dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments. We acknowledge  Landry Bailly  for the synaptic connection heatmap of the organoid emulation,  Andréa Combette  for the analysis of the spiking activity of the organoid emulation,  Mattia Di Florio  and  Marta Carè  for the preparation and execution of the experiment on open-loop in-vivo stimulation. We also acknowledge  Ryota Murai  for his advice on the RedPitaya system,  Hu Huaruo  and  Atsuhiro Nabeta  for their advice and assistance on the data acquisition from Maxwell system. Appendix A Section title of first appendix References.
Code availability. All Python codes will be provided by the corresponding author upon request. The Python 595 package spotpy  (Houska et al. , 2015)  used for the NSGA-II multi-objective optimization is available at https://github. com/thouska/spotpy. Data availability. Drainage data are not publicly available. Daily precipitation and ET0 data were provided by the Meteorological Service of Catalonia and are available at https://ruralcat. gencat. cat/agrometeo. estacions (accessed 2 March 2023). The 2 m resolution DEM was provided by the Institute of Cartography of Catalonia and is available.
Finally, datasets are cosmetically cleaned using higher level refinement algorithms (wavelets filtering and polynomial smoothing of the dark) to enhance band depth contrast without introducing significant biases. The remaining uncertainty on reflectance absolute level is mainly attributed to the error on the geometry of the illumination which requires a better modeling of local opography and the atmosphere diffusion. Some low frequency residuals are also miscalibrated by the current pipeline and still under investigation (e. g. , RF power stability, thermal effects). Notwithstanding these calibration uncertainties, the instrument signal to noise ratio (SNR) is high enough, and the relative (i. e. , spectral channel to spectral channel) calibration is precise enough to be sensitive to faint spectral features (down to a few percent band depth) even if few parts of the spectral range show very faint but high frequency effects.
Acknowledgements The authors would like to say thanks to  Elif Saçikara  for many useful discussions in the preparation of this work. The authors are grateful to  Heide Gluesing-Luerssen  and  Benjamin Jany  for their helpful remarks on q-polymatroid equivalence. This paper stems from a collaboration that was initiated at the Women in Numbers Europe (WIN-E3) conference, held in Rennes, August 26-30, 2019. The authors are very grateful to the organisers:  Sorina Ionica ,  Holly Krieger , and  Elisa Lorenzo García , for facilitating their participation at this workshop, which was supported by the  Henri Lebesgue Center , the  Association for Women in Mathematics (AWM)  and the  Clay Mathematics Institute (CMI).
Acknowledgements The authors gratefully acknowledge the  French Research Agency  for financial supporting in the framework of  DROPSURF  project  ANR-20-CE05-0030. The authors would like to acknowledge the  European Space Agency (ESA)  and the  French Space Agency (CNES)  through the  GDR MFA  for their support in the development and the operation of the  Multiscale Boiling Experiment RUBI  on the  International Space Station. Moreover, this work has been realized thanks to the granted access to the HPC resources of CALMIP supercomputing center under the allocation P23023.
, 2020)  or the creation of the COVID-19 Government Response Stringency Index  (Ritchie et al. , 2020). However, such datasets largely apply to the terrestrial domain. So far, in the marine domain, analyses have focused on the early reductions in maritime activity  (March et al. , 2021) , examining a limited range of vessel classes on a broad global scale or in specific locations  (Millefiori et al. , 2021 , March et al. , 2021 , Russo et al. , 2021 , Pita et al. , 2021). In such studies, it is crucial to disentangle lockdown effects from background changes. For example, changes to human mobility due to COVID-19 have directly impacted the marine environment, including reductions in underwater noise and coastal turbidity  (Gabriele et al. , 2021 , Diffenbaugh, 2022). However, reports that reductions in fishing activity may have had beneficial effects on the sustainable management of fish stocks may have been offset with potential increases in Illegal, Unreported and Unregulated (IUU) fishing due to a reduction in monitoring capacity  (OECD, 2021).
