dataesr/scientific-paragraphs-categorization huggingface dataset:
This layering was largely absent in the initial conditions from HYCOM (Figure  3a ); however, it was created within the higher-resolution MSEAS PE as the forecast simulation progressed (Figure  3b ). Similar findings were also obtained by comparisons of model forecasts to another Seaglider (SG137; not shown in figure). This indicates that the MSEAS-PE additional dynamics and increased resolution (1/60° and 70 optimized levels) were likely needed to develop and maintain the complex layered features observed in the region. The addition of tidal forcing also generated internal tides and waves in the western part of the domain at Socotra Island, as well as on nearby ridges (not shown). To evaluate the effect of model resolution on the forecast skill, Figure  4a-c  compares the salinity profiles from the initial conditions for March 11 (Figure  4a ) and the MSEAS PE three-day forecast for March 14 (without data assimilation; Figure  4b ) against in situ Argo profiles collected during March 14 in the region (Figure  4c ).
In the present NASCar experiment, 50-member ensemble forecasts were issued daily using ESSE from mid-March to April. To initialize multiscale ensembles  (Lermusiaux et al. , 2000; Lermusiaux, 2002 Lermusiaux, , 2007)) , historical CTD synoptic data for January, February, and March were used to create joint vertical EOFs (empirical orthogonal functions) for temperature (T) and salinity (S). To construct the three-dimensional T and S perturbations, the joint EOFs were combined with an eigendecomposition of a horizontal correlation matrix defined by a Mexican hat correlation function of 25 km decay-scale and 75 km zero-crossing (these numbers were obtained from the calibration PE runs and the literature). These T and S perturbations were used to generate velocity perturbations in accordance with close-to-geostrophic PE balance.
CONCLUSIONS Recent fundamental theories and methods for optimal planning and sampling predictions were reviewed and illustrated in real-time forecasting conditions in the northern Arabian Sea. The emphasis was on the concepts of reachability, path planning, and adaptive sampling. Reachability and path planning equations provide exact and practical solutions for time and energy optimal solutions, naturally including coordination, obstacle avoidance, anisotropic motions, and uncertain flows. A novel and rigorous framework for adaptive sampling was also outlined, integrating uncertainty quantification, Bayesian data assimilation, and information theory. For the real-time forecasting and planning experiment completed during February-April 2017 in the northern Arabian Sea, the predictive skill, layered fields, and uncertainty estimates obtained using our MIT MSEAS multiresolution ensemble ocean modeling system were studied and validated against independent in situ data.
ACKNOWLEDGMENTS We are thankful to all members of the MSEAS group, past and present. We are grateful to the NASCar DRI team members. We thank  Steve Jayne  for the ALAMO floats and  Luc Rainville  for the Seaglider pictures. We thank the reviewers and editors for their useful comments. We are grateful to the  Office of Naval Research  for support under grants  N00014-14-1-0476  ( Science of Autonomy -LEARNS ),  N00014-15-1-2616  (NASCar-OPS), and  N00014-14-1-0725  ( Bays-DA ) to the  Massachusetts Institute of Technology (MIT). We especially thank  Terri Paluszkiewicz ,  Marc Steinberg , and  Scott Harper  of the  Office of Naval Research  for their support and interactions. We also thank the  National Oceanographic Partnership Program (NOPP)  for research support under grant  N00014-15-1-2597  (Seamless Multiscale Forecasting).
The effect of solar wind high-speed streams (HSSs) on energetic particle precipitation at auroral and subauroral latitudes (L = 3. 8-5. 7) is studied by using cosmic noise absorption (CNA) data measured by the Finnish riometer chain during 95 HSS events occurring between 2006 and 2008. The data are divided into "long" and "short" HSS events, depending on whether the maximum solar wind speed is reached within 24 h or later after the arrival of the corotating interaction region (CIR) at the bow shock. We find that CNA is more frequent during long events and extends to subauroral latitudes, unlike during short events. CNA is observed for at least 4 days after the CIR arrival during long events, and only 3 days during short events. In addition, CNA is divided into three categories, depending on whether it is associated with local substorm activity, with ultralow frequency (ULF) wave activity, or neither of these.
