dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors acknowledge the contribution of  Rémy Huneke  who helped with the experimental setups and with icing experiments conducted at  TU Braunschweig. We would also like to acknowledge technical input from  Stephan Sattler  ( TU Braunschweig ),  Thomas Bürger  ( ACRT ) and  Bodo Haffner  ( ACRT ) for the design and the manufacturing of the test articles and experimental setups. Louis Reitter  and  Kilian Köbschall  ( TU Darmstadt ) invaluably contributed with their skills during data processing and analysis. This research work received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No  767560.
Introduction: Given the extreme conditions in the lower atmosphere of Venus, various in-situ missions faced instrumental failures. As a result, the thermal structure of the deep atmosphere, particularly below 12 km is not well known. In Venus International Reference Atmosphere (VIRA), the thermal structure of the atmosphere below 12 km altitude was constructed by extrapolating the data recorded in the upper atmosphere. Only VeGa-2 lander provided the high-resolution temperature measurements below 12 km altitude. However, these measurements indicated a region of high instability below 7 km altitude. Due to a lack of physical explanation, these measurements were not included in VIRA.
Data Processing: The near-IR observations from the VIRTIS instrument onboard the Venus Express and the IR1 imager onboard the Akatsuki orbiter are used in our study. The VIRTIS dataset has been already processed by  [2]  and contains the observations of the southern hemisphere having an altitude range below 4 km. The equatorial and northern highlands on Venus were observed by the IR1 imager. However, the IR1 observations are heavily contaminated by the bright straylight coming from the dayside of Venus. Also, the calibration had an uncertainty of±67%. To make use of the IR1data, we develop a correction procedure that includes (1) starylight correction, (2) limb darkening correction, and (3) cross-calibration using the VIRTIS data. Radiative Transfer: To retrieve the surface temperatures from the near-IR observations, we develop an atmospheric radiative transfer model based on the radiative transfer code from  [3].
The atmosphere is constructed by using VIRA profiles. We use the cloud model from  [4]  and Mie scattering is treated by using the code from  [5]. We model the absorption using the line-by-line code from  [6]  and considering eight major absorbing species. Appropriate spectral line dataset and lineshapes are used. To simulate the effect of topography on Venus, we generate the results in the form of a look-up table in which we vary the starting altitude of the atmosphere from -3 to 13 km altitude with respect to a 6051 km planetary radius. We validate our model based on the results generated by the model described in  [7].
Results and Conclusion: The coverage of the VIRTIS and IR1 datasets can be observed from the maps of retrieved surface temperatures shown in Figure  1  and Figure  2. Figure  3  shows the trendlines of mean values of the deviation of surface temperature with respect to VIRA temperature profile against the altitude for both the dataset. The dotted line shows the deviation of the VeGa-2 profile. We find that the VIRTIS and IR1 temperature trendlines show a lapse rate lower than VIRA from 0 to 1. 5 km altitude, as previously indicated by  [8]. Above this altitude VIRTIS trendline follows the VIRA lapse rate, however, the observations are limited up to an altitude of 3. 5 km. Above 2 km altitude, the IR1 temperatures fall even faster than the VeGa-2 profile and achieve a maximum deviation of∼5 K from the VIRA profile between 4-5 km and 7-8 km altitude range.
This indicates that the situation could be even more complex than indicated by the VeGa-2 profile. Above 8 km altitude, the IR1 data is less reliable. The reasons behind the differences in the IR1, and VIRA profiles are not clear. Possible reasons could be surface emissivity variations, a near-surface layer of aerosols, or a composition gradient  [9]. Thus, we find that both the VIRTIS and IR1 profile do not completely agree with either VIRA or VeGa-2 profile. However, observations from both VIRTIS and IR1 instruments were not ideal for the surface-emission studies. An optimized instrument could provide better coverage and quality of the data which could significantly help near-surface studies. Based on this, we highlight the need for future near-IR observations with an instrument optimized for the surface observing atmospheric windows of Venus.
