dataesr/scientific-paragraphs-categorization huggingface dataset:
Training Algorithm and Parameters We use the MNIST dataset of handwritten digits  [7]  which consists of 60000 samples of 28 × 28 gray-scale images of handwritten digits from 0 to 9 and 10000 further such samples to test the learned network. For training, we split the training dataset and use 15 % for validation purposes. Using a batch size of n batch = 100 and the Adam method for stochastic optimization  [10]  with a step size of 10 -3 , we train the weight variables of the network based on the training dataset, using the cross entropy between the known and predicted labels as error metric to be minimized. Every 50 epochs we compute the accuracy achieved on the validation dataset. We define a patience parameter to avoid overlearning on the training dataset: if the validation accuracy has not improved for seven consecutive times, we stop training.
Finally, we evaluate the achieved accuracy on the completely unseen test dataset. This is the accuracy we report in the main text. Since our measurements are complex-valued, we stack real and imaginary parts of the M measurements in a 1 × 2M real-valued vector M; then we feed M into the first fully connected layer. A subtle but crucial detail is the need to normalize M. If its standard deviation is several orders of magnitude above or below unity, training the weights of the fully connected layers may take much longer or be unfeasible, in particular in combination with the temperature parameter (see below). Hence, we first identify a normalization constant as the average of the standard deviation of M obtained for ten different random TX and RX masks (obeying the chosen constraint such as amplitude-binary) using the training dataset.
We then always divide M by that fixed normalization constant in the subsequent training, validation and testing. In other words, the normalization constant is fixed once and for all based on the training dataset. The employed activation functions are defined for realvalued inputs as follows. A "rectified linear unit" (ReLu) activation function returns 0 if its argument x is negative: (S17) f ReLu (x) = max(x, 0). ( S16.
ACKNOWLEDGMENTS M. F. I. , A. V. D. and D. R. S. are supported by the  Air Force Office of Scientific Research  under award number  FA9550-18-1-0187. The project was initiated and conceptualized by P. d. H. with input from M. F. I. and R. H. The project was conducted and written up by P. d. H. All authors contributed with thorough discussions.
Following this well-established theory that has been widely proven in practice, the resistivity of a resin was directly related to its viscosity while the Glass Transition temperature (Tg) can be estimated online using Synthesites proprietary algorithms via the Online Resin State (ORS) module ([9],  [10] ). This online estimation technique was extended successfully for the specific acrylic resin that is being used in the Recotrans project. The ORS module was initially developed for isothermal cure cycles and was further extended to deal with highly non-isothermal conditions i. e. high exotherms with long cooling stages. Similarly to the resistance correlation to the Tg in thermoset resins, for the reactive thermoplastic resin the correlation is with the residual of the monomer which corresponds to the degree of conversion as can be seen in fig.
treex is a Python package for manipulating rooted trees, ordered or not, with or without labels on their vertices. Basically, treex allows (i) random generation of trees, (ii) edit operations (e. g. , add or remove vertices or labels), (iii) visualization of structures and their properties (in command line or a Matplotlib figure and exportation to TeX), (iv) conversion to different formats, and (v) application of various algorithms. Concerning the latter two, coding processes  (Pitman, 2006)  have been implemented, as well as DAG compression  (Godin & Ferraro, 2010). In addition, comparison between trees can be performed via an edit distance algorithm  (Azais, Durand, & Godin, 2019). Self-nested approximations of trees  (Godin & Ferraro, 2010 , Azais (2017) ,  Azais et al. (2019) ) have been implemented through different algorithms.
