dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Boris Bolliet  and  Jens Chluba  for useful discussion, and  Aditya Rotti  for extensive comments on an early version of the draft. AR was supported by the  ERC Consolidator Grant CMBSPEC  (No. 725456 ) as part of the  European Union 's  Horizon 2020 research and innovation program. ML was supported by the project " Combining Cosmic Microwave Background and Large Scale Structure data: an Integrated Approach for Addressing Fundamental Questions in Cosmology ", funded by the  MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN)  Bando 2017 -grant  2017YJYZAH. ML also acknowledges support by the  University of Padova  under the  STARS Grants programme CoGITO, Cosmology beyond Gaussianity, Inference, Theory and Observations. FL was supported by a postdoctoral grant from  Centre National d'Études Spatiales (CNES).
, 2018a). These circulation anomalies widen the subtropical dry zones  (Previdi and Liepert, 2007; Sun et al. , 2013b). In addition,  Long et al. (2016)  highlighted that model uncertainty in tropical rainfall comes from the discrepancies of the atmospheric circulation anomalies among models. Thus, the spread of circulation changes in response to global warming across climate models leads to a diversity of responses in the hydrological cycle. Proxy data indicate that the mid-Pliocene (~3 million years ago) was one of the most recent warm periods with CO2 levels similar to the current anthropogenically elevated value of 400 ppm and can be considered an analog for future climate change  (Dowsett et al. , 2012; Burke et al. , 2018; Tierney et al. , 2019). Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) simulations have been used to investigate how the climate system responded to mid-Pliocene boundary conditions, including elevated atmospheric CO2 concentrations.
This might be induced by the uncertainties in PlioMIP1, including the uncertainty in atmospheric CO2 concentrations  (Salzmann et al. , 2013; Howell et al. , 2016; Samakinwa, 2018)  and paleogeography and bathymetry  (Otto-Bliesner et al. , 2016; Feng et al. , 2017; Samakinwa, 2018). In PlioMIP2 models, the boundary conditions have been updated using the new version of the U. S. Geological Survey PRISM4 dataset  (Dowsett et al. , 2016; Haywood et al. , 2016). These include the closed Canadian Archipelago and Bering Strait and a reduced Greenland ice sheet relative to PlioMIP1. In addition, PlioMIP2 focuses on a specific time slice during the mid-Pliocene at approximately 3. 025 Ma, which could reduce the uncertainties in reconstructions  (Mcclymont et al. , 2020). Researchers have been investigating the mid-Pliocene climate by using PlioMIP2, including Arctic warming  (De Nooijer et al.
Acknowledgements. This research has been supported by "the  Fundamental Research Funds for the Central Universities ":  B210201009  and the  Swedish Research Council  (Vetenskapsr det, grant no. 2013-06476  and  2017-04232 ). CJ acknowledges financial support from the  National Natural Science Foundation of China  (Grant No. 42005012 ) and the  Natural Science Foundation of Jiangsu Province  (Grant No. BK20201058 ). The model simulations with EC-Earth3 and data analysis were performed using ECMWF's computing and archive facilities and the  Swedish National Infrastructure for Computing (SNIC)  at the  National Supercomputer Centre (NSC)  partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. CJRW acknowledges the financial support of the  UK Natural Environment Research Council  funded  SWEET  project ( Super-Warm Early Eocene Temperatures) , research grant  NE/P01903X/1.
NJB acknowledges supported from the  National Science Foundation  ( NSF ;  AGS-1844380  and  OCN-2002448 ), as well as the  Alfred P. Sloan Foundation  as a Research Fellow. RF acknowledges the sponsorship by  U. S. National Science Foundation  grants  1903650  and  1814029. The contributions of BLO-B, ECB, and NR are based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  under Cooperative Agreement No. 1852977. The  CESM  project is supported primarily by the  National Science Foundation (NSF). Computing and data storage resources for the CESM and CCSM4 simulaitons, including the Cheyenne supercomputer (doi:10. 5065/D6RX99HX), were provided by the  Computational and Information Systems Laboratory (CISL)  at NCAR. XL acknowledges financial support from the  National Natural Science Foundation of China (NSFC , grant no.