Icarus 2019). In both cases, the interpretation of the surface Bidirectional Reflectance Distribution Factor extracted from these observations, in terms of composition and microtexture, is based on the inversion of the Hapke model, a semi-empirical photometric model that relates physically meaningful parameters x to the reflectivity y of a granular material for a given geometry of illumination and viewing  (Schmidt and Fernando Icarus 2015). This work presents an efficient method, based on a learning approach in a Bayesian framework, able to invert the Hapke model on large experimental or remote sensing datasets, as illustrated on a challenging CRISM multi-angular sequence of images.
Inversion characteristics In the CRISM case, the dataset provides both spatial and spectral dimensions. More specifically, each of the N spatial spatial point of the scene is observed at N spectral wavelengths, yielding N spatial × N spectral vectors of reflectance values. This high number of observations to be inverted usually makes approaches like Markov Chain Monte-Carlo simulations unacceptably slow. The approach we propose starts with a computer intensive learning phase that builds a statistical model for the Hapke model; it is then applied for all observables y obs. Each observable is a vector of D reflectances, one for each geometry of measure. In the case of CRISM data, D = 11 is moderate, but in laboratory, D may be one order of magnitude higher. The statistical model we propose has the advantage to be able to handle high dimensional settings while remaining computationally tractable.
Massive inversion of spectro-images The dataset comes from a multi-angular observation of the South Pole of Mars by CRISM. The targeted scene presents spatially segregated C02 ice, H2O frost, and mineral dust (Douté and Pilorget EPSC 2017). After fusion and atmospheric correction of eleven hyperspectral images  (Ceamanos et al. JGR-Planets 2013) , the dataset provides both spatial and spectral dimensions, totaling N obs = 154650 = 3093 × 50 measurements vectors y obs, which makes MCMC approach unacceptably slow. Maps of Hapke's parameter values are generated from the results of our inversion and superposed with transparency onto the full resolution CRISM nadir image, which serves as a geological control background image. The results are satisfying from the application point of view. The colour composition of Figure  1  reflects the variation of ω at three wavelengths and corresponds well with the spatial distribution of the three previous materials and their known spectral optical properties.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  810207  to M. W. and C. A. M. ). L. C. was supported by the  European Union 's  Horizon 2020   Marie Sklodowka-Curie individual fellowship   program  ( H2020-MSCA-IF-101028239 -MolecularArp ). A. J. was supported by the  ERC  (grant  StG-679116 ). MW is supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( CC2096 ), the  UK Medical Research Council  ( CC2096 ), and the  Wellcome Trust  ( CC2096 ). Cryo-EM data collected at the  Institute of Structural and Molecular Biology (ISMB) ,  Birkbeck  was on equipment funded by the  Wellcome Trust , U. K. ( 202679/Z/16/Z  and  206166/Z/17/Z ).
Data availability The cryo-EM maps and the corresponding structural coordinates were deposited under the accession codes PDB ID: 8P94 and EMDB: 17553 (Daughter filament consensus reconstruction), 17554 (Arp2/3 complex local refined reconstruction), 17555 (daughter filament local refined reconstruction), 17556 (capping protein local refined reconstruction), 17557 (mother filament local refined map reconstruction), 17558 (mother filament consensus reconstruction).
Acknowledgements We gratefully acknowledge funding from the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) Project -ID 433682494-SFB 1459  Intelligent Matter. We also thank the  Institute of Laue Langevin (Grenoble, France)  for allocating neutron beam time on FIGARO (DOI:10. 5291/ ILL-DATA. 9-11-1984  and doi:10. 5291/ILL-DATA. 9-12-652 ). Notes and references ‡ Footnotes relating to the main text should appear here. These might include comments relevant not central to the matter under discussion, limited experimental and spectral data, and crystallographic data. §, § §, etc.