10. 1002/2017JA023923 This paper applies the superposed epoch method to 95 HSS events to quantify the CNA produced by energetic particle precipitation as a response to HSSs for a range of latitudes covering L values from about 4 to 6. In particular, we will address possible processes, such as substorms and ULF wave activity, behind the observed magnetic local time (MLT) and geomagnetic latitude (MLAT) distributions of CNA. The same database of HSS events has previously been used to study the response of high-latitude (L=5. 2) ionospheric F and E region electron densities to HSSs in  Grandin et al. [2015]. The measurements used in this study are presented in section 2; their analysis and the obtained results are described in section 3. The results are discussed in section 4, and a summary of the main conclusions of the paper is given in section 5.
OMNI Data Solar wind data at 5 min resolution were obtained from the OMNI database  [King and Papitashvili, 2005] , which contains ACE satellite observations propagated to the terrestrial bow shock. This study uses the same solar wind data set as in  Grandin et al. [2015] , where 95 high-speed stream events were identified between 2006 and 2008 by searching for sharp increases in the interplanetary magnetic field (IMF) magnitude followed by enhanced solar wind speed values (reaching at least 500 km/s). For each event, the zero epoch was defined as the time of the sharp increase in the IMF, and a visual inspection ensured that it indeed also corresponded to the beginning of solar wind speed increase. The solar wind parameters considered in this study are speed V, IMF magnitude |B|, and dynamic pressure P.
We use riometer data at 15 min resolution in this study, which are obtained from the original 1 min data by selecting a maximum value inside each 15 min period. This approach was chosen for two reasons instead of using a mean. First, using mean values would lead to a severe underestimation of the peak absorption values in short-lived events. Second, in some cases the use of mean could make short-lived peaks to drop under the selected threshold, and these events would be entirely lost (see Figure  S1  in supporting information). Discussion of the threshold value will take place in section 3. 2. The data come from four riometer stations of the SGO chain: Ivalo (IVA, L = 5. 7), Sodankylä (SOD, L = 5. 2), Oulu (OUL, L = 4. 5), and Jyväskylä (JYV, L = 3. 8). Details of these stations, such as observed frequency and geographic and geomagnetic coordinates, are provided in Table  1.
Magnetic Data From the IMAGE Network In order to monitor the geomagnetic activity near the riometer stations, magnetometer data from the IMAGE network  [Tanskanen, 2009]  have been used. In this study, four magnetometer stations from the IMAGE network have been selected to provide geomagnetic data: Ivalo (IVA), Sodankylä (SOD), Oulujärvi (OUJ), and Hankasalmi (HAN). These stations are located close to the riometer stations at Ivalo, Sodankylä, Oulu, and Jyväskylä, respectively, and are shown in Figure  1. One aim of this paper is to study the possible effects of ULF pulsations on CNA, so we use 10 s resolution data from IMAGE. To estimate the ULF wave activity in the 1. 7-6. 7 mHz frequency range (corresponding to periods between 150 and 600 s), we derived a ULF index. The 1. 7-6. 7 mHz frequency band corresponds to the frequency range for Pc5 (continuous pulsations at 150-600 s), and the shorter period range of Pi3 (irregular pulsations with T > 150 s).
Satellite Data From NOAA/POES This study utilizes energetic electron data measured by the NOAA/POES satellites. During the 2006-2008 time period, there were four of these satellites orbiting the Earth on Sun-synchronous orbits: NOAA 15, NOAA 16, NOAA 17, and NOAA 18. Each of these satellites has a quasi-circular polar orbit at an altitude of about 850 km and with orbital period of 102 min. The energetic electron data come from the MEPED (Medium Energy Proton Electron Detector) instrument, which is a part of the Space Environment Monitor (SEM) instrument package. The MEPED Journal of Geophysical Research: Space Physics 10. 1002/2017JA023923 instrument consists of two telescopes measuring electron fluxes along two directions. One of them, referred to as 0 ∘ telescope, is oriented away from the center of the Earth, and the other one, referred to as 90 ∘ telescope, is pointing antiparallel to the spacecraft velocity vector.
The electron fluxes are measured in three energy channels called E1 (>30 keV), E2 (>100 keV), and E3 (>300 keV). Since in this study we are interested in comparing the CNA data from riometers to satellite measurements, we use data from the E1 (>30 keV) and E2 (>100 keV) channels of the MEPED instrument. The fluxes measured by the 0 ∘ telescope represent an estimate of the precipitating energetic electron fluxes, while the 90 ∘ telescope data give an estimate of the trapped electron fluxes. However, it is known that the 0 ∘ telescope views only part of the bounce loss cone and hence may produce an underestimation of the precipitating electron fluxes. The underestimation has been observed to take place during periods of low geomagnetic activity when weak diffusion is likely to dominate the pitch angle scattering  [Rodger et al.
