dataesr/scientific-paragraphs-categorization huggingface dataset:
Main text: A consistent research trend today is to design and leverage experiments that generate massive databases. The complexity of data is equally increasing, raising a major concern on data presentation and interpretation, especially in early phase research. Several fields share the common feature of generating multidimensional point cloud data such as single-molecule localization microscopy  [1] , flow cytometry, transcriptomics and astrophysics to name a few. To tackle the interpretation of high-dimensional data, it has become common to seek low-dimensional embeddings of the N-dimensional data (N >> 3); typically, in to mostly two dimensions. In this format, it becomes possible to extract exploitable visual graphs  [2] , although at the price of lower fidelity to the ground truth and the concealment of interesting features.
Exploiting any geometric information stored in SM localizations is nontrivial; linking it to the evolution of dynamic properties (e. g. diffusion or drift) is particularly difficult as the statistical properties of point density may not be directly linked to their dynamical properties  [5]. Recently, virtual reality (VR) has been re-introduced as a technology of interest for a vast number of applications due to the availability of low-cost consumer headsets and developer kits. The technology has already shown great promise for scientific research  [6, 7]. Our conviction is that there is an immediate dual opportunity for VR in scientific research:  Advanced representation and easy navigation of data to allow information to be disseminated rapidly  Human-in-the-loop data treatment that mixes data interaction in VR to automated algorithms With this motivation, we present Genuage, a VR-compatible open-source software platform compatible with any form of point cloud data, although we emphasize here its application to SM experiments.
In complement to the approach described in ref  [8]  focusing on superresolution, we extend the use of VR to dynamic data and higher dimensions. The design and architecture of Genuage were built around a simple basic concept that we see as essential in all scientific VR software: it is a 3D visualization vehicle. Specifically, VR serves to facilitate volumetric data interpretation by the user and can influence actions to take (i. e. human-in-the-loop data treatment) but it is an uncomfortable technology during extended uses. Genuage handles this issue with a dual visualization interface: a desktop mode for data loading and examination on a normal computer screen, and a VR mode for efficient data visualization, interaction and analysis. The desktop mode derives its general principles from the ViSP software by being ergonomic for users, allowing key parameters to be set while avoiding wearing the headset for long periods of time  [9].
We emphasize that while progress in VR is rendering the technology more accessible, the experience remains far from comfortable and efficiency in processing the data is essential. The main characteristics of Genuage are summarized hereafter. (i) Genuage features an efficient means for loading and presenting in VR millions of data points within seconds (Supplementary Movie1). Point cloud data can be overlaid with other highdimensional information such as molecular orientation, particle velocity, trajectories and color code in a robust and smooth way (Figure  1 ). (ii) The second main feature of Genuage, is a set of quantification tools specific to the VR mode. In VR, actions and measurements can be performed efficiently and precisely by the user in 3D (distances, angles, counting, local density calculator and histogram profiler) (Figure  1 ).
In addition, the quantification set includes a selection tool for highlighting regions of interest for further analysis or for export (Figure  1 , Supplementary movies 2-4 ). An automatically generated JSON metadata file records all the performed tasks for future sessions with Genuage and for posttreatment with other software. We provide a simple MATLAB script to illustrate how to read the JSON file and access the metadata. (iii) Genuage communicates with common software for data analysis such as Python and MATLAB, to exchange data sets after treatment (e. g. to be visualized in Genuage) or for further analysis (e. g. after selection and thresholding in Genuage). As such, applying already established quantification algorithms requires minimal effort in readapting the codes for specific programming language.
(iv) In addition, Genuage offers a Bayesian analysis package derived from the InferenceMAP software  [10]  for live analysis of dynamic data in VR. In Figure  1  (Supplementary movie 5) we show an example of the trajectories of diffusing single molecules in the nucleus. The Suppelementary movie 5 shows the user interacting with 4D localization data with Genuage. The diffusion coefficient and forces are calculated on the selected trajectory or region of interest. Genuage is an open-source software under a BSD license. Updated versions of the software will be regularly uploaded to here. We provide a detailed user manual describing the desktop and VR tools. While the examples presented in this paper are of single-molecule datasets, Genuage is adapted to other forms of multidimensional point cloud data generated synthetically or from other experiments.
