dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods First, we generated Triton haze analogue particles within the PHAZER (Planetary HAZE Research) chamber  (He et al. , 2017)  under Triton-relevant temperature and pressure with an AC cold plasma discharge as the energy source. We subjected the resulting solid particles to combustion analysis to obtain the elemental ratios of C, H, O, and N that make up the particles. Next, we performed a more in-depth analysis of the chemical composition of the solids produced in the PHAZER chamber by utilizing very high resolution Thermo Fisher Scientific LTQ-Orbitrap XL mass spectrometry. Once mass spectrometer measurements were complete, we employed custom software called idmol  (Hörst, 2011)  to make molecular identifications and perform data analysis from the mass spectrometry data. Finally, we obtained the transmittance and reflectance of the Tri-ton haze analogues by measuring thin films of the particles with a Bruker Vertex 70V Fourier Transform Infrared Spectrometer.
Future Triton Missions While ground-and space-based observations have furthered our understanding of Triton in the years since the Voyager 2 flyby (e. g. , Stern et al. , 1995; Young & Stern , 2001;Bauer et al. , 2010;Lellouch et al. , 2010;Stansberry et al. , 2015), in situ missions would dramatically improve our knowledge of the atmosphere, surface, and interior processes of the moon (Christophe et al. , 2012;Masters et al. , 2014; Hofstadter et al. , 2019;Fletcher -24-manuscript submitted to JGR: Planets Acknowledgments S. E. Moran was supported by  NASA Earth and Space Science Fellowship  grant  80NSSC18K1109. S. E. Moran thanks  R. M. Moran  for enlightening discussions during development of the manuscript. This work is supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ.
Grenoble-Alpes  and the  French Space Agency (CNES)  under their Exobiology and Solar System programs. C. Wolters acknowledges a PhD fellowship from  CNES/ANR  ( ANR-16-CE29-0015 2016-2021 ). We also thank  Darrell Strobel  for sharing the Khare Triton tholin data and  Lora Jovanović  for sharing the Pluto PAMPRE production rates. Data  generated as a result of this analysis can be found in the Johns Hopkins University Data Archive (Moran et al. , 2021).
The question of modernising these quality methods comes up with the digitalisation of the 21st century. On the other hand, with the recent development of new technologies, many IT tools have become available on the market and among them, Product Lifecycle Management (PLM) systems are very popular among companies thanks to the many functionalities they offer. In particular, Product Lifecycle Management (PLM) software enables the creation and maintenance of complex industrial products throughout their lifecycle. This brings multiple benefits to the company, such as faster time-to-market, improved productivity and collaboration, better product quality, reduced prototyping costs and much more. From this point of view, these Quality Management Systems (QMS) and Product Lifecycle Management (PLM) seem to be two systems that have proven their worth in supporting product quality improvement.
Overview of Available Systems Recently, the subject of PLM system is the subject of numerous research. Indeed, with the development of new technologies, companies need to adapt their tools to new trends. Among these, PLM System stands out by showing evidence of its advantages. However, there is still potential for improvement, especially regarding the issue of the importance of quality management in the system. As the subject is still quite new, very few papers directly address this problem. But a relationship between the PLM system and quality management has been noticed by many authors and some companies have seized the opportunity by developing PLM software that includes quality management.
Quality Management in PLM Systems Aras  [3] , an American developer, and publisher of product development software has rethought the way PLM is designed and now is offering Aras Innovator®, a full-featured Product Lifecycle Management software that supports companies in their development from all aspects. Among these, quality management is not negligible. That's why Aras has developed the Aras Quality Management System application, which provides companies with advanced product quality planning (APQP) tools to manage risk, improve quality, and achieve environmental, regulatory, safety, medical, and other compliance. In that respect, by combining PLM and quality, this approach can support regulatory compliance by reducing the number of systems companies use to manage quality information, manage product and process risks more easily by having all visibility on a single platform, and finally close the loop on product quality by having all necessary information in the same software allowing better coordination of tasks and organisation.
