dataesr/scientific-paragraphs-categorization huggingface dataset:
Verification Results Table  4  shows for each precipitation type the value of POD, FAR and ORSS plus the total number of minutes of each type in the Method3 and disdrometer data sets, which share generally a similar proportion. Note that the number of hail minutes is rather limited; however, it is included to illustrate the relatively good results achieved. During the verification of the lowest bin, no cases of Method3 unclassified precipitation type arose, but they are marginally present in some bins aloft. POD values indicate that both rain and snow, and also no precipitation, are the classes best detected (above 0. 93), rain being the highest (0. 99) and hail the lowest (0. 55). F ranges from 0. 29 (rain) to 0. 01 (hail) and 0. 05 (no precipitation). ORSS values indicate the substantial skill of Method3 hydrometeor classification, starting drizzle from 0.
Discussion and Conclusions A new methodology has been presented for processing K-band vertically pointing Doppler radar data recorded with Micro Rain Radar (MRR) systems. The methodology, referred to here as Verification scores based on a contingency table were calculated for each individual hydrometeor type, in particular the Probability of Detection (POD), the False Alarm Rate (FAR) and the Odds Ratio Skill Score (ORSS), which assess how good a forecast is compared to random chance (see Appendix A).
Additionally, the Method3 precipitation type classification is compared with Parsivel present weather observations using contingency table scores. Results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0. 97), satisfactory results for mixed and drizzle (PODs of 0. 79 and 0. 69) and acceptable for a reduced number of hail cases (0. 55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR < 0. 30, ORSS > 0. 70). The methodology presented in this article has been implemented in Python and is freely available in the repository github as RaProM (https://github. com/AlbertGBena/RaProM). The parameters calculated in Method3 and the new hydrometeor classification proposed will facilitate the analysis of precipitation profiles for the MRR data-user community.
Acknowledgments: The Cerdanya-2017 field campaign was a research effort organized by the  University of the Balearic Islands , the  University of Barcelona, METEO-FRANCE  and the  Meteorological Service of Catalonia. METEO-FRANCE/CNRM/GMEI/LISA , 4 M and TRAMM teams for the data acquisition during the campaign, especially  Jean-Marie Donier  and  Alexandre Paci  from  CNRM.
e. the soil water retention curve, and between the soil hydraulic conductivity and pressure head. These two relationships are generally described with parametric models involving empirical and physicals parameters. The water retention function and hydraulic conductivity can be determined by laboratory or field methods. These methods are either expensive or difficult to carry out or not reliable. Indeed the values of theses coefficients, when determined in laboratory  7  , are not necessarily representative of the field conditions  8. Actually, methods such as infiltration method  9  , internal drainage method  10  , and evaporation method  [11] [12] [13]  can be used in the field to determine the soil hydraulic properties from easily measurable data of infiltration rate, soil moisture content and pressure head.
Water flow model. A cell-centred finite difference based model was developed using Matlab tool to solve the Eq. (  3 ) in a vertical soil profile, with the pre-cited initial conditions (4), the boundary conditions (  5 ), (  6 ) and (7), and the functional relationships (8) and (9). The Eq. (  3 ) was discretised with cells centred at the equidistant measurement locations. However, the numerical artefacts created by the use of a non-transparent numerical boundary condition for a free drainage do not allow a proper representation of the saturation front passage through the bottom boundary. Therefore, the simulated soil profile thickness was extended to 1. 5 m. inverse method formulation. The soil hydraulic properties (θ r , θ s , K s , α and n) for the studied bare soil profile are unknown.
The optimisation problem consists in finding the vector X(θ s ,K s ,n,α) that minimises the objective function J(A,X). optimisation algorithm. Since the inverse problem for unsaturated water flow is an ill posed problem 8 , the values of the soil hydraulic properties are non-unique. Three optimisation methods were tested in order to resolve this problem. The first method is the Matlab gradient-based optimising function, in which both the objective and the constraint functions should be continuous and yields local solution. As J ψ of this problem is a non-monotonic function of n and α (it is actually very unstable and has several local minima as shown in Fig. 8 ), the optimum set of unknown properties could not be found using this method. The second one is the Particle Swarm Optimization algorithm, which is based on Swarm Intelligence techniques.
