dataesr/scientific-paragraphs-categorization huggingface dataset:
[39]  It is essential to use the known geometry of the sample and detector configurations to transform the 2D GIWAXS detector images into q z and q r (q xy ) in reciprocal space. These Ewald sphere image corrections are required prior to 1D analysis for accurate data interpretation, quantitative phase analyses, and structure refinement. Accurate structure refinement is complicated by information loss along the out-of-plane scattering axis that results from the GIWAXS experiment geometry. Pole figure construction is a useful method for obtaining an accurate representation of the orientational distribution of crystallites in the film. [39, 184]  Acquisition of both a grazing incidence diffraction pattern and a local specular diffraction pattern at the Bragg condition (q B ), also known as a "wide-angle rocking curve", allows for the merging of both datasets to construct a complete pole figure.
Within this   [267]  Copyright 2020, American Chemical Society. B) i,ii) In situ GIWAXS diffraction maps as a function of time and temperature for 2-TMAI and PEAI modified perovskite films, respectively. iii,iv) Respective intensities of the main peaks as a function of time. Adapted with permission. [268]  Copyright 2018, Royal Society of Chemistry. context, synchrotron-based GIWAXS methods have emerged as a characterization workhorse for quantifying structural features of perovskite thin films and supporting progress. Its proven utility is directly reflected by the rate at which important work now relies on GIWAXS to reveal insights into state-of-the-art optoelectronic devices and, most notably, solar cells. Rather than providing a thorough review of recent GIWAXS-related work and progress, our goal in this manuscript was to help newcomers develop a clear and comprehensive framework for designing, executing and interpreting synchrotron-based GIWAXS experiments.
• What analysis program was used to reduce and analyze the GIWAXS data. • The detector brand, dimensions (size) and pixel size. • What instrument or beam line was used. • The X-ray energy and its polarization. • The calibration procedure including the calibrants used. • The GIWAXS image exposure time. • The sample environment (e. g. , gas composition) and enclosure. The transparent reports of these experimental conditions will allow others to reproduce any experiments and provide clarity with respect to some of the advantages and limitations of each GIWAXS experiment.
Acknowledgements The authors acknowledge financial support from the  Research Foundation -Flanders [FWO : Grant No. 1242922N  (H. H. ),  12Y7221N ,  V400622N  (J. A. S. )]. J. H. and M. B. J. R. acknowledge financial support from the  FWO  through research projects (Grant No's  G098319N ,  G0A5923N ,  S002019N  ( PROCEED ),  S004322N  ( GIGAPIXEL ), and  ZW15_09-GOH6316 ), from the  KU Leuven Research fund  ( iBOF-21-085 PERSIST ) and from the  Flemish government  through long term structural funding  Methusalem  ( CASAS2 ,  Meth/15/04 ). J. A. S. and M. B. J. R. acknowledge financial support from the  KU Leuven Industrial Research Fund  ( C3/19/046 ). D. D. , D. L. , K. W. and M. F. T. acknowledge support from the  U. S. Department of Energy (DOE) Solar Energy Technology Office (SETO)  of the  Energy Efficiency and   Renewable Energy (EERE)  award for  Derisking Halide Perovskite Solar Cells.
We also considered the spatiotemporal evolution of the shock geometry (Θ Bn angle) and the shock strength. We examined if the changing shock properties of the field lines connected to an observer could be a pos- sible reason for the observed timings of the SEP releases. We emphasize that the values of the various parameters used in this study can vary from event to event in reality. They are used to build our discussion, analytically and quantitatively, on the possible delays of the SEP acceleration and release times from a propagating disturbance in the corona. The final results that we present in this study should be regarded in such a context. After our discussion, we show details of a novel python software package and a web application that can be used to model, in 2D, the shock wave parameters in the corona and IP space, and to calculate the connection times at different locations.
