dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This project was funded by a mobility fellowship of the  Swiss National Science Foundation  (Grant No. 177732 ). Claude Fermon  and  Aurélie Solignac  from the laboratory of nanomagnetism and oxides are acknowledged for hosting the project and providing the necessary infrastructure. Gregory Cannies  is acknowledged for assistance with the mechanical design and 3D-printed probe holders. Mathieu Thévenin  is acknowledged for fruitful discussions on FPGA programming. Hervé Hurdequint  is acknowledged for CW X-band EPR spectroscopy of the LiPc sample. The trityl radical solution used in the EPR measurements was a kind gift from  Gunnar Jeschke  and  Daniel Klose  from  ETH Zürich. Within this context,  Guido Grassi  and  Daniel Zindel  are acknowledged for the synthesis of the Finland trityl radical.
While FLC are not specifically assessed in UPP, abnormal urine FLC excretion may be suspected, and should be confirmed by urine electrophoresis and/or urine immunofixation (4). The software "MDI Lablink", developed by Regeniter et al. , allows a classification of proteinuria integrating these marker proteins  (1 -4). In this mini-review we illustrate the advantages of using the UPP and the software. (1) Technical considerations and interpretations UPP is performed on a minimal volume (0. 5 ml) of unconcentrated urine sample, ideally resulting from the second morning urine. The proteins are measured by immunonephelometry, a specific and analytically very sensitive method that can measure proteins reliably down to 0. 3 mg/l. The result is usually available on the day of collection. Urine marker evaluation by UPP is a two-step process:  (1)  Step 1 : measurement of total protein, urine creatinine, and two protein markers testing either glomerular (albumin) or tubular (alpha-1-microglobulin) dysfunction, respectively, and the urinary dipstick which estimates erythrocyte, white blood cell and nitrite count  (2)  Step 2 : only performed if albumin to creatinine ratio (ACR) and/or alpha-1-microglobulin to creatinine ratio (A1MCR) are higher than the upper normal limit, respectively > 3 mg/mmol and 2.
26 mg/mmol (9 -10): additional urine protein makers are evaluated to classify abnormal proteinuria. Transferrin confirms the glomerular alteration and IgG is indicative of severe damage of the glomerular filtration barrier. Tubulo-interstitial alterations, suspected by an increase of A1MCR, are further evaluated by retinol binding protein, which is particularly elevated in cases of severe proximal tubular cell dysfunction and when tubulo-interstitial fibrosis is present. UPP results are presented graphically (Fig. 1 -Fig. 5 ) with a diagnosis commentary generated by the MDI Lablink software, and an additional graph that show historical results when available. (2) UPP in the clinical practice UPP is indicated if a total proteinuria/creatinine ratio over 30 mg/mmol is detected, and/or for the evaluation of patients with a glomerular filtration rate (GFR) decrease below 60 ml/min/1.
Software, programs and calculations To analyze a large surface, recordings were first investigated with the Cellvizio Viewer software (Mauna Kea Technologies, Paris, France) to perform mosaicking, which consisted of stitching consecutive frames to "rebuild" the surface covered by the probe. Each mosaic was then processed to measure 14 parameters that were used to perform a quantitative analysis of the mucosa, hereafter referred to as "cryptometry. " After the mosaic was obtained, intrinsic architectural parameters of the crypts were calculated using the Icy software (Institut Pasteur, Paris, France)  [22]. To perform the calculation, we used the active cells plug-in that implements an active contour segmentation method, using exponential splines as basic functions to represent the outline of the crypt, which computes a region of interest.
The corresponding geometric data were thus obtained using the region of interest statistics tool, ie, the perimeter, the sphericity, the roundness, the maximal Feret diameter, the elongation factor and the ratio of the maximal axis to minimal axis. Concerning the crypt density measurement, we have developed a macro in ImageJ (U. S. National Institutes of Health, Bethesda, Md, USA)  [23]  software to calculate the ratio between the field of view area and the sum of the areas of the crypt. The measurement of the minimal and mean intercrypt distance and wall thickness have been adapted from a plug-in  [24]. The vessel parameters were measured using the IC Viewer (Mauna Kea Technologies, Paris, France) version 3. 8. 6 Vessel Detection plug-in. This plug-in enabled an automatic detection of the vessels directly from each endomicroscopic recording frame, based on fluorescence intensity detection with a threshold set manually at 10 µm.
