dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  Tim-Oliver Husser  who shared with us the tables with the results of the H16 analysis, and the  MUSE team  for making public the extracted spectra on their web site. We acknowlege the  LABEX Lyon Institute of Origins  ( ANR-10-LABX-0066 ) of the  Université de Lyon  for its financial support within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR) , and  IDEXLYON  for their support fo the  Indo-French Astronomy school  ( IFAS4 ), where this project was started. This work was supported by the  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. We thank  Yasna Ordenes ,  Yue Wu ,  Jean Damascène Mbarubucyeye ,  Sina Chen ,  Sonali Borah ,  Raghu Prasad , and  Saumya Gupta , who contributed to early investigations on this project.
Filters were implemented using MIA open source software  29. tissue segmentation and landmark collection. The filtered image stacks were processed using Avizo 6. 3 (https ://www. vsg3D. com) in order to produce surface models of the EDJ. Enamel and dentine were segmented using a semi-automatic process that separates voxels based on greyscale values. In some cases, tissue classes are less distinct even after image filtering, making segmentation difficult or impossible using this method. In these cases, a seed growing watershed algorithm was employed via a custom Avizo plugin to segment enamel from dentine, before being checked manually. Once enamel and dentine have been segmented, a triangle based surface model of the EDJ was produced using the unconstrained smoothing parameter in Avizo, and saved in polygon file format (.
ply). In some cases, dental wear removed dentine horn tips. Where this wear was minimal and multiple observers were confident of the dentine horns original position, dentine horns were reconstructed using surface modification tools in Geomagic Studio 2014 (https ://www. geoma gic. com). This was restricted to cases in which the wear was less than wear level 3 as defined by Molnar  30. Landmark collection and derivation of homologous landmark sets. 3D landmarks were collected in Avizo 6. 3, and homologous landmarks were derived using a software routine written by Philipp Gunz  31, 32  implemented in Mathematica 10. 0 (https ://www. wolfr am. com). This was done following a previously described protocol  16  outlined in Supplementary Figure  2. Inclusion of specimens from Lesedi Chamber. Two H.
Acknowledgements For assistance with scanning the H. naledi material we thank  Kudakwashe Jakata  ( Evolutionary Studies Institute, University of the Witwatersrand ). For assisting with the scanning of the comparative sample, we would thank  Bernhard Zipfel ,  Sifelani Jira  ( Evolutionary Studies Institute, University of the Witwatersrand), Miriam Tawane (Ditsong Museum ),  Emma Mbua  and  Job Kibii  ( National Museums of Kenya ),  Audax Mabulla  ( National Museum of Tanzania ), the  Tanzanian Commission for Science and Technology ,  Jakov Radovčić  ( Croatian Natural History Museum ),  Michel Toussaint  ( ASBL Archéologie Andennaise ),  Jean-Jacques Cleyet-Merle  ( Musée National de Préhistoire des Eyzies-de-Tayac) ,  Véronique Merlin-Langlade  ( Musée d' Art et d' Archéologie du Périgord ) and the  Leipzig University Anatomical Collection (ULAC).
All samples were calhttps://doi. org/10. 5194/bg-17-4663-2020 Biogeosciences, 17, 4663-4679, 2020 ibrated against a quinine sulfate monohydrate dilution series, performed once during sample analyses. EEMs were analysed by PARAFAC  (Stedmon and Bro, 2008)  and validated by split-half analysis using the drEEM toolbox for MATLAB after  Murphy et al. (2013). Four FDOM components that were identified during the PARAFAC analyses are expressed in quinine sulfate equivalents (QSEs). Samples for inorganic N compounds in the benthic lander samples (NO - 3 , NO - 2 and NH + 4 ) and the pore waters (NH + 4 ) were analysed following standard techniques after  Hansen and Koroleff (2007)  and will be published elsewhere  (Clemens et al. , 2020). NO - 3 and NO - 2 concentrations in the pore waters were assumed to be negligible  (Dale et al.
Net in situ fluxes of DOC (J DOC (Net)) and DON (J DON (Net)), measured in BIGO chambers, were evaluated as J s (Net) = V A × dC dt , (2) where J s (Net) is the net in situ flux of a solute, V is the chamber volume (m 3 ), A is the chamber area (m 2 ) and dC dt is the DOC (DON) concentration gradient over the time of the sediment enclosure (mmol m -3 d -1 ). The gradient was obtained by linear regression analyses ("polyfit" first-order, MATLAB, The MathWorks Inc. ) of the DOC (DON) concentrations over time. The error of the linear regression was used as a representation of the standard deviation of the evaluated net fluxes. In this study, fluxes directed out and into the sediment are reported as positive and negative values, respectively.
