dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 4. 0) with the RStudio toolkit (v. 1. 0. 143). Venn diagrams were constructed using assigned sequences independently at the genus and phylum levels using the VennDiagram package  (Chen and Boutros, 2011). Non-metric multidimensional scaling (NMDS)  (Kruskal, 1964)  based on the Bray-Curtis dissimilarity measure  (Bray and Curtis, 1957)  and Redundancy Analysis (RDA) were carried out using the Vegan package  (Oksanen et al. , 2007). RDA were performed using relative abundances of taxa at the taxonomical level 6 (genus level) transformed with the Hellinger transformation and standardized environmental data at zero mean and unit variance (z-scores). To select the significant explanatory variables, we performed a forward selection using the ordiR2step function of the Vegan package using adjusted R2 as the stopping criterion and 1,000 as the number of permutation in one step.
8% contribution), and ANME-2 (5. 2%), predominant in sulfide-rich microbial mat habitats (up to 54, 82, and 53%, respectively), were mainly responsible for the differences between the vesicomyid and microbial mat habitats (Figure  4 , Figure  S2 , and Table  S5 ). As major archaeal lineages responsible for the difference between sites, ANME-1, ANME-2 and MBG-D lineages were selected for detailed ribotype analyses. A ribotype represented several 16S rRNA sequences being identical at 99. 6% along the sequenced DNA fragment (sequences that are within 2 bp of a more abundant sequence have been merged to reduce sequencing errors). Network constructions revealed distinct identity, composition and diversification patterns depending on the analyzed lineage and the sampling sites (Figure  6 ). The network analysis performed on the ANME-1 related sequences showed a site-dependent distribution with a clear dichotomy between hydrothermal and cold seep sediments (Figure  6A ).
ACKNOWLEDGMENTS We are indebted to the crews of the research vessel l' Atalante and the submersible Nautile of the cruise "BIG " and the scientific team for their work on board. This cruise was funded by  IFREMER (France)  and has benefited from a work permit in Mexican waters ( DAPA/2/281009/3803 , 28 October 2009). We thank  Maria-Cristina Ciobanu ,  Philippe Vandenkoornhuyse ,  Alexandra Dheilly , and  Sophie Coudouel  for helpful scientific discussions. This study was supported by a  Carnot Institute  funding and by an  IFREMER/Brittany region Ph. D. grant for PC.
Fragment analysis was subsequently carried out using a 3730 DNA analyser (Life Technologies, Paisley, UK). Protists. Protists were extracted and enumerated simultaneously at the University of Cologne (Germany) using a modified Liquid Aliquot Method  45. Protists were morphologically identified up to genus level using an inverted microscope (Nikon Eclipse TS100, Japan) at Â 400 magnification. The abundance data obtained using this method were used in the network analysis. As protists were not counted in all replicates, they were excluded from the network analyses. Micro-fauna. Micro-fauna was extracted from the split-core rings using a Tullgren extraction setting for 7 days at Wageningen Environmental Research (The Netherlands) following ISO standards  46. We followed a two-step extraction, with a 3 days' initial temperature of 28 °C and a subsequent 4-day period at 45 °C, using a heat-generating carbon wire light bulb of 60 Watt above the samples.
The soil monoliths were stored at 4 °C and processed under laboratory conditions 2 days after collection. The collected earthworms were rinsed, weighed alive (with gut content), fixed in 4% formalin and, after a week, transferred to 70% ethanol. Adult and subadult individuals with sufficient sexual features were identified to species level based on external morphology, using Sims and Gerard  54. Juveniles were identified to genera. Network analyses. We removed single-sample occurrences per land abandonment stage before creating the Spearman's rank correlation matrix based on abundance data for preparation of the visualization of the correlation matrix using Cytoscape  55. We used aggregated groups consisting of species that are known to share a common function (that is, AMF). If function was unknown (that is, for bacteria and archaea), taxonomical classification was used (Supplementary Tables  5  and 6 ).