5 m respectively). As the last step, we should correct for the vertical shift observed on the profiles of the two lidars. This correction aims to remove the altitude difference between the two locations in the case of a sloping layer (more on the correction in Appendix B). We demonstrate the proposed depolarization characterization approach using profiles from two nights that follow the criteria described before. As the dataset was limited (due to the selection criteria i. e. dust layer above 3 km), the selected cases are taken from days with uniform dust layers over the island and for which the profiles of the two systems do not seem to be influenced by any local phenomena. The first timestamp 26 November 2021 22:00 is taken from a 5 day long dust event arriving from the Saharan desert (confirmed with HYSPLIT back trajectories), resulting in a daily average AOD 500nm value of 0.
In the above equation, ∆K * and ∆γ are the standard deviation values presented in Table  2. The measurement uncertainty ∆P ⊥ and ∆P ∥ for each polarization channel consists of the uncertainty of the raw counts signal (P 0 ) and the background correction (B). As the signal is received by a photon counting detector the distribution of the counts follows Poisson statistics and therefore the standard deviation is given by the square root of the number of counts in the measured interval (N). ∆P ⊥,∥ = ∆P 0 2 + ∆B 2 = N + σ(B) 2 n (C2) where σ(B) is the standard deviation of the background correction calculated over n = 67 ranges which corresponds to 1 km. These calculations are used for deriving the error bars presented in Figure  8. system. Financial support. This publication has been produced within the framework of the EMME-CARE project which received funding from the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement no.
The simulation tools for geometry description and particle transport FLUKA is a fully integrated Monte Carlo package for the simulation of transport and multiple physical processes of particles and nuclei interacting with matter  (Ferrari et al. , 2005; Böhlen et al. , 2014). In FLUKA, the amount of unstable residual nuclei produced by inelastic hadronic interactions in all the modeled materials, their time evolution with generation of daughter nuclei, as well as the tracking of the  emitted radiation, can be calculated according to a given irradiation history  (Ferrari et al. , 2005). FLUKA was already used to simulate the primary proton beam inside the PPB line, accounting for the proton interactions with collimators and beam line elements, as well as the isotope production in the SPES target and fission neutron fields  (Donzella et al.
The radioactive isotopes spread mostly on the Wien filter electrodes, however, relevant amounts are deposited also on the beam deflectors after the Wien filter and on the mass selection slits. The Wien filter contamination depends on the isotope beam required by the users and on the specific ion source employed to produce it. The continuous build-up of the radioactive isotopes sticking on the RIB line elements constitutes a radioactive source that becomes more and more critical with the accumulation of active periods of the SPES facility. This factor becomes especially significant when evaluating maintenance interventions close to the Wien filter. A previous study on the radiation protection constraints related to the transport of the radioactive ions along the SPES RIB line, and in particular on the Wien filter, was performed using the FLUKA code for Monte Carlo simulation  (Sarchiapone and Zafiropoulos, 2016).
In the present study, a more comprehensive and general computation approach is developed. The FLUKA Monte Carlo code is employed to simultaneously evaluate the build-up and decay of all radioisotopes deflected by the Wien filter, assuming that different mass values, chosen on the basis of the main user requests, are selected. Depending on the produced beam, the calculation of the different positions where the produced isotopes are deposited is performed, and the corresponding radioactive source sampled accordingly. The dose rate is then evaluated in the production bunker volume, especially in proximity of the devices located on the RIB line. Positions relevant for maintenance operations are reported in Fig. 4. The calculation procedure is achieved in the following steps: 1. simulation of the isotope production in the target due to 238 U fission; 2.
The deposition points of the different ions along the RIB line mechanical structures are obtained by analytical calculation of the ion trajectories inside the Wien filter electrodes, and by geometrical tracking using FLUKA in the elements downstream of the Wien filter  (Monetti, 2017; Donzella, 2021). The isobaric beams are supposed to have negligible size. The corresponding FLUKA source accounts for both the deposition points of the different isobars along the RIB line and the yields of the isotopes reported in Fig. 5. It is implemented in the FLUKA geometry model of the Front-End. The geometrical distribution of the deposited radioactive ions, with consequent build-up and decay times, depends on the mass value 𝑀 selected by the Wien filter. Based on the foreseen scientific program of the SPES facility (INFN Pisa, 2018; IUSS Istituto Universitario Studi Superiori, FERRARA, 2019), a realistic hypothesis for the isotope masses likely selected for the first period of the SPES activity with the 238 UC x target at full power is the following: • mass 𝑀 = 95 u, where 95 Rb and 95 Cs can be abundantly produced with the SPES Surface Ionization Source, and 95 Kr with the Plasma Ion Source; • mass 𝑀 = 132 u, where, in addition to the double magic 132 Sn, also 132 Te and 132 Sb can be produced with good rates.