In the future, we envision that further developments of this model will make it more sophisticated. This, for example, can be incorporated with the use of more realistic coronal and solar wind background models, such as the PFSS model or magnetohydrodynamic models. Additionally, the kinematics and the expansion parameters of the model can be more realistic, when analysing specific events, with the use of tools that perform reconstruction of the CME or shock wave 3D structure using remote-sensing observations from multiple viewpoints. For example, using PyThea 1 , a new Python software package that can be used to reconstruct shock waves and CMEs in 3D  (Kouloumvakos et al. 2022b ), the provided parameters can be more realistic. In Appendix A, we present an opensource Python software package and a web application that is 1 https://www.
Acknowledgements. This study has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101004159  (SERPENTINE). A. K. acknowledges financial support from the  SERPENTINE  project for the tool development and from  NASA   NNN06AA01C  (SO-SIS Phase-E) contract that allowed to complete this study. J. G. and R. V. acknowledge the support of  Academy of Finland (FORESAIL , grants  312357  and  336809 ). D. P. acknowledges the  ERC  under the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 724391 , SolMAG).
Furthermore, since a decade, we assist to the emergence of Deep Learning approaches, powered by the increase of computational power and by the amount of available data  [10]. Amongst those different methodologies, Virtual Screening (VS) is one of the most used in CADD to explore molecular databases and find interesting compounds for a considered target. It is the computational version of the experimental screening, which is by essence very expensive and time-consuming, but remains the experimental method of choice for hit identification and optimization. Virtual screening may be divided in two main approaches: the structure-based and ligand-based approaches. This last, requiring only the knowledge of molecules that bind the biological target of interest, was often used for molecular similarity, pharmacophore query or QSAR/QSPR modeling.
I) Generation of docking data: basis and reflexes Generating docking results is not as simple as it seems. Firstly, the choice of a correct protein structure and a set of ligands is not trivial. Moreover, those elements need to be carefully and meticulously prepared. a) Preparation of the protein for docking Protein structures can be solved by experimental methods or predicted by means of computational approaches. The Protein Data Bank (PDB) is an on-line database ( www. pdb. org) composed by almost two hundred thousand of protein structures, solved by experimental means such as X-Ray crystallography, NMR or cryo-EM techniques. Therefore, mining this database is the first thing to do when one is searching for a structure for docking experiment. But those structure needs to be prepared before the docking.
Indeed, charges must be put on every atom of the protein for a good conduction of the docking process. In this matter, some tools are very useful such as the H++  [19]  or ProPKA  [20, 21]  servers or standalone programs like MCCE. Especially, the buried residues must be treated with care, because the local pH can dramatically vary from the physiological ones, induced by the fact that water molecules cannot reach those chemical functions, hidden by the protein fold. Consequently, the pKa of those amino acids will vary and with sometimes important consequences. Finally, the structure of the protein deposited in the PDB database is only one rigid conformation, while proteins are dynamic macromolecules. Alternate stable conformations may exist for a binding site and an even limited change of the conformation of the binding cavity can lead to the recognition of new interacting molecules.
b) Selection and preparation of the ligand dataset The second point to consider before the docking calculation itself is the selection and the preparation of the ligand dataset. The number of chemical databases, meaning a library which repertory several compounds, is nowadays quite enormous. Since 2016, a list of 117 databases has been set in the article of Sabe et al. [4] , which have been consistently used during this 6year period. We retrieved amongst them the most known ones, such as ZINC  [23] , ChEMBL  [24] , DrugBank  [25] , PubChem  [27]  or ChemDiv. Those databases could be commercial or public and list lots of information in addition to the name and structure of the molecule of interest. We may add to this list all the private chemical libraries that may be used for a particular project.
