dataesr/scientific-paragraphs-categorization huggingface dataset:
Study area The seven investigated pits that constitute the experimental dataset of this study were located in five study areas, all included in the Langedoc (southern France). They were selected to be representative of the diversity of soil characteristics that may change the bulk density measurement conditions (e. g. , coarse fragments, texture, soil depth) and the bulk density itself (e. g. , structure). A morphological description was performed on each pit according to the FAO guidelines for soil description  (FAO, 2006). The final ten investigated horizons were chosen among this available different soil horizons, which is why not all the horizons at the same site were investigated. Five soil pits were dug in January, July and October 2018 for the Pech Rouge (43°08'33'', 3°07'59''), Lavalette (43°08'33'', 3°07'59'') and Mauguio (43°35'33'',4°00'39'') sites, respectively and two additional soil pits were investigated in September 2020 for the Angles (43°31'14'',2°31'45'') and Restinclieres (43°45'58'',3°51'15'') sites.
Samples of over 500 g were taken from each horizon for further laboratory characterization. The particle size distribution (texture), gravel and stone content, organic carbon content (OC), and calcium carbonate content (CaCO 3 ) were measured at the French INRAE-ARRAS laboratory. Each sampled horizon is denoted (Site name)_(Pit number when there were multiple pits in a site)_H(horizon number, from top to bottom). Tables  1  and 2  summarize the characteristics of this soil dataset. Lavalette_H2, Mauguio_2_H2, and Pech_H2 correspond to deeply tilled horizons with high structural variability. Lavalette_H4 and Mauguio_1_H3 show medium to high porosity due to high microfaunal activity. The clay content is highly variable between the investigated horizons, from 10% for Pech_H2 to 48% for Mauguio_3_H2.
General experimental setup Each of the ten horizons was investigated both on the side of the pit (core sampling, SfM, flash LiDAR) and from above after digging to reach the given horizon (rubber baloon, SfM, flash LiDAR). <figure 1 here> Investigations from the vertical side of the horizons: core sampling, SfM and Flash LiDAR The first investigations were performed directly in the side of the pit (figure  1a ) with the same procedure repeated for each investigated soil horizon. One unique flash lidar data acquisition of the whole horizon was performed immediately followed by an SfM acquisition that also covered the whole horizon. Both of these procedures were done before soil sampling. Then, five excavations were performed for each sampled horizon. Once the soil was sampled at these five locations, a second flash lidar acquisition of the whole soil horizon and a SfM acquisition of the whole horizon were performed after the soil sampling.
Manual post-processing method For the manual estimation of excavation volumes, 3D point clouds derived from flash lidar and/or SfM acquisition were processed together in the CloudCompare 2. 10. 1 software. A cloud-to-cloud distance was computed, and the 3D point clouds were coloured with this distance information. Each excavation was then manually delineated and clipped. Finally, the excavated volume was computed as a 2. 5D volume on a 1 mm resolution grid.
Accepted Article This article is protected by copyright. All rights reserved. After automatically trimming the borders of the excavations, dense 3D point clouds resulting from the flash lidar and SfM acquisitions were processed in several steps consisting of a combination of calls to CloudCompare 2. 10. 1 functions using R software and raster processes using the 'raster' R library. Excavated holes were detected using cloud-to-cloud distances between 3D point cloud before and after excavation, considering the minimal areas of the holes and the level of noises of the methods (Figure  2 ). The complete method is available as an R code in the open access repository Zenodo at the following URL (https://doi. org/10. 5281/zenodo. 4036423), and a sample dataset is available at the following URL (https://doi.
Range of raw density ρ b Based on the classical core sampling reference method, the studied horizons corresponded to a large observed range of ρ b [1. 04 -1. 71] mainly due to their various structural arrangements, organic carbon content and porosities. This dataset covered a large part of the bulk density measured in national databases (for example  Hollis et al. , 2012). The highest ρ b values measured with core sampling were in Mauguio_1_H2, due to tillage and wheeling degradations. The highest ρ b values (a mean of 2. 01, measured in Mauguio_3_H2 with flash lidar and SFM) could not be measured with core sampling due to high pebble and gravel content (the highest : 73% in mass). The lower ρ b values corresponded to the organic horizon Angles_H1 (1. 04). The ρ bFE were lower than the ρ b values due to the high content of coarse fragments in some horizons.
