dataesr/scientific-paragraphs-categorization huggingface dataset:
2019). Data integration requires that datasets share the same type of data, comply with well-established and sustainable file formats, and use common vocabulary and well-documented information. The outputs must ultimately address the needs of  intermediate-and end-users (e. g. , scientists, modellers, managers)  and support advice to policy-makers. Moreover, data should be easily understandable by humans and interpretable by machines in compliance with the FAIR (Findable, Accessible, Interoperable, Reusable) data principles  (Wilkinson et al. 2016). The first step for data integration is probably the agreement on the critical variables to focus on. GOOS adopted the Framework for Ocean Observing (FOO, Task Team for the Integrated Framework for Sustained Ocean Observing 2012) as a foundational document setting the common guidelines that comprise elements from Inputs (e.
In the context of meroplankton observations, the workshop participants emphasised the importance of standardizing sampling protocols, ensuring the consistency of types of data and file formats, and implementing quality control procedures. A recurrent issuemeroplankton identification -was widely debated. The use of flow imaging microscopy with particle analyzers  (Álvarez et al. 2011 ) and other integrated analysis systems for acquisition and classification of digital zooplankton images  (Gorsky et al. 2010 ) may enable a faster, semi-automatic processing and identification of meroplankton samples. However, these approaches rely extremely on the existing reference databases (e. g. ,  EcoTaxa -Picheral et al. 2017 , World Wide Web of Plankton Image Curation -wwwpic Team 2020) to facilitate the specimens' classification and minimize errors.
The use of the World Register of Marine Species (WoRMS, WoRMS Editorial Board 2020) was recommended as the most up-to-date reference database for scientific names. Another requirement fundamental to data integration is the existence of publishing platforms and repositories, which improve the interoperability of data (connections to related information). Biogeography online repositories such as OBIS (OBIS 2020) or GBIF (GBIF 2020), provide templates and dedicated publication tools that facilitate the data submission and ensures the maintenance and preservation of data, in an openly and freely accessible manner. The Darwin Core data format, used by OBIS and GBIF, was also recommended as the ideal vehicle for storage of meroplankton data. The Darwin Core facilitates data integration by offering a standard form for proper cataloguing and sharing of biological and environmental data  (Wieczorek et al.
Autonomous samplers for continuous monitoring, networked robotic systems for adaptive sampling, integrated systems for semi-automated processing and identification of meroplankton samples, as well as supervised machine-learning methodologies for imagery and metagenomics data processing were identified as some of the most relevant technological advances needed to underpin the irreplaceable humanpower in meroplankton research. The existing in situ components and land-based infrastructures of the Global Ocean Observing System are indispensable for implementing the envisaged global and regional schemes for sustained observing systems, as are many other initiatives for ocean observations at national and international levels. The EMSO-ERIC and the Ocean Networks Canada observatoies were identified as the most approachable consortia for collaborations in the short term.
Funding program This report is an outcome of the Euromarine Foresight Workshop Advances in Ocean Biological Observations: a sustained system for deep-ocean meroplankton (1804call_FWS_Meroplankton). The workshop was co-funded by the  Euromarine Consortium, InterRidge  and the  French National Agency for Research (ANR)  under the programme " Investissement d'Avenir" IsBlue (the  Interdisciplinary Graduate School for the Blue planet ), grant number  ANR-17-EURE-0015. Universidade de Aveiro  contributed in kind while Ifremer and CSIC sponsored the co-conveners (FP and MCH, respectively). Participants from  Universidade de Aveiro  were funded through  Centro de Estudos do Ambiente e do Mar  (CESAM,  UID/AMB/50017/2019 ) and by  Fundação para a Ciência e Tecnologia (FCT/MCTES)  through national funds.
