dataesr/scientific-paragraphs-categorization huggingface dataset:
The merged reads were quality filtered based on expected errors removing reads above the threshold of 1  56. The merged reads were turned into zero-radius operational taxonomic units (zOTUs) by compiling the sequences into sets of unique reads and performing error-correction using the UNOISE3 algorithm discarding sequences with fewer than 4 reads. The zOTUs were assigned taxonomy using DADA2's assignTaxonomy (minBoot = 50) and assignSpecies, using formatted version of the Silva v. 138 database. A phylogenetic tree of the sequences was created with the help of the MAFFT software (v. 7. 407  57  and the FastTree software (v. 2. 1. 10). Before analysis the data were filtered to remove low abundant and highly sparse zOTUs. The zOTUs were filtered based on fraction of total abundance, retaining all zOTUs contributing to more than 0.
Analysis of microarray data from mouse liver samples The linear model was fitted using the limma lmFit function  50  considering washing batches as a blocking factor. Pairwise comparisons between specific diets and MF diet were applied using specific contrasts. A correction for multiple testing was applied using the Benjamini-Hochberg procedure for false discovery rate (FDR). Probes with FDR ≤ 0. 05 and fold change >1. 5 compared to MF diet were considered differentially expressed. GO analysis was performed and redundancy within lists of GO terms was reduced by the online software Revigo (v. 1. 8. 1) with a similarity score set to 0. 5. Analyses of transcription factor enrichment were performed with Enrichr 60 (v. Sparse partial least squares regression using the regression mode was used to select liver mRNAs that were most likely to predict liver triglyceride content.
Analysis of 16S rRNA data from mouse cecum samples The graphical representations and statistical analyses of the microbiota were performed using R v. 3. 6. 1 with packages phyloseq v. 1. 28  63 and ggplot2 (v. 3. 3. 3) 64. Faith's phylogentic diversity and richness were calculated using the Picante (v1. 8. 2-package)  65. The analysis of β-diversity was performed on relative abundance normalized data. Bray-Curtis dissimilarity and unweighted UniFrac distance were calculated and principle coordinates analysis (PCoA) was performed with the vegan (v. 2. 5-6) R package  66. The Adonis test with 9999 permutations was used to assess analysis of variance in of composition and was complemented by analysis of similarities (n = 9999) (ANOSIM) using the vegan package. zOTUs were rarefied to read depth of the smallest sample (n = 20,701) using R-package rtk (v.
0. 2. 6. 1) and binned on genus and phylum level according to taxonomic assignment using R-package phyloseq (v. 1. 3. 0). Differential abundance analyses were performed using R-package metadeconfoundR (v. 0. 2. 6) under standard settings: Associations between rarefied bacterial read counts and diet were first tested using simple rank-based Wilcoxon tests. Using the "randomVar" parameter, we then controlled for cage effects in the detected associations by running an additional set of linear mixed-effects model likelihood ratio tests, adding the cage as random effect in all "diet X versus MF diet" comparisons (Figs. 3e, f and 7e).
Multi-omics data integration Bidirectional correlations between cecal OTUs and hepatic transcriptome or metabolic features of the host were investigated using N-integration discriminant analysis with DIABLO, an algorithm that aims to identify and select a highly correlated multiomics signature, while also maximizing discrimination between experimental groups 67 using the R package mixOmics (v6. 10. 9). Low abundance OTUs (representing <0. 01% of total OTUs) were removed leading to a dataset of 291 OTUs, and the 5000 hepatic genes with the highest variance were kept. We used two components in the models, and for the estimation of model parameters, the cross-validation procedure (CV) method was used. For the correlation networks, only correlations with a Spearman's rank correlation coefficient > 0.
75 were plotted. Bidirectional correlations between fatty acids, measured in cecal content or in serum, and cecal OTUs or liver transcripts were investigated using the regularized canonical correlation analysis (rCCA) implemented in mixOmics. This algorithm maximizes the correlation between two matching omics datasets measured on matching samples. Models using cecal fatty acids were built using n = 56 mice for which cecal fatty acids were measured, while those using serum fatty acids were built with n = 79 mice. OTUs and liver mRNA were filtered as described above for DIABLO analysis. The density of the correlation network was calculated by dividing the number of correlated features with ρ > 0. 55 by the total number of possible correlations in the model.
