dataesr/scientific-paragraphs-categorization huggingface dataset:
The Raymond et al. study only provides mean flux densities at the much coarser spatial resolution of the so-called COSCAT regions. All estimates were then downscaled to 0. 1° using the spatial distribution of European inland water bodies. Note that in contrast to  Hastie et al. (2019) , the areal distribution of lakes was extracted from the HYDROLAKES database  (Messager et al. , 2016) , to be consistent with the estimates of inland water N2O and CH4 presented by  Petrescu et al. (2021b). Uncertainty: Monte Carlo simulations were performed to constrain uncertainties resulting from both the pCO2 prediction equation and the choice of the k formulation.
A total of five managed grassland types with distinct temperature requirements, biomass productivity, and phenology were used to represent the C-cycle in European grasslands. High-productive generic winter pasture and tall fescue-based grasslands were used for Atlantic Europe, low fescue grasslands for the cool climates of Nordic regions and high mountains, high-productive tall fescue-based grasslands and low-productive bluegrass types for continental Europe, and low-productive bromegrass and high-productive winter pastures in the Mediterranean regions. Annual nitrogen and carbon inputs, including inorganic and manure fertilization, and atmospheric N deposition, were obtained from ISIMIP3  (Jägermeyr et al. , 2021). In this dataset, the annual manure production and the fraction of manure from livestock applied to cropland and rangeland were used from  Zhang et al.
For regional carbon budgets, any river carbon export outside the boundaries of the region of interest (in this case, EU27+UK) needs to be known to separate net uptake of atmospheric C from the actual land C sink. Carbon fluxes to the atmosphere from rivers and lakes were obtained from maps described in  Zscheischler et al. (2017). These methods are similar to those described previously in  Petrescu et al. (2021b). The primary difference is that the updated estimates include smaller lakes and reservoirs not represented in the Global Lakes and Wetland Database through the use of a scaling law, in addition to the older results being created specifically for Europe, while the newer results are part of a global product. The emissions from the previous work totaled 25. 5 Tg C yr-1 for the EU27+UK, while those used here are 19.
The CSR results reported to EUROCOM were not used, being instead replaced by the mean of the four updated CSR runs. CO2 fossil breakdown by fuel type  (Fortems-Cheiney and  Broquet, 2021). This figure presents the split per fuel type for year 2017. "Others" is other emissions in the UNFCCC's IPPU, and international bunker fuels (the white boxes) are not usually included in total emissions at sub-global level. Neither EDGAR 28 (v6. 0) nor PRIMAP publish a break-down by fuel type, so only the total is shown. For BP, the method description allows for emissions from natural gas to be calculated from BP's energy data, but the data for solid and liquid fuels are insufficiently disaggregated to allow replication of BP's emissions calculation method for those fuels.
Acknowledgements We thank  Aurélie Paquirissamy ,  Géraud Moulas  and all ARTTIC team, for the great managerial support offered during the VERIFY project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO. We acknowledge the work of other members of the EDGAR group ( Edwin Schaaf ,  Jos Olivier ). We acknowledge  Stephen Sitch  and the authors of the  DGVMs TRENDY  v10 ensemble models for providing us with the data. We thank all the national forest inventories that have made their data available: Ireland ( John Redmond ), Norway ( Rasmus Astrup ),  Sweden  ( Jonas Fridman ),  Poland  ( Andrzej Talarczyk ),  Germany  ( BMEL ),  The Netherlands  ( WUR & Stichting Probos), Belgium  ( Flanders: Leen Govaere ),  Luxembourg  ( Thierry Palgen ),  France  ( IGN ), Spain ( MAPA), Switzerland  ( Esther Thürig ), Italy ( CREA ),  Czech Republic  ( Emil Cienciala ),  Slovak Republic  ( Vladimír Šebeň ).
Financial support This research has been supported by the  European Commission ,  Horizon 2020 Framework Programme  ( VERIFY , grant no. 776810 , for AB, AFC, AMRP, AP, CG, GB, GJM, GJN, GM, GP, HACDG, JB, LP, MJ, MJM, MK, MV, https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0  License.