42005042 ) and  China Scholarship Council  ( 201804910023 ). The NorESM simulations benefitted from resources provided by  UNINETT Sigma2-the National Infrastructure for High Performance Computing and Data Storage in Norway. The work by ASvdH and MLJB was carried out under the program of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Ministry of Education, Culture and Science (OCW  grant no. 024. 002. 001 ). Simulations with CCSM4-Utrecht were performed at the SURFsara Dutch national computing facilities and were sponsored by  NWO-EW  ( Netherlands Organisation for Scientific Research, Exact Sciences)  (project no. 17189 ). The PRISM4 reconstruction and boundary conditions used in PlioMIP2 were funded by the  U. S. Geological Survey Climate  and  Land Use Change Research and Development Program.
Data availability. To access the PlioMIP2 database, please send a request to Alan M. Haywood (a. m. haywood@leeds. ac. uk). PlioMIP2 data from CESM2, EC-Earth3-LR, GISS-E2-1-G, IPSL-CM6A-LR and NorESM1-F can be obtained from the Earth System Grid Federation (ESGF, 2020, https://esgf-node. llnl. gov/search/cmip6/). CCSM4 and CESM1. 1 can be obtained from https://www. cesm. ucar. edu/models/. The reconstructed SST is from the alkenone-derived.
The lateral notched specimens show similar results, with an effect of the eGaIn in the propagation of the fracture, as shown by the SEM images. However, the differences are less evident in the force vs displacement curves. Indeed, notched SPT specimens are more appropriate to screen the system Cu-30 wt% Zn/eGaIn for LME. The specimens with a blind longitudinal notch are more suitable for this purpose than those with a lateral notch. However, the preparation of these specimens requires fine machining tools. The embrittlement effect in the notched specimens is present only at the higher displacement rate, indicating that the 30 wt% Zn brass is more susceptible to LME by the eGaIn at higher strain rates. This work was done at the Unité Matériaux et Transformations (UMET) with the support of the Agence nationale de la recherche (ANR) under the project ANR GauguIn (N° ANR-18-CE08-0009-01).
In the case of NO 2 , it is absorption fall in the LED-sensor response band  [46]. However, the reported estimated error for high concentrations of NO 2 events is around 1% from MODIS satellite AOD estimations  [47]. In our case, we measure AOD values under 0. 45, which means an expected error due to NO 2 lower than 0. 005, which is under the expected AOD error for our sensors (higher than 0. 01). In addition, we consulted the NO 2 concentration using TROPOMI NO 2 data  [48]  for the calibration and measurement days. From this search, we can tell that there were no relevant events that could affect the measurements. Thus, we neglect the NO 2 effect in our estimations and measurements. However, this NO 2 checking procedure will be included systematically to future campaigns. For that reason, we can use the optimization process proposed before without any additional consideration.
12 December 2018 The case presented in Figure  9  shows the significant spatial variation of aerosols in small areas. In this campaign, we used three LoCo-ASPs: the first was placed next to the AERONET station in Santiago Beauchef, in the border of downtown (Calibration unit, upper panel), the second was in Juan Gomez Milla Campus of the University of Chile in the east of the city (Ciencias, middle panel) at 6. 5 km from the first one, and the third was in Cerro Calan in the northeast of the city (Cerro Calan, lower panel) at 9. 6 km from the second station and 13. 7 from the first one. The data exhibit an increase in AOD around midday, and the displacement of this increase to the east and the northeast during the afternoon. Another relevant observation is an increment of AOD during the morning in Ciencias station.
Discussion In this work, we present three main contributions. First, we developed a monochromatic low-cost automatic sun photometer (LoCo-ASP) for AOD measurements. Second, we proposed and evaluated a calibration algorithm. The algorithm uses a pattern instrument to calibrate the LED-based sensors to estimate the unknown Langley constant and equivalent wavelength. The method takes advantage of the gasses contribution to the optical depth, and can be considered negligible at the operation wavelength; this is the reason for the chosen LED used in the LoCo-ASP. Finally, we performed city-scale measurements with several units to evaluate the performance of the instruments by comparing the AOD behavior over Santiago with previous works that used simulations, satellite data and handheld sun photometers.
