dataesr/scientific-paragraphs-categorization huggingface dataset:
The well-known curse of dimensionality is thus circumvented. The PGD solution is approximated as a truncated sum of modes and may subsequently be handled easily. The principle of separating representations has been recently extended to nonlinear regression applications in the s-PGD method  [13]. The PGD method was used for the first time in  [16, 17]  in the field of railway dynamics in an intrusive way. To the knowledge of the authors, the present paper is a first application of the method in a non-intrusive way using the s-PGD method. This is an advantage as intrusiveness inevitably would require the modification of the source code  [12] , here the railway dynamics multi-body system (MBS) software. The main goal is to demonstrate its relevance in this context. The principle is to build a numerical abacus to be used 'online' that may handle some of the external parameters highlighted above for the specific case of a turnout.
This abacus is created by first carrying out a finite number of 'offline' simulations using a design of experiments (DOE) approach. The principle of building a numerical abacus is not a novelty: in the fields of railway dynamics, the Kalker's book of tables USETAB  [18]  follows the same approach. The PGD method is still more versatile, as it allows to handle high dimensional domains. For instance, domains of up to ten dimensions may be handled  [19]  thanks to the separation of variables. Another difference is the model reduction, which allows to handle the model more easily for subsequent tasks, while keeping only the relevant information. A last difference lies in the nonlinearity of the PGD method. The same characteristics hold true for the s-PGD method. Here, railways dynamics simulations are handled with the MBS software VOCO  [20] , but other MBS packages may be used instead, as the process used in this paper does not require any modification in the source code of the MBS.
Building of the reduced model by s-PGD approach The s-PGD method has been used in this paper in order to build the reduced order model. The s-PGD approach is based on the same philosophy of separated representations as the PGD and enables the identification of complex physics existing in high dimensional settings from unstructured datasets. The data in these systems is often sparse due to the high dimensional nature of the phase space  [13]. Mathematically, the tensors of spatial correlation function between the dimensions are sparse, as shown in Figure  4  for the threedimensional space used in this study with variable vehicle speed, axle load and coefficient of friction. The s-PGD method does not require an a priori structure of the sampling points. The methods requiring such a regularity are usually anchored on Delaunay triangularization techniques, and use common interpolation techniques, i.
g. Picard or Newton) with a greedy algorithm. However, the sampling points (Figure  4 , left) are separated randomly into two groups of parameters: the 'inside training' group (Figure  4 , right), which gathers the points used to train the reduced model, and the 'outside training' group, used to validate the reduced model. This is done to avoid overfitting: if not, the reduced model would be tailored to a given dataset and cannot be generalised to other datasets.
In the studied case, the reduced order model is in good agreement with the full model. This seems to validate the applicability of the separated solution in the context of railway dynamics. As a verification of the method, a probabilistic study using the Monte Carlo method is subsequently carried out. The full model and the reduced one show comparable results, with a remarkable gain in terms of computation time. This is a significant advantage during the design phase of new railway network devices, when running full simulations with variable parameters can be significantly time consuming. The probabilistic study involves here a single parameter: the friction coefficient. Further studies should consider multivariate probabilistic analyse. The use of the s-PGD method is non-intrusive, in the sense that no modification is needed in the source code of the MBS code VOCO.
Acknowledgements: The authors are very grateful to the anonymous referees for their thorough review. This work was partially funded by the Chair " Modélisation Mathématique et Biodiversité " of VEOLIA-Ecole Polytechnique-MNHN-F. X and  ANR ABIM 16-CE40-0001. JCP acknowledge support from the  Royal Society  and  CONACyT  ( CB-250590 ). This work was concluded whilst JCP was on sabbatical leave holding a  David Parkin Visiting Professorship  at the  University of Bath. He gratefully acknowledges the kind hospitality of the  Department and University.
It promotes systemic and engineering principles (requirement, architecture,. ) and processes that are today standardized for different types of enterprises typology  [10, 11] , such an approach allows to overcome the issues aforementioned. MBSE inherits and enriches SE by highlighting, formalizing, and tooling the interest and roles of modeling and models all along with a design project. This scientific approach encompasses all activities to specify, design check, and implement a system. The use of models allows to carry out those activities more smoothly as it enables to successfully exchange data and to now and then simulate  [12]  the system behavior. Indeed, apart from verbal exchanges, documents (paper, files, or electronic databases) are the main means of communication and information sharing between the different stakeholders involved in a technological system development project, as well as the main means of integrating the design activity prior to the realization and physical integration of the system itself  [13] [14].
