dataesr/scientific-paragraphs-categorization huggingface dataset:
The advent of dedicated platforms like InnoCentive have made it easier to decompose the scientific and technological development process into component tasks and accordingly, to distribute it among multiple actors and entities  (Felin and Zenger 2014; Lakhani, Lifshitz-Assaf, and Tushman 2013). The free and open-source software movement provided inspiration to early calls for Open Science  (Willinsky 2005) , and today the transparency of open-source code allows the scientific community to cross boundaries around field-specific software packages and converge on common solutions like the software environment R. Another example is REANA, a research data analysis platform created by CERN to enable code and data reuse and reproduction, which has generated interest in disciplines well beyond physics  (Pujol and Wareham 2019).
2016) , which were later formally endorsed by the European Commission. Political support for making research data across Europe findable, accessible, interoperable, and reusable (FAIR) was thus translated into technical specifications for metadata, approaches to identification and indexing, protocols for access, and appropriate attributes for identifying provenance. Further efforts to support data sharing and reuse include the European Open Science Cloud, an ambitious effort to connect nationallevel infrastructures in an effort to harmonise data management according to FAIR principles, and the COVID-19 Data Platform, launched in April 2020 to bring together relevant datasets and accelerate research responding to the coronavirus pandemic. Another set of society-level factors affecting the application of OIS practices relates to what are considered valid reasons for limiting openness and collaboration.
2019) , and bioinformatics  (Blasco et al. 2019). Open and collaborative dissemination practices have also been tied to increased novelty, as with the uptake of scientific knowledge from sources like Wikipedia into original research agendas  (Thompson and Hanley 2018). Another type of impact stemming from the application of OIS practices relates to the reliability of scientific knowledge. The sharing of research data and protocols has led some fields to diagnose and begin responding to a so-called replication crisis  (Open Science Collaboration 2015). Even in fields where replication may not be valued or possible, parallel logics of transparency and reflexivity are being articulated in terms of local cultures of evidence (e. g. Elman, Kapiszewski, and Lupia 2018 ). Yet we should not assume that the impacts of applying open and collaborative practices in science are unambiguously positive.
Future research should examine the interdependencies between different levels of analysis; while this approach will require resourcefulness in collecting data across levels, the pay-off will be an ability to offer richer analyses that do justice to the complexity of the science system. Research designs that cross levels of analysis in this way promise a deeper understanding of OIS and stand to have important implications for the spheres of practice and policy. The OIS Research Framework also points to potential tensions and conflicts that may arise in pursuit of purposefully opening the scientific research process. For example, greater openness with respect to research data and other outputs may undermine efforts at technology commercialisation, which typically requires appropriation. Likewise, when designing the implementation of certain practices, attention will need to be paid to the multiple functions that these practices might have and the potential existence of trade-offs between them (e.
Conflicting goals may also complicate potential synergies between different OIS practices and stakeholders, as firms require appropriation and patenting while scientists may prefer openness of data and results  (Vedel and Irwin 2017). To this end, the development of a multi-dimensional measurement scale of OIS practices, which captures the intensity of effort when applying certain practices alone or in combination, may be beneficial. This would allow future studies to assess the benefits and costs of applying OIS practices in new contexts more holistically or applying multiple practices simultaneously. Future research also needs to advance our understanding of the multi-level antecedents and contingencies of OIS practices that influence the occurrence and success of open and collaborative practices in science.
These experiments, crucially, may involve piloting novel organisational forms beyond the received structures of departments, research centres, and the like. Such institutional changes should be aligned with policy-level efforts to encourage, monitor, and manage open and collaborative research practices while mitigating emerging risks (e. g. openly sharing research data while ensuring participants' data protection rights). In addition, technological advancements require ongoing attention to identify the potential for new research technologies like artificial intelligence and their implementation for OIS practices. For example, as discussed above, pioneering advances can already be observed in the area of computational citizen science, as well as in the creation of hybrid Open Science processes combining experts, crowds, and AI  (Kittur et al.
Both minerals represent around 0. 2% of the mineralized samples. The dataset is used to evaluate the possibility of extracting trace metals as by-products during Cu-sulfide processing, by the addition of unit operations to conventional plant designs. A remarkable advantage of the proposed workflows is that costs of mining, crushing, and grinding stages are accounted for in the copper production investments. The proposed geochemical characterization can be applied to other porphyry-type operations to improve the metallic benefits from a single deposit.
