dataesr/scientific-paragraphs-categorization huggingface dataset:
A120, page 8 of 13 Sipilä, O. , et al. : A&A, 678, A120 (2023) Acknowledgements. We thank the anonymous referee for useful comments and suggestions. O. S. and P. C. gratefully acknowledge the funding by the  Max Planck Society. L. C. acknowledges financial support through the  Spanish  grant  PID2019-105552RB-C41  funded by  MCIN/AEI/10. 13039/501100011033. This work is supported by  Chalmers Gender Initiative for Excellence  (Genie). A120, page 11 of 13 Sipilä, O. , et al. : A&A, 678, A120 (2023).
The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptative image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters such as mean and Gaussian blur, we can improve the quality of the diffraction pattern registration.
19, 20, 21  This 4D-STEM data analysis method based on the ACOM system of NanoMegas (Astar)  22, 23, 24, 25  uses pattern matching of a scanning nano-diffraction dataset with libraries of diffraction patterns simulated from known structures extracted from CIF files. This method enables to construct of crystalline phase and orientation maps to determine crystallinity  26, 27  , microstructures  28  , structural deformation  29  , and grain boundaries  30  using scanning nanodiffraction with precession mode in a nanometer resolution. 31, 32, 33  The recent use of high-speed cameras, pixelated detectors  34  such as CMOS cameras  35 , and hybrid-pixel detectors  10  enabled better compromises between signal-over-noise and dwell time of acquisition. However, using such cameras in the column implies strong changes in the acquired images in comparison to the use of a NanoMegas conventional external optical camera, as the quality of the image improves with the increased electron sensitivity and resolution.
35  As the Astar ACOM suite has been optimized for images acquired with the optical camera focused on the phosphorescent screen, the data preparation should be adapted to fit with images acquired using a CMOS camera such as the Oneview Gatan camera. The goal of the data preparation methods proposed here is to improve the quality of Astar pattern-matching using a dataset of diffraction patterns acquired with a CMOS Oneview camera. The high sensitivity of the CMOS camera and the data filtering developed here modify the diffraction images leading to a compromise between improving image quality and optimizing template-matching results. The study utilizes a data reduction technique that employs registration methods to identify electron diffraction spots within patterns. This process enables us to filter and capture the diffraction signal and then, reconstruct the patterns before feeding them into the Astar suite pattern-matching software.
A similar pre-processing workflow is used in the py4DSTEM software package from Savitzky et al. (2021)  36  and more recently in the pyXEM  46  library. The essential information of each reflection of a dataset such as intensity, size, and position are recorded in a few minutes with sub-pixel accuracy for the position of the order 10 -3 px, with a data reduction factor of the order 10 2 -10 3 , meaning the essential information of the diffraction pattern is stored in 100 to 1000 fewer times space disk. This adaptative method reduces noise and compresses nanodiffraction scanning data for ACOM and strain mapping analysis  37, 38, 39  , and can also be used on electron diffraction data acquired with other techniques such as 3D electron diffraction (3DED)  40, 41. Firstly, modifications on inside diffraction patterns are estimated through image quality metrics such as peak signal-to-noise (PSNR), structural similarity index measure (SSIM), and root-mean-square error (RMSE).
Secondly, the quality of the pattern-matching process on filtered and reconstructed images obtained by the proposed experimental data preparation method is evaluated using index and orientation reliability, as defined in the Astar software. We demonstrate that the experimental data preparation helps to improve the pattern-matching quality result, as it reduces noise overfitting, improves structural similarity index measure, and increases the orientation reliability. This 4D-STEM study demonstrates the significance of mapping crystal structures and orientations in understanding Na-ion extraction/insertion mechanisms at the individual particles of cathode materials used in Na-ion batteries  42  , such as NaxMnV(PO4)3 as studied here. 40, 43, 27  Furthermore, this image processing method, based on a large dataset of electron diffraction, enhances the reliability of phase determinations in complex cathode materials that undergo crystalline transformations and exhibit slight lattice parameter changes.
4D-STEM ACOM methods A 200 kV Tecnai FEI TEM equipped with the Astar system, and a quasi-parallel beam (semi-angle 0. 4 mrad) was employed to scan the sample in 10 nm steps using the 4D-STEM technique. To minimize the weight of dynamical effects in the diffraction patterns (DP) and Kikuchi line contrast, precession of the electron beam was utilized, as described in numerous studies. 44, 46  Indeed, the dynamic effects induce a change in intensity of the diffraction spot over the entire pattern adding an extra contribution to the diffraction signal, which can be reduced using the beam precession method. As shown in Figure  1   By utilizing a specific range of parameters, the software ensures precise similarity between the experimental and calculated patterns. The user can fine-tune these parameters using the overlay display in the pattern-matching software, selecting the optimal set that corresponds to their data.