We reported the vessel lengths and areas to the field of view area to normalize the data. Finally, the fluorescein leakage through the colonic mucosa (FLCM), defined by the increase of mucosal fluorescence over time, was measured using the "signal analysis" tool of the Cellvizio Viewer software. Because the Cellvizio confocal endomicroscopic probe in the clinical version requires internal calibration, we could not directly compare the fluorescence intensity between patients. Thus, we calculated the FLCM as the increase in fluorescence intensity reported in time units, independent from the calibration.
Statistical analysis Statistical analyses were performed using the GraphPad Prism software (GraphPad Prism 5. 0, GraphPad Software Inc). The mean comparisons were performed using the non-parametric Mann-Whitney test. The area under the receiver operating characteristic curve (AUROC), the multivariate regression and the logistic model were performed using STATA 14 (Stata Corp LP). Differences with a P value less than. 05 were considered statistically significant.
Acknowledgements. We thank the anonymous referee in their help improving the quality of the paper and making a number of constructive suggestions. The authors are indebted to illuminating discussions with  Jeffrey Fung ,  Gabriel-Dominique Marleau  and  Michiel Lambrechts. The new complex opacity tables have been provided by  Nick Malygin  himself for which we are very grateful. MS and AJ were supported by a project grant from the  Swedish Research Council  (grant number  2014-5775 ) and an  ERC Starting Grant  from the  European Research Council  (grant number  278675 -PEBBLE2PLANET ). AJ was further supported by the  Knut and Alice Wallenberg Foundation  (grant number  2012. 0150 ) and the  European Research Council  ( ERC Consolidator Grant   724687-PLANETESYS ). BB thanks the  European Research Council  ( ERC Starting Grant   757448-PAMDORA ) for their financial support.
As for the projects targets advantages of dual ratio transmission surpass disadvantages, it was decided to develop a dual ratio, switchable transmission. The ratios of the transmission are 21. 65 for the first gear and 12. 18 for the second gear which leads to a ratio step of 1. 78 The development process is described in the following: With the help of the gear synthesis software PlanGear from ZG, different gear configurations could be elaborated on the level of stick diagrams. Out of 34 possible configurations which fulfil the drivetrain's requirements, the four configurations with the highest efficiencies were depicted for further investigation. These configurations were then evaluated with respect to expected costs and overall size. Finally, the combination of a Ravigneaux planetary system and a pair of cylindrical gears was identified as most promising solution.
In a next step, gear and bearing dimensioning was elaborated with the use of the transmission calculation software MASTA by SMT. Shaft and bearing loads were based on highly demanding drive cycles. Fig. 13  illustrates the design process of the gear and bearing configuration. Several configurations were reviewed whilst taking the manufacturing methods for parts and the way of assembly of the powertrain into account. Subsequently, detailed gear and bearing lifetime calculations based on highly demanding drive cycles were performed. Load-capacity, efficiency, weight and costs of possible bearing arrangements were evaluated and compared. Where possible, ball bearings are applied to reduce transmission losses. Once the transmission layout was known, the oil routing was developed to assure all bearings and gears are lubricated sufficiently.
Results and implementation To make parameter balancing applicable, we provide software tools as well as Python and Matlab code. Our online interface allows modellers to balance model parameters with a few mouse clicks while the Python and Matlab packages can be integrated into modelling workflows  (Smith et al. , 2018; Stanford et al. , 2013). Our implementation supports established standard formats: models are given in Systems Biology Markup Language format while the generic table format SBtab  (Lubitz et al. , 2016)  is used for all other data. Based on a network model and kinetic parameter data, the tool returns a table with balanced parameter values and their uncertainties as well as a parameterized Systems Biology Markup Language model with modular rate laws  (Liebermeister et al. , 2010).