Indeed, SE promotes processes for both design and realization phases of a so-called System of Interest (SOI) identified as a complex system. We think it would be preferable to enrich these processes considering commissioning relevance and importance or to promote a transverse process allowing us to bridge this gap and take more attention to commissioning in both system design and system realization phases.  Issue 2: Limitations of principles, languages, modeling rules, and standards to provide models that could then be used and take attention to these commissioning objectives. In the SE domain, the MBSE approach promotes modeling as a crucial activity for many reasons  [20] , particularly the use of models will allow improving the interoperability between actors involved in both system design and system realization processes, covering naturally commissioning objectives such as safety and security requirement considerations.
Languages (Domain Specific Modelling Languages-DSML) The exponential growth of information accessible through databases represents a challenge for information analysts. The volume of data, their diversity, and the heterogeneity of their sources prevent any manual operation of extraction of such knowledge. The need was felt to propose new methodologies as well as efficient tools to achieve this. The visualization method will therefore have to allow the data visualization, as well as analysis results of their analysis by preserving the important relations between the information and by illustrating the behaviors of the concerned processes. Through the distribution of links, incoming or outgoing, between the associated sites, it will show the geographical distribution of the domains. The model-based orientation of the proposed method then needs to adopt a multi-viewpoint modeling approach as promoted in both systemic and system engineering-related literature.
 Informational view: includes all data generated and treated by the commissioning system. It also describes business objects (i. e. , information) managed, manipulated, or exchanged by the information system, and how these business objects are manipulated. To draw models that will correspond to each of those views and that allow the handling of the proposed concepts seen before, various DSML are requested. Some of them could exist, they concern modeling, simulation, or optimization languages used in the NF design phase in conformity with MBSE usage. However, they do not completely match with some of these concepts. Commissioning-specific new DSML can also be defined, being then interoperable with engineering languages. (e. g. , requirements, and architectures). By evidence, using the same DSML allows to easily irrigate and exchange the commissioning models with the NF model, the two models being then fully interoperable in terms of modeling concepts, modeling languages, and even modeling usages.
Tools Many tools can be used to instantiate those languages i. e. , to create models such as Capella developed by Thales in 2007, GENESYS developed by Vitech, or RFLP developed by Dassault. We present hereafter more in details Capella, but it is important to keep in mind that the method developed must be able to be deployed regardless of the tool if the languages that will be presented can be implemented by this tool. Capella is an open-source MBSE solution hosted by the Eclipse Foundation. It provides a tool-based process allowing the graphical modeling of system, software, or hardware architectures, in accordance with the principles and recommendations defined by the Arcadia method  [16]. The result of such an environment is intuitive and allows engineers to focus on defining their architectures rather than on learning and using complex generic modeling languages such as UML or SysML to capture their needs.
Dedicated to the underlying Arcadia method, it also guides engineers in their activities. The workspace has its own metamodel, which defines the concepts of the language that the user can manipulate through a project, i. e. , through a Capella model, an instance of this metamodel. Then, the user can visualize this model under different aspects, according to different concerns, through diagrams. Thanks to this tool, it would be possible to instance CoGuiNF directly in parallel of the Arcadia method, as the language used in Capella is like SysML, processes could be built and be directly linked to the NF model itself, allowing the team in charge to build all commissioning strategy regarding the architecture and the different requirements. As the language of Capella is close to SysML, to model processes need some adaptation.
Damage, cracking, and strain localization mechanisms often lead to unstable structural responses characterized by snap-backs (i. e. , force and displacement decrease simultaneously). Standard nonlinear Newton-based solution algorithms with displacement/force control cannot capture the equilibrium curve in its entirety. This can be overcome using path-following formulations. A general (i. e. , valid for any finite element code) implementation framework can be designed and applied to the Cast3M software by collecting the essential concepts and the formalism of partitioned path-following arc-length algorithms. Thanks to these developments, Cast3M is now capable of processing path-following equations without any major modifications. Three path-following constraints were selected to demonstrate the applicability of this framework: a first one on the combination of the displacement increment at a given set of nodes, a second one on the maximum strain increment over the computational domain, and a third one on the maximum elastic predictor of the damage/plastic criterion function over the computational domain.
Path-following methods were also used in strong discontinuity simulations of failure in solids, where cracks were represented as zero-thickness interface finite elements  [23, 35] , according to the extended  [36, 37]  or the embedded finite element method  [6, [38] [39] [40]. This overview of the evolution of path-following methods reveals the essential aspects of the present work. Firstly, although remarkable advances have been made in the last decades, the theoretical framework established in the pioneering works by  [1, 3] , and  [4]  remains unchanged. Secondly, the numerical procedures discussed above still haven't been made available to the Cast3M users community. 2  These two aspects bring us to the main purpose of this study, namely proposing a general framework, applicable to the Cast3M software, capable of processing instabilities generated by strain localization.
