dataesr/scientific-paragraphs-categorization huggingface dataset:
5 concentrations increased by a factor of 7%. Tang et al. (2015)  also improved hourly PM 2. 5 and ozone concentrations using OA over AIRNOW measurements as the mean bias improved from -7. 14 to -0. 11µg. m -  and from 2. 54 to 1. 06 ppbV. Similarly, the modeled AOD field over AERONET stations increased by a factor of 43% from 0. 21 to 0. 30. The correlation also slightly improved from 73 to 78%. Both the bias and error decreased from -45 to -27% and from 55 to 42%. While MODIS onboard Terra and Aqua pass at 10:30 and 1:  The number of compared data in the AERONET network in is lower than the the compared data of AIRBASE because numerous missing data were found in AERONET data. The correlation moved from 40 to 58% and the slope of the scatter plot increased from 0. 23 to 0. 52. The same result is found when it comes to the modeled AOD (the as the correlation increased from 73 to 90% and the slope of the scatter plot moved from 0.
20 The comparison of the two simulations against AirBase and AERONET data show that simulation with a radius of influence equal to the grid cell predicts PM 2. 5 and AOD that have a better agreement with observations. The simulated mean of PM closer to the observed means 38. 01µg. m -  (0. 37). Besides, the goal criteria is respected with analysis PM 2. 5 concentration field simulated using R 1.
Mangrove species classification: In the literature, it is possible to find species classification methods based on pixel based or object based classifiers  (Kamal & Phinn, 2011; Heumann, 2011; Kamal et al. , 2015). A pixel-based classification consists of comparing each pixel of an image with each other to form classes by grouping them together based on the spectral signature of each single pixel. An object-based classification uses in addition geometric and spatial context information (size, shape, adjacent objects). Kamal and Phinn (2011)  compared both methods using CASI-2 data with a spatial resolution of 4 m and concluded that the object-based method performs better results than the pixel-based method with overall respective accuracies of 76% and 69%. As well,  Cao et al. (2018)  attempted to classify mangrove species using object-based image analysis techniques from hyperspectral images obtained from the UHD 185 sensor carried on a UAV platform.
Data acquisition with UAV: A first acquisition was carried out using the DJI Mavic 2 pro sensor. A total of 1 906 images with a resolution of 1. 5 cm per pixel, using the 1" CMOS multispectral sensor on board have been acquired. The data were collected on November 3 and 4, 2021 in overcast conditions to avoid shadows and a wind speed below 20km/h to ensure the stability of the UAV. The acquisitions were made between 12 pm and 2 pm local time, with a water height of 0. 48 m at low tide. The flight altitude was approximately 50 m aboveground. For this first acquisition, a total of 6 targets were distributed in the field. The Metashape targets were printed on conventional paper previously glued on plywood boards. However, due to the size of the study area, it took us half a day to position the targets, convert them into the GNNC (Geodetic Network of New Caledonia) network and retrieve them at the end of the mission.
Data acquisition using TLS: In order to carry out TLS acquisitions in the mangrove, an adequate acquisition protocol must be defined. Indeed, the Trimble X7 scanner (Figure  6 ) has the particularity of automatically registering point clouds between them in real time. However, depending on the distance between two successive stations and the rate of detected overlap, the registration is done more or less accurately. In addition to the forest scanning, two specific scans are carried: the scan of an individual tree and the scan of pneumatophores. Thus, it is necessary to check that the select density of point clouds is sufficient to visualize perfectly the objects to be studied (pneumatophores, trunks, branches). Therefore, a compromise must be made between scan duration, point density and registration accuracy.
The TLS data were collected on March 21, 2022. The first experiment consisted in performing 5 scans respectively spaced from 3 m, 4 m, 5 m and 6 m (Figure  7 ) in order to investigate the optimal overlap. Also 2 different densities have been tested through different scan durations. It generates different scan durations (8 min and 13 min). The registration reports show errors of 2 to 5 mm for distances of 3 to 8m. Beyond that, the errors are greater than 5 mm, for example up to 1. 12 cm for a distance of 17 m. It can be concluded that for detecting pneumatophores, it is necessary to define a maximum distance between 2 scans of 8 m with a scan duration of 13 min to ensure sufficient density on the object (Figure  8 ). The individual trees have been scanned from 4 stations around the tree and with a scan duration of 13 min.