The internal standard methyl nonadecanoate fatty acid (19:0) was used for calculating concentrations. Three C20:0 methyl esters (Schimmelmann, Biogeochemical Laboratories, Indiana University) were used for calibration of the delta value. Identification of the compound was based on a BAME mix (Supelco 47080-u) and a FAME mix (Supelco 18919-1AMP). The following fatty acids were used as biomarkers for bacterial biomass: i14:0, i15:0, a15:0, i16:0, 16:1o7t, 17:1o7, a17:1o7, i17:0, cy17:0, 18:1o7c, and cy19:0 (ref. 69). PLFA10Me16:0 was used as specific indicator for actinomycetes  70. PLFA 18:2o6. 9 was considered as an indicator for fungal biomass  71, 72. The NLFA marker 16:1o5 was used as an indicator of AMF  73, 74. The percentage of  13  C allocated to a certain PLFA was calculated from the amount of  13  C in each PLFA and total  13  C accumulation (excess 13 C pmol g À 1 ) in all PLFAs used as biomarkers for different microbial groups and these values were used in data analyses.
Acknowledgements This study was carried out as part of the  EcoFINDERS research project  ( EU-FP7-264465 ),  NWO-ALW-Veni 863. 15. 021  to E. M. and  ERC-Adv  260-55290-SPECIALS to W. H. v. d. P. We thank  Rebecca Pas  for processing labelled material;  Eefje Sanders  for extracting PLFAs and NLFAs and sorting enchytraeids, earthworms and spiders for the labelling experiment;  Thomas Verschut  for sorting labelled mites and collembolans;  Javier Moliner Urdiales  and  Bekir Faydaci  for collecting living soil-cores for the pulse labelling experiment;  Agata Pijl ,  Valeria Bianciotto ,  Claudia Bragalini ,  Marine Peyret-Guzzon ,  Herbert Stockinger  and  Diederik van Tuinen  for preparing libraries for pyrosequencing; and  Peter de Vries  for drawing Supplementary Fig. 7. The  UMR1136  is supported by the  ANR  through the  Laboratory of Excellence Arbre  ( ANR-11-LABX-0002-01 ).
This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010-June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE).
Since the launch of SMOS of the European Space Agency (ESA), which operates at a low frequency (L-band), several similar satellites have been successively launched, including the Aquarius-and the Soil Moisture Active Passive (SMAP) mission  [1, 6, 16, 17]. All of them can provide global observations in the L-band, in which SMOS provides TB observations with multiple incidence angles, and the SMAP mission and Aquarius/SAC-D are supposed to provide global measurements at both the L-band TB and backscatter. However, the SMAP radar stopped working in July 2015. L-band has more advantages in the retrieval of soil moisture, because it can penetrate the atmosphere and vegetation coverage (up to ~5 kg/m 2 water content), and TB represents information on the upper 5 cm of soil  [1]. In order to make full use of the observations accumulated by different sensors, and to build a long time series dataset, multisource soil moisture data from historical and existing data should be merged for a long period of time.
In recent years, some researchers have used NNs method to rebuild long time series global SM products. Lu et al. [36]  reconstructed a time series soil moisture dataset using SMOS and AMSR2 soil moisture products to train the NN, with daily TB, NDVI, LST, PRC, and DEM information as input data. However, this study concentrated on the Heihe River Basin, and its time-series products only had a year of products in 2012 because the limitations of AMSR2 TBs. In order to develop longer time series products, based on the ESA-funded SM fusion study program, de Jeu et al. [37, 38]  designed three approaches to retrieve global time series soil moisture datasets during the 2003-2013 period, with SMOS and AMSR-E datasets over the June 2010-September 2011 period. Their goal was to carry out an integration of SMOS in a consistent soil moisture climate record.
The first approach was based on statistical regression, which was adopted by Al-Yaari et al. [39]. The authors retrieved global soil moisture during the 2003-2011 period using SMOSL3sm and AMSR-E TB observations as the training dataset. The second approach is a NN method developed by Rodriguez-Fernandez et al. [40] , using ECMWF SM predictions and SMOSL3sm products as reference to train the NNs, with SMOS TB, ASCAT backscatter (σ), NDVI, and soil texture information as input data. Rodriguez-Fernandez et al. [41] [42] [43]  compared different combinations of input data (TB, NDVI, texture) and obtained the best configuration of TB to train NN. Additionally, they analyzed the contribution of auxiliary data to the accuracy of SM retrieval. The third method, called Land Parameter Retrieval Model (LPRM) fusion, was derived by van der Schalie et al.