05±0. 3 mmol m -2 d -1 at St. 6 to 0. 3±0. 3 mmol m -2 d -1 at St. 5. In contrast to DOC fluxes, the diffusive DON flux on one of the middle shelf stations (St. 2) was also higher than the net in situ DON flux, exhibiting 3. 3 ± 1. 7 and -0. 03 ± 0. 3 mmol m -2 d -1 , respectively. At St. 1 both diffusive and net in situ DON flux estimates were very low. They displayed -0. 04 ± 0. 02 and 0. 08 ± 1. 4 mmol m -2 d -1 , respectively. Despite the clear apparent distinction between the different flux estimates for both, DOC and DON, no statistical differences were found between them at each station (p > 0. 05, Mann-Whitney rank sum test, SigmaPlot, Systat Software).
Acknowledgements. We are grateful to the chief scientist  Marcus Dengler  and the ship and scientific crews of RV Meteor (cruises  M136  and  M137 ). Jon Roa  is acknowledged for DOC analyses. We are grateful to  Bettina Domeyer ,  Anke Bleyer ,  Matthias Türk  and  Antje Beck  for technical and logistical support and  Ulrike Schroller-Lomnitz  for advice. We thank  Tomoko Komada ,  Piotr Kowalczuk  and the anonymous reviewer for improving this paper. Financial support. This research has been supported by the  Deutsche Forschungsgemeinschaft (DFG)  grant no. SFB754  " Climate-Biogeochemical Interactions in the Tropical Ocean " (miniproposal,  B9 ), the  Helmholtz Association  and funding provided by the  Inge-Lehmann-Fonds  through the  GEOMAR Helmholtz Centre for Ocean Research  awarded to ANL and by the funding provided by  DFG Excellence cluster Future Ocean   CP1403  "Transfer and remineralisation of biogenic elements in the tropical oxygen minimum zones" awarded to FACLM.
Data availability. All the measured DOC concentrations, a CDOM (325), S 275-295 and QSE of fluorescent components are available at pangaea. de at https://doi. pangaea. de/10. 1594/PANGAEA. 913476 (Loginova and Engel, 2020a) and https://doi. pangaea. de/10. 1594/PANGAEA. 913477 (Loginova and  Engel, 2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-17-4663-2020-supplement.
We additionally propose a heuristic to make it completely distributed. For the channel assignment subproblem, we show that our game possesses a weighted potential function and that, by extension, a pure NE exists and it can be reached via both best response and replicator dynamics. The game permits selecting the number of channels to be allocated to each operator, with its traffic being equally distributed on the channels it selects. We solve the decoupled problem for SF assignment first, and for channel assignment second. The joint algorithm can be semi-distributed (best response approaches) or completely distributed (learning approach). We describe our proposals and then compare them to the concurrent approach in SF and channel selection in LoRaWAN, which consists of adaptive data rate (ADR) for SF selection, along with random channel selection for each packet.
They illustrate how their algorithm increases the total packet delivery ratio in comparison to the legacy LoRaWAN SF selection approach. The authors of  [6]  propose a traffic oriented SF selection algorithm that equalizes the traffic loads on the SF channels. They additionally propose a K-means algorithms with the objective of relieving the crowded regions that are suffering from excessive collisions. The authors show that their proposed algorithms help to improve the scalability of LoRaWAN networks. With the objective of increasing the data delivery ratio in LoRaWAN, the authors of  [2, 7]  detail the limitations of the legacy SF selection algorithm and, thereafter, propose algorithms to tackle them. In  [2] , the authors argue that their approach can improve the scalability of LoRaWAN with a trade-off of a marginal increase in device power consump-tion.
System Model and Specifications We consider multiple LoRaWAN deployments belonging to different operators. Each network i ∈ N has N i nodes and r i gateways. The gateways for the different operators are coincident, i. e. , they are present at the same location sites. The devices are scattered across a square shaped area, as shown in Figure  1. All of the nodes generate packets with a rate λ i. Transmission attempts are done according to a Poisson process with packet size l being constant. Let T s be the time that is needed to transmit a packet of size l while using spreading factor s. LoRa supports SFs that range from 7 to 12. The latter represent a trade-off between coverage and data rate. For instance, SF7 yields the highest data rate, but it covers the shortest distance. The SFs are orthogonal meaning that packets transmitted during the same time frame with different SFs will be successfully received.
