show that FOCUS doubles the inspection efficiency compared with baseline approaches while maintaining strong resilience against common failures.

Drones are increasingly vital for disaster management, yet emergency fleets often consist of heterogeneous platforms, complicating task allocation. Efficient deployment requires rapid assignment based on vehicle and payload characteristics. This work proposes a three-step method composed of fleet analysis, area decomposition and trajectory generation for multi-rotor drone surveillance, aiming to achieve complete area coverage in minimal time while respecting no-fly zones. The three-step method generates optimized trajectories for all drones in less than 2 min, ensuring uniform precision and reduced flight distance compared to state-of-the-art methods, achieving mean distance gains of up to 9.31% with a homogeneous fleet of 10 drones. Additionally, a comparative analysis of area partitioning algorithms reveals that simplifying the geometry of the surveillance region can lead to more effective divisions and less complex trajectories. This simplification results in approximately 8.4% fewer turns, even if it slightly increases the total area to be covered.

Social robot teleoperation is a skill that must be acquired through practice with the social robot. Mobile neuroimaging and human-computer interface performance metrics permit the gathering of information from the operators' systemic and behavioral responses associated with their skill acquisition. Profiling the skill levels of social robot operators using this information can help improve training protocols. In this study, thirty-two participants performed real-world social robot teleoperation tasks. Brain function signals from the prefrontal cortex (PFC), and behavioral data from interactions with the system were collected using functional near-infrared spectroscopy (fNIRS). Participants were divided into two groups (high and low performance) based on an integrative metric of task efficiency, workload, and presence when operating the social robot. Significant differences were found in the operation time, width, and multiscale entropy of the hemoglobin oxygenation curve of the operator's PFC. Functional connectivity in the PFC also depicted differences in the low- and high-performance groups when connectivity networks were compared and in the leaf fraction metrics of the functional networks. These findings contribute to understanding the operator's progress during teleoperation training protocols and designing the interface to assist in enhancing task performance.