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Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats
No full textInternational audienceDuring the past two decades, the Space Industry has witnessed a constant evolution, primarily represented by a continuous increase in the number of operational satellites and market value with for instance CubeSats. This period has been marked as well by a pivotal shift from traditional Document-Based System Engineering to Model-Based Systems Engineering (MBSE) and Concurrent Engineering (CE) approaches, a transition led by both Industry and Academia. This shift responds to the increasing demand for innovative space technologies, and the necessity to accelerate design processes and lower costs
Juno Observations of Jupiter's Magnetodisk Plasma: Implications for Equilibrium and Dynamics
No full textInternational audienceAbstract The Jovian magnetodisk plays an essential role in the dynamics of the Jupiter system by coupling its various components. Here, we investigate the Juno (JADE, JEDI, and MAG) observations of the magnetodisk within 20–80 Jupiter radii () in the 0–6 hr local time sector. JADE and JEDI data are combined to generate equatorial plane distributions of density, pressure, temperature, and anisotropy of electrons, protons, and heavy ions. Results show: (a) Heavy ions dominate both the number density and pressure. (b) The number density and pressure of all species decrease with radial distance. (c) The temperature increases for electrons and heavy ions and decreases for protons as radial distance increases. (d) On average, the parallel pressure exceeds the perpendicular pressure for all species. Based on these distributions, we explore the equilibrium and dynamics of the magnetodisk and show that: (a) Radial force balance is primarily achieved between the inward magnetic stress and the outward plasma anisotropy force. (b) An examination of the kappa parameters indicates that electrons, protons, and heavy ions primarily undergo adiabatic motion, magnetic moment diffusion, and stochastic motion, respectively. (c) A radial diffusion coefficient is derived from the radial profile of mass, providing an estimate of the timescale for radial transport from 20 to 80 of 7 hr (d) The total mass ( kg) and thermal energy ( eV) of the magnetodisk between 20 and 80 are obtained
On the Optimality of Support Vector Machines for Channel Decoding
No full textInternational audienceIn this work, we investigate the construction ofchannel decoders based on machine learning solutions, andmore specifically, Support Vector Machines (SVM). The channeldecoding problem being a high-dimensional multiclass classifi-cation problem, previous attempts were made in the literatureto construct SVM-based channel decoders. However, existingsolutions suffer from a dimensionality curse, both in the numberof SVMs involved –which are exponential in the block length–and in the training dataset size. In this work, we revisit SVM-based channel decoders by alleviating these limitations and provethat the suggested SVM construction can achieve optimal BitError Probability (BEP) by attaining the performance of the bit-Maximum A Posteriori (MAP) decoder in the Additive WhiteGaussian Noise (AWGN) channel.<br /
SCvxPyGen: Autocoding SCvx Algorithm
No full textInternational audienceIn this paper, we address the embedded code generation for an optimal control algorithm, SCvx, which is particularly suitable for solving trajectory planning problems with collision avoidance constraints. Producing code compatible with embedded systems constraints will support the use of the SCvx algorithm in a real-time configuration. Existing uses of SCvx on drones or embedded platforms are currently handcrafted code. On the other hand, recent toolboxes such as SCPToolbox provide a simpler access to these trajectory planning algorithms, based on the resolution of a sequence of convex sub-problems. We define here a framework, in Python, enabling the automatic code generation for SCvx, in C, based on cVxpygen and the ecos solver. The framework is able to address problems involving non-convex constraints such as obstacle avoidance. This is a first step towards a more streamlined process to auto-code trajectory planning algorithms and convex optimization solvers
Modélisation statistique de trajectoires d'aéronefs
No full texthis thesis focuses on the statistical study of aircraft trajectories.First, we propose a literature review that identifies relevant statistical approaches in theanalysis of trajectory data. The framework of Functional Data Analysis (FDA) is partic-ularly instructive as it highlights two major challenges in processing such data: the needto reconstruct trajectories to evaluate them at different temporal resolutions and the exis-tence of phase variations that are important to correct statistically. The reconstruction oftrajectory data has specific features. We are particularly interested in taking into accounta positivity constraint for altitude and the reconstruction of the angular components of theflight (longitude, latitude, wind direction). Furthermore, several methods for correctingphase variations are compared. We apply elastic registration to drone and commercialaircraft trajectories with very good results. Moreover, we suggest a judiciously chosendistance in the amplitude space, allowing for the clustering of trajectories in the presenceof phase variations.The second part of the thesis is devoted to the comparison of spatial interpolation methodsfor meteorological data used in aviation. We develop a geostatistical framework adaptedto two specific case studies. Our model reliably associates meteorological conditions withtrajectory data, particularly for temperature values.Finally, we develop a Hidden Markov Model (HMM) for the segmentation of flight phases,whose nature, number, and sequence may or may not be known. We apply this modelto the segmentation of commercial aviation flights and a helicopter flight. Our methodproduces results of similar quality to existing approaches while providing an estimate ofthe uncertainty associated with the segmentation.Cette thèse porte sur l’étude statistique des trajectoires d’aéronefs.Dans un premier temps, nous proposons une revue de la littérature qui permet d’identifierles approches statistiques pertinentes dans l’analyse de données de trajectoires. Le cadrede l’analyse statistique des données fonctionnelles est particulièrement instructif car ilmet en lumière deux défis majeurs dans le traitement de telles données : la nécessité dereconstruire les trajectoires pour