dataesr/scientific-paragraphs-categorization huggingface dataset:
Notons que ne disposons pas de couche géographique d'usage des sols. Pour détecter les zones urbanisées, un seuil de densité minimale de 10 habitants par hectare a été utilisé, valeur obtenue à partir d'un travail spécifique realisé à partir de la base de données d'usage du sol Corine Land Cover. Pour calculer la densité nette, nous rapportons donc la population totale des zones au moins aussi denses que 10 hab / ha à la superficie totale de ces zones. La figure 2. B. détaille ce mode de calcul.
The pin-holder is fixed onto a dynamometer in order to measure the normal force F n and the tangential force F t (macroscopic forces). The apparent friction coefficient µ app is provided by the ratio between the tangential and the normal forces (Fig. 6 ), taken as an average value in the stable zone. t app n F µ F (1) The term 'apparent friction coefficient' is used since it differs significantly from the 'interfacial friction coefficient' induced by adhesion at the pin/workmaterial interface. Indeed the macroscopic forces measured by the tribometer include friction phenomena (adhesion -> µ adh ), elastic deformation and plastic deformation of the workmaterial, which cannot be neglected under such severe contact conditions (F n ≈600 N). The identification of the evolution of µ adh with the present data will be presented later in the paper.
Results and discussion 7 Table  1  summarizes the experimental data obtained in the present work. Fig. 7  outlines the image obtained by binocular microscope observation of a pin surface, presenting an example of the real contact area for velocity of 30 m/min (0. 5 m/s), where it can be clearly noticed the shape of the normally projected contact area with the elastic recovery. Table  2. Experimental data obtained by binocular microscope for four different sliding velocities.
Toutefois, à notre connaissance, il n'existe pas encore de veille internationale sur les réseaux intelligents au service de la mobilité électrique. Cette dynamique est émergente comme nous avons pu l'observer depuis 2012, à travers l'analyse des bases de données numériques, des publications scientifiques, de la littérature grise, des articles de presse, de la blogosphère…  (Némoz, 2014a)  L'exercice reste cependant délicat, car cet objet de recherche suscite des travaux dont les résultats sont pour partie confidentiels et non définitifs. Du processus de recensement des projets, il ressort une dynamique de transition énergétique très coûteuse, ayant pour moteur VertigO -la revue électronique en sciences de l'environnement, Volume 14 Numéro 3 | Décembre 2014 l'innovation technologique et avec comme principaux vecteurs : de grands groupes industriels, soutenus par des États ou dans le cadre de quelques programmes politiques internationaux, notamment européens.
Remerciements L'auteure remercie tous les participants au projet «  Smart Campus », ainsi que le centre international REEDS qui lui a confié la responsabilité de la recherche en sciences sociales. Il lui importe aussi de saluer les étudiants des masters professionnels sur l'énergie et la mobilité en milieu urbain, ainsi qu 'en management des éco-innovations (master international ECO-INNOV et master EMMU, promotion 2012/2013, UVSQ), pour leur contribution à l'enquête quantitative menée auprès des membres de l' Université de Versailles Saint-Quentinen-Yvelines. L'auteure souhaite enfin remercier les relecteurs anonymes de VertigO de leurs commentaires et suggestions.
Les simulations permettent de rendre compte fidèlement des concentrations d'acétate et de propionate mesurées lors des essais. Les auteurs remercient l'Agence Nationale de la Recherche (ANR) pour le soutien financier accordé au projet ANAMIX via le programme Bioénergie (n° ANR-08-BIOE-009-03). k m_ac Vitesse spécifique maximale de dégradation de l'acétate (mg DCO. mg DCOX -1. j -  ) k m_pro Vitesse spécifique maximale de dégradation de propionate (mg DCO. mg DCOX -1. j -  ) k m X ac Vitesse maximale de dégradation de l'acétate (mg DCO. kg -. d -  ) k m X pro Vitesse spécifique maximale de dégradation de propionate (mg DCO. kg -. d -  ).
