dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The research project was funded through  Medical Research Council (MRC)  (grant  G09018401 ) and the  Small Area Health Statistics Unit (SAHSU). The work of the  UK SAHSU  is funded by  Public Health England  as part of the  MRC-Public Health England (PHE) Centre for Environment and Health , funded also by the  UK Medical Research Council. The air pollution exposure assessments used in the research leading to these results were funded by the  European Community 's  Seventh Framework Program  ( FP7/2007-2011 ),  European Study of Cohorts and Air Pollution Effects projects  (grant agreement  211250 ) and Transport related Air Pollution and Health Impacts-Integrated Methodologies for Assessing Particulate Matter. The research was funded/part funded by the  National Institute for Health Research Health Protection Research Unit in Health Impact of Environmental Hazards at King's College London  in partnership with  PHE and Imperial College London.
Acknowledgements We are very grateful to  Jan Kretzschmar  for clarifying discussions and for kindly providing us with the Pythia8 setup used by  ATLAS  and with template predictions for the W and Z differential distributions. We would like to thank  Paolo Torrielli  for fruitful discussions on the topics presented here, and  Stefano Camarda  for useful correspondence on the impact of heavy flavours. We also thank  Fabrizio Caola ,  Lucian Harland-Lang ,  Jonas Lindert ,  Gavin Salam , and  Marek Schönherr  for constructive comments on the uncertainty prescriptions. The work of PM is supported by the  Marie Sk lodowska Curie Individual  Fellowship contract number  702610 Resummation4PS. LR is supported by the  ERC Starting  Grant  REINVENT (714788 ) and acknowledges the  CERN Theoretical Physics Department  for hospitality and support during part of this work.
The devices used to lead this comparison are the Nikon D90 APS-C sensor with Bayer technology and the Sigma Sd1 Merill APS-C FOVEON sensor. The study is carried out in two phases. First one aims to evaluate the quality of information reconstructed during de-mosaicking step by detecting potential additional colour distortion added during the de-mosaicking step. The de-mosaicking phase is done using Rawpy  ("RawPy" 2014) , an open source python wrapper for libraw ("LibRaw" 2018) library. It offers the possibility to process some de-mosaicking algorithms like Adaptive Homogeneity-Directed method (AHD)  (Hirakawa and Parks, 2005) , Discrete Hartley Transform method (DHT) (N. Bracewell, 1983) , Bilinear algorithm (DCB), Various number of gradient method (VNG)  (Chang et al. , 1999) , Pixel Grouping method (PPG)  (Lin, n.
Perko et al. , (2005)  gave an evaluation concept to assess the geometrical accuracy of resulting colour images. In that particular concept, only if no geometrical distortions were created during the de-mosaicking process, it is allowed to use the images for 3D reconstruction or arbitrary metrical photogrammetric processing. Li et al. , (2008)  made a comparative study evaluating 11 selective inter-channel de-mosaicking algorithms on two different benchmark data sets. The two benchmark data sets that were used are Kodak PhotoCD images and high-quality IMAX images. During their study the authors stated that in order to reduce the risk of mismatches and thus the presence of artefacts in the images, a fuse of de-mosaicking results by different algorithms is recommended. Stamatopoulos et al. , (2012)  took advantage of the camera's higher dynamic range without applying any pre-processing steps and applied various de-mosaicking algorithms to the RAW images of a Bayer sensor.
DE-MOSAICKING PROTOCOL The de-mosaicking testing procedure, wishes to quantify how much information is omitted in Bayern pattern colour images and how effective the tested algorithms are. The main concept is that the images taken from the FOVEON camera can be used as a reference, which is then converted in an artificial Bayer pattern file and then de-mosaicked with several de-mosaicking algorithms. The recreated image from the de-mosaicking process, can be compared against the FOVEON original image, to evaluate the loss of information. In technical terms, Rawpy Python module, was used for creating the artificial Bayern pattern from original FOVEON images and recreating the images using several de-mosaicking algorithms. The conversion of the FOVEON reference jpeg image I(ref) is done by replacing every pixel value of the Bayer sensor raw image with the radiometric values of FOVEON and respecting a GRBG Bayer pattern and then, de-mosaicking step is followed using the aforementioned 6 algorithms for every I(Bayer) that was derived.
