dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this work, we investigate the use of AIS maritime data and deep learning methods for the reconstruction of sea surface current. Within a 4D-Var assimilation framework, our deep learning approach combines a geophysically sound observation model with a trainable physics-informed prior. Numerical experiments show that the exploitation of reanalysis data provides relevant information to better constrain the training of the considered space-time prior onto sea surface dynamics stated as a reduced-order model  [37]. This training strategy results in a significant gain in terms of sea surface current reconstruction as well as in terms of generalization capabilities. Through numerical experiments on a real dataset, we show that AIS data in a casestudy region with a dense maritime traffic constitute a reliable source of observations to derive sea surface currents, which may better match in situ measurements than state-ofthe-art satellite-derived and assimilation-based products.
Interestingly, as AIS-derived sea surface currents do not rely on any geostrophy assumption, they can reveal the rotationalfree ageostrophic component of the sea surface dynamics, which cannot be informed by satellite altimetry and remains under-estimated by assimilation-based products. As such, the proposed framework could contribute better assess from in situ measurements the space-time variabilities of ageostrophic flows. From a methodological point of view, the proposed framework also opens new research avenues to exploit the synergies between different observation datasets. While, here, our learning-based inversion only relies on AIS data, we could complement the considered variational model with other observation terms. In this line of work, the synergy between altimetry and AIS data seems particularly appealing to better exploit these two data sources that may significantly complement each other in terms of space-time sampling and dynamical features they can reveal.
Acknowledgments: This work was supported by  Eodyn ,  LEFE program  (LEFE  MANU  project  IA-OAC ),  CNES  (grant  SWOT-DIEGO ) and  ANR Projects Melody and OceaniX. It benefited from  HPC  and  GPU resources from Azure (Microsoft EU Ocean awards ) and from  GENCI-IDRIS  (Grant  2020-101030 ).
9) is selected to represent the sample. In some cases, the second best-fit combination has R 2 value close to the best fit, but the difference between the two combinations always regards the less abundant of the four selected references, with negligible effects on the interpretation. The combinatoric package of the Athena software is used  (Ravel and Newville, 2005). Following the procedure adopted by  Shoenfelt et al. (2018) , the relative abundance of Fe-bearing minerals is estimated considering the percentage of linear coefficients obtained from OLS.
Another possible implementation is the concurrent analysis of soluble and insoluble impurities, including elements other than Fe. This will help to investigate the dissolution and precipitation of primary and secondary minerals and identify the geochemical reactions responsible for these transformations, G. Baccolo et al. :  Deep ice as a geochemical reactor: insights from iron speciation and mineralogy of dust paving the way for the development of englacial geochemistry. Data availability. Data used to meet the aims of the present study are available in the Supplement. Full XANES spectra are found in the PANGAEA open repository, https://doi. org/10. 1594/PANGAEA. 924114 (Baccolo et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10.
Acknowledgements. The Talos Dome Ice Core (TALDICE) project, a joint European program, is funded by national contributions from Italy, France, Germany, Switzerland and the United Kingdom. Primary logistical support was provided by PNRA at Talos Dome. This is TALDICE publication no. 62. This publication was generated in the frame of Beyond EPICA. The project has received funding from the European Union's Horizon 2020 Research And Innovation Programme under grant agreement no. 815384 (Oldest Ice Core). It is supported by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, the Netherlands and the United Kingdom. Logistic support is mainly provided by PNRA and IPEV through the Concordia Station system. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency or other national funding bodies.
Once the amplitude of harmonics is detected and controlled, and the phase deviations are compensated, the reference signal can be reconstructed on the time field with Inverse Fast Fourier Transform (IFFT). Then the output reference is converted into analog signal by digital to analog converter. In this paper, FFT-based selective harmonic current control is proposed for research on audible noise characteristics of AC capacitors. Firstly, in section 2, the mathematical model of HCIS is presented. In section 3, a detailed presentation about the detection and control strategy for harmonic currents will be given. Finally, the simulation and experimental results of the HCIS based on FFT are shown in section 4 and 5, based on the MATLAB/Simulink and RT-LAB, respectively, to validate the significance of the proposed strategy.
