dataesr/scientific-paragraphs-categorization huggingface dataset:
For the same reason, the method notify() is stereotyped hhrtFeatureii according to the rule VR2. Besides, the Controller represents an active entity responsible for checking the system state. Therefore, the rule VR4 is applied and the Controller class is stereotyped hhrtUnitii. In the resulting pattern, the calculateDerivedInfoTraffic() method is not present because it belongs to one application (COMPASS). However, in some cases it is essential to have a method that calculates derived data, in order to fulfill RT domain functional requirements (cf. Ef 2 of RT data control functionality). In this case, the CalculateDerivedValue() method is added by the designer as an optional element. Sim-  ilarly, the InitiateCorrection() method is added to the controller pattern as an optional method.
A. 3. Sequence diagram construction of RT controller pattern In order to illustrate the generation of RT controller pattern sequence diagram, we firstly present the sequence diagrams relative to the RT data control functionality of RT applications described in the Figs. A5-A7. The sequence diagram of RT controller pattern is obtained through the application of the unification rules (cf. Section 3. 4. 2) as follows: The rule UR12 case 1 is applied and the messages getMini-mumValue(), getMaximumValue() and getMeasure() are added to the pattern sequence diagram since these messages have equivalent names and their sender objects represent a variation of a concept and they are instances of a unique class which is Controller. The messages getMinimumValue () and getMaximumValue() are not common to all applications and they are placed in an optional combined fragment.
(7) The inductance and magnetic co-energy depending on the flux linkage are calculated according to the next equations: L(Θ, i) = φ(Θ, i) i , (8) W em = i 0 φ(Θ, i)∂i Θ=const. (9) The static torque can be calculated by differentiating magnetic co-energy with respect to angular position of the rotor for single phase excitation as follows: T (Θ, i) = ∂W em ∂Θ i=const. (10) ANSYS 12. 0. The non-linear FEM of 8/6 SRM has been implemented and analyzed by using ANSYS Parametric Design Language (APDL) because of its compatibility and ability to achieve the model with all the requirements and to reach the needed characteristics. To get the SRM characteristics, non-linear FEM must be analysed from an unaligned position to an aligned position for various excitation current (i = 2 A to 16 A). The analysis for one phase is sufficient to identify all the characteristics of the other phases because the SRM is homogeneous and symmetrical in physical characteristics.
However, the torque ripple defect remains undesirable in the SRM. On the other hand, we can reduce this defect by finding the most appropriate parameters to use in the control system. The values of the turn on, turn off angles (Θ on , Θ off ) and the supply voltage have a significant impact on the performances of SRM. For this pertinent reason, we relied on the parameter optimization strategy based metaheuristic techniques, as their efficiency in reducing the torque ripple will be demonstrated in the simulation results. The static torque characteristics transferred to the control system to determine the instantaneous torque by using the look-up table in Matlab Simulink. Furthermore, the proposed intelligent algorithms are adapted and applied to optimize specified parameters of SRM in order to reduce the torque ripple.
Besides, rand 1 and rand 2 are random numbers in the interval [0 -1]. The inertia weight in this study is not fixed but varies according to the following formula  [20] : The parameters k max and k are the maximum number of iterations and the current number of iteration, respectively. Also, w min and w max denote the maximum and the minimum inertia weight, respectively. w(k) = w max + w max -w min k max • k. (13) The coefficients in Eq. (  12 ) are constants: C 1 = C 2 = 2. The number of maximum iteration is equal to 100 and the inertia weight values are: w min = 0. 4 and w max = 0. 9. The PSO program has been developed using MAT-LAB language, for its ability to be associated with the control system in Simulink.
Conclusion and Perspectives We present in this paper an overview offuzzy-vector structures within GIS based on the OGC standard WKT encoding. It also presents constraints and basic operation such as sub-region extraction and fuzzy-polygon fusion. A complete description of the encoding of complex fuzzy polygons, as well as all of the operators, will be given in a future paper. Management scripts are developed in JAVA and Python for a better integration within a GIS tool. Currently, we are working on a better representation of fuzzy polygons. Indeed, when a two-fuzzy polygon model shows two structures evolving close to each other (such as two types of forest or other ecosystems), and having a transition that spreads over a long distance, the diffuse border involves an overlap of the corresponding fuzzy polygons.
