dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, oxidation and reduction processes, as well as the behaviour of clay minerals, cannot be assessed. The possibility of studying salt formation by alteration of iron-rich peridotite, which is the reaction thought to be responsible for the production of hydrogen and magnetite (Fe 3 O 4 ), is also disregarded. In addition, some phases are not available in the databases and thus cannot be included in the geochemical calculations, such as CaCl 2 MgCl 2 :12H 2 O and CaCl 2 MgCl 2 :6H 2 O, which should in practice replace tachyhydrite above 110 °C53. In summary, the major geochemical processes acting in the generation of highly concentrated brines by serpentinization can be described. Nevertheless, additional efforts are required to consistently extend the applicability of the thermodynamic approach to more complex and realistic systems.
calculation methodology. Salt formation is a response to water consumption by serpentinization. A script (calculation loops) was written especially for this purpose. Considering, for simplicity, the Mg end-member (enstatite) of orthopyroxene with a porosity of 10%, one kilogram of water is initially contained in a rock composed of 288 moles of enstatite. At each time step, a fixed quantity of enstatite (0. 1 mole) is placed in contact with the pore water. Then, this mineral dissolves and forms serpentine, which consumes water. Serpentine has a higher molar volume than Mg-rich orthopyroxene; thus, the reaction leads to a reduction in the porosity. Therefore, at each time step, a volume balance is made between the remaining fluid quantity and the remaining porosity in the rock. If this balance indicates a water deficit, the free volume inside the porosity is filled with seawater.
Acknowledgements We thank the  DIPS2  project funded by  Total for early calculations and concept development. Internal BRGM  funding for finalizing the calculations and writing the manuscript is also acknowledged. We also thank  Dr. Joël Casanova  ( BRGM ) for his enthusiastic support of this topic. We also gratefully acknowledge  Rowena Stead  ( BRGM ) for its help with the cover letter. We acknowledge  Springer Nature  for the English editing of the manuscript and  Sébastien Touraine (Com. communication)  for the artwork.
Introduction Constrained viscoelastic structures (CVS) are commonly used for noise and vibration control. They are usually composed of a viscoelastic layer sandwiched beetween two stiff layers. In the literature many works have been devoted to study the vibrational characteristics of viscoelastic damped sandwich structures. Rao  [1]  proposed a semi-analytical method to compute resonant frequencies and loss factors for three-layered 5 beams. Soni  [2]  developed a finite elements computer program Magna-D to compute frequencies, damping ratios and response frequency curves of frequency dependent viscoelastic structures. Johnson and Kienholz  [3]  used volumic finite elements along with modal strain energy method to determine modal frequencies, damping ratios and frequency response curves of frequency dependent viscoelastic structures.
For each mode, resonant frequencies and loss factors are determined. The damping is computed by the half power bandwidth method which remains accurate for low damping values  [58]. Let us denote by ( f i , η i ) the resonant frequencies and the loss factors. The identification strategy adopted here is to find the values of the viscoelastic complex modulus for each value of the resonant frequencies and then to perform interpolation on these values in order to obtain a continuous description of the shear complex modulus and loss factor over a wide range of frequencies. Let us set ω 2 i = (2π f i ) 2 (1 + iη i ). Since ( f i , η i ) are resonant modal data for mode i, they are supposed to solve the non linear eigenvalue problem 205  (36)  derived from the finite element model described in section 2.
Use case 2 Our goal is to identify the shear modulus and the loss factor of the viscoelastic beam using the experimental data presented in Table  2. In this case, we have no hint on the true complex shear modulus as in the previous example. The search space is fixed to 0, 10 9 × [0, 2] for the couple (G i , η i ). The optimization problem is solved thanks to a multistart strategy with 10 runs implemented via the function ms of Matlab. The interior point algorithm available through the generic function fmincon is used once again with the previous termination tolerances. This use case is more difficult than the first one since we have no hints on the true values and then we cannot check for convergence. Since gradient based methods are local methods, they need to be initialized with points near the optimum to converge.
Automatic or semi-automatic digitization performs the same task as manual process but with minimum and/or no human interference. These techniques can be developed for all possible features. With the advent of Semi-Global Matching (SGM) and Structure from Motion methods (SfM), dense point clouds can be made from images obtained from digital cameras. The quality of dense point clouds created from images using SGM are predominately affected by the ground sample distance (GSD) and image quality. In both 3D Geographical Information Systems (3D-GIS) and Building Information Modeling (BIM), 3D models are needed in vector geometry form. This means that the dense point cloud needs to be converted into geometrical primitives such as polygons. There are a lot of factors that will dictate such an undertaking; detail level, available equipment, point cloud processing and CAD software, to name a few.