Data availability The raw metagenomic sequence data of the 114 human subjects from the Ironmet cohort have been deposited in the European Nucleotide Archive (ENA) under the project number PRJEB39631 with the accession numbers ERS4859818-ERS4859933 (https://www. ebi. ac. uk/ena/ browser/view/PRJEB39631). The microarray data generated in this study have been deposited in NCBI GEO under accession code GSE222060. The mouse cecum 16S rRNA data generated in this study have been deposited in the European Nucleotide Archive (ENA) under accession code PRJEB58626. Data generated in this study are provided in the Source data file. The reference human genome GRCh38. p11 can be accessed here: https://www. ncbi. nlm. nih. gov/datasets/genome/ GCF_000001405. 37/. Silva can be accessed here: https://www. arbsilva.
Acknowledgements We thank  Anna Hallén ,  Louise Helldén ,  Carina Arvidsson ,  Zakarias Gulic  and  Per-Olof Bergh  for technical assistance,  Rosie Perkins  for commenting and editing the manuscript and  Martin Adiels  and  Chinmay Dwibedi  for help with mathematical calculations. We also thank  Claire Naylies  and  Yannick Lippi  from the  GeT-TRiX facility at Toxalim  for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT Genopole Toulouse Midi-Pyreńeés facility. Measurements of fatty acids in diet, cecum and serum were performed at CMSI, Chalmers University. Computations were enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at the  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under project  SNIC 2022/5-451  partially funded by the  Swedish Research Council  through grant agreement no.
2018-05973. This work was supported by the  European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)  coordinated by  Rémy Burcelin , the respective national/regional funding organizations:  Svenska Forskningsrådet FOR-MAS  ( 2017-02001 ). This work was further supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) as part of the clinical research unit ( CRU339 ); Food allergy and tolerance (FOOD@) -409525714. This work was also partially supported by research grants  FIS  ( PI18/01022  and  PI21/01361 ) and the project  PI20/01090  (co-funded by the  European Union  under the  European Regional Development Fund (FEDER)  "A way to make Europe") from the  Instituto de Salud Carlos III from Spain , by  Fondo Europeo de Desarrollo Regional (FEDER)  funds, and the  Catalan Government  (AGAUR, # SGR2017-734 ,  ICREA Academia Award 2021 ), J.
Acknowledgements The Imaging X-ray Polarimetry Explorer (IXPE) is a joint US and Italian mission. The US contribution is supported by the  National Aeronautics and Space Administration (NASA)  and led and managed by its  Marshall Space Flight Center (MSFC) , with industry partner  Ball Aerospace  (contract  NNM15AA18C ). The Italian contribution is supported by the  Italian Space Agency (Agenzia Spaziale Italiana, ASI)  through contract  ASI-OHBI-2017-12-I. 0 , agreements  ASI-INAF-2017-12-H0  and  ASI-INFN-2017. 13-H0 , and its  Space Science Data Center (SSDC)  with agreements  ASI-INAF-2022-14-HH. 0  and  ASI-INFN 2021-43-HH. 0 , and by the  Istituto Nazionale di Astrofisica (INAF)  and the  Istituto Nazionale di Fisica Nucleare (INFN) in Italy. This research used data products provided by the  IXPE Team (MSFC ,  SSDC ,  INAF , and  INFN)  and distributed with additional software tools by the  High-Energy Astrophysics Science Archive Research Center (HEASARC) , at  NASA Goddard Space Flight Center (GSFC).
Statistical analyses We built all graphical representations with R studio version 4. 1. 2, using the packages ggplot2 version 3. 4  (Wickham et al. , 2016)  and gridExtra version 2. 3  (Auguie and Antonov, 2017). To establish the limits of the two staminate phases through extrapolation, we used the package Hmisc version 4. 7  (Harrell, 2022). To test the difference in insect abundance between staminate phases on trees and on female flowers, we selected chestnut clones with at least two copies in the experimental plot. To facilitate comparisons between staminate phases, we selected those trees for which we had performed at least two inventories during each phase. For each insect taxon, we performed a negative binomial family generalized linear mixed model (GLMM) to account for overdispersion in the data.
