dataesr/scientific-paragraphs-categorization huggingface dataset:
However,  Tamburello et al. (2015)  did not consider interstudy differences in the probability density estimator used (i. e. , KUD smoothing factor). Presumably similar confounding factors also affected data collection, since data were drawn from essentially the same body of literature as that used in  Nash et al. (2015). Additionally, measures of activity space and mass used to construct allometric scaling in the past have used species average values, with limited consideration of the effect of intraspecific variability in both measures  (Welsh et al. 2013). Therefore, at this juncture the allometry of activity space in marine animals remains unclear. To address these methodological issues, we compiled and assessed allometric scaling in a data set comprising 1,596 animals across 79 marine species and 34,890,047 location detections, collected over a decade and across 32 degrees of latitude.
The data were collated by the Integrated Marine Observing System's (IMOS's; https://www. imos. org. au) Animal Tracking Facility. Since 2007, the IMOS Animal Tracking Facility has coordinated a continental-scale network of acoustic receiver stations that covers a significant portion of Australia's coastline  (fig. 1a; Hoenner et al. 2018). This collaborative network of receivers and associated information infrastructure is used by independent researchers, universities, and government organizations to understand animal movement patterns and collect continental-scale detection data around Australia (fig. 1b ). This study used a comprehensive quality-controlled data set of the movements of a range of marine taxa monitored using the IMOS Animal Tracking Facility network  (Hoenner et al. 2018).
Using this database, we assessed activity space scaling laws across four major marine taxonomic groups-teleost fish, sharks, rays, and reptiles-that inhabit Australia's coastal waters, with our a priori hypothesis being that allometric scaling would be apparent and similar across these taxa. In contrast to earlier efforts (e. g. ,  Nash et al. 2015; Tamburello et al. 2015) , we examined marine ectotherms that forage in benthic and pelagic habitats and assessed activity space against body size, taxonomic group, geographic location, and trophic group. Given that activity space for all taxa was estimated at an individual level, using the same technology and analyzed using the same standardized approach  (Udyawer et al. 2018) , rather than summarized species-level data, this study overcomes the potential biases incurred by varying methods used to estimate activity space and incorporates intraspecific variability in measurements used to define allometric scaling relationships.
Data Collection and Processing Metrics of activity space used to examine allometric scaling relationships for marine animals were calculated from acoustic telemetry detection data of tagged organisms collated over a decade  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017)  by the IMOS Animal Tracking Facility (fig. 1 ) and stored in an openly accessible data repository (https://animaltracking. aodn. org. au). Data available in the repository undergoes strict quality control, eliminating erroneous raw detections using specific rules that interrogate each data point in light of spatiotemporal patterns in detection and known species ranges (for the primary quality control process, see  Hoenner et al. 2018). Raw detections were processed to estimate short-term centers of activity (COAs) for 60-min intervals.
COAs were estimated prior to subsequent activity space estimation, to account for the varied transmission settings across tagging projects and spatial biases in raw detection data inherent in node-based telemetry studies as well as to incorporate movements of tagged animals between fixed receivers  (Udyawer et al. 2018). We conducted a second round of data filtering by excluding individuals that were not detected at five unique COA locations. A minimum of five unique COA locations meant that tagged individuals recorded on a minimum of two receivers were included, provided that sufficient movements were recorded between the receivers over the tracking period. This process ensured that subsequent estimates of activity space were not biased based on lack of positional data. Detection data were used to calculate standardized metrics of activity space using the Animal Tracking Toolbox functions within the R package VTrack  (Campbell et al.
We collated species identity, release location, body mass, trophic group, and foraging habitat data for each tagged individual. Each species used was classified into one of four broad taxonomic groupings: teleost fish, shark, ray, or marine reptile. Where information on body mass was not recorded, it was estimated from total length, snoutvent length, or carapace length using length-weight relationships in published literature  (Webb and Messel 1978; Hirth 1982; Froese et al. 2014; Froese and Pauly 2017; fig. S1b). Trophic level indices for each species were sourced from published literature that used standardized indices based on diet composition  (Cortés 1999; Jacobsen and Bennett 2013; Froese and Pauly 2017). Each individual was then categorized into one of three broad trophic groups: primary consumers, secondary consumers, and tertiary consumers.