Changes in overall global maritime vessel activity We produced global shipping and fishing vessel activity maps reflecting the extent and intensity of vessel activity and VIIRS Boat Detections in 2020 (Fig. 1a-c ). Activity footprints were highly variable between datasets. Shipping vessel activity, encompassing classes such as cargo, passenger and tanker vessels, largely reflected well-established thoroughfares such as the major transoceanic shipping routes around the Cape of Good Hope and through the Suez and Panama Canals (Fig. 1a ). This differed significantly from fishing vessel activity, which exhibited a footprint that was far more diffuse and widespread across the globe (Fig. 1b ). Fishing vessels were tracked in both EEZs and in the Areas Beyond National Jurisdiction (ABNJ), although certain EEZs, such as the Maldives in the Indian Ocean, and St.
Helena, Ascension and Tristan da Cunha islands in the South Atlantic Ocean, were clearly delineated by the absence of AIS-tracked fishing vessel activity (Supplementary Figure  1 ). VBDs were more highly concentrated in coastal regions and within EEZs than either of the two AIS datasets, and especially prevalent in the Gulf of Thailand, the South China Sea, and the Yellow Sea (Fig. 1c ). While matching VBD data to individual vessels is impossible, certain classes of vessels that emit more light than others, such as passenger vessels or squid jigging fishing fleets  (Ruiz et al. , 2019) , are likely to have been detected reliably by this dataset, for example, off the coast of Argentina in the South Atlantic Ocean and to the south and west of the Galápagos Islands in the central Pacific Ocean.
While there was a clear lockdown signal overall (Fig. 1d -f), the spatial extent and magnitude of changes varied between datasets. Most changes in shipping vessel activity between the two periods occurred within transoceanic shipping routes (Fig. 1d ). Areas with both the largest increases (positive change, in red) and decreases (negative change, in blue) in activity included the Strait of Hormuz and coastal waters in proximity to the port of Shanghai, with changes of ± 200 vessel detections compared to the baseline period. Change in fishing vessel activity was spatially heterogeneous, with widespread areas of positive and negative fluctuations in both EEZs and in ABNJ (Fig. 1e ). Changes in VIIRS Boat Detections were also spatially variable (Fig. 1f ). Certain coastal areas, such as the Mediterranean Sea and the Sea of Japan, presented large-scale reductions compared to the baseline.
The first category for each country, 'All shipping vessels', is the total sum of all nine categories of shipping-related vessels, and includes cargo and passenger vessel counts. below the 2017-2020 baseline throughout 2020, whereas passenger vessel activity peaked in June 2020, with 75 % higher activity than in the baseline period, only dipping below the baseline in April and May 2020 (Fig. 7c ). A spike in passenger vessel activity in June 2020 did not occur in any other year in the dataset (Fig. 7b ). VBDs also exhibited large increases from March to May, and in June 2020. In the Agoa Marine Mammal Sanctuary, which encompasses the EEZs of multiple Small Island Developing States (SIDS; Guadeloupe and Martinique), cargo vessels were less active in 2020 than during the baseline period, with the lowest levels recorded in November and December 2020, at 25 % below the baseline (Fig.
Value of using multiple vessel datasets Finally, the method used to monitor activity will influence the resulting data. For instance, there were always fewer VBDs than AIStracked vessel detections. This is likely caused by reduced activity on the ocean at night combined with environmental factors affecting detectable levels of illumination, such as clouds, moonlight glint, and the fact that many vessels only carry dim lights. However, VBD data offer the opportunity to map the changing activity patterns of vessels that do not use AIS, such as smaller vessels or those fishing illegally  (Elvidge et al. , 2021) , which are absent from much of the broad-scale, AIS-monitored vessel analyses  (Kroodsma et al. , 2018). Thus, we found that when used alongside other data sources such as Automatic Identification Systems (AIS) and Vessel Monitoring Systems (VMS) on ships, VBD data became a powerful tool in the continued monitoring of human activity on the ocean  (Ruiz et al.
Satellite AIS data Satellite AIS tracking data (see Supplementary Methods for further details on AIS) for shipping (monthly) and fishing (daily) vessels from January-December 2017-2020 were obtained from the Global Fishing Watch (GFW) at 0. 1 • × 0. 1 • and 0. 01 • × 0. 01 • resolution, respectively. The GFW passes data on vessel characteristics derived from vessel registries through a neural network classifier, allowing for the classification of AIS transmissions by vessel type. The shipping vessel dataset is sorted into 9 classes (bunker, bunker or tanker, cargo, cargo or reefer, cargo or tanker, container reefer, passenger, specialized reefer, tanker), and the fishing vessel dataset is sorted into 16 classes (fishing, squid jigger, drifting longlines, pole and line, trollers, fixed gear [including pots and traps, set longlines and set gillnets], trawlers, dredge fishing and seiners [encompassing purse seines, tuna purse seines, other purse seines, and other seines]).
