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Low-frequency radio interferometry is crucial to understanding the universe and its very early days. Unfortunately, most of the current instruments are ground-based and thus impacted by the interferences massively produced by the Earth. To alleviate this issue, scientific missions aim at using Moonorbiting nano-satellite swarms as distributed radio-telescopes in outer space, keeping them out of Earth interference range. However, swarms of nano-satellites are systems with complex dynamics and need to be appropriately characterized to achieve their scientific mission. This paper presents a methodology based on graph theory for characterizing the swarm network system by...

For a long time, Cognitive Load Theory has considered working memory models as tools to advance research on learning. It has used working memory capacity models, where working memory is viewed as being composed of a discrete number of slots (i.e., chunks) that can be kept active. However, recent results have shown that for a fixed quantity of information, the mere pace of information presentation can affect learning performance. Commonly used working memory models cannot explain such results. Here, we propose to use a new model in the field of Cognitive Load Theory, the Time-Based Resource Sharing model, which enables time to be taken into account when describing working...

Cognitive workload is of central importance in the fields of human factors and ergonomics. A reliable measurement of cognitive workload could allow for improvements in human machine interface designs and increase safety in several domains. At present, numerous studies have used electroencephalography (EEG) to assess cognitive workload, reporting the rise in cognitive workload to be associated with increases in theta band power and decreases in alpha band power. However, results have been inconsistent with some failing to reach the required level of significance. We hypothesized that the lack of consistency could be related to individual differences in task performance and...

The purpose of this work is to present an improved energy conservation method for hyperelastodynamic contact problems based on specific normal compliance conditions. In order to determine this Improved Normal Compliance (INC) law, we use a Moreau-Yosida α-regularization to approximate the unilateral contact law. Then, based on the work of Hauret-LeTallec [1], we propose in the discrete framework a specific approach allowing to respect the energy conservation of the system in adequacy with the continuous case. This strategy (INC) is characterized by a conserving behavior for frictionless impacts and admissible dissipation for friction phenomena while limiting penetration...

GNSS radio frequency interferences (RFI) sources are often classified in two categories in the civil aviation community: aeronautical and non-aeronautical RFI. Aeronautical RFI sources gather systems with an aeronautical radio navigation system (ARNS) frequency allocation which radiate in or near the GNSS band and consequently affectthe GNSS performance. Non-aeronautical sources include systems with no ARNS frequency allocation also radiating in the GNSS band, either voluntarily or involuntarily. To precisely estimate the impact of RFI sources on GNSS receiver has one main stake. Indeed, it allows to assess the GNSS receiver capability to meet minimum International Civil...

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In the context of civil aviation, GNSS L5/E5a Radio Frequency Interference (RFI) environment is dominated by pulsed interferences such as JTIDS/MIDS and DME/TACAN causing a degradation of the effective C/N_0 observed by the receiver. The mitigation of the pulse RFI as well as its precise characterization is fundamental to guarantee the provision of the civil aviation Safety-of-Life service. In order to mitigate the pulsed RFI impact, in civil aviation standards, a temporal blanker is used as a counter measure. The effective C/N_0 is thus computed accounting for the temporal blanker introduction through the derivation of the blanker duty cycle, bdc, and the equivalent...

We investigate the feasibility of using machine learning methods for predicting the Quality of Experience (QoE) of end users in the context of video streaming over satellite networks. To achieve this, we analyzed QoE and traffic data from 2,400 YouTube video sessions over emulated geosynchronous (GSO) satellite links. The objective is to determine whether existing learning methods, originally developed for wired or mobile networks, can be adapted to accurately predict key QoE factors over SATCOM. We particularly investigate a specific existing framework, which achieves outstanding performance in predicting resolution and initial delay. However, we point out some...