dataesr/scientific-paragraphs-categorization huggingface dataset:
As an example, any significant permanent variation of the reflected power is most likely related to a hardware failure (e. g. , antennae, cables, or amplifiers). Signal to Noise Ratio is usually not stable because of environmental conditions and periodically fluctuates with repeated daily pattern, but any significant permanent change in this pattern or in its variation range should be investigated. HF radar management software often provides diagnostic tools and warning messages. The SeaSonde Radial Suite generates hardware and software diagnostics that are saved in so called DIAG files. An extension with *. hdt refers to hardware diagnostics and an extension with *. rdt refers to radial diagnostics. Much of this information is reported within the radial file itself, but diagnostic files help to aggregate and show them, through DiagDisplay application, in a bigger picture.
Plots of diagnostic parameters over custom time windows can be also exposed in a web page served by the RadialWebServer. Another application, the RadialSiteReporter, is able to perform a scheduled detailed check of all the software and hardware components and to produce and send to the operator detailed email alerts with highly customizable rules. These alerts are also shown by the RadialWebServer, plus are added to an alert log. The WERA software provides hardware and software diagnostics on all levels of data acquisition or data processing. Automatic status and warning messages ( *. status) will automatically be sent out to defined recipient groups at different intervals depending on defined priority levels. High priority messages will be sent out immediately, lower priority messages only once per day or per week.
(Emery and Washburn, 2007). a Recommendations given by remote site monitoring scripts rs_warn. pl. This script compares the parameters of the hardware against accepted values as listed on the tables. b Recommendations given by the documentation (http://hfradar. msi. ucsb. edu/brian_emery/files/reports/2008_diagnostics_evaluation. pdf). c Warning: the new GPS modules (MINI-JLT REV v1. 0) do not provide information about the number of GPS satellites. The rs_warnm. pl must be modified to include this exception.
The two most common commercial software packages for HF Radar acquisition and pre-processing are: • the suite of WERA/Helzel (available for Linux Os only, suggested distribution open SUSE), based, for the signal processing algorithms, on Klaus Werner Gurgel works  (Gurgel et al. , 1999) , and supporting the two HF radar implementations and spectral information processing methods: Beam Forming processed on a regular cartesian grid (or optionally with polar coordinates), and Direction Finding by Least Mean Square developed for compact antenna system. • the Seasonde radial suite (available for Mac Os X only) from Codar Ocean Sensors, based, for the signal processing algorithms, on the works of  Lipa and Barrick (1983)  and  Barrick and Lipa (1997). Third party software for data acquisition and preprocessing is available under commercial license for beam forming HF radar systems, see Table  8  for more details.
, 2019). As a data post-processing example, the authors want to report here the case of the application of QC tests and their description, according to the definition of data levels and to the European common data and metadata model for surface currents 1 https://github. com/rowg/hfrprogs (Corgnati et al. , 2017 (Corgnati et al. , , 2018a,b),b). The flow is summarized in the following scheme:.
These thresholds should not be determined arbitrarily but based on historical knowledge or statistics derived from historical data. The software tools for HF radar Real Time data postprocessing into the EU standard, described in Table  10 , have been developed by the EU HFR Node and they are freely shared (provided under the license: Attribution-Non-Commercial-ShareAlike 4. 0 International -CC BY-NC-SA 4. 0-. More information about the license in this link: http:// creativecommons. org/licenses/by-nc-sa/4. 0/) and kept updated on a GitHub repository.
Data Archival and Preservation On-site archiving on a separate storage system is recommended as the first step for data preservation. Both the Codar SeaSonde and WERA softwares include scripts and applications for scheduled data archiving. For Codar systems, at the very least, it is advisable to save all Range Series (. rs) data files, from which radial diagnostic information, Cross Spectra (. cs) files, and all the derived oceanographic variables can be recalculated, allowing also for different reprocessing settings. From a practical point of view, in order to ensure quicker checks of processing settings, it is suggested to save also the Cross Spectra files (at least CSStype averaged files). Another bunch of deployment-specific files must be archived for SeaSonde systems, they are: in the RadialConfigs folder, Header.
txt file and MeasPattern. txt files; gps Track data and Time Series files generated during APM, needed for recalculating Antenna Pattern in case of need; the folder/Codar/SeaSonde/Logs/or any equivalent log file containing the operator comments on all the changes performed on the radial site, including periods of validity of one or more Antenna Pattern files; alert and diagnostic files for later troubleshooting of the station behavior. The WERA toolbox contains a script named "BackupDataFiles. sh, " which is used to automatically create a backup copy of raw data and other data products onto an external backup hard disk. If two hard disks are used for redundancy, the script should be duplicated for optimum performance. For WERA systems using beam forming software it is not required to save Cross Spectra files.