In this light, the following data sources have been examined: Science Direct, Web of Science, SAGE, Springer, Taylor & Francis, Google Scholar, Google, Ellen MacArthur Foundation, Institut de l'Economie Circulaire (French Institute working on the circular economy), ADEME (French Environmental Agency). Keywords included: "circular economy" OR "circularity" AND "indicator" OR "measurement" OR "assessment". The selection process was as it follows. First, based on scanning titles, abstracts and/or short contents, works-including peer reviewed academic journals, conference papers, research reports, postgraduate dissertations, books, websites and tools-which were considered as non-relevant regarding product-level circularity measurement have been discarded. Then, those which were dealing directly with or getting indirectly connections with product circularity measurement have been looked at extensively and critically.
Case Study: Tools Experimentation Based on a Real Industrial Product To test and compare the outputs provided by each tool, the same industrial product-and its associated dataset-is used. The industrial product used in this case study in a catalytic converter that equipped heavy off-road vehicles such as excavators or mobile cranes. The manufacturing company, one of the European leader in machinery construction equipment, seeks to improve the traceability and circularity of their products, notably their catalytic converters considered as a key component due to high value of precious metals-platinum-containing inside. This system is significant for conducting the present case study because, according to Hagelüken et al. [38] , while metallurgical recovery rates for platinum group metals from automotive catalysts may be over 95%, the effective recycling rate is currently around 60%.
e. , the effectiveness with which indicators communicate with the stakeholders). Future circularity measurement and monitoring tools should be designed and developed in software accessible to most users. For instance, Microsoft Excel could be used due to its high level of diffusion and utilization across multiple business sectors. Last but not least, in accordance with business common sense and rules of thumb, it is suitable that the framework is user-friendly, time-efficient and intelligible (easy and pleasant to use, understand and communicate) for all industrial practitioners non-expert in circular economy, and provides gateway for enhancing products and components circularity by offering a comprehensive and operational view of circularity improvement opportunities. Required, desired and ideal features are synthesis and classified within a proposed hierarchy inspired by Maslow's pyramid of needs  [42]  and adapted here to appropriate design of tools aiming at product performance measurement in a context of circular economy, as illustrated in Figure  2.
2017). These examples, together with the empirical evidence provided by free and open source software, allow for a foreseeing of a flourishing future for OSH (ibid. ). Like free and open source software, OSH is an IT-enabled internet phenomenon. Fjeldsted et al. (2012)  as well as  Bonvoisin and Boujut (2015)  point out the integral part IT platforms play in fostering product-related data sharing, community-based product development as well as the emergence of OSH-based business models. However,  Raasch and Herstatt (2011)  draw a contrast: compared with OSS development, the aspect of physical object design in OSH has strong impacts on required skills, tools, and infrastructure. This aspect is even increasingly salient as the focus of OSH progressively expands towards many other forms of hardware than electronic hardware, like mechanical, construction, medical, optical, agricultural or textile hardware.
6 The second is the use of subjective evaluation of the openness factors with the help of Likert scales. In contrast to the approaches of  Balka, Raasch, and Herstatt (ibid. ) , the research reported here strives at a systematic analysis of published documentation with the help of measurable indicators, from the part of an impartial observer. In the following subsections, the approach adopted for the acquisition and the analysis of empirical data is presented. The first subsection introduces methodological choice made in focusing on non-electronic complex hardware products. The second subsection describes the method applied for the establishment of a representative pool of those products. The third subsection defines a set of criteria used to characterize products and their related documentation.
g. textile). It may eventually include electronic hardware and consequently software. Purely electronic hardware or software products such as Arduino or Linux are therefore excluded. In-house means that the piece of non-electronic hardware is ' original equipment' created by the product originator and not a component bought off the shelf. • The product is complex. While well-defined metrics assessing product complexity on a positive real scale have been proposed in the scientific literature (e. g. Rodriguez-Toro, Jared, and Swift 2002), using those metrics requires having sufficient access to detailed data such as the number of parts. This condition is difficult to satisfy in the context of observation of partly documented and defined products. Therefore, in the following analysis, the complexity of products is evaluated with a rule of thumb: products are considered as complex if they consist of more than two parts of different materials.