All models and scaling relationships were constructed with a Markov chain Monte Carlo (MCMC) sampler accounting for correlated random effects arising from phylogenetic correlations between all species using the MCMCglmm package  (Hadfield 2010)  in the R statistical environment (R Core Team 2021). Model coefficients were estimated using diffuse prior specifications, with 10 4 MCMC iterations, thinned every 10 iterations with a burn-in value of 3,000. Model convergence was assessed using the Gelman-Rubin statistic. Measures of variance in models were quantified by calculating marginal and conditional coefficients of determination (R 2 ) using custom R scripts as formulated by  Nakagawa and Schielzeth (2013). Marginal R 2 values provided the variance explained by the fixed effects in models, whereas conditional R 2 values provided the variance explained by the full model.
Phylogenetic correction in models were applied following methods described by  Hadfield and Nakagawa (2010)  by including a phylogenetic covariance matrix within models calculated using branch distances from published phylogenetic trees (henceforth indicated as "1 phylo" in formulas). The phylogenetic covariance matrix was generated using the inverseA function before applying it to each model using the ginverse parameter in the MCMCglmm function  (Hadfield 2010 ). Taxonomic relationships between species for phylogenetic corrections was extracted from the Open Tree of Life (OTL) taxonomy database (https://tree. opentreeoflife. org). The OTL project assembles current phylogenetic relationships across all organisms on Earth by synthesizing published phylogenetic trees across multiple taxonomies  (Hinchliff et al.
2015). The phylogenetic tree utilized in this study was built by subsetting the OTL data set for the species for which activity space data was available and combining results into a single phylogenetic tree (fig. S2 ; Udyawer 2022). Accessing the OTL data set, subsetting, and building the phylogenetic tree used in this analysis was conducted using the rotl R package  (Michonneau et al. 2016). To allow for comparisons between taxonomic groups and with previous studies (e. g. ,  Tamburello et al. 2015) , model coefficients were used to predict the area of activity space, with estimated standard errors, for a 1-kg individual (AS 1kg , in square kilometers).
2020). A multilevel correlation structure was used with taxonomic group as a random effect to account for the lack of independence of observations within taxonomic groups. Collinearity testing highlighted that all variables used in subsequent models were not strongly correlated (Spearman coefficients ! j0:5j; fig. S3 ). Candidate models were compared using a deviance information criterion (DIC) index  (Spiegelhalter et al. 2002)  using the MuMIn package  (Bartoń 2020 ) in R, with the marginal R 2 estimated for each candidate model. Only main effects were considered in the model selection process. Variables present in the models with the lowest DIC score were regarded as influential biological or geographic factors. Second, the influence of each covariate on allometric relationships was explored using a series of simple bivariate interaction models.
These tend to be slow-moving and sedentary species, again reducing the potential for interspecies variation. In our study, fish data were collected across a wide range of orders (30 teleost species within 18 orders), habitats, and temperature regimes. The broad range of teleost species examined here may have overridden any significant allometric effects of body mass on activity space found in previous studies and provides a more accurate picture of allometric scaling in teleost fishes. Much of the literature around allometric scaling of activity space has been developed from location data collected for bird and mammal species (although see  Hirt et al. 2017). In these groups, basal metabolic rate appears to be a good size predictor of activity space, and variation in allometric Note: Scaling coefficients presented are in the form AS p aM b , with numbers of species (n spp ), conditional R 2 , and deviance information criterion (DIC) values for interaction models.
In marine ectotherms, a hierarchical approach to examining allometric scaling exponents reveals a more complex relationship between body mass and activity space across taxa, trophic groups, and foraging behaviors. Here, we demonstrated the utility of collaborative passive telemetry networks to assess activity space-body mass allometry in a range of marine taxa and across a wide geographic scale, an approach not previously feasible because of the difficulty of obtaining long-term and standardized metrics of activity space over biologically relevant spatial scales. Data were sourced from Australia's Integrated Marine Observing System (IMOS). IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as lead agent.