, 2013]. In addition to the problems related to the viewing direction of the telescope, instrumental problems (radiation damage, cross contamination of particle species, instrument efficiency, etc. ) occur  [Asikainen and Mursula, 2013]. However, in this study we use the POES/MEPED data only in a qualitative way to compare the latitudinal profile of CNA to the latitudinal profiles of energetic electron fluxes in one example event. Figure  1  shows the foot points of satellites used in this study for this selected HSS event.
Occurrence of Cosmic Noise Absorption Events Figure  3  presents the frequency of occurrence of CNA events at three riometer stations (IVA, SOD, and OUL), corresponding to the three rows in each panel, from 2 days prior to zero epoch and until 4 days after zero epoch. If t 0 refers to zero epoch, days are noted D-2 (from t 0 -48 h to t 0 -24 h), D-1 (from t 0 -24 h to t 0 ), D1 (from t 0 to t 0 + 24 h), D2 (from t 0 + 24 h to t 0 + 48 h), D3 (from t 0 + 48 h to t 0 + 72 h), and D4 (from t 0 + 72 h to t 0 + 96 h). Hence, days D-2 and D-1 contain pre-HSS data, whereas days D1 to D4 consist of data measured following the arrival of the HSS. To estimate the frequency of occurrence of energetic particle precipitation, we defined a threshold value for CNA data, above which the measured absorption can be considered significant.
CNA Events Associated With Substorms and ULF Pulsations In order to better understand the origin of cosmic noise absorption during HSS events, we will next address the global and local geomagnetic conditions under which CNA events occur. Figure  4  shows an example of HSS event as time series, from day D-1 until the end of day D3. The first panel shows the solar wind speed (blue, left axis) and the SYM-H index values (red, right axis). The grey areas indicate times with southward IMF B z. The second panel shows the AE (global, red) and IE (local, blue) indices. The third panel shows the variations of the magnetic X component at the magnetometer stations in Ivalo (IVA, black), Sodankylä (SOD, red), Oulujärvi (OUJ, green), and Hankasalmi (HAN, blue). It must be noted that, for legibility purposes, the baselines of the data from SOD, OUJ, and HAN have been shifted by 100, 200, and 300 nT, respectively.
, 2007]. In fact, it is fairly well established that the occurrence of a substorm on the nightside is a necessary condition for the observation of dayside CNA  [Kavanagh et al. , 2002 [Kavanagh et al. , , 2004;; Behera et al. , 2016]. Figure  9  shows cross correlations between CNA and the AE index. The CNA measured at IVA and SOD was cross-correlated with the AE index, separating (i) substorm-type (shown in blue) and ULF-type (in red) CNA events, and (ii) long and short HSS events. This was done for days D1 to D3 for long events, and for days D1 and D2 for short events (day D3 not shown because of insufficient statistics). The same was also done with the CNA measured at OUL, but without separating long and short events and only during days D1 and D2. To obtain better time resolution for this analysis, 5 min data have been used instead of 15 min data.
The 5 min data were obtained by the same process as the 15 min riometer data, i. e. , by keeping the maximum of 1 min CNA values over 5 min periods. Figure  9. Normalized cross correlation of CNA and AE index at (first row) IVA, (second row) SOD, and (third row) OUL. Blue lines correspond to substorm-type CNA events, and red lines correspond to ULF-type CNA events. For IVA and SOD, left panels show results for long events during days D1-D3, and right panels show results for short events during days D1 and D2. For OUL, all events are shown together for days D1 and D2. Positive delay means CNA lagging the AE index. Substorm-type CNA is well correlated to the AE index at all stations, during long and short HSS events (all events for OUL), with no delay between these two parameters. This is valid during each day separately.
