dataesr/scientific-paragraphs-categorization huggingface dataset:
Events in the XDF category must have 10 < m X < 70 GeV and p miss T > 20 GeV. Production of ZZ pairs is the main background in this category. Additional contributions arise from t tZ, VVV, and Vγ processes. These backgrounds are all modeled with MC simulation. The ZZ normalization is extracted from data in a dedicated CR defined by the requirements 75 < m X < 105 GeV and p miss T < 35 GeV. The event selection and categorization in the ZH4 category is summarized in Table  10. A BDT approach is used to discriminate between signal and background. The BDT is trained on events passing the global selection, with p miss T > 20 GeV and 10 < m X < 70 GeV. The number of inputs used in the BDT is eight, and these include separation in the η-φ plane between the leptons in each dilepton pair, transverse masses of combinations of leptons and p miss T , as well as p miss T itself.
Nonprompt lepton background The nonprompt lepton backgrounds originating from leptonic decays of heavy quarks, hadrons misidentified as leptons, and electrons from photon conversions are suppressed by the identification and isolation requirements imposed on electrons and muons. The nonprompt lepton background in the two-lepton final state primarily originates from W+jets events, while the nonprompt lepton background in the threelepton final state primarily comes from Z+jets events. Top quark production with a jet misidentified as a lepton also contributes to the three-lepton final state. The nonprompt lepton background gives a negligible contribution in the fourlepton final state. This background is estimated from data, as described in detail in Ref. [7]. The rate at which a nonprompt lepton passing a loose selection further passes a tight selection (misidentification rate) is measured in a data sample enriched in events composed uniquely of jets produced through the strong interaction, referred to as QCD multijet events.
In order to assess the uncertainty in the pileup modeling, the total inelastic pp cross section of 69. 2 mb  [78, 79]  is varied within a 5% uncertainty, which includes the uncertainty in the inelastic cross section measurement, as well as the difference in the primary vertex reconstruction efficiency between data and simulation. A theoretical uncertainty due to the modeling of the PS and UE is taken into account for all the simulated samples. The uncertainty in the PS modeling is evaluated by varying the PS weights computed by pythia 8. 212 on an event-byevent basis, keeping the variations of the weights related to initial-and final-state radiation contributions uncorrelated. The uncertainty in the UE modeling is evaluated by shifting the nominal templates according to alternative MC simulations generated with a variation of the UE tune within its uncertainty.
The corresponding nuisance parameter is correlated among all samples and between 2017 and 2018 data sets. An uncorrelated nuisance parameter is used for the 2016 data set, as the corresponding simulations are based on a different UE tune. The PS uncertainty affects the shape of the templates mainly through the migration of events across jet multiplicity bins, while the UE uncertainty is found to have a negligible impact on the shape of the templates and a normalization effect of ≈ 1. 5%. Additional theoretical uncertainties in specific background processes are also taken into account. A 15% uncertainty is assigned to the relative fraction of the gluon-induced component in the WW background process  [62]. An uncertainty of 8% is assigned to the relative fraction of single top quark and t t processes.
The H → WW selection is subject to some degree of contamination from events in which the Higgs boson decays to a pair of τ leptons that themselves decay leptonically. These events are included in the signal definition, and their contribution ranges from below 1% in the ggH and VBF categories up to ≈ 10% in some of the WH categories. As described in previous sections, CRs are used to fix the normalization of dominant backgrounds from data. This is achieved by scaling the corresponding background contributions jointly in the CR and SR. Given that the procedure effectively amounts to a measurement of the cross section of the background in question, the contributions from the 2017 and 2018 data sets are scaled together. The 2016 data set is kept separate in this regard because a different pythia tune was used.
Summary A measurement of production cross sections for the Higgs boson has been performed targeting the gluon fusion, vector boson fusion, and Z or W associated production processes in the H → WW decay channel. Results are presented as signal strength modifiers, coupling modifiers, and differential cross sections in the simplified template cross section Stage 1. 2 framework. The measurement has been performed on data from proton-proton collisions recorded by the CMS detector at a center-of-mass energy of 13 TeV in 2016-2018, corresponding to an integrated luminosity of 138 fb -1. Specific event selections targeting different final states have been employed, and results have been extracted via a simultaneous maximum likelihood fit to all analysis categories. The overall signal strength for production of a Higgs boson is found to be μ = 0.