Conclusions In the present study, a methodology is developed for the calculation of the radioactive contamination of the components of the RIB line of the SPES facility after one year of activity with the 238 UC x target at full power. The FLUKA Monte Carlo simulation code is used for the calculation of the isotope production in the target due to 238 U fission, the ion deposition downstream of the mass separator device, the Wien filter, and the prediction of gamma ambient dose equivalent rate around the Front-End and its time evolution. The radioactive isotopes forming the ion deposition are extracted from the many isotopes produced in the target, accounting for the relative decay chains, the efficiency of the ionization method, and following a set of appropriate selection criteria. The obtained results are analyzed together with the results of previous studies devoted to determine the residual activation of the SPES Front-End, due to the primary proton beam, and the radioactive contamination of the ion source complex, at different times during and after the target irradiation.
VCD maccess modes and availability At the time of writing this abstract, we are finalizing a prototype. We plan to release a first version of the VCD in September 2021. Based on our experience with the MCD, the VCD will be distributed as: A main Fortran subroutine that users can interface and directly call from their own software. Interfaces to call this gateway routine using other programming languages (e. g. C, Python, IDL, Matlab, …) will also be available. A web interface, based on the MCD one (at http://www-mars. lmd. jussieu. fr), for quick looks will be set up and maintained on http://www-venus. lmd. jussieu. fr.
The 3 datasets are only shied in energy to be superimposed. This leads to values of C 8 H + 6 AEs from Naph and Azu precursors, and IE of PA to coincide within 0. 3 eV. Although it is not a surprise that the reaction cross sections from Naph and Azu are similar, the reaction cross section from PA also overlays perfectly. This a decisive observable that indicates that PA + is the reactive species at all photon energies up to the start of region 4 upon direct ionization of PA or dissociative ionization of Naph and Azu near dissociation thresholds. This result is a strong support for our hypothesis that close to 100% of PA + is formed by dissociative ionization (for a timescale shorter than 40 ms), whatever the precursor but also informs that all internal energy is conserved by the ionic fragment when PA + is formed by C 2 H 2 elimination from Naph and Azu.
Acknowledgements We thank the referees for their critical and constructive comments, which enabled us to rectify part of the manuscript and thus strengthen it signicantly. U. J. is grateful to the  SOLEIL facility  for providing beamtime under project  20201123. C. A and C. R. acknowledge the synchrotron  SOLEIL  for the support to the associated CERISES setup of ICP since 2008 and subsistence expenses during beamtime periods. This research/ project was undertaken with the assistance of resources and services from the  National Computational Infrastructure (NCI) , which is supported by the  Australian Government. PMM thanks the  Natural Science and Engineering Research Council of Canada  for continuing nancial support and the  Digital Research Alliance of Canada  for computational resources.
A search for SVJ signatures in the 𝑠-channel production mode has been presented by the CMS Collaboration  [8] , and excludes mediator masses up to 5. 1 TeV, depending on other signal parameters. This Letter presents a first search for 𝑡-channel production of SVJs using the full LHC Run 2 dataset of 13 TeV proton-proton collisions with an integrated luminosity of 139 fb -1 recorded by the ATLAS experiment. Searches for 𝑡-channel production modes probe a broad class of non-resonant signals. In the 𝑡-channel production mode, a scalar mediator, Φ, acts as a portal between the SM and dark sectors. It couples to a SM quark and a dark quark, and mediates the production of dark quarks, which then hadronise to stable and unstable dark hadrons through the DS process described above and shown in Figure  1.