POINT CLOUDS COMPARISON PROTOCOL The second phase of our study was to evaluate which camera delivers the best datasets to produce accurate 3D point clouds, in correlation with each colour channel. Three different testing sites were chosen for testing each one giving a large emphasis to each colour channel. As reference, point clouds captured by a structured light hand-held scanner sensor, which provides an accuracy better of 1mm at 1m object-scanner distance were used. The capture protocol was designed so that to have a digital image GSD of approx. 2. 25 mm and a corresponding accuracy on the camera lens axis of approx. 4. 5 mm. To achieve that, distance from the objects should have been 11m for the FOVEON and 10m for the Bayer as their physical pixel sizes differ slightly (Table  1 ), and it was necessary to have exactly similar GSD, to be able to compare their geometric accuracies.
SIGMA SD1 NIKON D90 Focal lens 24mm 24mm Effective pixels 15M 4700x3200 12. 2M 4288x2848 The base between captures has been computed for an overlap of 80% and resulting datasets are composed of three photos where the working section is totally visible (Figure  4 ). Nine Ground control points have been measured with total station to build a common local coordinate system in every test site separately. The photogrammetric pipeline was executed using Agisoft Photoscan. From RGB Bayer and FOVEON datasets red, green and blue radiometric values were extracted to build a Red, a Green and a Blue dataset for each camera. Grey-scale datasets were also computed for each camera, which resulted in 10 blocks in total (RGB, R, G, B and grey-scale for each camera) for each site. According to software's reports, the effective acquisition distance is 10.
CONCLUSIONS AND FUTURE WORK This paper describes the difficulty to reconstruct missing colour values of a Bayer pattern image and tries to evaluate and compare overall quality of 3D point clouds obtained with datasets resulting from different sensor colour capturing technologies. Additionally, our test using 6 selected de-mosaicking algorithms showcased the difficulties of true colour reconstruction from a Bayer pattern raw file. FOVEON technology is simpler in terms of post-process. It captures more information and less processing is needed for the final image, but more time is needed for recording data from sensor to card. One of the main limitations is its use to low light conditions as high ISO shots produce a lot of noise. It still needs white balance and brightness adjustment to deliver sharp and properly coloured images.
(2019)  assessed the impact of the initial hydration condition on infiltration and wetting front characteristics. Through a series of irrigation experiments, they showed that the wetting front moves faster and deeper under dry initial conditions, than in wet soils. Furthermore, soil water content is a primary explanatory variable for the formation and connectivity of shrinkage crack networks, making it critical to obtain accurate soil water content readings in these soils. Similarly,  Bagnall et al. (2018)  assessed the influence that (i) air-and water-filled cracks and (ii) uncertainty in bulk density sampling have on neutron moisture probe readings. Their results provide critical observational data on how cracks influence moisture readings from neutron probes depending on their size and state.
Acknowledgments The work of  Majdi Abou Najm  was supported by  University of California-Davis Agricultural Experiment Station  and the  Multistate Hatch Program   W4188  of the  USDA National Institute of Food and Agriculture. Ryan D. Stewart  was supported in part by the  Virginia Agricultural Experiment Station  and the  Hatch Program  of the  USDA National Institute of Food and Agriculture. We would like to acknowledge  Keith Beven ,  Horst H. Gerke ,  Peter Germann ,  Jan Hopmans ,  Markus Flury ,  John S. Selker , and  Harry Vereecken  for their discussions and intellectual contributions as we jointly developed this introduction to the special section and that are helping to shape our continued work examining the broader state of the science of nonuniform flow.
