dataesr/scientific-paragraphs-categorization huggingface dataset:
However, individual variation in foraging behaviour persisted at each site, reflecting differences in intrinsic factors. Some individuals remained very local, foraging predominantly on the benthos throughout the winter, while others moved further afield, foraging more pelagically. A potential limitation in this study is that bathymetry is poorly known in remote regions like the Southern Ocean  (Millan et al. , 2020). The Weddell seal dive data reflects this inaccuracy with 24% of the dives recorded being deeper than the ocean floor provided by GEBCO19. To account for this potential source of error, we designated foraging behaviours conservatively. Many studies have reported that seal dive depths recorded by SRDLs and CTD-SRDLs are accurate  (Boehme et al. , 2009; Costa et al. , 2010; Padman et al.
FUNDING Field support was provided in the  Ross Sea by Malcolm O'Toole ,  Rupert Woods , and  Antarctica New Zealand  and in  Prydz Bay  by  Malcolm O'Toole ,  Andrew Doube ,  Iain Field , and the  Australian Antarctic Division. The tagging study in Terre Adélie had logistical support from  IPEV  ( Institut Paul Emile Victor ) and the  French Polar Institute. New Zealand  funding was provided by the  Ministry for Business, Innovation and Employment Endeavour Fund   C01 × 1710 : " RAMPing-up protection of the Ross Sea ". The 2014 field event was funded by  NZARI  ( NZ Antarctic Research Institute ) and  Fisheries New Zealand  (respectively), with Regina Eisert as CI, and tags and some field personnel funded by  IMOS. The IMOS deployments in Prydz Bay were supported logistically by the  Australian Antarctic Division  through the  Australian Antarctic Science Grant Scheme  ( AAS Projects 2794 & 4329 ).
The objective of this work was to benchmark different deep learning architectures for noise detection against cardiac arrhythmia episodes recorded by pacemakers and implantable cardioverter-defibrillators (PM/ICDs) and transmitted for remote monitoring. Up to now, most signal processing from ICD data has been based on classical hand-crafted algorithms, not AI or DL-based ones. The database consist of PM/ICD data from 805 patients representing a total of 10471 recordings from three different channels: the right ventricular (RV), the right atria (RA), and the shock channel. Four deep learning approaches were trained and optimized to classify PM/ICDs' records as actual ventricular signal vs noise episodes. We evaluated the performance of the different models using the F 2 score. Results show that the use of 2D representations of 1D signals led to better performances than the direct use of 1D signals, suggesting that the detection of noise takes advantage of a spectral decomposition of the signal, which remains to be confirmed in other contexts.
Introduction Remote monitoring of pacemakers and implantable cardioverter defibrillators (PM/ICDs) identifies early signs of lead failure, which lowers patient morbidity and mortality. Remote monitoring generates a large amount of data with low yield. It is estimated that only 7 to 9% of the transmitted data requires a medical opinion, and less than 2% motivate early patient management  [1]. In case of non-sustained ventricular arrhythmia, a major challenge is to identify lead noise (4% of the tracings). Noise episodes may eventually reveal electrode failure, the consequences of which can be disastrous (syncope, inappropriate shock, death)  [2] [3] [4]. The analysis of these large datasets requires a lot of human resources, thus the deployment of remote monitoring surveillance in clinical practice remains difficult.
Dataset The database was composed of PM/ICD remote records collected at Bordeaux University Hospital and managed by IHU Liryc. A total of 805 patients has been followed-up, creating 10,471 records, each composed of right ventricular bipolar signals, and possibly atrial, left ventricular and/or far field EGM signals, depending on the type of device (single, dual, or triple-chamber pacemaker or implantable cardioverter defibrillator). The database was randomly divided into 3 subsets: All Recordings were unevenly sampled at the maximum rate 200 Hz for efficient storage in the device. We applied data interpolation so that all time-steps were 5 ms. Considering that the recordings were not of the same length, window sliding was applied. It consisted of taking the first W points from the recording of L points, and then moving forward by S points and taking the following W points, at the end getting (L-W)/S + 1 samples of each recording.
