dataesr/scientific-paragraphs-categorization huggingface dataset:
To study the dissimilarities between invertebrate communities, we computed the total beta diversity within each field campaign across all ponds using the beta. multi function of the betapart package 1. 5. 2  (Baselga et al. , 2020). The two additive components of beta diversity namely, turnover and nestedness, reflect species replacement and species richness difference, respectively  (Baselga et al. , 2020). The beta. multi function partitions the total beta diversity into turnover and nestedness components. We calculated the contribution of each pond to beta diversity, that is, the local contribution to beta diversity (hereafter LCBD) following  Legendre and Caceres (2013)  using the beta. div function of the adespatial package 0. 3-8  (Dray et al. , 2019). As pond dissimilarity may be different if computed with the abundance or presence-absence (PA) of morphotaxa, we calculated both using the Hellinger and Jaccard dissimilarity coefficients, respectively.
The LCBD values, which represent the uniqueness of a pond in terms of taxa composition, were tested for significance with the null hypothesis of a random distribution of species among ponds within a sampling campaign  (Legendre and De Caceres, 2013). We also computed the species contributions to beta diversity (hereafter SCBD) to identify the morphotaxa that contribute the most to beta diversity. This last index is calculated only with morphotaxa abundance. We also explored the temporal beta diversity, that is, the variation of morphotaxa diversity between two field campaigns using the temporal beta-diversity index (TBI) computed using the TBI function of the adespatial package  (Dray et al. , 2019). The TBI is composed of the morphotaxa losses (B) and the morphotaxa gains (C). The B-C plots were produced with the plot.
Acknowledgements We thank  Sébastien Breuil  and  Amélie Trouvé  ( Ecosys Versailles ) for their help in sampling and packaging during all the campaigns and for DOC analysis,  Nathalie Bernet  ( Ecosys Thiverval-Grignon ) for PAH analysis, and  Philippe Beguinel  and  Henri Roche  ( CEA ) for their advice in carrying out the project and their participation in the trace analyses. We thank  Yves Levi  for constructive discussions and his advices.
Aeolian dune movement poses a threat to critical infrastructure, urban areas, water resources as well as agriculture. This threat is expected to increase in the coming years due to land degradation, desertification and climate change. Several approaches have been used to investigate the evolution of dune fields. Satellite remote sensing can be considered one of the most accurate tools for the continuous monitoring of global sand covered surfaces. Although early studies found a great potential in synthetic aperture radar (SAR) for dune assessment, the full potential has not been explored as of yet. Therefore, in this study, we present a novel method for assessing complex dune field morphology based on the easily accessible and globally available Sentinel-1 ground range detected (GRD) SAR dataset.
This means that sand is transported upslope and westwards through the wind where it is bound by the river system and transported downslope and eastwards again. The Bor Khyar Els, however, lacks a strong adjacent fluvial system that can act as a natural barrier to the aeolian processes and transport sand downslope into the source lake  (Grunert and Lehmkuhl 2004). Instead, after traveling 800 m vertically up-slope, the sand disperses into several smaller dune fields situated within mountain valleys. Preliminary visual analyses of the dune field based on high resolution optical satellite imagery reveal a large variety of dune types and related sandy surfaces including sandy plains, transverse dunes, grid dunes, longitudinal dunes, parabolic dunes as well as barchans. A preliminary analysis of the ERA5 climate dataset reveals large seasonal differences in wind with the strongest winds in winter from west to northwest (C3S, 2017).
This can be attributed to the very low backscatter on sand and the large variation in backscatter within small distances resulting in a very low coherence, which is challenging for interferometric SAR analysis. As such, the DEM-approach was dismissed. Instead, we focused on the indirect derivation of morphological features from Sentinel-1 ground range detected (GRD) data. The pre-processing and extraction of Sentinel-1 GRD scenes as well as the ERA5 wind analysis was conducted within the Google Earth Engine (GEE), an openly accessible cloud-based platform for geospatial analysis, powered by the Google server infrastructure  (Gorelick et al. 2017). For Sentinel-1, we chose to work with the VV-polarized dataset, as it shows the highest dependency on the local incidence angle. To achieve analysis-ready-data (ARD), the pre-processing included additional border noise correction, speckle filtering and radiometric terrain normalization.