Indeed, this connection of data relating to quality activities, from the design phase of the product life cycle through to production, in a single source ensures visibility and accountability of related processes. In this way, quality is improved through this two-way communication of controls and results obtained by linking quality planning with feedback along the life cycle. Siemens PLM  [4]  software is also equipped with tools to manage quality management directly to support the optimisation of products throughout their life cycle. Siemens' PLM software has been developed in response to the needs of the industry, as they have understood the many benefits that this represents, including system homogeneity resulting in data concentration, reduction of manual activities and paper documentation, standardisation of reports, reduction of process time and, above all, transparency of processes.
All of this allows a solution that can be shared across engineering, quality manufacturing, supply chain and regulatory teams and consequently improve their collaboration, thus accelerating the development process of high-quality products. The value of integrating quality management into the life cycle management system is then supported by the development of all these quality functions in the latest PLM software. Unifying the Engineering, quality, and regulatory teams by introducing best practice change, document, and design control processes. The challenge now is to know how to adapt and use these new features of the PLM system is to achieve the quality objectives set by the company. Furthermore, it is interesting to note that these software suppliers do not stop at this innovation and continue their research, by furthering the Digital Twin approach.
To address this problem of heterogeneity of systems, PLM software as Windchill is used within the company. Indeed, as it is responsible for such issues as data access, storage and recall, information security and integrity, concurrent use of data, and archival and recovery, it connects engineering to the manufacturing and supply chain by integrating both systems. The benefits of these proposed changes in the application have convinced the company, which now uses it as an essential tool for managing product data and using the change management functionality for releasing the product information thru the life cycle approval process which gets interfaced to the ERP system. For demonstration purposes, a CAD assembly model, see Appendix, was created to allow better tracking of the information flow managed by the PLM system of the partner company.
As a supporting document, a Manufacturing Change Request (MCR) will be created at the end of its examination, followed by the Manufacturing Change Notice (MCN) and then the (Manufacturing Change Activity) MCA if everything is validated. These latter documents and information, once checked and validated by reviewers are stored in the ERP system, which is not only used by the manufacturing group but also by other functions such as Order Management, Product Configuration, CRM, Supplier Management and Product Cost. This information will then be submitted to the quality function for checking based on the Service Quality Index (SQI), thus creating a reusable database of identified problems. This ensures the overall quality of the product is improved. Finally, this information is passed on to suppliers and product service providers to establish relationships for business exchange.
The idea is to email or sends a notification to Quality Function through the PLM system, in this way they can see all documents related to the task including the drawings (see Appendix for instance), and this allows them to identify if there is any problem with the parts and assembly. Based on their database of problems already encountered, they can then easily give feedback at this stage of the process and this simultaneously enables them to update their issue database. Their response will then be considered by the CN Auditor before releasing and if the quality aspect of the part or product is not validated, the CN planner is directly contacted back to make the necessary changes instead of losing time and energy compared to the previous scheme. Indeed, everything else works in the same way as described above, facilitating tasks and collaboration between the different actors.
We build an original synthetic dataset of 2D mechanical designs alongside their mechanical and geometric constraints, GMCAD. Such a dataset allows training Deep Learning (DL) models for Design for Additive Manufacturing (DfAM) to incorporate and control Computer-Aided-Design (CAD) features with mechanical performance. Geometric AM constraints are often complex to describe, depending on applications, processes, materials. They often lack explicit mathematical descriptions, belong exclusively to the CAD world, and hardly can be integrated into mechanical design, hampering AM design freedom. DL models have recently emerged as a potential to reconcile both CAD and Computer Aided-Engineering (CAE) worlds. They derive data-driven geometric rules over mechanical designs, allowing fine-grained control over the geometry during the design phase, contrary to the conventional CAD-to-CAE sequential approach.