2D-DenseNet To take advantage of Deep Convolutional Neural Networks, we converted the time series data into images. In this part, we only considered the recordings of the right ventricular channel, so we obtained two-dimensional images of the temporal traces of the ventricular signal over 2000ms. Then these images were fed to a Dense neural Network (DenseNet) for image classification. Input image dimension of the network was (248,372,1). DenseNet is an improved version of convolutional neural networks that minimizes the vanishing gradient problem, and can be trained with few parameters which takes less time and is found to achieve better results. [5]  The figure depicts the architecture of a typical DenseNet model, we have dense blocks composed of interconnected layers : each layer is receiving information from all preceding layers and in between there are non-linear transformation (Batch Normalization, Rectified Linear Unit (ReLU), Convolutions ).
2DTF-VGG In this part, RV-time/frequency maps are also used as inputs of the network. Each RV recording is first divided into regular time segments each 10 seconds wide. We compute then for each part the spectrogram in the range [0-100Hz] with a frequency resolution of 2Hz, and a 50% overlapping sliding windows of 1s leading to one 50x20 image for each 10-second segment. These 2D representations feed a VGG-like neural network. Final architecture was selected using a 5 fold cross-validation process over the training dataset. In order to manage the unbalanced data-  set, we train a cost-sensitive classifier and penalize missclassifications from the minority class y more than the majority class n by introducing weights to our loss function as follows: Loss = 1 n n i=1 W y i log(ŷ i ) + (1 -W )(1 -y i )log(1 -ŷi ) Where y i is the probability of input i to be be a noise event; and n the total number of example in the training database.
Discussion and conclusion In this work, we suggest a deep learning approach to automatically identify PM/ICD episodes with lead noise, results show that the use of 2D representation and a convolutional neural network (2DTF-CNN, 2D-DenseNet, 2DTF-VGG) outperforms the use of 1D representation of the signal (1D-AgResNet). The 2DTF-CNN has the best performance in terms of F 2-score reaching 91% on the test set. This may suggest that noise detection can also benefit from spectral decomposition of the signal, which remains to be confirmed in other contexts. Regarding the imbalanced data, we used a weighted loss function which potentially improved the F2-score. It will also be interesting to work on resampling the training set as shown in  [7]  where the authors propose an under-sampling approach to make a balanced dataset out of the imbalanced one.
A total of 8218 pelagic microplastic samples from the world's oceans were synthesized to create a dataset composed of raw, calibrated, processed, and gridded data which are made available to the public. The raw microplastic abundance data were obtained by different research projects using surface net tows or continuous seawater intake. Fibrous microplastics were removed from the calibrated dataset. Microplastic abundance which fluctuates due to vertical mixing under different oceanic conditions was standardized. An optimum interpolation method was used to create the gridded data; in total, there were 24. 4 trillion pieces (8. 2 × 10 4 ~57. 8 × 10 4 tons) of microplastics in the world's upper oceans.
Similarly, real data on microplastic abundance in the oceans is needed to validate the accuracy of numerical models (e. g. ,  [5, 6] ). A few studies have synthesized microplastic abundance data for the world's oceans to generate datasets. Eriksen et al. [7]  created a publicly available dataset of microplastic abundance based on data obtained from 680 surface net tows conducted by different researchers during 2007-2013. These data were standardized to reduce uncertainty derived from vertical mixing induced by oceanic turbulence, because abundance estimates based on surface net tows are influenced by oceanic conditions: particle counts for light-weight microplastics, which are produced mostly from polyethylene and polypropylene (polymers less dense than seawater,  [8] ), decrease (or increase) near the sea surface under stormy (or calm) oceanic conditions.
They used a formula to estimate the vertical distribution of the particle counts  [9] , to deduce the total particle count throughout the entire water column under wind speeds measured on the Beaufort scale. However, no description of the significant wave heights required for the formula was provided in Eriksen et al. [7]. Cózar et al. [10]  synthesized microplastic abundance data obtained from 841 surface net tows (442 wind-corrected samples), including a circumnavigation cruise of the earth. Published and unpublished microplastic abundance data from 1979 through 2013  (11, 632  samples in total) were synthesized by van Sebille et al. [6] , although their dataset was not made available to the public. They statistically standardized the data obtained by different researchers using a generalized additive model incorporating the year in which each study was conducted, the geographical locations, and wind speeds given by an atmospheric reanalysis product.