To the best of our knowledge, treex is the only Python library that permits encoding of rooted trees in various formats together with such a diversity of treatments. Let us mention the related Java library TED  (Pawlik & Augsten, 2016)  for efficient edit distance algorithms. treex offers converters to the standard encoding of nested brackets (see for instance  (Aho, Hopcroft, & Ullman, 1974) ) and L-strings as manipulated by L-Py, a simulation framework for modeling plant architectures  (Boudon, Pradal, Cokelaer, Prusinkiewicz, & Godin, 2012). Numerical experiments and/or figures of recent publications  (Azais, Genadot, & Henry, 2019 , Azais (2017) ,  Azais et al. (2019) ) have been made using the current or previous versions of treex. Furthermore, ongoing academic projects on the development and implementation of supervised classification methods for tree data, the study of lineage trees, as well as investigations on plant modeling, make intensive use of structures and algorithms implemented in treex.
treex is open source and distributed under the LGPL License. The source code is hosted on GitLab. Releases are automatically built and tested for 64-bit Linux and Mac OS X machines using Jenkins CI, and packaged on Anaconda Cloud  (Azais, Cerutti, Gemmerle, & Ingels, 2019a). The documentation of all classes, methods and functions is built upon release and made available online  (Azais, Cerutti, Gemmerle, & Ingels, 2019b).
Purpose -The building of an Integrated Catalogue of Research Assets Metadata should boost multi-disciplinary research. Such an integrated catalogue should enable researchers to solve problems or analyze phenomena that require a view across several scientific domains. Design/methodology/approach -There are two main approaches for integrating metadata catalogues provided by different e-RIs: centralized and the distributed. We decided to implement a central metadata catalogue that describes, provides access to, and records actions on the assets of a number of e-RIs participating in the system. We chose the CERIF data model for description of assets available via the Integrated Catalogue. Analysis of popular metadata formats used in e-RIs has been conducted, and mappings between popular formats and the CERIF data model have been defined using an XML-based tool for description and automatic execution of mappings.
Findings -An Integrated Catalogue of Research Assets Metadata has been created. Metadata from e-RIs supporting Dublin Core, ISO 19139, DCAT-AP, EPOS-DCAT-AP, OIL-E, and CKAN formats can be integrated into the Catalogue. Metadata are stored in CERIF RDF in the Integrated Catalogue. A web portal for searching this catalogue has been implemented. Research limitations/implications -Only five formats are supported at this moment. However, description of mappings between other source formats and the target CERIF format can be defined in the future using the 3M tool, an XML-based tool for describing X3ML mappings that can then be automatically executed on XML metadata records. The approach and best practices described in this paper can thus be applied in future mappings between other metadata formats.
Practical implications -The Integrated Catalogue is a part of the eVRE prototype, which is a result of the VRE4EIC H2020 project. Social implications -The Integrated Catalogue should boost the performance of multidisciplinary research, thus it has the potential to enhance the practice of data science and so contribute to an increasingly knowledge-based society. Originality/value -A novel approach for creation of the Integrated Catalogue has been defined and implemented. The approach includes definition of mappings between various formats. Defined mappings are effective and shareable.
This project aims to bridge across existing e-RIs such as EPOS, ICOS and SeaDataNet by taking VREs one step further towards the enhanced-VRE (eVRE) model with a standard reference architecture, generic reusable building blocks for VRE development and explicit cross-e-RI interoperability support. A cross-e-RI metadata catalogue essentially enables scientists to discover and utilize data and services from different communities and domains; however, the development of such a catalogue requires a context-rich metadata schema which can glue together different metadata schemas from different individual e-RI catalogues. As mentioned, VRE4EIC has developed a reference architecture and software components for VREs based on the concept of the eVRE, a modular VRE based on standard building blocks that can easily be adapted for use by different research communities.
Because the eVRE is intended to hide the IT complexity of the underlying implementation layers from its users, this implies that one of the main concerns that an eVRE has to deal with is heterogeneity in terms of protocols, data and metadata formats, techniques for accessing data, etc. The Reference Architecture  (Meghini et al. 2016 ) defines six main conceptual components that interoperate with well-defined interfaces. In this paper, we focus on the Metadata Manager, responsible for the building, maintaining, and querying of the eVRE's internal metadata catalogue. The motivation for creation of the cross-e-RI metadata catalogue described in this paper is to allow researchers to solve problems or analyse phenomena that require a view across several scientific domains. The Integrated Catalogue should boost the performance of multidisciplinary research, and thus enhance the practice of data science and so contribute to an increasingly knowledge-based society.
