dataesr/scientific-paragraphs-categorization huggingface dataset:
Geometrical Reconstruction of the Human Cornea In this research, we have reconstructed both anterior and posterior corneal surfaces from the raw tomographic data (unprocessed by the device software, which avoids possible biases) generated by the corneal tomographer Sirius® (CSO, Italy) in the form of two clouds of points. To do so, we used the surface-handling CAD software Rhinoceros® V 5. 0 (MCNeel & Associates, Seattle, USA), which offered two alternative reconstruction methods, between which we wanted to compare: • "Patch" surface generation function. Uses a NURB surface to reconstruct both corneal surfaces, minimizing the nominal distance between the 3D point cloud and the solution surface. A cubic NURB surface was used with a segmentation of 256 in each parametric direction. The surface so obtained, approximates the point cloud.
Practical Application This comparative study included 161 eyes of 161 patients selected at random from the official database "Iberia" of KC cases (RETICS-OFATARED). The sample was later divided into two groups: a "healthy" group formed by 100 healthy eyes of 100 individuals, and a "keratoconus" group that included 61 KC eyes of 61 patients. The presence and degree of the disease was stablished according to state-of-the-art evaluation techniques and the Amsler-Krumeich classification  [12] , creating subgroups for the Grades I (27 subclinical KC eyes) and II + III + IV (34 clinical KC eyes). The research was developed at Vissum Corporation Clinic in Alicante, Spain, and was ratified by the institution's Ethical Board, according to the tenets the Declaration of Helsinki (7th revision, October 2013, Fortaleza, Brazil).
Statistical Analysis The software used to perform all statistical analyses was SPSS V23. 0 (IBM, Armonk, EEUU). To check the normality of data, Kolmogorov-Smirnov test and Shapiro-Wilks test were used. To determine if the measured values of the morpho-geometric parameters depended on the function used when reconstructing the corneal surface ("mesh" or "patch"), Student and Mann-Whitney tests for unpaired data were used. A significance level of 0. 05 was fixed for p-values in all statistical tests. Bland-Altman diagrams were used with comparison purposes.
4. Process the point cloud to obtain the polygon model (triangle mesh) of the impression (Fig. 2d ). This process involves principally reducing the noise in the data collected, merging the point clouds collected in different captures and creating a unique polygon model. This process was carried out in the 3D scanner's software and was performed automatically after adjusting certain parameters such as: resolution (0,4 mm), optimize scan mesh (level 0 of 100), decimate scan mesh (level 0 of 100), automatically fill holes (level 5 of 100) and remove isolated patches (level 0 of 100). 5. Clean the polygon model (Fig. 2e ). This process consists of removing from the polygon model all the unnecessary data for the design. This task was carried out in the 3D scanner's software. 6. Define the global alignment of the polygon model (Fig.
This process was carried out in the 3D scanner's software and was performed by using all the necessary tools to obtain a clean and watertight mesh by varying the values of its parameters for different regions of the polygon model depending on its geometrical characteristics. 8. Generate an organized patch layout surface model from the polygon model (Fig. 2h ). This process was carried out in the 3D scanner's software and as it was an organic geometry, it was performed by using the auto surface tool. 9. Export the surface model to the CAD software in STL format.
Phase 2: Design of the Grip This second phase consists of designing the grip from the 3D geometry obtained in the preceding phase. To carry out this phase, the following steps were followed in the CAD software: 1. Transform the surface model into a solid model (Fig. 3a ). 2. Generate the paddle shaft's hole (Fig. 3b ). To perform this task, a circular passing hole with the same diameter and direction as the paddle shaft was created. 3. Generate paddle shaft's sleeve (Fig. 3c ). To perform this task, a thin cylinder with the same inner diameter and axis as the paddle shaft was created. 4. Design the joining system and divide the resulting solid into two parts (Fig. 3d ). As a kayak paddle has a blade at either end, to enable its assembly on the paddle shaft, it must be composed of at least two parts.
Orthopedic appliances rely on customized geometries that fit the patient's anatomy to be effective. Accordingly, the development of 3D scanners with specially designed features for the acquisition of specific anatomies could improve the treatment of several injuries and pathologies. In this context, the authors designed a system for the acquisition of the geometry of the arm-wrist-hand district, called Oplà 1. 0, deputed to the fabrication of 3D printed casts for the treatment of wrist fractures. The system is composed of a 3D optical scanner and an acquisition software that is responsible for collecting the reference data and processing it. For the interested reader, all the details pertaining to the development and testing of the first version of the system can be found in  [1, 5, 6]. The main features sought in the development of the system were: i) fast acquisition speed, required in order to minimize the incidence of human movements, ii) relative low-cost, iii) a maximum acquisition error of 1 mm.