Acknowledgements We thank  Jan Carl Budich ,  Sergio Ciuchi ,  Michele Fabrizio ,  Alessandro Toschi  and  Björn Trauzettel  for useful comments and  Lukas Müchler  for discussions in the early stage of this project. N. W. is supported by the  SFB 1170 Tocotronics , funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) -Project -ID 258499086. L. C. acknowledges financial support from the  DFG  through the  Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter-ct. qmat ( EXC 2147 , project-id  390858490 ). G. S. acknowledges support from the  DFG  through  FOR 5249-449872909  (Project  P05 ). We gratefully acknowledge the  Gauss Center for Supercomputing  e. V. (www. gauss-center. eu) for funding this project by providing computing time on the  GCS Supercomputer SuperMUC at Leibniz Supercomputing Center  (www.
lrz. de). Part of the numerical calculations were carried out using the Julia programming language 74. We thank the computing service facility of the  MPI-FKF  for their support. We gratefully acknowledge use of the computational resources provided by the  Max Planck Computing and Data Facility. We thank  José M. Pizarro  and the  FAIRmat consortium  for the support given on the management of our data. A. A. acknowledges funding from the  National Recovery and Resilience Plan (NRRP)   MUR  Project  PE 0023 ,  CUP B53C22004180005-NQSTI. J. C. acknowledges support from the  Air Force Office of Scientific Research  under Grant No. FA9550-20-1-0260. A. J. M. was supported in part by  Programmable Quantum Materials , an  Energy Frontier Research Center  funded by the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) , under Award No.
The heliospheric community needs an interstellar probe with a magnetometer onboard to understand the solar magnetic field in the heliosphere and beyond. To date, only Voyager 1 and Voyager 2 have sampled the magnetic field in the heliosheath and interstellar medium, with New Horizons being the only other satellite destined to enter these regions but without a magnetometer. The solar magnetic field plays a critical role both in the region of supersonic solar wind and in the heliosheath, however many questions remain open regarding the interplay of the solar and interstellar magnetic field at the heliopause and beyond. An interstellar probe with a proper onboard magnetometer would provide observational data to answer many questions about the magnetic field in the heliosphere, including the effect of magnetic reconnection on ion acceleration in the heliosheath and the draping of the interstellar magnetic field around the heliopause.
CASMO5 CASMO5 is a Studsvik two-dimensional deterministic transport code which also contains a simplified module for the calculation of SNF characteristics (called SNF-lite, corresponding to an older version of SNF, prior to 1. 6)  (Ferrer and Rhodes, 2014). In order to obtain a number of SNF characteristics (such as decay heat values, but also nuclide concentrations), one needs to describe the assembly in terms of geometry, initial fuel content, mass, various materials (e. g. cladding, spacers, impurities), followed by the irradiation history: for each cycle (and eventually for a number of time or burnup steps within cycles), fuel and moderator temperatures, boron concentration, power density. Between each cycle, downtime or cooling time is also defined. With the additional information of the cooling time at the end of the last cycle, one can get the SNF decay heat and nuclide concentrations.
Different version of CASMO5 are compared in the following, as well as the use of different processed nuclear data libraries. A processed library will be identified by the library name (such as ENDF/B-VII. 1) and its processing number in brackets (such as 201). This allows to uniquely identify which library version is used: the ENDF/B-VII. 1 library, processed (  201 ) and (  202 ), ENDF/B-VIII. 0 (300), JEFF-3. 2 (202),  and JEF-2. 2 (300). In the case of the JEF-2. 2 library, as the original library is not as complete as the more modern ones, it is complemented with nuclides from ENDF/B-VIII. 0. Regarding the CASMO5 versions, different ones are used with the same library ENDF/B-VII. 1 (either (201) or (202)): 2. 03, 2. 13  and 3. 05.