UN BILAN GLOBAL ET DES ÉTUDES DE CAS ISSUS D'ENTRETIENS, D'ENQUÊTES ET D'UNE REVUE DE LITTÉRATURE L'analyse de la situation des villes françaises et les études de cas reposent sur la mobilisation de la base de données empirique et qualitative constituée à partir d'une large enquête auprès de villes françaises conduite entre 2012 et 2013 dans le cadre de la thèse de  DUCRET (2015). Dix-neuf villes françaises de tailles différentes et présentant des contextes géographiques, fonctionnels, politiques et économiques différenciés ont été enquêtées (Tableau 1).
Dans le cadre du projet VALI-URB, une méthodologie multi-scalaire basée sur des images Pléiades et des données géographiques existantes (bases de données vectorielles de type OSM et BDTOPO ® IGN) est proposée pour cartographier les surfaces bâties et vertes dans les zones urbaines et périurbaines. L'objectif est montrer l'intérêt d'utiliser une information d'occupation et d'utilisation des sols dérivée des images Pléiades combinées à des données vecteurs pour cartographier de manière semi-automatique les structures grises et vertes à l'échelle locale (1/10 000) par une classification orientée-objets. Les premiers résultats sont présentés sur deux villes de taille moyennes aux formes urbaines différentes: Strasbourg et Rennes (France).
Introduction Improving our knowledge on the urban patterns with their grey and green structures and their dynamics at multiple spatial scales (from the urban block to the morphological urban zone) plays an important role for a wide range of applications, such as urban planning and management  (Herold et al. , 2005) , disaster management  (Okada et al. , 2000) , or energy consumption modelling in urban environments  (Heiple et al. , 2008). Many studies have mapped and analysed European cities and their evolution using optical remote sensed data but a consolidated and reproducible methodological framework is still missing. In the context of the VALI-URB Project, a multi-scale methodology based on Pléiades images and existing ancillary data (vector databases or the Open Street Map -OSMdatabase) has been proposed to map built-up and vegetation land features in urban and suburban areas.
Study sites and datasets Two medium-sized cities with different urban forms have been chosen to apply the standardized methodological approach (Figure  1 ). Both cities present several typical morphological urban characteristics representative of some western urban areas. Strasbourg is a compact urban city of 415,000 inhabitants (2013) which covers an area of 316 km with a density of 1498 inhab. /km. This city is organized as a concentric dense city centre inherited from the Middle-Ages with surroundings organized in some rings (Figure  1a ). Rennes is a multi-nodal city of 473,187 inhabitants (2013) surrounded by a green-belt and covering an area of 705 km with a density of 587 inhab. / km. The city is structured as a polycentric city (Figure  1b ). Both cities are submitted to a gradual urbanisation process since the last century with an urban sprawl achieving the third ring (Strasbourg) and the peripheric cities (Rennes).
Thus, several urban patterns or Urban Fabrics (UF) can be observed in both cities along a gradient of urbanization from the city-centre and its suburbs (Figure  2 ). A bundle (MS + P) Pléiades image has been acquired in both cities during leaf-on period (Figures  1a  and 1b  and Table  1 ). Radiometric and atmospheric corrections are applied to Pléiades images using the 6S model (Second Simulation of a Satellite Signal in the Solar Spectrum vector code) proposed by  Vermote et al. (1997). Corrections are performed with the Orfeo Tool Box (OTB) using the Optical Calibration/6S module. Both images are orthorectified by the French National Geographic Institute. Pan-sharpened orthoimages with a 0. 5m spatial resolution are created based on the combination of panchromatic and multi-spectral bands.
Methodology The proposed methodology is organized in three steps (Figure  2 ) and follows a bottom-up approach in order to be close to the visual interpretation process. The first step (Figure  3  step 1) concerns the extraction of the Morphological Urban Area (MUA) and the delineation of 'urban blocks' based on ancillary data. A protocol based on  Loriot (2008)  was proposed to extract impervious surfaces (housing, communication ways, commercial or industrialized zones). The MUA was firstly delineated using (1) the built-up layer derived from the combination of the BD TOPO ® IGN and the OSM databases and (2) the road, railway and hydrographic network layers, extracted from the BD TOPO ® IGN database. In this study BD TOPO ® IGN database is considered as the reference layer to be updated by the OSM database.