Summary The aim of this paper was to study statistical properties of cosmic radio noise absorption measured at high latitudes (L = 3. 8-5. 7) under solar wind high-speed stream driving. HSS events were first divided into two categories, labeled "long" and "short," depending on the duration of the solar wind speed increase to a maximum value. In addition, at each of the four riometer stations used in this study, CNA events were defined as times when the measured CNA exceeded 0. 5 dB, in the 15 min data. Such CNA events were classified into three categories: substorm associated, ULF wave associated (T = 150-600 s), and nonclassified, and the MLT distribution and time evolution of those three types were analyzed. The time evolution of the HSS effects was studied by looking at the statistical behavior of the data during each day, from 2 days prior to the beginning of the solar wind speed enhancement (day D-2) and until 4 days after it (day D4).
Acknowledgements We would like to thank  Raphaël Paris  ( Laboratoire Magma Volcan -UCA, CNRS ) for the insightful discussions we had on seismic risk. We also thank  Simone Bertoli ,  Michaël Goujon ,  Michele Imbruno ,  Mathilde Maurel ,  Jaime de Melo , and participants at the  Ferdi-Banque de France research seminar , the DIAL 2017, the AFSE 2017, the  DEGIT XXIII , the RES 2019 conferences, and participants of the GRETHA research seminar, for their valuable comments. We are also grateful to  Martine Bouchut  ( CERDI-UCA ) for helping us in the programming of some variable calculation.
THEORETICAL FRAMEWORK AND TECHNICAL DETAILS In order to probe for possible new physics effects in multiple-Higgs interactions, we modify in a modelindependent fashion the SM Higgs potential, V h = m 2 h 2 h 2 + (1 + κ 3 )λ SM hhh vh 3 + 1 4 (1 + κ 4 )λ SM hhhh h 4 , by introducing two κ i parameters that vanish in the SM. In our notation, h denotes the physical Higgs-boson field, m h its mass and v its vacuum expectation value. The SM self-interaction strengths moreover read λ SM hhh = λ SM hhhh = m 2 h 2v 2. We simulate our triple Higgs signal and the associated backgrounds by implementing the above Lagrangian in the FeynRules package  [18]  that we use along with the NloCT program  [19]  to generate a UFO library  [20]. The latter allows for event generation for both tree-level and loop-induced processes within the Mad-Graph5 aMC@NLO  [21, 22]  framework, that we use to convolute hard scattering matrix elements with the next-to-leading (NLO) set of NNPDF 2.
Acknowledgements: We are very grateful to  Minho Son  for valuable help and discussions, in particular on tau reconstruction, as well as to  K. C. Kong ,  Ian M. Lewis  and  Graham Wilson  for useful comments and suggestions during the course of this project. We also thank the  HT-CaaS group  of the  Korea Institute of Science and Technology Information (KISTI)  for providing the necessary computing resources and acknowledge the  Korea Future Collider Study Group (KFCSG)  for motivating us to proceed with this work. JHK is supported in part by  US-DOE  ( DE-FG02-12ER41809 ) and by the  University of Kansas General Research Fund allocation 2302091. SL is supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MEST)  (No. NRF-2015R1A2A1A15052408 ), and by the  Korean Research Foundation (KRF)  through the  Korea-CERN collaboration program  ( NRF-2016R1D1A3B01010529 ).
Simulations showed that the inclusion of even small numbers of African Americans in control cohorts probably would have prevented these misclassifications, highlighting the importance of diversity in variant databases. The problem of erroneously assigning mutation status to rare, sometimes population-dependent benign DNA variants is widespread. It is of most actionable importance in the clinical arena, but as editors of Neurology: Genetics, we face similar challenges when deciding on acceptance of manuscripts reporting novel mutations in established disease-causing genes. We wish to acknowledge the individuals who have completed reviews for the journal since its launch in April 2015. Your thoughtful comments are tremendously helpful and highly appreciated. We are also grateful for your cooperation in returning reviews in a timely manner.
This paper presents 23 years  of quasi-continuous measurements of the total ozone column (TOC) over the Southern Space Observatory (SSO) in São Martinho da Serra, Brazil (29. 26 • S, 53. 48 • and 488 m altitude). The TOC was measured by a Brewer spectrometer, and the results are also compared to daily and monthly observations from the TOMS (Total Ozone Mapping Spectrometer) and OMI (Ozone Monitoring Instrument) satellite instruments. Analyses of the main interannual modes of variability computed using the wavelet transform method were performed. A favorable agreement between the Brewer spectrophotometer and satellite datasets was found. The seasonal TOC variation is dominated by an annual cycle, with a minimum of approximately 260 DU in April and a maximum of approximately 295 DU in September.
