dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Classification of the stratigraphic units (SUs) in Lake Imandra is based on a combined analysis of seismic and echo-sounder data (Figs. 3-10; Table  3 ), sedimentological data (Fig. 11 ) and chronological information (Fig. 12 ). Three main units (SU I-III) can be identified based on the seismic data. The echo-sounder data with a higher vertical resolution in combination with the sedimentary properties derived from the two cores Co1410 and Co1411 allow further subdivision of SU I into five subunits (SU Ia-Ie). An overview of all stratigraphic units and reflectors is presented in Table  3.
Stratigraphic unit I The sediment of SU I comprises the uppermost deposits and cover the entire basin of Lake Imandra (Fig. 10A-E ). In the seismic and echo-sounder data, SU I is delineated at the top by the uppermost reflector R Ia representing the sediment-water interface and at its bottom by the reflector R II. The identification of reflectors within SU I based on the seismic data is difficult, because the reflectors partly appear blurred and/or disrupted (Figs. 3, 4  and 7-9 ) or the vertical resolution of the seismic data is too low. The echo-sounder data, however, in combination with information from the two sediment cores, Co1410 and Co1411, indicate five sub-units referred to as SU Ia-Ie. A long succession of SU I, comprising SU Ia to SU Ie, was identified along the eastern slope of the trench (Figs.
Stratigraphic unit Ic Deposits of SU Ic are characterized by thin, continuous, stratified and high-amplitude reflectors. They are bordered at the top by the prominent high-amplitude reflector R Ic, at the bottom by the low-amplitude reflector R Id, and can be identified in the entire study area (Fig. 10C ). Echo-sounder data indicate a maximum thickness of 3 m, which is confirmed in the sediment succession of core Co1410, where SU Ic has a thickness of 2. 8 m. Deposits of SU Ic are missing in core Co1411 (Fig. 11 ). The reflectors of SU Ic drape the relief of the underlying stratigraphic unit. Increased sediment accumulation can be observed in depressions or channel structures (Figs. 3B  and 6B ), but the stratified deposits are partly truncated in the trench deeper than ~30 m water depth (Fig.
Conclusions This paper presents the evolution of the Lake Imandra basin as inferred from high-resolution seismic and echo-sounder data combined with the results from two sediment cores, Co1410 and Co1411. The characterization of the stratigraphic units demonstrates a basin development with phases of glacial, deglacial and postglacial processes. 1. The glacial history of the lake basin is characterized by recurring glacial advances. At least two advances (at MIS 4 and MIS 2) are indicated in the seismic data. Glaciolacustrine and paraglacial deposits overlie these glacial deposits and were probably formed during the Lateglacial, when glacial ice was still present in the catchment. Lacustrine conditions without any glacial influence prevailed throughout the Holocene.
Acknowledgements. Financial support for this study was provided by the  German Research Foundation  (DFGgrant no. ME 1169/28 ),  St. Petersburg State University  (SPBUgrant no. 18. 65. 39. 2017 ) and the  German Federal Ministry of Education and Research  (BMBFgrant no. 03G0859A ). We are grateful to various colleagues from the  Geological Institute of the Kola Science Centre of Russian Academy of Sciences  ( GI KSC RAS ) in Apatity for their expertise and logistical help during fieldwork in summer 2017. We thank  Nicole Mantke  and  Dorothea Klinghardt  from the  University of Cologne  for assistance and help with the laboratory work. Free academic licences for the Kingdom (seismic interpretation) and VISTA Desktop Seismic Data Processing Software (seismic processing) were granted IHS Markit and Schlumberger, respectively.
Data will be available via the PANGAEA repository after publication. The authors declare no conflicts of interest. Open access funding enabled and organized by Projekt DEAL. Abbreviations. AMS, accelerator mass spectrometry; MIS,  Marine Isotope Stage ; MS, magnetic susceptibility; SIS, Scandinavian Ice Sheet; SU, stratigraphic unit; TC, total carbon; TIC, total inorganic carbon; TN, total nitrogen; TOC, total organic carbon; TS, total sulphur; XRF, X-ray fluorescence.