Overview Our approach relies on an algorithm of searching for the best plane among a 3D point cloud, which approximates a model with data features. Figure  1  provides an overview of our procedure. The planning stage includes the selection of study object, planning the photography, selection of devices and establishment of control point and check point. The ground control points (GCPs) and check points (CPs) need to be planned properly before the acquisition of digital photograph. It is necessary to relate measurements derived from the photographic images to a 3D site coordinate system. The most effective means of achieving this involves placing a number of photogrammetric target points throughout the area of interest. Then, image sequence captured by an unmanned aerial vehicle's camera is carried out on the object.
Photographs obtained and calibrated were subsequently carried out by the SfM and dense matching process through Photomodeler software to obtain a dense cloud point. The resulting point clouds will then be exported to carry out the next process, namely automation of polygons into 3D models. The 3D model produced by PolyFit needs to be transformed through Cloud Compare software so that the 3D model can be used as a basis for further digitization. After the 3D model is oriented and in accordance with the initial data, the digitization process can be performed on the details of the building that has not been modeled such as windows, screen walls, and other parts. Digitization is done by using the space intersection principle. This digitization is done to improve details on 3D models (upgrading).
Data Acquisition The proposed technique was tested on 2 types of buildings, namely modern architecture and traditional architecture (temples). New building of an office complex in Bandung, named Pondok Hijau Indah Complex, is classified as the modern architecture and one temple in Jogjakarta, named Candi Sari, is classified as the traditional architecture. Figure  2  and Figure  3  show both of the buildings image and point cloud. In the case of Pondok Hijau Indah Complex, a total of 30 aerial and 19 terrestrial images of the building were captured from the perimeter of the building toward the center, with an image taken about every 10 meters (30 feet). To minimize foreground obstructions in the images and expedite fieldwork, the camera was mounted on a small quadcopter that flew at an elevation of 35 to 45 meters (120 to 150 feet) above the ground.
These images were combined with images taken at ground level. A total of 18 points were measured using a reflectorless Total Station device; 10 of these points were used as control points for the geo-referencing of the images. The point cloud was generated using the software PhotoModeler. A 3D polygon model of the main building in OBJ format was generated by PolyFit and used as a control/base for drafting directly on the oblique images using Photomodeler. In addition to the OBJ file, a list of all measured points in TXT format was generated. This included the point names, codes, descriptions, coordinates, and their measurement accuracy in all axes. In total, 135 new measurements were made from the images. The measurement accuracy of all points was better than 2 centimeters (5-6 hundredths of a foot) in both position and elevation.
In the case of Candi Sari, laser scanning was performed to obtain the interior of the temple while serving as a "bridge" between the interior and the exterior which was acquired using photogrammetry (both terrestrial and UAV). Several carvings inside the temple were also documented using photogrammetry, in order to render them in a higher resolution. The laser scanning data was processed using FARO Scene, while photogrammetric work was done using the software PhotoModeler. A traverse network was established around the temple; with reference points measured using static GNSS. The topographical network was measured in the national cartographic system. The integration of the sensors was performed by using the control points scattered around and inside the temple in the form of coded targets and artificial spheres.
SfM-Photogrammetry and Dense Image Matching Structure from Motion (SfM) has a fundamental difference from conventional photogrammetry, where in terms of geometry, the position and orientation of the camera are completed automatically without the need to determine targets or ground control points that have been predetermined, which are known to position in 3D. Instead, these parameters are solved simultaneously using repetitive file alignment procedures, based on a feature database that is automatically extracted from a series of overlapping images  (Snavely, 2008). SfM-photogrammetry is a solution that integrating SfM and ground control. It is necessary to relate measurements derived from the photographic images to a 3D site coordinate system. The most effective means of achieving this involves placing a number of photogrammetric target points throughout the area of interest.
The sparse point clouds resulting from the SfM process and the camera position distribution can be seen in Figure  4. The principle of Structure from Motion is to calculate the position and orientation of the camera that is completed automatically without the need to determine targets or ground control points that have been predetermined, whose positions are known in 3D. These parameters are then solved simultaneously using a repetitive file alignment procedure, based on a feature database that is automatically extracted from a series of overlapping images. The quality of photos oriented in file alignment can be seen in Figure  5. The quality of photo orientation in the Photomedeler software is a representation of the results of the position of the corresponding object points in each photo due to the alignment process of the file that is influenced by the position and orientation of each camera towards the resulting photo or photos that overlap with the surroundings.
3D Polygon Fitting PolyFit is software that offers a new framework for reconstructing lightweight polygon surfaces from cloud points. PolyFit processing is done with the aim to generate 3D models of a building automatically  (Nan, 2017). The point cloud from the MVS Photomodeler results is then selected on the part of the building you want to be modeled so that it discards unwanted parts using Cloudcompare or Photomodeler software. After that, normal estimation and primitive extraction is carried out in the Mapple software by entering the appropriate RANSAC parameters to be modeled automatically in PolyFit. The stages of modeling in PolyFit are done automatically.
