dataesr/scientific-paragraphs-categorization huggingface dataset:
Different computation software can be used together in a complementary fashion, bringing different information and interpretations to the same situation. An example of this can be coupling thermodynamic computation results generated by the Thermo-Calc® software (analyzing the phases and possible compositions and microstructures of the alloys) with plots conducted by the Ansys® Granta EduPack and Selector software. As a consequence, it is possible to create robust comparison charts of properties of interest, prices, energy costs, and other environmental factors  4, 5. Thermo-Calc® works based on a set of databases. It is centred on thermodynamic equilibrium calculations, being able to build phase diagrams, among other phase and path analyses. It is very useful in order to improve/comprehend solidification and solid-state behaviors of various materials  4.
The Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022 is widely employed for improving materials and process engineering learning. The software has a vast and robust database that provides great assistance to teachers, students, and professionals in the field through the exploration, investigation, and selection of materials and processes. It is possible to examine the most diverse properties of the materials, cost, energy costs, and emission of pollutants in production  5. Studies have been conducted with the aim of improving the autonomy of electric vehicles in order to make them competitive with vehicles powered through internal combustion. As a result, the efficiency of the electric motors, battery, transmission, the weight of each component, and even intelligent acceleration control profiles are being developed to obtain the best balance between autonomy and performance  3, 6, 7.
Moreover, the main alloys applied in the extruded form are: 6061, 6063, 6070, and 6082, generally applied as structural components of vehicles  9, 11. Therefore, the 6000 alloys are the ones that have had the most employment in the studies discussing the use of Al alloys in vehicle chassis, such as the research by Miller et al. 11. However, despite these alloys being composed of the same alloying elements (i. e. , Mg and Si), there is still a variation in composition and properties. Consequently, the present investigation aims to analyze several 6000 alloy types in terms of thermodynamics and properties, producing a comparison and classification between them, using the Thermo-Calc® and the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022. As such, the challenge of this work lies in the correlation of the results obtained by these computations and the selection of the 6000 alloys with the greatest potential for application in electric vehicle chassis in terms of performance, processability, environmental impact, and cost.
Methodology For the comparison of the 6000 alloys in the possible structural application in electric vehicle chassis, thirteen alloys were firstly selected. Eight of them were already mentioned because they can be found in the literature as alloys of interest, that is: 6009, 6010, 6016, 6061, 6063, 6070, 6082, and 6111  9, 11. The other five alloys were the 6005A, 6013, 6022, 6060, and 6066 alloys. The selected alloys were all considered under the T4 and/or T6 heat treatment conditions. Obtaining results for comparison of these alloys followed four steps. The first step was to determine the maximum composition of each alloy using the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022. Furthermore, the Thermo-Calc® software with the TCAL7 database was used to obtain data on the formation of the phases as well as the variation of the phase fraction as a function of the alloying contents.
The second step was the comparison of the alloys based on the mechanical properties considered important for the required application. The analyzed properties were elastic modulus, yield tensile strength, fatigue strength, toughness, density, and corrosion. All data related to the mentioned properties were taken from the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022 using property maps or tables. The elastic modulus (E), yield tensile strength (σ y ) and density (ρ) were part of the maps that had the function of expressing the adopted performance indices for Al chassis. A performance index means a combination of certain properties of a material that can be used to represent a relation for a particular set of criteria with a particular objective in mind. In the case of the chassis, the calculations were intended to obtain property relationships in order to minimize the part's mass, as indicated by Ashby et al.
This data was acquired in the form of graphs plotted in the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022 with the axis of the ordinates fixed as the alloy prices. As a result, the alloys were compared according to the ability to be processed by cold working, hot working, and stamping. Due to the need to join the manufactured parts, their weldability was also analyzed. The fourth step was the analysis of the environmental impact of each alloy. In this step, the Eco Audit tool of the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022 was employed. This tool gathered information on energy costs and CO 2 gas emissions in the production and processing of these alloys. Candidate alloys were compared against commercially pure Al. Finally, the averages of the properties and features obtained from the aforementioned steps were summarized for comparison and classification of the alloys under three distinct scenarios: (a) better performance, (b) better processability, and (c) lower environmental impact.
Thermo-Calc results The fundamental difference between the alloys of interest is their chemical composition. Table  1  shows in detail the chemical composition range of each of these alloys. Information on the alloying contents as described in Table  1  was taken from the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022. The main alloying elements are Mg, Si Cu, and Mn, and their contents can change important properties such as mechanical strength, ductility, and toughness. Moreover, they have an impact on the ability of the alloys to be processed, as well as their pricing. Moreover, thermodynamic calculations associated to the studied alloys were made by using the Thermo-Calc® software. It was possible to quantify the proportion of the phases considering the alloys being processed in equilibrium conditions.