1. General domain of study. 1 Called "global" since they account for all degrees of freedom (DOFs) of the problem. 2 Cast3M (http://www-cast3m. cea. fr/index. php) is a well-established finite element software  [41]  developed and distributed by the CEA, the French Alternative Energies and Atomic Energy Commission (https://www. cea. fr/ english). p(u, η) = 0 x ∈ Ω (6) where σ(u) stands for the Cauchy stress tensor, ϵ(u) is the infinitesimal strain tensor,  is the constitutive relationship between σ and ϵ, Sym denotes the symmetric part operator, u imp is the imposed displacement, û is the direction of the indirectly-controlled displacement, t imp is the imposed traction vector, t is a vector providing the direction of the indirectly-controlled tractions, "∇⋅" denotes the divergence operator and "∇" the gradient.
Cast3M procedures The main modifications implemented in the Cast3M code concern three existing procedures (PAS DEFA, PAS INIT, and UNPAS) of the main nonlinear incremental Cast3M solver (PASAPAS). These procedures are modified (see Fig. 2  for more details) in order to create and handle all the data structures for the path-following methods; to compute the solution variations δa k+1 I and δa k+1 II ; and to build the total correction δa k+1 once the load multiplier variation δη k+1 has been determined. Moreover, an additional procedure called PILOINDI is added to handle the constraint equation's solution (i. e. , to compute δη k+1 depending on the selected path-following equation) once δa k+1 I and δa k+1 II have been determined.
Building a data file Using the path-following solver with the standalone implementation is easy and only requires minor modifications to existing Cast3M data files. 3  In particular, the main steps for building the simulation file can be summarized as follows: (i) The definition of the simulation always starts with the definition of the geometry of the problem, the definition of the material model, and the force/displacement boundary conditions. At this stage, only the known (imposed) contributions to the boundary conditions are prescribed. (ii) Some initializations are then necessary for the path-following solver. In particular, one has to prescribe the domain's boundary over which the external load (displacement or force) is indirectly controlled. The direction of this loading is also given.
User-defined constraint equations The CMSI formulation can be used without needing to perform any additional developments. However, user-defined constraint equations can be added in a standalone manner by overloading the PILOINDI procedure in the simulation's script (i. e. , without touching the source code). The general syntax used to write new constraint equations is presented in Listing 4. Two possible user-defined constraints are implemented to showcase this feature: (i) The first constraint equation, the so-called Control by Nodal Displacement Increment (CNDI), is fully general  [4]  and can be used with any material model. It is particularly interesting for simulating experimental loading conditions; for instance, when a combination of displacement measures is used to drive the variation of the external load (see, e.
In addition to predicting the peak load and the existence of the two snap-backs, they describe the entire equilibrium path all the way to the rupture. Remarkably, the proposed formulations provide solutions even in the presence of severe instabilities. Figure  14b  shows that the value of Δτ is attained with each criterion and for every analysis. However, convergence cannot be ensured when using the CNDI algorithm, in particular during the second snap-back phase. Notice that, as was already observed by  [6] , the jump in the global response observed with the CNDI formulation (where the other methods instead predict the first snap-back) cannot be attributed to the non-fulfillment of the constraint equation. However, this is directly related to the controlled DOFs chosen. While the simulation ends prematurely with CNDI, this can be prevented by activating the "forced convergence" (a mixed implicit-explicit formulation in which the novel solution is predicted based on previous ones) feature in the Cast3M software (dashed green line).
Conclusions The present study proposes path-following methods for the finite element software Cast3M  [41] , developed by the CEA (French Alternative Energies and Atomic Energy Commission, France) and freely distributed for research and educational purposes. This framework preserves the essence of the successful numerical methods in this field of study: (i) Breaking down the load into two components, one known and one unknown, the latter depending on an additional unknown: the load multiplier  [15]. (ii) In a Newton-like solution process, breaking down the unknown displacement correction solution as the sum of two contributions that can be determined from two linearized systems of equilibrium equations that are independent of the load multiplier variation  [16]. (iii) Determining the load multiplier variation from the solution of an additional equation of the problem: the path-following constraint equation.
Methods We identified past or current IICTs registered and starting in two European countries (Czech Republic and Portugal) from 01/01/2004 to 31/12/2017 to identify detailed studies´ characteristics such as the funding sources and the respective scientific impact. This period of time starts in the year after which the International Committee of Medical Journal Editors' implementation (ICMJE), policy required the registration of clinical trials as a prerequisite for consideration for publication (2004) until the end of 2017. A separate search in four clinical trial registries (CTRs) was performed for each country: ClinicalTrials. gov, European Clinical Trials Registry (EU-CTR), International Standard Randomised Controlled Trial Number (ISRCTN) registry, and the Australian New Zealand Clinical Trials Registry (ANZCTR) by selecting trials registered and with recruitment sites in Czech Republic or Portugal.
