A series of DCB tests were conducted to characterize the fracture behavior of the EA9394 structural adhesive under mode I loading, used as a joint between two aluminum substrates. A dual-actuator test system was used to load the specimen in a symmetrical manner while maintaining the bondline in a fixed position during the test. This configuration enabled for detailed monitoring of crack propagation using cameras and facilitated the use of image correlation techniques to obtain a large amount of data compared to the standard outputs usually provided by standard universal tensile testing systems (e.g. force and displacement). More specifically, the data obtained through image correlation, such as the longitudinal evolution of deflection and rotation of substrate cross-sections, was analyzed. Of particular interest was the evolution of cross-section rotational, which, although rarely utilized, offers valuable insights. These measurements enable more precise tracking of crack position and allow for the in-situ calibration of the substrates' flexural rigidity, which is essential for reconciling the GIc values determined using the J-integral calculation, the energy method, and the effective crack length method.
Recommendation ITU-R P.1853-2 includes a stochastic approach to generate timeseries of total impairments that are correlated in time and space to support the design and operation of Satcom fade mitigation technique (FMT). Because it lacks any meteorological realism, it cannot reproduce realistic rainy/nonrainy periods or credible outage durations. We present a new modeling approach that combines meteorological data from the ERA5 reanalysis with stochastic components, preserving the required spatial-temporal correlations among several locations. The relative contribution of ERA5 and random terms is calibrated with high-resolution WRF-ARW simulations coupled to an electromagnetic module that converts 3D atmospheric information into microwave attenuation. With this hybrid model, ERA5 drives the slow dynamics while the stochastic part adds rapid fluctuations to the signal. Long-term ITU-R P. statistical models for rain, clouds, and water vapor transform normally distributed mixed processes into attenuation series; oxygen attenuation is computed directly from ERA5 variables, and scintillation is generated as in Rec. ITU-R P.1853-2. Model performance is assessed by comparing the complementary cumulative distribution function (CCDF) of many synthetic series with long-term ITU-R P. predictions, showing good agreement and revealing the climatic variance in the synthetic CCDF. Joint statistics are also evaluated against a new site-diversity prediction model that can handle more than two locations. Finally, fade and interfade durations are analyzed, demonstrating that the proposed synthesizer produces more realistic and longer outage and nonoutage periods than the original model from Rec. ITU-R P.1853-2.
The CNES-CLS22 Mean Dynamic Topography (MDT; https://doi.org/10.24400/527896/A01-2023.003, Jousset, 2023) represents an incremental update to previous CNES-CLS solutions, combining altimetry, satellite gravity, and in situ observations (drifters, hydrography profiles, and HF radar data). The main improvement lies in the Arctic, where enhanced Mean Sea Surface (MSS) coverage eliminate artifacts present in CNES-CLS18 and enable a more physically consistent representation of circulation, including the Norwegian Atlantic Front Current along the Mohn Ridge. Globally, CNES-CLS22 remains close to CNES-CLS18, with modest improvements in validation against independent datasets: RMS differences in geostrophic velocities decrease by only ∼ 0.2 %–0.5 % at the global scale and the average variance reduction at the global scale compared to heights derived from profiles is ∼ 0.5 %. Though regional gains are significant in the Arctic and Nordic Seas. HF radar integration in the Mid-Atlantic Bight demonstrates progress but highlights persistent challenges in shelf regions dominated by ageostrophic processes. At very small scales (< 40 km), noise from in situ data may introduce unrealistic kinetic energy, underscoring the need for improved filtering. Overall, CNES-CLS22 consolidates previous advances and provides better representation of key circulation features, but further progress will require enhanced coastal observations and refined processing methods, particularly for high-latitude and shelf areas.
This paper describes the flow of events which led ESA to select this Mars Express mission for a launch in 2003, following the failure of the Russian Mars 96 in 1996. The unique genesis of Mars Express is reflected in its system design, the payload selected, and the operation strategy. Although a low cost “F-mission”, Mars Express achieved unprecedented objectives, with in particular coupled analyses of all Mars envelopes, from its exosphere, high and low atmosphere, to the surface and subsurface characterization. For ESA, Mars Express constitutes the first ever Mars mission, on which the Agency, the European space industry and the science community built its level of excellence, opening wide cooperative partnerships.
This work introduces a novel unsupervised method for solving sparse linear systems related to a Poisson equation problem in plasma physics simulations. The approach leverages recurrent Graph Neural Networks (GNNs) trained iteratively in an online unsupervised manner to generate an initial guess, which is designed to lower the computational cost of traditional iterative solvers in terms of the number of iterations and execution time. By employing recurrent GNNs, we aim to model arbitrary simulation domains, accommodating computational meshes that are structured or unstructured, small or large, in 2D or 3D. The proposed unsupervised method seeks to improve previous works in solving the Poisson equation with GNNs by using a supervised, data-driven approach. Particular emphasis is placed on the interaction between the GNN-generated initial guess and the Flexible Generalized Minimal RESidual (FGMRES) solver. Through this hybridization, the method evaluates whether the unsupervised method can accelerate the convergence rate of the FGMRES solver. Ultimately, the proposed method will demonstrate whether a hybridization between a recurrent GNN and FGMRES solver can accelerate the solving process for the Poisson equation, which has been discretized into a linear system.