Wim Bogaerts is professor in the Photonics Research Group at Ghent University -imec. He received his PhD in the modelling, design and fabri-cation of silicon nanophotonic components at Ghent University in 2004. During this work, he started the first silicon photonics process on imec's 200mm pilot line, which formed the basis of the multi-project-wafer service ePIXfab. Wim's current research focuses on the challenges for large-scale silicon photonics: Design methodologies and controllability ofcomplex photonic circuits. In 2014, Wim co-founded Luceda Photonics, a spin-off company of Ghent University, IMEC and the University of Brussels (VUB). Luceda Photonics develops unique software solutions for silicon photonics design, using the IPKISS design framework. Since 2016 Wim is again full-time professor at Ghent University, looking into novel topologies for large-scale programmable photonic circuits, supported by a consolidator grant of the European Research Council (ERC).
Acknowledgments This issue is the result of the effort and inspiration of many people. First, we would like to express our appreciation to all the authors of the high-quality invited and contributed papers, as well as to the many reviewers who found time in their busy schedules to contribute constructive criticism to make the manuscripts even better. We thank the  IEEE  publications staff for their general support, and in particular  Ms. Chin Tan Lutz  for being always-present with technical and editorial assistance and advice. It takes quite some coordination skills to make sure a collection of busy scientists around the world stick to a schedule. Finally, we would like to thank  Prof. José Capmany , JSTQE Editor-in-Chief, for his behindthe-scenes coaching and for giving us the opportunity to put together this special issue.
Experimental Setup Figure  4  displays the experimental testbed. It includes a DC Machine emulating the primary energy source. The SRG with a 8/6 four-phase topology whose characteristics are given in Table  1  feeds the 24V DC bus. An Asymmetric Half-Bridge Converter (AHBC) is used to control the SRG operation. The AHBC is energized from the external excitation source during magnetization mode and provides power to the load (an equivalent resistance) through the DC bus during generation mode. Both the excitation source and load are connected to the same 24V DC bus. For higher tolerance, the DC bus can be split into one for excitation mode and another for generation mode. An incremental encoder is mounted on the rotor for rotor position and speed measurements. The control part includes a host PC for programming, supervision (Matlab-Simulink, Control-Desk), and data acquisition.
A more detailed study of the SR Motor losses is presented in  [31]. Ferromagnetic losses (P mag ) are due to variations of magnetic fields in iron and windings. In this calculation, we assume that magnetic losses in the windings are negligible. The ferromagnetic losses (hysteresis and eddy currents losses), thanks to the finite element analysis, are calculated in the different parts of the yoke and, in the stator and rotor teeth based on geometry of the machine, iron volume V iron , flux density B, frequency f , and K F and K H constant coefficients depending on the material. The hysteresis (P H ) losses and eddy current losses (P F ) are defined according to Equation  (8). P F = V iron. (K F ( dB dt ) 2 ); P H = V iron. (K H. f. (∆B) 2 ) ( 8 ) Finite Elements Analysis software MRVSIM  [36]  computes the conduction and switching losses in the switches P cond and P com.
The calculation of these losses depends on the control method, switching frequency, current reference waveform, and characteristics of power switches and diodes. Only aerodynamic losses are evaluated in the calculation of mechanical losses in bearings. The pre-determination of aerodynamic losses in electrical machines is a serious issue because of poor quantification of surface texture. In the software MRVSIM, a correction parameter K a is set based on the comparison between measured and simulated aerodynamic losses at maximum speed. The copper losses correspond to the Joule losses in SRG windings P J = 4rI 2 , where I stands for RMS phase current. The minimization of copper losses implies the minimization of the RMS phase current. The objective function to be maximized is the efficiency defined in Equation (  9 ), where P em is the electromagnetic power.
Simulation and Experimental Results The proposed control strategy is first implemented in Matlab-Simulink for dynamic simulation in order to evaluate voltage regulation performances. The SRG operation requires mechanical power as an input. For simulation and experimental studies, a DC machine is used to emulate the mechanical power extracted from the wind. The load is emulated with variable resistance, and a shunt capacitor is used to smooth the DC bus voltage. The performance of the voltage controller is verified in Figure  13  through step-changes of the reference voltage when speed is set at 800 rpm and load at 15 Ω. The controller determines control parameters (I re f , ψ, θ p ) to set output voltage to the desired reference value. We can observe satisfactory DC voltage regulation performance; the voltage controller reacts very fast to track voltage reference, despite some ripples.