EXPERIMENTS Here we present an example that illustrates our approach. The algorithms were implemented in MATLAB R2019b on MacBook Air (2014, 1. 4 GHz Intel i5, 4GB RAM). We consider r = 2 branches and filter lengths L 1 = 3, L 2 = 3 with the following coefficients: A = 0. 1 (x 1 ) = 3x 3 1 -x 2 1 + 5, g 2 (x 2 ) = -5x 3 2 + 3x 2 -7. ( 10 ) We use N = 30 operating points generated randomly on the unit circle. We run Algorithm 1 for 10 different starting points (i. i. d. Gaussian distributed), maximum 250 iterations, and show the convergence plots in Fig. 2. We see that the algorithm converges linearly for all but one initialization, which is reasonable due to nonconvexity of the problem. For one of the realizations, the final residual is P([[A, B, H]])y 2 = 8. 48 • 10 -9 , and the estimated factor A is (with the first row normalized to 1 and shown with 4 fractional digits of the mantissa), A =   1 1 -1.
ACKNOWLEDGEMENTS This research was supported by the  ANR (Agence Nationale de Recherche)  grant  LeaFleT  ( ANR-19-CE23-0021 );  KU   Leuven start-up-grant   STG/19/036 ZKD7924 ;  KU Leuven Research Fund ;  Fonds Wetenschappelijk Onderzoek -Vlaanderen  ( EOS  Project  30468160  (SeLMA),  SBO  project  S005319N ,  Infrastructure  project  I013218N ,  TBM  Project  T001919N , Research projects  G028015N ,  G090117N , PhD grants  SB/1SA1319N ,  SB/1S93918 , and  SB/151622 );  Flemish Government  ( AI Research Program );  European Research Council  under the European Union's  Horizon 2020 research and innovation programme (ERC AdG  grant  885682 ). P. Dreesen is affiliated to Leuven. AI -KU Leuven institute for AI, Leuven, Belgium. Part of this work was performed while  P. Dreesen  and  M. Ishteva  were with Dept.
Given the effects of steep dips and large lateral variations in seismic velocity beneath the Vancouver Island continental shelf, seismic processing and travel time inversion are inadequate to obtain a detailed velocity model of the subsurface. Therefore, seismic full waveform inversion is applied to multichannel seismic reflection data to obtain a high-resolution velocity model beneath the Tofino fore-arc basin under the continental shelf off Vancouver Island margin. Seismic velocities obtained in this study help in understanding the shallow shelf sediment structures, as well as the deeper structures associated with accreted terranes, such as Pacific Rim and Crescent terranes. Shallow high velocities, as large as ∼5 km/s, were modeled in the mid-shelf region at ∼1. 5-2. 0 km depth. These coincide with an anticlinal structure in the seismic data, and possibly indicate the shallowest occurrence of the volcanic Crescent terrane.
The landward dipping Crescent thrust bounds the Crescent terrane to the west. The Tofino forearc basin lies above the accretionary wedge, the Crescent terrane and the Pacific Rim terrane (Figure  1 ). It has a width of ∼60 km and a length of ∼200 km, and it contains up to 4 km of sedimentary rocks  [1]. Shell drilled six exploratory wells in the Tofino basin in the 1960s, which show up to 3 km of Miocene to Recent mudstones and minor sandstones  [4]. Some of these wells also indicate Eocene sediments and volcanics  [4, 5]. The volcanics are the source of the Prometheus magnetic anomaly [6], with a magnetic anomaly high over the Crescent terrane and a low over the Pacific Rim terrane  [1]. Beneath the eastern portion of the shelf, seismic reflection data indicate a localized thickening of the Tofino basin sediment section to more than 3 km, and this was interpreted as a fossil trench  [1].
We also present the imaging challenges encountered while inverting structurally complex, noisy, short offset data devoid of low frequencies. However, the primary objective of this study is to quantitatively image the structure of the upper part of the Tofino fore-arc basin under the continental shelf to further our understanding of the margin. In particular, we aim (1) to obtain the detailed P-wave velocity structure of the Tofino basin sediments, (2) to understand the nature of deformation within Tofino basin sediments, and (3) to understand the relationship between Tofino basin sediments and the underlying Crescent terrane and Pacific Rim terrane.