Typical profiles In order to identify possible typical profiles, the k-medioids algorithm has been computed on the dataset leading to the identification of five clusters. The similarity matrix reordered according to the identified clusters is displayed in Figure  6. The openness of the products of each cluster is displayed in Table  3  and described hereafter: • Cluster 1. Fully open products which satisfy almost all openness criteria and having hence a high average OI value (mean value 6. 83/8). • Cluster 2 and 3. Products being transparent, replicable and commercially usable but not accessible. Their average OI value is medium to high (mean value respectively 5. 23 and 3. 81/8) and is mainly handicapped by the lack of accessibility. • Cluster 4. Products being transparent, accessible and commercially usable but not replicable.
Framing the "source" of open source hardware It appears from the reported analysis that the "source" of OSH tends to be interpreted in practice as a dataset whose contents and properties evolve along the product lifecycle. This is summarized in the open source hardware lifecycle depicted in Figure  7. This original framework defines two states of OSH characterized by specific motivations and shared product documentation as well as two modes for developing open source hardware products. It also specifies in more detail the concepts which contours have been previously defined in scholarly contributions. Within the product development process, an open source approach may be used as a means to support the emergence of community-based product development. The OSH product is then before all the object of an open source product development process (state 1 in Figure  7 ).
At the time the data acquisition campaign was terminated (April 2017), the marginal search time for finding a new project was high enough to justify freezing the dataset since the risk of omitting a significant part of the studied phenomenon was reasonably low. Additionally, the broad field of OSH was narrowed down to discrete, tangible, non-electronic and complex products, which excludes a large part of the field (such as millions of gimmicks). In terms of how far the results discussed in this article are valid for the whole range of OSH products, including low complexity products as well as purely electronic products, remains an open question. Further research could make use of the methods defined in this article in order to perform a similar analysis specifically addressing electronic hardware.
Documentation regarding electronic hardware and software has not been regarded as a practical cut-off in data acquisition (see Section 4. 3 "Data acquisition"), hence, parts of openness have been omitted. Needless to say, the openness of electronical hardware is as important as those of other types of hardware. Consequently, the openness of a mechatronic product should be assessed upon those of its electronical and mechanical components. The dataset on which this article is based upon has been published with a CC-BY licence (Bonvoisin and Schmidt 2017b, http://doi. org/10. 14279/depositonce-5977). An actualized version of this dataset is available under the same licensing terms in the Open Source Hardware Directory. 16  This database of complex OSH products allows any interested person to add references of OSH products, or to edit existing ones.
The content of this database can be used for any purpose, such as future research as suggested in this article. The objective of the methodology pursued in this article and in the generation of the data was to generate an overview of the field of OSH. The authors cannot guarantee that the presented data of more than 2000 items is free of errors. Should the originators or contributors to the OSH products referenced in this dataset consider their products misrepresented, the authors kindly invite them to make corrections. They are free to edit the provided database or inform the authors of detected errors.
Conclusion This article provides an overview of how the concept of OSH is interpreted in practice, in other words, what is the "source" of OSH for practitioners. Starting from a critical analysis of existing definitions, evaluation criteria have been defined and used to analyse the published documentation of a pool of 132 complex and non-electronic OSH products. The gathered data illustrates how the field of OSH has moved well beyond the scope of electronic hardware, towards the field of non-electronic complex product development. The reported research required the establishment of a database of OSH products which is the largest published to date. This database provides empirical evidence of the evolution of OSH outside the sphere of electronics-a phenomenon which presently has only been described by scholarly publications based on isolated cases.
