On the secondary electron emission phenomenon when originating from very thin layers

International audienceThe secondary electron emission phenomenon lays down the principle of operation of many physical devices and processes. Although it is fairly well described in the case of irradiation of metals there is still lack of information on the secondary electron emission when originating from dielectrics. In this work we report on the secondary electron emission resulting from very thin layers. It is found that for dielectric SiO 2 layers of less than 100 nm of thickness a departure from the general behaviour occurs for incident primary electrons with energy of around 1 keV. The departure in the electron emission yield heavily depends on the layer thickness. The case of nanostructured layers-dielectric matrices containing metal nanoparticles is also considered in the study

Study of required conditions to limit the dielectric charging phenomenon when measuring the electron emission yield from thin dielectric layers

International audienceThe electron emission yield of materials is an important quantity to be determined in various fields of physics. Among them, dielectric materials have a strong ability to retain charges and remain charged when submitted to electrical field, in particular when irradiated by electron beam. Without the use of specific measurement methodology, experimental investigation of dielectric materials may lead to an inaccurate measurement of the total electron emission yield (TEEY). This paper shows that a particular attention should be paid to the pulse duration of the incident electron beam and to hysteresis effects induced by charge trapping

Etude theorique de la statique du minifixateur externe du service de sante des armees

SIGLECNRS RP 174 (166) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model