We considered two fixed effects on insect abundance, staminate phase effect and clone effect, and a random effect corresponding to the tree effect. We also calculated a marginal R² and conditional R² using the trigamma function  (Barton, 2022)  for each GLMM. We used package lme4 version 1. 1-28  (Bates et al. , 2015)  and MuMIn version 1. 46. 0  (Barton, 2022)  for this purpose.
ACKNOWLEDGMENTS The authors thank the following persons who contributed to the monitoring in spring 2021:  A. Bedani ,  C. Bodénès ,  O. Bonnard ,  B. Curtit ,  J. Fernández-Cruz ,  C. Lalanne ,  E. Petit ,  M. Rheinheimer , and  I. Van Halder. We thank  T. Renard  for providing feedback on the manuscript and  M. Petit  for expert advice on figure visuals. We also thank  B. Wenden  ( UMR BFP, INRAE ) for valuable advice on processing the phenology data, and  B. Castagneyrol  ( UMR Biogeco ) and two anonymous reviewers for useful feedback on the manuscript. We are grateful to the  Société Botanique de France  for awarding the Jussieu prize 2021 for our work on chestnut pollination, thus financially facilitating our ongoing research. The study was funded by the  Région Nouvelle-Aquitaine  ( Regina  project no.
The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.
Background & Summary The data from European Space Agency's (ESA) and Centre National d'Etudes Spatiales (CNES) SMOS satellite are widely used in a variety of applications over both land and oceans. Applications over land include the water cycle (freeze/thaw 1 , soil moisture  2, 3  , evapotranspiration  4  , precipitation  5  , net water flux  6  , etc. ) and carbon cycle (proxy by vegetation optical depth, VOD)  7  , as well as applications for agriculture studies  8. The SMOS satellite was launched on November 2, 2009, to a low-earth, polar sun-synchronous orbit with a mean altitude of 758-km, and flight forward with a fixed forward titled angle of 32. 5° between the boresight and the local nadir. The SMOS satellite is equipped with a synthetical aperture radiometer, which is a Y-shaped structure with 69 antenna elements regularly distributed (0.
Being the first L-band radiometer, problems were not expected in the protected band, so no filtering approaches were implemented in the receivers. This leads to data loss or meaningless retrievals of SMOS TBs  13, 14. Great success was obtained in reducing RFI problems since the SMOS launch, but many RFI sources still need to be located, filtered, or canceled, and new RFI sources also appear every day  15. Therefore, apart from the flagging approaches developed at the Level 1 and 2 products in the SMOS processors, different kinds of methods have been proposed to post-process SMOS multi-angular TB. For example, a fixed width binned average has been used in the CATDS Level-3 (L3) daily SMOS multi-angular TB product, which also transforms the observed TB polarizations to a ground-based reference frame  16.
Some studies fitted multi-angular SMOS TBs with an exponential function  17  , a quadratic function  18  , or a third-order polynomial function  19. Zhao et al. proposed a sophisticated method, named two-step regression, to refine the multi-angular SMOS TBs  20. The approach was proposed based on a large number of simulations with a wide range of natural conditions using radiative transfer models. The two-step regression approach has been shown to perform best compared to other methods  21  and has been used in the retrieval of soil moisture, snow density, and sea ice  [22] [23] [24] [25]. The current research aims to produce an RFI-suppressed SMOS multi-angular TB dataset using the two-step regression approach. This dataset is intended to provide improved opportunities for better scientific applications over land.
Methods The ESA SMOS Level-1C Full Polarization (SCLF1C) product  26  from 2010 to 2021 (version 724) was refined using the two-step regression approach. The full polarized TB observations at the antenna frame are provided to 15-km in an equal-area discrete global grid (DGG) system (Icosahedral Snyder Equal Area projection, ISEA 4H9) in the SCLF1C product. The flowchart of the post-processor is shown in Fig. 1. It mainly contains four parts after extracting TBs at each DGG node from the SCLF1C orbit file. Firstly, TB observations at the antenna frame were filtered with various criteria (details presented in antenna level rows in Table  1 ). The snapshot acquisition sequence in the full polarization mode is XX, XX/XY, YY, and YY/YX  27. As there is no complete dataset for any epoch, a simple first-order spline interpolation was employed to fill the missing acquisitions.