The radius is determined using an algorithm detecting local standard deviation variation. The mean is the average gray value in this radius. The reflections, in this example, belong to one image whose position is (100,100) in the scan. The reduction in data size is significant, on the order of 10   S11 , as the number of reflections per pattern increases, the compression rate decreases, whereas fewer reflections per pattern result in a higher compression rate. For the NMVP dataset presented in Figure  2  and Figure  5 , the data reduction factor attained for data written to disk is 614 with an average of 28 reflections per diffraction pattern registered. To go further in detail, there are 10. 0 reflections/pattern for the full scan and 28. 3 reflection/pattern considering only the patterns with more than 1 reflection (direct beam).
• The "spot enhance": The Spot enhance loops parameter is the number of times the 3 by 3 square matrix filter is applied to the spots of the image. The noise is reduced by the following 3 by 3 square matrix filter applied on every pixel ([1,1,1],[1,8,1],[1,1,1]). Increasing the Spot Enhance Loops number increases the number of times the filter matrix is applied to each of the spots. • Noise threshold: a threshold of grey values cutting the low intensities. The range is 0-255, and the default value in the software is set to 10. Increasing the Noise threshold will increase the maximum value that sets the intensity values to zero for all pixels whose value is lower than the selected maximum. The ePattern filters used to extract the features are : • The Subtract background is a rolling ball strategy that has been chosen for its accuracy rather than the gain in performance compared to the "softening loop" filter.
The default range is set to 20 px as the maximum radius of a peak in the dataset. • Gaussian blur filter of 2 px range by default to (better) detect the positions of the peaks with the 2D prominence algorithm. Two results using Astar filters with default (Figure  5b , f, j) and optimized (Figure  5c, g, k ) parameters show the impact of parameter choice on enhancing pattern-matching quality. Finally, the "register and rebuild" result (Figure  5d, h, l ) only utilizes the reconstruction method described in this study, without any Astar filters. The grain ROI is defined by a threshold of 5 reflections on the spot count map, which is intended to isolate the crystalline regions from the rest of the map where we find the amorphous grid membrane. A comparison of methods to isolate crystalline points of the scan is discussed in Figure  7.
The simultaneous representation of amorphous and crystalline phase maps is crucial, for instance, to investigate ion diffusion in grains in carbon-coated active cathode materials, which is particularly important in battery material research. Figure  7  shows the use of Astar metrics to cut points from the map that do not contain grain. Indeed, as non-crystalline points cannot give relevant results through the pattern-matching calculation, it is necessary to mask them. In practice, the reliability threshold can generally be set at a recommended level to keep the data reliable, for example at a value between 10 and 20, and the index threshold is adjusted to cut out the few remaining outliers. However, in the case of this dataset, we notice that the filtering that has been optimized leaves artifacts that lead to the overfitting of non-crystalline points, which results in an increase in the index value at these points.
However, as can be seen in Figure  7b-d  before pattern matching. In the reconstructed dataset, we notice that the points considered non-crystalline give much lower index and reliability values, as shown in Figure  7g-i. Thus, we assume that overfitting is much more limited for the reconstructed dataset than for a dataset filtered by Astar by optimizing the filtering parameters. However, from our experience, this hypothesis holds especially when the SNR of the raw pattern is relatively low. In the case of the reconstructed dataset, a threshold by the count of spots makes it possible to refine the mask applied to the pattern-matching results before applying the reliability threshold. However, in the case of a non-reconstructed dataset, the spot count threshold keeps only the grain areas while strongly limiting user interaction with the results as well as the loss of data.
Thus, the spot count metric obtained after registration offers a non-crystalline data mask solution whether the dataset is reconstructed or not. The use of the spot count followed by the application of a reliability threshold is particularly effective on data with a low SNR and helps to avoid user influence, in particular by using the ePattern reconstruction instead of the Astar filtering. A similar usage of the number of reflections used to separate the crystalline and non-crystalline areas is implemented in the pyXEM library. 46, 47.
Pattern matching Simulation of ED template banks, matching of the experimental ED with simulated templates, and generation of the phase maps were performed using DiffGen2, Index2, and MapViewer2 respectively (NanoMEGAS software package). The input parameters specific to pattern matching are the calibration of the image (camera length) in scale and the filtering parameters integrated into the software (value 0 for this study). During the automatic crosscorrelation calculation, the point clouds of the templates (orientations) of each bank are superimposed on the experimental images to determine an index score linked to the intensity products between the points and the corresponding aligned pixel. To rate the quality and reliability of the performed template matching (and, thus, the reliability of the obtained phase maps) two parameters were employed: the cross-correlation index (Qi) and the reliability (R).