Here, we provide a new dataset of pelagic microplastic abundance in the world's oceans which incorporates different sampling methods. The dataset includes both published and unpublished microplastic abundance data obtained from 2000 to 2019. The number of samples is ~10-fold (n = 8218) higher than Eriksen et al. [7]  and Cózar et al. [10]. We standardized the data obtained by different researchers in a physical manner. The dataset is publicly available as the Supplementary data in a CSV format.
Categorization of data Different from the datasets mentioned above, the data in the present study were categorized as raw, calibrated, processed, and gridded data, similar to satellite products (https://climatedataguide. ucar. edu/climatedata/nasa-satellite-product-levels). Raw data (hereinafter referred to as Level-0 data) were mostly obtained by surface net tows and are provided as "particle count per unit seawater volume (partly, per unit area)". First, these raw data were calibrated to the abundance of microplastics (< 5 mm), except fibrous microplastics (filaments and fibers), as a quality control (Level 1). Second, to reduce uncertainty derived from vertical mixing, integrating microplastic abundance vertically from the sea surface to the infinitely deep layer yielded processed data for both the total particle count (Level 2p) and weight (Level 2w), over the entire water column per unit area, where the subscripts 'p' and 'w' represent the particle count and weight, respectively.
Third, the Level-2p and -2w data were gridded to obtain the particle counts (Level 3p) and weight (Level 3w) per unit area using an optimum interpolation method (OIM). Last, these gridded data were converted to monthly particle counts (Level 3 pm; 'm' represents monthly data) and weights (Level 3wm) per unit seawater volume in the uppermost layer. The present paper describes the detailed procedures to create this multilevel dataset.
Thus, as defined in the present study, the surface layer included seawater from the sea surface to a depth of 3 m. The Projects #25 and #27 collected data via continuous seawater intake at the depth deeper than 3 m, so that these data were included only in the Level-0 and Level-1 (shown next) data. The number of samples obtained after 2014 was smaller than that before 2014, but observations were conducted over all seasons (Supplementary Fig. 1 ). Except for duplicated data (the same location, time/ date/year, and observer) which were removed because of no relation to dataset reliability, we used all data obtained by these 27 projects to ensure that the amount thereof was sufficiently large, although parts of these projects adopted procedures that differed from the latest guidelines. Almost all projects adopted a tow net with a mesh size of 0.
2% × 50%). Both sizes and surface areas of microplastics show a continuous distribution  [37]  and, thus, the overestimation in small microplastics could be observed even if equivalent lengths computed from areas (e. g. ,  [38] ) were used for a measure of microplastic size. The microplastic abundance metric for the Level-0 data is the particle count per unit seawater volume (pieces m -3 ). Abundance was measured directly using a flowmeter (12 projects) or intake water (4 projects). However, 11 projects measured abundance per unit area, which was computed by converting flowmeter (projects #5, #6 and #21) or global navigation satellite system data (projects #1, #4, #7, #9, #14, #15, #16, and #20). The seawater volume for each of these 11 projects was computed by multiplying the area by tow depth (half the height of the tow net).
Level 1calibration by removal of fibrous microplastics Including fibrous microplastics can cause a pseudo difference in microplastic abundance estimates obtained from different projects; while one group of projects provided abundance data for microplastics including fiber, another group omitted fibrous microplastics from their estimates. Fibrous microplastics were unlikely to have been quantified precisely, unless clean-air devices were used to prevent airborne contamination during sampling or processing, or airborne contamination was removed by a blank test  [39, 40]. In addition, sampling gear, such as a tow net made from synthetic fibers, might be a source of contamination. Thus, some of the projects (#2, #3, #5, #7, and #17) excluded fibrous microplastics when creating their datasets. Meanwhile, fibrous microplastics constituted a non-negligible fraction of microplastics collected in the ocean close to the coast (projects #13 and #18), or in an estuary (Project #  19).