For this, we used a slightly modified version of the openly accessible ARD script by  Mullissa et al. (2021) , applying a 10scene multi-temporal improved Lee sigma speckle filter with a kernel size of 15 cells  (Lee et al. 2009). For the terrain normalization, the NASA SRTM global digital elevation model was used  (Farr et al. 2007 ). Based on a preliminary visual interpretation, pre-processed Sentinel-1 scenes from 4 dates were selected and exported for further processing. The dates include  2015-01-31, 2018-06-14, 2018-10-12 and 2021-12-07 , each for which a Sentinel-1 GRD mosaic of the whole dune field was created. The GEE was also used to calculate wind direction and speed from ERA5 wind u-component and v-component. Monthly data was used to aggregate speed and direction over the full timespan (2015-2021) while wind vectors for visualization were created from daily data.
Visual interpretation and profile analysis In the visual interpretation of the whole dune field (AOI A), Sentinel-1 scenes, Sentinel-2 true colour composites and the SRTM DEM were compared. This comparison confirms the expectations about the C-Band sensitive backscatter characteristics of active and inactive dunes, stated in the materials and methods. As such, it could be observed that active dune fields can be separated from inactive dune fields and surrounding areas by their lower backscatter. For a simple thresholding approach, a backscatter of < -20 dB yielded the best results in differentiating active dunes across all scenes. The only other surfaces in the study area that fall below this threshold are lakes. Confusion between these two surface types, however, can be easily avoided by the complementary use of optical imagery and/or surface water datasets.
Conclusion In this study, we have shown the possibilities as well as limitations in using the currently most easily accessible SAR dataset, Sentinel-1 GRD, to access the morphology of a complex dune field and changes therein. As the very low areal coherence within active complex dune fields does not allow for interferometric approaches such as the calculation of a DEM, we used the GRD backscatter values to extract morphological features indirectly. Using this method, three main dune features can be extracted based on surface roughness and local incidence angle. These are namely active dune sand, dune ridges and inter-dune ripples. Based on this information, a comparative analysis to optical Sentinel-2 imagery and the SRTM DEM as well as a profile-based continuous wavelet transform frequency analysis were conducted for the complex dune field Bor Khyar Els in western Mongolia.
Using these methodologies, three areas of interest within the dune field were analysed, revealing significant differences in aeolian activity, wind direction and wind seasonality. Based on the results of this study, we see a good suitability of Sentinel-1 for complex dune field analysis. This suitability is additionally amplified by the comparison to similar approaches based on optical imagery and DEMs. In comparison to these datasets, Sentinel-1 allows for a far better detection of dune-relevant morphological features. In comparison to optical imagery where these features only show slight differences in their reflectivity, SAR-based analysis offers an easy discrimination. The main advantage over globally available DEMs is the comparably high spatial resolution and especially the multi-temporality.
Acknowledgement This work was carried out in the frame of the exploratory research space (ERS), funded by the  Federal Ministry of Education and Research (BMBF)  and the  Ministry of Culture and Science of the German State of North Rhine-Westphalia (MKW)  under the  Excellence Strategy of the   Federal Government  and the  Länder  [project  ID G: ( DE-82 ) EXS-SF-OPSF612 ]. We thank  Alexandra Weber  for the valuable help in reviewing the figures and structure,  Damian Stawinoga  for testing the interferometric approach and  Martha Wingen  for the conceptual and administrative help. We would also like to thank the reviewers for the time and effort invested into reviewing the manuscript. Their comments and suggestions helped in improving the quality of the manuscript significantly.
In the latter case, the tides do not propagate with the rotation of the sun but according to the passage of the sun. They should be generated by a zonal asymmetric thermal forcing induced by large longitudinal variations in tropospheric humidity and have been observed in the lower stratosphere using GPS radio occultation observations from space  [24]. The meteorological analyses have been extended to the mesopause (80 km) with a larger data set (ERA5), allowing for the study and update of tidal characteristics and the specific contribution of non-migrating tides. To better provide direct information on temperature tides, a constellation of small satellites has been suggested  [22] , while long-term observations from space clearly show difficulties in providing continuity using successive sensors  [17, 18].