125° resolution). CABLE-POP also used a higher resolution land use land cover change (LULCC) dataset for the results submitted to VERIFY (0. 25° as opposed to 1. 0°). The increased IAV from the high-resolution CABLE-POP compared to ORCHIDEE is suspected to have been introduced through the construction of the LULCC dataset as described in Appendix A2. Gross fluxes are, by definition, larger than net.
work represents an update of the Petrescu et al. (2021b) European CO2 synthesis paper presenting and investigating differences between the UNFCCC NGHGI, BU data-based inventories, both coarse and high resolution https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License. process-based BU models, and TD approaches represented by both global and regional inversions. Datasets used in the previous work have been updated by extending the temporal coverage and updating the models and data behind the calculations. In addition, several new models to expand the number of independent approaches compared have been added. Additional efforts have been made to improve uncertainty characterization in two approaches, along with a first attempt to present as many datasets as possible in a clear single figure to draw overarching conclusions.
A methodology for reconciling LULUCF country estimates from the FAOSTAT datasets with the NGHGIs is presented inGrassi et al. (2022a)  andGrassi et al. (in prep)  for the global scale. The next steps needed to improve and facilitate the reconciliation between BU and TD estimates are the same as those discussed in Petrescu et al. (2021b): 1) BU process-based models incorporating unified protocols and guidelines for uniform definitions should be able to disaggregate their estimates to facilitate comparison to NGHGI and 2006 IPCC practices (e. g. , managed vs. unmanaged land, 20-year legacy for classes remaining in the same class, distinction of fluxes arising solely from land use change, Grassi et al. (2022a)); 2) for sector-specific models, in particular for cropland and grassland, improving treatment of the contribution of soil organic carbon dynamics to the budget; 3) for TD estimates, using the recently developed Community Inversion Framework(Berchet et al.
, 2021)      to better assess the different sources of uncertainties from the inversion set-ups (model transport, prior fluxes, observation networks), 4) standardize methods to compare datasets with and without interannual variability, and 5) develop a clear way to report key system boundary, data, or definitional issues, as it often necessary to have deep understanding of each estimate to know how to do a like-for-like comparison. https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License.
first edition of the Emissions Database for Global Atmospheric Research was published in 1995. The dataset now includes almost all sources of fossil CO2 emissions, is updated annually, and reports data for 1970 to year n-1. Estimates for v6. 0 are provided by sector. Emissions are estimated fully based on statistical data from 1970 till 2018 https://data. jrc. ec. europa. eu/dataset/97a67d67-c62e-4826-b873-9d972c4f670b. Uncertainties: EDGAR uses emission factors (EFs) and activity data (AD) to estimate emissions. Both EFs and AD are uncertain to some degree, and when combined, their uncertainties need to be combined too. To estimate EDGAR's uncertainties (stemming from lack of knowledge of the true value of the EF and AD), the methodology devised by IPCC (2006, Chapter 3) is adopted, that is the overall uncertainty is the square root of the sum of squares of the uncertainty of the EF and AD (uncertainty of the product of two variables).
and vegetation classes in the dataset were reclassified to correspond to PFTs represented in CABLE-POP. Land use https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License. transitions as well as land management (harvest) were prescribed from the LUH2v2h dataset over the entire simulation period. Crops and pastures are treated as C3 grasses but are subject to agricultural harvest fluxes as given by LUH2v2h. The use of HILDA+ data for the land cover distribution and the LUH2v2h for the representation of land cover/land use change likely introduced additional uncertainties resulting from a potential mismatch between the two data sets.
B2, Appendix B; Abad-Viñas, pers. comm, 2022). This weakening occurred mostly in Forest land, due to a combination of increased natural disturbances, forest aging and increased wood demand (Nabuurs et al. , 2013; EU NIR, 2022). Natural disturbances, including fires (especially in the southern Mediterranean), windthrows, droughts and insect infestations (especially in central and northern European countries), have increased in recent years (e. g. , Seidl et al. , 2014) which explains most of the interannual variability of NGHGIs. Forest aging affects the net sink both through the forest growth (net increment) -which tends to level off or decline after a certain age -and the harvest, because a greater area of forest reaches forest maturity and Petrescu et al. (2021b), expanding on the scope of those works by adding additional datasets and years.