Two reasons might explain this: a) the height of trees is the same or b) medium Rhizophora are generally taller than Avicennia but because of the slightly downward slope towards the seashore, the difference can not be detected. Point altitude thus seems to be a good criterion for delimiting tannin zones as well as large Rhizophora but is not sufficient to distinguish medium Rhizophora from Avicennia. In order to automatically delimit tannin zone using the criteria of point altitude, an algorithm has been implemented in Matlab. Firstly, it sorts the set of points along the X-axis. The algorithm then scans the x-axis according to an interval defined by the user. The extrem points (Ymin and Ymax) are recovered in order to delimit the contour of the study area. For each given X interval, if the algorithm detects a difference in altitude (tolerance defined by the user), then a point is created, otherwise, the calculation continues until there is no more point for a given X value.
Results The proposed sizing methodology is applied to the Electrical Engineering Building (EEB) at the Industrial University of Santander (UIS). The Backup system solution must respect the demand conditions shown in Eq. (  10 ) and (  11 ) and the restrictions of the energy sources. The local electricity network operator provided the historical data on power outages for the distribution network of the case study. The load profiles were obtained from the meters of the building. The following presents the results of the case study.
Loads characterisation Before applying the sizing, it is necessary to characterise the distribution of outage length probabilities and categorise the loads. Thirty-five historical data samples of power outages from the last two years were used to develop TSP. When applying the PDF fit, it was found that the Inverse Gaussian distribution has the best fit with an 8. 8% MAPE, followed by the Non-parametric and Gamma distributions with 16. 9% and 18. 1% MAPE, respectively. Figure  4  shows the CDF fit and cumulative probability for the historical data. The loads are grouped according to their functionality and the disconnection via the circuit breakers for categorisation. Table  4  shows the categorisation of the loads, and Figure  5  shows the equivalent circuit to tackle the optimisation problem.
Acknowledgements The authors wish to thank the  Department of Electrical, Electronics and Telecommunications Engineering (Escuela de Ingenierías Eléctrica ,  Electrónica y de Telecomunicaciones) , the  Vice-Rectorate for Research and Extension (Vicerrectoría de Investigación y Extensión ) from the  Industrial University of Santander  ( Universidad Industrial de Santander). The same as the electricity company  Electrificadora de Santander S. A E. S. P. -ESSA ESP (Bucaramanga, Colombia ). Moreover, to  ECOS Nord , and the  FEMTO-ST Institute. This work has been supported by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ) and the  Region Bourgogne Franche-Comté.
Such physically based framework is designed to analyse and simulate geophysical fields exhibiting extreme variability over wide range of space-time scales as wind. Drop oblateness is also accounted for. The paper is organized as follow. In section 2, a deterministic equation for the fall of oblate drops in a 3D field is derived and validated through the comparison of obtained terminal fall velocity with commonly used formulas. In section 3, the framework of Universal Multifractals is briefly reminded. Then, the drops are subjected to simulated multifractal fields as wind input and multifractal behaviour of horizontal drop velocity is assessed. Finally, in section 4, 3D wind is reconstructed from high resolution 3D sonic anemometer data and strong scaling assumptions. This field is used to study the trajectories of drops between falling from 1 500 m down to the ground.
For the two last points, data is available within the RW-Turb project. Acknowledgements. The authors gratefully acknowledge partial financial support from the Chair "Hydrology for Resilient Cities" (endowed by Veolia) of Ecole des Ponts ParisTech, the Île-de-France region RadX@IdF Project, and the ANR JCJC RW-Turb project (ANR-19-CE05-0022-01).