Many difficulties can be encountered, including misunderstandings and how to manage different versions of the mapping definitions; there is also a need for an exhaustive knowledge of the schemas being mapped to and from. There have been some attempts to automate part of the task  (Rahm and Bernstein, 2001). To simplify and accelerate different parts of the task, mapping tools can help  (Choi et al. , 2006 , Xu et al. , 2006). We used the X3ML toolkit  (Marketakis et al. 2016)  for implementation of mapping tasks in our project. The X3ML toolkit describes schema mappings in such a way that they can be collaboratively created and discussed by experts. We decided to use this tool in order to accelerate mapping process, as well as to enable collaboration of metadata formats (CERIF, Dublin Core, etc) experts and mapping experts which we had in our research team.
Methodology The methodology for implementation of the integrated eVRE metadata catalogue includes the following steps: 1. Selection of an approach to the cooperation between e-RIs and a VRE -Pros and cons of the centralized and the distributed approaches for integrating metadata catalogues coming from different e-RIs were analyzed. The projected usage of the eVRE system was taken into account in the process of selection of the final approach. 2. Definition of a metadata model (schema) for the integrated catalogue -To identify the metadata model to be used for the eVRE catalogue, an analysis of various e-RIs was carried out. The goal was to identify the most widely used metadata models. To support interoperability, as well as interdisciplinarity, across various metadata models, we have to select one model to service as the data model of integrated catalogue, based on the following criteria: 1) the model must be an accepted standard; 2) it must include all basic research entities, and in particular be able to cover non-community-specific entities; and 3) the model should also be able to flexibly deal with the various semantics used by the different communities.
3. Creation of the integrated catalogue -In the context of the central eVRE catalogue, after selection of e-RIs which could be data providers and definition of an integrated catalogue schema (see the previous bullet), there is a need to match and map information existing in the individual e-RI catalogues to the central eVRE metadata catalogue. The process of matching, harvesting, mapping and transforming data coming from different e-RIs is shown in Figure  1. The first step is to define the semantic matching between the source schema and the eVRE catalogue schema (steps A1 & A2). Mapping should be defined for all metadata formats dominant in the VRE related initiatives listed in Table  1. The lost of information in the process of metadata transformation (step A4) should be as less as possible.
Metadata which can be used as metadata criteria for searching and filtering of catalogue are especially important to be mapped and transformed well. A transformation engine applies this matching to the set of data that have been harvested from the participating e-RI, following the source schema (steps A3 & A4). The output of this mapping populates the eVRE metadata catalogue (step A5). The final step A5 is implementation of information retrieval features over the integrated catalogue. Search of the integrated catalogue should enable researchers to solve problems or analyze phenomena that require a view across several scientific domains. Thus, metadata criteria for searching and filtering data in an integrated catalogue should be created in accordance with the previously stated main purpose of integrated catalogue.
In our case, it was decided to implement a central metadata catalogue (answering Research Question 1) that describes, provides access to and records actions on the assets of the e-RIs participating in the eVRE. It facilitates one important service, namely the construction of workflows across one or more RIs. The construction of workflows requires numerous accesses to resource descriptions, followed by optimisation for parallel/distributed operations; the centralized approach makes it possible to implement this access in the most efficient way possible.
ii. ISO19115/19139 The ISO19115 standard defines the ISO schema for describing geographic information 2. The schema provides information about the identification, extent and quality of spatial and temporal data, spatial references, and distribution of digital geographic data. The standard is applicable to cataloguing of datasets, especially geographic datasets, dataset series, individual geographic features and feature properties. The ISO19115 standard can be used to provide the metadata about geographic datasets required by the European INSPIRE directive 3. ISO19139 defines the XML profile for ISO19115.