DL models, however, need high-quality labeled data, and merging CAD features to CAE aspects is challenging as they rely on different formats, rules, and tools. GMCAD dataset solves this issue following these building steps. (i) Building a DL-mechanical conditions predictor from a dataset generated by a density-gradient-based Topology Optimization method (TO); an AM-synergetic design generation tool. (ii) Creating a CAD dataset inspired by the TO-based designs. (iii) Predicting the mechanical conditions of the CADs using the DL predictor of mechanical conditions. Last, we evaluate the mechanical performance of GMCAD's designs and derive statistics over CAD and CAE features. Designs of GMCAD show the significant influence of minor geometric changes, explaining the intricate design task of conforming both with functionality and geometric constraints.
Consequently, in this work, we propose to solve the problem via Deep Learning (DL) architectures and particularly convolutional neural networks (CNNs), for they have proven efficient and robust in extrapolating spatial correlations and solving geometric-related problems  [11, 20, 36]. Their computational cost, in terms of operations and prediction time, is independent of the inputs' complexity, unlike FE-methods, which grow exponentially with input's complexity. Henceforth, instead of trying to integrate analytically geometric constraints into TO's formulation and generate designs using FE-solvers, we propose to explore DL-architectures to impose member-wise and inter-member geometric AM constraints. Nevertheless, creating a DL-AM-driven-TO, which grasps the geometrical and mechanical variations in designs, needs a rich training dataset with a wide variety of pairs of these designs alongside their constraints.
Creating this dataset is challenging, especially since no FE-based TO method in the literature handles several geometric conditions at once. Thus, to achieve our goal, we propose to resolve the problem inversely (detailed in section 3, Fig. 1 ). First of all, we generate designs from given mechanical constraints (boundary conditions, loads configuration, and volume fraction) using a modified version of the open-source code of Solide Isotropic Material with Penalization (SIMP) written by Sigmond  [25]. Second, a DL model that maps designs to their corresponding mechanical conditions, using the SIMP designs, is built; the model is referred to as DL-Mechanical-Conditions-Predictor. Third, synthetic designs (inspired by the shapes of SIMP designs) with various geometric constraints are generated using pygmsh  1.
Fourth, the previously learned DL-Mechanical-Conditions-Predictor is used to predict their mechanical conditions. Finally, the target dataset "GMCAD" is consolidated; it consists of pairs of designs and their mechanical and geometric constraints. Consequently, we can train DL-AM-TO, which integrates mechanical and AM-geometric constraints simultaneously at the conceptual level and allows the user to tailor its input constraints easily and generate designs instantly. It is worth mentioning that, in the AM field, a controllable design generation, combining the mechanical and geometrical natures exhaustively, is recently further explored  [21]. In fact, with an adapted synthetic dataset as GMCAD and DL techniques, DL-driven generative design approaches offering to manage several parameters and constraints jointly will motivate the implementation of lighter and faster modules in CAD software.
The major contributions of this article can be summarized as follows:  (1)  The creation of a synthetic dataset of designs alongside their mechanical and geometrical conditions, which (2) allows the inverse problem resolution regarding generating designs complying with mechanical and geometric constraints simultaneously. (3) Finally, the usage of DL techniques to mitigate the difficulty of integrating geometric constraints at the conceptual level of mechanical design and the expensive computational costs of iterative FE methods  [3]. The rest of the paper is organized as follows: section 2 provides a theoretical overview of density-based TO. Section 3 details GMCAD's conception workflow. Section 4 evaluates the DL-Mechanical-Conditions-Predictors, presents and evaluates a sample of the resulting target dataset "GMCAD".
V 0 and V(x) are the design domain volume and material volume respectively and f the volume fraction. To solve the problem stated above, Sigmund  [25]  used the Optimality Criteria (OC) method and added a mesh-independency filter to ensure the existence of solutions to the problem and avoid checker-board patterns  [23]. In this study, a modified Python version of the 99-line-of-code of the SIMP method written by Sigmund  [25]  is used to generate a dataset used to train the Mechanical Conditions Predictors (sections 3. 1 & 3. 2) 2.