As a result, the 6066-T6 alloy was eliminated since it had the lowest specific stiffness among the the alloys. The 6066-T6 alloy has a larger density than the others, but with a similar Young's modulus. The 6060-T4 alloy, in addition to having a lower alloying content, is subjected to the T4 heat treatment condition, which includes solution followed by natural aging, and hence has the lowest yield strength of all alloys, ranging between 60 and 70 MPa. The very low Mg 2 Si fraction of this alloy can be seen in Table  1. The results of its specific calculations using Thermo-Calc® software can be seen in Figure  3. Since the studied alloys pertain to the same Al series and have a relatively low content of alloying elements, the density does not change very much, with a variation between 2. 66 g/cm 3 and 2.
Processability Another important parameter in comparing these alloys is the material's ability to be processed and formed into the final part for the application. Since this is a qualitative property, Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022 categorizes the materials in four scales: Excellent, where the material is frequently subjected to this type of processing and presents no significant problems;  Acceptable, where the material is generally subjected to this type of processing but is not fully optimized for it; Limited use, where the material can follow this type of processing in limited cases or requires special measures to avoid problems; Inadequate: the material cannot follow this processing. Figure  5  shows the classification of the candidate alloys in their suitability for cold working and hot working processing.
Environmental impact The comparison of the candidate alloys in terms of their environmental impact was performed using the Eco Audit tool of the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022. To make this comparison, commercially pure Al was used as the basis for differentiating the energy consumption and carbon dioxide production of all examined alloys. Then, the production and extrusion of 100 kg of each of the alloys were used as a parameter, adopting a typical percentage of recycled material in the material production and recycling the part at the end of the vehicle's life. As a result, both Figure  7  and 8 were generated. Figure  7  shows information regarding the energy expenditure, in (Mega Joules) MJ, while Figure  8  presents information regarding the amount of CO 2 gas emission, in kg.
Discussions In order to quantify qualitative properties, such as processability and weldability, scores from one to four were given according to the classification levels in which these properties were categorized by the Ansys® Granta EduPack and Selector software, ANSYS, Inc. , 2022, that is: Unsuitable -1, Poor -2, Good -3, Excellent -4. Table  2  shows the average properties of all examined 6000 alloys. In addition, three scenarios were considered for comparison, one aiming at better performance, another aiming at better processability, and the last one aiming at the lower environmental impact. The weighting for performance was defined giving higher percentage for the performance index related to the Young's modulus. 70% was defined for properties divided into 28% for MI 1 , 14% for MI 2 , 14% for toughness and 14% for fatigue.
Acknowledgments The authors are grateful to  CNPq -National Council for Scientific and Technological Development, Brazil  (grant #  308931/2019-0 ), to  FAPESP -São Paulo Research Foundation, Brazil  (grant: # 2019/23673-7 ) and to  CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil -Finance Code 001  for their financial support. The authors are grateful to  Ansys, Inc  for allowing the use of Ansys Granta EduPack and Selector software,  ANSYS, Inc. , 2022 on this paper. This work was developed using the Granta EduPack and Selector.
RustOnt can contribute to the major objectives of these institutions, such as FONTAGRO and ICAFE, for Latin American and Caribbean countries through its integration into the SRAT and SART projects, helping to minimize the heterogeneous information used within the different modules that make up these EWSs. The remainder of the paper is organized as follows. Sections 2 describe the climatic variables considered after a detailed analysis of related work and the methodology selected to develop the RustOnt ontology. Sections 3 and 4 respectively describe the RustOnt ontology, the process followed for its evaluation, and the main results obtained. Finally, Section 5 presents the main conclusions and future research lines.
Shadow Attributes: synonyms, the correspondence value, expressions, and the units of the value of the variable. The Country class includes the name and three-digit international code features. The instances of this class correspond to countries that consider some variables belonging to the Weather Conditions class, which can be Temperature, Humidity, Shade, Precipitation, and Wind. The Mapping class is a type of class that denotes a transformation or mapping and is divided into two subclasses Lexical or Sample that applies the Converts relationship to transform the instances of the Weather Conditions class. The Weather Conditions class includes weather variables considered by RustOnt, e. g. , the Humidity, Precipitation, Temperature, and Wind classes. These classes include different characteristics, such as conditions (greater than, less than), scale (average, minimum, maximum, etc.
Rules Rules define a set of explicit rules to constrain the correct operation of RustOnt. This section describes the rules that are stored in the ontology, including the natural language description and the expression that formally describes each of them. To represent the set of rules, the semantic rules language (SWRL) was used, which allows expressing OWL concepts (classes, attributes, and instances) combined with RuleML to have a high-level syntax. Rule expressions follows the structure: <antecedents or conditions> (body) →< consequence > (head) where antecedents or conditions are conjunctions of atoms A 1 ∧. ∧ A n and functions F 1 (?A 1 , ?A 2 ) ∧. ∧ F n (?A n ), variables are represented with the character '? using the notation ?A n , and the consequence is a single atom. A total of five rules have been defined for RustOnt.