In this context, storage technologies are considered the suitable solution. These can be necessary in order to solve and fill the problems of the renewable distributed sources are introducing in the management of the network infrastructure. The aim of this work was to create a model in order to evaluate the impact of power generation considering PV systems in Australia along with a model to simulate Battery Energy Storage Systems (BESSs) and Electric Vehicles future contributions using MATLAB. The methodology used to develop these models was based on statistical assumptions concerning the available details about PV systems installed and current storage technologies. It has been shown that in all the scenarios analysed, the future adoption of rooftop PV panels and impact on the CG is incredibly higher than the uptake of energy storage systems.
The model is developed in Matlab environment. The variables that influence the PV generation depend on different parameters; in this model, these variables are calculated distinguishing between the following divisions: k: postcode; x: system azimuth angle (orientation); y: system slope (inclination); t: time and j: system size. The flexibility of the model Fig. 4  Hourly frequency of the daily peak in the period 2009-2015 on a trimester base consists in the capability to simulate the behaviour of one system or of a group of aggregated systems. The size of the group simulated can be an area of a feeder, a single 2-digit postcode, a state or an aggregation of these different options. Data concerning the list of all postcodes and the relative coordinates (latitude and longitude) are available in CSV format from the ABS (Australian Bureau of Statistics) and on the Australian Post' website (auspost.
PV simulation results and correlation development The simulation data of the PV generation model are correlated with the APVI's website measured data in the period April 2015-August 2016. The data from this institute are available only for this period and represent the monthly total output from PV systems in NSW. These data are measured from a group of few thousands PV systems scattered around the country; then the data are scaled to the totality of the capacity installed in Australia assuming that the data from these systems are representative of the total 1. 6 million of systems installed. Once the data are collected, they are transformed in the monthly mean daily output and then are compared to the data generated by the PV model. In Fig. 8 , the column chart represents the monthly data from the PV generation model with the green columns and the data from APVI's website with the light blue columns, and they refer to the left axis.
This reduction, though, is particularly greater during the central hours of the day and its profile assumes the same trend of the solar irradiance. The decrease in consumption during the day results in a steeper ramp of the demand in the afternoon when the demand starts to growth towards the evening peak, especially in the cold season. Therefore, in this work, a model to evaluate the aggregated generation of rooftop PV systems along with a model to simulate BESSs and EVs future contributions has been  developed in MATLAB, to assess the current and future impact of these technologies on the centralised paradigm. The simulation of the future aggregated impact of PV systems in New South Wales (NSW) under the scenario of high penetration (3. 7 GW in 2025 and 6. 5 GW in 2035), showed a clear alteration of the profile of the demand during the warm season that has always been characterised by a demand almost constant during the day and a slight peak in the late afternoon.
Acknowledgements We would like to dedicate a sweet and deep thought for our  Philip Ciufo. He  has not just been an inspiring guide for his dedication, his professionalism and rectitude but more than anything a friend. All the people, who had the opportunity to work closely together with him, know how he has always been ready to give his support, advice and encouraging words. This research was started thanks to his desire to consolidate stronger relations between universities and to be on the front lines facing the challenges which the energy field transformation is requiring and will require in the future for the sustainability of our planet. Thank you Philip.
Sources of uncertainty Since accidental nuclear releases are related with the routes undertaken by the submarines, there will be two main sources of uncertainty in our case study 3  : • The sea conditions (wind, currents,. ), at the time of the release, identified by a parameter β. In the case of Toulon, they are dominated by wind  [13] , and their probabilities can be estimated using a meteorologic database. • The position RP = (x RP , y RP ) where the release takes place, being, respectively, the latitude and longitude. We identify the main typical routes for submarines with some uncertainty around them. We modeled uncertainty about the accident parameters through representative scenarios. We shall consider three sea conditions with their associated probabilities, as described in positions with their associated probabilities, as specified in Table  5 , displayed by red crosses in Figure  1.