The *. URFI or *. RFI files containing information about radio frequency interference during the measurement and optionally the frequency pre-scan *. RAW files may be archived beside the raw data files *. USORT or *. SORT. The results ( *. CAL) of the automatic direct path test measurements will contain information about antenna health. In addition to the on-site archiving, the HF radar remote stations should backup the most important data in real time to a remote repository through a network connection, as much as the bandwidth allows. The full backup of the data can be executed periodically on-site with a portable storage device. The minimum estimated amount of data to be archived per year and per station is in the order of 100 GB for CODAR systems, and 1 TB for WERA systems.
Data Dissemination Homogenization of file format, data description and Quality Control procedures are prerequisites for data sharing among data centers and data aggregators through the concepts of data interoperability and machine to machine interaction. The authors strongly encourage HFR data providers to investigate the state of progress of regional and global HFR standards and infrastructures, and to take advantage of the service provided for enabling a data flow in standard interoperable format toward the community. The European HF radar Node is an example of a recommended way for channeling HFR standardized data toward European and global data portals (Table  11 ). The Node is responsible for the NRT HFR current data collection from the HF radar operators in Europe, for the application of the standard QC model, for the conversion of the QCed data files into the European standard data and metadata model.
It is operational since April 2019 for the Copernicus Marine Service (CMEMS-INSTAC), SeaDataNet (SDN/SDC), and EMODnet Physics data delivery. The connection with US network for reciprocal data exchange is under development. In US, radial files are collected in near real-time at regional level and then pushed into national nodes for total vector maps production and distribution through a Thematic Real-time Environmental Distributed Data Services (THREDDS) server  2. The Australian Ocean Data Network provides open access to real time quality controlled and delayed-mode quality-controlled HF radar derived surface currents, winds and waves through their THREDDS catalog 3  as well as through the data portal 4. One advantage of the data portal is that it allows for data to be aggregated over a user-defined time window, platform or region.
Standard NetCDF format that adhere to the CF-1. 6 and IMOS-1. 4 conventions are available, either unaggregated or aggregated. The Global HF Radar network web site 5  shows a map with all of the locations of the HF Radar sites all over the world automatically connected. In 2017 "the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS)"  (Roarty et al. , 2019). This is expected to help coordinate the effort at international level and hopefully to define a unique global standard within the next few years. It is worth mentioning that the data management plan for HF radar data should follow other few very general best practices as: • follow the FAIR  (Wilkinson et al.
, 2016)  paradigm as part of making research data findable, accessible, interoperable and re-usable. • include, inside metadata and as part of the data management plan, information about data policy, and possibly share unrestricted data, to make them freely available, thus facilitating access to a wider public. Creative Commons license Attribution 4. 0 International (CC BY 4. 0) 6  is an example adopted by some HF radar data provider. • associate a Digital Object Identification (DOI) to each dataset, and thus helping to make the data discoverable, accessible and citable. Other general best practices on How to develop a Data Management Plan (International Oceanographic Data and Information Exchange, 2016) are available at the Ocean Best Practices repository.
ACKNOWLEDGMENTS This paper is based on work carried on within the  JERICO-NEXT project  ( Joint European Research Infrastructure network for Coastal Observatory -Novel European eXpertise for coastal observaTories ), funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 654410. We would like to thank the projects RITMARE (Italian national funds),  INCREASE  ( CMEMS Service Evolution Call  for Tenders  21-SE-CALL1 ),  SeaDataCloud  ( EU-H2020 GA n. 730960 ), in whose framework the activities of data harmonization and standardization have been carried on. We acknowledge financial support from  Copernicus Marine Environment Monitoring Service (CMEMS)  through  IBISAR  ( CMEMS User Uptake Call  for Tenders  67-UU-DO-CMEMS-DEM4_LOT7 ) and INSTAC (http://www.