Parameter balancing can be customized by modifying the priors, e. g. by specifying new mean values, standard deviations and possible ranges for various types of kinetic constants. Metabolic fluxes cannot be used directly as input data because typical kinetic rate laws do not fit into the regression model underlying parameter balancing. However, if flux directions are known, they can be used to define the signs of thermodynamic forces, and the resulting metabolic state will comply with these flux directions. In the Matlab version, enzyme levels and catalytic constants can be automatically adjusted to match predefined fluxes; moreover, parameter sets can be sampled from the posterior distribution, where corrections are applied if parameters violate predefined bounds. In a model with 50 reactions, the calculation takes less than a second on a 2.
In this work, a domain is an area of knowledge specified through an ontology document, called repository domain ontology (RDO) document. Figure  1  illustrates the modules composing the QoDisco architecture, described as follows. The Ontology Module consists of repository domain ontology (RDO) documents describing the concepts composing the information model of QoDisco. In this module, the Ontology Manager provides operations for adding, removing, and modifying RDO documents, whereas the QoC Criteria Manager is responsible for adding, removing, and modifying QoC criteria (also defined in RDO documents). A QoC criterion represents an attribute that qualifies a context information (e. g. , accuracy, upto-dateness) and it can be calculated in different ways according to the situation in which it is used.
In QoDisco, QoC criteria are described through ontologies to allow for unambiguous identification and representation. The Repository Module manages repositories and maps each one to (a group of ) specific RDO document(s) in the Ontology Module. As the search and management of records is based on domains, a repository joining QoDisco has to be mapped to at least one domain specified by an RDO document and multiple repositories can be mapped to the same domain by their respective owners. The Record Manager is responsible for adding, removing, and modifying records in repositories. As repositories maintain their operational and managerial independence, their owners can manage their own records independently from QoDisco. Nonetheless, the information model of QoDisco (see Section 2. 3) is shared among all of these repositories.
Implementation QoDisco is implemented as a Web application using the Java programming language and the Spring framework, being deployed to an Apache Tomcat server. Spring provides support for multithreading using a thread pool, which was required towards implementing the parallel search request to the repositories (see Section 2. 2). For the sake of performance, QoDisco uses multithreading facilities to throw searches in parallel. In the current implementation of QoDisco, search queries are specified in SPARQL  [31] , a semantic query language standardized by W3C for information retrieval. SPARQL supports both multi-attribute and range queries over documents in OWL, the W3C's recommended language to semantically describe ontologies. SPARQL is one of most popular approaches for discovery services due to its relative simplicity of implementation, besides being an open standard.
Experimental results reported by Bröring et al. [32]  recommend the use of SPARQL for discovery services based on directories (such as QoDisco) since it provides queries with richness and result ranking. In particular, search queries in QoDisco comply with SPARQL 1. 1, the latest version made available by W3C. The underlying infrastructure for Operation, Search, Repository, and Ontology Modules is implemented using Apache Jena  [33] , an open-source framework to search for, add, and modify OWL descriptions while supporting SPARQL queries. QoDisco uses Jena-ARQ as query engine and it follows five steps to perform a query: (1) Parsing -structuration of the query string as query object; (2) Algebra Generation -translation of the query object to an algebra expression using the SPARQL specification algorithm; (3) High-level Optimization -transformations applied to the algebra expression, e.
g. , replacing equality filters with a more efficient graph pattern; (4) Low-level Optimization -choice of the evaluation order for basic graph patterns; and (5) Evaluation -execution of algebra expressions to generate solution graph patterns. Information about repositories (HTTP address and supported operations), RDO documents (name and URI), and topics (topic name) is handled by their respective modules to be registered at a relational database. Repositories are implemented using Apache Fuseki  [34] , a server providing SPARQL query, update, and storage operations via HTTP requests. Fuseki was chosen due to its easy deployment and because it provides a SPARQL HTTP endpoint built upon the Jena framework, but other existing technologies could be used to implement repositories. The broker service within the Asynchronous Search Handler uses Moquette  [35] , a lightweight open-source Java library to implement publish-subscribe message brokers for the MQTT messaging protocol.