Methods The local ethics committee approved this prospective observational study (Kantonale Ethikkommission KEK, Bern, Switzerland, reference number 060/10) and the study was registered in a clinical study registry (ISRCTN 96726807). For this observational, nonrandomized quality control study, we prospectively included two hundred adult patients (>18 years old) admitted to the PACU who were extubated via an AEC (Cook Airway Exchange Catheter, Cook Medical Inc. , Bloomington, IN, USA) and who gave written informed consent to use their data obtained during the PACU stay. As the decision for an AEC was made by the attending anesthesia team in the OR, obtaining informed consent while the patient was still awake prior to surgery was not possible. Therefore, informed consent was obtained at the time of discharge from the PACU when the patient was awake, fully oriented, free of pain and with stable vital signs.
When analyzing dislocation rate, 16 datasets (5 oral AEC, 11 nasal AEC) had to be excluded because of insu cient data regarding AEC position at the time of removal. These 8% missing data represented catheters being removed without prior checking by the attending anesthesiologist (Table  2 ). Regarding the primary outcome parameter, overall dislocation rate, there was no signi cant difference between the oral and the nasal position of the catheter and thus, the null hypothesis could not be rejected (7. 2% vs. 2. 7%, p=0. 16). The odds ratio of dislocation of oral AECs vs. nasal AEC was 2. 86 (95% CI: 0. 66-12. 39). Interestingly, signi cantly more patients were retching when an oral catheter was in place compared with a nasal catheter (26% vs. 8%, p<0. 01). In the group with oral catheters, 41% were coughing, compared to 28% in the nasal group (p=0.
Capnography as indicator of correct position CO 2 data from 20 catheters were incomplete and had to be excluded. In both groups, oral and nasal, there was one catheter dislodged as veri ed by nasal endoscopy even though CO 2 could always be measured (oral AECs: 1 of 59 vs. nasal AECs 1 of 96). Six of 8 oral AEC and 13 of 15 nasal AEC did not show CO 2 , even though the intratracheal position was con rmed by nasal endoscopy. As a test of correct tracheal position, the presence of CO 2 showed an overall sensitivity of 89% and a speci city of 67%. The overall positive predictive value (PPV) was 98. 7% (indicating that a correct tracheal position was likely if CO 2 present), the negative predictive value (NPV) was only 17% (waveform capnography often did not show CO 2 even in correctly positioned catheters).
The CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3. 8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity (η) coverage provided by the barrel and endcap detectors. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. The CMS two-level trigger system  [37]  reduces the event rate from the bunch crossing rate of 40 MHz down to around 1 kHz for data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the kinematic variables, can be found in Ref.
Data sample and simulation The data sample analyzed in this search corresponds to an integrated luminosity of 35. 9 fb -1 , collected in 13 TeV proton-proton collisions with the CMS detector during the 2016 data taking period. The signal processes pp → X → VH → qqτ + τ -and pp → X → HH → bbτ + τ - are simulated at leading order (LO) using the MADGRAPH5 aMC@NLO v2. 2. 2  [39]  Monte Carlo (MC) event generator, for resonance masses between 900 and 4000 GeV, where the Higgs boson is forced to decay to τ pairs and the other boson to a pair of quarks. The signal processes where pp → X → HH → bbVV and pp → X → VH → qqVV are also considered, in which VV → 2 2ν or 2τ2ν, as they can yield final states similar to those of the primary signal process. The natural width of the resonance is assumed to be smaller than the experimental resolution of its reconstructed mass, as consistent with the benchmark radion, graviton and HVT models.
