dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the SOM according to ocean geography We first made a spatial analysis of the SOM in order to check the data series and the performance of the SOM algorithm. We decomposed the studied period (January 2010 -December 2016) into 635time steps of 4 days. The SST, SSS, ADT, τx, and τy variables are organized as follows: at each time step, each variable is represented by a latitude X longitude geographical map of 771 pixels (the continental values have been removed). The full dataset is therefore represented by a matrix of dimensions (771, 3175) which contains the merged SST, ADT, SSS, and wind information for 5X635 (3175) time dependant data. The aim of our analysis is to summarize the information contained in the learning set by producing a small number of referent vectors that are statistically representative of the learning set.
Climatological analysis In this analysis we focused our attention on the second step of our methodology and estimated the seasonal climatological signal from the 6-year data set. For each variable, a database of 12 monthly climatological data and 771 pixels was formed (12X771) and we apply the HAC using the Ward distance for the intra-classes similarity. As mentioned above, the number of classes has been selected by choosing the most significant discriminative partition with respect to the full dendrogram of the HAC according to the Davies-Bouldin and to the gap indexes. Figure  6  shows, as an example, the SST dendrogram and histogram of the data after the HAC clustering. This SST example is representative of all the other oceanic parameters. These figures help to determine the most significant discriminative partition.
2010-2016 seasonal signal We applied the same method to the whole 2010-2016 data of the oceanic parameters, but now we used the SOM quantization and the HAC onto the neurons of the maps to determine the 3 "generic cases" of each variable. The "strong form" clustering is applied during the climatological analysis. The time-space dimensions of the starting matrices are  (635, 771). We obtained about 70 strong forms, which are numbered according to the description given in section 2. 2. 3. Figure  14  gives the time evolution of these different 'strong forms' characterized by their associated number over the studied period. The signal is seasonal. From an oceanographic point of view, this is not a surprise due to the seasonal character of the tropical Atlantic Ocean, contrary to the Pacific Ocean for instance, where inter-annual variability associated with the El Niño-Southern Oscillation phenomenon prevails (e.
Summary and Discussion After checking the consistency and the ability of our methods to extract information from a spatial analysis which gives coherent results, we analysed how the seasonal cycle of SST, ADT, SSS, and wind stress data sets are linked over the western tropical Atlantic Ocean, first from their climatological averages, then from the whole 2010-2016 period. The dominance of the seasonal cycle with respect to other time periods is clearly depicted. Every year during the boreal spring, our results show that the ITCZ is located around 2-4°N so that the trades are more intense in the north-eastern part of the region. The SSTs are minimum while beneath the ITCZ, a large area of a maximum SST appears. Due to the evaporation, the SSS increases north-eastwards while river runoffs freshen the signal along the American coast.
Conclusion In this paper, we investigated how machine learning algorithms could help in understanding oceanic dynamics from satellite observations. Indeed, with the arrival during the past decade, of a new generation of satellite missions dedicated to the Earth observing system (e. g. Topex/Poseidon/Jason altimeters, SMOS, Aquarius radiometers. ) oceanographers are facing the crucial problem of exploiting voluminous data sets to extract information and knowledge. For this, we applied machine learning algorithms (SOM and HAC) to SST, ADT, SSS, and wind data sets over the north-western tropical Atlantic Ocean to evidence coherent structures and the potential relations among the different variables. We demonstrated that the computations over the seasonal cycle of our data sets give a coherent picture of the concomitant variability of the variables.
Acknowledgement This work has been funded by the french  CNES  ( Centre National d'Etudes Spatiales ) and the  IRD (Institut de Recherche pour le Développement)  organisations. The  Group for High Resolution Sea Surface Temperature (GHRSST) Multi-scale Ultra-high Resolution  (MUR) SST data were obtained from the  NASA EOSDIS Physical Oceanography Distributed Active Archive Center (PO. DAAC)  at the  Jet Propulsion Laboratory, Pasadena, CA  (http://dx. doi. org/10. 5067/GHGMR-4FJ01 ). The Ssalto/Duacs altimeter products were processed by Aviso+, with support from  CNES. They are now distributed by CMEMS (Copernicus Marine environment monitoring service). The  L3_DEBIAS_LOCEAN_v2 Sea Surface Salinity maps have been produced by LOCEAN/IPSL  ( UMR CNRS/UPMC/IRD/MNHN ) laboratory and ACRI-st company that participate to the  Ocean Salinity Expertise Center (CECOS)  of  Centre Aval de Traitement des Donnees SMOS (CATDS).