Occurrence data collection For the eight SHEVs, we compiled contemporary occurrence data from three available public databases: The Ocean Biogeographic Information System (OBIS, http://www. iobis. org), the Global Biodiversity Information Facility (GBIF, https://www. gbif. org) and Fishbase (http://www. shbase. org). To build the most up-to-date datasets and to reduce the risk of spatial niche truncation (i. e. , when only a subset of the environmental conditions experienced by a species across its full range is characterized  21  ), we also retrieved all available species observations from the literature (Supplementary Material 1). The resulting occurrence data ranged from 1950 to 2017, recent records (since 1990) prevailing over past (before 1990) and undated observations: 72. 82 ± 6. 12%, 10.
79 ± 3. 7% and 16. 37 ± 4. 58%, respectively. To avoid a biased estimation of the niche due to low quality occurrence records  49  , past or undated occurrence were only kept along the distribution edge when con rmed by recent records from the literature (Supplementary Material 1). For each species, we preprocessed the data and improved the quality of the eight occurrence record datasets by removing (i) unreliable observations -such as preserved specimen-and incorrect taxonomic identi cations, (ii) duplicate occurrences and (iii) locational errors, such as geographical outliers. The resulting number of observations ranged from 1211 for gilthead seabream to 15827 for common sole. For each SHEV, we aggregated occurrences on a 0. 1°× 0. 1°s patial grid (from 70°N to 70°S and from 180°E to 180°W) that corresponds to the resolution of the preprocessed environmental variables (see 2.
Environmental lter and pseudo-absence selection Spatially biased effort in presence-only species datasets -i. e. , when data sources are not comprehensive across the study area -can induce a bias in the environmental space in which the spatial distribution of species is modelled  54. To consider this potential effect for each species, we used an environmental lter to keep only a single observation among a group of occurrences characterized by a similar combination of environmental values (Supplementary Material 2), as performed in the GARP modelling system (program RASTERIZ  55  ). We determined the following resolution for environmental ltering: 0. 5°C for temperature-related variables, 0. 5 psu for SSS and 0. 5 mol. m - 2. s - 1 for PP (used in logarithm). The same environmental domain was used to generate pseudo-absences outside the space delimiting environmental suitable conditions using the convex hull method 56 while excluding the 2.
Ensemble model selection For each species and combination of environmental variables, the algorithms that best re ected the observed distribution were selected according to the Continuous Boyce Index (CBI), an evaluation metric speci cally designed for presence/pseudo-absence datasets. We retained algorithms with a CBI value over 0. 5 57. To ensure the ecological realism of our models, we discarded spurious responses to environmental factors (e. g. , bimodal response to temperature) and selected the simulations for which response curves matched a priori expectations (see  51  for further details). For each species, we quanti ed the uncertainty related to our ensemble modelling procedure (e. g. , algorithm selection) by means of the standard deviation (SD) computed from all retained simulations.
Pre-treatment of future temperature data To assess possible bias between contemporary and future temperature-related we performed Taylor diagrams  59  to estimate the consistency between current and future climate data (Supplementary Material 3): considering a common period (i. e. , 2006-2017), we calculated the Pearson correlation coe cient, the root-mean-square difference (RMSD) and the standard deviation (SD) difference for each temperature-related variable. For each GCM and RCP scenario, we then corrected model-based temperature data according to their difference with observation-based data for each geographical cell. This procedure ensured a perfect correlation (Pearson coe cient r = 1), no RMSD and the same SD between model-and observation-based datasets for a common period 60.
Projected changes in environmental suitability For each future period, we estimated the occurrence of each SHEV species per geographical cell (0. 1° x 0. 1°) by combining our ensemble modelling method with environmental data originating from the ve GCMs and the three RCP scenarios. For each species and future period, we calculated the proportion (in km 2 ) of the studied area that was projected to contain a suitable habitat in order to quantify (as percentage) potential changes in the spatial extent of species, relative to the contemporary (1990-2017) period. We then assessed the potential consequences of distributional shifts at the scale of each Mediterranean and European EEZs: we calculated the total value of landings for each SHEV over the period 1990-2017 (in logarithm) and computed changes in ESI values at the scale of Exclusive Economic Zones (EEZs) 61 , areas stretching from the coastline out to 200 nautical miles over which a country has special rights regarding the use of marine resources.