, N=2 À 1 ð Þ Df. Through the mathematical computation, the information of each harmonic will be extracted from the spectrum, and the Fourier coefficients are scaled by N/2. If an exact integer period of signals is included during the sampling window at the signal acquisition stage, the final results of FFT algorithm will be precise. The well-known result of ''harmonic leakage'' is caused by incompleteness within the data acquisition window, and the harmonics ''leakage'' will result in the inaccurate detection. Under these circumstances, some relevant measures have been proposed to compensate the impact of non-cycle sampling, such as: adding the window function or zero padding. When the amplitude and phase angle of a specific harmonic are extracted from the data frames, the corresponding harmonic current regulator will be adopted to ensure the accurateness of the amplitude, and the related phase compensation angle is applied to ensure the synchronization.
Harmonic control method The configuration of the proposed HCIS is shown in Figure  5. The tested current measured by hall sensor is converted to digital data with the help of AD converter. Then the magnitude and phase of a specific harmonic current are detected based FFT method. And a PI regulator for specific harmonic is used to track the reference with phase compensation. At last, the reference signal for power amplifier is reconstructed in time domain by IFFT. A PI regulator is applied to control the harmonic current amplitude to achieve zero-error control. 22 The transfer function of PI controller for the fundamental current is expressed as: H 1 (s) = K p1 + K i1 s ð9Þ where K p1 is the proportional gain and K i1 is integral coefficient for the fundamental current. Let U Ã 1 be the output voltage of PI regulator for the fundamental current, the transfer function is: U Ã 1 k AMP st + 1 = I 1 (s) sCr + 1 sC ð10Þ where I 1 (s) is the fundamental current.
Simulation results In order to verify the effectiveness of the harmonic current detection and control strategy of the proposed HCIS, simulation model has been built based on MATLAB/Simulink. Table  2  shows the harmonics reference. The current is sampled with 2560 Hz sampling rate, and 256 points are used for FFT calculation. The frequency resolution ratio of this detection is 10 Hz, so the frame has integer number of cycles, and the spectrum leakage can be avoided. Figure  8(a)  shows the current waveform of tested capacitor. As shown in Figure  8 (b), the fundamental current is 75. 01 A, the 7th harmonic is 29. 0 A and the 13th harmonic is 90. 99 A. It can be found that the harmonic components of the injected current are consistent with the reference current values shown in Table  2.
Experimental results In this section, experiments about HCIS are carried out based on the RT-LAB platform. The RT-LAB developed by Opal-RT Technologies has been widely employed in various domains. RT-LAB software can immediately compile and import mathematical model built with MATLAB/Simulink into real-time simulation platform for control and test. It is equipped with 16 channels for 16-bit ADC and the input ranges up to 620 V. It also has 16 channels for 16-bit DAC and the output ranges up to 615 V. The gap between the simulation clock and natural clock is only nanoseconds. The experimental test bench is shown in Figure  9. Due to the limits of power level in lab, the transformer is not used and the compensation reactor is applied to reduce the active output power. In this system, considering the sampling delay and the calculation burden of FFT algorithm, the sampling points is 1280 points and the model step size is 1/12,800.
2. The time span of top publications analysed in the present review goes from  Conrad, 1996 , with a scientific piece that describes in detail the microbiological processes occurring in the soil, that produce atmospheric trace gases, and ends with a high resolution (30 m) satellite imaging database from 1984 to 2015 of the global surface water  (Pekel et al. , 2016). Pekel et al. , 2016 ; resulted three times the most cited article when running different keyword sets (wetland* modelling, global wetlands and global wetland modelling), meaning that is an important input dataset for the description and dynamics of wetlands surface in long term. The high number of citation prove the popularity that Earth Observation data has gained in the last decade. Nitrogen cycling scored the greatest number of results (44352), followed by Denitrification (26699) with around half of the record found by the first keywords set.