Acknowledgments: We acknowledge funding from  Fondation pour la recherche médicale -FRM -DEI20151234398 (B. H. ),  Agence National de la recherche   ANR-19-CE42-0003-01 ,  Labex Celtisphybio  ( ANR-10-LBX-0038 ), and  Institut Curie  (B. H. ). We recognize the support by  France-BioImaging infrastructure  Grant  ANR-10-INBS-04  Investments for the future (B. H. ). We acknowledge the financial support of  Agence pour la Recherche sur le Cancer (ARC Foundation ) ARC (B. H. ) and DIM ELICIT (B. H. ). We acknowledge funding from the  Pasteur Institute  (J. B. M), the sponsorships of  CRPCEN ,  Gilead Science and foundation EDF  (J. B. M), the  ANR-17-CE23-0016  TRamWAy (J. B. M), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG) (J. B. M), the programme d'investissement d'avenir supported by  L'Agence Nationale de la Recherche   ANR-19-P3IA-0001  Institut  3IA  Prairie (J.
Simulation and model-based design software packages are widely used in many engineering disciplines. When it comes to robotics those tools are very important for robot design, simulation and the development of control algorithms before the implementation on the real robot. Simulink by MathWorks R is an advanced model-based design tool. It is popular in education, industry and research. In addition, Simulink supports several hardware components, facilitating a rapid deployment of the developed programs on the target hardware. In this study, the SimulinkIIWA interface for controlling KUKA iiwa robots from Simulink is presented and compared to the KUKA Sunrise Toolbox (KST). This interface is based on the User Datagram Protocol (UDP) and allows graphical real-time control of iiwa robots from Simulink without a need for writing any code.
Thus, combining the ease of use of Simulink on an external computer with the capabilities of KUKA iiwa robot has its merits for many applications. In robotics, it is not uncommon to have an external computer run-ning along with the robot-controller, where the PC can be used for performing computationally expensive operations, for treating data from sensors or even for implementing control algorithms. The robotics community has presented various software packages related to robot simulation and control. The Robotics Toolbox for MATLAB (RTB) described in  [3]  is used by many students and researchers worldwide. This toolbox provides packages for the fundamental algorithms in robotics, which are essential for their modeling and control. Roy Featherstone described the algorithms for modeling robot dynamics  [4]  accompanied by software implementation (including Simulink)  [5].
The SpaceDyn toolbox introduced in  [6]  integrates floating base dynamics as such it can be used for simulating space robots. Another toolbox which can be used for the modeling/simulation of manipulators is described in  [7]. This toolbox integrates graphical user interface (GUI), 3D simulator and utilizes symbolic math for producing the physical quantities of the robot. The Machine Vision Toolbox (MVT) introduced in  [8] , is a MATLAB toolbox which integrates vision and image-processing algorithms, as such the user can extend his/her robotic application with machine vision capabilities  [9]. While the previous software packages are used for performing advanced mathematical operations related to robotics, they do not support direct control of robots from an external computer, consequently other packages have been proposed for this purpose.
The Kuka Control Toolbox (KCT)  [10]  and the JOpen-ShowVar  [11, 12]  are used for controlling 6 DOF KUKA robots from MATLAB/Java. However, KCT and JOpenShowVar support the KRC controller only, as such they can not be used for controlling KUKA iiwa robots (provided with the newer Sunrise controller). The Robot Operating System (ROS) is another option for controlling robots from an external computer. Several packages have been proposed for different types of manipulators. In  [13]  the authors used the ROS interface, iiwa stack, for controlling iiwa robot to perform autonomous MRI-guided ultrasound, and in  [14]  the authors presented the KUKA-IIWA-API, an interface for controlling iiwa using ROS. This interface provides data acquisition and point-to-point (PTP) motion features, but it does not support on-the-fly control feature.