Accepted Article This article is protected by copyright. All rights reserved. cm 3 on average) is at least comparable to that of the core sampling with an equivalent of 15 samples. However, the sampling must follow important recommendations mainly: (i) maintain a smooth shape of the excavation, (ii) be careful to maintain a good lighting of the image without potential moving objects , and (iii) prevent external soil particles from falling during the excavation. Almost all image data processing is automated, with the exception of the application of the Time-SIFT method. Currently, operator intervention is still needed between the two main steps (image alignment and dense matching) of Time-SIFT processing to separate the so-called before and after datasets prior to the computing of the dense 3D point clouds.
This was not a limiting factor in the case of this study, which suggested that this study had a manageable amount of samples (several dozens), but may become a true limitation for the normal operational use of SfM-based soil bulk density measurements. There is hence a strong interest in developing a Time-SIFT plugin for Photoscan so that the whole image processing process would be fully automated, from image dataset provision to individual volume estimations. This work is already ongoing but is out of the scope of this study. Other further work may assess the potential for the use of simple sensors such as those on smartphones for image acquisition and further 3D processing. Even if the lens and sensor quality of smartphones is lower than that of single-lens reflex digital cameras, the quality of the latest phone cameras and the availability of multi-lens smartphones may provide enough quality to allow for accurate and precise volume measurements.
The promise of flash lidar The soil bulk densities measured from the flash lidar data were generally significantly different from the values estimated with other methods. A prominent finding is the large standard deviation of flash lidar data compared to that from the SfM method. This calls into question the accuracy of the flash lidar method, which also depends on a large set of local environmental conditions, such as wind and scene illumination. Compared with that in our results,  Scanlan et al. (2018)  achieved better accuracy on BD measurements while using a Kinect™ sensor. The main factor explaining the difference between our findings and the findings of  Scanlan et al. (2018)  is most likely the sensor resolution. The Kinect sensor used by these authors is a 2 M pixel sensor (1920x1080), while the Pico Flexx used in our study is only a 38 k pixel sensor (224 x 171).
The bulk density of fine earth from the stony soils The studied dataset showed large differences with the measurement of bulk density in relation to the coarse fragment contents. Core sampling was considered as a reference for the measurement of bulk density in numerous studies. In case of low coarse fragment contents (i. e. <15%), the sampling was possible with the core sampling and a simple correction is sufficient to study the bulk density of the fine earth. Restinclieres_H2 and Angles_H1, with respectively 24 and 36 % in mass of coarse fragment contents, presented lower bulk densities of the fine earth than expected with the core sampling method. During the sampling in the field, the fine gravels and gravels were easily included in the core stainless steel and did not significantly deflect the driving of the core.
Accepted Article This article is protected by copyright. All rights reserved. even not applicable at all. The sensor-based excavation methods denoted low and noisy ρ bFE values, which probably depend on the irregular voids between the stones. Stony soils (i. e. soils with more than 30 % in mass of coarse fragments) represent a large part of the Mediterranean soils and more generally of the European countries (The soil map of Europe, 2014). New studies focusing on the relation between stony soils and water retention show the importance of stones and the relation between stones and fine earth (for example,  Korboulewsky et al. (2020) ). The structural arrangements within the fine earth in stony soils needs to be studied and the new proposed excavation methods may improve future datasets.