[44]. Their research updated roughness parameterization, and optimized AMSR-E LPRM parameters for the C-and X-bands to match SMOS retrievals. In our study, for comprehensive consideration, we adopted the second solution to rebuild long time series SM products, using a BPNN method with SMOSL3sm products as a reference. The main target of this study was to develop a reconstruction approach to obtain long time series global soil moisture datasets, on the basis of previous studies. The initial purpose of this study is to find a way to refine the former observations from the future WCOM soil moisture products, which mainly rely on L-S-C band observations. Therefore, the best option is to use SMOS observations to train the former AMSR-E/AMSR2 data. The data fusion of multi-source satellite observations lies outside of the aim of this study.
On the choice of reference, from Aquarius, SMAP, and SMOS, we selected SMOS SM as the standard reference of SM products. SMOS provides multi-angular global microwave TB observations, from 2010 until the present, and its SM products have the high accuracy of 0. 04 m 3 /m 3. A great deal of research has evaluated SMOS SM products and demonstrated its high accuracy  [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56]. For the consideration of long time series, we choose AMSR-E/AMSR2 data as the training dataset because it has the same configuration and constitutes a continuous time series, for AMSR-E, lasting from 2003 to 2011, and AMSR2, lasting from 2012 to the present. In contrast to previous studies, we implemented the Microwave Vegetation Indices (MVI) as inputs in order to provide information on the effects of vegetation.
Additionally, this study serves as part of the pre-feasibility studies of the scientific objectives of WCOM. We will join the modeled and WCOM observed soil moisture to produce an even better product, and then use this to refine the former observations (including SMOS, AMSR-E/AMSR2, etc. ). The rest of this paper is organized as follows. Section 2 presents the different data used in the study and the BPNN method used to retrieve soil moisture. Section 3 shows the results of BPNN and evaluates NNsm with SCAN in situ SM observations. Then, Section 4 discusses the accuracy and advantages of NNsm in comparison to satellite SM products, and the resulting datasets of other methods. Finally, Section 5 provides conclusions.
1. 3, are descending nighttime data (at 01:30 local time), because, at night, surface temperatures (Ts) are more stable than during the daytime, and the vegetation temperature is closer to that of soil temperature  [58]. AMSR-E L3 TB data are provided by the National Snow and Ice Data Center (NSIDC), and can be downloaded free of charge from the NSIDC. These data are provided in EASE-Grid projections  [59, 60]  (global cylindrical) at a 25-km resolution, which is the same as SMOSL3sm data. For the dataset, spatial coverage is global, data are daily, and coverage begins 19 June 2002, and ends 27 September 2011. The TB data (in tenths of Kelvins) are two-byte unsigned integers. We used two kinds of AMSR-E L3 SM products. One kind was the standard products of AMSR-E, derived by the NSIDC, and can be downloaded from  [61].
AMSR2 Data The AMSR2 onboard the Global Change Observation Mission-Water (GCOM-W) satellite is operated by the Japan Aerospace Exploration Agency (JAXA). The aim of this satellite is to observe water circulation changes. As a follow-up mission for AMSR-E/Aqua, the sensor and satellite configurations of AMSR2 are almost the same as those of AMSR-E/Aqua, as described in last section. AMSR2 L3 TB is produced by JAXA, and has an equi-rectangular (EQR) projection. For the dataset, spatial coverage is global, data are daily, the number of pixels for EQR projection is 0. 25 • , the resolution is 1440 × 720, and coverage began 3 July 2012 and is continuing today. Data can be accessed freely from the GCOM-W1 Data Providing Service. We used two kinds of AMSR2 L3 SM products. One is the standard products of AMSR2 from JAXA, based on the lookup table method.
In Situ Network Data To evaluate the NNsm time series, we used in situ data from the Soil Climate Analysis Network (SCAN)  [65, 66]. SCAN sites are comprehensive automated weather stations that provide agricultural producers and resource managers with hourly data to monitor soil and weather conditions. Each SCAN installation can measure soil moisture and temperature at depths of 5, 10, 20 and 50 cm below ground surface. This database is available from 1 January 1996 until the present, and can provide long time series of in situ soil moisture observations that met our demands. It is an essential means of the geoscientific community for validating and improving global satellite observations and land surface models. The sampling interval of SCAN is one hour; we use the daily average soil moisture, at a depth of 5cm, corresponding to the measurement depth of the L-band.