SNF SNF is a also Studsvik code, designed to provide SNF characteristics, such as decay heat, nuclide inventory, and neutron and gamma emission  (Simeonov and Wemple, 2019, 2021). It combines calculated quantities from CASMO5 (isotopic concentrations, fluxes, and cross sections) with the irradiation history from a core simulator such as SIMULATE. In the absence of irradiation history from SIMULATE, one can simply tabulate the power history in the SNF input file. In the present work, data coming from CASMO5 were obtained as described in the previous section (based on processed data obtained with the code CMSLINK5). There is therefore a consistency of information between the input data used in CASMO5 and in SNF. Regarding the decay heat, SNF applies the summation method, based on the CASMO5 nuclide concentrations at the end of irradiation (with specific modifications from the irradiation history), then passed to a Bateman equation solver.
03, with the library ENDF/B-VII. 1 (  201 ) is good. Similar agreement is obtained with SNF version 1. 6, as it is based on CASMO5 files (using the CMSLINK5 code) using the same CASMO5 version. A number of observations can be made on the present results: • The global agreement is nevertheless less good for other CASMO5 versions and libraries. Again, we remind the reader that the same CASMO5 input files were used in all cases. A number of changes in CASMO5 were implemented after version 2. 03, such as the depletion method or the two-dimensional transport solution method. This can influence some nuclide concentrations by one or two percents, leading to different decay heat values. Similar variations are observed between SNF versions 1. 6 and 1. 07, as both versions are using data from CASMO5 version 2.
Finally, as mentioned, the modelization of the assemblies was performed with CASMO5 version 2. 03. This has led to average C/E values close to 1. 00, but the use of the same input files with different CASMO5 versions provided sensibly lower decay heat values. Posterior adjustments could have been done, by slightly changing the boron concentration, the coolant density, or the assembly burnup, to reach a better agreement with the measured data. Simply changing the coolant density by 1% or the assembly burnup can affect the decay heat by almost the same amount. This indicates that different parameters can allow a number of adjustments. In conclusion, it was observed that the effect of the nuclear data libraries are within 1 to 2% range, whereas the impact of modifications of the depletion or two-dimensional transport solution method can be D.
Conclusion Calculated decay heat values were compared to 256 measured values of the published Clab and GE-Morris spent nuclear fuel assemblies. The comparisons were performed for different CASMO5 and SNF versions, together with various nuclear data libraries. Four standard methods were also applied. Among the different code versions and methods, all calculations were based on the same description of the assembly geometries and irradiation histories, rendering them consistent from a computational aspect. For CASMO5 and SNF, a number of variations in terms of C/E were observed, due to nuclear data (1 to 2% range) and changes in depletion and transport solution methods (1% range). Such variations were nevertheless smaller than the spread of the 256 C/E values, being from 4 to 5% (1). In the case of the standard methods, larger calculated decay heat values were obtained, globally indicating some degrees of conservatism.
Acknowledgements One of the authors ( D. Rochman ) acknowledges that part of this work was partly supported by swissnuclear, the association of the Swiss nuclear power station operators, with the COLOSS-2 project. It was also partly funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No  847593  (project  EURAD ,  Work Package 8). All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Nitrogen (N) deposition is a significant nutrient input to cropland and consequently important for the evaluation of N budgets and N use efficiency (NUE) at different scales and over time. However, the spatiotemporal coverage of N deposition measurements is limited globally, whereas modeled N deposition values carry uncertainties. Here, we reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale. We utilized different data sources to estimate N deposition input to crop production at national scale and compared our estimates with 14 N budget datasets, as well as measured N deposition data from observation networks in 9 countries. We created four datasets of N deposition inputs on cropland during 1961-2020 for 236 countries.