Recently, the KUKA Sunrise Toolbox (KST)  [15]  is introduced. It is a MATLAB toolbox that allows controlling KUKA iiwa robots from an external computer. KST implements Transmission Control Protocol Internet Protocol (TCP/IP) to connect with the robot. It integrates several functionalities including (1) networking functions, (2) point-to-point motion functions, (3) on-the-fly control functions, (4) setters and getters of robot parameters, (5) general purpose control functions and (6) physical interaction functions. KST allows the user to control the robot remotely from MATLAB scripting language. Due to its complex design, it does not implement Simulink integration. As such, we developed the SimulinkIIWA interface for controlling KUKA iiwa robots from Simulink. Unlike the KST and the previously listed ROS-interfaces (all TCP/IP based), the SimulinkIIWA has a different nature, Table  I.
SimulinkIIWA interface The KUKA iiwa 7R800 and 14R820 are programmed using the Java based Sunrise. Workbench engineering suite. In addition, various packages for controlling the robot from an external computer have been proposed, by using MATLAB, ROS and C++. However, no Simulink interface is available for these kind of robots (according to our knowledge). Simulink is a popular tool for developing control algorithms, because it implements relevant libraries, including interfaces to a wide range of sensors, actuators and other hardware components. In addition, Simulink allows the development and implementation of powerful control algorithms graphically, without requiring to write any code. Due to those advantages, we present an interface for on-the-fly control of KUKA iiwa manipulators using Simulink, this interface supports several operation modes elaborated in subsection 2.
On the other hand, Fig. 6 , shows the timing interval between each two consecutive UDP motion commands received by the robot from Simulink, where the timing data was collected at the controller side. Using the Sunrsie. OS a timestamp is recorded when each motion command message is received. The results show that the SimulinkIIWA interface is able to achieve 1KHz rate for motion commands stream capacity. The figure also shows that the timing intervals are not regular, there are sporadic spikes with a maximum of 42 milliseconds. However, the average time interval is 1 millisecond. At the same time, the interface achieved an average 177 Hz rate for the feedback data stream, Fig. 7 , where the maximum time interval between two consecutive feedback data measurements sent from the robot to Simulink is around 20 millisec- onds, the average time interval is 5.
Acknowledgements This research was partially supported by Portugal  2020  project  DM4Manufacturing POCI-01-0145-FEDER-016418  by  UE/FEDER  through the program  COMPETE 2020 , and the  Fundacão para a Ciência e a Tecnologia (FCT)   SFRH/BD/131091/2017  and  COBOTIS (PTDC/EME-EME/32595/2017 ). This research is also sponsored by  FEDER  funds through the program  COMPETE Programa Operacional Factores de Competitividade , and by national funds through FCT  Fundacão para a Ciência e a Tecnologia  under the project  UIDB/00285/2020.
| Species selection and presence data Six VME indicator taxa and six commercially important deep-sea fish species representative of both Eastern and Western North Atlantic deep-sea habitats were selected based on their wide spatial distribution, ecological significance or catch relevance in deep-sea fisheries, and on the availability and spatial coverage of existing occurrence records (Table  1 ). The VME indicator taxa included three scleractinian corals that form aragonite skeletons (Lophelia pertusa, 1 Madrepora oculata and Desmophyllum dianthus) and three octocorals forming calcitic axial skeletons (Acanella arbuscula), and with sclerites in their axis or coenenchyme and polyps (Acanthogorgia armata, and Paragorgia arborea). Despite the widespread occurrence of these two groups of VME indicators in the North Atlantic  (FAO, 2019) , they are expected to respond differently to future water mass conditions properties.
The six deep-sea fish species selected were the commercially harvested round nose grenadier (Coryphaenoides rupestris), Atlantic cod (Gadus morhua), blackbelly rosefish (Helicolenus dactylopterus), American plaice (Hippoglossoides platessoides), Greenland halibut (Reinhardtius hippoglossoides) and beaked redfish (Sebastes mentella). Although many of these fishes are not strictly considered deep-sea species, they all occur beyond 200 m depth (Table  1 ) and are relevant to deep-sea fisheries including in areas beyond national jurisdictions. Georeferenced presence-only records were obtained from institutional databases of partners participating in this work (see Supporting Information Appendix S1, Table  S1 ) as well as from public databases (Table  1 ) such as the Ocean Biogeographic Information System portal 2 (OBIS), the NOAA Deep Sea Coral Data Portal, 3 and the ICES Vulnerable Marine Ecosystems data portal.