(2020)  and TableA3inFriedlingstein et al. (2019)      https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License. CO2 I I N E N N E Acc. table E N i N i N i fertilizat A1 in ion GCB 2021 Climate I I N E f I b I c E (Friedling E N i N i N i induced stein et impacts al. , 2022) Natural I I N N E N E N N i N i N i disturba nces (fires, insect, wind) Soil I E E E E E E N N N Organic C 2253 dynamic Lateral N N N N N N N N N N N Descri-C NGHGI Global Process-based models DGVMs Bookkeeping Models Inversions # ption transport database (river) Flux E U F N E N E N I C E N d E C T Acc. table E O E B E B E H from N A C P B F A R A1 in R L L & Harveste F O O I M I B E GCB C U U N d Wood C S S C S L N 2021 H E E Products harvest ass Crop/Gr from Flux ? C C a T A T a N E S E -I I A S A E e N C E N -Space N E E -P O P D (Friedling Y stein et V 1 0 al.
int/parties-observers, last access: February 2022). 2 For most Annex I Parties, the historical base year is 1990. However, parties included in Annex I with an economy in transition during the early 1990s (EIT Parties) were allowed to choose one year up to a few years before 1990 as reference because of a non-representative collapse during the breakup of the Soviet Union. For the EU27+UK, this includes  Bulgaria (1988) ,  Hungary (1985 Hungary ( -1987)) ,  Poland (1988) ,  Romania (1989), and Slovenia (1986). (Tiered methods) based on the absolute contribution of the sector to the national GHG balance and the country's national circumstances (availability and resolution of national activity data and emission factors). Generally, Tier 1 methods are based on global or regional default emission factors that can be used with aggregated activity data, while Tier 2 methods rely on country-specific factors and/or activity data at a higher category resolution.
Acknowledgements M. Z. , C. P. S. and A. A. acknowledge support by the  Max Planck Society (Max Planck Research Group ). M. Z. acknowledges support by the  Federal Ministry of Education and Research  under 'Make our  Planet Great Again -German Research Initiative ' (grant no. 57427209 , ' QUESTforENERGY ') implemented by  DAAD. J. W. F. acknowledges  Department of Energy  grant no. DE-SC-0012375. Y. Z. , Y. L. , D. D. F. , S. T. , S. G. E. t. V. and J. G. C. were supported by the  US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division. W. S. D. acknowledges support from the  Joint Center for Artificial Photosynthesis , a  DOE Energy Innovation Hub , supported through the  Office of Science of the US DOE  under award no.
The image subtraction method (ISM) This section describes the ISM method performed with LabVIEW ®; the procedure is also described in  Danzi et al. (2021). Each video was filmed at 2000 fps for about 500 ms to capture the motion of the cloud before the triggering instant suggested by current standards for MIE measurement (in the range of 60-180 ms). Since no ignition is performed, the cloud dynamic may be studied for a long time until it starts settling toward the tube bottom. The ISM is divided into successive phases, described in detail by  Danzi et al. (2021) , and defined as: • Video filtering, • row brightness detection, • brightness data presentation and recording.
Dust cloud structure A method to retrieve cloud structure information was developed using a LabVIEW® code. The code operates on the post-processed movies to identify particle lumps. As a result, information on the cloud structure and turbulence could be obtained. Furthermore, the code allows 1) the extraction of an image from each frame; 2) the application of a brightness value threshold; 3) the generation of an image modified by the imposed threshold; 4) to analyse lumps size. Fig. 19  shows the steps of the process and the corresponding images. Here, the method is applied to a frame obtained from the original movie using the PositiveDeltaFrame method. The quality of the images extracted from the video depends on the threshold value adopted; The aim is to achieve the most accurate possible representation of dust cloud clumps while eliminating background effects such as reflections on the glass tube and background noise.