In this study, we propose to investigate meteorological analyses (ERA5) to extract migrating and non-migrating tides in the middle atmosphere while analyses are performed up to the mesopause. In Section 2, data are presented. The methodology to extract local tides is described in Section 3, as well as the separation of migrating and non-migrating modes. In Section 4, migrating tidal amplitudes and phases are compared with the Global Scale Wave Model (GSWM), while Section 5 presents non-migrating components. Section 6 discusses the results and their impact on observation requirements. Section 7 provides conclusions.
Description of ERA5 Re-Analyses The ERA5 numerical model is a weather and climate model  [25]  developed by the Copernicus Climate Change Service of the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 is a reanalysis dataset combining various observations (ground, weather balloon, satellite) and a mathematical model constructed around primitive dynamic equations  [26]  governing atmospheric motions with equations of moist processes and convection, ground interactions, and physico-chemistry equations. The model is therefore constantly adjusted according to the arrival of observations. This method makes it possible to best approach the reality of the phenomena and to make forecasts  [27]. This model simulates many atmospheric, oceanic, and terrestrial variables, including dynamic parameters (pressure, temperature, wind) and density of atmospheric species, including radiatively active ones for tides like ozone, water vapor, and oxygen.
Discussions and Conclusions If migrating and non-migrating tides are correctly simulated in numerical weather and climate models, it means that both radiative and dynamic processes are correctly taken into account and can somehow validate models on hourly scales. The ERA5 model is currently one of the most sophisticated representations of our atmosphere for weather and climate forecasting. The analysis of the new data from the European center ERA5 allows us to develop a new methodology to separate migrating tides from non-migrating tides by an objective analysis of histograms by longitude where migrating tides by definition must have similar characteristics. It appears that the characteristics of the migrating modes are in fairly good agreement up to 60 km, even if the amplitudes at mid-latitudes remain twice as large compared to the simulations with the GSWM model, as already reported by previous studies.
The use of a satellite constellation could partially solve this weakness. Constellations are only feasible if several small satellites are deployed simultaneously at low cost. With the limb observations, it is possible to derive temperature profiles with a good vertical resolution (1-2 km). This technique can be developed within small satellites like CubeSat and can then be deployed through a large constellation that can provide several observations each day over a single location, allowing us to take into account tidal fluctuations. These data would provide global coverage with an improved sampling rate, which could be incorporated into meteorological models. This will be crucial for providing accurate mesospheric temperature information. Space observations could be supplemented by specific continuous observations of the tides from the ground in convective regions to observe local non-migrating tides.
Data Availability Statement: ERA5 model output data have been downloaded from the ECMWF portal at https://www. ecmwf. int/en/forecasts/dataset/ecmwf-reanalysis-v5 (accessed on 2 April 2023) in addition to documentation and support concerning the 137-level model. GSWM model monthly characteristics were provided by Maura Hagan through a dedicated portal https://www2. hao. ucar. edu/gswm-global-scale-wave-model (accessed on 2 April 2023).
Wé strictly followéd thé protocol éxcépt that wé startéd diréctly at thé fragméntation stép aftér thé rRNA rémoval stép and wé adjustéd thé numbér of cyclé réquiréd to amplify librariés. For RNAsé tréatmént, 1 ul of micrococcal nucléasé (Biolabs M02475, 2x10 6 U/ml) was addéd to 200 μl buffér containing 1 mM CaCl2 and incubatéd for 10 min 37°C. An RNA aliquot was harvéstéd prior to TAP purification for thé analysis of total RNAs (input) and was tréatéd thé samé way as thé purifiéd fractions éxcépt for thé purification. Illumina RNA sequencing results analysis Réads wéré alignéd along thé 16 chromosomés and mitochondrial séquéncé of S. cerevisiae S288C génomé. For thé mapping, wé uséd thé STAR program (vérsion 2. 7) with défault paramétérs éxcépt for thé following préféréncés: alignIntronMax 1500, alignMatésGapMax 1500, alignSJovérhangMin 25.