Model validation. The direct problem model was tested using the Philip solution  46. It gives suitable results. The inverse procedure was also validated using data of simulated (synthetic) infiltration experiment. The identified parameters are close to the input data (Table  6 ). Figure  9  shows that the computed pressure heads values match the given pressure heads profiles, at different times from the beginning of the infiltration test. This demonstrates that our model is able to give good results. conclusion The inverse problem method developed in this study has allowed the determination of the soil hydraulic properties in a bare soil located in the Segdoud oasis. The data used for this work were collected in situ and they reflect the real condition of flow processes occurring in this ecosystem.
The homeostatic energy W 0 = 1 × 10 -5 mJ was determined based on an average femur cross-sectional area and its corresponding bone density and body weight load conditions. The other two energies W 1 = 1. 456 × 10 -5 mJ and W 3 = 3 × 10 -5 mJ were defined based on identification of the four parameters of the model by k 1 = 1×10 5 kg•mJ -1 •m -3 •unit time -1 , k 2 = 0. 7×10 5 kg•mJ -1 •m -3 • unit time -1 , A 1 = 1. 456 kg•m -3 •unit time -1 , and A 2 = 2 kg•mJ -1 •m -3 •unit time -1 , where k 1 > k 2 and A 2 > A 1. The intermediate energy W 2 is the linear interpolation between W 1 and W 3. The model was implemented within the software COMSOL Multiphysics, and the results are presented in the following section.
Type II TA Systems in Pathogenic Bacteria Since 2011, the studies of TA systems have been assisted by the Toxin-Antitoxin Database (TADB)  [23] , updated to version 2. 0 in 2017  [24]. Currently, TADB 2. 0 lists 6194 type II TA loci  [24]  categorized, where possible, in two partially interdependent ways. The first classification scheme uses a system based on the structural and functional characteristics of the toxin proteins  [25, 26]. There are 11 families of two-component type II TA systems: ccd, hicBA, hipBA, mazEF, parD(PemKI), parDE, phd-doc, relBE (with 5 subfamilies-relBE, higBA, yfeM-yoeB, ygiTU(mqsAR) and prlF-yhaV), vapBC, mosAT and yeeU. The less numerous three-component type II TA systems are ω-ε-ζ, pasABC and paaR-paaA-parE. The second classification scheme is based on a set of 44 conserved toxin-antitoxin protein domain pairings and better reflects the versatility and modularity of TA systems  [27].
, Burkholderia spp. and species of the ESKAPE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. )  [19, [28] [29] [30] [31] [32] ]. An increasing number of studies are being undertaken to describe the repertoire of TA systems in pathogenic bacteria at the level of a given strain or of a whole taxon. Not only do such approaches shed light on the importance of overall TA system networks with respect to the physiology, virulence and evolution of pathogens, but thanks to the large-scale genome comparisons involved, they also lead to the discovery of novel TA loci. M. tuberculosis is notable for its abundance of TA systems, especially of the vapBC, mazEF and relBE families (Figure  1 ), and their activity has been linked to the regulation of adaptive responses to Type II toxin-antitoxin (TA) systems identified in human pathogenic bacteria according to the toxin family classification system  [25, 26]  as collected in the Toxin-Antitoxin Database (TADB) 2.
In response to this need, Akarsu and colleagues created the "discovery-oriented" database TASmania in 2019  [50]. Using this newly developed pipeline, they annotated a set of over 41,000 assemblies from the EnsemblBacteria database, resulting in the identification of >2 × 10 6 candidate TA loci  [50]. The greater flexibility of TASmania compared to the TAFinder search tool in TADB 2. 0 allowed for the identification of a higher number of putative TA loci, thus providing a starting point for experimental analyses  [50]. Moreover, the "guilt-by-association" strategy used throughout the annotation process, i. e. , targeting loci directly neighboring orphan toxin or antitoxin genes, facilitated the discovery of new TA protein families. In the case of Listeria monocytogenes, TASmania-assisted large-scale genomic comparisons led to the identification of 14 putative TA genes  [51].