FUNDING Research was supported by the following projects:  JERICO-NEXT (EU-H2020 GA No. 654410 );  RITMARE (Italian flagship  project);  INCREASE (CMEMS Service Evolution Call for Tenders   21-SE-CALL1 ),  SeaDataCloud  ( EU-H2020 GA No. 730960 ),  Copernicus Marine Environment Monitoring Service (CMEMS)  through  IBISAR  ( CMEMS User Uptake Call  for Tenders  67-UU-DO-CMEMS-DEM4_LOT7 ). Publication fees are funded by:  JERICO-NEXT  project ( Joint European Research Infrastructure network for Coastal Observatory -Novel European eXpertise for coastal observaTories ), funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No. 654410.
Data Manager and Data access EMODNet-Physics European Marine Observation and Data Network: Physics http://www. emodnet-physics. eu/Map/ http://thredds. emodnet-physics. eu/thredds/ HFRADARCatalog. html Sea Data Net Pan-European infrastructure for ocean and marine data management. The SeaDataNet portal offers different services: discovering, visualization, access and data downloading http://seadatanet. maris2. nl/v_cdi_v3/ search. asp.
Previous releases administrator permissions pre-installed by can be used, but it is on the computer. attendees) recommended to have at least the R2016b version. • HFR_Progs-2. 1. 2 Unidata NetCDF package • M_Map for Java TM installed: • GSHHS https://www. unidata. ucar. • Nctoolbox-1. 1. 3 edu/software/thredds/ mysql-connector-java- current/netcdf-java/ 5. 1. 17 JDBC driver for connecting to the MySQL database • Rdir Graphical user NO YES interface Converts CODAR YES YES TUV files (Totals) to NetCDF European HFR Standard Performs radial YES NO combination Performs QC YES YES Can run in real time YES YES Requires scripting YES NO skills Allows batch YES YES processing.
e. , situation (b), but sometimes, the importance of picoplankton could exceed phytoplankton, as reflected by the biomass being grazed (Table  2 ). The impact of the trophic environment on microzooplankton grazing was also detected in the Mediterranean Sea  (Zoccarato et al. 2016). There, in oligotrophic and mesotrophic conditions, microzooplankton mostly preyed on heterotrophic prokaryotes (56. 7% and 60. 6%, respectively), which was different from the data presented here. In an analysis of the global impact of microplanktonic grazers on marine phytoplankton, microzooplankton consumption was found to be the main source of phytoplankton mortality, accounting for up to 67% of the daily phytoplankton growth for the full dataset. Under eutrophicated conditions, the potential production of microphytoplankton exceeded the ingestion rate, while under meso-and eutrophic conditions, the ingestion rate on autotrophic prokaryotes exceeded the potential production  (Calbet and Landry 2004 ).
Data elaboration Apparent growth rate (u, /day) of Chl a, picoplankton and flagellates in the dilution incubations were calculated using the equation u = ln(P t /P 0 )/t  (Landry and Hassett 1982)  where t was incubation time (day), P 0 and P t were the density of Chl a, picoplankton and flagellates at the beginning and after the end of the incubation, respectively. According to  Landry and Hassett (1982) , u is the net result of grazing (g, /day) and growth rates (k, /day): Values of k and g were determined from linear regression of the apparent growth rates against dilution factors (c). u = k -c × g. Here, we considered only the results with significant linear regression for the preys considered (R 2 > 0. 8). Microzooplankton grazing pressure on standing stock (P i ) and primary production (P p ) were calculated according to  Verity et al.
Acknowledgements We thank the crew of  RV Beidou  for their help and cooperation in the field work. Our colleague  Peng Ji  collected all the seawater used in the incubations. Dr. Chaolun Li  provided hydrographical and nutrients data. This study was supported by  National Key Research and Development Program of China  ( 2017YFA0603204 ), the  National Natural Science Foundation of China  (Nos. 41706192 ,  91751202 ), the  CNRS-NSFC   Joint Research Projects Program  ( NSFC 41711530149 ) and 2017-2019 Sino- French Cai Yuanpei Programme.