Data Records The refined SMOS multi-angular TBs dataset is located at National Tibetan Plateau Data Center (TPDC) and is provided in three types of projections  32  , including the ISEA4H9 grid and the EASE-GRID 2. 0 25-km and 36-km grid system. All files are stored in the netCDF4 format. For the ISEA4H9 gridded data, it includes: (1) basic observation information, such as geolocations, DGG time, counts of strong or other types of RFI, and aliasing from the SCLF1C flag. (2) refined SMOS multi-angular TBs and corresponding flag range from 2. 5° to 62. 5°, with an interval of 5°, including TB and flag at the incidence angle of 40°. (3) abundant auxiliary statistics at the incidence angles of refined TB. These statistics comprise the overall and binned standard deviation of the original and filtered TBs, the bias, and the RMSD between the original, filtered TB, and the refined TB, as indicated in the yellow rhomboids in Fig.
1. (4) regression results, providing all parameters in Eqs. (2, 3), degree of freedom in the second step at H-polarization, and goodness of fit parameters such as the reduced chi-square  33  , Akaike and Bayesian information criterion 34 statistics. Additionally, the dataset in the EASE-GRID 2. 0 projection contains variables that can be resampled to 25-km and 36-km grids, along with corresponding uncertainty. However, variables such as counts in (1) and regression results in (4) that cannot be resampled are not included in the dataset. To be noted, the resample method may introduce uncertainties in the results, and the standard deviation of a variable for resampling in an EASE-GRID 2. 0 cell was used to evaluate the interpolation artifacts. More details can be found in the user guide. The ISEA4H9 gridded filename was inherited from the SMOS SCLF1C orbit file with ascending or descending flag added in the filename.
The EASE-GRID 2. 0 grided files have a suffix about the resample method and grid size. All the datasets  32  can be accessed at https://doi. org/10. 11888/Terre. tpdc. 300406 or https://cstr. cn/18406. 11. Terre. tpdc. 300406. Detailed description of the data records 32 can be found in the readme. pdf file and codes and procedures for processing and plotting the figures are also included.
technical Validation Below analyses are presented to support the technical quality of the refined SMOS multi-angular TB dataset. Demo of the two-step regression fitting procedure. Figure  2  shows the fitting procedure of the two-step regression approach with the SCLF1C observations from the DGG id 4060112 (longitude: 104. 591°E, latitude: 54. 173° N) on June 2, 2021. Figure  2 (a) shows aliasing exists in the snapshots with large oscillations, especially at small incidence angles in the "extended alias-free" field of view region. At this DGG node, aliasing at small incidence angles results in significantly higher total intensity for small incidence angles. However, the aliasing effect was effectively mitigated by the first-step regression procedure. This is primarily because the observations at intermediate and large incidence angles do not exhibit aliasing, which leads to its mitigation, as shown in Fig.
Further, it is necessary to demonstrate the potential advantages of using this refined SMOS TB for soil moisture retrieval. The total emissivity was calculated using the refined SMOS TBs and compared with in-situ soil moisture from 19 dense networks (Table  3 ) at the same incidence angles as the CATDS L3 TB product, with the following equation: e TB T ( 5 ) P P s = θ θ where θ e P is the total emissivity at incidence angle θ and P polarization. T s is the soil temperature extracted from the CATDS auxiliary data (https://data. catds. fr/cpdc/Common_products/GRIDDED/AUX/), projected in EASE GRID 2. 0 25-km. For each soil moisture observation network, a bounding rectangle was defined using the maximum and minimum longitude and latitude of network stations. The emissivity of the EASE-GRID 2. 0 cells located within this rectangle was averaged and compared with the average soil moisture for validation.
Spatial comparison of the refined TB at 40° with SMAP TB. To illustrate the performance of the refined SMOS TB dataset, TB at 40° (resampled to EASE-GRID 2. 0 36-km) was compared with the SMAP TB product. To assess the correlation and differences between the two datasets, R, bias (unit: K), RMSD (unit: K), and unbiased root mean square deviation (ubRMSD, unit: K)  41  were used as evaluation metrics. Figure  6  shows the details of the above statistics. The comparison spans from 31 March 2015 to 31 December 2021. While SMOS and SMAP cross the equator at the same time, they pass over an area at higher latitudes either before or after 6 am/pm. This results in overlapping orbits at a certain time. To ensure a fair comparison, a threshold of 3600 seconds was used to filter the overlap area. Additionally, SMAP flags including "Static Water", "Radar-derived Water Fraction", "Coastal Proximity" and "Urban Area" were used to filter the TB.