iii. DCAT-AP DCAT stands for Data CATalogue vocabulary. It is a recommendation by W3C 4  that is designed to facilitate interoperability between data catalogues published on the Web. The main entities managed by the DCAT vocabulary are Catalog, Dataset and Distribution. It relies on the FoaF 5  vocabulary to describe persons and organisations. A specification of DCAT for data portals in Europe has been designed: DCAT-AP 6. This specification introduces several additional mandatory and recommended classes like Agent, Category, Category Scheme and License document. It also introduces a status for the properties: DCAT recommends usage of some properties for each class, whereas DCAT-AP defines sets of mandatory, recommended and optional properties for each class.
iv. EPOS-DCAT-AP EPOS-DCAT-AP is an extension of the DCAT-AP for Research Infrastructures in the environmental domain, with a specific focus on the EPOS Research Infrastructure in the subdomain of earth science. It extends the description of datasets, dataset series, equipment and services. It largely leverages existing models and vocabularies, like schema. org. EPOS-DCAT-AP was implemented by defining an RDF (Resource Description Framework) syntax that can be used for the exchange of descriptions of spatial datasets, dataset series, and services among communities.
vi. CKAN CKAN is an open source and open architecture software platform for data management 9. The CKAN data management platform is in use by numerous governments, organizations and communities around the world. It can be easily installed and customized for the specific needs of some organisation. Also, taking into account its open architecture it can be extended with some plugins. The CKAN platform can preserve various data typesdatasets, source codes, documentations, etc. Note that CKAN itself is a platform, not a metadata standard, but it has a widely used internal metadata model, also used by some e-RIs, so we decided to consider that model (the 'CKAN model') as one of the source schemas to map from.
ii. Vocabularies homogenisation In order to facilitate data retrieval from various sources, the e-VRE has to understand both the format and the semantics of the metadata describing the resources from those sources. The format issues are taken into account by the matching and mapping operations. The semantics define the meaning and definitions of the resources and the links among them. As CERIF allows use of any semantics, one step towards data integration must concern the integration of the semantics of the different sources. Various information used in a dataset can be expressed using different terms in different standards: author or creator, creation dates and type of resources, etc. Various terms can be used to indicate that some resource is a dataset. For instance, in a Dublin Core record, one can define a dataset by using the property dcterms:type (http://purl.
org/dc/terms/type) with the value Dataset (http://purl. org/dc/dcmitype/Dataset) coming from the Dublin Core Type Vocabulary ( http://purl. org/dc/terms/DCMIType), while in a CKAN record, the free text "dataset" term can be used. All these terms and vocabularies should be harmonised so that e-VRE users can retrieve both results by asking the search engine for "datasets". A specific task was conducted to achieve harmonisation among metadata mapped into CERIF. We used a bottom-up approach by first listing in the matchings all the attributes that match the cerif:Classification entity, with the corresponding values identified in the source, or in the vocabularies identified or recommended by the documentation of the standard used by the source. We then categorised the attributes regarding their values in the following categories:  Terms introduced by the matcher -the expert that does the matching.
The matching and mapping progresses by building step-by-step the first matching rules and running test mappings and transformations. Matching of all identified schemata Dublin Core, ISO19139, DCAT-AP, EPOS-DCAT-AP, OIL-E, and CKAN to CERIF RDF has been provided in the context of the VRE4EIC project  (Remy et al. 2017). The full matching between those models to CERIF RDF is expressed in the 3M tool. Those mappings may be found at the following Zenodo link: https://zenodo. org/record/2548732 or by using 3M tool instance. To view the full mapping in 3M: 1) open the link http://www. ics. forth. gr/isl/3M-VRE4EIC 2) log in using username vre4eicGuest and password vre4eic 3) find the certain mapping project in the list and select it 4) click on the icon. There are a few basic options provided via the 3M menu for each selected mapping project.