les évaluer à différentes résolutions temporelles, ainsi quel’existence de variations de phase qu’il est important de corriger sur le plan statistique. Lareconstruction de données de trajectoires présente des spécificités. Nous nous intéressonsnotamment à la prise en compte d’une contrainte de positivité pour l’altitude et à lareconstruction des composantes angulaires du vol (longitude, latitude, direction du vent).Plusieurs méthodes de correction des variations de phase sont par ailleurs comparées. Nousappliquons un alignement élastique à des trajectoires de drone et d’avion commercial avecde très bons résultats. De plus, nous suggérons une distance judicieusement choisie dansl’espace des amplitudes permettant de faire un clustering de trajectoires en présence devariations de phase.Un deuxième volet de la thèse est consacré à la comparaison de méthodes d’interpolationspatiale pour des données météorologiques utilisées dans l’aviation. Nous développonsun cadre géostatistique adapté à deux cas d’étude en particulier. Notre modèle permetd’associer des conditions météorologiques à des données de trajectoires avec une grandefiabilité, notamment pour les valeurs de température.Enfin, nous développons un modèle deMarkov caché pour la segmentation de phases de vol,dont la nature, le nombre et l’enchaînement peuvent être connus ou non. Nous appliquonsce modèle à la segmentation de vols de l’aviation commerciale et d’un vol d’hélicoptère.Notre méthode produit des résultats de qualité similaire à ceux des approches existantestout en fournissant une estimation de l’incertitude liée à la segmentation
Feasibility Study of GBAS/INS and RRAIM for Airport Surface Movement Under Low-Visibility Conditions
No full textInternational audienceCurrently, surface movement, encompassing all operations on the airport surface prior to take-off and after landing, cannot be achieved under low-visibility conditions by an aircraft-guidance-only solution. In addition to surface movement radar and Automatic Dependent Surveillance–Broadcast, pilots also rely on signage, lighting and reports/commands from the airport traffic control tower, which are partly based on visual inspection of the airport, to aid in guidance from the runway to the gate. Therefore, low-visibility conditions caused by meteorological effects can significantly affect the continuity of operations on the airport surface. Global navigation satellite systems are considered to overcome these difficulties by enhancing guidance and situational awareness on the airport surface. This paper explores the feasibility of utilizing a ground-based augmentation system, which is potentially available at the airport, an inertial navigation system, and relative receiver autonomous integrity monitoring to support surface movement operations in low-visibility conditions. The paper provides results assessing the compliance of the proposed solution to accuracy and integrity requirements
A passive brain–computer interface for operator mental fatigue estimation in monotonous surveillance operations: time-on-task and performance labeling issues
No full textInternational audienceObjective : A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue (MF). This makes detecting MF a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT)—the popular method—and performance metrics for labels. Yet, to our knowledge, they have never been directly compared. Approach : This study was designed to address both issues: the use of a realistic task to elicit MF during a monotonous visual search task and the labeling type used for intra-participant fatigue estimation. Over four blocks of 15 min, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines. Main results : The results show that the capability of a machine learning algorithm to detect MF depends less on the input data but rather on how MF is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if MF is estimated based on behavioral performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%). Significance : TOT-based MF estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of MF are neglected
Reducing flight upset risk and startle response: A study of the wake vortex alert with licensed commercial pilots
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Towards more automated airport ground operations including engine-off taxiing techniques within the Auto-Steer Taxi at AIRport (ASTAIR) project
No full textInternational audienceThis paper discusses the SESAR's ASTAIR (Auto-Steer Taxi at Airport) project, which seeks to advance airport ground operations including engine-off taxiing to move a step forward to sustainable airports. The ASTAIR concept integrates Human-AI Teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR's AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describes operational challenges for implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human-automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization
Scientific Evaluation of the Impact of an Increase in the Retirement Age on the Cognitive Functions and Well-Being of Air Traffic Controllers (ATCOs)
No full textIn 2020, the Swiss Federal Council made it a strategic objective to encourage Skyguide (Switzerland’s private air navigation service provider) and social partners (HelvetiCA) to work together to raise the retirement age from the current 56/59 to at least 60. In this context, HelvetiCA and Skyguide agreed to carry out a scientific study (RAFA study) to assess the possible impact of this increase in age, in particular on the psychological well-being and cognitive performance of ATCOs. Two studies were carried out following a review of the literature. The first study aimed to identify the factors relating to working conditions, individual characteristics and coping strategies that may be affected by ageing and sought to assess whether these factors have an impact on the ability to perform operational tasks according to the demands and conditions of the job carried out by ATCOs. The second study aimed to assess the cognitive functions of ATCOs of different ages using a battery of neuropsychological tests to examine the impact of ageing on cognitive performance, a crucial aspect of air traffic control activity. After a 13-month study period, a final report containing 17 recommendations was submitted to HelvetiCA/Skyguide. Within the context of the Collective Labour Agreement agreed in January 2024, the social partners agreed to implement the recommendations to help HelvetiCA and Skyguide manage this change safely and efficiently, with a transparent programme