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Antimony mining and metallurgy in the Brioude-Massiac district (Massif Central, France): historical features and perspectives
No full textInternational audienceThe Brioude-Massiac district played a key role in antimony mining in France. While in Roman times, complex polymetallic veins (Pb-Ag-Sb) were exploited, most likely for Pb and Ag only, Sb veins gained interest in the 17 th century. Numerous mines were opened, playing a significant role in the French production. Nowadays, mining and smelting activities are long gone but remnants are still clearly visible. On one hand, numerous galleries, shafts and mining wastes are witnesses of historic mining activity; on the other hand, scarcer smelting remains can be observed, as ruins of plants or as accumulation of artisanal smelting artefacts. Based on a literature review, field examination and classical petrography, the present study documents the Brioude-Massiac district in order to discuss its value as a geoheritage site and to broaden it towards an international audience, since most of the relevant works about this district are in French. As a A type-locality for a major Sb ore (berthierite FeSb 2 S 4 ), it is also known since at least the 18 th century for magnificent stibnite (Sb 2 S 3 ) crystallisations. Smelting activities (liquation and oxidizing roasting) have left traces such as ruins of former plants and various slags (locally used as aggregates) which yield interesting samples. Hence, it can be seen that the patrimonial value of this district allows a holistic approach of all aspects of Sb, from geology, mining and smelting. Based on a literature review, field examination and classical petrography, the present study documents the Brioude-Massiac district in order to discuss its value as a geoheritage site and to broaden it towards an international audience, since most of the relevant works about this district are in French.</div
Revisiting VOC and PM1 Emission Factors from ships in Dunkirk
No full textNational audienceGlobally, maritime transport is by far the most widely used transportation mode for goods. This comes with considerable impacts on climate and air quality. Besides inorganic emissions, like SO2 and NOx, that are subject of recent regulations, particles and VOCs are emitted but not regulated. However, these pollutants influence both air quality and the chemical composition of the marine atmosphere. Here, we present results from measurement campaigns in two major French harbours for which new emission factors (EFs) for inorganic gasses and VOCs were determined. Importantly, speciated VOC emission factors show that although oxygenated VOC are comparable to literature reported EFs, EFs for the non-oxygenated VOCs observed in France show important differences (2-3 orders of magnitude). Observations of non-combustion related emissions of methane will also be discussed. These results will be used in regional models to better estimate the contribution of shipping to particle formation, atmospheric composition and air quality
Role of mechanical representativity in multiaxial and transverse mechanics of human annulus fibrosus: A microstructure-based biphasic finite element study
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Détection d'événements acoustiques par DAS et CNN : application à la détection de cris de baleines
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Discrete-Element Method Study of the Effect of Ballast Layer Depth on the Performance of Railway Ballast Bed
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Mode I crack propagation in polydimethylsiloxane-short carbon fiber composites
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Topology optimization of rectangular parallel plate heat exchanger unit
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Enhancing The Assessment of the Quality of Explanations for AI-based Network IDS
No full textInternational audienceThe rise of cyber threats makes Intrusion Dectection Systems (IDS) essential for network security. With the development of machine learning, these IDS have been significantly improved even if the trade-off between performance and interpretability remains an issue. In recent years, several authors have proposed white-box IDS systems to enhance trust and confidence of security analysts. However, only few of these works provide a systematic evaluation of the proposed explainable AI (XAI) techniques. In this paper, we propose a thorough analysis of LIME and SHAP explainers on a high performance ensemble-based IDS. The proposed IDS is trained on the publicly available datasets Edge-IIoTset, N-BaIot and CIC-IDS2017 with AGRU and XGBoost, and the results show that XGBoost classifies better with an accuracy of 1 on two datasets compared to 0.99 for AGRU. We then assessed the performance of the explainers under three metrics (stability, fidelity and sparsity) on XGBoost predictions. The results revealed that SHAP was more stable (stability ) for various noise values in the feature values and more faithful (Fid+ ) on N-BaIoT dataset, while it achieved the highest sparsity for the classes of the CIC-IDS2017 dataset