Typically, in such N budgets, the N deposition inputs are calculated by overlaying the modelled N deposition maps with a cropland distribution map, as well as country boundaries (Table  1 ). However, the spatial and temporal coverages of these maps vary among studies, and the precision of the maps varies too. Based on a summary of the methods, data availability, and survey of expert opinions, we chose to use two different global N deposition maps (i. e. , Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP)  10  and Wang et al. [18] [19] [20]  ) and two cropland maps (i. e. , Land-Use Harmonization 2 (LUH2)  21  and History of the Global Environment database (HYDE)  22  ) in this study (Tables  1  and 2 ). While some region-specific models that emphasize cropland  [23] [24] [25]  might yield a more precise evaluation of N deposition, their restricted spatial extent doesn't align with the objectives of this study, which seeks to quantify N deposition for the majority of countries worldwide.
In addition, few regional models provide a time span that extends over the extensive period from 1961 to 2020. Combining these different sources of maps resulted in four products of N deposition inputs on cropland at annual scale for 236 countries for the period 1961-2020. We compare our N deposition estimates with existing literature and ground observations, and discuss the four products and their usability. The four products developed were the results of the combinations of four maps: AH: ACCMIP + HYDE, AL: ACCMIP + LUH2, WH: Wang et al. + HYDE, and WL: Wang et al. + LUH2. On balance, we recommend using the WL data product for global estimates of N deposition on cropland. This data product was included in the Cropland Nutrient Budget database, a joint release by the Food and Agriculture Organization of the United Nations (FAO), the International Fertilizer Association (IFA), and various research groups  26  , available at https://www.
Methods Data sources. The datasets used for developing N deposition at a national scale include N deposition maps, and cropland maps. Both types of maps have different spatiotemporal resolutions (Table  2 ). The N deposition maps from ACCMIP include dry and wet deposition of NHy and NOx, while Wang et al. comprise bulk N deposition without discriminating between the types of N deposition. Cropland maps (i. e. , LUH2 and HYDE) with finer resolution were adjusted by summing their values to a coarser resolution to match the grid resolution of the N deposition map. Since the N deposition data from Wang et al. are available for a shorter time period than the period targeted in this study, we used N emission data from the Emissions Database for Global Atmospheric Research (EDGAR)  27  to extrapolate and interpolate the values of N deposition to complete the time series from 1961-2020.
We compared our national scale estimates with observed N deposition data from measurement station networks in the UK, China, the USA, and East Asia, and with the N deposition estimates used in 14 N global budget datasets from Zhang et al. 14. The ACCMIP dataset is a multi-model ensemble dataset providing the mean of N deposition across 11 models  10. Out of 11 models, 10 models include NOx chemistry, while only 5 models include NHy chemistry. All the models varied in their spatial resolution to model deposition (see supplementary information in Lamarque et al. 10  ). Both dry and wet depositions of oxidized and reduced N are estimated and simulated for five time slices: 1850, 1980, 2000, 2030, and 2100 years. The deposition estimates are averaged across models that were originally at monthly time scale.
1  Historical trend of cropland N deposition in (a) Tg N yr -1 and (b) kg N ha -1 yr -1 by regions. Data are adopted from FAOSTAT's Cropland Nutrient Budget  26. Chemistry and Aerosols) models. This is the global aerosol chemistry climate model, which couples the General Circulation Model and Aerosol Model  28, 29. In the model, emissions data are from oceans (NH3), vegetation emissions (NO), and other agricultural activities (such as fertilizer application), and fuel combustion (NOx.
N Deposition Land area Zhang 2015; Zhang Reorganized † Zhang et al. 13  Used deposition map from Bouwman et al. 12  and cropland distribution map of Ramankutty et al. 57  Harvested area from FAOSTAT Lassaletta Lassaletta et al. 39, 40  Dentener et al. 30  , Bouwman et al. 58  Used a combination of harvested area and cropland from FAOSTAT Conant and Dorich Conant et al. 16  Dentener et al. 30  Cropland area from FAOSTAT Bodirsky with (or without) forage ‡ Bodirsky et al. Deposition map from Dentener et al. 30  for year 2000. Using historical and projected emission, the deposition maps are scaled to other years. Land cover data from HYDE 3. 1  59  Gerber and Mueller Mueller et al. 45, 46  , West et al. 47  Deposition map for 2000 from Dentener et al. 30  Cropland area: FAOSTAT, irrigated area: Portmann et al.