The BPNN Method A neural network (NN) is a powerful nonlinear regression method. BPNN, one of the commonly-used NNs, is a type of multilayer feedforward network, in which the algorithm is trained according to error back propagation. Its learning rule is the steepest descent method, adjusting the network weights and threshold by error back propagation in order to minimize the sum of squared error of the network. The structure of the BPNN model includes the input layer, the hidden layer, and the output layer. Our research adopted the three-layer network model with a single hidden layer. We used Matlab software to realize the training and verification of BPNN. After several tests, we selected the appropriate configurations and built the following network: net = newff(P, T, 7, {'tansig', 'purelin'}, 'trainlm') ( 1 ) where P is the input vector; T is the output vector; 7 is the number of neuron nodes in the hidden layer, which is determined when the learning error does not significantly decrease with the increase of neuron nodes, after several training tests, with a small initial value of neuron nodes; 'tansig' is the node transfer function of the hidden layer, and it is a hyperbolic tangent sigmoid transfer function, with an arbitrary input and output, ranging from -1 to 1, which is different from the linear transfer function 'purelin' etc.
Development of the NNsm Product In the simulation phase, our input layer Rs and MVIs are derived from TBs, thus, we considered the time coverage of the AMSR-E and AMSR2 TBs. Based on the BPNNs trained in the previous step, we can develop a long-term SM dataset over 2003-2015 (except 2012) on each grid, with available AMSR-E TB from 1 January 2003 to September 2011 and AMSR2 TB from 2013 to 2015. Normally, soil moisture is not retrieved over frozen soils, thus, before the simulation, we preprocessed the TBs and T S data, removing those data where T s were lower than 273. 15 K.
Comparing with Satellite Products In Section 3, we compared NNsm with the reference SMOSL3sm, in situ SCANsm, and AMSRsm. It turns out that NNsm has a high consistency and accuracies relative to the reference. However, the AMSRsm from NSIDC and JAXA underestimate SM significantly. In this section, we discuss the performance of NNsm by comparing it with AMSR-E and AMSR2 soil moisture products (we call it AMSR_LPRM), generated by the same algorithm, namely the LPRM algorithm. These long time-series datasets are from two similar sensors, namely AMSR-E and AMSR2 satellite sensors, and further more they are retrieved both from the LPRM algorithm and are from one dataset, so we treated them as one long time-series datasets. We average the statistical results of the 15 selected sites, as shown in Table  3.
From the point of view of the time series (upper panel) and anomaly time series (lower panel) shown in Figure  10 , it is obvious that NNsm is more consistent and continuous with SCANsm than the performance of AMSR-LPRM. AMSR_LPRM overestimate soil moisture over these sites. Additionally, the soil moisture from AMSR-E and AMSR2 are not consistent over these sites. Some panels show a shift in soil moisture in the AMSR-E period compared to the AMSR-2 period that could be explained by not accounting for possible bias between the LPRM datasets. Over site 2076, there is a big shift between the AMSR-E and AMSR2 data, so after anomaly process, AMSR-E underestimates the soil moisture and the AMSR2 overestimate the soil moisture. With respect to accuracy, NNsm has an average value of CC = 0. 52 for 15 selected sites and CC = 0.
Comparing with a Regression Method As mentioned in the Introduction Section, Al-Yaari et al. [39]  used a regression approach to retrieve global time series soil moisture datasets, with SMOSL3sm as a training reference target and AMSR-E TB as training input. We compared results NNsm with Reg_sm of the algorithm used by Al-Yaari. The comparative results are listed in Table  4  for the training and simulation period. In the training period, compared with the reference SMOSL3sm, NNsm has a slightly higher CC 0. 67 than 0. 60 of Reg_sm, and NNsm has a slightly lower RMSE 0. 055 m 3 /m 3 than 0. 057 m 3 /m 3 of Reg_sm. By the statistical comparisons, we can see that our NNsm has a better training result than the Reg_sm. Moreover, the input of NNsm includes TBs from AMSR-E and AMSR2, while the input of Reg_sm is only AMSR-E TB.