Samples and Analytical Protocol For this study, we sampled ten drill cores of mineralized material from the Los Sulfatos deposit at depths from 100 to 800 m, which are located in the expected exploitation area to be developed as the Los Bronces underground project. From these drill cores, fifteen 5 m-mineralized intervals were selected and two hand-picked samples were collected from each interval (Figure  2 ). It is important to note that each 5 m interval corresponds to a composite-sample analyzed by whole-rock chemical analysis, which is a routinely analytical procedure in the mining industry (Figure  2a ). The goal of that correspondence is to compare the whole-rock geochemical data with the obtained results from.
Samples and Analytical Protocol For this study, we sampled ten drill cores of mineralized material from the Los Sulfatos deposit at depths from 100 to 800 m, which are located in the expected exploitation area to be developed as the Los Bronces underground project. From these drill cores, fifteen 5 m-mineralized intervals were selected and two hand-picked samples were collected from each interval (Figure  2 ). It is important to note that each 5 m interval corresponds to a composite-sample analyzed by whole-rock chemical analysis, which is a routinely analytical procedure in the mining industry (Figure  2a ). The goal of that correspondence is to compare the whole-rock geochemical data with the obtained results from an advanced micro-chemical characterization (Figure  2b ). Thus, thirty representative samples Minerals 2020, 10, 531 4 of 18 were collected from four mineralized zones, Min-Zones, defined at the Los Sulfatos deposit  [10] , i.
At GET, the tuning routine is optimized to reduce production of molecular oxide species (typically 232 Th 16 O⁄ 232 Th < 1%) and doubly charged ion species (typically 140 Ce++⁄ 140 Ce+ < 2%). This allows an analytical precision of <15% for the relative standard deviation (RSD) to be reached  [30, 31]. The gas blank is measured for 30 s before switching on the laser for 60 s. Raw data were processed online using the GLITTER software package (4. 0, ARC National Key Centre for Geochemical Evolution and Metallogeny of Continents, Macquarie University, Sydney, Australia) (e. g. ,  [32] ), using certified reference materials, such as pyrrhotite-Po-726  [33] , an in-house natural chalcopyrite, Cpy-RM  [30] , and NIST SRM 610  [34] , as external calibrators, in bracketing mode standard-sample-standard.
The two metallogenic units are spatiality related with the Min-Zones, recognized in the geological 3D model for the Los Sulfatos deposit. Minerals 2020, 10, x FOR PEER REVIEW 5 of 18 deviation value. At GET, the tuning routine is optimized to reduce production of molecular oxide species (typically 232 Th 16 O⁄ 232 Th < 1%) and doubly charged ion species (typically 140 Ce++⁄ 140 Ce+ < 2%). This allows an analytical precision of <15% for the relative standard deviation (RSD) to be reached  [30, 31]. The gas blank is measured for 30 s before switching on the laser for 60 s. Raw data were processed online using the GLITTER software package (4. 0, ARC National Key Centre for Geochemical Evolution and Metallogeny of Continents, Macquarie University, Sydney, Australia) (e. g. ,  [32] ), using certified reference materials, such as pyrrhotite-Po-726  [33] , an in-house natural chalcopyrite, Cpy-RM  [30] , and NIST SRM 610  [34] , as external calibrators, in bracketing mode standard-sample-standard.
Acknowledgments: We are grateful to  S. Gouy  and  Ph. de Parseval  for their help during SEM and EMP analyses at the  Centre de Micro-Caractérisation Raimond Castaing  ( UMS 3623 ) in Toulouse, France, and  T. Aigouy  for their help during the SEM analyses at the  GET Laboratory in Toulouse, France. We would also like to thank  J. Chmeleff  and  C. Duquenoy  for their technical assistance on the LA-ICP-MS analyses at the  GET Laboratory. G. Velásquez  and  H. Estay  acknowledge the  National Commission for Scientific and Technological Research  ( CONICYT Chile ), through  CONICYT-PIA  Project  AFB180004  they were benefited to conduct part of this research. Constructive comments by two anonymous reviewers substantially improved the quality of this manuscript.