For this study, fishing vessels were aggregated to 9 classes (fishing, squid jigger, drifting longlines, pole and line, trollers, fixed gear, trawlers, dredge fishing and seiners). Additional details on fishing vessel classification can be found in Supplementary Figure  8  (Global Fishing Watch Datasets and Code, 2023). For fishing vessels, "activity" refers to both the positions of fishing and non-fishing vessels and is therefore not representative of the fishing effort being carried out.
VIIRS Boat Detection (VBD) VIIRS Boat Detection (VBD) data were obtained for 2017-2020 from the Earth Observation Group, Payne Institute for Public Policy. The Visible Infrared Imaging Radiometer Suite (VIIRS) is the primary imager on the Suomi National Polar Partnership, a polar-orbiting satellite flown by NASA and NOAA  (Elvidge et al. , 2021). The VIIRS day/night band (DNB) spectral bandpass spans 0. 5 to 0. 9 µm, straddling the visible and near infrared, detecting light sources present on the Earth's surface. At night, the DNB maintains a nearly constant pixel footprint of 742 m on a side, with latitude and longitude positions located at the pixel centres. The 'Boat Detection' algorithm developed by  Elvidge et al. (2015)  filters out any features on land and other unwanted signals, such as gas flares, and assigns each detection a quality flag, yielding information about the quality and type of detection (e.
Maritime activity and COVID-19 All analyses were run in R 4. 1. 2, and raster visualisations were carried out in QGIS (3. 24. 2-Tisler). For all datasets, vessel activity does not represent the number of unique vessels in an area, but instead the spatial extent and relative activity occurring within an area. To enable clear visualisation of the data at the global scale, GFW fishing and VBD data were re-gridded to a lower resolution (0. 25 • × 0. 25 • ). Likewise, cargo and passenger data from the GFW shipping dataset were re-gridded to a lower resolution (0. 2 • × 0. 2 • ). At the global scale, we compared vessel activity during 2020 to the average vessel activity across a baseline period (average yearly vessel locations for each grid cell across 2017-2019). This approach is consistent with other studies examining impacts of  COVID-19 (Le Quéré et al.
We selected case studies to showcase (non-exhaustively) the variety of activity patterns observed in these regions across different vessel classes. Where vessel classes are grouped (e. g. fixed gear = pots and traps, set longlines, set gillnets), we followed the hierarchy set out by the GFW. Please see supplementary methods for further detail on these jurisdictional frameworks, and for the full set of results (by vessel class). We calculated vessel activity as the daily (fishing vessel dataset) or monthly (shipping vessel dataset) total number of unique vessel identifiers present in each grid cell. As vessels can move between multiple grid cells in a day, extents larger than a single grid cell may capture the same vessel multiple times. For VBD, since no unique identifier is associated with detections, vessels cannot be filtered by class.
Time series forecasting We chose the Holt-Winters time series forecasting method chosen because of its capacity to capture seasonality in a dataset  (Chatfield, 1978 , Winters, 1960 , Swapnarekha et al. , 2021)  to model expected monthly global maritime vessel activity in 2020 based on patterns observed in the three previous years of data  (2017) (2018) (2019). For each vessel class, activity values were separated into training (January 2017-May 2019), testing (June 2019-December 2019), and 2020 (January-December 2020) subsets. The training dataset was decomposed to extract the trend, seasonality and random components (Supplementary Figure  4 ). Both additive and multiplicative Holt-Winters models were fitted to each training dataset, and the best model for each dataset was selected. A multiplicative model was used to account for increasing long-term trends in the passenger and tanker data, whereas an additive model was used for cargo and container reefer activity data, as the seasonal variation was relatively constant throughout the time series.
Models were examined for fit using smoothing functions, and autocorrelation plots informed on the correlation of fitted residuals between points of various temporal separations in the time series. Ljung-Box tests indicated that model residuals were independent and had a normal distribution  (Lima et al. , 2019). The predictive power of the model was examined by comparing the forecasted data from the training dataset to the test dataset, by visualising the confidence intervals and by calculating Mean Absolute Percentage Error (MAPE) for each vessel class forecast. MAPE is the mean of all absolute percentage errors between the predicted and actual values. A higher MAPE indicates a bigger distance between the forecasts and the model predictions. The forecast was then compared to the 2020 data to examine how different the first year of the pandemic was to the forecasted activity.