Introduction Non-linear extended MHD allows to describe a broad variety of different large-scale instabilities in magnetically confined fusion plasmas accurately and with lower computational costs than (gyro)kinetic approaches. Numerically, however, there are significant challenges to overcome: large spatial and temporal scale separations need to be bridged. Highly anisotropic heat transport, fast plasma flows and a large scale separation in time render it virtually impossible to simulate realistic parameters without an implicit time integrator. The following Sections 1. 1 to 1. 3 provide background on the JOREK code and the sparse matrix solvers, explain the numerical stages executed during a time step, and describe the setup used for the numerical tests. Section 1. 4 contains the outline of the main part of the article, which describes the developments implemented before they are tested via a series of benchmarks.
JOREK overview JOREK  [1, 2]  is a state-of-the-art code for the simulation of large-scale instabilities in tokamaks (magnetic confinement fusion devices). It solves extended reduced  [3]  or full  [4]  MHD equations using a 2D iso-parametric G 1 continuous finite element discretization  [5] , which was recently extended to arbitrary order  [6] , combined with a toroidal Fourier decomposition. Main applications are simulations addressing the dynamics of major disruptions including vertical displacement events and runaway electrons as well as edge localized modes. The goal is to advance the physics understanding for these potentially very harmful phenomena and to develop reliable control schemes applicable in present and future fusion devices. A free boundary and resistive wall extension exists  [7]  and a framework for kinetic effects is available  [8].
The time integration is fully implicit such that a very largeand, due to the properties of the equations, badly conditioned -sparse matrix system needs to be solved in every time step. The present article describes improvements of the computational efficiency of the code by a variety of different approaches enhancing the solver performance. These methods may also be beneficial in other codes. We describe the key properties of the JOREK solver and the sparse matrix system in the following Subsections 1. 1. 1 and 1. 1. 2.
Summary In this article, we have described the iterative solver with the physics-based preconditioner used in the non-linear MHD code JOREK, the properties of the sparse matrix system, and the interfaces to parallel sparse matrix solver libraries. We also explained some limitations faced with this solver in particular regarding memory consumption and convergence in highly non-linear scenarios. The new interface was developed, which allows using complex numbers in the preconditioner, leading to improved performance and memory utilization. Significantly reduced memory consumption with the complex preconditioner allows to perform a simulation with less compute nodes and thus allows to shift the point of operation towards the lower (ideal) part of the strong scaling curve. The most important development is a generalization of the preconditioner to "mode groups".
Acknowledgements The authors would like to thank  Omar Maj  for fruitful discussions, and  FJ Artola ,  A Cathey ,  F Wieschollek ,  I Krebs ,  R Ramasamy  for providing "real-life" test cases,  Mathieu Faverge  and the rest of the PaStiX team for their assistance with PaStiX 6. One of the author (I. H. ) would like to thank  Sebastian Ohlmann  (MPCDF) and  Pieter Ghysels  ( LBNL ) for technical advising. Part of this work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training program 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. Some of the work was performed using the Marconi-Fusion supercomputer.
Subjects We conducted our data collection during the 2011-2012 mating season, from early November to late January. We collected data from a captive social group of 39 Japanese macaques living in a 1,210 m 2 outdoor enclosure at Kyoto University Primate Research Institute (KUPRI, Inuyama, Japan). Group composition was as follow: 13 adult females (> five years, mean ± SD = 10. 64 ± 6. 84 years, range = 5-27), 6 sexually immature females (2. 79 ± 0. 81 years, range = 1-4), 3 adult males (> six years, 10. 67 ± 3. 06 years, range = 8-14), 12 sexually immature males (2. 99 ± 1. 05 years, range = 1-4), and 5 infants less than one year old. We only selected all sexually active adults as focal subjects to limit the potential effects of maturational age on traits, ovarian cyclicity, and mating decisions (see Table  A1  for female characteristics).
The group composition is similar to other captive and wild populations from studies of sexual behaviours and mating strategies  (Fooden & Aimi, 2005; Fujita, 2010). The Center for Human Modeling Research of KUPRI provided data on female age and weight calculated at the beginning of the mating season (to the nearest 100g). Parity was calculated by dividing the total number of infants born by the number of years post-adulthood. Animals had ad libitum access to water and were provided with food twice a day.