1812 experimental measurements were recovered from 11 studies. Their variability was discussed, providing an aerodynamic reading. Nonlinear regression was carried out on the consolidated dataset in order to assess the average value. Two well-known laws initially conceived for spheres were refitted to natural particles providing high coefficients of determination equal to 0. 90 and 0. 93. Then, copula-based regression was performed in order to seize the variability of sedimentation velocity. Finally, the results from the two approaches were compared and critically discussed.
This motivates our re-examination of existing data to refit existing well-established laws, and to develop a novel statistical characterization of the sedimentation velocity based on copula regression. The paper is organized in five further sections. Section 2 briefly reviews the equilibrium of freely falling particles, collects a number of experimental measurements from the literature and critically analyzes them. In Section 3, nonlinear regression is applied to the dataset and the average sedimentation velocity for aeolian particles is recovered. Copula-based regression is described and applied in Section 4 and the results are discussed and compared in Section 5. Finally, conclusions and perspectives are outlined in Section 6.
(  6 ) in order to homogenize measurements obtained in different Newtonian fluids (air, water, glycerine, oil) and for different particle densities so to expand the cardinality of the dataset and make it suitable for application by different scientific communities. Moreover, the Re-C d plane highlights the behavior of the falling particles by referring to well-established aerodynamic regimes  (Stokes, Newton and intermediate regimes, as in Achenbach, 1974; Johnson and Patel, 1999). This provides a physical basis for the statistical treatment of the data. The resulting dataset for natural particles is plotted in Fig. 2(a). The colours correspond to the nature of the fluid, while the symbols correspond to the experimental campaign. The high quality Re-.
C d dataset for smooth spheres collected by  Brown and Lawler (2003)  and their aerodynamic regimes are included for reference only in Fig. 2 (a) and (b), respectively. The present study addresses the careful collection of sedimentation velocity measurements from the literature, and it is not intended to provide new experimental measurements. The dataset includes two main sub-ensembles: sand grains falling in air, and natural particles falling in other fluids. Recently, Farrell and Sherman (  2015 ) collected all available s measurements of sand grains falling in air from the past literature. They identified five studies, resulting in a collection of 352 distinct measurements from  Bagnold (1935) ,  Wilson and Huang (1979) ,  Cui et al. (1983) ,  Malcolm and Raupach (1991) ,  Chen and Fryrear (2001).
The dataset is then complemented by Re-C d measurements of natural particles falling in fluids of different densities and viscosity. Overall, 911 measurements of particles of different sizes and densities falling through water, solutions of glycerine and water, and oil are obtained from the sources reported in the popular study of  Dietrich (1982) , i. e. Corey (1949) ,  Wilde (1952) ,  Schulz et al. (1954) ,  Briggs et al. (1962) ,  Alger (1964) ,  Romanovskiy (1966) ,  Stringham et al. (1969) ,  Komar and Reimers (1978). In addition, the high-cardinality Re-C d measurements of volcanic deposits (304) and calcareous sand particles (521) falling through solutions of glycerine and water are obtained from the recent studies of  Dioguardi et al. (2017)  and  Wang et al. (2018) , respectively.
According to  Loth (2008) , effects induced by non-spherical shapes may be attributed to the increased sensitivity of boundary layer separation to increased shape convolutions. Effects induced by local irregularities are ascribed to local high curvatures of the surface that induce the early boundary layer separation, the wider wake and the higher C d. Furthermore, as Re increases, the sedimentation pattern is chaotic and tumbling  (Bagheri and Bonadonna, 2016)  resulting in a greater dispersion of measurements. We critiqued the fall velocity datasets to remove potentially unreliable Re-C d measurements, and discarded those for the following reasons (see gray-colored symbols in Fig. 2a ). In particular: Re [-] C [-] d.