). 7 Source: https://github. com/. 8 Source: https://www. thingiverse. com/. 9 Source: https://ultimaker. com (accessed 30. 01. 2017). 10 Source: http://echopen. org (accessed 30. 01. 2017). 11 Source: https://perma. cc/7LXQ-PGKN (accessed 30. 01. 2017). 12 Source: https://perma. cc/96W5-ND74 (accessed 30. 01. 2017).
). 7 Source: https://github. com/. 8 Source: https://www. thingiverse. com/. 9 Source: https://ultimaker. com (accessed 30. 01. 2017). 10 Source: http://echopen. org (accessed 30. 01. 2017). 11 Source: https://perma. cc/7LXQ-PGKN (accessed 30. 01. 2017). 12 Source: https://perma. cc/96W5-ND74 (accessed 30. 01. 2017). 13 Source: http://www. nimbro. net/OP (accessed 30. 01. 2017). 14 Source: https://perma. cc/N7Y2-J5YC (accessed 30. 01. 2017). 15 Source: http://echopen. org (accessed 30. 01. 2017). 16 Source: https://perma. cc/T9CS-UE28 (accessed 07. 07. 2017).
In many cases a gravity model including the first zonal term due to the Earth flattening is sufficiently accurate at the mission analysis stage. The aerodynamics is generally modeled by the drag and lift components tabulated versus the Mach number and the angle of attack. The atmosphere parameters (density, pressure, temperature) can be represented by an exponential model or tabulated with respect to the altitude. A higher accuracy may be required on some specific occasions, for example to forecast the possible fall-out of dangerous space debris, to assess correctly low thrust orbital transfers or complex interplanetary space missions. In such cases the dynamical model must be enhanced to account for effects of smaller magnitudes. These enhancements include higher order terms of the gravitational field, accurate atmosphere models depending on the season and the geographic position, extended aerodynamic databases, third body attraction, etc, and also other effects such as the solar wind pressure or the magnetic induced forces.
Direct Method In this section we envision a direct approach for solving (P S ), with a piecewise constant control over a given time discretization. The solutions obtained with such a method are sub-optimal, especially when the control is chattering (the number of switches being limited by the time step). Since the initialization of a direct method may also raise some difficulties, we propose the following strategy. The idea is to start from the problem (P S ) with relaxed terminal requirements, in order to get a first solution, and then to reintroduce step by step the final conditions (20) of (P S ). We implement this direct approach with the software BOCOP and its batch optimization option (see  [13] ). • Step 1: we solve (P S ) with the initial condition x(0) = x 0 and the final conditions ω y (t f ) = 0, θ(t f ) = θ f , v z (t f ) sin θ f -v x (t f ) cos θ f = 0.
ALSFRS-R and manual muscle testing were performed on the same day as the MR imaging acquisition. Clinical evaluation was performed by an experienced neurologist specialized in ALS (P. -F. P. , 15 years' experience). One patient with ALS was excluded from the study because the MR imaging acquisition was interrupted for medical reasons, and 3 patients were excluded because of excessive motion during acquisition. The total number of patients used in this study was 25. Table  1  lists demographic data, Table  2  lists clinical data commonly used for prognosis, and Table  3  lists clinical scores at baseline and 1 year after.
MR Imaging Acquisition Data were acquired on a 3T MR imaging system (Tim Trio; Siemens, Erlangen, Germany) by using the product 12-channel head, 4-channel neck, and the 3 most rostral elements of a spine coil for signal reception. A 3D T2-weighted fast spin-echo sequence (sampling perfection with application-optimized contrasts by using different flip angle evolution, or SPACE) was acquired as an anatomic image for subsequent registration to a common template (see "Data Processing" section). Parameters were: TR, 1500 ms; TE, 120 ms; 52 sections; field of view, 280 ϫ 280 mm 2 ; voxel size, 0. 9 ϫ 0. 9 ϫ 0. 9 mm 3 ; acceleration factor, R ϭ 2; and acquisition time, 3 minutes. An axial 2D T2*-weighted multiecho gradient-echo sequence (multiecho data image combination, or MEDIC; Siemens proprietary sequence) was acquired for GM cross-sectional area (GMCSA) measurements thanks to the good white-to-gray matter contrast available in this type of sequence.