The lumps size analysis could be based on different size estimates, depending on the measurement method: Feret diameter is adopted in this work, which considers the non-spherical morphology of dust dispersed. The last step is done using image processing software (in this case, ImageJ®), which works similarly to the LabVIEW® code. Finally, a dimensional analysis instrument is used to measure the numerical distribution of the size of the lumps. Fig. 20  shows the cloud structure obtained using different dust or shot conditions. Detailed analysis of the cloud structure will be the subject of further work. Here we limit to underlining that the method identifies the structure of the cloud and reveals significant differences as the test conditions vary. These differences are probably due to the intrinsic properties of dust, such as particle density, ability to accumulate electrostatic charge and/or test conditions such as mass concentration and air blow pressure.
Numerical simulation set-up and validation In this work the ANSYS Fluent software (Ansys Inc 2020) is chosen to perform diffusiophoresis simulations, with help of the coupled algorithm (Ansys Inc 2021) solver that solves the momentum and continuity equations simultaneously. The software supports dynamic meshing, which is a required feature to implement the TEF model described in § 4. 1. Typically, the computation time for one transient simulation remains within less than 24 h for a large number of time steps (up to 400) and refined mesh (up to 1. 3 × 10 6 elements) using four processors. To implement the equations, we use the laminar model available in Fluent to simulate fluid flow, and define a user-defined scalar to describe solute transport (Ansys Inc 2021). The extra term -k B Tn ∇Π ic appearing in the momentum balance equation for the fluid, which corresponds to the force exerted by the interface on the solute (transferred to the solvent), is handled via a source term.
FundingA. E. and K. S. have been supported by the Hausdorff Center for Mathematics. Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der Länder -GZ 2047/1, Projekt-ID 390685813. The work of A. G. has been (partially) supported by the Project EFI ANR-17-CE40-0030 of the French National Research Agency A. D. acknowledges support of the Lagrange Mathematical and Computing Research Center.
Acknowledgments The author deeply thanks his PhD advisors,  M. Massot  and  B. Graille , for the fruitful discussions and advice on the subject, his brother  P. Bellotti  for the useful tips to improve the style of manuscript and  S. Simonis  for having read and commented the preprint of this paper. The author also thanks the two anonymous referees for the valuable questions and suggestions. The author is supported by a PhD funding (year 2019) from the  Ecole polytechnique.
Fracture analysis 2D Fracture analysis Georeferenced orthoimages were imported to QGIS using the local coordinate system for the TDP models and the WGS84-UTM38N coordinate system for the UAVDP models. These were used to map visible fractures. The fracture maps were analyzed using the FracPaQ  (Healy et al. , 2017)  and NetworkGT  (Nyberg et al. , 2018)  algorithms. The FracPaQ algorithm is a suite of MATLAB scripts created for the analysis of fracture orientations, dimensions, intensity, and to calculate the 2D spatial variability of the equivalent permeability ellipsoid. The NetworkGT algorithm is a QGIS plugin used to analyze the spatial variability of the fracture network connectivity, as those of the X-, Y-and I-nodes types of fracture terminations and those of the fracture branches.
3D Fracture analysis The obtained 3D dense clouds and textured meshes were analyzed using CloudCompare software (CloudCompare, 2020), which allows visualization and mapping of the 3D features of the outcrops (e. g. , fractures geometry;  Menegoni et al. , 2018; Inama et al. , 2020). The 3D fracture mapping was performed using both the manual and semiautomatic sampling/tracing methods embedded in CloudCompare software. The manual method consists of manually picking the DOM points belonging to the fracture traces and/or surfaces and the estimation of the 3D planes that best-fit the point selections (Figure  7 ). The semi-automatic method used in this study is based on the Compass plugin of the CloudCompare software as presented by  Thiele et al. (2017). This plugin uses a least-cost path algorithm to semi-automatically map the fracture trace based on only a few points as the start and end points of the fracture trace.