Methods All patients in REIN having initiated HD from 2010 to 2013 were classified according to their exposure to the different dialysate calcium concentrations in their dialysis unit. Data on the individual patients' treatments for MBD were extracted from the French national health database. Cox proportional hazard models were used to estimate mortality hazard ratios (HR) associated with time-dependent exposure to dialysate calcium concentrations and MBD therapies, adjusted for comorbidities, laboratory and technical data.
Population Data are extracted from REIN registry, which includes all patients with end-stage kidney disease in France on chronic renal replacement therapy. Details of methods and quality control of the REIN registry have been described elsewhere  [11]. Data were fully anonymized. Approvals from the National Commission on Informatics and Liberty and from the Advisory Committee on Information Processing in Material Research in the Field of Health were obtained through the national REIN registry. The patients have an opt out option if they don't want to be included into the registry and the patients associations are participating to the monitoring of the registry. We included all incident adult patients having initiated dialysis between January 1 st , 2010, and December 31 st , 2013, and who were dialyzed for more than 3 months (flow chart,  Fig 1).
MBD therapies Individual data on MBD therapies were extracted from French national health database which retrieved the therapies bought by each subject in number of canister per month, recalculated as a dose per day. This access allows evaluating therapies exposure by day and by drugs dose. Oral calcium, native and active vitamin D (1α calcidol), sevelamer, lanthanum and cinacalcet were studied. On the 25629 subjects included, 21497 patients were identified in the database. In the lack of unique identifier between French national health database and the REIN registry database, we proceeded to a stepwise indirect linkage using the following data: gender, age, residency code, a national hospital identifier, and date of dialysis start.
Data The REIN data at dialysis start included age, gender, nephropathy, emergency start, laboratory results (albumin, hemoglobin, and estimated glomerular filtration rate (eGRF)), and comorbidities. We used the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation to calculate eGFR. Baseline comorbidities included active malignancy, cirrhosis, handicap status, obesity (body mass index �30 kg/m 2 ), arrhythmia, respiratory insufficiency, heart failure, and at least one arterial disease (among stroke, transient ischemic attack, coronary insufficiency, abdominal aortic aneurysm, and lower limb arteritis). Furthermore, treatment data including frequency and duration of dialysis, hemodiafiltration use, vascular access and center type (center, medicalized unit or self-assisted unit) were updated annually and when the patient moved to another dialysis center.
Survival times were censored at the time of event for kidney transplantation, weaning from dialysis, loss to follow-up, moving out of France, transfer to a dialysis unit with inaccurate dialysate exposure, peritoneal dialysis, or home dialysis. The all-cause and cardiovascular mortality models were lastly adjusted for the dialysate acid type classified yearly for citric acid, HCl and standard as a time dependent covariate. Missing values on adjustment covariates were treated by multiple imputations using multivariate imputation by chained equations (MICE, packages mice and miceadds of R). Twenty iterations were used and 20 imputed datasets were created. All covariates presented in the Cox models were included in the imputation procedure. Sixty percent of patients had at least one missing data on adjustment covariates.
Unfortunately, Africa is a region where the ground-based rain-gauge network is of very low density and operational radar installations are almost non-existent  [2]. Furthermore, the gauge networks have been degrading over the last few decades  [3]. In this context, satellite-based precipitation products represent an unavoidable alternative for providing precipitation knowledge in Africa. In recent decades, significant progress has been made in satellite precipitation estimation. This progress is mostly due to the introduction of new sensors (e. g. Global Precipitation Measurement Core Observatory satellite), but also to the improved sensor accuracy, and efficiency of proposed algorithms that take advantage of the many observational data (including multi-channel VIS/IR sensors, and passive microwave).