using SPARQL queries. In the experiment, we have calculated the time spent by QoDisco to search for all the observations related with carbon monoxide pollution levels with a QoC indicator of precision greater than or equal to 95%. This is expressed by the following SPARQL query: SELECT ?obs ?qocValue ?sensorData WHERE { ?obs a ssn:Observation ; ssn:observedProperty pol: Carbon_Monoxide ; ssn:observationResult ? observationResult. ?observationResult qodisco:has_qoc ?qoc ; ssn:hasValue ?resultValue. ?resultValue ssn:hasQuantityValue ? sensorData. ?qoc qodisco:has_qoc_criterion qodisco: Precision. ?qoc qodisco:has_qoc_value ?qocValue. FILTER (?qocValue >= 95) } This query returns a response in JSON or XML containing (i) an URI to the observation, (ii) an URI referring to the sensor output, (iii) a reference to the QoC indicator, and (iv) the QoC value of the observation.
For E1 and E2, a test plan was set with sending an HTTP GET request to the QoDisco API with the above-stated SPARQL query. In E3 and E4, a repository initially populated with 1,000 random observations related to carbon monoxide pollution levels was deployed in a virtual single-core machine with 1 GB of RAM. QoDisco was deployed in a virtual dual-core machine with 2 GB of RAM, multiple publishers were deployed in a virtual single-core machine with 1 GB of RAM, and subscribers were deployed in another singlecore machine with 1GB of RAM as well. A time Web service intended to provide the relative time was deployed in a virtual single-core processor with 512 MB of RAM. These settings are depicted in Fig. 6. As JMeter does not provide native means of subscribing to an MQTT broker, we developed a Java client program to assess the response time of asynchronous notifications sent by QoDisco in E3 and E4.
An additional, but important caution must be taken when attempting to precisely measure a Java application since it is necessary to consider the complex interaction among the components of the Java Virtual Machine, including the application bytecode, the core runtime system, the just-in-time compiler, and the garbage collector. To obtain a realistic performance measurement, it is necessary to wait for QoDisco to reach its steady state after a large number of interactions. In both E1 and E2, we noticed that the synchronous search method reaches a steady state after 1000 requests. For E3 and E4, the steady state was reached after 200 notifications. In all four experiments, we also monitored the processes and threads running in each virtual machine using the htop Linux tool  [38]  to ensure that only QoDisco and its Java threads would have considerable amount of time in the dual-core processor.
Similarly to QoDisco, CASSARAM is built upon an ontology-based information model comprising the SSN ontology and the SPARQL language is used in the search process. Despite CASSRAM addresses context information, it does not consider QoC criteria in the discovery process, which is valuable towards providing more accurate results according to application/user requirements. Indeed, we have not found any proposal in the literature addressing the use of QoC criteria for discovery services in IoT, thereby constituting a significant contribution of our work. The main motivation of the Wang et al. 's work  [47]  is to solve two major challenges involved in IoT environments, namely interoperability and scalability. They adopt the sensor-as-a-service vision, in which the ultralarge-scale sensing infrastructure (consisting of wireless sensor networks) provides sensing services for multiple applications and geographically distributed users.
e. , the type of context explored is a spatial one. It is not within the scope of their proposal to deal with other types of contextual data nor to consider information about QoC as in our proposal. Another difference with respect to our work is that the search process is based on the use of the SPARQL semantic query language, which adopts a synchronous interaction model. QoDisco provides both synchronous and asynchronous queries, which gives it greater flexibility of operation and support for more diverse types of IoT applications. Fredj et al. [48]  propose a hierarchical-based approach of semantic gateways to improve the discovery of IoT semantic Web services in a dynamic context. In such an approach, the IoT environment is modeled as a tree hierarchy of smart spaces. Each smart space is controlled by a semantic gateway that maintains information about the IoT services in its scope (located within the space) and processes discovery requests.