4 In order to reduce potential errors in the spatial position of the occurrence records, we compared the depth values given by OBIS and NOAA with depth values extracted from the depth raster layer, excluding any occurrence records with no depth information or with depths that differed more than 30% and more than 50 m in absolute depth. In the case of the ICES VMEs database, those records with a position accuracy lower than 5000 m of linear distance were excluded. Species occurrence provided directly by co-authors was crosschecked for accuracy of reported depth prior to submission of the data and were considered accurate, and took priority over OBIS data from the same institutional sources. Maps of the presence records used in the models are provided as supporting information (Figure  S1 ).
Most HSM approaches require information on the location of both species presence and absence. However, existing biological datasets rarely include information on species absence, despite its importance for model performance and precision  (Iturbide, Bedia, & Gutiérrez, 2018; Iturbide et al. , 2015; Wisz & Guisan, 2009). To overcome this obstacle, we generated pseudo-absence data (a. k. a. background points) by adapting the methodology in  Iturbide et al. (2015)  to our specific data. In the first step, we used the function OCSVMprofiling from the R package MOPA  (Iturbide et al. , 2018)  to limit the geographic region for pseudo-absence data generation using environmental profiling based on presence data. In the second step, pseudo-absence data were randomly generated in the region defined above using the function pseudoAbsences from the MOPA package, but excluding a buffer distance to presence records of 6 km.
008°) using a bilinear interpolation. The bathymetry layer consisted of EMODnet data, where available, merged with GEBCO 2014 and rescaled to the final resolution of 3x3 km using bilinear interpolation. Rescaled EMODnet is considered higher in accuracy than GEBCO 2014 at the same resolution because it contains the most recent multibeam coverage for the Northeast Atlantic  (EMODnet, 2018; Schmitt, Schaap, Spoelstra, Loubrieu, & Poncelet, 2019). Slope (in degrees) was derived from the final bathymetry grid using the Raster package in R  (Hijmans, 2016)  and the Bathymetric Position Index (BPI) was computed using the Benthic Terrain Model 3. 0 tool  (Walbridge, Slocum, Pobuda, & Wright, 2018)  in ArcGIS 10. 1 with an inner radius of 3 and an outer radius of 25 grid cells. In order to avoid extreme values, BPI was standardized using the scale function from the Raster package.
Maxent models were developed using the function maxent from the R package Dismo  (Hijmans, Phillips, Leathwick, & Elith, 2017) , with prevalence set as the proportion of presences over the pseudo-absences generated. GAMs were fitted with the function gam from the R package mgcv  (Wood, 2018)  using a binomial error distribution with logit link function, constraining the smooth curves to four knots to avoid overfitting; we used three knots for temperature and aragonite in cold-water coral models. Finally, we computed RF models using the function randomForest from the R package of the same name  (Liaw & Wiener, 2002). Variable selection in GAMs used the Akaike information criterion and the function dredge from the R package MuMIn  (Barton, 2018) , whereas the other two models (Maxent and Random Forest) were fitted with the original set of variables.
(2006). Performance for the present-day model was evaluated using a cross-validation method based on a random 'block' selection of training and testing data  (Guinotte & Davies, 2014). Past work suggests this method of partitioning a unique dataset provides the best spatial independence between training and testing datasets  (Fourcade et al. , 2018). We implemented this methodology with the get. block function from the EnMEVAL package in R  (Muscarella et al. , 2014)  Fielding and Bell (1997) provide a complete description of these statistics. Each model was then used to predict a relative index of habitat suitability (HSI) across the study area under present-day  conditions and to project the HSI for the period 2081-2100 by projecting the present-day niche onto the environmental layers of projected future conditions.
We, therefore, cannot state whether previous records from several databases include some of these new species. The deep sea remains one of the least studied and sampled areas on the planet, with many undescribed species and unresolved taxonomy that constrain determination of the full spatial distributions of many species. Extensive exploration of the deepsea environment may eventually reduce this uncertainty, but it will take time. Further integration of species-level biogeochemical and physical data, as well as results of the ecophysiological performance of deep-sea organisms from ex situ experimental work, will improve suitability and distribution mapping, but noting the need for additional mechanistic (experimentally derived) understanding of how climate drivers elicit ecological responses.