Comparison of the two layers allowed us to extract the elements present only in the OSM database, to validate them by photointerpretation and update the BD TOPO ® IGN database. Each limit (Figure  1 ) is validated using MUA's limits derived from aerial photographs and cadastres provided by the urban agencies of both cities. Then, units called 'urban blocks' were delineated in using the MUA limits previously defined and the main roads, rivers and all the railways. In the second step, the MUA is classified using an object-based image analysis (Figure  3  step 2). The Pléiades images are firstly segmented before being classified to identify the urban objects defined in the nomenclature (Table  2  in grey). Three supervised classification algorithms were tested. The segmentation and classification results are then evaluated.
The training and validation schemes proposed are detailed in section 3. 1. Section 3. 2 explains the segmentation and classification algorithms. In the step 3, the grey and green structures are analysed at the 'urban block' scale (Figure  3 , step 3). This analysis is based on a series of metrics classically used in landscape ecology. Eight spatial metrics (% of land cover per class, number of patches, mean and standard deviation of patch size, patch and edge density, shape index, Shannon index) are calculated on the land cover classification (Figure  3,  Step 2) at the scale of the urban blocks. These blocks were previously classified into six classes of urban fabrics. In Rennes, a supervised photo-interpretation process is applied to build these classes. In Strasbourg, a regional land use database is available ®CIGAL (Figure  2 ).
Training and validation steps A rigorous statistical accuracy assessment generally requires suitable reference data, an appropriate sampling scheme and independence between the training and validation datasets  (Congalton, 1991). Training and validation samples are generated through a stratified random sampling scheme adapted from  Puissant et al. (2014). The stratification is based on an urbanization index, which represents the complexity of a city with several density levels. The urbanization index was built using three land cover classes ('vegetation areas' (AV), 'non-vegetation areas' (ANV) and water areas (Aw)). Vegetation areas are extracted in thresholding a NDVI computed from the Pléiades images. Water surfaces were derived from the BD TOPO ® IGN database and other areas that did not belong to AV or Aw are considered as non-vegetation areas.
Segmentation and classification methods The Pléiades images are segmented using the Multiresolution segmentation algorithm of eCognition® software  (Baatz and Shäpe, 2000; Burnett and Blasche, 2003). The optimal parameters (shape, compactness, smoothness) are performed using two methods: an unsupervised method, the Estimate Scale Parameter (ESP) tool  (Drǎguţ et al. , 2010; eCognition® software, 2012)  and a supervised method, the distance index  (Levine and Nazif, 1985; Clinton et al. , 2010). The supervised method is validated using one hundred and eighty reference polygons randomly selected from the OSM or the BD TOPO ® IGN databases, or digitized from the orthophotoplan (IGN®)  (Belgiu and Drǎguţ, 2014). In total, 28 parameter combinations were tested to determine the optimal scale, smoothness and compactness values (Tables  3  and 4 ).
Scale shape Compactness 70 0. 5;0. 9 0. 5;0. 9 80 0. 5;0. 9 0. 1;0. 5;0. 9 90 0. 5;0. 9 0. 1;0. 5;0. 9 100 0. 5;0. 9 0. 5;0. 9 110 0. 5;0. 9 0. 5;0. 9 120 0. 5;0. 9 0. 5;0. 9 Three supervised classification methods are applied to the segmented images: K-Nearest Neighbor (KNN)  (Cover and Hart, 1967) , Random Forest (RF)  (Breiman, 2001)  and Support Vector Machine (SVM)  (Vapnik, 1998). The classifications are performed using 14 image features with spectral information (mean and standard deviation values), indexes (NDVI, MSAVI, and brightness), geometric information (area, compacity, asymmetry) and textural parameters (Homogeneity and entropy). Spatial post-processing is then conducted by crossing the classifications with the ancillary data (Figure  2 , step 2). Seven classes of landcover are considered: building from OpenStreetMap or from BD TOPO ® IGN, network (roads and railways) and water from BD TOPO ® IGN, agricultural parcel from 'Registre Parcellaire Graphique' (RGP), tree, grass and other artificialized surfaces with classification of Pléiades images.