For each species and EEZ, we downloaded the mean catch data from the Sea Around Us database (http://www. seaaroundus. org/) for the period 1990-2017 (i. e. , the most recent available information). Focusing on EEZs allowed the estimation of changes at the scale of basic units for sheries management (e. g. , attribution of maximum allowed catches by EEZs) and conservation perspectives 61. In addition, EEZs are relevant regions for biogeographic research, and are commonly investigated for assessing the socio-economic consequences of climate change on sheries  5.
, 2014). Still, there is little consistency in the Manta net design with different sizes of the device and sampling criteria in aquatic environments (sampling duration, sampling speed, mesh size used. ). This review aims to (i) analyze the methodologies and the characteristics of Manta nets used for MPs sampling, (ii) discuss factors that can impact the efficiency of the sampling and the MPs quantification, and (iii) propose recommendations in order to improve and standardize the sampling protocol. In order to reach this objective an extensive literature review, using a scientific database (Scopus ® ) and a scientific article browser (Google Scholar ®   S1 , whereas others studied freshwater areas like rivers  (Ta et al. , 2020; He et al. , 2021; Frank et al. , 2021 i. e. ) , lakes  (Uurasjärvi et al.
A blank sample should also always be analyzed, when possible, at every step during the treatment and conditioning of the sample to check for possible contamination during the various stages of the process. Moreover, some parts of the Manta net device are often made of plastic polymers, such as the net that is, usually made of nylon, or the cod end collector that is, made of PVC. These plastic parts can be collected, analyzed and integrated in identification databases, in order to avoid overestimation of MP loads due to contamination by sampling devices  (Scopetani et al. , 2020).
FUNDING This review is part of a PhD project named " Microplastics: Assessment  of the contamination of organisms and aquatic environments in connection with the human consumption of aquatic product" funded by  Université du Littoral Côte d'Opale, France  as part of the  IFSEA Graduate School. This work has been partially financially supported by the  European Union (ERDF) , the  French Government , the  Région Hauts-de-France  and  IFREMER , in the framework of the project  CPER IDEAL 2021-2027.
In 2017, OSPAR (the Northeast Atlantic regional seas commission) evaluated each indicator individually as part of a Northeast Atlantic ecosystem assessment  (OSPAR, 2017m). In many cases, the assessment was challenging due to the scale of assessment, methodological or data limitations, or due to the novel approach of assessing biodiversity across datasets, ecosystems, and multiple scientific experts from OSPAR Contracting Parties at this scale. It was often equally challenging to identify pressures causing changes in the marine environment, either because of scientific uncertainty or due to resource and time limitations. In most cases (e. g. pelagic habitats, food webs, NIS) no quantitative targets for GES were agreed at the OSPAR level at the time, so no overall assessment of the state of marine biodiversity was made during IA2017.
Data Data were gathered through OSPAR formal data calls, requests to ICES Working Groups and ICES data calls, national expert group contributions, and direct requests to data providers, many of whom were members of the OSPAR Expert Groups. Spatial and temporal data coverage varied by dataset and indicator due to differences in data availability; for this reason, and because ecological dynamics vary throughout the ecosystem, a common temporal baseline was not used across indicators and ecosystem components. For example, broad-scale benthic habitat, cetacean abundance, and plankton abundance and biomass data were used throughout several regions, while some other datasets, such as benthic community condition, harbour porpoise bycatch data or changes in plankton diversity, were available only subregionally.
For NIS, data were available on a national scale and then processed to meet the regional scale. Approximately 86 datasets were used to develop the indicators, but in some cases (e. g. pelagic habitats, benthic habitats) lack of formal data contribution by Contracting Parties restricted the inclusion of all possible datasets. A wide spectrum of data types was used to construct indicators. The harmonisation of diverse datasets was often required (e. g. to construct pelagic and benthic habitats and food web indicators). Data and metadata can be obtained from each indicator's IA2017 webpage (see Tables  1 2 3 4 5 6 7 ) and associated Coordinated Environmental Monitoring Programme (CEMP) guidelines, which contain official and detailed methodologies for all indicators contributing to IA2017 (see Tables  1 2 3 4 5 6 7 ; further information in CEMP at https://www.