Quality-checked telemetry data used in this study were accessed via the Australian Ocean Data Network portal (https://portal. aodn. org. au/) and the IMOS Australian Animal Acoustic Telemetry database (https://animal tracking. aodn. org. au). We thank M. Braccini, R. Bradford, M. Davidson, A. Hobday, I. Keay, M. Lansdell, P. McDowall, S. Mountford, J. van den Broek, B. Walker, and J. Welch for their contributions. We thank S. O'Donnell and two anonymous reviewers for their insightful suggestions on the manuscript and C. Mull for advice during the reanalysis. We also acknowledge the contributions of all collaborators and their institutions to the contents of the IMOS Australian Animal Acoustic Telemetry database and data. This work was supported by the Fisheries Research and Development  Corporation (FRDC; project 2018-091).
2; Fisheries Research and Development Corporation (2010/062, 2012/020); LabexMER (ANR-10-LABX-19); PRESTIGE program (PCOFUND-GA-2013-609102); Sea World Research and Rescue Foundation; Save Our Seas Foundation; Oceania Chondrichthyan Society/ Passions of Paradise; ECOCEAN; Winifred Violet Scott Estate; Holsworth Wildlife Research Endowment; Ningaloo Whale Shark Industry; Caring for Coasts; Department of the Environment, Water and Natural Resources; New South Wales Department of Primary Industries; Sydney Aquarium Conservation; Neiser Foundation; Adelaide and Mount Lofty Ranges Natural Resources Management Board; Nature Foundation of South Australia; Tracking Research for.
Later these findings were quantified by subsequent observations over a full year. E. g. , based on twoyear continuous observations with the Moveable Atmospheric Radar for Antarctica (MARA) at the Antarctic station Troll,  Kirkwood et al. (2015a)  showed that, in winter these echoes were significantly weaker than in summer, and that they come from lower altitudes, 55-80 km. Whereas in summer, the radar echoes arose from altitudes between 80 and 95 km and were more frequent and intense. Similar results were obtained with the Middle Atmosphere Alomar Radar System (MAARSY)  (Latteck et al. , 2012)  at Andøya, northern Norway by  Latteck and Strelnikova (2015). The PMWE mean diurnal occurrence rate estimated from MAARSY data for the winter seasons of 2011-2013, was 16% (95% for PMSE in June/July).
The radar transmits vertically with the whole antenna array, but for reception one can use 12 segments in different combinations. More detailed descriptions of ESRAD can be found in  Chilson et al. (1999)  and  Belova et al. (2021). The radar runs continuously, cycling between experimental modes optimised for the lower troposphere, troposphere/stratosphere, or mesosphere. A typical cycle measures for about 1 min in each mode, repeating every 3-4 min. Vertical wind is derived from the Doppler shift of the return signal by coherently combining the data from all receivers. Horizontal winds are derived by using the full correlation analysis (FCA) method developed by  Briggs et al. (1950)  and  Briggs (1984). The principle behind FCA is that an ensemble of scatterers of the radar wave in the atmosphere leads to a diffraction pattern on the ground that moves across the antenna array, as the scatterers move horizontally advected by wind.
The meteors were also disregarded. In this study we used a value of 0. 9 as SNR rejection threshold. This was subjectively chosen based on a compromise between the number of observations that passed quality control and quality of the data set. When we apply a value of 1 for the threshold, we still have these high-speed values, e. g. above 300 m/s, meanwhile, the number of other data points is reduced. We used the data from the operating modes covering the mesosphere and lower thermosphere (50-100 km): mode 'fca_4500 0 was in operation until April 2016, then it was changed to 'fca_900 0. The parameters of the experiments are shown in Table  1. The velocity profiles were calculated with about 1 min. resolution and the measurements in these modes were repeated every fourth minute.