Primitive extraction Primitive extraction is carried out through the RANSAC method. The RANSAC method contains three unspecified general parameters  (Fischler, 1981) : fault tolerance used to determine whether a point fits a model, the number of subsets to try, and the threshold t, which is the number of compatible points used to imply that the correct model has been found. Through Mapple software, primitive extraction is done by entering RANSAC parameters that are suitable for point clouds and building models. The RANSAC parameter values used in primitive extraction on Mapple can be seen in Table  3. Of the five parameters, the most influential in primitive extraction are the Minimum Support and Distance Threshold. The value of 500 at the minimum support is determined based on the minimum number of points needed to form a surface.
Optimization This stage is the last stage to reconstruct 3D objects automatically through PolyFit. It can be seen that the fields formed will be intersected with other intersecting fields. This intersection produces various kinds of combinations of surface products that are formed. The problems contained in this stage can be resolved using Gurobi solver  (Nan, 2017). The results of optimization can be seen in Figure  10. Figure  10. Final result of PolyFit Optimization in front look.
Detail Digitizing A 3D polygon model of the main building in OBJ format was generated by PolyFit and was used as a control/base for drafting directly on the oblique images using Photomodeler. The modeling results in PolyFit are still referenced in the local coordinate system. Therefore, it is necessary to transform so that the 3D model can be referenced in a coordinate system, namely UTM Zone 48S by using Cloudcompare software. This referenced model can later be used as a base for digitizing the Photomodeler. The transformation parameter values shown in Figure  11  show that the X translational value = 786066,625, Y translation = 9241495. 00 and Z translation = 940. 594299 with no rotation and scaling elements of 1. 00.
g. Dhonju et al. 2017; Seitsonen 2017; CReAAH 2019; MOLA 2019; SCAPE 2019; Schweizerische Eidgenossenschaft 2019). Since the quality of research results and heritage management decisions is highly dependent on the nature of the available data, issues of sustainability of digital data repositories, accessibility and reliability of data, standardization of data formats and management of property rights are currently widely debated. The lack of consistency in (spatial) data standards greatly inhibits the ability to develop sustainable solutions for managing, sharing and analysis of that data. Cross-regional or even supranational analysis of data sets is therefore in most cases a highly time-consuming or even impossible task. Even when datasets are managed according to welldocumented standards, the development of appropriate tools to access and present relevant (spatial) information to researchers, heritage managers and general public is still very much in the stage of exploration and focuses mainly on project-specific contexts that often have a short lifespan.
Software solutions typically have a short lifecycle as well and will cater for different user demands. The flexibility of interfaces is usually limited and does not support interoperability with other datasets. It can therefore be questioned whether these systems really provide the end users with the data and information that they would like to have. In this position paper, we discuss the existing solutions and state-of-the-art approaches, and their effectiveness in providing the end users with relevant and up-to-date information, based on our experiences in developing data retrieval systems and spatial data infrastructures for various purposes. From there, we will reflect on how to develop more sustainable approaches and technologies for management and use of spatial data, particularly in research and archaeological heritage management, but also within the broader contexts of planning, design and public involvement.
Development of national archaeological databases and their use The origins of large (national) archaeological databases stem from the post-Second-World War shift in cultural heritage legislation and expansion of the archaeological enterprise in a number of developed countries  ( Willems 2000; Demoule 2012 ), leading to systematisation and standardization of archaeological surveys and the compilation of, at first, paper-based registries of archaeological sites  (Norman & Sohlenius 2009; Niedziółka 2016 ). Although early computerised databases date back to the 1960s  (Lock 2003) , larger-scale deployment of digital information systems began to take place in the late 1980s and early 1990s (e. g. Willems 1997; Kuna 2002; Niedziółka 2016). The need to coordinate and manage archaeological fieldwork and effective decision-making required effective management of information.
Role of GIS and spatial database infrastructures The large-scale development and use of spatial databases began only after the introduction of GIS software that was reasonably easy to use. Together with affordable personal computers this opened up the possibility to access and build databases and conduct spatial analysis for individual archaeologists and heritage administrators. The emergence of digital spatial data analysis has been one of the most influential factors affecting archaeological work during the past few decades, even when it has been subject to criticism (cf. Rajala 2004; Valenti & Nardini 2004; Conolly & Lake 2006). Apart from the evident usefulness of GIS in archaeological research (see e. g. Lock & Stanc ˇic ˇ 1995) , its potential was also soon noted in archaeological heritage management  (Limp 2000).