Behavioral observations One observer collected behavioural data using 30-min continuous focal samples every day for 3 months (7:00 AM to 5:00 PM). We used a female-centered approach because of the need to assess female trait variation in order to address the question of male responses. We equally distributed focal samples across days and between females to follow each focal female 2 hours per week. We recorded all occurrence social and sexual behaviours directed by the focal female and received from any identified adult male: i. e. , female presentations, female approaches, female mounts, female vocalisations (oestrus and copulation calls), male approaches, male holding behaviours, male inspections of female genitals (i. e. , visual and/or tactile and/or olfactory inspections of the female anogenital area), and male mounts (i.
e. , ejaculatory and nonejaculatory). The number of observed records for each behaviour is given in Table  1. Each clear win/loss outcome of an agonistic interaction was recorded into an agonistic interaction matrix (541 female-female interactions, mean ± SD per female = 38. 64 ± 11. 93, range = 17-76). Female social rank positions were calculated using Normalized David's Score (NDS)  (de Vries et al. , 2006)  (Table  A1 ). Given that there were only three adult males in the group, male ranks were assessed as alpha (winning all agonistic interactions), beta (defeating all males except the alpha male), and gamma (losing all agonistic interactions) directly from the agonistic interaction matrix (data provided by the Center for Human Modeling Research of KUPRI).
Female coloration Data collection and analyses are extensively described in  Rigaill et al. (2019). Briefly, one experimenter took digital images of female face and hindquarters every two days using a Canon EOS 350D camera with an 8 megapixel CMOS censor and an EF28-135 mm f/3. 5-5. 6 IS USM lens. The experimenter standardized data collection daily using white balance and colour standards (GretagMacbeth ColorChecker). Colour standards were photographed in the exact same conditions as those for the subject (i. e. , camera settings and distance, orientation from the camera). For each female's photo, colour was assessed separately from the face and hindquarter skin. A technician who was blind to females' identity and characteristics extracted reflectance spectra using Colourworker software (Chrometics Ltd.
Data analysis We restricted our analyses to the prefertile, fertile, and post-fertile phases. We used 173 focal observations with matching female photo (i. e. , focal and photo on the same observation day) for the analyses (mean ± SD = 7. 53 ± 1. 21 per female and per cycle). We built our dataset based on the 30-min focal observations and attributed one line to each focal female -male dyad per focal observation (i. e. , three lines per focal female), which was the unit of analysis. We conducted our analyses in R version 3. 3. 3. Before running the statistical analyses, we checked for correlations between our variables of interest. None of the female behaviours (i. e. , presentations, approaches, and mounts) were correlated with each other (all P > 0. 05). None of the male behaviours (i. e.
e. , approaches, holding behaviours, genital inspections, and copulations). Similarly, we also computed a binary variable that corresponded to a female behavioural display (hereafter, female behaviours), as the occurrence of at least one of the behaviours involved in establishing and maintaining consortship that were displayed by the interacting female of the dyad (i. e. , presentations, approaches, and mounts). We built a series of generalized linear mixed models with a binomial error structure and logit link function with the function glmer from the lme4 package  (Bates et al. , 2015)  to investigate the relationship between male behavioural response (binary variable) and female coloration (continuous variables). Hindquarter luminance followed a normal distribution, face luminance, face redness, and hindquarter redness followed a lognormal distribution.
Observations and data reduction HD 163296 was observed with MUSE  (Bacon et al. 2010 ) in narrow field mode (NFM) during the night of 29 April 2019 (program 0103. C-0399(A), PI: de Boer). Laser tomography adaptive optics assisted NFM provided a spatial resolution of ∼75 mas with the spatial pixel (spaxel) size of 25 mas × 25 mas in a field of view of 7. 5 × 7. 5 , covering 480 nm -930 nm with the spectral resolving power of 1740 at 480 nm and 3450 at 930 nm. The data were calibrated and reduced using the ESO MUSE pipeline, version 2. 6. The details of the MUSE pipeline can be found in  Weilbacher et al. (2020). Three exposure settings were adopted to optimize the observations of this bright star with an R band magnitude of 6. 86 mag. The details of the MUSE observations can be found in Table  A.