Ontology Editor RustOnt has been modeled with the Protégé ontology editor. Protégé supports OWL 2, RDF, and XML schemes to create and edit ontologies. This software provides logical reasoners, such as HermiT and Pallet, to check for inconsistencies and support inference over the ontologies  [61]. Protégé supports two types for ontology modeling: Protégé-Frames and Protégé-OWL  [62]. This editor also provides a graphical user interface with tabs to model ontologies. The OntoGraf tab allows visualizing classes, instances, attributes and relationships in a graph. The entities tab allows managing data types, individuals and properties of annotations, data, classes, and objects. The individuals tab allows managing the instances of each class declared in the ontology. The DL query tab allows searching a classified ontology in a simple way using DL queries.
To solve the competency questions Q3, Q4, and Q5, a scenario is proposed for each question. The scenario approach is used to represent the queries in an ontology and modeled using the SPARQL language  [63, 67, 68]. The solution to the proposed scenario indirectly answers each competency question. The following are the competency questions, the scenarios that are used as a tool to answer, the expected response against the scenario, the SPARQL query that represents the solution, and the result that is obtained from the ontology.
Coherence Coherence or consistency of an ontology denotes that inferred statements should be correct  [58, 63]. At the very least, the defining axioms should be logically consistent. In addition, the natural language documentation should be coherent with the formal statements. Figure  10  shows the use of the Protégé's Pellet reasoner to assess the coherence of the RustOnt ontology. Using this tool, we verified that it is a consistent ontology model according to class hierarchy, object property hierarchy, data property hierarchy, and class assertions.
More complex queries required the definition of scenarios implemented using SPARQL queries. The quality assessment of RustOnt consisted of three criteria: clarity, consistency, and modularity. We defined formal axioms on the main classes to assess clarity, which allowed us to define the terms used and the hierarchy between them. The consistency was related to the clarity of each concept defined in the RustOnt ontology and its validation using the Pallet reasoner of the Protégé tool to infer knowledge about the elements of the ontology (classes, object property, data property and individuals) without contradictions. Furthermore, modularity was achieved by splitting RustOnt into two different modules (weather conditions and mapping), where each module is a representation ontology that can be used as an independent ontology for other domains.
Finally, RustOnt can help the process of data collection, integration, and knowledge exploitation in the field of coffee rust as an asset to create recommendation systems or predictive models that help farmers and experts in decision-making processes avoid adverse scenarios related to coffee rust. The next step to improve RustOnt is to expand its information basis to include other coffee-producing countries that have not been considered due to the lack of previous studies. This objective can be achieved through initiatives such as the Regional Cooperative Program for the Technological Development and Modernization of Coffee (PROMECAFE) for Latin America and the Uganda Coffee Development Authority (UCDA). We also want to integrate RustOnt as part of a case-based reasoning (CBR) system widely used in agriculture for crop management  [3] , traceability  [73] , yield estimation  [74] , and pest and disease protection  [75] [76] [77].
Acknowledgments: The authors thank the  Ministry of Science, Technology and Innovation (Minciencias)-Colombia  under project " Sistema basado en ingeniería del conocimiento para el manejo agroecológico de la roya del café ", grant 823-Formation of high-level human capital for the regions-Cauca,  Universidad del Cauca. This work was also supported by the  Sistema General de Regalías (SGR)  and  University of Cauca  through the  Project "Incremento de la Oferta de Prototipos Tecnológicos en Estado Pre-Comercial Derivados de Resultados de I+D Para el Fortalecimiento del Sector Agropecuario en el Departamento del Cauca"  ( BPIN 2020000100098 ).
This method was implemented for the Vercors massif, and later extended to all mountain regions in France, using the SAFRAN meteorological reanalysis system  (Durand et al. 2009)  as an observation reference. The ADAMONT method was used to downscale and adjust 13 GCM/RCM model pairs for Representative Concentration Pathways RCP4. 5 and RCP8. 5, and four pairs for RCP2. 6, from the CMIP5/EUROCORDEX database  (Jacob et al. 2014). Verfaillie et al. (2018)  provide an example of the use of this data set, including atmospheric conditions and also simulated natural snow cover using the Crocus model as an impact model.