Satellite and ground-based data As mentioned before, the high PMWE occurrence rate might be related to extra ionisation in the middle atmosphere at the associated altitudes. This can be caused by solar protons, solar X-rays and high-energy electrons precipitating from the magnetosphere after the arrival of HSS at the Earth (e. g. ,  Tanaka et al. , 2019). For this study, we analysed satellite measurements of the solar proton and X-ray fluxes, and solar wind velocity provided by the Geostationary Operational Environmental Satellites (GOES) and the Deep Space Climate Observatory (DSCOVR) satellite. The respective datasets are available through the database of the Space Weather Prediction Center at National Oceanic and Atmospheric Administration/. (https://www. swpc. noaa. gov/products/). For local monitoring of ionisation in the lower ionosphere, two ground-based Relative Ionospheric Opacity meters (riometers) were used.
A riometer is a radio receiver that continuously measures cosmic noise at the ground after the noise has passed through and has been absorbed within the ionosphere. By comparing the observed level and known quiet day level, one can measure additional cosmic noise absorption (CNA) due to elevated electron density caused by e. g. , energetic electron precipitation associated with HSS. The CNA occurs primarily in the D-layer of the ionosphere at 60-90 km altitude. Measurements of radio noise absorption (as a proxy for energetic particle precipitation and ionisation at PMWE altitudes) in this study were provided by the La Jolla fast-response solid-state riometers observing at 30 and 38 MHz, which were operated in Kiruna [dataset] (https://www2. irf. se/Observatory/?link=Riometers) [dataset] Kiruna riometers https://www2.
PMWE events Unlike PMSE occurring almost every day during summer months, the winter mesospheric echoes have been observed by ESRAD only sporadically. Among all PMWE observations the echoes with the high horizontal travel speed are very rare. We have screened the data for days with PMWE moving with horizontal velocity above 350 m/s, for the period starting from December 2013, the time when the microbarom measurements at IS37 in Bardufoss, Norway were started, until December 2020. Seven days were found and from these, three longer lasting PMWE events were selected for the study. After 2018, ESRAD gradually lost 75% of its transmitter power, which implies that only very strong echoes could match the FCA quality criteria to allow horizontal velocities to be estimated.
e. at the place and time of the PMWE events. These data are not available. In-situ atmospheric and plasma measurements have been performed during a limited number of PMWE-dedicated rocket campaigns. However, PMWE moving with high horizontal speed were not registered at these occasions. Moreover, infrasound measurement instrumentation is mainly ground-based, with a limited number of balloon-borne observations (e. g. ,  Bowman and Albert, 2018). Also, it is difficult to measure electron density gradients directly and with a high vertical resolution of a few hundred meters in the lower ionosphere, i. e. at the PMWE altitudes. Therefore, we present here the infrasound observations, microbarom source regions and propagation conditions for three PMWE events shown in Sec. 2, which might support the proposed high-speed PMWE generation hypotheses indirectly.
In this study, data from one ground-based infrasound array, IS37 are analysed. The station IS37 is located in Bardufoss, Norway (69. 07°N, 18. 61°E) 170 km north-west from ESRAD. The station consists of 10 MB3-type microbarometers and has an aperture of around 2 km (see, e. g. ,  Gibbons et al. , 2019). IS37 is a part of the International Monitoring System for verification of the Comprehensive Nuclear-Test-Ban Treaty  (Dahlman et al. , 2009; Marty, 2019). Microbaroms detected by the IS37 array have recently been analysed and compared to simulations  (Vorobeva et al. , 2021; De Carlo et al. , 2021) , revealing a good agree-ment between the observed and predicted microbarom fields.
Vespagrams For each PMWE event we analyse the microbarom fields recorded by the IS37 arrays during the whole day of the event. This data is analysed on a sliding time window of 1 h, over which the average microbarom-band pressure is estimated every 30 min. This is complemented with a vespagram-based array signal processing analysis  (Vorobeva et al. , 2021) , which shows the directional distribution of the microbarom energy impinging the array for a given apparent velocity. Fig. 4  displays vespagrams obtained from 0. 1 to 0. 4 Hz bandpass filtered infrasound recordings at the IS37 station and integrated over apparent velocities above 350 m/s. The apparent velocity is the horizontal velocity with which infrasound travels across the array, and can be used as an indicator of the altitude from which the signal is coming.