Workflow As previously mentioned, GMCAD aspires to bridge the mechanics to the geometry at the design level. TO generates layouts given mechanical constraints as inputs. However, it is rather complex for it to handle geometric constraints. Therefore, we choose to resolve the problem inversely. We can always learn to inverse TO's job via DL, i. e. , create a DL-Mechanical-Conditions-Predictor that takes a design as input and predicts its mechanical conditions. Then, we create designs (with layouts inspired by SIMP's suggestions) accounting for the desired geometrical constraints, deduce their mechanical constraints using the former DL-Mechanical-Conditions-Predictor. Now that we dispose of a complete dataset with mechanical and geometrical constraints, we can go to the next step and train a DL model that generates designs accounting for the mechanical and geometrical aspects jointly.
The global work is divided into two steps. The first step consists of consolidating the target training dataset GMCAD, which makes the subject of the present article, in an inverse problem resolution way, from the geometry to the mechanics. The second step involves training the DL-AM-driven-TO model and is reported to future work. The first step is partitioned into four stages: (1) The generation of DB1, a dataset of 2D designs from mechanical conditions using SIMP-TO. (2)  The training of a DL-Mechanical-Conditions-Predictor to learn to map designs to their corresponding mechanical conditions. (3) The inception of DB2 a synthetic dataset of CADs (inspired from SIMPdesigns' shapes) from geometric conditions. (4) The completion of the synthetic dataset by predicting the mechanical conditions of the CADs.
Generation of the SIMP dataset DB1 DB1 comes from the mechanical SIMP-TO code and consists of generating 2D designs (as images) from various mechanical conditions (boundary conditions, loads configuration, and volume fraction). For the present work, only edge-like boundary conditions and edge-like punctual loads were considered; i. e. , boundary conditions and loads were chosen along the circumference of the design space. Additionally, the maximum number of loads is one. A sample of SIMP designs alongside their mechanical constraints are presented in Fig. 6. It is important to emphasize that using SIMP-TO, the output layout is independent of the load intensity, and the load orientations are of modulo 180°, i. e. , a design subject to a load of 90°is similar to another subject to a load of 270°.
Instead of predicting BC and F in a single shot, we create a model per constraint; the BC-DL-Predictor and the F-DL-Predictor. The SIMP-based dataset DB1 was split into a train (4538 samples) and a test (1140 samples) set to train the DL models. In this work, n x and n y are set to 100, θ ∈ [0 • , 180 • ] and |F| = 1N.
BC-DL-Predictor The BC-DL-Predictor's architecture was inspired by the convolutional Res-U-Net architecture  [32]. The network is constituted of an encoder, a bridge, and a decoder. The encoder is formed of 4 blocks, each consisting of a downsampling layer (a convolution of stride 2) and a residual unit  3. The decoder comprises 5 blocks, each consisting of an up-sample layer (a transpose convolution of stride 2) and a residual unit followed by a convolution of kernel size 1 × 1 and a sigmoid activation. The bridge connection has the same architecture as an encoder-block and combines the encoder with the decoder. BC-DL-Predictor takes as input the 2D design and outputs a two-channel matrix corre-Fig. 4 : A sample of the synthetic dataset DB2, inspired from SIMP layouts, with various geometric constraints: minimum/maximum element-length, minimum/maximum element-thickness, minimum overhang, and the number of elements.
Generation of the synthetic geometric dataset DB2 The synthetic 2D CAD dataset (DB2) is built using pygmsh (a python library, FreeCAD); the CAD shapes are inspired by SIMP-TO's designs. A synthetic design is defined as a connection of beams where we know the four coordinates of every design's beam. This definition of geometry allows us to extract all the geometric information we need from the length, width, overhang (corresponding to a chosen build direction), etc. , and consequently obtain a dataset of a wide variety of geometric constraints. Next, the CADs are converted to 2D images. The conversion from CADs to images consists of reading the CAD as a cloud of points, then applying a convolutional filter followed by a bilateral filter to smooth the image without compromising the design's beam thicknesses.