Monitoring of experiments and post-processing of data The experimental device is equipped with white-light LEDs and UV LEDs, alternating every 15 s during an experiment. An array of nine cameras acquires photographs (every 15 s) both in white light (all cameras) and in UV light (red camera only; Fig. 2a ). The transparency of the injected water and the silicone cap in white light enables manual mapping of bedforms and reconstruction of digital elevation models (DEMs) of the bed surface from the white-light photographs, with a precision of ±0. 1 mm, through a photogrammetric method developed by  Lelandais et al. (2016). The morphometric properties of bedforms and drainage features (Fig. S1  in the Supplement) are calculated from interpreted snapshots and DEMs. Maps of water distribution at the silicone-bed interface are derived from the UV photographs, thanks to the fluorescence of the injected water.
Figure12. Manual mapping of ribbed bedforms, lineations and ice stream margins along different palaeo-ice stream tracks in Canada. Left panels show digital elevation models and hillshade maps using ArcticDEM database (Porter et al. , 2018)  and lidar data (Alberta Geological Survey). Right panels show our morphological interpretations and the rose diagrams compiling bedform orientations. https://doi. org/10. 5194/tc-15-2889-2021The Cryosphere, 15, 2889-2916, 2021.
Acknowledgements. This study is part of the  Ice-Collapse project (The dynamics of ice sheet collapse in deglaciation periods ) funded by the  French Agence Nationale de la Recherche  through grant  ANR-18-CE01-0009. This project has benefited from the PAL-GLAC team of researchers and received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 787263 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-18-CE01-0009 ) and the  European Research Council ,  H2020  (PALGLAC grant no. 787263 ).
Code availability. The code for the DEM reconstruction of experimental beds is available from the corresponding author of Lelandais  et al. (2016). The code for the calculation of the horizontal deformation of the silicone cap is available under https://github. com/ JeanVerite/Verite_et_al_2021 (last access: 23 June 2021) (GNU General Public License v2. 0). Data availability. All datasets used in this paper are available from the corresponding author on request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-15-2889-2021-supplement.
Search Criteria All search results were evaluated using the PRISMA statement  (Page et al. , 2021). In a systematic review, information was collated to answer the following two research questions: (1) how DPS and STRUBIAS production on in-situ and ex-situ treatment and processing units affect product characteristic, fertilizer replacement value (FEV) and P dynamics in soil, risks of GHG emissions and pathogen and heavy metal pollution? (2) how to combine this information to develop production systems necessary to certify DPS and STRUBIAS products as P-fertilizers in accordance with technical proposals for new fertilizing materials under the Fertilizing Products Regulation? A comprehensive systematic literature search of three online databases was performed, Scopus (www. scopus. com), PubMed (https://pubmed.
Greenhouse Gas Emission Some data needed for the holistic review were not available in the literature and needed to be developed outside of the systematic review within the present study. Greenhouse gas emission from DPS sludge and secondary STRUBIAS products was calculated using a whole systems approach. Herein, methods presented calculate CH 4 , N 2 O, and ammonia (NH 3 ) emissions from sludge and secondary STRUBIAS products from production to field application. Ammonia emission is included in the calculation due to risk of N 2 O emission from NH 3 deposition to land. In this analysis, various conceptual scenarios (from production to field application) are compared with the aim to find potential scenarios that can minimize or eliminate emissions. Assumptions for management of sludge and STRUBIAS products for a Danish dairy production site are used in the scenarios as follows: each month 1/12 of 1 ton of annual produced standard sludge is transferred to a store and the temperature in the stored sludge is similar to the monthly average air temperature of Denmark.