, 2003). As a result of these differences, pelagic data could not be simply aggregated across datasets. Instead, pelagic indicators were purposely developed to deal with this wide range of plankton data types, regardless of sampling and enumeration method or level of taxonomic identification (see Table  1 ). Indicator Changes in phytoplankton and zooplankton communities (PH1/FW5) aggregates plankton taxa into "lifeforms" based on common functional traits. Changes in lifeforms can be interrogated temporally, spatially, or as ecologically-relevant lifeform pairs  (McQuatters-Gollop et al. , 2019a).
Benthic habitat mapping data provide information on seabed type, classified according to EUNIS (European Nature Information System) levels, and distribution. Benthic habitat maps were produced using EMODNet methods and data (EUNIS level 3), combined with local datasets, at different spatial resolutions, depending on methods used (e. g. groundtruthed sampling, acoustic mapping, and modelling)  (Diaz et al. , 2004; Ellwood, 2014; Kenny et al. , 2003). The information on environmental variables (e. g. depth, exposure, sediment type, etc) was used to classify and model habitats and produce habitat maps  (Brown et al. , 2011). Information on the extent and distribution of benthic habitats is used alongside the associated sensitivity, in terms of resilience and resistance capacity of benthic species, biotopes and habitat types.
Data for surface and sub-surface abrasion by bottom-trawling fisheries provided an indirect measure of pressure on the seabed by fishing. These data were obtained from ICES (2016b). The pressure layer on abrasion by bottom trawling was produced from bottom trawl data provided by OSPAR Contracting Parties, the assessment of related pressure by ICES, and finally an assessment of disturbance of habitats by the OSPAR benthic habitat expert group.
Fish data Quantitative spatio-temporal data across the OSPAR area were available from scientific trawl surveys coordinated by ICES and supporting the fisheries management process. These surveys collect data on the number and weight of each species of fish along with measurements of body length and mass. To maintain transparency and for quality assurance, a selection of surveys was used for the analyses. The data resulting from the surveys were made available through the routinely updated Database of Trawl Surveys (DATRAS). Relevant ICES Working groups (ICES, 2021a; ICES, 2021b)) were consulted on the appropriate usage of these data and a single data product was subsequently created and, following discussion within ICG-COBAM, stored in an online repository to support the assessment process with comprehensive documentation available  (Greenstreet and Moriarty, 2017; Moriarty et al.
Marine bird data The assessment used data on marine bird species that, at some point in their annual life cycle, rely on coastal and/or offshore marine areas; the term 'marine birds' therefore includes seabirds and waterbirds. Taxonomic groups considered include Anseriformes (ducks, geese, swans), Procellariiformes (fulmars, petrels, shearwaters), Suliformes (gannets, cormorants), Gaviiformes (divers), Podicipediformes (grebes).
Some colonially breeding species were also assessed using data on breeding successthe average number of chicks fledged per breeding pair per year. Non-breeding bird abundance did not derive from at-sea surveys, but was restricted to counts of birds in intertidal areas or close to the shore and surveyed from land during migration or over the winter. During these times the birds in these habitats aggregate in large numbers and are much easier to count than during the breeding season, when they are dispersed over large areas of remote nesting habitat, such as the Arctic tundra. Many marine bird species assessed in the non-breeding season (mainly shorebirds, ducks, geese, swans, divers, and grebes) breed inland and in areas outside the OSPAR Maritime Area but are reliant on the Northeast Atlantic most of the year during migration and/or over the winter.
, 2021). ICES Working groups (ICES, 2014a) were consulted on relevant population-specific assessment units as well as threshold setting methods for population trends and bycatch. Data on bycatch rates of harbour porpoises were primarily obtained through observer schemes that operated according to European Union Council Regulation (EC) 812/2004, and were analysed by the ICES Working Group on Bycatch of Protected Species (WGBYC). ICES advise (2015a) was used for assessment of this pressure indicator.