At this point, they remained qualitative with predefined variation or disturbance modalities (e. g. increase, decrease, temporal variability increase, increase of extreme values). Nevertheless, the design of the model made it possible to introduce quantitative indicators and some characteristic variations and thresholds as they became available. Adaptation cases, including climate variables and climate and socio-ecological indicators, were filled in a documented sheet on the basis of the material collected during the workshops, and then adjusted with the help of small expert focus groups (Fig. 1 , Adaptation cases FG). During the phase of operational transfer of the research, all data was reworked and enriched with data from other participatory experiments, helping to consolidate the whole knowledge model and its indicators, while being implemented in a database and a physical data model.
5x0. 5km, 4x4km and 7x7km. These data were then analysed using Principal Component Analysis and Hierarchical Ascending Classification to obtain a map of functional units of the landscape in the Vercors massif. 2 -Expert assessment of ecosystem services -At the same time, ecosystem services were assessed by a focus group formed with 12 scientists and 15 experts from the Regional Natural Park, technical institutes, local administrations and environmental protection associations in the fields of ecological sciences, forestry, biodiversity and bird conservation. Scientists and experts were individually asked to assess the range and intensity of ecosystem services on the main natural habitats of the massif, using an adaptation of the CICES nomenclature (CICES, 2016) and a methodology based on a capacity matrix  (Hermann et al.
-Expert assessment of vulnerability and adaptability of social-ecological systems under climate forcing. Data on social-ecological units and ecosystem services were reworked by a focus group of two scientists and five experts from the Vercors Regional Natural Park for three main types of climate forcing based on the 30-year climate projections. The experts sketched projection maps for the impact, vulnerability and adaptability of social-ecological systems undergoing these climate forcings. Ecosystem service bundles were also reassessed in order to estimate the vulnerability of the provision of these services for each social-ecological unit of the Vercors massif. This reassessment referred to the initial land use, without taking into account the impact of climate forcing on the spatial shift of habitats and thus on the spatial extension of social-ecological units.
ACKNOWLEDGMENTS This project was funded by the  French Ministry of the Environment  and has benefited from the key support of the political and technical staff of the  French Prealps Regional Natural Parks. The project also benefited from the extensive mobilisation of the researchers and engineers of the  INRAE Grenoble research centre. We thank CNRM colleagues involved in this work, in particular  Raphaëlle Samacoïts  for producing Fig. 3. Many colleagues from research support teams also offered their assistance for this research, and we would like to thank all of them, while extending a special thanks to  Emmanuel Jonas , who made a major contribution in the field of business analysis and quality management and to  Arnaud Cosson  for his valuable insight as a sociologist of organisations.
We would also like to acknowledge the team of contractual engineers in charge of the operational course of the project for their investment and significant participation, with particular recognition of  Delphine Piazza-Morel. The work of maturation and transfer of the research results was supported by the  Technology Transfer Acceleration Company SATT Linksium in Grenoble  and  INRAE. LESSEM and CNRM/CEN are part of LabEX OSUG@2020. This study contributes to the  Trajectories Cross-Disciplinary Project (CDP) Trajectories  funded by the  French Agence Nationale pour la Recherche  within the framework of the  IDEX  project  of Grenoble-Alpes University.
Implementation of Selected DKF Algorithms The simulation scripts were written using the Sense Script language in CupCarbon, while for the laboratory experiments, the firmware uploaded to the nodes was written in C/C++ and compiled using the Arduino IDE, version 1. 8. 9. The KF parameters A, H, Q and R were assigned the values 1, 1, 0. 001, and 0. 081, respectively. Table  2  provides a summary of the values of the parameters assigned to the various DKF algorithms.
Implementation of Selected DKF Algorithms The simulation scripts were written using the Sense Script language in CupCarbon, while for the laboratory experiments, the firmware uploaded to the nodes was written in C/C++ and compiled using the Arduino IDE, version 1. 8. 9. The KF parameters A, H, Q and R were assigned the values 1, 1, 0. 001, and 0. 081, respectively. Table  2  provides a summary of the values of the parameters assigned to the various DKF algorithms. For the ICF algorithm, the value of the network size N was set to 2 (since we are working on a two-node linear WSN), and the number of consensus iterations (L) was set to the value 5 for both the simulation and the laboratory experiments. The number of For the ICF algorithm, the value of the network size N was set to 2 (since we are working on a two-node linear WSN), and the number of consensus iterations (L) was set to the value 5 for both the simulation and the laboratory experiments.
Simulation Setup 2. 3. 1. Description of Simulation Setup The simulation setup shown in Figure  4  is composed of two sensor nodes (S1 and S2) and natural event generators (A4 and A5). The natural event generators emulated the physical accelerometer sensors and were loaded with acceleration data collected from the laboratory testbed. The sensor nodes (S1 and S2) were loaded with scripts that implement the selected DKF algorithms during the simulations. and the simulations and experiments conducted on the simulation platform and the laboratory testbed, respectively.