For thé éxon annotation wé uséd thé GTF filé for Saccharomyces cerevisiae (R64-1-1. 104) from ENSEMBL. Réad counts wéré obtainéd using thé féaturéCounts function of thé Subréad packagé, with an annotation filé that contains non-coding RNA coordinatés in addition to yéast géné transcripts  (Malabat et al, 2015). Thé analyzéd data was visualizéd using thé Intégratéd Génomé Viéwér, IGV  (Robinson et al, 2011)  and furthér data procéssing was doné using R (R Coré Téam, 2022). It consistéd in filtéring out féaturés with léss than 10 réads, followéd by a normalization by thé total numbér of réads for éach samplé and avéraging of thé log2 transforméd résults for thé thréé réplicatéd éxpériménts for éach condition. Input normalizéd counts wéré substractéd from purification counts for éach condition.
Another widely used automatic segmentation method is proposed by FreeSurfer  [10] , which offers hippocampal subfields delineation with multiple input options. Reliability of volumetric estimations has been reported for FreeSurfer's procedure using images acquired by two different MR scanners  [7] , as well as using different input images (T1w, T2w, T1w and T2w)  [11]. By using a dataset collected in a healthy young adult population by the Human Connectome Project (HCP)  [12] , we tested the inter-method (i. e. ASHS and FreeSurfer) reliability for volumetric analysis using automatic segmentation of hippocampal subfields. In a second moment, we explored the relationship between IHI scores, rated according to the criteria reported in  Cury et al. (2015) , and hippocampal subfields' volumes extracted with both ASHS and FreeSurfer methods.
MRI data All HCP subjects were scanned on a customized Siemens 3T housed at Washington University in St. Louis using the gradients of the WU-Minn and MGH-UCLA Connectome scanners. T1w 3D MPRAGE were acquired with TR=2400 ms, TE=2. 14 ms, TI=1000 ms, flip angle=8 deg, FOV=224x224, 0. 7 mm isotropic voxel, bandwidth=210 Hz/px, iPAT=2, and acquisition time=7:40 (min:sec). T2w 3D T2-SPACE were acquired with TR=3200 ms, TE=565 ms, variable flip angle, FOV=224x224, 0. 7 mm isotropic © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Automatic Segmentation of Hippocampal Subfields The open-source ASHS software, developed by the Penn Image Computing and Science Laboratory (PICSL) at the University of Pennsylvania  [8] , invoke image analysis algorithms from FSL  [14]  and ANTS  [15]. ASHS implements the joint label fusion (JLF)  [16]  and corrective learning (CL)  [17]  algorithms. The steps of the ASHS segmentation pipeline are described in  Yushkevich et al. (2015). Preprocessed T1w and T2w in AC-PC line were provided as input for the segmentation. ASHS was run using the IKND Magdeburg Young Adult 7T Atlas, provided by Ottovon-Guericke-University Magdeburg. The atlas has been developed by manual segmentation of hippocampal and the adjacent medial temporal lobe regions  [18]. The subregions segmented with this atlas are: entorhinal cortex (ErC), area 35 and 36 of the perirhinal cortex (PrC), parahippocampal cortex (PhC), subiculum (Sub), cornu ammonis (CA) 1, 2 and 3, dentate gyrus (DG), cysts, and tail.
FreeSurfer segmentation of hippocampal subfields The tool for segmentation of hippocampal subfields and nuclei of the amygdala offered by FreeSurfer 7, was run using the T1w scan from recon-all method (segmentHA_T1. sh). Preprocessed T1w in AC-PC line were provided as input. This tool segments the different subfields by using a Bayesian inference approach based on image intensities and prior knowledge of a probabilistic atlas which was generated from in vivo manual segmentations and ultra-high-resolution ex vivo MRI data  [19]. The subfields segmented with this method followed the head-body-tail subdivision  [10]  and are: CA1 body, CA1 head, CA2/3 head, CA2/3 body, CA4 head, CA4 body, fimbria, granule cell layer of dentate gyrus (GC-ML-DG) head, GC-ML-DG body, hippocampus-amygdalatransition-area (HATA), tail, hippocampal fissure, molecular layer head, molecular layer body, parasubiculum, presubiculum head, presubiculum body, subiculum head, subiculum body.