Acknowledgements The authors thank all the institutions mentioned in the methods section for their valuable support and access to study fossil and extant comparative collections. They thank  D. Gutiérrez ,  Andrés Eloy Reyes ,  José Gregorio Hernández ,  Rodolfo Salas Gismondi ,  Mauro Grano ,  David Flores ,  Brian Sidlauskas ,  Rodolfo Isaac Sánchez ,  Tito Barros ,  Gilson Rivas ,  Oscar Lasso Alcalá ,  Mark Sabaj Pérez ,  Kyle R. Luckenbill ,  Frank Gary Stiles ,  Paula Bona ,  Javier Luque ,  Emily Lindsey ,  Gabriel Aguirre-Fernández , the  Sánchez  family and the  Urumaco community  for their valuable support, and to  Judith Recht  for corrections to the text. They also thank  German Bayona ,  Andrés Gómez ,  Felipe Lamus ,  Sara Morón ,  Luis Quiróz , and  María C. Ruiz  for their support in the elaboration of the stratigraphic sections of  Northward Chiguaje Hill , and  San Gregorio Río Seco.
0 for MSS(PAS), SM(FSN), and LII, while they were lower for MAAP and EC(TOA) (b = 0. 91-0. 94) and the highest for AE (b = 1. 26). A tendency for lower or higher BC results in comparison to the average over instruments is indicated by ∆(BC). For the whole dataset, ∆(BC) was positive for SM(FSN) (0. 66 ± 0. 50 mg Sm -3 ) and for AE (0. 90 ± 1. 91 mg Sm -3 ), while it was slightly negative for MSS(PAS) and MAAP (-0. 05 ± 0. 74 and -0. 02 ± 0. 62 mg Sm -3 ), and particularly for EC(TOA) (-0. 60 ± 0. 53 mg Sm -3 ). For sulphur-free fuels in the HSD engine, the results from different instruments were particularly close to one another: ∆BC was slightly negative for MSS(PAS) (-0. 10 ± 0. 04 mg Sm -3 ) and slightly positive for LII (0. 10 ± 0. 08 mg Sm -3 ), while the results obtained with SM(FSN) were close to average.
Data Processing Data processing varies for different BC measurement instruments. Output from the SM(FSN) is on screen or saved on a computer in an easily accessible format. The MSS(PAS) saves the results at high resolution: one second intervals were used in this study, but 10 Hz intervals would be an option. For the MSS(PAS), the data format is easily readable with common programs (e. g. , Excel). For the LII 300, data resolution is adjustable, and for these measurements, recording in 5Hz intervals was recommended and used, which led to large file sizes. Data files from LII were not readable with common programs and data processing required special software. LII 300 data processing also required a user familiar with instrument settings, since the results are influenced by the choices made.
The observed 90th percentile of ∆BC was ±1. 5 mg Sm -3 , which is a narrow range when considering the wide marine exhaust matrices studied. Typically, BC concentrations measured with SM(FSN) and MAAP were higher than those measured with MSS(PAS) or EC(TOA). The ∆BC was consistently positive for SM(FSN) (+0. 66 ± 0. 50 mg Sm -3 ) and negative for EC(TOA) (-0. 60 ± 0. 53 mg Sm -3 ), while for MSS(PAS) and MAAP the direction of ∆(BC) (-0. 05 ± 0. 74 mg Sm -3 and -0. 02 ± 0. 62 mg Sm -3 ) depended on the aerosol properties, ratio of AAExBC:SO 4 , or on particle sizes and morphology, which is a new finding. Regression analysis of the results revealed correlations between ∆BC and exhaust properties in individual measurement campaigns, but not over the whole dataset, indicating that combined effects of aerosol properties could be significant.
Acknowledgments: The authors wish to thank  Jorma Kämäräinen  and  Anita Mäkinen  from  Traficom  for their support and consultation related to the BC emissions from marine engines. Thanks are given for assistance in measurement planning to  Heikki Korpi  (Wärtsilä). The authors also wish to thank  Liisa Pirjola  ( Metropolia ) for lending an Aethalometer in campaign A. Authors deeply thank personnel for their invaluable contributions to the project's measurements:  Pekka Piimäkorpi  (campaigns A-C),  Sami Nyyssönen ,  Jarmo Kuusisto ,  Jarno Martikainen ,  Matti Niinistö  (campaign A),  Mikko Karppanen  (campaign C), at  VTT ,  Matthew Bloss  at  FMI ,  Pauli Simonen ,  Teemu Lepistö  at  TAU.