The largest dimension for the study of denitrification is at Earth scale (10 7 m). Modelling and compilation of data of many ecosystem and landscape level studies can give more information about denitrification and its role to the regulated fluxes into the oceans in a more integrative context and with a long term dynamic vision  (Fabre et al. , 2020; Trepel and Palmeri, 2002). Hence, integration of information and multidisciplinary research, at different spatial-temporal scale need to be promoted. How to simplify microorganism's biological processes (10 -6 m) on a global scale (10 7 m)? How to upscale from controlled laboratory conditions and field case studies to a global change scenario? Translation of this knowledge into models has begun, thanks to the increasing improvement of analysing tools and datasets availability worldwide.
, 2017; Czuba et al. , 2018; Hansen et al. , 2018; Yao et al. , 2017). Kirschke et al. (2013)  calculated decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions. Their result showed three contrasting emission scenarios (differing in fossil fuel and microbial emissions), to explain the decadal variability in atmosphere methane levels detected, their budgets overestimates the total natural emissions, so definitive conclusions cannot be drawn, but trends of changes during these three decades were clearly visible. The dataset was extended from 2010 to 2017  (Saunois et al. , 2019). Overestimation and uncertainty in global scale analysis is unavoidable, but quantification and report of it is important to improve future research.
This model would require the use of several databases to be accomplished. In , a collection of possibilities with the associated properties is provided. The limitation and suitability of each of them for the modelling of this specific process has to be proven. A priori, we suggest that soil moisture dynamic datasets seem to be the most suitable parameter for detailed denitrification and precise hot moments due to its time span (daily), and capacity to sense soil moisture under vegetated areas in a depth of 30 cm. The present proposed approach has the potential to contribute to the understanding of denitrification dynamics at global scale that so far has been the least monitored transformation for nitrogen biogeochemical cycling modelling.
Surface water Wetlands extents can exhibit large seasonal variations, which has a direct impact on denitrification quantification, thus monitoring the surface water extents is an important information. This can be achieved using EO data in the optical and microwave (active or passive) domains or a combination of the two. We consider here the datasets that provide open water extents at a sub-monthly time resolution. For this reason, static land cover datasets like Globe Cover (GC)  (Broxton et al. , 2014; Friedl et al. , 2010)  and Global Lakes and Wetlands Database (GLWD)  (Lehner and Doll, 2004)  while widely used for the identification of wetland ecosystems do not meet this requirement. Worth mentioning, as it appeared second in this bibliometric review, that the GC product, which is part of the International Geosphere-Biosphere Programme (IGBP) program, is obtained using images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 0.
005°covering years 2001 to 2012. Dynamic water surface masks derived from MODIS optical acquisitions are also available at 500 m resolution from the MOD44W product  (Carroll et al. , 2017). Recently, the Landsat- derived Global Surface Water (GSW) dataset  (Pekel et al. , 2016)  has raised considerable attention for the description of wetlands from a high resolution (30 m) optical EO. It appeared as the most cited article of recent times (2015-2019) on three of the research exercises (wetland* modelling, global wetlands and global wetland modelling) of the present bibliometric analysis proving the importance that EO data has gained in the last decade. GSW provides global surface water dynamics over the past 32 years, obtained through three million highresolution optical (30 m) Landsat satellite images.
In GSW, an expert system, that corrects for Normalized Difference Vegetation Index (NDVI), Hue-Saturation-Value (HSV) colour and space transformations, is used to tag at global grid pixels into three classes: water, land or nonvalid observations. The non-valid observations are associated to snow, ice, cloud or sensor-related issues. Since it is based on optical data, the GSW dataset has a lower temporal resolution over tropical and highlatitude regions. In addition, water surfaces under dense or floating vegetation are not well depicted even under clear sky conditions. These constitute the main two limitations for its application for denitrification studies. Outside these limitations, GSW dataset provides relevant and high resolution information about surface water changes  (Yamazaki and Trigg, 2016).