Step 3: Exploratory analysis of urban blocks and classification The series of metrics are computed at two scales: the MUA and the urban blocks. First, the distribution of urban fabrics is not the same in the two cities (Table  6 ). Dense urban fabrics and individual residential areas are more present in Strasbourg whereas specialized urban areas are more numerous in Rennes. Some errors about the identification of urban blocks can be observed because urban block classes were assigned differently in the two cities, i. e. by photo-interpretation and using a regional land cover database respectively for Rennes and Strasbourg. This explains why some small blocks delimited by roads, like places or roundabouts, are affected to urban classes whereas this kind of objects are classified as other objects in the case of Rennes.
Acknowledgements This research is co-funded by the  STEREO II programme of BELSPO  and contributed to the  RTU (Pléiades © CNES  ( 2012 ), distribution Airbus DS / Spot Image),  ORFEO Accompaniment Program  (CNES). Thanks to the IGN services for the orthorectification of the images, to the  Rennes Planning Agency (AUDIAR)  for giving us access to MUA data and to CIGAL for the Land cover Regional database in Strasbourg.
Interdependencies and Feedback Loops The screening process results in a list of potential candidates for CO 2 storage, and in the end one or more specific sites are chosen, assumed to be suitable for storage. Especially when the various geo-scientific disciplines are invoked in the quantitative aspects of site characterisation, a strictly linear workflow (i. e. without loops) is an illusion. For instance, new data or intermediate results may necessitate changes in the 3D static earth model, which then may have consequences for the reservoir engineer studying the fluid dynamic properties of the potential storage strata. These new results may raise further questions for the geologist, leading again to adaptations. The iterations depend on the specific properties of the site and cannot be listed at a generic level.
Continuous RA during Site Characterisation Site characterisation will be undertaken in roughly this order: 1. data acquisition, 2. quick analysis, 3. qualitative RA, 4. geological assessment (static model building), 5. geomechanical assessment and potential for induced seismicity, 6. dynamic behaviour, 7. geochemical assessment, 8. migration path analysis, 9. well integrity analysis, 10. consequence analysis (health safety and environment, HSE). Step 4 leads to steps 5 to 9, and steps 7, 8 and 9 can largely be performed in parallel. Steps 5 and 6 tend to be strongly linked. The given ordering is not carved in stone, but more important: returning to an earlier issue might be necessary on account of later emergence of unanticipated risks. Hence: we have to deal with a continuous risk assessment process.
Critical Path of Site Characterisation Study The critical path of a RA project is defined by the acquisition of the necessary data plus the modeling sequence of static geological assessment and dynamic simulation of the CO 2 injection process. The experience from the SiteChar partners is that steps 1 (data acquisition), 4 (static model building) and steps 5 and 6 (geomechanical assessment and dynamic behaviour, respectively) often define the duration of the site characterisation process.
g. , are there faults clearly seen from the data, or is it a matter of interpretation?). This clearly adds to the workload. An alternative is to successively update the static model as deemed necessary from the inputs/requirements of the other disciplines. Model extent. The model should encompass the volumes that might contain CO 2 in steady state. A practical way might be to construct a detailed model of the reservoir (containment volume) with its overburden/underburden, whereas the laterally equivalent strata are modeled on a coarser grid. Potential CO 2 migration paths should be modeled with different tools, and the results compared and discussed. Model resolution. The resolution of the model depends on the data resolution. However, it should be kept in mind that if lateral resolution is not an issue, simulation of CO 2 migration calls for a grid with coarsescale horizontal resolution.