Software Design The use of the Oplà scanning system is facilitated thanks to the development of a simple and intuitive Graphical User Interface (GUI). In this work, a significant effort has been devoted to the design of a renewed GUI to improve the user experience. The main steps of the procedure are confirmed in 2. 0 version of the system, but the graphic design was restyled mainly on the basis of the results of usability tests  [5]. As illustrated in Fig. 2 , software tools are now divided into two main tabs in the main window, one dedicated to the scanning phase and the other to custom orthosis modelling. This separation is intended to give the user some guidance on the order of operations and reduce the possibility of human errors. Moreover, 3D navigation and interaction tools have been moved in the GUI upon the 3D viewer, following the typical convention of 3D modelling environments.
Results and Discussion The core of the fitting algorithm has been implemented in MATLAB®, which is able to call the built-in functions of SolidWorks® to perform the successive CAD model updates and ensure the consistency of the resulting B-Rep model during the optimization loops. All the steps h k used to compute the sensitivities are equal and only depend on the type of parameter they are associated to. Thus, step h k = 0. 1 mm in case p k is a length, and h k = 0. 1 • in case it is an angle. Those values have been chosen in coherence with the accuracy of the adopted CAD modeler. In order to compare the sensitivity of the parameters to the way the SA algorithm modifies the parameters values step after step, the evolution of the parameters can be tracked in an absolute or relative manner, as follows: δp k,j = p k,j+1 -p k,j (5) p k,j = δp k,j p k,j (6) Thus, the value δp k,j characterizes the absolute evolution of the parameter p k between iterations j and j + 1, whereas p k,j refers to its relative evolution.
Digital Tool User Interface The web oriented tool consists of a software that allow the architect (for example) to better manage its projects centered to the human use of the environment. After the creation of the project, the user friendly interface shown in Fig. 1  oblige the architect to select all combinations of principles before closing it. Thus, he has to think about solutions/proposals for each of them during the creativity phase of the project. Logically, he select the most adapted combinations of principles at first (depending of the project characteristics) … knowing that sometime, new solutions come from others points of view. He can add illustrations, comments, schemas etc. and then print the document to constitute the archives of the complete project.
Conclusion and Perspectives Design processes used by architects and industrial designers are complex and need frequent adaptation with the context of use in the future working environment. Products or working spaces have to be dedicated to all the characteristics of the final users. Theoretical approaches exist to follow and support these needs. The software proposed is based on 7 Universal Design and Usability principles and is complete by 5 human sensorial systems. The combination give 35 couples of principles that the architect or industrial design has to use to propose final solution. 4. Van Der Linden, J. C. d. S. , Lacerda, A. P. : Metodologia projetual em tempos de complexidade. In: Martins, R. F. d. , Van Der Linden, J. C. d. S. (org. ). Pelos caminhos do design: metodologia de projeto.
Due to diversified complications of mold design with mold manufacturing and the coupling effects of plastic with molding machine, it is quite difficult to consistently molding the products with higher quality and precision. Even with high-end commercial software and hardware, the seamless integration of mold design, mold manufacturing and molding process needs innovative thinking  [3]. Cyberphysical systems in industrial processes are characterized by their ability to connect the physical devices that compose it with ICT systems capable of accessing and processing the set of technological data obtained from the process. This paper presents a new smart application integrated into a web platform. The new application differs from other mold designing applications in that it only requires the CAD model of the plastic part in a discrete format, the precision of the analysis, the thermoplastic material of the plastic part and the number of parts to be manufactured per year as application inputs.
On the other hand, the algorithms for obtaining the mold designing can be applied in any CAD system, being independent of the model and the modeler. Thus, the presented application differs from other software linked to the CAD format of the model which requires access to the internal information of the part. The new platform is the first one capable of working completely autonomously and intelligently, performing the complete process of optimal dimensioning and mold design without requiring, at any time, mold designer interaction during the process. Furthermore, the design of the resulting mold does not require numerical simulations for functional validation, since the expert calculation algorithms incorporate technological requirements that guarantee the correct performance of the mold. The application is designed for future implementation in an Industry 4.