To minimize the discovery cost, the approach proposes creating clusters of similar services that can be optimized over time in terms of number of clusters and number of services per cluster. The discovery cost is measured in terms of the number of service request matching operations performed in a gateway to discover services matching an incoming request. Similarly to QoDisco, this approach is based on distributing data structures to support the discovery process. However, the goal is to minimize the discovery cost instead of dealing with scalability issues as is the case in QoDisco. Moreover, Fredj et al. [48]  use only service location as context information and QoC is not considered in the scope of their proposal.
Conclusion The heterogeneity of the widely distributed plethora of devices in IoT poses a significant challenge to find, select, and use IoT resources (devices, sensors, actuators, services and context data). Therefore, it is important to provide a flexible mechanism that enables clients to easily discover, retrieve, and use device capabilities and data produced by them in an unambiguous way. Due to the imperfect nature of data provided by IoT devices, it is valuable to complement observations with QoC criteria towards augmenting the capability of the search procedure and better supporting decisions made upon these data. Our research in this context has proposed QoDisco, a semantic-based discovery service for IoT structured upon a set of repositories storing resource descriptions and QoC-related information.
Acknowledgements This work was partially supported by  CNPq -Brazilian National Council for Scientific and Technological Development  and  INES 2. 0 -Brazilian National Institute of Science and Technology for Software Engineering  (http://www. ines. org. br),funded by  FACEPE  grant  APQ-0399-1. 03/17  and  CNPq  grant  465614/2014-0. TB,  FCD  and  PFP are CNPq fellows.
Introduction The shaping of the plasma facing components (PFCs) is a fundamental challenge for future fusion reactors like DEMO. The first-wall (FW) shape has to be adapted to one or more steady state scenarios, but be sufficently protected to ensure that the PFCs will not reach their thermal limits during perturbations (minor disruption, Vertical Displacement Events, etc). The EUROFusion program WPLSI has been tasked with designing the FW of DEMO  [1]   [2]. Optimizing the number of limiters is a key issue for the design of a tokamak  [3]. Too many limiters lead to a more complex design and increase the cost and maintenance time. On the other hand, too few limiters will be exposed to too much heat load, or fail to protect the FW sufficiently. Thus, simulation is needed to optimize the number of limiters.
Background This paper describes a dataset of fish, crustacean and mollusc occurrences extracted from the "Experimental Fishing" section of the IRD's PPEAO information system. PPEAO stands for "Fish communities and artisanal fisheries of West African estuarine, lagoon and freshwater ecosystems". This database contains information collected using two different methods: experimental fishing and surveys of the artisanal fisheries that exploit these ecosystems. The database is accessible at http://ppeao. ird. fr.
New information The current dataset is available on GBIF. org at 10. 15468/ra4voa. It comprises the occurrences of 314 fish, crustacean and mollusc taxa collected in experimental sampling surveys of different aquatic ecosystems in West Africa between 1979 and 2013. Different types of fishing gear were used including purse seines, gill nets and fyke nets. The taxa were identified by IRD scientists or by scientific partners well trained in systematics. Most taxa were identified at species level (97% of cases). This dataset is the result of 213 fishing.
The ecosystem approach to fisheries advocated by FAO particularly requires this type of information. The dataset presented here is of special importance in this context because it combines both old data (the taxonomy of which have been reviewed in the light of current knowledge) and recent data, thus allowing an approach to the temporal evolution of biodiversity. This database is the largest experimental fisheries catch database for West Africa and covers both a large temporal range (more than three decades) and a large spatial range in West Africa (12 ecosystems in seven countries). It comprises fish occurrences mainly from estuarine and lagoon ecosystems (Ebrié lagoon, Sine Saloum estuary including Bamboung MPA, Fatala estuary, Rio Grande de Buba, Gambia estuary), but also from coastal ecosystems (Bijagos archipelago including Urok Island MPA, Banc d'Arguin National Park and Dangara inlet) and also two reservoir lakes (Manantali and Selingue).