Starting and Final Velocity Models In this study, we used multichannel seismic data acquired in 1989 by Geological Survey of Canada using a 144-channel streamer with maximum offset of 3783 m  [30]. The shot spacing was 50 m, and the airgun array (128. 15 L) was towed at a depth of 10 m. The data were recorded for a time of 14 s at a sample interval of 4 ms. The data has excellent quality with high S/N ratio. No major problems were encountered during the data acquisition. First arrival traveltimes are identified out to maximum offset. The apparent velocities of these arrivals are about 1. 9 km/s but increases up to 4. 3 km/s in some areas. An example of the data is shown in Figure  3. A starting model was carefully constructed using travel times from every 5th shot (every 250 m) along the line by ray tracing first in a block model  [23]  and then in a grid model  [24].
Comparision with Drill Hole Data Waveform inversion velocities are compared to sonic logs from Zeus I-65, Zeus D-14, and Pluto I-87 that were drilled by Shell in the 1960s (Figure  18 ). These wells are not exactly on the line, and were projected on to the seismic line by 18 km, 10 km, and 11 km, respectively. Zeus I-65 and Pluto I-87 were drilled on top of anticlinal structures. Zeus I-65 penetrated through the shallow part of the Crescent terrane, while Pluto I-87 penetrated through a thin gas-bearing sand at depth. Zeus D-14 penetrated through volcanic rocks on the flank of an anticlinal structure  [4]. Since the original logs were not available, the digitized logs of Zeus I-65, Zeus D-14, and Pluto I-87 were assigned uncertainty values of ±200 m/s, ±100 m/s, and ±150 m/s, respectively, based on the wiggles in the sonic logs.
As illustrated in Figure  2 , the overall antibody response was divided into 4 groups, each group representing a period of infection. A remarkable neutralization pattern with high antibody levels was seen in convalescent patients recovered from the original Original/B strains (lineage B), as determined by CLIA. Regarding the global humoural response, our data highlight a strong variability in the antibody levels and in the neutralization profiles. The reason behind this "interindividual heterogeneity" is not yet clear. However, multiple studies have shown a sort of positive correlation between the serum neutralizing capacity and disease severity, highlighting the highly heterogeneous nature of nAb responses against the SARS-CoV-2 spike protein  [15, 16]. In response to the question of what the immunity and protection levels are following a natural infection against the currently circulating variants, especially the VOCs, we also studied the reactivity between nAbs and 9 different variant isolates.
The investment and innovation environment of the agricultural sector is important, but not always characterized by coherence and systematic measures taken by public authorities  [6]. With this in mind, we have built a clustering model to identify the most successful group of countries in terms of implementing intensification of production activities, the effectiveness of innovation policy and the ability to meet food demand, taking into account the requirements of sustainable development. To this end, a number of indicators ( These indicators were selected from various data sets that characterize such areas of countries as economic, social, environmental and reflect, in particular, investment and technical equipment of agricultural production. This simulation was implemented on the basis of DataFrame libraries Pandas, Matplotlib, NumPy and others.
For  Riva (2002)  VR is 'A collection of technologies that allow people to interact efficiently with 3D computerized databases in real time using their natural senses and skills'. Xia et al. (2012)  state that 'IoT technologies refers to the networked interconnection of everyday objects, which are often equipped with ubiquitous intelligence'. For  Armbrust et al. (2010)  cloud computing refers 'to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services'. Oztemel and Gursev (2020)  say that data analysis is 'having the capability to handle big amount of data and performing well defined analysis to be able to run the overall system aligned with the manufacturing goals'. From Figure  5 , we can see that there is scientific evidence that the system has changed.
Enomoto, Yamamoto, and Suzuki (2013) ,  Azizi, Yazdi, and Hashemipour (2019) , and  Peruzzini et al. (2020a)  used VR to design workplaces in which the ergonomic parameters of assembly tasks were respected. Gao, Shao, and Liu (2016) ,  Plantard et al. (2017) ,  Wu et al. (2018) ,  Xiao et al. (2018)  and  Bortolini et al. (2020)  instead applied sensors to collect different forms of data in order to design an ergonomic workplace. The data collected from time-and-motion and ergonomic analyses of an AS can then allow for the assignment of tasks within a workplace. Papakostas et al. (2016)  created a software application that could assign assembly tasks in an AS based on the data available at the moment of the request. Huo et al. (2020a)  and  Huo, Zhang, and Chan (2020b)  developed two real-time monitoring systems that were capable of reassigning tasks based on the states of the different resources in the AS.
