dataesr/scientific-paragraphs-categorization huggingface dataset:
4   surements and PTA audiogram thresholds partly converge (Spearman's rank correlation ρ = -0. 64, p-value < 0. 0001; Figure  2A ), suggesting that they capture similar but not equal auditory characteristics. In relation to cognitive and neurological variables, the mean Mini-Mental State Examination (MMSE) score was 27 ± 3. 4, while the mean CDR SoB was 0. 94 ± 2. 3. A summary of demographic data is reported in Table  1. Less DPOAE is correlated with worse dementia rating. Next, we analyzed the possible relations between the number of DPOAE and PTA with cognitive traits, such as global cognitive performance evaluated through MMSE, functional status quantified using Pfeffer, and the CDR SoB, which was obtained by clinical neurological evaluations that were blind to the DPOAE results, providing a quantitative index of the level of cognitive impairment as a dementia rating.
e. 0 to 8-was counted and a number of "0" -no detectable DPOAE in that ear-was considered as cochlear amplifier dysfunction while "8" implied normal cochlear amplifier function. We used the total number of detected DPOAE of both ears (range from 0 to 16). Image acquisition. Imaging data were acquired using a MAGNETOM Skyra 3-Tesla whole-body MRI Scanner (Siemens Healthcare GmbHR, Erlangen, Germany) using a T1-MPRAGE sequence. Contiguous images across the entire brain were acquired with the following parameters: time echo (TE) = 232 ms, time repetition (TR) = 2300 ms, flip angle = 8, 26 slices, matrix = 256x256, voxel size = 0. 94x0. 94x0. 9 mm3. We also registered T2-weighted turbo spin echo (TSE) (4500 TR ms, 92 TE ms) and fluid-attenuated inversion recovery (FLAIR) (8000 TR ms, 94 TE ms, 2500 TI ms) to inspect structural abnormalities.
The acquisition duration was 30 min with a total of 440 images for each subject. Image preprocessing and analysis. The morphometric analysis was carried out by FreeSurfer, version 6 running under Centos 6. A single Linux workstation was used for the T1-weighted image analysis of individual subjects as suggested by  (33). The FreeSurfer processes cortical reconstruction (34) through several steps: volume registration with the Talairach atlas, bias field correction, initial volumetric labeling, non-linear alignment to the Talairach space, and final volume labeling. Briefly, the automatic "recon-all" function produces representations of the cortical surfaces. It uses both intensity and continuity information from the entire threedimensional MR volume in segmentation and deformation procedures.
ACKNOWLEDGEMENTS This research work was supported by the  National Agency for Research and Development of Chile (ANID) ,  FONDEF ID20I10371 ,  FONDECYT 1221696 ,  FONDECYT 1220607 ,  FONDEQUIP EQM210020 ,  Fondo Basal ANID   FB0008  to PHD,  Centro Nacional de Inteligencia Artificial CENIA ,  FB210017 ,  BASAL ,  ANID  to RV,  Proyecto Milenio   ICN09_015 , and  Fundación Guillermo Puelma.
Soil Sampling and Sample preparation Sampling was carried out on May the 26 th 2020, at the tillering stage, by collecting a soil core of 8 cm diameter per plot, just below a plant, after cutting the shoot, down to 30 cm. We divided the soil core into the 0-15 cm and 15-30 cm soil depths. Considering the spatial variability of soil depth, the sampling volume of soil cores was not always the same. To integrate this variability, the data were normalized to 100 cm 3 for analysis. Forty-eight soil samples were retrieved at 0-15 and the same number at 15-30 cm depth. For each sample, we collected rhizosphere and bulk soil. Rhizosphere soil was collected by manual shaking to keep only 1 to 2 mm soil around the roots and brushing of the roots with caution, and, bulk soil was collected by sampling the loose soil not aggregated around the roots and sieving at 2 mm.