Finally, the energies of neutral hadrons are obtained from the corresponding corrected ECAL and HCAL energies. The identified particles are clustered into jets using the anti-k T algorithm  [48] , implemented in the FASTJET package  [49]. Two distance parameters are used in the analysis, 0. 4 and 0. 8, yielding jet collections referred to as AK4 and AK8 jets, respectively. The AK4 jets are used primarily to reject or select events with top quarks, while the larger AK8 jets are used to identify and contain hadronically decaying W, Z, and Higgs boson candidates. The charged-hadron subtraction (CHS) algorithm for mitigating pileup  [47]  discards charged particles not originating from the primary vertex (PV). The PV is defined as the vertex with the largest p 2 T sum of the physics objects. Here, the physics objects are the AK4 jets, and the associated missing transverse momentum, which is taken as the negative vector sum of the p T of the jets.
8, and then matched to the CHS AK8 jets described above used for the kinematic selections. Subsequently, the soft-drop algorithm  [53, 54] , designed to remove contributions from soft radiation, is applied to the AK8 PUPPI jets. The soft-drop jet mass m j is defined as the invariant mass associated with the four-momentum of the soft-drop jet. Dedicated mass corrections, derived from data in a region enriched with tt events containing merged hadronic W decays, are applied to m j to remove any dependence on jet p T , and to match the jet mass and resolution observed in data. After the application of these corrections, the W(qq) jet mass resolution is measured to be 10% in the jet p T range considered. The two-prong hadronic decays of W and Z boson candidates are used to discriminate against jets initiated from single quarks and gluons.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI  and  FEDER (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Therefore, we will adopt here a 1 ppm uncertainty on hourly CO 2 data as the target performance for new urban lower-cost medium-precision CO 2 sensors. Today, the continuous CO 2 gas analyzers used for continental-scale observing systems like ICOS (https://www. icos-ri. eu/, last access: 30 April 2019), NOAA (https://www. esrl. noaa. gov/gmd/, last access: 30 April 2019) or ECCC's GHG network (https: //www. canada. ca/en/environment-climate-change. html, last access: 30 April 2019) follow the WMO GAW guidelines and are at least 10 times more precise than our target of 1 ppm, but are also quite expensive. For urban inversionbased flux estimates for Paris,  Wu et al. (2016)  found that the number of instruments is more important than their individual precision. Furthermore,  Turner et al. (2016)  reported that weekly urban CO 2 fluxes in the San Francisco Bay Area (California, USA) can be estimated at a precision of 5 % when deploying a dense network of sensors (ca.
5 • C. Its range is 0 % RH-100 % RH and -40 to +80 • C, respectively. The response time for all sensors was less than 1 min (which is the time step to which data were integrated). A Raspberry Pi3 (RPi3) (Raspberry Pi Foundation, 2015) is used to collect the data of all sensors. The RPi3 is a small (85 × 56 mm 2 ) single-board computer running Raspbian OS, an open-source GNU/Linux distribution. The HPP3 sensors were connected via USB. A 7 in. touch screen monitor is connected via an adapter board, which handles power and signal conversion. The package is powered by a switching power supply providing 12 V, but can also be run on a 12 V battery pack. An image of the components of the portable instrument package is available in Fig. 1.
g. by including a linear drift that only becomes more apparent for longer-term observations. Different calibration windows were tested for both the Saclay field site and Jussieu field site ambient air measurements and their results were evaluated against CRDS data that were not used for calibration. Those sites exhibit the typical {CO 2 } levels in urban GHG monitoring networks where future low-cost medium-precision instruments could be deployed. Regular (6-weekly) recalibrations are found to be appropriate to capture sensor drifts and changes in relevant cross-sensitivities, while not increasing the burden of performing calibration too often. A dedicated set of calibration gases was not necessary if the low-cost instrument was calibrated against {CO 2 } from a CRDS instrument using the same air.
Acknowledgements. We would like to thank the anonymous reviewers and the editor whose comments have helped to significantly improve this paper. The work conducted here was partially funded through the  LOCATION  project of the  Low Carbon City Laboratory  and a SME-VOUCHER from  Climate-KIC  (EIT) as well as the  Chaire BridGES of UVSQ, CEA ,  Thales Alenia Space and Veolia S. A. Review statement. This paper was edited by  Martin Steinbacher  and reviewed by two anonymous referees.