Introduction A diverse set of data has suggested for more than 50 years the existence of a non-thermal population of cosmic particles confined in our Galaxy for a characteristic timescale of ∼ 10 7 years (see  [1]  and references therein), much larger than the crossing time of the Galactic halo associated to a free-streaming particle, thus suggesting a diffusive transport rather than a ballistic motion. The canonical framework to interpret particle diffusion in the Interstellar Medium is the socalled quasi-linear theory (QLT), able to predict particle confinement as a function of rigidity due to the interaction with isotropic, transverse magnetic fluctuations  [2, 3]. This picture has inspired most of the phenomenological interpretations of the cosmic-ray (CR) diffuse emission, computed by solving the so-called transport equation with numerical or semi-analytical methods, such as GALPROP or DRAGON.
In particular, the DRAGON package has provided significant steps forward in the context of inhomogeneous CR transport, by moving away from the zero-order modeling of isotropic, homogeneous diffusion and implementing in some contexts independent parallel and perpendicular scalings ( ≠ ⊥ ), as well as position-dependent diffusion coefficients. Supporting the importance of such inhomogeneous paradigm, in  [4]  different scaling relations of the diffusion coefficient in the Galactic disk and in the halo were invoked to explain the hardening in the proton spectrum (first discovered by PAMELA and then characterized by AMS-02 with large accuracy) for a peculiar combination of parameters in the disk and halo. These studies have provided a precious insight on the impact of the transport properties on the CR observables, although they do not contain a theoretical explanation of the microphysics of the particle-wave interactions.
PoS(ICRC2021)426 CR-scattering on pre-existing MHD modes Ottavio Fornieri linear modification of the QLT theory of CR scattering in MHD turbulence has been developed e. g. in  [11, 12] , and then further reconsidered in  [13]  to explicitly address the role of fast-magnetosonic modes. Based on the above-mentioned modified-QLT framework, we consistently compute the diffusion coefficients for a wide rigidity range in both environments, and depending on several parameters, including the plasma and the amplitude of the injected turbulent fluctuations. We then implement these coefficients in the DRAGON2 numerical package and test the predictions of the theory against the most recent data provided by the AMS-02 Collaboration.
Then, we implemented the coefficients in the DRAGON2 code and tested the theory against current experimental data, with particular focus on the extremely accurate AMS-02 dataset. We found that the high-energy behaviour of the ( ) nicely matches the secondary-over-primary slope in that regime, and a reasonable range of the aforementioned parameters allowed us to reproduce the correct normalization as well, without invoking any ad hoc tuning. Overall, we found a natural agreement with all CR channels within a reasonable choice of both the ISM parameters governing the transport process, and other parameters (e. g. injection slope) that characterize our setup. The theory is therefore adequate to describe the microphysics of Galactic CR confinement in the high-energy domain, in particular above the 200 GeV.
Description of Atmospheric Observations To synthesize observational atmospheric Mo data, we collected data from multiple observational networks and sites that record particulate matter (PM) atmospheric concentrations, some of which include elemental and/or chemical speciation. Observations were taken using several different methods and span many years as described in each paper for each set of observations (Figure  1 ; Data Set S1). From a compilation of these observations (Supporting Information Supplementary Methods), we can both infer the sources from a compositional analysis and by comparing observations against model predictions (Figure  1 ; Data Set S1). We compiled observations of aerosols in both the PM 2. 5 and PM 10 size categories (PM < 2. 5 and 10 μm in aerodynamic diameter, respectively)  (Mahowald et al.
Analysis of Atmospheric Observations Annual means of atmospheric observations at each site were calculated for all values above the detection limit at each station and reported here. Because Mo concentrations are usually low in atmospheric aerosols (<1 ng m -3 ), we considered whether we were biasing our results toward higher values because in some cases, a large proportion of the data were below the detection limit. If more than 50% of values at a site were reported as above the detection limit, we used one-third of the minimum detection limit for any samples below the detection limit. However, if less than 50% of the data at a site were above the detection limit, we excluded the data in our annual values. Instead, we included the data to calculate an upper bound using   b ) the number of number of Mo concentration observations (PM 2.