Acknowledgements This article is a contribution of the  COVID-19 Bio-Logging Initiative , which is funded in part by the  Gordon and Betty Moore Foundation  ( GBMF9881 ) and the  National Geographic Society  ( NGS-82515R-20 ) (both grants to C. R. ), and endorsed by the  United Nations Decade of Ocean Science for Sustainable Development. Specifically, A. L. 's, R. P. 's and B. R. 's postdoctoral positions were funded by the  Gordon and Betty Moore Foundation  ( GBMF9881 ), and J. S. 's contributions were funded by the  National Geographic Society  ( NGS-82515R-20 ). D. W. S. was supported by a  Marine Biological Association   Senior Research Fellowship  with additional support from the  Natural Environment Research Council  ( Discovery Science   NE/R00997/X/1 ) and the  European Research Council  ( Advanced Grant   883583 OCEAN DEOXYFISH ).
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  grants  ANR-16-CE13-0007-02   PHAGOMECANO  and  ANR ANR-18-CE13-0026-01 RECAMECA  (to CLC). HW is supported by a  PhD fellowship  from the  China Scholarship Council (CSC). RS is supported by a PhD fellowship from  Association de Spécialisation et d'Orientation Scientifique. The present work has benefited from the Light Microscopy facility of Imagerie-Gif, (http://www. i2bc. paris-saclay. fr), member of IBiSA (http://www. ibisa. net), supported by  "France-BioImaging " ( ANR-10-INBS-04-01 ), and the  Labex "Saclay Plant Sciences " ( ANR-10-LABX-0040-SPS ). We thank the members of the  "Cytoskeleton Dynamics and Motility" team  for helpful discussions.
Only 8 photographs per polarity are chosen for further analysis due to the fact that the they do not differ from each other significantly when comparing the same polarity and similar propagation time. This is done not to repeat the calculation for the same or very similar data (photographs). The average velocities of streamers are of 2. 4 km/s and 1. 8 km/s for positive and negative streamers, respectively. These values are of the same range as those reported elsewhere  [6, 9, [24] [25] [26]. At the first glance, if we limit ourselves to the classification of streamers according to their form, this result is surprising regarding the effect of polarity. Indeed, it was commonly accepted that the positive streamers are faster and more branchy than the negative ones, whereas we observe the opposite: the positive streamers are a little bit faster (i.
Calculation method of fractal dimension There are many methods to estimate the fractal dimension 𝐷 of a self-similar structure  [10]. For its ease of use, the box-counting method is the one used in this work. This has been described in detail in previous work  [19, 21, 22]. For the sake of continuity, let us recall here the main lines of this method. This consists, in the first step, in converting the original images of the discharges into binary (black and white), as illustrated by Figs. 4(a  the streamer image is transformed into a 580 × 780 element matrix. The elements of the matrix are integers with a value between 1 (represents black) and 255 (represents white) representing the data relating to the color of pixel in the image. Values between 1 and 255 represent shades of gray. These images are then processed by a computer code designed for this purpose in Matlab, described in early work  [21].
Aknowledgments The authors thank  V. Niess  and  M. Tueros  for their careful reading, as well as the  GRAND-Paris team ,  T. Huege  and the GRAND team at  KIT  for fruitful discussions. This work was supported by the  Programme National des Hautes Energies of CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. Simulations were performed using the computing resources at the  CCIN2P3 Computing Centre (Lyon/Villeurbanne -France) , partnership between  CNRS/IN2P3  and  CEA/DSM/Irfu. This work is part of the  NuTRIG  project, supported by the  Agence Nationale de la Recherche  ( ANR-21-CE31-0025 ; France) and the  Deutsche Forschungsgemeinschaft  (DFG; Projektnummer 490843803; Germany).