The Consolidation of the target dataset GMCAD At this stage, we dispose of DB2 , a geometric dataset, a BC-DL-Predictor, and an F-DL-Predictor. Consequently, we predict the mechanical constraints of DB2's designs and end up with GMCAD, a dataset with designs along with their mechanical and geometrical constraints. This step consists of first predicting the BC of the designs and then their loads' locations and orientations; since the loads-predictor also needs the BC input. GMCAD's mechanical and geometric-AM-related variables distributions are shown in Fig. 7.
We would like to point out that improving the precision of the F-DL-Angle-Estimator is still a work in progress. According to our knowledge, we are the first to predict the mechanical constraints from designs via DL models to consolidate a complete dataset of designs with geometric and mechanical conditions altogether. A sample of designs with their predicted versus true loads and boundary conditions is shown in Fig. 6. As we can see, the BC predictions and loads locations are very accurate. Also, most of the load orientation predictions deviate from the true values within a range of [-20°, +20°].
"GMCAD": dataset of designs with their geometrical & predicted-mechanical constraints GMCAD's variables distributions and a sample of the synthetic designs along their predicted mechanical conditions and performance are shown in figures 7 and 8, respectively. GMCAD contains 36181 distinct combinations of the geometric (Min/Max length/thickness, Min Overhang, Nbr of elements) and mechanical (F orientations and BC nodes) variables. From Fig. 7 , we can see that GMCAD comprises a wide variety of designs with 3 to 12 elements, Min bar length ∈ [0. 07, 1. 0], Max bar thickness ∈ [0. 003, 0. 4], Min overhang ∈ [0 • , 65 • ], etc. In the future, we could complement GMCAD with designs featuring more than 12 bars, a Max bar thickness ≥ 0. 4, and a Min bar length ≥ 1. 0. The data can be found in GMCAD's Git repository.
To further understand the influence of geometric constraints on mechanical performance, we generate 16 four-element designs by varying a constraint at a time and compute their compliance (Fig. 9 ). We can observe that the compliance decreases gradually with the non-constant thickness. However, it fluctuates with the minimum overhang and minimum constant thickness. Thus, there is no apparent relation between constant thickness/minimum overhang and the mechanical performance that can be deduced or generalized. To sum up, several changes in geometric constraints can improve mechanical performance. However, the combinations of constraints are uncountable, and the inter-correlations or dependencies between them are unknown. Moreover, the influence of geometric constraints over mechanical performance cannot be generalized, which encourages the establishment of this dataset and its usage to train a DL model that can jointly capture these correlations and generate designs complying with geometric and mechanical conditions.
Conclusion and Future Works A synthetic dataset of 2D designs alongside their mechanical and geometrical conditions is consolidated in this work. First, we generate mechanical designs using the FE-density-based-TO method (SIMP) and train DL models to predict the mechanical conditions. Then, we build synthetic CADs inspired by the SIMP designs' layouts accounting for various complex geometric constraints, which we complete by predicting the mechanical conditions using the learned DL-Mechanical-Conditions predictors. Finally, we evaluate the synthetic designs' mechanical performance. We showed that even slight geometric changes could deteriorate the design's functionality, and finding the best compromise between mechanical performance and geometry can be very challenging because of the unlimited combinations of geometric modifications and their unknown inter-correlations.
6). Section 4 is devoted to proving the polynomiality structure, Theorem 1. 3, within this framework. In Section 5, we introduce the Chiodo classes and review the d-orbifold ELSV formula. We then show how it can be deformed to prove Theorem 1. 5, and obtain the vanishing Theorem 1. 6 by expressing the consistency of our computations. The paper concludes with appendices that provide a new proof of the previously known formula for the generating series for DH 0,2 (µ 1 , µ 2 ), and examples of our intersection-theoretic results tested numerically with the help of the SageMath package  [19].