These data were used to monitor and control the AS, and allowed the user to create a digital copy or digital twin of the all the actors in an AS. Bao et al. (2020)  explained how to create a digital twin application, and such applications were reviewed by  Cimino, Negri, and Fumagalli (2019) , who then presented a case study of a digital twin for energy consumption monitoring. Zhuang, Liu, and Xiong (2018)  and  Nikolakis et al. (2019a)  proposed two different digital twin solutions for monitoring an AS. The data necessary for this monitoring can be also obtained from videos and pictures of the AS, and  Tarallo et al. (2018) ,  Oyekan et al. (2019) , and  Chen et al. (2020c)  collected the data used in their studies from an analysis of videos and pictures of assembly activities. These data allowed the authors to monitor and improve the performance of the assembly processes under study.
Although human workers can be guided during their activities, they may still make mistakes, and hence several solutions for greater control over the quality of the products, using different sensors and techniques, were proposed by  Yu and Wang (2013) ,  Lei et al. (2017) ,  Colledani et al. (2018) ,  Jiang et al. (2019) ,  Chen et al. (2020a) ,  Negri et al. (2020) ,  Runji and Lin (2020) ,  Wagner et al. (2020) ,  Wang et al. (2020a)  and  Židek et al. (2020). The collection of information about these errors can allow a company to create databases of similar situations, which can then be studied in order to avoid them happening again. For example, Carvajal  Soto, Tavakolizadeh, and Gyulai (2019)  and  Sassi, Tripicchio, and Avizzano (2019)  used machine learning and deep learning, respectively, to analyse data collected from quality inspections of assembled products.
In conjunction with this time analysis, it may be necessary to evaluate the ergonomic impact of the technologies while they are being used, in order to understand where and when it is best to use them. The time analysis and ergonomic impact can be evaluated due to the sensors applied in the technologies and by the human workers, such as IoT sensors or wearable devices. Another possibility is the combination of cameras that monitored the movements of the technologies and the human workers as a result of computer vision algorithms. At this level, a company is aware of which data it must collect and now needs to know where to install the sensors to collect these data. The sensors need to be installed in protected and proper places in order to reliably and safely collect the required data. Therefore, the significant amount of data that companies can collect from the sensors needs to be analysed and validated.
Background To counter the COVID-19 pandemic, mRNA vaccines, namely tozinameran and elasomeran, have been authorized in several countries. These next generation vaccines have shown high efficacy against COVID-19 and demonstrated a favorable safety profile. As widespread vaccinations efforts are taking place, incidents of myocarditis and pericarditis cases following vaccination have been reported. This safety signal has been recently confirmed by the European Medicine Agency and the U. S. Food and Drug Administration. This study aimed to investigate and analyze this safety signal using a dual pharmacovigilance database analysis.
Methods This is as an observational study of reports of inflammatory heart reactions associated with mRNA COVID-19 vaccines reported in the World Health Organization's global individual case safety report database (up to June 30 th 2021), and in the U. S. Vaccine Adverse Event Reporting System (VAERS, up to May 21 st 2021). Cases were described, and disproportionality analyses using reporting odds-ratios (ROR) and their 95% confidence interval (95%CI) were performed to assess relative risk of reporting according to patient sex and age.
Data Extraction and Analysis Reports including a COVID-19 mRNA vaccine (tozinameran or elasomeran) as the suspected drug in the adverse reaction have been extracted from both databases. From that, cases of inflammatory heart reactions, i. e. myocarditis, myopericarditis, pericarditis, and pleuropericarditis, were retrieved using the ad-hoc high-level terms from MedDRA, "non infectious myocarditis" and "non infectious pericarditis", coded as reactions. Cases included in this study were registered up to May 21 st and June 30 th 2021, in VAERS and in VigiBase, respectively. In VigiBase, for each report of interest, the following data were extracted or assessed: demographic features, continent of reporting, type of reporter, month of reporting, patient sex, patient age, time to reaction onset, seriousness, reaction outcome and type of heart inflammatory reaction Serious cases were defined, according to the WHO, as the occurrence of death, lifethreatening adverse event, inpatient hospitalisation or prolongation of an existing hospitalisation, significant disability or requirement of intervention to prevent any of these.