The growth of the field is reflected in a plethora of existing and new software developments that implement aspects of electronic structure theory either for specialized or rather broad general use cases. The community-wide psi-k. net website lists over thirty "codes" at the time of writing (December 2019) and 74 individual code projects are listed at the "Community Code Database" of the U. S. based Molecular Softa) Electronic mail: micael. oliveira@mpsd. mpg. de b) Electronic mail: nickpapior@gmail. com c  Electronic mail: yann. pouillon@materialsevolution. es ware Sciences Institute (MolSSI),  4  another communitybridging organization working to support a broad set of "codes" and their users. 5, 6 ile the electronic structure community (ESC) is thus extremely active in developing software that enables a host of scientific insights, developments have historically occurred in the form of different individual software packages that are largely distinct from each another at the code level.
E. pspio For a long time, the development of pseudopotentials has generally been coupled to a parent DFT code. This has resulted in a proliferation of file formats and incompatibilities, preventing or severely limiting collaboration involving different codes. Even worse, some versions of a pseudopotential format are not compatible with some versions of the DFT code they originated from. To address this issue, many discussions took place from 2002 on to define a common file format for pseudopotentials. While this led to the successful creation of the PAW-XML format for projector augmented-wave (PAW) datasets,  78  no agreement was reached at the time for norm-conserving pseudopotentials (see, however, Section IV F below). pspio takes exactly the reverse perspective: since many file formats exist and will continue to exist for the foreseeable future, let us design and implement a library that is able to read and write all of them, including the different versions of each format.
PSML (for PSeudopotential Markup Language) 80,81 is a file format for norm-conserving pseudopotential data which is designed to encapsulate, to the greatest extent possible, the abstract concepts in the domain's ontology, and to provide appropriate metadata and provenance information. PSML files can be produced by the ON-CVPSP  30  and ATOM 82 pseudopotential generator programs, and are a download-format option in the Pseudo-Dojo database of curated pseudopotentials. 83, 84 he software library libPSML  80, 81  can be used by electronic structure codes to transparently extract the information in a PSML file and adapt it to their own data structures, or to create converters for other formats. It is currently used by Siesta and ABINIT, making full pseudopotential interoperability possible and thus facilitating comparisons of calculation results.
The code is hosted here https://gitlab. com/Electro nicStructureLibrary/esl-demo. ABINIT pioneered the open-source model within the electronic-structure community. It is a plane-wave-based code that has allowed contributions from quite an open community of developers, some of them coding new features, tools, etc. in modules being integrated into the code. In that sense it can claim to have taken the first intellectual step within the electronic-structure community that led to the ESL. In addition to this contribution to the ESL concept, ABINIT benefits from the usage of Libxc for the local and semi-local exchange-correlation terms, and it now incorporates libGridXC on top of Libxc for the global grid treatment on non-local functionals. It also makes use of PSolver for the computation of the Hartree terms to energy and potential, and the PSML standard and associated library for pseudopotential input.
FHI-aims FHI-aims developers have greatly contributed to the ESL through joint involvement in the broader, U. S. NSFfunded ELSI project, described in Section IV H. ELSI was inspired by the ESL and represented an early U. S. initiative in this effort, in an otherwise primarily European endeavour. Through ELSI, FHI-aims now benefits from a range of Hamiltonian solvers in a seamless framework. This list includes ELPA, which originated within FHIaims and is maintained as a standalone solver library led by the Max-Planck Computing and Data Facility, as well as PEXSI, NTPoly and all other solvers supported by ELSI. The libraries used by FHI-aims are not restricted to solvers, as Libxc is supported for the calculation of the exchange-correlation contribution for local and semilocal functionals.