Level 2pprocessing for wind/wave correction The Level-1 data were standardized to obtain the total particle count, by vertically integrating microplastic abundance over the entire water column using the wind speed and significant wave heights during each The authors stated that the "vast majority" of collected microplastics were fragments. l The abundance without fibrous microplastics was provided by the coauthor. m Intake seawater, n The lower size limit in this project, o 88% of fragments collected in this project were smaller than 10 mm, while fragments between 5 and 10 mm in size account for approximately 5% of all microplastics shown in Supplementary Fig. 2. Thus, 83% (0. 88 × 0. 95) was categorized as microplastics < 5 mm in size. p Bongo net, q Plankton net, r These data were included only in Levels 0 and 1 data because the intake depth of 11 m was largely different from other studies.
(  5 ). The weight obtained by Eq. (  8 ) (W Eq  (8)  ) is expressed approximately by W in Eq. (  7 ) as follows: log 10 W Eq: 8 ð Þ ¼ 1:2 log 10 W Eq: 7 ð Þ -2:0: ð9Þ The dataset converted using Eq. (  7 ) is referred to as the Level-2w1, while Eq. (  8 ) created the Level-2w2 data. The difference between the Level-2w1 and 2w2 data was described in the first subsection in Results and discussion.
Nonetheless, these data should allow for accurate laboratory-based studies on impact to aquatic organisms exposed to microplastics, so that microplastic concentrations used for exposures are comparable with those in reality. In addition, these data may be capable of predetermining appropriate months and locations of a field campaign to collect sufficiently large numbers of microplastics. The wind speed and wave height data used to create the Level-2 dataset were averaged monthly for the period 1993-2018. Using Eqs. (  2 ) and (3), we converted abundance at Level 3p and 3w1 (M in the equations) to the Level-3 pm and -3wm surface concentrations, respectively, for each month using the monthly averaged wind speed and wave height. Other parameters, such as terminal rise velocity, were the same as those in creating the Level-2 dataset.
Sensitivity of parameter choices on microplastic abundance Because of limited available knowledge regarding microplastics in the ocean, the present study had to make some parameter choices for processing the data at each level. Here we demonstrate how microplastic abundance depends on the choices made by using different parameters such as terminal rise velocities (w) in Eq. (  3 ) and formulae to convert from the total particle count to weight. The early plastic projects ca. 2010s may have overestimated the particle count by approximately 30% because of misidentification of small fragments in the absence of spectrometry. To quantify how the overestimation diminished the quality of the dataset, the Level-2p data were created from the Level-1 so that the particle counts were reduced by 30% in the projects without spectrometry (Table  1 ).
2D maps and statistics The present study's objective was to generate a new, publicly available dataset and facilitate microplastic research based on actual and reliable ocean data. Although further and more detailed interpretations, analyses, and processing are expected to be carried out by researchers who download the dataset, we present twodimensional (2D) maps with brief explanations of the features of the dataset. Figure  2a  and b provide 2D maps of the Level-0 and Level-1 data, respectively, including the microplastic abundance obtained by Project #21, conducted in the Great Lakes of the United States. Microplastic surveys have been conducted in the seas around the United States, European countries, such as the Mediterranean Sea and the eastern North Atlantic, and Japan. Approximately 46% of microplastic surveys have been conducted in the mid-latitude ocean between 30°N and 60°N, while low-latitude surveys of the Indian Ocean and western Pacific (between 30°S and 30°N, and 40°E and 180°E, respectively) account for only 5% of all data.
In particular, a notable shortcoming of the present dataset is the lack of microplastic data for the Indian Ocean and the seas around Southeast Asia (including the South China Sea). Besides waters close to land masses, surveys in the subtropical convergence zones approximately across the 30°-latitude in both hemispheres should be prioritized to determine the total amount of plastics in the world's oceans. Second, microplastic abundance in the subsurface layer of the ocean should be explored. Recent observations of pelagic microplastics have revealed that a non-negligible fraction of microplastics exists in the subsurface layers of coastal waters  [36] , and in intermediate and abyssal layers of the open ocean  [30, 54, 55]. It has been suggested that biofouling  [56] , inclusion within marine aggregates  [57] [58] [59] [60] , and inclusion within fecal pellets  [61]  allow microplastics lighter than seawater to settle in the abyssal ocean.