The chemical fingerprints of lipophilic fractions obtained with the extraction method described below (see Section 2. 3) were also acquired (data not shown) and compared to those of MeOH-CH 2 Cl 2 extracts. Since only some minor differences in the relative number of peaks in the polar and mid-polar region of the chromatograms could be observed, only the chemical fingerprints of MeOH-CH 2 Cl 2 extracts were presented in the results Section 3. 2. Since the LC-MS method used was designed to study mid-and non-polar secondary metabolites, the chemical fingerprints of the hydrophilic fractions obtained with the extraction method described below for the toxicological analyses (see Section 2. 3) were not acquired and thus not shown.
Absorbance data were fitted to a sigmoidal dose-response curve (variable slope) based on the four-parameter logistic model (4PL) allowing the calculation of half-maximal effective concentration (EC 50 ) values using Prism v6. 07 software (GraphPad, San Diego, CA, USA). The EC 50 values of samples were expressed in ng dried extract µL -1. According to Viallon et al. [37] , samples inducing a loss of N2a cell viability between 50% and 80% at the MCE under OV + conditions were considered as weakly toxic. Calibration of the assay was achieved using a P-CTX3C reference material sourced from the Institut Louis Malardé (Papeete, French Polynesia) that had been quantified gravimetrically. The EC 50 value obtained for P-CTX3C was 3. 1 ± 0. 8 fg µL - 1. The toxin contents (T) of samples were estimated using the formula T = (P-CTX3C EC 50 /sample EC 50 ) and expressed in ng P-CTX3C equiv.
9, 3. 8 and 4. 5 ng dried extract µ L -1 , corresponding to toxin contents of 7. 8, 2. 0 and 21. 8 ng P-CTX3C equiv. g -1 freeze-dried cyanobacteria, were estimated for lipophilic fractions of MOR-076, MOR-139 and MOR-167, respectively. Interestingly, lipophilic fractions from an Anabaena sp. bloom from Lifou Island (New Caledonia)  [16]  and two Anabaena sp. 1 blooms from Tahiti and Mangareva Islands (French Polynesia) (M. Roué, unpublished data) previously showed a similar activity using CBA-N2a. However, further LC-MS analyses failed to detect the presence of any known VGSC activators, including CTXs (data not shown). Therefore, the weak toxicity detected on neuroblastoma cells could be consistent with the cytotoxic activities against cancerous cells exhibited by laxaphycins A and B  [24, 65] , two compounds detected in Anabaena sp.
Acknowledgments: The authors wish to thank the technicians of CRIOBE (Moorea) and  Mathieu Grellier  for their help and support during field expeditions. The authors also greatly acknowledge  Jérôme Viallon  and  Hélène Taiana Darius  from the  Institut Louis Malardé (Tahiti)  for the CBA-N2a analyses. Chromatographic, spectroscopic and structural analyses were performed using the facilities of the  Biodiversité et Biotechnologies Marines platform at the University of Perpignan (Bio2Mar , http://bio2mar. obs-banyuls. fr/fr/index. html). Finally, authors thank the two anonymous reviewers whose comments greatly helped improve the manuscript.
Data The GRACE satellite measures the vertical terrestrial water storage from the land surface to the deepest aquifers and can be used to monitor spatiotemporal variability in terrestrial water storage anomalies  (Scanlon et al. , 2016). The advanced mass concentration (mascon) approach contains a much higher signal-to-noise ratio in TWS retrieval than the traditional global spherical harmonics (SH) technique because of reduced leakage errors  (Scanlon et al. , 2018 (Scanlon et al. , , 2019)). Notably, the GRACE mascon solutions derived from the Jet Propulsion Laboratory (hereafter JPL-M, http: //grace. jpl. nasa. gov, last access: 21 July 2020) and the Center for Space Research (CSR-M, http://www2. csr. utexas. edu/ grace/RL05_mascons. html, last access: 21 July 2020) represent two fundamentally different approaches to applying con- straints.
The constraints applied in the JPL-M processing are based on both GRACE data and geophysical models for mass changes over land, oceans, ice, inland seas, earthquake areas, and areas affected by glacial isostatic adjustments  (Watkins et al. , 2015) , whereas CSR-M constraints are based solely on GRACE data  (Save et al. , 2016). Meanwhile, the JPL-M solution has the unique characteristic that each 3 • ×3 • (approximately to the native resolution of GRACE data) mascon element is relatively uncorrelated with the neighboring mascon elements  (Rodell, 2018). In this study, both the JPL-M and the CSR-M datasets were used to detect TWS changes across the Asian and eastern European regions with spatial resolutions of 0. 5 • × 0. 5 • for the period between April 2002 and June 2017, for a total of 183 solutions.