60  , crop area: Monfreda et al. 61  , pasture area: Ramankutty et al. 57  Chang et al. Skalský et al. 48  , Herrero et al. 49  , Valin et al. 50  , Havlík et al. 51  , Chang et al. 52  Atmospheric N deposition for the year 2000 is derived from the International Global Atmospheric Chemistry (IGAC)/Stratospheric Processes and Their Role in Climate (SPARC) Chemistry-Climate Model Initiative (CCMI) N deposition fields  62. Harvested area from crop-specific and spatially explicit SPAM dataset is harmonized with cropland land cover from GLC2000  48. Further, at regional level to harvested area from FAOSTAT. Some additional adjustment of total cropland to total arable land from FAOSTAT. FAO FAOSTAT Soil N Budget Domain  44  Zhang et al. 13  Cropland area from FAOSTAT  and NHy). To capture N in aerosols and gases and simulate global dry and wet deposition, the LMDZ-INCA was run at a spatial resolution of 1.
27° latitude by 2. 5° longitude for the years 1850, 1960, 1970, 1980, 1990, and  1997-2013. The detailed methodology to model transport and removal processes is in Wang et al. 20. Model evaluation was performed using recent global modeled datasets on wet N deposition rates  9  , while the evaluation of dry deposition was not performed due to a lack of data  30, 31. The evaluation of wet deposition against station measurement includes North America, Europe, Africa, Asia, South America, and forest areas. The major difference in the ACCMIP and Wang et al. lies in the emission data used to model the deposition, that leads to differences in the N deposition estimates. Between the two cropland maps, HYDE is created using simple time-varying land allocation algorithms. It develops land use maps (e.
g. , cropland) from 10,000 BCE to present. The HYDE dataset is developed for each 100-year from 800 to 1700, at a decadal scale between 1700 and 2000, and at an annual scale from 2000 to 2015. The key input data include historical national level "arable land and permanent crops" and "permanent meadows and pastures" from the Food and Agriculture Organization of the United Nations (FAO)  32. Additionally, subnational datasets are utilized. These coarser statistics on land use are then converted to finer resolution croplands using European Space Agency (ESA) Land Cover Consortium maps  33, 34  and following a sequence of allocation steps. The ESA land cover provides a land mask for HYDE and is available for three years, but the most recent epoch is used to develop the dataset. For more details on steps, see Goldewijk et al.
22. At a national scale, HYDE dataset has shown consistent estimates with FAO's land use data but suffers from larger differences compared to other satellite-based products  35. The input dataset in LUH2 land-use states includes the HYDE database to develop a historic dataset. The data from HYDE are on a decadal scale from 1700 to 2000 and annually from 2000 to the present. This data was linearly interpolated to establish an annual time series of gridded cell area fractions of different land use types   (e. g. , cropland). The cropland area fraction includes five different functional types: C3 annuals, C4 annuals, C3 perennials, C4 perennials, and C3 nitrogen fixers. This dataset also takes crop rotation and agricultural management practices into account. Although LUH2 is an improvement over HYDE, it also underestimates cropland area in some regions compared to other studies  36.
2 ). Then, using cropland area as weights within a country's boundary, we aggregate N deposition to a national scale. The N deposition data from Wang et al. are available as bulk estimates, whereas the ACCMIP dataset provides deposition in separate forms (i. e. , NOx, and NHy). For separate cases, the N deposition is calculated for different forms of reactive N, and then aggregated together to represent bulk N deposition. Adjustments to cropland maps. HYDE cropland maps are available at decadal scale from 1960 to 2000, and at annual scale after 2000, until 2017 (Table  2 ). To create a continuous time series of N deposition, the following adjustments were done to the cropland map: The LUH2 cropland map is reported as the fraction of land in a grid cell. These fractions are available for 12 possible land-use categories, including the classification of primary and secondary natural vegetation into  β β = + N Y ( 1 ) deposition 0 1 where Y is the year, and β 0 and β 1 are intercept and slope, respectively.