Microbarom source and propagation In this section, we assess infrasound propagation conditions from the microbarom source location to the infrasound station. We combine models for the microbarom source location and strength with the winds and temperatures of an atmospheric model product. The microbarom source modelling product used in this study is based on the WAVEWATCH III ocean wave model (The The WAVEWATCH III: Ò Development Group, 2016, hereafter WWIII). The model output is given on a latitude-longitude grid with 0. 5°Â0. 5°spacing and 3hour time resolution. One of the WWIII outputs estimates the spectral density of the equivalent surface pressure forcing microbaroms ([dataset] denoted p2l and available via ftp://ftp. ifremer. fr/ifremer/ww3/HINDCAST/SISMO/). This parameter from the time closest to the PMWE event is used as an estimate of microbarom source strength.
Assessment of generation mechanisms As pointed out in the summary of Sec. 3, all proposed mechanisms work more effectively with increasing electron density and infrasound signal amplitude at the point of PMWE generation. We found that at the time of the selected PMWE events the infrasound waves generated by the sources located in the North Atlantic could reach mesospheric heights above ESRAD due to effective ducting. In the absence of in-situ infrasound measurements, the maximum of the microbarom amplitude with apparent velocity of more than 350 m/s at IS37 is used to make an assumption of the acoustic signal strength at mesospheric heights. The infrasound data used in this study were obtained from IS37 which is located about 170 km from the ESRAD site. This distance is much shorter than the distance between the ESRAD site and the microbarom source (see Figs.
Acknowledgments ESRAD operation and maintenance is provided by the  Esrange Space Center of the Swedish Space Corporation (SSC). We acknowledge partial support from the project  Middle Atmosphere Dynamics: Exploiting Infrasound Using a Multidisciplinary Approach at High Latitudes (MADEIRA ), funded by the  Research Council of Norway  basic research programme  FRIPRO/FRINATEK  under Contract No. 274377. Evgenia Belova  thanks  Sheila Kirkwood  for the fruitful discussions and help with the ESRAD data analysis.
Discretization: The results of the preceding section will be illustrated by numerical simulations for the simple problem (1) on the torus with b ≡ 1. Discretization is based on an equidistant grid ϕ k = (k -n)π n , k = 0,. , 2n , with an even number of grid points, guaranteeing that the grid is invariant under the reversal collisions, i. e. , with ϕ k also ϕ ↓ k = ϕ k+n is a grid point. Solutions of (  1 ) are approximated at grid points by f n (t) := (f 1 (t),. , f 2n (t)) ≈ (f (ϕ 1 , t),. , f (ϕ 2n , t)) , extended periodically by f k+2n (t) = f k (t). This straightforwardly leads to the discrete model df k dt = Q n REV (f n , f n ) k , with Q n REV (f n , f n ) k := π n |k * -k|>n/2 b k,k * (f k+n f k * +n -f k f k * ) , and b k,k * := b(ϕ k , ϕ k * ). For the time discretization the explicit Euler scheme is used, such that the total mass is conserved by the discrete scheme, which has been implemented in Matlab.
L. K. and C. S. acknowledge support from the  Austrian Science Fund , grants no. W1245  and  F65 , as well as the  ÖAD , mobility grant  FR 01/201. L. K. received funding by a grant from the  FORMAL  team at  ISCD -Sorbonne Université. A. F. acknowledges support from the Project  EFI   ANR-17-CE40-0030  of the  French National Research Agency. A. F. thanks the hospitality of the  Laboratoire de Mathématiques et Applications (LMA, CNRS)  in the  Université de Poitiers , where part of this research was conducted.
Its conception was performed by achieving a comparative study of proposed deep learning network architectures, enhanced with pre-and post-treatments and applied on our dataset provided by the ICU of the University Hospital of Saint-Etienne. Our framework is designed to perform two tasks sequentially. The first one is a classification task, aiming to eliminate all the pulses without the P1 and P2 subpeaks. The second one, only performed on the remaining pulses, aims to identify the subpeaks P1 and P2 to calculate the ratio of their relative amplitudes. As an output, our framework provides a discontinuous signal of P2/P1 ratio values, post-processed to make it as readable as possible for the clinician. In this article, we provide a description of the neural network (NN) architectures we compared for pulse selection (3.