Science return Even if its requirements are driven by planetary defense, Hera will also provide unique information on many current issues in asteroid science. The reason is that our knowledge of these fascinating objects is still poor, especially for the smallest ones. The recent data obtained by the JAXA Hayabusa2 and NASA OSIRIS-REx missions have revolutionized our understanding of carbonaceoustype Near-Earth Objects. Hera has the the potential to do similar as it will rendezvous for the first time with a binary asteroid. Its secondary has a diameter of only 160 m in diameter. So far, no mission has visited such a small asteroid. Moreover, for the first time, internal and subsurface properties will be directly measured. From small asteroid internal and surface structures, through rubble-pile evolution, impact cratering physics, to the long-term effects of space weathering in the inner Solar System, Hera will have a major impact on many fields.
NEO-MAPP NEO-MAPP (Near Earth Object Modelling and Payload for Protection) is a project funded by the H2020 program of the European Commission. Hera is its reference mission, and most of the NEO-MAPP activities are aimed at supporting the preparation of Hera. The main goal of NEO-MAPP is to provide significant advances in our modeling of impact physics, binary dynamics and internal properties, as well as in instrumentations and associated measurements by a spacecraft (including those necessary for the physical and dynamical characterization in general). In particular, innovative and synergetic measurement and data-analysis strategies are developed that combine multiple payloads, to ensure optimal data exploitation for Hera and other NEO missions.
Acknowledgements We thank  ESA  and  CNES  for support. We also acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870377  (project  NEO-MAPP ). Hera Science Team :  Patrick Michel ,  Adriano Campo Bagatin ,  Benoît Carry ,  Sébastien Charnoz ,  Julia de Leon ,  Alan Fitzsimmons ,  Simon F. Green ,  Alain Hérique ,  Martin Juzi ,  Özgür Karatekin ,  Tomas Kohout ,  Monica Lazzarin ,  Naomi Murdoch ,  Tatsuki Okada ,  Ernesto Palomba ,  Petr Pravec ,  Colin Snodgrass ,  Paolo Tortora ,  Kleomenis Tisganis ,  Stephan Ulamec ,  Jean-Baptiste Vincent ,  Kai Wünnemann ,  Elisabetta Dotto ,  Nancy Chabot ,  Andy F. Cheng ,  Andy Rivkin ,  Olivier Barnouin ,  Carolyn Ernst ,  Angela Stickle ,  Derek C. Richardson ,  Cristina Thomas ,  Masahiko Arakawa ,  Hirdy Miyamoto ,  Akiko Nakamura ,  Seiji Sugita ,  Makoto Yoshikawa ,  Paul Abell ,  Erik Asphaug ,  Ronald-Louis Ballouz ,  William F.
C. Reformulation and solving programs Both the operation model and planning model can be reformulated as a mixed-integer linear programming (MILP) problem with some general techniques. We implemented the optimization models via Gurobi Optimizer in Python. Our base planning case can be solved in 3 -5 minutes with MIP gap set as 1% on a personal computer. 3 When implemented as MPC, steps on the horizon tail are reasonably simplified. The tailored operation model can be solved in 10 seconds on average per 15-minute rescheduling step.
A. Data description and system configuration PEV charging records from the Olser Parking Structure charging station on UCSD campus in 2019  [34]  are used as the data source. The dataset includes detailed information of the start and end times and energy consumption for 12259 charging events (daily mean ±std : 33. 6 ±18. 6 ). Charging demands varied between weekdays (42. 9 ±13. 0 ) and weekends (10. 2 ±4. 4 ). We construct two typical demand profiles by randomly sampling 43 and 10 sessions from all the sessions on weekdays and weekends respectively. The sampled profiles are used as representative demands in planning. The capital cost of FCs (including installment and maintenance fees) is estimated to be $ 5400 each with a ten year lifespan via market survey  [35]. We estimate the capital cost of a RC to be twice that of a FC in the base case, considering the complexity of its hardware, software, manufacture and maintenance.
Note that the receiver locations of PD involved in the experimental analysis follow a uniform distribution. Then, the measured signal is filtered and the corresponding positioning beacon signal is reconstructed with high precision after filtering. Finally, the measured data is processed with MATLAB. Based on the semi-physical experimental setting, the performance results of positioning are analyzed in the following. Firstly, the characteristic of the positioning beacon is analyzed. Fig. 8  shows the transmission beacon waveform of each LED to the reference position point. It can be seen from Fig. 8  that the transmission beacon has experienced severe distortion due to the LED driving and photoelectric conversion. Thus, an efficient signal recovery is meaningful. Fig. 9  shows the received waveform at the reference point.