Analysis of Specific Sites First, to better understand the nature of the sources of atmospheric Mo, we considered a few data sets where data were collected from cities and in nearby rural areas. Data collected from the sites in Switzerland, where a substantial fraction of the observations were above the detection limit for Mo, suggested that there was a clear gradient in the spatial distribution of Mo with higher concentrations in the more urban areas (Figure  2a ), along with higher PM in general  (Gianini et al. , 2012a (Gianini et al. , , 2012b;; Hueglin et al. , 2005). Second, compositional analysis on the observations across all sites in Switzerland  (Hueglin et al. , 2005)  using the approach by Qu et al. (  2020 ) indicated that Mo is typically found in urban aerosols (Figure  S2 ).
AcknowledgmentsWe acknowledge the Atkinson Center for a Sustainable Future at Cornell University for funding for this project, and thank Adina Paytan for comments on an earlier version of this manuscript. Simulations were undertaken at the NCAR facility (National Center for Atmospheric Research, 2019). We acknowledge many observational networks and sites that were used in this study, including, but not limited to APAD and ASFID: Airborne Particulate Matter Databases Related to the Asia-Pacific Region (http://www. ansto. gov. au/aspdatabases), DEFRA (https:// uk-air. defra. gov. uk), the European Monitoring and Evaluation Programme (https://www. emep. int/), the Ministerio del Medio Ambiente de Chile (https:// mma. gob. cl), and the Research State Agency of Spain. Houston area measurements were made possible by funding from the Texas Air Research Center and the Texas Commission on Environmental Quality to S.
Outline This paper will more precisely address issues regarding the organization of exams in terms of the number of papers, requirements, and grading in the English department at UTA for undergraduate students during the 2019 Spring semester. Methodologically, I will first analyze the online descriptions of all the English courses offered that semester. This quantitative analysis will highlight the main features of assessments in the context of this university and make it possible to confront them with the issues of reliability and fairness. Then, I will conduct a qualitative analysis of six interviews with professors who explain how they evaluate and grade their students. This second type of data analysis makes it possible to discuss the validity of the construct and, to some extent, the issue of proper grading.
Take-home final exam Prof. 1' described as follows his assessment procedure: Class preparation and participation: 10%; Weekly discussion posts: 30%; Short paper: 20%; Final Exam (take-home): 40%. In my French university, due to different events (student strikes followed by the COVID crisis), exams have been carried out online for the past two years, which has been a negative experience according to the majority of professors. One main reason for this negative opinion is the risk of cheating and plagiarism. Contrary to this experience, Prof. 1 said that at UTA: Plagiarism is rare. It has often been students that are struggling in the class and are just trying to sort of find the easiest way of dealing with this problem. I haven't really found issues of plagiarism. Especially because they are fully aware that when they submit it on Canvas [a CMS equivalent to Moodle], there is often a plagiarism detection software on Canvas.
We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO's and Advanced Virgo's third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density ΩGW ≤ 5. 8 × 10 -9 at the 95% credible level for a flat (frequencyindependent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20-76. 6 Hz; ΩGW(f ) ≤ 3. 4 × 10 -9 at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20-90.
While some sources of the GWB, such as slow roll inflation, have a fundamentally stochastic character, others like the background from CBCs are a superposition of deterministic sources. The LIGO Scientific Collaboration and Virgo Collaboration have previously placed upper limits on isotropic  [43]  and anisotropic  [44]  GWBs using data from the first two observing runs, in the frequency range 20-1726 Hz. The searches were performed by calculating the cross correlation between pairs of detectors. An extension of this method has been applied to searching for a background of non-tensor modes  [43, 45, 46] ; see  [47, 48]  for recent reviews. Cross-correlation methods have also been applied to publicly released LIGO data  [49]  by other groups, who have obtained similar upper limits  [50] [51] [52].