ACKNOWLEDGMENTS The research presented in this paper has received support from multiple sources. Specifically, funding has been provided by the  French Research Agency (ANR)  as part of the  DestiNa-ion_operando project  ( ANR-19-CE42-0014 ). The authors express their appreciation for the proofreading efforts of  D. Boursier. Additionally, the UPJV and RS2E electron microscopy platforms were utilized for this research. The authors acknowledge funding from the  RS2E  network via the  STORE-EX Labex  Project  ANR-10-LABX-76-01. The authors would like to thank  Mohammad Ali Akhavan Kazemi  and  Frédéric Sauvage  ( LRCS lab ) for their assistance in providing photovoltaic perovskite materials and extend their special thanks to the  NanoMegas company  for their support during the acquisition process.
In addition, the comparison between the raw reliability maps (Figure5e-h) and those masked by a spot count of 5 (Figure5i-l) show non-zero reliability values in non-crystalline zones (amorphous carbon membrane) for raw and filtered data by Astar. In particular, the case of Astar(1, 4, 10) filtering optimized to maximize reliability shows high-reliability values that are close to that crystalline zone of the map. At the same time, applying the mask to the reliability map of the reconstructed dataset has a minimal impact. This point is discussed further in Figure7.
, 2019)  or to supplement satellite altimetry data to assess relative sea level change for coastal studies and planning  (Poitevin et al. , 2019). In the last decades, significant efforts have been undertaken to produce accurate estimates of vertical land motion at tide gauges using Global Navigation Satellite System (GNSS) data (e. g. Sanli and Blewitt, 2001; Wöppelmann et al. , 2007; Hammond et al. , 2021). Wöppelmann et al. (2007)  showed the importance of applying a homogeneous GNSS data reanalysis strategy across the entire data span (i. e. using the same modelling, corrections and parameterisation) to Published by Copernicus Publications. address the demand of accurate position time series and velocities for sea level studies. This conclusion was reached independently by  Steigenberger et al.
Input data Although the term GNSS is employed throughout the paper, the ULR-repro3 reanalysis considered Global Positioning System (GPS) observations only. The GNSS measurements were retrieved from the SONEL archive (http: //www. sonel. org, last access: 26 January 2023) in the form of station-specific daily files in the international standard RINEX (Receiver INdependent EXchange) format (https: //igs. org/wg/rinex/, last access: 5 July 2022). These contain dual-frequency carrier phase and pseudo-range measurements with a typical sampling of 30 s. SONEL holdings include data from over 1200 stations around the world, amounting to over 6 300 000 daily files. A station selection was applied with the criteria of targeting time series with over 3 years of continuous GNSS measurements and 70 % completeness, located at or near a tide gauge (within 15 km).
The term "continuous" denotes that no offset dis-continuity in the station position was anticipated from the metadata available, i. e. from the station operation log files (which should report changes in instrumentation) or from the co-seismic displacements predicted using the earthquakes database and modelling described by  Métivier et al. (2014 Métivier et al. ( ) (updated to 2020)). Some exceptions to these selection criteria concerned the French GNSS stations at tide gauges, as part of the ULR commitment for France to the Global Sea Level Observing System (GLOSS) programme of the Intergovernmental Oceanographic Commission. This programme was initiated in 1985 to establish a well-designed, high-quality in situ sea level observing network to support a broad research and operational user base.
, 2015)  to obtain the daily global solutions, which include all stations available each day with their positions expressed in a common but yet undetermined terrestrial frame. These daily global solutions were then stacked into a long-term solution using the CATREF software package  (Altamimi et al. , 2018)  with a time-dependent functional model that included translation, rotation, and scale transformation parameters between daily and long-term frames, estimated simultaneously with the mean station positions (at the reference epoch 2010. 5), annual and biannual signals, and velocities. The scale parameters, which represent the mean height changes of all the sites, are available upon request, especially for users interested in global sea level rise. Note that position offset discontinuities (mostly due to equipment changes and earthquakes) as well as station velocity changes and post-seismic displacements (PSDs) were added to the above modelling, where appropriate.
As expe- rienced analysts still tend to perform better than automatic methods  (Gazeaux et al. , 2013) , the position offsets were identified and adopted via expert visual assessment using all positioning components (i. e. including the north and east components). To facilitate this task, the equipment changes reported in the GNSS station logs were considered along with the co-seismic displacements larger than 2 mm predicted with the earthquakes database and modelling by  Métivier et al. (2014). When a position discontinuity was detected in a time series, the station position was estimated separately before and after the discontinuity along with the offset amplitude. The velocities before and after each position discontinuity were tightly constrained (0. 01 mm yr -1 ), unless a velocity discontinuity was suspected.