Usage Notes In this study, we have presented an RFI-suppressed SMOS multi-angular TB dataset  32  spanning over a decade. The two-step regression method proposed by Zhao et al. 20  was used to refine or interpolate the multi-angular SMOS TB affected by field aliasing and RFI. The resulting refined TBs in DGGs were then resampled to the EASE-GRID 2. 0 projection, with two grid sizes of 25 km and 36 km, using a Hamming window approach with a footprint of 43 km. As described in the previous section, this refined SMOS TB dataset  32  has demonstrated great RFI-suppression performance. It exhibits strong agreement with SMAP TB at 40°, and the corresponding emissivity derived from the refined TBs shows a better relationship with in-situ soil moisture compared to the CATDS TB in most cases. This dataset 32 can be used to retrieve essential land parameters such as soil moisture and snow density etc.
Acknowledgements This study was supported by the  Second Tibetan Plateau Scientific Expedition and Research Program  (No. 2019QZKK0206 ). The CATDS data were obtained from the  CATDS , operated for the  Centre National d'Etudes Spatiales (CNES, France)  by  IFREMER (Brest, France) ". We acknowledge  ISMN  for providing in situ soil moisture observations. We thank the anonymous reviewers' comments improved the paper.
Code availability The software and codes for processing the collected data and for plotting the figures are conducted in Python 3. 7 and included in the dataset at https://doi. org/10. 11888/Terre. tpdc. 300406 or https://cstr. cn/18406. 11. Terre. tpdc. 300406. And they are also available on GitHub: https://github. com/thimpeng/RFI-Suppressed_SMOS_L-band_multi-angular_TB_Refinement.
e. , a signal-processing algorithm whose parameters have been derived with the help of a database and some statistical optimization algorithms. Such an approach provides the digital signal processor the ability to potentially change its parameters within a given signal analysis structure. Analogue intelligent radars go further by replacing the digital signal processor with an analogue integrated circuit: instead of performing calculations through a complex computer structure in the digital domain, the whole processing process happens in the analogue domain in one or multiple very-low-power ASICs. This technology aims at increasing the radar signal Sensors 2023, 23, 3036 2 of  24  processing speed and decreasing the energy consumption (multiple applications for AI can be found in  [9] [10] [11] [12] [13] [14] [15] [16] [17] ).
• Electrocardiograph We can measure the differential voltage on the body with electrodes directly stuck on the skin. The order of magnitude of this differential is 10 -4 V. This relatively small voltage needs some amplification and filtering to be readable by a computer. Instead of making the circuit ourselves, we chose a circuit sold on the Internet. We integrated this circuit directly with an Arduino board, which is the computer we chose to use. On the other side, we connected wires and the electrodes. To detect heartbeats, Figure  5  shows the heartbeat module with the electrodes. small voltage needs some amplification and filtering to be readable by a computer. Instead of making the circuit ourselves, we chose a circuit sold on the Internet. We integrated this circuit directly with an Arduino board, which is the computer we chose to use.
Data-Saving Mechanism There are two ways to save data: either to have a data storage device on-board or sending it directly to the computer via WiFi.  Using a SD card It is possible to buy on the Internet SD card adaptors directly connectable on Arduino boards. They are provided with computer code directly implementable on Arduino. The data can be saved in a. txt format file that is easy to use for signal processing (see Figure  12 ). The Arduino code can be found on the official Seeed Studio Wiki and also on the Arduino website  [83, 84].
• Using a SD card It is possible to buy on the Internet SD card adaptors directly connectable on Arduino boards. They are provided with computer code directly implementable on Arduino. The data can be saved in a. txt format file that is easy to use for signal processing (see Figure  12 ). The Arduino code can be found on the official Seeed Studio Wiki and also on the Arduino website  [83, 84]. NiMH battery 300 mAh, LiPo charger, LiPo 2S 4500 mAh battery.