ACKNOWLEDGMENTS The authors would like to thank  CECAM  for launching and pushing the ESL, as well as hosting part of its infrastructure, and partly funding the extended workshops where most of the coding was done, both in the Lausanne headquarters as in the Dublin, Trieste and Zaragoza nodes. Within CECAM, particular thanks to  Sara Bonella ,  Bogdan Nichita , and  Ignacio Pagonabarraga. The authors also acknowledge all the people that have supported and contributed to the ESL in different ways, including  Luis Agapito ,  Xavier Andrade ,  Balint Aradi ,  Emanuele Bosoni ,  Lori A. Burns ,  Christian Carbogno ,  Ivan Carnimeo ,  Abel Carreras Conill ,  Alberto Castro ,  Michele Ceriotti ,  Anoop Chandran ,  Wibe de Jong ,  Pietro Delugas ,  Thierry Deutsch ,  Hubert Ebert ,  Aleksandr Fonari ,  Luca Ghiringhelli ,  Paolo Giannozzi ,  Matteo Giantomassi ,  Judit Gimenez ,  Ivan Girotto ,  Xavier Gonze ,  Benjamin Hourahine ,  Jürg Hutter ,  Thomas Keal ,  Jan Kloppenburg ,  Hyungjun Lee ,  Liang Liang ,  Lin Lin ,  Jianfeng Lu ,  Nicola Marzari ,  Donal MacKernan ,  Layla Martin-Samos ,  Paolo Medeiros ,  Fawzi Mohamed ,  Jens Jørgen Mortensen ,  Sebastian Ohlmann ,  David O'Regan ,  Charles Patterson ,  Etienne Plésiat ,  Markus Rampp ,  Laura Ratcliff ,  Stefano Sanvito ,  Paul Saxe ,  Matthias Scheffler ,  Didier Sebilleau ,  Søren Smidstrup ,  James Spencer ,  Atsushi Togo ,  Joost Vandevondele ,  Matthieu Verstraete , and  Brian Wylie.
Daniel G. A. Smith  was supported by  U. S. National Science Foundation (NSF)  grant  ACI-1547580. Mike Payne  acknowledges support from  EPSRC  under grant  EP/P034616/1. Arash Mostofi  acknowledges support from the  Thomas Young Centre  under grant  TYC-101 , the  Wannier Developers Group  and all of the authors and contributors of the wannier90 code (see Ref. 116 for a complete list). Alin M. Elena acknowledges support by  CoSeC , the  Computational Science Centre for Research Communities , through CCP5: The Computer Simulation of Condensed Phases,  EPSRC  grants  EP/M022617/1  and  EP/P022308/1. Alberto García  and  José M. Soler  acknowledge grant  PGC2018-096955-B-C42  from  Spain's Ministry of Science. Emilio Artacho ,  Alberto García  and  José M. Soler  acknowledge grant  FIS2015-64886-C5  from  Spain's Ministry of Science.
Yann Pouillon ,  David López-Durán  and  Emilio Artacho  acknowledge support from grant  RTC-2016-5681-7  from  Spanish MINECO  and  EU Structural Investment Funds. Martin Lüders  acknowledges support from  EPRSC  under grant  EP/M022668/1. Martin Lüders ,  Micael J. T. Oliveira , and  Yann Pouillon  acknowledge support from the  EU COST  action  MP1306. Jan Minar  was supported by the  European Regional Development Fund (ERDF) , project  CEDAMNF , reg. no. CZ. 02. 1. 01/0. 0/0. 0/15-003/0000358. Victor Wenzhe Yu ,  William Paul Huhn ,  Yingzhou Li , and  Volker Blum  acknowledge support from the  National Science Foundation  under award number  ACI-1450280  (the  ELSI  project). Victor Wen-zhe Yu furthermore acknowledges a  MolSSI fellowship  ( NSF award   ACI-1547580 ). Simune Atomistics S.
In this research, we considered SPOT 6 and 7, and PLEIADES A and B, as well as the capability of scheduling all these satellites by the same mission planning center. Additionally, we considered the scheduling time horizon from 2019-10-30-09-50 and 2 h ahead. During the time horizon all four satellites were able to capture images of some of the requests; see Figure  2  for an illustration of this scenario. The satellite information included satellite path information given by the Two-Line Element set (TLE) and specifications about the payload, memory, attitude maneuvering speed, etc. The TLE can be found for any publicly known satellite via the website www. n2yo. com. The TLE is a data format that constitutes a list of orbital elements for an Earth-orbiting object for a given point in time, the epoch.
In this study, we integrated forecasted cloud coverage and its uncertainty to mimic a real problem scenario. The cloud cover data specific to the considered time horizon and locations of area of interest were obtained from www. openweathermap. org. Therefore, the real-time uncertainty was considered as a criterion in the scoring process to make sure that the schedule satisfied the cloud coverage condition. For example, if weather parameters are not considered in pre-processing and a bad quality image is captured, then the satellite resource is wasted without any fruitful outcome from the perspective of both operators and customers. Note that we were not ignoring any requests, but this heuristic increases the system efficiency, and these infeasible requests were considered in later scheduling as presented in our computational scheme in Figure  1.