We also analyzed the CO 2 cumulated at the end of the incubation period (60 days) (Table  3 , Fig. 3 ). We represented the results of these ANOVAs by plotting the dependent variable in response to the two environmental modalities (without "w/o" and with "w" inputs) and in conjunction with phenotype's responses related to a given breeding type. This representation is the one used to represent phenotypic Y ijkm = + i + j + k + ( ) ij + ( ) ik + ( ) jk + ( ) ijk + E ijkm Content courtesy of Springer Nature, terms of use apply. Rights reserved. 1 3 Vol:. (  1234567890 ) plasticity and more generally interaction between a genotype and its environment  (Oyserman et al. 2021). Analyses of root traits, MicroResp™ and incubations data were performed with the RStudio software (RStudio Team 2020).
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Acknowledgements We thank  Institut Agro Dijon  for experimental facilities. We are grateful to  Jean-Philippe Guillemin ,  Wilfried Queyrel  and  Etienne Gaujour  for their advice in wheat culture and the management of the experimental site. We thank  Pauline Souche-Suchovsky  and  Marie Spetebroot  for their contribution to the study of root systems. We thank  Graines de Noé , especially  Hélène Montaz , for providing the seeds of ancient varieties, as well as  RAGT Semences, Secobra, Saaten Union and Limagrain  for providing the modern varieties. We are grateful to  Jacques Le Gouis  and  François Balfourier  for providing the information about the origins of ancient varieties.
This study investigates the impact of motion on the line-of-sight (LOS) turbulent velocity fluctuations derived from lidar profiler measurements. Onshore tests were conducted using a WindCube v2. 1 lidar, referred to as the mobile lidar, mounted on a hexapod to simulate buoy motion, with a fixed lidar used as a reference. To assess the motion-induced effects on turbulent velocity fluctuations measured by floating lidar systems, the root-mean-square error (RMSE) of LOS velocity fluctuations obtained from the fixed and mobile lidars was calculated. A comprehensive wind dataset spanning 45 hours was analyzed, with a focus on regular motions involving single-axis rotations and combinations of rotations around multiple axes. The investigation of single-axis rotations revealed that the primary influencing factor on the results was the alignment between wind direction and the axis of rotation.
4a ). For this study, our focus lies on the 15 measurement cycles recorded between October 6th, 2022, and November 9th, 2022. Each cycle consisted of 12 sequences, with nine 10-minute "regular" sequences involving rotations around the y-axis at various amplitudes and periods, as well as movements that coupled rotations around different axes, namely (i) x and y, (ii) y and z, and (iii) x, y, and z (Table  2 ). Following these regular sequences, there were three 30-minute sequences, two consisting in random motions induced by real sea-states and one representing "pink noise. " However, these latter sequences were not used for the purposes of this study and will be utilized later for validating the motioncompensation algorithm. The signals from both lidars were divided into 10-minute ensembles, each containing 600 data points.
The two lidars recorded data at 10 different elevations, evenly distributed between 40 m and 220 m. Throughout the measurement campaign, the data availability remained at 100%. At the test site, wind characteristics captured by the fixed lidar over the 57-day deployment period indicated wind speeds ranging between 0. 5 m/s and 23. 3 m/s, with a mean of 10 m/s at 140 m elevation. The prevailing wind directions were predominantly from two main sectors: the South-West and the South-East (indicated by gray shading in Fig. 3 ). These sectors align with the average wind directions recorded during the 15 measurement cycles of the mobile lidar (depicted by boxes in Fig. 3 ). Moreover, the 15 cycles encompassed various wind speed ranges, with speeds ranging from 1. 2 m/s to 17 m/s and a mean of 7. 5 m/s at 140 m elevation (Table  1,.
Discussion An experimental campaign was conducted to evaluate the influence of motion on LOS velocity fluctuations derived from lidar profiler measurements. The tests were conducted onshore, utilizing a WindCube v2. 1 lidar mounted on a hexapod designed to replicate the motion of a buoy. As a reference, another fixed lidar of the same type was installed nearby. A comprehensive wind dataset spanning 45 hours, comprising 15 cycles of 3 hours each, was recorded during October and November 2022. For this study, the focus was on regular motions involving rotation around a single axis, as well as combinations of motions around multiple axes of rotation. This analysis pertains to half of the collected dataset (the first 9 sequences, i. e. 1. 5 hours, of each cycle). In the case of rotation around a single axis, the primary factor influencing the results was found to be the alignment between the wind direction and the axis of rotation.