One potential strategy for improving satellite precipitation products is to use soil moisture measurements from satellite microwave sensors. Soil moisture can be seen as the trace of precipitation, and a knowledge of the temporal and spatial variability of soil moisture could benefit to rainfall retrievals from space. Pioneer studies  [12] [13] [14] [15] [16] [17]  exploited this signal to correct existing satellite precipitation products. Later,  [18, 19]  developed the SM2RAIN approach to directly derive a rainfall amount from soil moisture variations exclusively. Since 2015, these approaches were improved and applied at the global scale  [20, 21]  and on different locations in the US  [22] , in Australia  [23] , over selected sites  [24]  and in China  [25]. One of the main advantages of these methodologies based on soil moisture measurement is that they can replace, in near-real-time, scaling procedures of state-of-the-art satellite precipitation and avoid significant latency for dataset availability.
Ground-based precipitation measurements Five reference data sets based on in situ rainfall measurements are used in this study (Table  1 ). The first two datasets are provided by the AMMA-CATCH Observatory in Niger and Benin  [30, 31]. Both sites cover about 1x1° and are composed of 34 gauge stations in Niger and 30 gauge stations in Benin. A spatial interpolation (block kriging) was performed at the 0. 25x0. 25° spatial resolution in order to obtain a reference rainfall amount at the commonly used satellite spatial scale (0. 25°). The selected 0. 25° sites in Niger and Benin are respectively centered at 2. 625°E; 13. 625°N and 1. 625°E; 9. 625°N (see Figure  1 ). The number of gauge stations that directly affect the rainfall amount at the 0. 25° resolution are 12 in Niger and 10 in Benin.
The third data set is composed of 20 in situ gauge stations covering the whole of Burkina Faso. The dataset was provided by National Meteorological Department of Burkina Faso. The fourth dataset is the "WaTFor" data set which documents Western Central Africa. Built-up by  [5]  it contains monthly and daily in situ rainfall data collected from global datasets, national meteorological services and monitoring projects for Cameroon, Gabon, Congo and Central African Republic. Finally, the last data set is a gauge network composed of 78 stations covering 7 countries in East Africa (Ethiopia, Djibouti, Somalia, Kenya, Uganda, Tanzania and Rwanda  [32] ). The datasets at national scale comprise both synoptic stations (whose data are generally incorporated in post-processed products) and independent stations.
Motion vectors are determined from half-hourly geostationary infrared measurements, and used to propagate the estimates obtained from the microwave data. The CMORPH rainfall product is proposed in two modes: the CMORPH-Raw product which is not merged with any raingauge observations and the CMORPH-Adj product which use a monthly ground calibration procedure to remove bias and uncertainties. In this study, the 0. 25° 3-hour version of the product was used. TRMM 3B42 products  [34]  originality lies in incorporating precipitation radar and microwave measurements to better evaluate rainfall intensity. It combines these data with polar-orbiting and geostationary satellite images to obtain 3-hourly rainfall estimates at 0. 25° spatial resolution. Similar to CMORPH, the TRMM 3B42 products are available in two modes: the TRMM-3B42RT product (hereafter called TRMM-Raw) available in near real-time (latency of about 7 hours) which is not merged with any raingauge observations and the TRMM-3B42 product (hereafter called TRMM-Adj) which includes GPCP monthly gauge aggregations and is available with a latency up to 6-week.
We used the TAMSAT V3. 0 version product  [38]  available at the daily time scale. Similar to IMERG, the product was regridded to the 0. 25° spatial resolution, and is called TAMSAT_025. The GPCC daily product is provided by the Global Precipitation Climatology Center  [26] , and is available since 1 January 2009 with a spatial sampling grid of 1°. GPCC is a gridded gauge-analysis product derived from quality-controlled station data (more than 85,000 different stations). This dataset is characterized by an uneven spatial distribution: some regions are characterized by dense rain gauge networks (Europe, US, China) while other regions such as Africa, Amazonia and Northern areas suffer from low density networks. To enable comparison with other products, GPCC was downscaled to 0. 25° spatial resolution with a linear interpolation method.
CHIRPS (Climate Similar to PrISM, SM2RAIN precipitation product  [18, 19]  takes advantage of satellite soil moisture observations to derive a precipitation product. SM2RAIN is based on the inversion of the soil water balance equation and allows to estimate the amount of water entering the soil by using as input soil moisture observations from in situ or satellite sensors (e. g. ,  [20, [42] [43] [44]. The SM2RAIN product used in this study is the GPM+SM2RAIN precipitation dataset (hereafter called SM2RAIN) which is based on the integration of IMERG-ER with SM2RAIN-based rainfall estimates derived from ASCAT, SMOS and SMAP L3 soil moisture products. The merging methodology is using an Optimal Linear Combination approach, OLI  [46, 47]. This approach provides an analytically optimal linear combination of ensemble members (precipitation products in this case) that minimizes mean square error when compared to a reference dataset.