This model even goes further by clustering instantiations to improve the probabilities. Finally, we systematized the method from  [SQH19]  to precisely compute the probability of a boomerang. We applied our new set of tools to the block cipher SKINNY [BJK + 16] and found many new distinguishers on all versions of the ciphers. Our results are given in Table  1. All previous results from  [SQH19]  are improved, in particular we found a new boomerang distinguisher on 18-round SKINNY-128/256 (i. e. on the TK2 model) with probability 2 -47. 37 while the previous best distinguisher had probability 2 -77. 83. We experimentally verified some of the distinguishers to confirm the probabilities. All our source codes and results (including characteristics) are publicly available at: https://gitlab. inria.
Truncated boomerang characteristics were generated for all the versions of SKINNY using the MILP solver Gurobi. 4 For truncated boomerangs, there is no difference between the 64 and 128 bits versions as for each table the optimal probability is the same for both versions. Gurobi allows to either get one optimal solution, or get the N best solutions. Both solvings are important: finding the optimal objective allows to know the number of rounds when the boomerang characteristics have probability lower than 2 -64 or 2 -128 ; finding the best solutions allows to search for different characteristics in the next step, hence increasing the chances to find a good distinguisher. Table  2  shows all the optimal objectives for different number of rounds on different attack models. Recall that this objective has to be multiplied by two to get the best boomerang characteristic probability exponent, because of optimizations done in the objective function dedicated to SKINNY.
Optimizations For SPNs, a table showing the minimum number of active Sboxes for a given number of rounds is often available. For example on SKINNY, the table is given by the authors [BJK + 16] for up to 30 rounds on the different attacker models (SK, TK1, TK2, and TK3). We can use this table to add non-trivial redundant constraints to the model. Let tab[r] be the minimum number of active Sboxes on a differential characteristic with r rounds. Then we add the constraints: ∀ r ∈ [1, R], r ∈ [0, R -r], r-1 k=0 15 i=0 isActive[r + k][i] ≥ tab[ r] We experimentally noticed that adding those constraints really speeds up the solving process as it is now easier for the solver to prove lower bounds. There are many minor optimizations that are possible but really dependent on the solver used. For Gurobi, we found out that solving a sub-problem first and then solving the full model really helps in the resolution (helping to know in advance what variables to branch on in the solving).
( E. Davies ) The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement №  339109. ( R. de Joannis de Verclos ,  R. J. Kang ) Supported by a  Vidi grant  ( 639. 032. 614 ) of the  Netherlands Organisation for Scientific Research (NWO).
We deduce easily by formula (9) that, for all fixed j, 8u ! 0, A À1 j ðωÞ ¼ 1 ffiffi ffi 2 p 0 B B @ " 8ω π 2 þ d 4 1j #1 2 þ d 2 1j 1 C C A 1 2 ω ! 0 P ÀÈ min i T j i < T 1jjT1j¼u ÉÁ ¼ 1 À e ÀΛ T j ðuÞ (10) By integrating this quantity between À∞ and þ∞ with respect to the density of the random variable T 1j (the density of the normal distribution), we get γ ¼ 1 À 1 σ ffiffiffiffiffi 2π p X j2J Z þ∞ À∞ e ÀðuÀkd 1j Þ 2 2σ 2 ÀΛ T j ðuÞ ! du (11) Interpretation of the result: In the following tables, we take in the preceding formula, m ¼ 2 (meaning J ¼ f2g ) and then a model of two honest miners and one SM-pool. We simply take the constant k ¼ 1, and we give some values of γ with respect to some arbitrary values of d 12 ; λ; σ. Under the simple hypotheses, the elements which allow us to compute the following tables are : & Λ T 2 ðuÞ ¼ Z þ∞ 0 λF Nð0;1Þ u À kA À1 2 ðωÞ ffiffi ffi 2 p σ dω; ' 8 > > > < > > > : γ ¼ 1 À 1 σ ffiffiffiffiffi 2π p Z þ∞ À∞ e ÀðuÀkd 12 Þ 2 2σ 2 ÀΛ T 2 ðuÞ ! du 9 > > > = > > > ; Let's see the tables generated by MATLAB in the next pages.