Following sufficient ground-truthing, habitat suitability models can become valuable tools to inform environmental management and conservation policy  (Robinson, Nelson, Costello, Sutherland, & Lundquist, 2017)  and provide a basis for taking climate change into consideration  (Johnson et al. , 2018; Johnson & Kenchington, 2019) , Incorporating these needs into the Deep Ocean Observing Strategy can help fill data gaps, and prioritize spatial locations for the collection of key physical and biogeochemical data  (Canonico et al. , 2019; Levin et al. , 2019). As forward-looking international entities, the United Nations' Decade for Ocean Science, the Global Ocean as Observing System, and the Regular Process for Global Reporting and Assessment of the State of the Marine Environment (a. k. a. World Ocean Assessment) can help set such science agendas.
ACK  N OWLED G EM  ENTS This work contributes to the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  678760  ( ATLAS ) and No 679849 (SponGES). This output reflects only the authors' views and the  European Union  cannot be held responsible for any use that may be made of the information contained therein. TM was supported by  Program Investigador FCT  ( IF/01194/2013 ), IFCT  Exploratory  Project ( IF/01194/2013/CP1199/CT0002 ) from the  Fundação para a Ciência e Tecnologia (POPH and QREN) ,  PO2020   MapGes  ( Acores-01-0145-FEDER-000056 ). TM, CDC, JMGI, GHT and MCS also acknowledge funds provided by the  Fundação para a Ciência e a Tecnologia (FCT)  through the strategic project ( FCT/UID/MAR/04292/2013 ) granted to MARE. LAL is supported by  NSF   OCE OCE-1829623  and a DOOS subcontract from the  Consortium for Ocean Leadership.
, dividing the study area into four subareas containing similar numbers of occurrence data points. Selection of three of these four subareas provided training datasets, leaving the fourth area as the test dataset. This operation was repeated 10 times with a random selection of evaluation and training areas to com- pute the mean and the standard deviation of each metric. From each training and test dataset, we randomly selected 80% of the data in each iteration to avoid repeating the exact same selection in different iterations. Analysis of the ability of the training data to predict the test data in each iteration used the evaluation data and five different statistical metrics: area under the curve (AUC) of the receiver operating characteristic, kappa statistic, specificity (a. k. a. true negative rate), sensitivity (a.
II. PROBLEM FORMULATION A. SHORT-CIRCUIT RATIO (SCR) CALCULATION In this work, system strength is evaluated by the indicator SCR. For SCR calculation, a balanced three-phase fault is applied at the PCC of a wind farm to investigate the most severe condition. SCR at the wind farm PCC is then calculated using (  1 ) and (2)  [7] ,  [9] ,  [25]. SCC = √ 3V n I f (1) SCR = SCC P max (2) where V n is the line-to-line pre-fault voltage at the PCC (in kV), I f is the fault current at the PCC in (kA), SCC is the short-circuit capacity in MVA and P max the rated generation capacity of the wind power plant (in MW). The work presented in this paper has been carried out on the software PSS R E, which is widely used in the engineering community. While calculating SCR, V n is determined by load flow analysis using PSS R E.
Thus, additional fuel price needs to be paid. If wind power is not curtailed, this additional cost is saved. As such, yearly fuel saving R fuel is given by  (10)  and is included as a revenue in the NPV. C gas is the cost of gas in SA and C mix is the combined cost of gas and coal in VIC. R fuel = 365 • 24 • (α SA P SA C gas + α VIC P VIC C mix ) (10) All the data, for instance the cost of fuels, yearly averaged wind power production in South Australia and Victoria etc. , are given by the AEMO database for the next 20 years  [5]. Consequently, the NPV analysis is performed over a period of 20 years which implies conservative results as the synchronous condensers are expected to run longer than 20 years  [27]. Note that in (5) R i and C i are respectively the summation of R wtg and R fuel and the summation of C elec and C main for year i.