Code and data availability. The Python scripts for the data collection from the HPP3 and DHT22 for Raspberry Pi 3, and the data from the experiments described here are available from the corresponding authors upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-12-2665-2019-supplement. www. atmos-meas-tech. net/12/2665/2019/ Atmos. Meas. Tech. , 12, 2665-2677, 2019.
RUL Prognosis for Data Simulation Approach 1 The variance of the degradation process, αβ 2 , depends on the choice of the Gamma process parameters α and β. It stands for the similarity in degradation behavior of identical PEMFC stacks. As being discussed in the presentation of the simulation procedure (see Appendix 2), the objective of introducing the variance is to represent stack-to-stack variability around the average behavior. Figure  10  shows three examples of degradation paths with different levels of variance: 1) low variance (α = 0. 6, β = 2. 5e-4); 2) medium variance (α = 0. 1, β = 1. 5e-3); 3) high variance (α = 0. 03, β = 5. 0e-3). The RUL predictions for all the degradation trajectories are carried out by the Particle Filteringbased approach described in Section 3. For each trajectory in the test set (J = 50 trajectories), the RUL predictions are made every 100 time steps with Model 1 and every 10 time steps with Model 2.
, 2002). Altogether, these data support an effect of temperature on the interaction between the bee and its pathogens. To investigate the influence of temperature on the honey bee/virus relationship, we measured the course of DWV infection and bee survival at different temperature regimes. Given a critical maximum temperature of about 42. 8 °C ± 2. 8 °C for the Western honey bee  (Atmowidjojo et al. , 1997) , we tested temperatures ranging from a low of 15 °C (close to the predicted critical minimum temperature) to a high of 37 °C (with or without heat shocks at 40 °C and 43 °C). Finally, by measuring both bee survival and viral multiplication, we searched for a potential tradeoff between bee performance and virus load, i. e. , Is there a temperature allowing host survival, but severely affecting the virus? Such a temperature could be used as a physical method against DWV in honey bees.
Statistical analysis We used the Kaplan-Meier method with Log-rank test to analyze variation in bee survival according to temperature regimes, colony and experiment variables. A Bonferroni correction for multiple comparisons was used if needed. When citing the median values, the 95% confidence intervals are indicated by square brackets. The median survival time was calculated as the survival time at which the mortality is greater than or equal to 50%. A possible replicate effect on survival was tested only on the reduced dataset corresponding to temperatures tested in all 3 experiments (T28, T34, T37). Regarding viral loads, the number of DWV copies per bee was log10transformed. As the temperature treatment at 15 °C was tested only in the third experiment, and both survival and viral load were different in this experiment in comparison to the two previous ones, we separated the dataset into two distinct sets: one for DWV load at temperatures ranging from 28 °C to 43 °C (n = 253 bees, 1st and 2nd experiments), and a smaller dataset comparing DWV load at 15 °C and 34 °C (n = 36, 3rd experiment).
We then used post-hoc tests with Tukey correction for multiple comparisons among qualitative variables. The statistical analyses were performed by using RStudio software (RStudio-Team, 2016) and the packages survival  (Therneau, 2014) , MuMIn  (Bartoń, 2016) , lme4  (Bates et al. , 2016) , lmerTest  (Kuznetsova et al. , 2013) , multcomp  (Hothorn et al. , 2016)  and lsmeans  (Lenth, 2016). Regarding viral loads of in-hive bees, we fitted a linear mixed model with experiment and three interactions (Table  S1, Models 1, 2  and 3 ). Finally, we compared the variation of in-hive and in vitro DWV viral loads. We did not include experiment 3 as only two temperatures were tested, and one of them (T15) was tested only in this experiment. We fitted a linear mixed model with experiment and an interaction between IVL and treatment.