The modelling process In this configuration, XYZ positions are obtained by linear combinations of each interferometer (I 1 , I 2 , I 3 , I 4 ) according to the following equations: (1) With such a configuration, the evaluation of certain contributions in the uncertainty budget is experimentally difficult if not impossible. To precisely determine their impact on the mAFM, a numerical model of the mAFM has been developed. It represents a faithful modelling of the metrological chain. It has been developed under Matlab using object oriented programming. This model takes into account the laser beams, the AFM's tip position, the measurement and reference prisms and their mirrors as shown on figure  3. Displacements in six degrees of freedom can be generated on the mobile prism (sample translation) or any object to evaluate measurement errors.
The interferometer beam modelling The interferometers are not modelled in our first approach. Only the four beams emerging from the differential double pass interferometer are taken into account. These beams are modelled with a source point, a direction defined with a vector targeting the mirror and a Gaussian profile. In this way, the four beams act as four mechanical probes that only measure the distance from the source to the corresponding mirror. It takes into account the Gaussian profile and the impacted surface of the mirror as shown in the figure  4. To feed the model with experimental data, the real interferometer beams have been analysed to get their shapes and dimensions. The data are computed with Matlab to found the best coefficients that define the modelled Gaussian profile (standard deviation, mean and amplitude).
Improving the performance of the monitoring system in intensive care units is a major issue about the recent research topic on medical devices. Therefore, an automatic and intelligent real-time monitoring system is necessary to increase quality of medical-service in intensive care units. We feel that video monitoring based on multi-camera perception enables to supplement the existing monitoring system in ICU because it is quite obvious that hearing and vision are complementary. This video layer can provide visual-information in real-time for pro-active actions in medical emergency, and design an image database that we term Medical Black Box (MBB) for future diagnosis and analysis. The proposed video surveillance system must be able to analyze and interpret the medical video scenes for recognizing significant events of a patient's hospital stay and to help physicians in the monitoring tasks.
Proposed ICU Monitoring System The intensive care units are a specialized section of a hospital containing medical equipment, physicians and the nursing staff, monitoring and life support devices necessary to provide continuous care to the patients that are severely ill and medically unstable  [14]. We can find in a typical ICU environment a ventilator which assists the patient's ventilation, pumps to supply of various drugs, and various devices to the heart or respiratory functions  [3, 11]. The set of these devices is handled by a multidisciplinary team that may consist, intensivists (clinicians who are specialized in critical illness care), clinical pharmacists, nutritionists and others. These facilities are connected to the data collector which displays the collected data  [14]. The vital sign information acquired from bedside monitoring ICU system is analyzed by supervising physicians for decision making.
Afterwards, we have the step of scenario recognition which enables to make the summary of the clinical scenes of the physician and important or critical events of the patient during his hospital stay through the video selection. The latter computer vision task helps us to design an especial database in ICU, that we term Medical Black Box (MBB) in order to store interesting images of clinical scenes and important behavior of the patient. Figure  1  is an illustration of the proposed architecture for the visual-monitoring system in ICUs. Temporal and hierarchical HMM for activity recognition applied in visual medical monitoring using a multi-camera system -51.
The aim of this system is to extract good visual content representative key-frames. In order to achieve this, we used an efficient method for human activity recognition in medical videos which leads to the extraction of better key-frames as representative (interest scenario) for important events summary. The MBB is a database containing interesting images obtained by video selection which represents the summary of critical or important events from hospital stay. The concept of the medical black box is an original new idea that presents double interest : firstly, it is a decision support for medical monitoring in intensive care units and secondly, it allows, after analysis and diagnosis of MBB's content, to contribute to the development a new intervention protocol which improves the performance of medical care.
Experiments and Results Significant tests are carried out on dataset that are gathered from the proposed experimental monitoring system in intensive care units. Our Algorithms have been implemented using Matlab, C/C++ and OpenCV library). The database is characterized by its heterogeneity with a high variation of illumination (day and night) and several people such as physicians and patients (male, female with different actions, skin colors) other moving people as the visitors among others, may appear at defined and known instants. Furthermore, the database has been collected from the experimental videosurveillance system that we installed in the cardiology department at the hospital (CNHU: Centre National Hospitalier Hubert Manga) in Benin.