It makes it possible to derive continuous and longer soil moisture dataset from AMSR-E and AMSR2 TB in the next simulation step. In the simulation period, from an accuracy perspective against SCANsm, NNsm has a slightly higher CC 0. 52 than 0. 49 of Reg_sm. However, the two algorithms have nearly equal RMSE when compared with in situ SCANsm. For the long-term dataset, the NNsm can simulate soil moisture from 2013 to 2015 based on AMSR-E/AMSR2 TBs, longer than the dataset from Reg_sm.
NNsm has significant advantages relative to the regression method, with a higher accuracy and longer time series. For improvement and further research, we wish to continue the following work: (1) for the improvement of our BPNN method, we can test different combinations of Rs and MVI. For example, we can use low or high frequency bands of Rs and MVI, as we used all frequencies in this study, which may not be the optimal input combination for BPNN training; (2) Another direction is to train BPNNs for future WCOM. Because WCOM will have no overlapping data with AMSR-E data, we can train BPNNs using only the AMSR2 data and apply the BPNNs to cross-calibrated AMSR2 and AMSRE data. In conclusion, through BPNNs training, this study provides a promising method to build long time series of global soil moisture products.
Acknowledgments: This work was supported in part by the  National Key Basic Research Program  ( 2015CB953701 ),  Key Research Program of Frontier Sciences, CAS  (Grant No. QYZDY-SSW-DQC011 ),  International S&T Cooperation Program of China  ( 2016YFE0117300 ), the  National Natural Science Foundation of China  ( 41371354 ,  41301396 ) and the  Youth Innovation Promotion Association  (No. 2016061 ). The authors would like to acknowledge  CATDS  for providing the SMOS L3 datasets. The authors thank the NSIDC and the  JAXA  for providing the AMSR-E/AMSR2 TB and SM data, the  NASA  for providing the AMSRsm products and the  ISMN  for providing SCAN observations.
The TEMPO instrument will be delivered in 2017 for integration onto the nadir deck of a NASA-selected GEO host spacecraft for launch as early as 2018. TEMPO and its Asian (GEMS) and European (Sentinel-4) constellation partners make the first tropospheric trace gas measurements from GEO, building on the heritage of six spectrometers flown in low-Earth orbit (LEO). These LEO instruments measure the needed spectra, although at coarser spatial and temporal resolutions, to the precisions required for TEMPO. They use retrieval algorithms developed for them by TEMPO Science Team members and that are currently running in operational environments. This makes TEMPO an innovative use of a well-proven technique, able to produce a revolutionary data set. The 2007 National Research Council (NRC) Decadal Survey "Earth Science and Applications from Space" included the recommendation for the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission to launch in 2013-2016 to advance the science of both coastal ocean biophysics and atmospheric-pollution chemistry  [78].
TEMPO overview and background TEMPO collects the space-based measurements needed to quantify variations in the temporal and spatial emissions of gases and aerosols important for air quality with the precision, resolution, and coverage needed to improve our understanding of pollutant sources and sinks on suburban, local, and regional scales and the processes controlling their variability over diurnal and seasonal cycles. TEMPO data products include atmospheric ozone profile, total column ozone, NO 2 , SO 2 , H 2 CO, C 2 H 2 O 2 , H 2 O, BrO, IO, aerosol properties, cloud parameters, UVB radiation, and foliage properties over greater North America. Fig. 1  shows the average tropospheric column NO 2 for 2005-2008 measured from the OMI satellite over the TEMPO field of regard. Every hour during daylight, TEMPO will scan this entire greater North American domain that extends from Mexico City to the Canadian oil sands and from the Atlantic to the Pacific.
The mechanism is actuated inductively using a network of voice coils and magnets. The mirror position is measured using differential impedance transducers (DITs). The SMA is closeloop controlled using real-time attitude data supplied by the host spacecraft. The Opto-Mechanical Subsystems consist of a telescope and a spectrometer assembly. Primarily, the optical design employs reflective optics with simple geometries (Fig. 4 ). The f/3 Schmidt-form telescope consists of the T1 Scan mirror, the T2 Schmidt mirror and a T3 and T4 with final projection onto the slit of the spectrometer assembly. The telescope mirrors are coated with UV-enhanced aluminum, with the exception of the T2 optic, which has a bandblocking coating to minimize stray light biases within the spectrometer. The Offner-type spectrometer was chosen due to its compact design and superior re-imaging performance and is similar to the Ozone Mapping Profiler Suite (OMPS) nadir spectrometer  [24].