Generally, those databases are provided at 2D SDF format (Structural Data File), SMILES format (Simplified Molecular Input Line Entry Specification, in 1D) or as CVS file. However, some discrepancies could exist in those databases, despite the care of their curator to update and maintain their viability. Those mistakes can be originated from errors during the recording of the compounds, or more complex cases difficult to understand. In addition, sometimes the entry encompasses also other impurities, which couldn't have been separated from the molecule of interest. Hence, some steps of preparation must be realized before the docking. The first step aims to filter the entries to remove the erroneous compounds and duplicates, and keep only one molecule by entry. Second, a conversion from 1D or 2D format to 3D coordinates must be carried on in order to generate compounds that can be used for docking calculations.
c) Docking process Once protein target and chemical libraries prepared, they may be submitted to the docking process. Numerous software could be used for the docking, such as Autodock Vina  [28] , Glide  [29] , Gold  [30] , DOCK  [31] , rDock  [32] , PLANTS  [33] , etc. Numerous papers and reviews have already precisely described and efficiently compared the several docking software available and the power of the different algorithm used  [11]. The last step consists in verification and selection of the most interesting ligands for the target. Lots of methods could be applied in order to sort the best molecules: ranking with scoring functions, RMSD comparison with already known ligands, ROC curve in order to see the efficiency of the calculation to separate active form inactive compounds, etc.
The most known one is GPCRmd  [42] , an online database concerning the MD trajectories of all class of GPCR. For each PDB structure associated to a GPCR, this website contains molecular dynamics simulations starting from those structures. All the files associated to the creation of the simulations are available on a special page of the trajectory, in addition to a visual point of view of the trajectory itself, and a list of simple analysis that can be done on the data. Of course, the several data file (input, output, topology, trajectory) are also available for download. One of the most important tools proposed nowadays is the publication of Simulation Foundry in 2020 by Gudrun Gygli and Juergen Pleiss  [43]. This automated workflow on MD trajectory is the first that highlights the implementation of the FAIR principles within the treatment of the data.
It allows the generation of the entire set of parameters for the calculation and making the calculation locally. In addition, the workflow comprises analyses of the trajectory generated and also a report in PDF format of the entire protocol and results. Regarding docking data, unfortunately, few progresses have been made about their sharing to the scientific community. To our knowledge, no solutions, such as the ones already existing for MD data, have been currently proposed for the sharing of docking ones with the community. However, it exists some resources online to help docking users in their processes to generate results. We can for example cite the PDBind database  [44] , which list several protein-ligand structures with known experimental affinities, but also LIT-PCBA, a curated dataset for virtual screening and machine learning (ref Rognan) and the DUD-E, a dataset comprising active compounds by targets and associated decoys  (ref).
With this dataset, one could test his own protocol in order to estimate his robustness. Furthermore, a new breakthrough on the sharing of data is the proposal of entire datasets in ready-to-dock format, following the most classical parameters of physiological environment. This is made for example by ZINC database. Obviously, initial dataset could be download by the users that need to prepare their dataset in another way. Following this lead, other datasets, comprising even billions of molecules are now emerging. The most notable one is VirtualFlow  [45]  which encompass a workflow for ligand preparation and a ligand dataset comprising 1. What can be done about the sharing of docking data? Of course, normalized protocols, such as the one proposed by VirtualFlow or the one described in the paper of Bender et al.
[34] , could be proposed any further, for example specialized on some target families of importance (GPCR, kinases protein, etc. ), as what was already made for MD data. In addition, the results of large-scale docking campaigns are for now cruelly missing for sharing and availability. One could imagine a specialized repository containing the results of large-scale docking, such as ZINC database or others, against one or several members of protein family. Indeed, we can assume that numerous docking processes against popular targets, with therapeutic potential, are made by researcher around the world, without knowing that others have already realized their virtual screening before. Such sharing using a dedicated infrastructure to list and register the several docking campaign could be of great interest for the entire community of drug design researchers.