To work around the limitations of optical EO, passive and active microwave has been used for the monitoring of inundated areas at large scale. Prigent et al. (2007)  used a combination of active and passive microwave from SSMI/AMSR-E and ERS/ASCAT to derive the Global Inundation Extent from Multiple-Satellites (GIEMS) dataset. Schroeder et al. (2015)  derived the Surface WAter Microwave Product Series (SWAMPS) product from the ERS/SSMI and ASCAT brightness temperatures. Parrens et al. (2017)  demonstrated the ability of L-Band microwave brightness temperatures  (Al Bitar et al. , 2017)  from SMOS to provide the SMOS WAter Fraction (SWAF) under the vegetation in the Amazonian basin. An enhanced algorithm using multi-angular and dual-polarization brightness temperatures has been applied to obtain a global dataset G-SWAF  (Al Bitar et al.
, 2020). All of the above products suffer from the coarse spatial resolution of the microwave sensors (>25 km). For this several fusion or disaggregation algorithms have been developed  (Aires et al. , 2017; Parrens et al. , 2019). They rely on complementary information from Digital Elevation Models (DEM) from for example the Shuttle Radar Topography Mission (SRTM) in combination to optical data to convert the fractional information to a water mask at higher resolution. A complementary information to the surface water extents is the surface water height, as the denitrification will depend on the water depth. Water height is observed over land using.
The available global surface soil moisture datasets are provided from microwave radiometers or scatterometers at a coarse spatial resolution >25 km and high revisit frequency (1-3 days) with all-weather capacities (except during very strong precipitation)  (Al Bitar et al. , 2017; Dorigo et al. , 2017; Entekhabi et al. , 2010). The ESA CCI SM (European Space Agency Climate Change Initiative Soil Moisture)  (Dorigo et al. , 2017)  is an ongoing effort to provide a long time series soil moisture product from the fusion of multiple sensors (SSMI, AMSR-E, ASCAT, SMOS, SMAP). Obtaining deeper soil moisture (root zone soil moisture) information would require higher frequency radiometers like in P-Band  (Garrison et al. , 2018)  which is not available currently. On the other hand, parsimonious water balance models  (Kerr et al.
Soil characteristics It has been proven that the rates of N2O vary among soils at the same wet conditions  (Pihlatie et al. , 2004) , in case of paddy soils, nitrification seems to happen in aerobic/anaerobic conditions, but the net nitrification rate was recorded at 60% WHC (53% WFPS)  (Yang et al. , 2019). Thus, ancillary static soil properties maps are needed to parametrize the denitrification  (Colombani et al. , 2020) (Appendix A. 3 -Table A. 3. 2). The first comprehensive global soil map is the Food and Agriculture Organization of the United Nations (FAO) Soil Map  (Van Engelen et al. , 2005) , delivered now as the Digital Soil Map of the World (DSMW). Recognizing the need of an updated database the FAO and the International Institute for Applied Systems Analysis (IIASA) created the Harmonized World Soil Database (HWSD)  (Nachtergaele et al.
, 2010). Batjes (2015)  used a Taxotransfer procedure over HWSD to produce the WISE30sec database which includes estimates of global carbon stocks that is a mandatory criteria for denitrification  (Burgin et al. , 2011; Huang and Gerber, 2015; Kumar et al. , 2018). More recently machine learning was used in combination to EO and the WoSIS database to produce the SoilGrids 250 m database  (Hengl et al. , 2017). Large discrepancies exist between the aforementioned datasets  (Tifafi et al. , 2017)  showed that the use of the different datasets induces large uncertainties in the carbon budget, it is expected that this result would also provide large uncertainties for denitrification studies.
Acknowledgements The research presented in this paper is funded by  CONACYT  (The Mexican  National Council for Science and Technology)  in the frame of a  PhD research grant (Scholar/Scholarship reference :  625261/471711 2017-2021 ) at  Université Toulouse III -Paul Sabatier, Toulouse, France. Financial support for open access to this article was provided by the  TOSCA SOLE and SWOT downstream programs of CNES  ( Centre National  d'études Spatiales). We would like to thank the  Environmental Initiative research unit of CUNY, New York, USA  for their hospitality during a research stay funded by the  Laboratoire Ecologie  fonctionnelle et environnement,  Université de Toulouse, CNRS, INPT, UPS, Toulouse, France. We would also like to thank  Michelle Bruce  and  Arnaud Mansat  for their skills in visualizing graphics and figures that contributed to improve the present manuscript.