Data Processing All data were processed by using the open-source software Spinal Cord Toolbox v3. 0. 11 All processing commands are available at the Open Science Framework public repository. In brief, T2-weighted and T2*-weighted images were automatically registered to a common template image, which was generated in a previous study by averaging spinal cord MRIs from 50 adult subjects. 12, 13 The T2weighted image was used to precisely identify vertebral levels, and the T2*-weighted image was used to automatically measure the GMCSA thanks to its good white-to-gray matter contrast. Figure  1  illustrates the processing pipeline, which can be broken down into 7 steps: 1) The spinal cord was automatically segmented by using "PropSeg" 14 on T2-weighted and T2*-weighted images. Note that the segmentation was slightly manually corrected when needed.
Several models were tested with 1) clinical predictors only; 2) clinical predictors and SCCSA; and 3) clinical predictors, GMCSA, and the ratio of WM CSA to GMCSA. MR imaging biomarkers were progressively added to the clinical predictors to test the impact of each MR imaging measure. The hypothesis was that at 1 year, the prediction score of disability would be higher when adding MR imaging measures at baseline to clinical predictors. Here, WM CSA was used instead of SCCSA to avoid colinearity between predictors (because SCCSA ϭ WM CSA ϩ GMCSA). As in the previous subsection, GMCSA and WM CSA values were averaged across the C4 -C6 vertebral levels. A leave-one-out cross-validation was performed to evaluate how each prediction model will generalize to a new dataset. A patient was randomly discarded from the list of patients, and a new prediction model was created from the remaining (n Ϫ 1) patients.
7 Moreover, the exclusion of the lumbar and thoracic regions (unavailable in the T2*weighted images) precluded studying the correlation between GMCSA and the total ALSFRS-R score, which includes a clinical evaluation of the lower limbs. Optimized MR imaging sequences now provide the possibility to image the thoracic and lumbar GM with satisfactory quality  23  and should be investigated in future studies. Measuring CSA with MR Imaging. Being able to measure spinal GM atrophy has potential applications in other diseases, such as spinal muscular atrophy. 26 Furthermore, an original aspect of the present study is the fully automatic segmentation of the cord and its GM, providing minimum user bias and facilitating the reproducibility of the current technique to other centers (software and processing scripts were made freely available).
ACKNOWLEDGMENTS We would like to acknowledge all members of the  NeuroPoly Lab , especially  Benjamin De Leener  and  Gabriel Mangeat  for the useful discussions, and  Dominique Eden  for the English review. This study was supported by the  Association Francaise contre les Myopathies-Telethon  ( AFM-Telethon ) and the  Institut pour la Recherche sur la Moelle epiniere et l'Encephale (IRME). The research leading to these results has also received funding from the program " Investissements d'Avenir "  ANR-10-IAIHU-06. We thank the neurologists of the Paris ALS center who participated in the recruitment of the patients in the study:  Drs. Nadine Le Forestier ,  Vincent Meininger ,  Gaelle Bruneteau ;  Francois Salachas ,  Maria del Mar Amador , and  Rabab Debs. We thank  Sophie Blancho  ( IRME ) for her help in the management and coordination of the study.
We deeply thank the subjects who participated in this research. Disclosures: Marie-E ˆve Paquin-RELATED: Grant:  Canada Research Chair in Quantitative Magnetic Resonance Imaging (JCA) ,  Canadian Institute of Health Research  ( CIHR FDN-143263 ),  Canada Foundation for Innovation  ( 32454 ),  Fonds de Recherche du Quebec -Sante ( 28826 ),  Fonds de Recherche du Quebec -Nature et Technologies  ( 2015-PR-182754 ),  Natural Sciences and Engineering Research Council of Canada  ( 435897-2013 ),  Quebec BioImaging Network*. Mohamed M. El Mendili-RELATED :  Grant: Association Francaise contre les Myopathies-Telethon (AFM-Telethon ),  Institut pour la Recherche sur la Moelle epiniere et l'Encephale (IRME), program  " Investissements d'Avenir "  ANR-10-IAIHU-06 , Comments: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript*.