The missing data for the duration of 20 months in the original time series were filled by the linear interpolation method  (Long et al. , 2015; Yang et al. , 2017). The GRACE anomalies reported in these mascon solutions are relative to a 2004-2009 mean baseline  (Scanlon et al. , 2016). For details on the data processing, please refer to  Watkins et al. (2015)  and, for the mascon solutions,  Save et al. (2016). The Global Land Data Assimilation System (GLDAS) data between April 2002 and June 2017 were used to partition the GRACE-observed TWS changes into SW (snow water equivalent, canopy water, lakes and glaciers), SM and groundwater. The monthly data products from the GLDAS version 2. 1 Noah model contain 36 variables, including canopy water storage, snow water equivalent and SM data. Noah has a total of four layers of SM thickness: 0-10, 10-40, 40-100, and 100-200 cm.
To compute the GLDAS TWS, the SM in all layers, the snow water equivalent, and canopy SW are summed. The summed GLDAS TWS is comparable to GRACE TWS over land  (Rodell et al. , 2004) , and, notably, the GLDAS version used here does not include groundwater and separate SW components (such as rivers and lakes). Therefore, deviations from the GRACE total water storage changes can be expected. A comparison between GRACE and GLDAS is shown in Fig. S1  in the Supplement. The native spatial resolution of the GLDAS dataset is 0. 25 • × 0. 25 • ; we resampled these data to a 0. 5 • × 0. 5 • spatial resolution using the nearest neighbor interpolation method prior to the analysis. The sea level (Caspian Sea, East Aral Sea, West Aral Sea, and North Aral Sea) data derived from multimission altimeter observations were obtained from the Database for Hydrological Time Series of Inland Water  (Schwatke et al.
Cross-correlation analysis Contemporaneous weather conditions impact TWS residuals and often show evident time lags. Therefore, in this study, we employ the cross-correlation method to explore the relationship between the TWS residual and teleconnection indices. The cross-correlation measures the similarity of the two time series datasets as a function of the displacement of one set relative to the other  (Oppenheim et al. , 2009). The cross-correlation is defined as follows: ρ (τ ) = σ 12 (τ ) √ σ 11 σ 22 , (3) where σ 12 (τ ) is the cross-covariance function of x 1 (t) leading the lagging x 2 (t), τ is the time lag, and σ 11 and σ 22 represent the auto-variances of x 1 (t) and x 2 (t), respectively. The value of ρ (τ ) lies between -1 and +1. Moreover, we focus on the current and historical influence of TCs on the TWS residual, and hence we constrained the value of lag τ to range between 0 and 24  (Ni et al.
There are several recommendations for future hydrological studies: (1) withdrawal of freshwater from groundwater in water-limited regions is important for the sustainable development of water resources and food supplies  (Rodell et al. , 2018). However, groundwater drought is a distinct phenomenon resulting from a decrease in groundwater storage  (Thomas et al. , 2017). Understanding groundwater drought is important in water-limited regions where the interplay between groundwater recharge and abstraction results in variable groundwater stress conditions. GRACE has the unique potential to obtain data on groundwater storage by introducing subsidiary datasets. (2) Glacier mass loss in mountainous areas can relieve drought stress in drought years  (Huss et al. , 2018) , but it can additionally result in hydroclimatic extremes, e.
g. , floods. Neither of these phenomena can be detected using only precipitation datasets, such as those commonly used in monitoring drought and flood events  (Sherwood et al. , 2014) ; this highlights the importance of TWS-related hydroclimatic extremes. With the release of the GRACE follow-up satellite  (Famiglietti et al. , 2013) , consecutive prolonged data records could provide a valuable solution for evaluating hydrological conditions from a long-term perspective and would lead to considerable improvements in our knowledge of TWS-related hydroclimatic extremes  (Famiglietti et al. , 2013). (3) A recent study found that the CO 2 growth rate is strongly sensitive to observed changes in TWS  (Humphrey et al. , 2018) , and the coupling between the water and carbon cycles highlights the need for stronger interactions between the hydrological and biogeochemical research communities to achieve a sustainable development of the Earth system.