Chemistry Prompted by our experience in the area, we submitted this project to the ASL (Automated Synthesis Lab) as part of the Eli Lilly's Open Innovation Drug Discovery program  [22] , as this was an opportunity to synthesize compounds remotely, explore novel synthetic approaches, improve reaction efficiency, and test the feasibility of automatic synthetic processes to maximize the yield of targeted compounds. The proposal was accepted, and we started the remote synthesis of the designed ligands (Figures  2  and 3 ). N H O N ( ) n QN1 (n = 2) QN2 (n = 3) QN3 (n = 4) O N H O N ( ) n QN4 (n = 3) QN5 (n = 4) N N H O N ( ) n QN10 (n = 1) QN11 (n = 2) QN12 (n = 3) QN13 (n = 4) N H O N QN14 N H O N ( ) n QN15 (n = 2) QN16 (n = 4) N H O N QN19 O O N H O N ( ) n QN6 (n = 1) QN7 (n = 2) QN8 (n = 3) QN9 (n = 4) N O Me O O O N H O N ( ) n QN17 (n = 3) QN18 (n = 4) O O Me Figure 2.
Chemistry Prompted by our experience in the area, we submitted this project to the ASL (Automated Synthesis Lab) as part of the Eli Lilly's Open Innovation Drug Discovery program  [22] , as this was an opportunity to synthesize compounds remotely, explore novel synthetic approaches, improve reaction efficiency, and test the feasibility of automatic synthetic processes to maximize the yield of targeted compounds. The proposal was accepted, and we started the remote synthesis of the designed ligands (Figures  2  and 3 ). In this structure-activity relationship (SAR) analysis we had the opportunity to evaluate the biological activity of different amines at variable distances from the quinolinone and dihydroquinolinone cores. In Figure  2 , we show the structures of the "quinolinones" QN1-19 that we prepared under Lilly's ASL program.
Computational Chemistry: Docking of Compound QN8 To justify the observed in vitro enzymatic activity and shed light into the hrAChE active site and binding mode of compound QN8, we performed molecular docking studies using software AutoDock Vina 1. 1. 2 [29]. Compound QN8 showed quite high binding affinity (-10. 3 kcal/mol), indicating a tight binding to the enzyme. The docking analysis revealed that compound QN8 spanned the narrow AChE active site interacting with the anionic subsite (AS) halfway down the gorge, and in the mouth of the gorge at the peripheral anionic site (PAS), but not with the catalytic triad (CT: residues His447, Glu334, and Ser203) of hAChE (Figure  7 ). In this binding mode, the quinolinone ring interacted with Trp86 (at the AS) and with Tyr337 via π-π stacking interactions.
Predicted Physico-Chemical Properties Analysis for Ligand QN8 The physico-chemical properties of ligand QN8 were evaluated with QikProp module of Schrodinger (QikProp, version 5. 1, Schrodinger, LLC, New York, NY, 2017-1). The selected properties are known to influence metabolism, cell permeation, and bioavailability (Table  2 ). Central Nervous System (CNS) drugs tend to be more lipophilic, less polar, less flexible, and have lower molecular weight and molecular volume than drugs used for other therapeutics. Usually, CNS drugs show values of molecular weight (MW) < 450, number of hydrogen bond In conclusion, docking results based on the interactions of compound QN8 at the AS, PAS and OH are in good agreement with the experimental kinetic data, indicating and confirming a non-competitive type of inhibition.
Predicted Physico-Chemical Properties Analysis for Ligand QN8 The physico-chemical properties of ligand QN8 were evaluated with QikProp module of Schrodinger (QikProp, version 5. 1, Schrodinger, LLC, New York, NY, 2017-1). The selected properties are known to influence metabolism, cell permeation, and bioavailability (Table  2 ). a MW: molecular weight of the molecule (130-725). SASA: total solvent accessible surface area, in square angstroms, using a probe with a 1. 4 Å radius (limits 300-1000). Volume: total solvent-accessible volume, in cubic angstroms, using a probe with a 1. 4 Å radius (limits 500-2000). donorHB: estimated number of hydrogen bonds that would be accepted by the solute (limits: 0-6). accptHB: estimated number of hydrogen bonds that would be donated by the solute (limits: 2-20).