Third, we perform a careful analysis of correlated magnetic noise that could impact the search. In addition to constructing a correlated magnetic noise budget, as in past runs, we use a Bayesian statistical framework developed in  [61]  to constrain the presence of magnetic noise. Perhaps the most interesting source of an astrophysical GWB, given the current network sensitivity, is the GWB from CBCs. Previous studies have shown that this GWB may be detectable with Advanced LIGO and Advanced Virgo running at design sensitivity  [62, 63] , and the ability to detect such a background has been confirmed with mock data challenges  [64] [65] [66]. Therefore in this work we carefully consider the implications of our results for the CBC population. We estimate the GWB using the most up-to-date information from observations during O3  [67] [68] [69] [70] [71] [72]  and compare with the sensitivity of the current and future detector networks.
We discuss the data quality procedures and studies we performed in Section III. We present the main results of the search in Section IV: we derive upper limits on the GWB in Section IV A, put constraints on the presence of scalar-and vector-polarized backgrounds in Section IV B, and in Section IV C we extend these results by simultaneously fitting for an astrophysical GWB and an effective GWB arising from magnetic correlations of terrestrial origin. We compare our upper limits with a fiducial model for the GWB from CBCs in Section V A, and derive constraints on the BBH merger rate using the upper limits on the GWB and observations of individual CBCs in Section V B. We conclude in Section VI.
A. Cross correlation spectra Let us label the GW detectors in the LIGO-Hanford, LIGO-Livingston, and Virgo (HLV) network by the index I = {H, L, V }. We denote the time-series output of the detectors by s I (t), and the Fourier transform by sI (f ). Following  [47, 58] , we define the cross-correlation statistic for the baseline IJ as ĈIJ (f ) = 2 T Re[s I (f )s J (f )] γ IJ (f )S 0 (f ) , (2) where γ IJ (f ) is the normalized overlap reduction function  [58, 77, 78]  for the baseline IJ, the function S 0 (f ) is given by S 0 (f ) = (3H 2 0 )/(10π 2 f 3 ), and T is the observation time. In practice, because the noise is nonstationary, we break the data into segments, and then take T to be the segment duration. We then average together segments using inverse noise weighting  [58]. If the noise were stationary, this average would reproduce Eq.
(9) In Section V B, we will apply this method to combine the upper limits on the GWB with observations of individual BBH events from  [67] , similar to that performed in  [74]. III. DATA QUALITY A. Data We analyze strain data taken during O3 by the LIGO-Hanford, LIGO-Livingston, and Virgo detectors. The O3 run is divided into two sets. The first, O3a, began April 1, 2019, 15:00 UTC, and continued until October 1, 2019 15:00 UTC, while O3b ran from November 1 2019, 15:00 UTC, to March 27, 2020 17:00 UTC. The HL baseline had 205. 4 days of coincident livetime, HV 187. 5 days, and LV 195. 4  days, before applying any data quality vetoes. We look for correlated magnetic noise using magnetometers located at the sites. Each LIGO detector has installed two low-noise. The Virgo detector has two low-noise MFS-06 magnetometers by  Metronix [83].
We perform the crosscorrelation search with a publicly available implementation [84] of the algorithm described in Section II using Matlab  [85]. As an end-to-end test of the entire system, we added stochastic signals in the Hanford and Livingston detec-tors by actuating the test masses, following the procedures described in  [86]. We injected the same realization of the stochastic background with a flat power law index and strength of 4. 3 × 10 -5 in two 15-minute segments of data. We found for the first injection Ω GW = (4. 6±0. 4)×10 -  and the second Ω GW = (4. 7±0. 4)×10 -  ; both recoveries are statistically consistent with the injected signal.
9 and 36. 7 Hz. These lines were moved below 20 Hz on  April 16, 2019. In principle, the CBC signals known to be present in the data contribute to the integrated cross-correlation. A simple estimate by using the median values for the masses and redshifts in O3a from  [67] , the livetime for O3a, and the inspiral approximation, in Eq. 16 of  [66] , yields Ω GW (25 Hz) 10 -10 , which is well below the O3 sensitivity. Therefore we do not remove the observed CBCs from the data. After applying the category 1 vetoes and nonstationarity cut, we found that 17. 9% of available livetime was lost in the HL baseline, 22. 1% in the HV baseline, and 21. 9% in the LV baseline. We do not analyze frequency bins where there is evidence of coherence between instruments that is determined to be caused by the instruments themselves.