In contrast to other applications, the new application does not require additional information or designer interaction during the analysis process, dimensioning and designing the mold in a fully automated process. The presented platform incorporates four databases with information about technical characteristics of thermoplastic materials, standard components of injection molds, technical characteristics related to the injection manufacturing process such as the characteristics of the coolant fluid and a data base with information related to the injection machines. Figure  2  shows a logical operation scheme of the smart web platform, including the phase of data processing introduced by the user, the phase of calculation of the results from the input data, the phase of designing and executing the algorithms and the phase of management of the stored information in the databases.
Use Case and Results To implement the proposed methodology of data analysis, the authors have constructed a representative summary dataset to validate that the experiments are correct. This dataset respects, in a very flexible way, a certain amount of causality rules that have been previously defined to be compared with the resulting causality links. The use case addresses one KPI: the production cycle time. The rest of the dataset is made of variables that may affect, or not, the addressed KPI: the day of the week, the time slot, the month, the indoor temperature, the operator's heart rate, his stress level, the defaults number, and the training level. The goal is to identify, among this dataset, variables that affect the production cycle time, and to prioritize them. To build a realistic and more relevant use case, the authors have made sure that the dataset does not follow these rules in an exclusive way.
2. b). The ANN that gave us these predictions is made up of 5 hidden layers. This ANN's parameters were found using an experimental approach, since the parameters' optimization algorithm is still being under development. Concerning (iv) the impacting factors prioritization, ANN's weights used for the prediction were employed to give a ranking of all available factors, and the ranking corresponds to the starting assumptions. Fig. 2. c shows that we obtain the same prioritization even if the prediction models' structures are different (conditioned on the fact that they have a predictive power upper than 85% in our case). The figure shows three lists, all with the same nodes ranking (4-0-3-1-2-5-6). To prove that this ranking is consistent, we have created a new dataset with the same rules and assumptions as the first one.
First, we have used this dataset to test our prediction model; the resulting prediction accuracy was 89%. Then, we have replicated the same dataset five times, and each time, we changed the values of one influencing variable individually (day (1), temperature (3), stress (4), training  (7) , or defaults (6)), to evaluate the impact of each of these influencing variables. Each time, we replaced one variable (e. g. stress) by random values in the same range of variation as the initial values, letting the other variables as they were. Then, we predicted our KPI with the new dataset, using the same model that we have previously built, then we evaluated the prediction. We predicted five times, in addition to the first prediction, in order to see the impact of each of the five influencing variables.
This experiment had some limitations: the sample was homogenous but small (about twenty students). Some of the data should be used with caution as some are based on subjective judgments (video coding, perception evaluated by a survey), on the capacity of each individual to follow a standard and the associated reactions (even if data were triangulated to limit the subjectivity). The results can vary greatly from one individual to another. It will be necessary to repeat the experiment on a larger sample. In addition, the measurement of emotions through image processing software can provide a better exhaustiveness of human behaviors interpretations.
While a designer is working on a product, he or she often needs to check design rules if he or she wants to provide a design free of errors. An expert designer may have memorized all crucial rules of his domain. A non-expert designer does not have in mind all applicable design rules. He/she has no other alternative than to spend a large amount of time finding the subset of design rules that matches his design context by either searching in design manuals or asking expert-colleagues to guide him/her. This task is even more time consuming because design rules often come from different domains and are stored in documents or databases that may not be correctly organized. Unstructured documents are still common in the industry  [1]. Many companies still store design rule knowledge in unstructured documents -mainly PDF format -which are over tens or hundreds of pages.
Design Rules For this paper, design rule definition is based on the work of Fu et al. [4]  who propose a formal definition of a Prescriptive Design Principle: It is stated in the grammatical imperative form. It includes a prescriptive action for a designer to take. It increases the likelihood of reaching a desirable consequence. It is situated within a particular context and point in time. In this paper, we will reduce the scale of this definition and consider a more technical definition. Calkins et al. [5]  in the field of Knowledge Based Engineering (KBE) consider that design rules synthesize the knowledge of a company and indicate how it should be used to create a proven design. In our case, we also want to put the emphasis on how this knowledge is stored in databases or in design books.
Introducing a Context-Aware Cognitive Design Assistant In  [17] , A. K. Dey describes the context of a software user as: "Any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves. " In our case, any information describing the design process, the digital mock-up or the team members in charge of the design, is part of the design context. A. K. Dey gives a definition of a Context-Aware System (CAS): "A system is contextaware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user's task. " Therefore, a context-aware application for rule application would have to provide rule information and design guidance when the user needs it to progress without mistakes in his design task.