, 1997). This study presents measurements of TOC over the Southern Space Observatory (SSO) in southern Brazil. Data have been recorded by a Brewer spectrophotometer quasicontinuously for 23 years (1992 to 2014); these measurements are compared with data from spaceborne satellite sensors (TOMS -OMI). The climatology is derived to determine the seasonal variability in the TOC, and wavelet analysis is used to identify the variability modes and quantify this influence in multidecadal datasets. These analyses are complementary to global-  (Fioletov et al. , 2002; Hudson et al. , 2006)  and regional-scale  (Rigozo et al. , 2012)  studies of TOC variability and contribute to the understanding of the seasonal and interannual ozone variability over southern Brazil. The Brewer spectrophotometer is a fully automated instrument developed for the ground-based measurements of spectral irradiances of the UV-B part of the solar spectrum at five wavelengths,  306.
and provides calibrating services  (WMO, 1996). The TOC monitoring over the south of Brazil is accomplished by formulating the TOC daily average time series obtained by Brewer spectrophotometers using the direct-sun (DS) method on cloud-free days. The Brewer DS analysis considered in this study follows criteria similar to that performed by  Fioletov et al. (2005) , as follows. The individual DS ozone observations are performed five times in 3 min for each half hour. The standard deviations of the measured ozone were computed in a group of five individual DS measurements for each wavelength. Data were only considered if the standard deviation was lower than 2. 5 DU. To obtain a better statistics analysis, the number of the individual DS data should be at least 35 (about seven sequences of five observations), and only typical clear-sky days at solar zenith angles below 65 • were considered in the calculation of ozone.
The daily ozone measurements by satellite used in this study were obtained through overpass measurements from the Total Ozone Mapping Spectrometer (TOMS), developed by National Aeronautics and Space Administration (NASA), on board the Meteor-3 and Earth Probe satellites and the Ozone Monitoring Instrument (OMI), which is on board the EOS-Aura satellite. The TOMS instrument started operating in 1978, with the launch of the Nimbus-7 satellite, continuing from 1991 to 1994 aboard the Meteor-3 satellite and since 1996 aboard the Earth Probe, stopping operation in late 2005. The TOMS ozone overpass data used in this work are from the version 8 TOMS algorithm, available on the NASA website http://acdisc. gsfc. nasa. gov/opendap/ (Bhartia and Wellemeyer, 2002; Wellemeyer et al. , 1997 Wellemeyer et al.
gsfc. nasa. gov/index. php?site=1593048672&id=28. Just like TOMS, OMI performs measures through the BUV technique with two images feeding the spectrometer grid. It has two ultraviolet bands, namely the UV-1 270 at 314 nm and UV-2 306 at 380 nm with a spectral resolution of 1-0. 45 nm  (McPeters et al. , 1998). The Brewer daily average measurement used in this analysis is considered satisfactory because the overpass satellite measurements show only one daily value, allowing the comparison between a large number of pair measurements  (Antón et al. , 2010b). Figure  1  shows a time series for each instrument groundbased (panel a) and satellite (panel b) used for TOC monitoring over the South Space Observatory (SSO) between 1992 and 2014, noting a well-defined annual cycle in all datasets together with large periods of missing data in the Brewer spectrophotometer dataset between 1999 and 2000 and between 2009 and 2011 and the TOMS satellite dataset between 1994 and 1996.
Comparison analysis The TOC daily averaged data from the Brewer instruments were compared with the satellite instrument datasets (TOMS and OMI) to analyze the correspondence between both datasets and to confirm the effectiveness of the long-term Brewer spectrophotometers TOC monitoring over the Southern Space Observatory. Two datasets were used: Dataset #1: TOMS -Brewer; June 1992-December 2005. Dataset #2: OMI -Brewer; October 2004-December 2014. A linear regression analysis was performed for each dataset, and the correlation coefficient (R 2 ) and the root mean square error (RMSE) between the measurements were calculated. In addition, the mean bias error (MBE) and the mean absolute bias error (MABE) were calculated for all the possible data pairs of each dataset. The RMSE, a measure frequently used to estimate the difference between values predicted by a model (or satellite), and the values observed (in this case a Brewer spectrophotometer), also called residuals, which aggregates them into a single measure of predictive power, were also calculated using the following formula: RMSE = n i=1 (Satellite i -Brewer i ) 2 n , ( 1 ) where n is the data pair number.