The sample was then centrifuged (16,500× g) for 10 min. The deamination product of kynuramine formed during the enzymatic reaction 4-hydroxyquinoline (4-HQ) was determined by HPLC-MS on a 2. 1 mm × 50 mm, 1. 8 µm Zorbax RRHD Eclipse plus C18 column (Agilent) by using a 6470 Series Triple Quadrupole mass spectrometer (Agilent) (electrospray ionisation-positive ion mode). Three MRM transitions were followed for kynuramine (165. The hrAChE and hrBuChE were prepared as recombinant proteins at the University of Hradec Kralove. For their production, the mammalian expression system was used. Briefly, the DNA sequence encoding human AChE and BuChE was obtained from UniProtKB Server (www. uniprot. org, accession numbers: P22303 and P06276) and de novo synthesized as GeneArt Strings DNA fragments by GeneArt Gene Synthesis Service (Thermo Fisher Scientific, Pardubice, Czech Republic).
Docking Analysis Compound QN8 was prepared using "build" option within Discovery Studio 2. 1 to create three-dimensional geometry, and assign proper bond orders and ionization states prior to virtual screening. Protein crystal structure of hAChE (PDB ID: 1B41) was prepared prior to docking, using protein model tool in Discovery Studio in order to add hydrogen atoms and to assign proper bonds, bond orders, hybridization, and charges. In addition, cocrystal ligands and water molecules were removed. AutoDockTools (ADT; version 1. 5. 4) was used to add hydrogens and partial charges for proteins and ligands using Gasteiger charges. To give flexibility to the binding site, residues lining the AChE (Trp286, Tyr124, Tyr337, Tyr341, Tyr72, Asp74, Thr75, Trp86) were allowed to move during the docking search as performed by software AutoDock Vina 1.
1. 2 [29]. The ADT program was used to generate the docking input files. Docking calculations were performed with the program AutoDock Vina 1. 1. 2 and default parameters were used except num modes, which was set to 40. The grid box was built with a resolution of 1 Å and 60 × 60 × 72 points, and it was positioned at the middle of the protein (x = 116. 546; y = 110. 33; z = -134. 181). The best Vina scored poses were considered and the docked ligand output files were viewed and analysed using Discovery Studio.
Focus of the camera was fixed and had 9. 5 mm depth of field and 56 mm × 77 mm field of view. The synchronization at 60 frames per second (fps) was used in order to record the whole welding operation. The limitation of the frame rate was due to the lighting Cavitar system, whose laser is not cooled, so that it can work with a duty cycle of 0. 03 % only to record a welding scene of duration more than 10 s. According to the chosen pulse length (5 μs), the 60 fps is the maximum rate reachable with the present system. For the metal transfer viewing, a rate >300 fps must be achieved to control GMAW due to droplet frequency detachment. Therefore, it is necessary to wait that Laser diode cool and, thus, total duration of high speed film cannot exceed 10 s. An image processing algorithm was developed in MATLAB programming language.
Voltage and current acquisition For the measurement of voltage, a differential probe has been used. The probe has been connected to the welding circuit. The positioning of the measurement was made to be as closest as possible to the arc. A connector was attached to the drive roller of the wire electrode, the other one was attached to the mass of the welding source. In order to measure the welding current, a Hall Effect amperometric clamp was used. The clamp was fixed to the mass cable. Acquisition of the voltage and current signals has been programmed on a FPGA Compaq RIO controller with the LabView software. The sampling frequency was set to 40k samples per second and per channel. In addition to voltage and current signals, the control signal of the illumination system corresponding to exposure camera opening was acquired with the same controller in order to get a perfect synchronisation between signals and image acquisition.
They also described the potential ways in which a hotel can engage in CSR and two alternative methods to provide evidence of the results. The descriptors used for the attributes and attribute levels were refined through a multi-step process that included initial development, focus groups, and two phases of pilot tests, with feedback from the respondents at each stage regarding the clarity of the descriptors. The survey comprised 24 choice scenarios divided into three blocks. The attribute levels (see Table  1 ) differed between the scenarios based on a D-efficient design specific to choice modeling  (Hensher et al. , 2015) , and results from a pilot study provided the estimator priors required to develop it. To create the design, we utilized Ngene (Choicemetrics, 2014), a specialist software for choice model experimental design.