Links and dependencies, which appear between heterogeneous 3D models, become increasingly complex in the daily activities  [3]. The legacy nature of data, specific to CAD vendors, limits the possibility of open-ended analysis of 3D model, sharing 3D data, modify or revise 3D model, sometimes the retrieval of the 3D model or any information related to the design intent. One of the main problems faced by users of these systems is the lack of compatibility between systems. As a result, 3D models created using a particular software package may become inaccessible to its creators once that software is obsoleted. Incompatibilities of all kinds and at all levels impede the sharing of information and prevent the exchange of services between different systems. And developing interoperability means developing knowledge and solutions to eliminate incompatibilities  [3].
The semantic PMIs will allow access to all the information that characterizes it (reference elements, parameters, etc. ), and possibly to modify them. The second input is based on a semantic descriptor. Once some information are stored inside the ontology, he can directly query the system to retrieve a particular information based on the concepts already registered within the ontology. Ontology, being considered itself as a database and knowledge base, the semantic definition and enrichment of the 3D model will allow an efficient and fast reutilization of the information contained within the enriched file format. The use of the ontology allows a quick annotation and modification of the information stored in the different classes. The fact that the ontology can be written in XML helps the stored information to be share.
Graph Database Consideration Querying information can be achieved through Information Retrieval Systems that need to access to data in order to provide the most relevant one. This objective is reached by managing the data in NoSQL databases rather than traditional relational databases, as the former is faster, more efficient and flexible  [2]. The main categories of NoSQL like column database, key-value store and document-oriented database includes indexing and quick access to the information but lack expressing of the relationships between data in their schema. The graph databases answer to this issue and consequently are the more suitable in our context. To emphasize the benefit of the graph database, different researches have shown the importance of analyzing data with strong relational nature as in  [3] , applied in different manufacturing use cases as in  [4].
Other works define a framework to allow data querying by transforming structured data  [5]  and unstructured data  [6]  into a graph, with enrichment by data linkage  [6]  with possible using ontologies for example in  [7]. In this article, the authors aim to define the prerequisites for a manufacturing querydata system by answering this question: "What are the minimum issues to be taken into account for a querying manufacturing data system based on the graph database?".
Methodology In order to define only the bare minimum issues to consider when defining the query system, an iterative method has been implemented. This method is detailed below: (1) Integration of data into a graph database. The data includes the minimum of information at initialisation (only metadata without text content). Metadata means all the properties carried by unstructured data and all metadata carried by structured data. Thus, each data is transformed into a node and each metadata integrates the properties of this node. On the other hand, the explicit relationships of relational databases are translated into relations between nodes (see Fig. 1 ). (2) Application of queries to the graph database, here refers to the translation of the user query adapted to the graph. Query transformation includes, in particular, the path of relations between data (e.
Experimentation Conditions and Results The study was based on a dataset composed of 686 elements, representing data from a drone manufacturing company, and distributed as following: 47% unstructured data including spreadsheets, videos, photos and textual documents; 22% tree structure data; 17% of data from relational databases and 15% of geometrical data. All these elements represent the data necessary for the development of a mechanical system (from design to prototyping through logistics, purchasing and project management). 19 queries have been written in response to innovative use cases characterized by Capgemini 4  (e. g. : a designer is looking to identify the product requirements or the justification for a product, a manager looking for identify an available team with the right skills, a salesperson looking for a customer's usage parameters, etc.
). The expected performance thresholds are less than 1 s for time; this was fixed according to study conclusions on the impact of response latency in web search  [10]. The tools used are Neo4J 5  for storage and querying in a graph database and Standford CoreNLP 6  for the exploitation of natural language. These tools are open source and relatively well documented. After the first cycle of the methodology, the results are insufficient (see Table  1 ). Analysis of the results of this first cycle has shown that more than half of the anomalies are caused by the lack of textual content of the data in the graph database. For example, the search for the battery reference does not give any result because the information is carried by the content of an excel named "Bill of Materials". In order to treat this issue, a second cycle was therefore launched to integrate the text content of unstructured data.
• To define the concept of eco-design and to become aware of the environmental, economic and social implications of product design. • To apply the ecodesign methodology and manage the tools available for ecodesign in the industrial field • To define the concept of life cycle and identify the phases of the life cycle of a product, as well as list the regulations of the Life Cycle Analysis. • To know and apply the evaluation methodologies and software tools for product life cycle analysis. • To formulate guidelines for communication and marketing with a life cycle approach. • To define and understand corporate and competitive strategies to integrate the environmental variable in the company and select the most appropriate strategy for each particular case. • To identify business opportunities in the field of the Circular Economy for different sectors, stakeholders and design proposals.