In a first approximation, it was shown in this study that τ value can be appropriately estimated with 30days smoothed air temperature (Tair) using the following relationship: 𝜏(𝑡) = 400 -( 350 (1+𝑒 -0. 1(𝑇𝑎𝑖𝑟-7. 5) ) ) (Eq. 5) where 30-days smoothed Tair values (°C) are obtained from MERRA-2 database (3-hours, 2013). At the continental scale of Africa, the τ parameter ranges from 80 h to 350 h.
The CDF matching procedure The PrISM methodology is based on the assimilation of the SMOS soil moisture retrievals into the API model (Eq. 2). Thus, a preliminary work consists in scaling the SMOS soil moisture retrievals to the API simulation using a simple CDF-matching procedure. To that end, a reference rainfall dataset was selected to provide a reference soil moisture simulation with the API model at the Africa scale. The evaluation of two adjusted products (CMORPH-adj and TRMM-3B42) against in situ rainfall measurements in Niger and Benin sites led to the selection of the CMORPH-Adj precipitation product as the reference for the CDF-Matching procedure. Then, the API model (Eq. 2) was run for the whole of Africa using CMORPH-Adj precipitation product and parameters were derived from Eq.
Assessment at the local scale (Niger and Benin) The eleven selected precipitation products were compared to ground-based precipitation measurements using commonly used statistical scores: the Pearson correlation (R), the Root Mean Square Error (RMSE in mm/day) and the annual bias (in mm). Null values were accounted for in the scores calculation and the comparison was performed at the daily time scale and at the 0. 25° spatial resolution. As the ground-based precipitation datasets (Niger and Benin) are available at the hourly time scale whereas some satellite products are provided at the daily time scale, the matching of the two time-series was carefully checked to avoid the known ambiguity between a 6am-6am day (commonly used in precipitation measurements) and a 0-24h day. Lastly, we also examine the number of rainy days (cumulative daily rainfall > 1 mm) compared to in situ measurements.
Hazards group Infrared Precipitation with Stations) dataset is a quasi-global (50°S-50°N), high resolution (0. 05°), daily, pentadal, and monthly precipitation dataset [41]. CHIRPS uses the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis version 7 (TMPA 3B42 v7) to calibrate global Cold Cloud Duration (CCD) rainfall estimates. CHIRPS incorporates station data in a two phase process. In the first phase, Meteorological Organization's Global.
25° 3-hourly 1998-present >1 month yes TRMM-3B42 0. 25° 3-hourly 1998-present >1 month yes IMERG-Final 0. 1° 30 min present 03/2015- >1 month yes 2. 3 The SMOS soil moisture dataset [48,49]l Moisture and Ocean Salinity[48,49]satellite was launched in November 2009 and started delivering data on January 2010. The primary goals of this Earth Explorer mission are to measure globally and frequently surface soil moisture over land and sea surface salinity over the oceans. The SMOS data used in the study as the main input to the PrISM algorithm corresponds to the CATDS level-3 soil moisture data obtained through https://www. catds. fr site. The SMOS-L3SM.
Material EMG recordings. EMG activity was recorded using TrignoTM Mini sensors (Delsys Inc. ) with a sampling rate of 1926 samples/s, a band pass of 20 Hz (12 dB/oct) to 450 Hz (24 dB/oct) and were amplified by a TrignoTM 16-channel wireless EMG system (Delsys Inc. ). These sensors consist of two 5 mm long, 1 mm wide parallel bars, spaced by 10 mm, which were attached to the skin using double-sided adhesive interfaces. The skin was cleaned by scrubbing it with 70% isopropyl alcohol. EMG signals were synchronised using the PowerLab 16/35 (ADInstrument, PL3516). Raw data from the EMG sensors were then resampled at a rate of 1 kHz and stored in digital format using Labchart 8 software (ADInstrument, MLU60/8). EMG sensors were positioned over five muscles: the corrugator supercilii (COR), the frontalis (FRO), the zygomaticus major (ZYG), the orbicularis oris inferior (OOI), and the flexor carpi radialis (FCR).