Funding: This research was funded by  Business Finland  ( SEA-EFFECTS BC No 40356/14  and BC footprint No  1441/31/2019 ),  Traficom  ( 198717/02. 03. 00/2020 ), the  European Union 's  Horizon 2020 programme  ( H2020 TUBE No 814978 ),  Academy of Finland Flagship funding  (grant nos. 337552 ,  337551 ), and from industrial partners. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
thaliana. The dataset was validated by quality assessments including RNA sample quality, microarray quality and global gene expression profiling. The raw and normalized formats of these transcriptomic data are available via GEO repository with the accession number GSE168830. The dataset can be used to provide insights into the plant's early and later mechanisms induced or repressed by RLs. It can be compared to data obtained with other plant defense elicitors, including the well described compounds perceived by membrane protein receptors. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Data Description This dataset represents a large scale transcriptome comparison of Arabidopsis seedlings not exposed or exposed to Mono-RLs for 1 h or 3 h. Total RNA samples were extracted from the seedling tissues and were subjected to the microarray experiments using Agilent Arabidopsis V4 Gene Expression Microarrays (Agilent Technologies, Santa Clara, CA, USA). The flowchart of the experimental design of the study is presented ( Fig. 1  ). The raw and normalized formats of these data are available in National Center for Biotechnology information (NCBI) Gene Expression Omnibus (GEO) gene expression data repository ( https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE168830 ). The data obtained after each processing step are provided as supplementary data files. Quality Control (QC) reports generated after array scanning and data extraction are provided in Supplemental Appendix S1.
Data acquisition and analysis The arrays were immediately scanned with the SureScan Microarray Scanner  (Agilent). The data extraction was performed with the Agilent software Feature Extraction version 11. 5. 1. 1 using the default settings  [4]. Quality Control (QC) reports generated showed that all evaluated metrics matched defined criteria. Processing of the dataset was realized using LIMMA (Linear Models for Microarray and RNA-Seq Data) package version 3. 40. 6  [5]  of the bioconductor project  [6]  on R software version 3. 6. 1  [7]. The different steps were made following LIMMA user's guide  [8]  and in particular with the single-channel case study on Agilent arrays. Briefly, a background correction was realized before a quantile normalization between arrays using respectively the « backgroundCorrect » and «normalizeBetweenArrays» functions.
Control probes and probes above background on less than five arrays were filtered out. No more transformation was done on data transmitted to GEO NCBI (except removing replicates as requested). To validate the experimental process, a principal component analysis was realized using the PCA function of FactorMineR package version 2. 4  [9]. It confirmed three distinct groups of values corresponding to the control condition, the 1 h Mono-RL treatment and the 3 h Mono-RL treatment. Boxplots were generated with log(FC) values and were well centered on 0. Venn diagrams were realized, first considering probes with a corresponding p-value < 0. 01 and with probes for which the condition |log(FC)| > 1 is also respected.
Acknowledgments We thank  Dr Stéphan Dorey  ( Résistance Induite et Bioprotection des Plantes laboratory, Université Reims Champagne-Ardenne ) for having provided us Mono-RLs purified by the  Institut de Chimie Moléculaire de Reims. This research was supported by the  Hauts-de-France (HdF) Council  and  European Regional Development Fund (ERDF) , and was realized in the context of the.
However, prior attempts to estimate the impact of planned dams on the sediment load to the delta have suggested that future sediment loads could be significantly affected:  Kummu et al. (2010) , for example, estimated a sediment load reduction of 51-69% if all planned dams are to be built, while  Kondolf et al. (2014)  found that the reduction could be as high as 96%. However, previous empirical studies are now outdated, while the existing projections of future sediment loads: (i) focus exclusively on the impacts of damming; (ii) have employed damming databases that do not always identify all relevant infrastructure reliably, and; (iii) do not take into account other drivers, such as anthropogenic climate change. Given the extreme importance of the Mekong delta for the livelihoods of the millions of people living there  (Nguyen et al.
, 2019) , and the enormous anthropogenic pressure that the Mekong River has been subject to in recent years, due to river damming and climate change, it is essential to assess accurately the impact of natural and anthropogenic stressors on the sediment flux of the Mekong River. In this paper we provide a comprehensive assessment of the potential impacts of future environmental change on future (to 2100) Suspended Sediment Flux (SSF) to the Mekong delta. We use a dynamic, semi-distributed, catchment model to identify how SSF has changed in the historical period  and will change in the future (2020-2100) throughout the Mekong's large, transboundary, basin, under the combined influence of dam construction and anthropogenic climate change. Importantly, we set the model up using the latest and most comprehensive database of damming available for the Mekong (provided by the Mekong River Commission).