Signal and background event simulation Signal events were modelled at the leading order (LO) in QCD, using the MadGraph5_aMC@NLO  [11]  event generator to calculate matrix elements (ME) with up to two extra partons at the leading order. The NNPDF3. 0lo  [12]  parton distribution function (PDF) set was used. The mediator mass was varied within the range 1-5 TeV in 0. 5 TeV steps. The Hidden Valley (HV) module  [13]  of Pythia 8  [14]  was used to shower the ME-level event and produce dark hadrons, using the NNPDF2. 3lo  [12]  PDF set and the A14 set of tuned parameters  [15]. The MLM  [16]  jet matching scheme, with the matching parameter set to 100 GeV, was employed. A detailed description of the HV module is beyond the scope of this Letter, and the HV parameter settings adopted here provide a simple way to generate this mostly unexplored collider-event topology.
2 ). We processed images containing a "doublet" feature using a new feature extracting algorithm  (Saranathan and Parente, 2021)  based on Generative Adversarial Networks (GANs). GANs are a class of neural network models, called generative models designed to learn and mimic the patterns/modalities of a specific dataset. Given their ability to learn the various intricacies of a specific dataset from unlabeled samples, they have been successfully used as feature extractors to improve the performance of classification  (Zhang et al. , 2018)  and mapping algorithms  (Saranathan and Parente, 2021)  for hyperspectral datasets. The GAN-based mapping has proven to be effective in identifying and mapping the boundaries of mineral deposits in CRISM images. In this study, the various images were mapped using a base set (of well-known mineral shapes known to be present in the CRISM image database) and manually selected CRISM spectra representing the most distinct "doublet" type units.
We used these as a guide to check the spectra of individual locations, typically 5 × 5 or 10 × 10 pixels, but sometimes 3 × 3 pixel regions for smaller outcrops. For these analyses we used ENVI software with the CRISM Analysis Tool (CAT) plugin version 7. 4 available at the NASA's Planetary Data System (PDS) Geosciences Node: https://pds-geosciences. wustl. edu/missions/mro/crism. htm#Tools. Once regions were identified in the image containing a spectral doublet between 2. 2 and 2. 3 μm, we classified these into three groups manually: those having a deeper band closer to 2. 2 μm, those having roughly equal band depths, and those having a deeper band closer to 2. 3 μm (Fig. 3 ). CRISM spectra (averaged from several locations to minimize noise) of these three new spectral classes were added to the general set of classes identified with the algorithm for all CRISM images  (Saranathan and Parente, 2021).
Fig. 4  displays an example of this output, where the BestGuess images include the spectral features of the three newly classified doublet types, as well as other components. The doublet type spectra are the most common components in the two example images. We initially analyzed both the MTRDR and SUBCONV images for CRISM images FRT0000A202, FRT00009B27, and FRT0000823A (Table  2 ), where the doublet signatures were strongest to compare the results observed from both techniques. Following the initial confirmation, we expanded the mapping of the doublet materials and associated minerals along the southern wall of the northern canyon using SUB-CONV and GAN techniques. The GAN method is optimized for SUB-CONV images and cannot be reliably performed for MTRDR datasets. Additionally, there are no established method for mapping such small variations in the doublet absorption features using the MTRDR dataset nor other spectral parameters.
The stratigraphy for the different colors does not correlate to elevation as shown by the DTM perspective view and topographic profile shown in Fig. 11. All the units are draped along the lower wall rock slopes of northern Geryon Montes between elevations ~ -3600 m to -4100 m. Both the green and purple colored regions correspond to shallower slopes relative to the pink and gold colored regions. Because loose debris is more favorable to collect on shallower surfaces, the purple and green areas are more likely to have more dark debris on their surfaces that in turn would affect the CRISM spectra relative to the pink and gold regions. Topography derived from MOLA data has also been useful for understanding why the doublet materials could have formed here. The section of Geryon Montes mountains that collocates with the doublet material in Ius Chasma has a significantly lower ridge height than ridge localities to both the east and west of the observed doublet unit (Fig.