These traces are then used to estimate the best-fit 3D plane. The main advantage of the semi-automatic sampling method is the rapid measurement of the fracture traces observed over the outcrop. This method works only with the point cloud (not with textured mesh) and can map only traces. Therefore, when working with the 3D meshes or when visible fracture surfaces are present, the manual sampling procedure is used. The fractures were mapped along their entire visible extensions to compute their orientation (dip and dip direction), position and dimensions (discontinuity length). The 3D geometrical features of fracture were exported as CSV files to perform further quantitative investigation. The dataset was analyzed with two software: Stereonet  (Allmendinger et al. , 2011; Cardozo and Allmendinger, 2013)  and MATLAB  (The Mathworks Inc.
2022)  version 30. 2. 1 1 using the Calibration database released on 2022 November 17. Source photons were collected from a circular region with radius R src of 60 centered on the source position. Following the prescription by Di  Marco et al. (2023)  for the sources with high count rate ( 2 cnt s -1 ), the background has not been subtracted from the data, because it is negligible. For the timing analysis, the photon arrival times were corrected to the Solar System barycenter using the standard barycorr tool from the ftools package, with the effects of binary motion also taken into account using the orbital parameters from  Kühnel et al. (2013). For the spectro-polarimetric analysis, the flux (Stokes parameter I) energy spectra have been binned to have at least 30 counts per energy channel.
The same energy binning has also been applied to the spectra of the Stokes parameters Q and U. Taking into account the high number of source counts and low background level, the unweighted approach has been applied. All the spectra were fitted using the xspec package version 12. 12. 1  (Arnaud 1996)  and version 12 of the instrument response functions, and applying the χ 2  statistic. The uncertainties are given at the 68. 3% confidence level unless stated otherwise.
Polarimetric analysis To understand whether also the polarimetric analysis supports this conclusion, we conducted pulse phase-averaged and phaseresolved analysis of IXPE data for both observations individually. First, we performed the polarimetric analysis of the data using the formalism by  Kislat et al. (2015)  implemented in the ixpeobssim package  (Baldini et al. 2022 ) under the pcube algorithm in the xpbin tool. Phase-averaged analysis in the 2-8 keV energy band resulted in low PD values of 4. 1 ± 0. 9% and 3. 4 ± 1. 1% for the bright and low states, respectively, with corresponding polarization angle (PA, measured from north to east) values of -3 •. 8 ± 6 •. 4 and -9 •. 1 ± 9 •. 2; that is, the results are consistent within uncertainties. However, we note that, because the PA is expected to be strongly variable over the pulsar spin phase, these phaseaveraged results do not contain a significant amount of useful information.
Therefore, as the next step, we performed a phase-resolved polarimetric analysis. For that, we used all the available data in each observation in the 2-8 keV band binned into 14 phase bins. The number of phase bins was selected to trace the strongly variable PA from one side and to get sufficiently high statistics from another. The results are shown in Figs. 3  and 4. We see that the normalized Stokes parameters q = Q/I and u = U/I are indeed strongly variable over the pulse phase, resulting in a relatively low phase-averaged PD value. To properly take the energy dispersion and the spectral shape into account, we also conducted a spectro-polarimetric analysis through a joint fit of the I, Q, and U spectra (prepared with the PHA1, PHA1Q, and PHA1U algorithms in the xpbin tool) using the xspec package (see  Strohmayer 2017).
In the phase-resolved analysis, all these parameters (including N H ) were allowed to vary over the spin phase to reflect possible inhomogeneities in the flow of matter around the NS. An energybinned (pcube) analysis was performed on the combined dataset as well. The obtained results are summarized in Figs. 4  and 6  and Tables  1  and 2. Modification of the polconst polarization component of the best-fit model with pollin and polpow, which correspond to a linear and a power-law dependence of PD and PA on energy, respectively, did not lead to any significant improvement of the fit quality. Namely, for the phase-averaged data combined over both observations, this modification resulted in ∆χ 2 ∼ 1 for 2 d. o. f. for both pollin and polpow models. For the phaseresolved data, and for each phase independently, the ∆χ 2 value did not exceed ∼5 for 2 d.