1. Short exposures (2 s) were used to obtain the unsaturated stellar PSF and the stellar emission. Two long exposures of 250 s were obtained to perform the main analysis. Due to the brightness of the star, we masked the saturated data within a radius of 0. 5 and replaced the data with the exposures of 8 s. We did not combine all the data with multiple exposure settings due to the read-out noise in 8s exposures, which degrades the signal-to-noise ratio (S/N) of the jet. The jet emission is much fainter than the stellar halo. We used the high-resolution spectral differential imaging (HRSDI) technique to remove the stellar spectrum, which was first used in the discovery of the protoplanet PDS 70 c  (Haffert et al. 2019). HRSDI is suitable to retrieve sharp emission line features while removing the stellar halo.
Unfortunately, being a Herbig Ae star, HD 163296 does not have other prominent emission or absorption features. Hence, we cannot estimate the residual offset after our extra wavelength calibration. Overall, MUSE can be used to observe the velocity shifts caused by high-and low-velocity components at < 1. However, the relatively large offset of the wavelength solution prevents us from measuring any potential jet rotation at a 5σ confidence level in the dataset of HD 163296. Future instruments will be able to measure the jet rotation in the visible and the near-infrared. For optical wavelengths, VLT/MAVIS  (McDermid et al. 2020)  will provide the necessary spatial and spectral resolving power (R ≈ 10000), while VLT/ERIS  (Davies et al. 2018) , which has a similar resolving power, can be used to probe emission lines in the near-infrared.
Future observations of HD 163296 with MUSE and with longer integration times are recommended to get better measurements of the kinematic structure of the jet. A&A proofs: manuscript no. ms_40602corr Notes. For atomic lines we could not identify, only observed line properties are provided. The line properties were obtained by fitting a Gaussian profile. The rest-frame wavelength (λ rest frame ) is obtained from  Kramida et al. (2019). ( a) We did not correct for the flux loss caused by the low Strehl ratio (25% at Hα) because the Strehl ratios are relatively constant for most of line ratios we measured, except for Hα/Hβ in A4-out. ( b) The measured FWHM includes instrumental line broadening, which should be considered as the upper limit. The spectral LSF is about 116 km s -1 at Hα  (Bacon et al.
Acknowledgements. This work is based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  0103. C-0399 (A). This research made use of Astropy 6 , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013; Price-Whelan et al. 2018). Support for this work was provided by  NASA  through the  NASA Hubble Fellowship  grant # HST-HF2-51436. 001-Aawarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555.
Interaction performance The virtual tour experience was used to see interaction performance. Since we have dependent samples of 3 interaction systems, we need to test parametric assumption first. The Shapiro-Wilk  [19] , W test was used for normality testing and the data respect to normal distribution then 1-way ANOVA was used to analyze the differences among group means in a sample. The time spent for the virtual tour experience showed in Figure  5. The navigation time for all 3 interaction systems were quite similar. While the CAVE system took more manipulation time than other systems and the 3D system lead to higher selection time. The 1-way ANOVA was used to confirm this observation. The result showed that the navigation time was not significantly different (p = 0. 137787 > 0. 05). The manipulation time was different with highly significance (p = 5.
However, in the actual usage, the user may have a different interaction from the design. We should capture the user behavior, how often do users interact with each object within the designed scene? If the design did not meet the user needs, VM designer should know which objects or locations should be updated in order to meet the user requirement. The storytelling platform was designed to support this idea. We kept all interaction data which can make a report to the developer when user finished using the system. The report will save the amount of interactions with the scene and where is the user point of interest. Virtual museum developers can use this report to improve attraction and interaction respect to user requirements. The interaction design within the scene compared to the actual usage showed in Figure  9.