Conclusions The motivation for the results presented in this paper arose from a noticeable absence of experimental testing in the literature, specifically addressing the evaluation of motion-induced effects on turbulent velocity fluctuations measured by a FLS. Understanding and quantifying the impact of motion on turbulent velocity fluctuations, as measured by FLS, are of paramount importance. The conducted experiment has facilitated a comprehensive analysis of motion-induced effects, paving the way for improved calibration and interpretation of FLS-derived wind turbulence data. However, to establish the validity of the findings from the present onshore experimental campaign, it is essential, as a further step, to conduct validation studies in real-world offshore environments or perform similar analysis on historical dataset.
The dataset presented in this paper will serve as a foundation for the formulation of a preliminary motion-compensation algorithm, which will be addressed in further research. This algorithm will then be tested on data collected during real-world deployments of FLS at sea, alongside traditional anemometers, which are still considered the reference for turbulence measurement. Through such comparative analysis, the accuracy of the proposed motion-compensation algorithm and its effectiveness in estimating turbulence can be validated. This process will be integral to refining and optimizing the algorithm's performance to ensure its applicability and reliability in practical offshore conditions. Furthermore, conducting a comprehensive investigation into novel stabilization mechanisms and motion-damping techniques is highly recommended as a crucial next step to mitigate motion-induced turbulence error estimates.
6 and z = 3. 2-4. 5, respectively. Twenty percent of our catalog, or 424 galaxies, have no HST counterpart. The vast majority of these new sources are high equivalent-width z > 2. 8 Lyα emitters that are detected by MUSE thanks to their bright and asymmetric broad Lyα line. We release advanced data products, specific software, and a web interface to select and download data sets.
Data reduction After DR1, described in  Bacon et al. (2017) , we continued to work on the data reduction process, which led to several major improvements. The overall process is similar to DR1, with important changes in the self-calibration algorithm, in the skysubtraction with the Zurich Atmospheric Purge (ZAP) software  (Soto et al. 2016, Appendix B. 3) , and the use of a "superflat. " This process was first applied to the UDF-10 and MOSAIC (278 exposures) and then to the MXDF (373 exposures). The resulting depth and color images for the three data sets are shown in Figs. 4  and 5 , respectively. A detailed description of this improved data reduction process is given in Appendix B.
80 for the MXDF, UDF-10, and MOSAIC data sets, respectively. Source detection is performed on both convolved images with SExtractor  (Bertin & Arnouts 1996). We tune SExtractor's detection parameters to obtain similar segmentation maps between HST and MUSE convolved images. The two catalogs are crossmatched and the mean offset and its standard deviation are derived using 3σ sigma-clip statistics on the matched catalog. The astrometric differences between the MUSE datacubes and HST ACS F775W image are given in the upper part of Table  2. If we exclude the faint sources with AB > 27, the mean astrometric relative error is about 0. 1 arcsec, which is half of a spaxel or about 1/6 of the spatial resolution. We note that astrometric errors are larger for MXDF than for the MOSAIC data set. Such an increase with depth is expected given the increase of source confusion.
We used the same crossmatched catalogs to evaluate the achieved accuracy in source AB magnitude for MUSE data sets with respect to HST photometry. The results, reported in the lower part of Table  2 , are based on SExtractor computed automatic aperture magnitude 2. We note that the magnitude difference is below 0. 1 for MXDF sources with AB < 27. As expected, 2 SExtractor's automatic aperture photometry routine is derived from Kron's algorithm  (Kron 1980) ; see https://sextractor. the magnitude differences decrease with the source brightness and increase with depth.
MXDF spatial PSF Spatial resolution is an important ingredient for most analyses. The spatial PSF was first estimated for each individual raw exposure to reject bad exposures before the final datacube was produced. Then, the final PSF was estimated on the combined datacube. readthedocs. io/en/latest/Photom. html for more detailed information. A4, page 4 of 42 Table  2. Estimated astrometric and photometric accuracy for the three data sets when compared to the HST ACS F775W image.