, 2014; Meshkova and Carling, 2012; Nie et al. , 2018; Van et al. , 2012)  and thus significant geomorphological changes are expected to take much more than just a century. This might not be true for the delta part of the river, however, as pointed out previously, this paper focuses on the inland part of the river and estimates the influx of sediment at the apex of the delta. 1B  and Figure  1D ). Daily precipitation and temperature data from a climate model were employed to project changes in the future climate. The PRECIS Regional Circulation Model coupled to the GFDL-CM Global Circulation Model was employed in this study  (Thuc et al. , 2016; Whitehead et al. , 2019). The Representative Concentration Pathways (RCPs) 4. 5 and 8. 5 were used to drive the climate model  (Edenhofer et al. , 2014; Janes et al.
, 2019). RCP 8. 5 reproduces greenhouse gas emissions rising continuously throughout the 21st century and represents a relatively challenging situation. Emissions in RCP 4. 5 peak around 2040, then decline. The results of the climate model were bias-corrected, applying a quantile mapping correction  (Lafon et al. , 2013)  to reproduce the observed climate. These climate scenarios allow climate changes to be projected up to the year 2100. A database of existing and planned dams was obtained from the Mekong River Commission. A total of 284 dams were identified, 30 of which are located on the main stem of the Mekong (7 of them are already in place), and the rest on tributaries (Figure  2 ). This database includes information on reservoir volume and year of construction (or planned year of construction).
Segmentation des images Pour identifier les zones des images correspondant aux plantes, nous utilisons l'indice de végétation Excess Green (ExG) en association avec la méthode de segmentation automatique d'Otsu  [20]. Chaque pixel d'une image est remplacé par sa valeur verte amplifiée :  = 2 - -. L'association de ExG avec la méthode d'Otsu pour la segmentation des images de champs de culture a été étudiée et validée dans des travaux précédents  [20, 6, 7, 21]. À la fin du processus de segmentation, les images des champs (1) Le code implémentant la méthode de détection décrite dans cette section est disponible à l'adresse suivante : https://github. com/LittleCoinCoin/Plant_Counting. de culture RGB sont transformées en images en noir et blanc, appelées images Otsu, où les pixels blancs sont censés correspondre à une plante (culture ou adventice).
Le protocole expérimental En raison de l'absence de bases de données publiques bien étalonnées portant sur les variations de densité dans les champs, les expériences réalisées l'ont été sur des images virtuelles de champs. Cela permet de contrôler les conditions de variations de densité et de comparer les résultats obtenus avec la « vérité » terrain qui est contrôlée. Nous avons ainsi généré des jeux de données avec une probabilité de levée des plantes qui dépend de la hauteur et de la largeur d'un champ ainsi que de leurs positions dans le champs. Dans nos expériences, un gradient linéaire artificiel est défini de sorte que le couvert végétal de la parcelle soit le plus faible en bas à gauche (aux coordonnées (0,0) du champ) et maximal en haut à droite (aux coordonnées  (14, 14) ). Nous avons échantillonné quatre intensités de gradient avec des variations allant de 0 % à 100 % (0-100), 25 % à 100 % (25-100), 50 % à 100 % (50-100) et 75 % à 100 % (75-100).
Slow-moving landslides exhibit persistent but non-uniform motion at low rates which makes them exceptional natural laboratories to study the mechanisms that control the dynamics of unstable hillslopes. Here we leverage 4. 5+ years of satellite-based radar and optical remote sensing data to quantify the kinematics of a slow-moving landslide in the tropical rural environment of the Kivu Rift, with unprecedented high spatial and temporal resolution. We measure landslide motion using sub-pixel image correlation methods and invert these data into dense time series that capture weekly to multi-year changes in landslide kinematics. We cross-validate and compare our satellitebased results with very-high-resolution Unoccupied Aircraft System topographic datasets, and explore how rainfall, simulated pore-water pressure, and nearby earthquakes control the overall landslide behaviour.