We found that a total of 0. 4% of Hanford data was gated in the data that we analyzed, and 1% of Livingston data for each baseline. We refer the interested reader to  [59]  for further details of the procedure, including the whitened channels and precise thresholds used. This was not necessary for Virgo data due to the lower rate of large glitches. The impact of gating can be clearly seen on the coherence spectra, as we show in Figure  1. Compared to the non-gated data, many more segments are analyzed after applying non-stationarity cuts, and the spectrum is much closer to what is expected from uncorrelated Gaussian noise. It was discovered that from April 20-25 a 1/120-Hz comb was visible in the Livingston data around large calibration lines. The comb was caused by an inadvertently running diagnostic camera clicking at regular two minute intervals.
VI. CONCLUSIONS In this work, we have performed a search for an isotropic GWB using data from Advanced LIGO's and Virgo's first three observing runs. Since we did not find evidence for a background of astrophysical origin, we placed upper limits, improving previous bounds by about a factor of 6. 0 for a flat background. We considered the implications of the results, and by combining the upper limits with measurements from GWTC-2 we have constrained the BBH merger rate as a function of redshift. Our results can be used to constrain additional models such as cosmic strings or phase transitions, using the cross correlation spectra we have made publicly available  [57]. Our results can also be combined with other measurements of the GWB at other frequencies  [81]. Moving forward, we expect currently proposed groundbased facilities such as A+ have the potential to probe a large range of the model space for CBC backgrounds.
The analysis is limited to observations acquired over ocean surfaces to minimize uncertainties in clear-sky computations  (Garnier et al. , 2021). Polar regions, where sea ice can arise, are excluded from the analysis for the same reason. The CAD scores in the CALIOP V4 and in the new IIR cloud-aerosol classifications are compared and cases where the CALIOP V4 algorithm could benefit from IIR observations are discussed. Section 2 presents the IIR and CALIOP data and the 1-D radiative transfer model. Section 3 presents the IIR 2-D BTD signatures in cloud and aerosol monolayer atmospheric columns using radiative transfer IIR simulations with a 1-D radiative transfer model and quantify the uncertainty in the observed clear-sky IIR signatures. Section 4 describes the new IIR-based CAD algorithm.
IIR observations The IIR L2 track data V4 provides the brightness temperatures measured at 8. 65, 10. 60, and 12. 05 µm at 1 km resolution, with collocated to the lidar track. Also reported is the clear-sky brightness temperature computed using the fastcalculation radiative transfer (FASRAD) model  (Dubuisson et al. , 2005; Garnier et al. , 2021). Here, we average those brightness temperatures over the 5 km atmospheric columns defined in the CALIOP L2 5 km merged layer data product. Then, using the CALIOP level-2 (L2) 5 km merged layer data product V4, we retain monolayer cases. Monolayer columns are defined as 5 km (15 lidar single shots) horizontal averages of CALIOP attenuated backscatter profiles in which the CALIOP layer detection algorithm identifies only a single atmospheric layer (i.
CALIOP observations The CALIOP L2 5 km merged layer data product V4 reports tropospheric and stratospheric cloud and aerosol detection information on a 5 km horizontal grid. However, the amount of horizontal averaging required to detect a layer may ex-ceed 5 km and hence the search for features is also 20 and 80 km averaging intervals  (Vaughan et al. , 2009). Here, we do not retain feature layers detected with a horizontal averaging of 80 km because their optical depths are typically very small and are therefore considered as transparent for the IIR (see Appendix A). This means that a 5 km atmospheric column containing, for example, a cloud layer detected with a horizontal averaging of 5 km and another cloud layer detected with a horizontal averaging of 80 km is considered as a cloud monolayer column here.