Data set AB < 25 25 < AB < 27 27 < AB Average astrometric offsets (arcsec) MXDF 0. 12 ± 0. 04 0. 13 ± 0. 05 0. 13 ± 0. 06 UDF10 0. 08 ± 0. 02 0. 10 ± 0. 05 0. 15 ± 0. 09 MOSAIC 0. 05 ± 0. 02 0. 08 ± 0. 05 0. 16 ± 0. 10 Average AB magnitude differences MXDF 0. 08 ± 0. 04 0. 09 ± 0. 07 0. 25 ± 0. 19 UDF10 0. 07 ± 0. 04 0. 23 ± 0. 13 0. 35 ± 0. 24 MOSAIC 0. 10 ± 0. 08 0. 24 ± 0. 16 0. 32 ± 0. 23 Notes. Mean and standard deviation are given for three AB magnitude intervals. The mean AB magnitude error reported by SExtractor for the HST convolved image amounts to 0. 002, 0. 011 and 0. 030 in the AB < 25, 25 < AB < 27 and AB > 27 intervals, respectively. Unfortunately, there is no point source bright enough in the MXDF field of view to estimate the PSF in a single exposure of 25 min integration.
5. The wavelength (λ) and FWHM units are Å and arcsec, respectively. The β value and the FWHM polynomial coefficients (a n ) are given in Table  4. We compared this PSF estimate with the values derived by convolution of the HST images for the F606W, F775W, F814W, and F850LP broadband filters. The method is identical to that used in the paper I  (Bacon et al. 2017, Sect. 5. 1)  for the MOSAIC and UDF-10 datacubes. For ease of comparison, a   Notes. β is the Moffat shape parameter and a n are the coefficients of the polynomial approximation to FWHM(λ) defined in Eq. (  1 ). In the case of the MOSAIC data set, we provide the median PSF for the nine fields. FWHM values in arcsec (e. g. , F 4850 ) are given for the blue (4850 Å), central (7000 Å) and red (9350 Å) wavelengths. direct Moffat fit was performed on the M star for the four reconstructed HST broadband MUSE images.
Line spread function As in DR1, we measure the line spread function (LSF) on a combined cube of data where the sky has not been subtracted. A Gaussian fit was performed on the bright sky lines taking into account the contribution of adjacent fainter lines. As shown in Fig. 8 , the MXDF LSF shows the expected instrumental evolution with wavelength. A good model of the median LSF FWHM is given by the UDF-10 model: LSF(λ) = 5. 866 × 10 -8 λ 2 -9. 187 × 10 -4 λ + 6. 040, with LSF and λ in Å. The LSF is approximately constant in the field of view, except in the outer rings, which show larger variation. This is mainly due to the limited number of exposures that are combined together at the edges, which leaves the intrinsic variation between the 24 IFUs more visible.
We note that the noise level has not been corrected for correlations caused by the interpolation. where V s (λ) is the variance by spaxel, ∆λ the wavelength bin size (1. 25 Å), ∆s the spaxel bin size (0. 2 arcsec), 25 the number of spaxels in 1 arcsec 2 and σ(λ) is derived from the LSF approximation given in Sect. 4. 2. 2 with σ(λ) = LSF(λ)/2. 355. The wavelength interval (k = 1. 29) was chosen to capture 90% of the line flux. The variance by spaxel was computed on the central MXDF area with a depth larger than 120 h after masking the bright sources and taking the median of the datacube variance. We note that the variance is already corrected for the correlated noise as described in Appendix B. 6. SB line (λ) = √ V line (λ) ∆λ √ 25 ∆s 2 withV line (λ) = λ 0 +kσ(λ) λ =λ 0 -kσ(λ) V s (λ ), (2) The same computation was performed for the UDF-10 data set, restricted to the area with depths greater than 30 h, and the MOSAIC data set.