The results could be download by the user, and ranked using several criteria, such as different scoring functions, or experimental data if they exist. However, the principal problems of this kind of architecture that can be easily predict is the issue of the storage and the size of data. Obviously, dataset of millions, even billions of compounds take large space for the storage. Indeed, infrastructure keeping results of docking campaign must take into account several different docking poses for each ligand conformations in a receptor, making the data significantly heavier when the dataset comprises million or billions of compounds. This problematic has been relevantly discussed in the paper of Hospital et al. [46]  This interesting review was focused on the MD data, which are obviously more affected by the size issue due to the inherent nature of the data.
III) Concluding remarks CADD is now an important field in life science research, thanks to the technological progress made during this last decade. In particular, virtual screening using docking protocols remains the main methodology for drug discovery processes. Our goal here was double: i) to sensibilize users of docking methodologies to the main points in order to create relevant docking data, from preparation of inputs to verification of the results and ii) what is done now to the sharing of data regarding docking calculations and what can be done in the future. As we have described, there is actually a lot of online resources for assistance and help to the users, starting from the large number of chemiotheques, to the several software and workflow that can be used for making docking campaign, even some respecting the principles of FAIR data for the sharing and a good understanding.
However, there are only scarce elements about the sharing itself of docking results. We believe that, even with the issues that can from this sharing and storage, the entire CADD scientific community could benefit from the creation of such online storage of docking campaign results and freely shared to everyone. However, questions of data size and sustainability in long-term are two major bottlenecks to creation of such infrastructure. Here are some good practices guidelines to follow for the sharing of such data: (i) precise and clear description of the structure used (with or without optimization, but clearly noted), with date and place. For example, structure of the PDB supplemented by Modeller (ii) If a molecular dynamics approach or other method are used to have conformers, the set of parameters used for the simulation and also for the structure clustering.
It is advisable to have at least the different configurations and if possible the trajectories (with the parameter files). (iii) a precise description of the origin of the libraries, the protocol and the software used with their versions for the 3D conversion, the refinements used to generate the entire dataset. (iv) The same for docking with the different scoring approaches used. The difficulty here is identical to that of MD simulations results, the place that raw data can take. Nevertheless, efforts must be done on the application of FAIR principals for docking data, in order to favorize a better connection between the molecular model of protein-ligand interactions and biological activities and by extension, the experimental methodologies. The availability of such data freely could also be used for the development of deep learning approaches applied to docking and analysis of protein-ligand interactions, such as recent papers just begin to realize  [49].
4 billion commercially available compounds in ready-to-dock format. The workflow is compatible with lots of docking software and scoring functions, and respects all along the process the FAIR principles. Both ligand dataset for docking calculations by the user, and entire workflow of the VirtualFlow program, are available freely. In addition, through an openaccess GNU GPL license, everyone can contribute to improve this workflow. The workflow is available on GitHub (http://www. github. com), a platform of program collaborative development and sharing, with a versioning process very powerful, that can allow following every modification bring on the program. Since the last decade, GitHub has become the standard platform to propose and share online all kind of program, scripts, with an idea of sharing and improvements for and by the concerned community.
Water-cooling channels flow through them, with 6 mm tube adapters at the ends. The heat cartridges are connected in parallel. The heater has been tested and calibrated using lattice parameters, determined using X-ray diffraction of Ag and Pt, as well as with the melting points of Pb, Ba(NO 3 ) 2 , NaCl and Ag up to 1200 C and 100 C min À1 [Fig. 3(a) ]. A thermocouple log is shown in Fig. 3(c ). These data were measured on the 'inline' furnace. Lattice parameter calibrations were performed using a cubic relation; details are given in Appendix A. The software Tcal was used to quickly calculate the calibrated temperatures from the diffraction data; details are given in Appendix B. The heater is useful for measuring phase and lattice parameter changes due to its wide temperature range [e. g. the melting of silver (and crystallisation of quartz) shown in Fig.