Those evaluation workshops have been co-located with ICIST 2018, CRIS 2018 and IWSG 2018 conferences, respectively. Collected feedback has been used to improve the prototype. Also, a set of competency queries was created to test and validate the contents of the eVRE Metadata Catalogue. A metadata catalogue covering the datasets provided by a group of e-RIs can be created using defined mappings similar as prototype implemented for the needs of the VRE4EIC project. Also, a similar approach can be applied in order to build an integrated metadata catalogue for some other domain. An integrated catalogue, whether the prototype developed for the VRE4EIC project or a new catalogue developed in the future based on the methodology and mappings defined above, should provide a boost for multi-disciplinary research, thus enhancing the further development of data science as well as contributing to knowledge-based society.
Moreover, vocabularies (controlled or free) used or recommended by the standards have been collected and harmonized. They form a core set of vocabularies part of the semantic layer of CERIF. The X3ML toolkit has been used to construct automatic mappings and a common matching language. The methodology used to build the matching can be reused for other standards that may also need to be integrated within any VRE built according to the eVRE reference architecture. By being expressed using the same standard, metadata are easily findable for researchers, no matter the original format of the metadata. The homogenisation of the metadata also allows homogenisation of the vocabularies used to categorise the metadata. The prototype implementation of the e-VRE Metadata Catalogue proved the feasibility of building an integrated, context aware catalogue that supports interoperability and reuse across RIs.
The work presented in this paper resume a numerical analysis of the concrete cover effect, on the resistance of the steel-concrete interface. The effect of friction on the interface behavior is also included. For this, a brief description of the experimental steps generally used for the characterization of the steel-concrete interface is presented. Also, the CDP model, Concrete Damage Plasticity, is illustrated. The results of the numerical simulation using the Abaqus code are presented with different diameters of coatings with and without friction.
Pull out test It is the most experimental test used to study the decohesion at the interface between the concrete and steel. Many studies carried out to describe the effect of high temperature on the interface between concrete and steel. We quote the experimental research carried out at INSA Rennes  [1] , on cylindrical specimens (110 mm in diameter and 130 mm in height) reinforced with a steel bar (12 mm diameter). The bar is in the middle of the specimen and anchored on his half height (65 mm) figure  1. Tensile tests are carried out on the cylindrical test pieces using a traction machine. This machine "TESTOMETIC" is driven at speed of 0. 5 mm / min by a computer with WinTEST analysis software.
). These data are easily recovered for scientific catches  [26, 51, 67] , but this could be more difficult when samples are purchased directly from fishermen or market or sold as processed seafood  [35, 60, 75]. When individuals are still whole, biometric data have to be recorded, such as total or standard length with an ichthyometer or a calliper and weight. Recording whole sample size will help scientists to determine whether this corresponds to a commercial size; as an example, commercial sizes for major European species can be found in Regulation (EC) No 850/98  [91]. Moreover, it could help to estimate the physiological state of studied organisms. Studies on bivalves and crustaceans usually meet this standard, but it is more difficult to find such studies on fish. However, these data are important, especially for risk assessments related to human health.
Proposals for standardisation Based on these findings, some advice can be proposed concerning sampling and isolation, in order to standardise practices. A minimum of 50 individuals must be sampled per studied species (Figure  3 ), even though statistical analyses are required to assess whether this is representative of population variability. It is important to gather metadata as much as possible, including GPS coordinates for the catch. The second important point to keep in mind is to select individuals that correspond to the commercial size, if risk assessment for human health is considered. Biometric data, i. e. whole size, whole weight and analysed tissue weight are mandatory for study reports in order to improve standardisation of digestion techniques. Once sampled, tissues are to be stored as frozen samples, since the effects of chemical preservatives on polymers are not clearly documented.