Overview A schematic of the process is shown in Fig. 12. The first phase is the production of inspection data sets (process chart one in Fig. 12 ). Candidate sources are produced in two parallel branches: (i) from blind detection using ORIGIN software (Sect. 5. 2) and (ii) from de-blended extraction using the ODHIN algorithm (Sect. 5. 3) based on the R15 HST catalog and segmentation map. For each candidate source, the first five redshift solutions obtained with the pyMarZ software are recorded (Sect. 5. 4). For each redshift solution, the corresponding emission and absorption lines are computed with pyPlatefit (Sect. 5. 5) and narrow bands for the highest S/N lines are derived  (Sect. 5. 6). The data resulting from the different processes are organized into a database with a set of candidate source catalogs, one for each input datacube and each detection branch (i.
ORIGIN blind source detection The ORIGIN software  (Mary et al. 2020 ) has been developed to automatically detect faint line emitters in MUSE datacubes. It is optimized for the detection of compact sources with faint spatialspectral emission signatures and provides an automated and reliable estimate of the purity (i. e. , one minus the fraction of false positives,  Benjamini & Hochberg 1995; Mary & Roquain 2022). All three data sets were processed with the same input parameters. Below we present the example of the MXDF data set. The preprocessing steps (Fig. 13 ) perform a first pass continuum removal using a discrete cosine transformation of order ten along the datacube wavelength axis. The resulting white light continuum image is then segmented to mask the bright continuum sources. A second segmentation is also performed on the S/N residual image (i.
SourceInspector The inspection package provided to each expert consists of a small database, a set of interactive html files and the SourceInspector software. SourceInspector is a PyQt tool that allows the expert to display each candidate source, to select the redshift solution or to provide a new one, to give a confidence to the redshift assignment and to match the source to other sources (e. g. , ORIGIN to ODHIN) and to HST catalogs. All operations are performed locally. An example of the SourceInspector interface is shown in Fig. 19. The inspection results are exported at the end of the process to be used later for the reconciliation step.
The version refer to the version of the data reduction process. Notes. Computations are performed for an unresolved emission line. Surface brightness and point-source 1σ limiting flux are given in erg s -1 cm -2 arcsec -2 and erg s -1 cm -2 units, respectively. gresSQL relational database 23. User can perform advanced source selection: for example, selecting all MUSE sources with Lyα flux higher than 8 × 10 -19 erg s -1 cm -2 and S/N > 7, redshift in the z = 3 -4 range, and without HST counterparts (Fig. A. 1). The resulting list can be visually inspected by looking to spectra, emission and absorption lines fit, reconstructed broadband and narrowband images and ancillary information (e. g. , HST images). This process allows users to refine the 23 www. postgresql. org selection, possibly taking notes for sources of interest, and then download the selected data.
B. 5. musered To deal with the complexity of this data reduction, given the number of exposures and the number of reduction steps, we developed a Python package called musered on top of the MUSE pipeline. This package uses python-cpl 27  to run the pipeline recipes, and it uses a database to gather information about the raw files and to keep track of everything that has been done, for each file, each recipe, and each version of the reduction. musered takes care of file associations when running a recipe, to find the correct calibration files, and provides a convenient command line interface to run the various recipes, either from the pipeline or from custom Python recipes. It also makes it possible to inspect what was done for each exposure, when it was processed, and with which parameters and which calibration files.
Acknowledgements. We warmly thank  ESO Paranal  staff for their great professional support during all GTO observing runs. We thank the anonymous referee for a careful reading of the manuscript and many suggestion for improving its readability. We thank  Marc Rafelski ,  Katherine Whitekaer ,  Emiliano Merlin  and  Adriano Fontana  for providing information on their published spectrophotometric catalogs and associated segmented maps. RB, YG acknowledge support from the  ANR L-INTENSE  ( ANR-20-CE92-0015 ). NB acknowledges the support of the  ANR 3DGasFlows  ( ANR-17-CE31-0017 ). SLZ acknowledges support by The  Netherlands Organisation for Scientific Research (NWO)  through a  TOP Grant Module 1  under project number  614. 001. 652. A4, page 37 of 42 A&A 670,  A4 (2023 ).