Data collection and analysis We obtained a panel of 308 UK respondents (2464 choices) from Qualtrics, a market research provider. This exceeded the minimum sample size of 227 required for estimator significance, as calculated using the Ngene software for experimental design. The respondents were a minimum of 18 years old and had at least considered a trip to a destination similar to the scenario. Thus, they were responsible for their own choices and familiar with the scenario they faced, supporting survey validity  (Hensher et al. , 2015). We did not use survey-instrument-or situation-design methods to reduce SD bias since the analysis incorporates it in the results. Before estimating the HCM, we verified the reliability and validity of the latent variables, SD bias (SDB) and cynicism bias (CB).
This supports the existence of systematic biases over the arbitrary variation that random variables would suggest and reinforces the validity of our approach. We used Biogeme 2. 5  (Bierlaire, 2016b) , an open-source software developed specifically for choice models, to estimate the HCM and MMNL models. As the integrals in these two model specifications do not have closed-form solutions, we used maximum simulated likelihood estimation and employed modified Latin hypercube sampling to generate the random draws required for the simulation (Abou-Zeid and Ben-Akiva, 2014). The results were estimated using 1000 draws and further verified by 1500 draws without significant changes; this suggests the results are consistent.
Improvements on modelling and simulation have been proposed and tested on different simple test cases  [19]. An update of the ongoing model evaluation and development is reported in  [20]. The extension of the validation base for flow separation, jets, mixed convection and a rod bundle represent one of the main topics of the SESAME project. An overview of experimental and numerical activities performed, is presented in Figure  3. In  [21] , new reference data from open literature on a backward facing step was used. It shows encouraging results for the AHFM-NRG model for turbulent heat transport coupled to an isotropic linear model for momentum. The same authors explain in  [22]  that they have extended their turbulent heat flux model to the use of an anisotropic non-linear model for momentum.
Finally,  [20]  summarizes the latest developments with respect to advanced turbulent heat flux model developments. In the frame of the SESAME project, new reference data are assessed for a variety of advanced turbulent heat flux models, i. e. the second order TMBFeq-ATHFM model, an implicit AHFM model and the AHFM-NRG. Three different sets of reference data are assessed covering the various flow regimes. For the natural convection flow regime a Rayleigh-Bernard Convection case has been considered from literature, for the mixed convection flow regime, new data from the SESAME project has been considered and for the forced convection flow regime, again the impinging jet case has been considered. Once again, the AHFM-NRG showed good results in all flow regimes. The implicit AHFM model showed good results in the forced convection  regime, while it became clear that the promising second order TMBF-eq-ATHFM will need further calibration especially for applications involving non-negligible buoyancy effects, before definite conclusions on the performance of this model can be drawn.
Core thermal hydraulics The core thermal hydraulics work package, within the SESAME project was focused on the development and validation of numerical models for the thermal hydraulic simulation of liquid metals fast reactor cores. The developed models include sub-channel codes, reduced resolution CFD, coarse-grid-CFD and CFD models. New reference data were generated from the considered experiments, high fidelity numerical models and DNS. Experimental data is generated for wire-wrapped bundles, a bundle with spacers, the effect of blockage, and inter wrapper flow. All intended data was prepared and applied in the model development or in the validation of the used model. In the SESAME project, a 7-pin rod bundle experiment was performed adopting water as coolant, allowing to implement a validation database for the flow field.
The interaction of turbulent flow with the fuel pins (flow induced vibrations in a fuel assembly) was experimentally investigated in a seven pin bare rod bundle using water as coolant (SEEDS-1 experimental facility). Obtained data were used to support the development and validation of numerical approaches. Simulations were based on a URANS approach with an SST k-v turbulence model and strongly coupled algorithms to account for the fluid-structure interaction. The frequency of the flow pulsations was reasonably well predicted. However, the results of the Fluid Structure Interaction (FSI) calculations deviated from the experiments in that they underpredicted the amplitude of the flow-induced vibrations and in that they over-predicted the respective frequency. Several possible reasons for the mismatch were identified, but will need future investigations to draw conclusion.