, 2017)  by applying minimal constraints to all the transformation parameters (translation, rotation, scale, and their rates) with respect to the positions and velocities of a stable subset of about 35 well-distributed reference frame stations. This step resulted in daily position time series expressed in the ITRF2014 frame for all (601) stations considered in ULR-repro3. From this set of position time series, only stations with more than 3 years between two consecutive position discontinuities and with data gaps not exceeding 30 % were retained for the next step as input (546 stations, among which 161 are reference stations and 457 are near a tide gauge).
, 1994). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-497-2023-supplement. Author contributions. The project was defined by MGr and GW as a contribution to the third reprocessing ("repro3") campaign of the International GNSS Service (IGS). MGr processed the GPS data with support from TH (GAMIT/GLOBK software packages, net-work design, and strategy for subnetwork design and orbit adjustment), ZA (CATREF software and reference frame alignment), PR (product quality assessment within "repro3"), and MGu (strategic use of the high-performance computing centre). KG prepared the NTAL corrections, assessed the stochastic properties of the time series, and produced the final velocity field. All authors contributed to the analysis and discussion of the results.
Ice cores included in the database used by  Stenni et al. (2017)  are unevenly spatially distributed, especially the records covering the full 2000year period, as ice cores are often drilled on domes or divides to avoid the glaciological advection of ice, leaving a vast gap of undocumented areas between coastal domes and the plateau summits. In particular, the East Antarctic plateau temperature reconstructions are limited by low temporal resolution caused by the low accumulation in the dry highelevation sites. In addition, most temperature reconstructions in Antarctica rely on water stable isotopes, which could be seasonally biased  (Werner et al. , 2000; Persson et al. , 2011)  or altered by post-deposition effects  (Landais et al. , 2017; Casado et al. , 2018 ). The regional climate would be better understood with temperature records from new locations to increase the spatial coverage and more diverse temperature proxies  (Christiansen and Ljungqvist, 2017).
Recent bubbles in the ice around this depth were probably enclosed by a thin ice wall at the time of sampling and may have been contaminated with more recent air before the pores were fully sealed. Another possibility is that vacuum pumping on thinly closed pores may have altered the isotopic composition during gas sample preparation. These samples were considered outliers and will not be included in the final dataset, which consists of 95 duplicates and three singletons. After removal of outliers, the pooled standard deviation of remaining samples is 0. 0137 ‰ for δ 40 Ar and 0. 0036 ‰ for δ 15 N. In this article we rely on mass-independent fractionation and thus use 15 N excess = δ 15 N -1 4 • δ 40 Ar  (Kobashi et al. , 2008 ; further description will be given in Sect. 3. 2). Vertical heterogeneities in firn density of the order of 10 cm  (Hörhold et al.
, 2016). Specifically, we defined tied points so that the methane content in bubbles at ABN fits the published methane dataset from the West Antarctic Ice Sheet (WAIS) Divide ice core, named WD2014  (Sigl et al. , 2016). A total of 14 tie points were identified where there are clear, quick transitions or extrema in methane records (Fig. A9 ). For the most recent part (1800 to 2000 CE, Fig. 4b ), the methane data from WAIS Divide were not available, and hence the ABN methane record was tied to the revised Law Dome record  (Rubino et al. , 2019)  with four additional tie points. Again, samples taken above the close-off depth of ∼ 104 m below the surface, which corresponds to 1925 CE, are likely to be contaminated with recent air before the pores were sealed, resulting in higher methane concentrations.
, 2019) , so it is more reliable to average over several years to retrieve the climate information from the water isotope record. Therefore, a 30-year average is used to better determine the climate signal. The 30-year averaged δ 18 O series has a mean value of -40. 8 ± 0. 5 ‰ (1σ ) and shows minimal variation over time. We chose to determine the ABN δ 18 O-temperature slope using the δ 18 O record from the 12 m shallow core described in  Servettaz et al. (2020)  and temperatures from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional, available at https://gitlab. com/ Mar-Group/MARv3, last access: May 2023; we use a simulation described in  Agosta et al. , 2019)  nudged to ERA-Interim climate reanalysis  (Dee et al. , 2011). The MAR model was developed with implementation of specific physical parameterizations for polar regions, with a turbulent scheme adapted for stable conditions of the Antarctic plateau.
It has a high vertical resolution with five levels within the first 10 m, which enables a good representation of temperature inversion. Consequently, MAR was shown to model the surface temperature more accurately than any other available dataset when compared with automatic weather station observations at GC41 (48 km south of ABN), with a bias lower than 1 • C  (Servettaz et al. , 2020). Water stable isotopes in the shallow core were measured at high resolution with continuous flow analysis (CFA) at the Desert Research Institute  (Servettaz et al. , 2020). The shallow core is dated back to 1968 and overlaps with the MAR modelled temperature for the 1979-2013 period, although annual layers could only be identified down to the Pinatubo eruption  (1991). Below this depth, uncertainties in the dating do not allow for clear annual averages, and multi-year average could lessen the range of variability.