Physico-chemical characterization studies The different studied sorbents were fully characterized to gain insights on their chemical, textural and structural properties. Elemental ICP analyses (Ag, Na, Al and Si), powder X-ray Diffraction (PXRD) measurements, N2 adsorption-desorption isotherms at 77 K as well as DRS-UV-Visible spectroscopic analyses were then performed. Detailed experimental procedures and associated results have been already presented during our previous works  [7 12]. A summary of characterization data is presented in Electronic Supporting Information (Table  S2 ). In the present study, the morphology of some zeolites was also assessed from Scanning Electronic microscopic (SEM) characterizations. These p* alys 's were performed on a JEOL (JSM 6010-LV) microscope. Powdered samples we-d^positei on an aluminum grid coated with a thin layer of adhesive carbon.
Population In total, 11 patients, 5 women and 6 men, with the mean age of 57 years old (34-78), who were operated on with unilateral horizontal semicircular canal plugging (HSCCP) for intractable unilateral MD from 2006 to 2016 were included in this study. They corresponded to criteria of definite MD (Barany Society 2015) and grading among saved data were considered. They had no previous surgery for their MD. All patients had severe disabling vertigos for more than 6 months (more than 1 crisis/month with vomiting) before surgery. They were included for surgery with symptoms as previously described by Charpiot et al. [1] : fluctuating hearing loss associated with pure rotatory vertigos in the horizontal canal plane. All participants gave informed consent before data collection, in accordance with the current ethical laws, and all experiments were performed in accordance with the Helsinki II Declaration.
A unique data set derived from remote sensing, airborne, and ground-based in situ measurements is presented. This measurement report highlights the known complexity of comparing multiple aerosol optical parameters examined with different approaches considering different states of humidification and atmospheric aerosol concentrations. Mie-theory-based modeled aerosol optical properties are compared with the respective results of airborne and ground-based in situ measurements and remote sensing (lidar and photometer) performed at the rural central European observatory at Melpitz, Germany. Calculated extinction-tobackscatter ratios (lidar ratios) were in the range of previously reported values. However, the lidar ratio is a function of the aerosol type and the relative humidity. The particle lidar ratio (LR) dependence on relative humidity was quantified and followed the trend found in previous studies.
Modeling of aerosol optical properties The aerosol optical properties are calculated following the flowchart displayed in Fig. 1. A model utilizing Mie's theory  (Mie, 1908)  allows the calculation of the optical properties of aerosol particles under the assumption that these particles are spherical. The Mie model applied here fulfilled three main tasks. First, it is tested to what extent it can reproduce measured σ abs (λ) with the given constraints. Second, it is compared to lidar-based σ bsc (λ) and σ ext (λ) based on airborne in situ measurements accounting the ambient RH. Third, it derives LR(λ) at ambient aerosol conditions to examine the LR-RH dependence. For both campaigns, an adapted Mie model, written in Python (package PyMieSca v1. 7. 5; Sumlin et al. , 2018) , simulates the aerosol optical properties, including, in particular, σ bsc (λ), σ ext (λ), σ sca (λ), and σ abs (λ) for eight different wavelengths.
Experiments The data assembled during two campaigns near Melpitz, Saxony, Germany, are examined in this study. The first campaign, named the Melpitz column or MelCol summer, is, unless otherwise stated, ongoing and referred to as the summer campaign, was conducted in May and June 2015 with an intensive measurement period including ground-based and airborne in situ measurements between 13 and 28 June. The second campaign, MelCol winter, took place in February and March 2017 and, thus, is referred to as the winter campaign in the remainder of this paper. The upcoming sections overview the conducted experiments, introduce the Melpitz observatory with its characteristic features, and provide an overview of the applied instrumentation on the ground and the air.