Before starting the rating of the database, we evaluated intra-and inter-rater agreement. For intra-rater coherence, A. F. rated the same database of 30 subjects with a distance of 15 days. For inter-rater agreement, C. C. rated the same 30 subjects. Kappa scores were calculated for C5, and kappa weight scores were calculated for criteria C1, C2, and C3. All intra-and inter-rater scores were assessed independently for each hemisphere and all of them were above 0. 65.
Volumes were previously corrected by whole brain volume (BV) calculated on FSL using fslstats function. Gender was also included as independent variable in all models, but age was not included due to the very narrow range. The forward stepwise method was used to identify the best grouping of independent variables that account for the most variance in the outcome (R-squared). This approach enters independent variables one at a time after considering the marginal contribution of a variable controlling for other variables already in the model. The absence of multicollinearity between variables included in each model was tested though the variance inflation factor (all VIF<5. 00). Finally, using the automatic linear modeling of IBM SPSS Statistics 25, the predictor importance of each independent variable was determined by computing the reduction in variance of the target attributable to each independent variable, via a sensitivity analysis  [20].
The score indicates the relative importance of each variable in estimating the model, with values for all predictors on the display summing 1. All p-values reported were Bonferroni corrected for multiple comparisons (10 correlations + 24 Mann-Whitney tests + 2 hemispheres x 11 subfields segmented with ASHS + 2 hemispheres x 19 subfields segmented with Freesurfer = total of 94). All statistical analyses were conducted on IBM SPSS Statistics 25.
For the ðBDM þ B 0 Þ case, to calculate the E i values, we sum the BDM signal and the background B 0 for each energy bin. Clearly, if the BDM contribution explains experimental data, Δχ 2 must be less than 0 corresponding with a better fit. The XENON1T Collaboration had reported a 3. 5σ excess of events in the electron recoil range of 1 < E R < 7 keV  [6]. However, a recent dataset from the XENONnT experiment  [7] , aimed at verifying the aforementioned excess, shows that no such excess exists. We use the data from this experiment for our analysis. To derive the exclusion limit with the 95% confidence, we demand Δχ 2 > 40. 1 that corresponds to 27 d. o. f. For Super-K, we use the SK-I data, which was taken for total 1497 days of live time  [56]. The detector originally looked for the diffuse supernovae background events via inverse beta decay ν e þ p → n þ e þ.
In the present work, we assume that the observed events are consistent with the background and hence the signal due to DM should be consistent with the data within the uncertainty. Since an estimate of the background is not found in the literature for SK-I data, we take χ 2 ðB 0 onlyÞj SK ¼ 0. The excluded region satisfies Δχ 2 > 26. 3, which corresponds to 95% exclusion limit for 16 d. o. f. Both Super-K and XENONnT experiments are located deep underground to reduce background, but this also attenuates the DM flux entering the detector. The attenuation of DM particles happens mainly due to the interaction with electrons in Earth's surface, significantly altering the DM flux reaching the detector. While a detailed study of the effects of attenuation on boosted DM is beyond the scope of this paper, we have determined the attenuation bound considering a DM particle with T χ ¼ 1 GeV.
Bounds from stellar and supernovae (SN 1987) cooling  [66, 67]  are also shown for the light mediator case. Constraint due to solar reflection of DM, relevant for the heavy mediator case  [30, 45] , is shown in amber color. section relevant at the detector end as well. As discussed in Sec. II, the vector mediator case yields slightly stronger bounds than the scalar mediator case. We also plot the DM attenuation bound for XENONnT and Super-K for the heavy mediator scenario. Similar results have been obtained for vector mediator in Ref. [30] , but it should be noted that the dataset used in Ref. [30]  is based on XENON100 and XENON1T's S2-only analysis  [68, 69] , while we use the data based on XENONnT's S1-S2 analysis; thus the exclusion bounds we provide are slightly different from those obtained in Ref.