Introduction Bioinformatics has become the core of modern biology, especially in the context of highthroughput workflows that are becoming commonplace in many fields, in particular related toomics approaches. The big data volumes obtained by these techniques require ever more efficient and sophisticated software, which is being developed and refined at a vigorous pace. In the field of systematics, powerful programs for phylogenetic analysis have been developed, and databases and data aggregators have been set up to deal with the massive globally-generated taxonomic dataset comprised of over one million species and many millions of specimen records. Yet, only few bioinformatic tools so far have been tailored to specifically fit the practical work of taxonomists, who diagnose and name some 15,000-20,000 new species of organisms per year, a task that still is largely performed by single or small teams of (professional and amateur) researchers  (Miralles et al.
2020). Most existing tools are aimed at the construction of identification keys (e. g. Dallwitz, 1974; Clark 2003; Delgado Calvo-Flores et al. 2006; Zhang et al. 2006; MacLeod 2008; Vignes Lebbe 2015; Tofilski 2018) , which in some groups help field identification. Only a handful of software packages (EDIT: cybertaxonomy. eu, TaxonWorks: taxonworks. org, Scratchpads: scratchpads. org) are tailored towards facilitating descriptive work itself, but none of these is so far widely used; furthermore, these programs do not include various important aspects of the alpha-taxonomic workflow, such as species delimitation or molecular diagnosis  (Miralles et al. 2020 ) which can also be of high relevance for other fields such as molecular ecology. Although most taxonomic studies are still relying on morphology only (as shown in a recent review;  Miralles et al.
The concept of iTaxoTools We aim to develop a bioinformatic platform to facilitate the core work of taxonomists, that is, delimiting, diagnosing and describing species. Our initiative produced an integrative taxonomy toolkit -iTaxoTools (Fig. 1 ; Table  1 ). The concept of iTaxoTools rests on four pillars: (1) fully open source code; (2) a diversified set of stand-alone programs ('modules') that in future versions will become increasingly interconnected; (3) a specimen-centered architecture, where at present tables (tab-delimited text files) with specimen identifier columns serve as main input format; and (4) a focus on user-friendliness, accessibility, and clear and transparent documentation. All of the code developed by us is fully open source and available from a dedicated GitHub repository (https://github.
com/iTaxoTools). In the case of tools programmed by other researchers, we make this information transparent, and the GUI we added specifies the original references and programmers. The current pre-release of compiled executables is available from https://github. com/iTaxoTools/iTaxoTools-Executables. See Table  2  for repositories of each single tool. The toolkit is diversified, including simple format converters of molecular or geographic data, text and spreadsheet merging and pruning, simple statistical analyses e. g. of morphometric data, but especially focuses on two main aspects: species delimitation and diagnosis, based on multiple kinds of data. The distribution of the tools is also diversified, including command-line tools for those users familiar/comfortable with Python; standalone GUI executables of each module for Windows, Linux, and Mac operating systems for those looking for a single functionality, e.
g. a converter, to be called from a single and easily portable file -these tools will necessarily be 'heavier' and slightly slower than command-line executables; and a single software package containing all libraries (currently developed for Windows and Linux), from which each module can be launched (Fig. 2 ). In the future, the latter software package will also enable data transfer between different modules. The GUI software versions are designed to be stable over many different versions of the respective operating systems, e. g. , from Windows 7 to Windows 10. Alpha taxonomy is a primarily specimen-centered research field in which specimensmostly single individual organisms or parts thereof, or cultured isolates composed of multiple individuals -are grouped into species. Consequently, iTaxoTools has implemented tab-delimited text as standard format for most tools, with one column indicating the specimen identifier.