Therefore, the level of standardisation of some spatial datasets is high, particularly in ones developed for regulatory purposes, whereas comparatively little effort has been made to standardize primary evidence from fieldwork and research. This has undoubtedly contributed to the fact that, currently, of all archaeological information, spatial data is probably best represented in digital repositories even if there are many obstacles to managing, preserving and making spatial information accessible. A central ambition of national archaeological databases was also to make archaeological information more accessible. Many of the archaeological databases and repositories compiled during the 1990s were entirely, or partly, searchable on the Internet or had an ambition to open up for online access at a later date (e.
Creation of digital archives The digitalisation of the production of archaeological information in general and spatial data in particular from the 1990s onwards meant that the old paper-based practices of archiving archaeological data were rapidly becoming obsolete  (Huvila 2016). Even if a large number of digital archaeological databases were developed already during the 1990s with an aim of recording and making available information on archaeological sites to support both research and heritage management (e. g. Roorda & Wiemer 1992) , they were generally designed for hosting only a subset of all documentation. As  Wise and Richards (1999)  underline, with few exceptions (e. g. Eiteljorg II 1995) , there was a chronic lack of consideration of longterm preservation of information. Obsolescence in both software and storage formats are major risks to the sustainability of digital data.
In spite of the large-scale acquisition of spatial data, the paper report remained (and in many cases, still remains) the standard deliverable. The digital data either remains undiscoverable with the data creator  (Shaw et al. 2009)  or is deleted, hence of no subsequent value beyond the project lifespan. Another problem was, and still is, the proliferation of project and site-specific databases with their own peculiar data structures, concepts and vocabularies  (Oikarinen & Kortelainen 2013)  that have proved to be difficult to harmonise on a meaningful level. This is a problem common to many disciplines  (Bowker & Star 1999). In the UK, the need to interrogate data from multiple projects has been acknowledged and excavation and other fieldwork datasets are produced by commercial archaeology often creating data ' on a per-site basis structured according to differing schema and employing different vocabularies.
The increasing availability and use of digital spatial data have been, however, some of the cornerstones and central enablers of the integration of archaeological datasets as a whole  (Huggett & Ross 2004). Even when ensuring that data are accessible in the long term, they are mostly treated on a project by project basis with little or no standardisation across datasets: that is, the mechanisms to ensure the creation, exchange and use of spatial data do not exist for archaeological data within a Spatial Data Infrastructure (SDI), defined as a framework of technologies, policies, and institutional arrangements that together facilitate the creation, exchange, and use of geospatial data and related information resources across an information-sharing community  (ESRI 2016). A lot of significant work for the development of (nonspatial) data infrastructures was initiated in the context of research and development projects around the turn of the century.
International guidelines and standards The value of geospatial data is recognized internationally through the coordinating efforts of the Open Geospatial Consortium (OGC 2019a) in defining Open Data standards for the global geospatial community. Globally, the value of geospatial data is recognised through the United Nations Global geospatial Information Management (UN-GGIM) strategic framework (UN-GGIM 2018). Many international and national initiatives, including the INSPIRE Directive (European Commission 2019) that aim at realizing the potential of environmental data in Europe acknowledge the value of spatial data for a range of activities including decision making processes. Governments including The Netherlands, through Geonovum  (Geonovum 2019 ) and the United Kingdom, through the Geospatial Commission (Gov.
Public institutions are focused on delivering their key corporate objectives often with little opportunity for innovation whilst the growing commercial sector is predominantly driven by competitive processes focusing on project delivery rather than contributing to the bigger picture. Financial constraints, the need to demonstrate impact of your organisation coupled with institutional inertia in the face of the digital revolution reinforce data silo mentalities leaving the potential offered by digital datasets far from being realised. Moreover, Valletta could not foresee the revolution and opportunities offered by digital data so there are no incentives or drivers to work collaboratively towards developing an SDI for cultural heritage data. Despite the obvious benefits of frameworks developed to contribute to interoperability such as the CIDOC CRM  (Doerr 2003; ICOM CIDOC 2019a)  or more pragmatic solutions required to collate and share data locally, there is no true coordination, leadership or mandate either at European or national level from within the profession to coordinate harmonising spatial data across organisations and jurisdictions.
Instead, the key drivers mandating the harmonisation and publication of datasets are external. In particular, the European Union INSPIRE Directive, transformed into the national legislation of member countries, requires that Protected Sites are published as WMS and WFS to agreed technical specifications, to inform Community environmental policies and policies or activities which may have an impact on the environment. As legislative frameworks and working practices differ in detail across Europe, approaches to the publication of 'protected sites' are uneven with some authorities only releasing formally designated data rather than full inventories.