Search Methodology for Clinical Trials Identification in Each Database The inclusion criteria were: clinical trial starting from 01/01/2004 until 31/12/2017; non-commercial sponsor; sponsor or recruiting sites in Portugal or in Czech Republic. The following exclusion criteria were used: industry sponsored trials; starting date before 01/01/2004; sponsor or recruitment site not involving the Czech Republic or Portugal; non-intervention trials. Step 1: The search in the EU-CTR allowed to include as general term "non-commercial" therefore eliminating the commercial studies. The timeframe selected was from 01/01/2004 until 31/12/2017 as well as the countries, the Czech Republic or Portugal. No other requirement in the advanced search field was selected. Step 2: As the discrimination between commercial and non-commercial clinical trial is not possible at the Clinical-Trials.
Data Extraction, Duplicates and Complementary Information Step 5: Information about trial identification (Trial ID) number (main and secondary), recruitment status, sponsor name and country, trial title, trial phase (when applicable), type of intervention, therapeutic area, type of funding and publications of the completed studies, were extracted manually and independently from the registered records (from 1/03/2018 to 31/08/2018), gathered and organized in an Excel sheet for each country. No restrictions on each of the referred information was applied. Forty percent of the records were double-checked by two individuals from each team and discussed until a consensus was reached. Step 6: Duplicates were identified in each database, when secondary IDs were provided or when the sponsor and the study title were the same.
The same study registered in different CTRs was considered duplicated and removed at this point of the search. When complementary information was provided in different registrations about the same study it was added to the respective entry in the working Excel sheet. None of the studies identified had recruitment sites in both countries, so no clinical trial is considered in both databases. Sponsors were coded as: Disease-Specific Organization (Disease associations or research institutes dedicated to a specific therapeutic area), Foundation (Research Institutes or Health Institutions legally constituted as foundations), Hospital, Research Institutes (non-specific therapeutic area), University, and others (Private health clinic, Funder Agency, acting as sponsor). In the four CTRs, the funding source was identified in different fields.
Both in ISRCTN and ANZCTR there is a specific field entitled "Funder". In Clinicaltrials. gov CTR the funders were identified from the field "Sponsor and Collaborators". In EU-CTR the funder was identified in the field "sources of monetary support ". All the funders identified in these CTRs were classified as: (i) Public organizations (non-profit organization such as public institutions, funding agencies, disease specific organizations); (ii) Private organization (for-profit organization-Industry); (iii) Both, when the funding is provided by the industry and one or more public organizations; (iv) Not Indicated, when the information was not provided in any registry or in the publication. When only the sponsor was mentioned in the Clinical-Trials. gov registry, we considered it as the funder.
Results A total of 3496 and 1427 registrations were found in the four CTRs involving the Czech Republic and Portuguese institutions, respectively: ClinicalTrials. gov, EU-CTR, ISRCTN, and ANZCTR. Clinical trials were screened to isolate those with a non-commercial sponsor, recruiting sites in Czech Republic or Portugal, and a start date between 01/01/2004 and 31/12/2017.
Identification of Non-commercial Studies The total number of trials for Czech Republic and Portugal, identified in all databases is respectively 3496 and 1427 as shown in Fig. 1. After discarding industry sponsored trials, non-interventional studies, or those with no recruiting sites in the Czech Republic or Portugal, 485 and 378 noncommercial trials (i. e. IICTs) were respectively considered eligible from all the screened databases. The same trial registered in different CTRs was separated and considered as duplicate. After discarding duplicates, a total number of 439 IICTs were identified involving the Czech Republic whereas for Portugal 328 trials were found. Forty-one percent (n = 180/439) and 44% (n = 144/328) of the studies were already completed and from those 41% (n = 73/180) and 55% (n = 79/144) were published, in the Czech Republic and Portugal, respectively.
Among the 61 trials funded by the Czech Health Research Council (Table  1 ), 32 (52%, n = 32/61) are testing medicinal products (data not shown). On the other hand, among the 60 IICTs in Portugal receiving financial support from funding grant agencies, only half of these (50%, n = 30/60) were funded by national funding grant agencies or funding programs (Fig. 3b ) while the other half was funded by international funding agencies. Among the 27 trials funded by Fundação para a Ciência e Tecnologia (Table  1 ) only one (4%, n = 1/27) is testing medicinal products (data not shown). During the time period studied in the present work, FCT launched only one call, in 2007, specifically focused in clinical research. The Ministry of Health had a financial contribution to this call and a total amount of 9 M € were used to fund 63 projects  [7].