• the results obtained by Chen and Fryrear (2001) are not included herein for two main reasons. First, some studies questioned their validity, see e. g. Roux (2002) ,  Chen and Fryrear (2002) , or Le  Roux (2005). Secondly, those measurements are much different from those of other experiments, with no apparent rationale (see Fig. 2a ). • a few pairs of Re and C d measurements fall just below those found for spheres within the Stokes and intermediate regimes. Such outcomes could be caused by measurement errors or partially uncontrolled experimental procedures. In the following, all the Re-C d measurements less than those for equivalent spheres are discarded in order to define a physically sound dataset, in agreement with the well-established aerodynamic concepts detailed above. The datasets used for our final analysis are summarized by source in Table  1 , including the particle diameters and densities, and subset cardinality numbers #.
Recently,  Bagheri and Bonadonna (2016)  proposed a new, relatively simple law for natural particles by defining two shape-dependent parameters called Stokes' drag correction k S and Newton's drag correction k N : = = = = = = k k k k k k 1, 0. = + + + C k k k k k 24 Re 1 0. 125 Re 0. 46 1 d S N N S k k N 2/3 5330 Re / N S (9) In this study, Eqs. (  8 ) and (  9 ) are fitted to the Re-C d dataset through a nonlinear regression by considering k S and k N as free parameters. The regression analysis allows the estimation of the average value of the dependent variable C d for fixed values of the independent variable Re, by providing an estimate of each free parameter. For Eq. (  9 ), they are and = = k k 1. 2807, 2. 6893 S N. The two curves are perfectly overlapped up to = Re 300. After that, they show different trends.
In particular, Eq. (  8 ) better follows the data for very high Re because of the large number of free parameters. Conversely, Eq. (  9 ) conserves the typical trend of C (Re) d for spheres resulting in higher C d for very high Re. In Fig. 4 , the Re-C d dataset and the laws plotted in Fig. 3  are made dimensional through Eq. (  7 ) in the plane ds , by standardizing for quartz particles falling in air. Fig. 4  is complemented with some of the best performing d ( ) s laws for spheres and natural particles (see  Le Roux, 2014; Farrell and Sherman, 2015 and cited references) , i. e. the laws of  Cui et al. (1983)  and Le  Roux (2014)  for spheres, the ones proposed by  Cui et al. (1983)  and  Farrell and Sherman (2015)  for sand only, and the ones of  Ferguson and Church (2004)  and Le  Roux (2014)  for natural particles of any diameter.
It is worth stressing that the support of is bounded between 0 and 1 since s s sphere ,. The copula C provides the dependence structure between the random variables d and , by establishing their correlation and by discarding the effect of their marginal distribution. Several copulas have been proposed in the literature. Interested readers can refer to  Nelsen (2007) , for a complete review. Generally, the choice of the copula can be performed by visually comparing the F d F ( ) ( ) d scatter plot of the observed dataset to an artificial dataset generated from the fitted copula  (Genest and Nešlehová, 2014) , as done by  Raffaele et al. (2016). In this study, a goodness of fit quantitative assessment is carried out. It is based on the rigorous numerical methodology recently proposed by  Sadegh et al.
(2017)  and implemented in the MvCAT toolbox. Table  3  reports the 25 tested copulas sorted by the overall ranking on the basis of the Root Mean Square Error (RMSE). The Frank and Nelsen copulas produce the lowest RMSE. In this study, the Frank copula is chosen because of its performance in this test and because its use is widespread. The Frank copula is defined as follows: = + C u v u v u v ( , ) 1 ln 1 (exp( ) 1)(exp( ) 1) exp( ) 1 , , [0, 1], 0 (11) where = 2. 72 is the best fitting parameter. In Fig. 5 (c), a 3000 point sample is generated with the Frank copula. This replicates the quasi symmetric tail dependence in the lower-left and upper-right corners, i. e. close to (0, 0) and (1, 1). However, a slight discrepancy between the Frank copula-generated sample and the original dataset remains in the lower-right corner, i.