A1. Calculation of temperature from thermal expansion The thermal expansion is defined as " ¼ a À a 20 a 20 ; ð1Þ where a is a unit-cell dimension of the reference material measured as a function of temperature, and a 20 is the value taken at 20 C. Temperature dependence of the thermal expansion can be approximated as a polynomial function, " ¼ a 0 þ a 1 T þ a 2 T 2 þ a 3 T 3 : ð2Þ The temperature at the sample position is calculated from the thermal expansion of a reference material placed at the same position as the sample. For calibration purposes it is convenient to use an inverted function, T ¼ b 0 þ b 1 " þ b 2 " 2 þ b 3 " 3 ; ð3Þ where coefficients b i are refined from the tabulated data on thermal expansion of the reference materials (Ag, Pt) and given in Table  1.
A2. A note on the accuracy of temperature calibration from thermal expansion We first assume the same resolution R = ðaÞ=a ' 10 À4 for all datasets. Error propagation [equation (1)] gives " ð Þ 2 ¼ a ð Þ 2 a 2 20 þ a 20 ð Þ 2 a 2 a 4 20 ; ð4Þ with a linear coefficient of thermal expansion ¼ 1 a 20 da dT ; ð5Þ and, with the prior assumption, one obtains T ¼ R 1 þ a 2 a 2 20 1=2 ¼ R 1 þ " þ 1 ð Þ 2 Â Ã 1=2 : ð6Þ For silver at T = 1200 C, = 29. 6 Â 10 À4 K À1 and " = 0. 21180, this gives T = 0. 05 K. This estimate has to be taken as a lower limit for the calibration based on thermal expansion, as it neglects uncertainties for " and , as well as errors coming from the experimental instabilities.
In areas of activity where the notion of accountability is strong, the adoption of artificial intelligence (AI) is limited by the opacity and lack of understanding of its behavior, all the more so in the embedded domain where neural networks are compressed and executed on microcontrollers. While the NIST introduced in 2021 several principles allowing the AI explainability, this paper introduces a novel scheme, HistoTrust, combining secure hardware and blockchain technology to bring trust in the traceability of AI behavior and allow its explainability. HistoTrust attests in an Ethereum ledger all the relevant data produced by a physical device, especially the heuristics inferred by AI. Thus, the audition of the ledger allows security verifications and AI behavior analysis.
HistoTrust introduces a device-centric  [5]  solution based on Ethereum technology that conciliates the need for security and privacy with the trust required between stakeholders. HistoTrust provides an architecture that ensures end-to-end security and privacy by design while enabling the traceability of embedded NN inferences. The authenticity of the issuer device is attested through secure hardware components such as Trusted Platform Module (TPM) and ARM TrustZone technology as Trusted Execution Environment (TEE). Hardware component serves as root-of-trust for the digital data processed by the embedded NN. Thus, each of the smart robots operating on the production line sends to the ledger the attestations of the digital data it produces. An attestation includes the cryptographic fingerprint of a set of raw data, the authentication of the issuing embedded applications, and the timestamp of the record.
The ledger maintains the history of transactions received from the smart robots distributed around the production line. In a context where several stakeholders cooperate in the manufacture of a product, each protecting its own interests, business and personal data, sharing attestations through the ledger brings trust between them. While each one keeps and protects its raw data, and must be able to explain the behavior of its embedded AI if requested. HistoTrust has several objectives:  (1)  to protect the embedded NN from logical and physical attacks by ensuring the cyber robustness of the AI,  (2)  to protect the data produced by the embedded applications and processed by the NN in order to allow the explainability of the AI behavior,  (3)  to attest and trace the data produced in a blockchain in order to provide authentic non-repudiable attestations shared between the different stakeholders.
The following section positions the work done in HistoTrust in relation to existing solutions. The use case is described in Section 3. Section 4 presents the embedded NN used in HistoTrust. Section 5 outlines the attestation process of the data produced to the ledger. The integration with the embedded NN and the deployment are discussed in Section 6. A security analysis is led in Section 7 followed by the audition process in Section 8 before concluding this work.