dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, if a 10 km × 10 km pixel has a water cloud fraction under 0. 75, it is excluded for the same reason. The MOD06ACAERO  (Meyer et al. , 2015)  products are also used. These use reflectance observations at six MODIS spectral channels (0. 46, 0. 55, 0. 66, 0. 86, 1. 24 and 2. 1 µm) to simultaneously retrieve ACAOD, COD and the cloud effective radius (CER) of the underlying marine boundary layer clouds. Retrievals are performed at the pixel level (here, every fifth native 1 km pixel) on both Terra (morning) and Aqua (afternoon) MODIS data. Output includes pixel-level estimates of retrieval uncertainty that accounts for known and quantifiable error sources (e. g. , radiometry, atmospheric profiles, cloud and aerosol radiative models). Assumptions regarding the cloud forward model and ancillary data usage are consistent with those of the operational MODIS cloud products (MOD/MYD06)  (Platnick et al.
, 2017). Note that both these datasets represent only the partial column AOD, i. e. , the AOD above the liquid cloud top, and that ice-phase clouds are not processed. In addition, we have also used MODIS Terra and Aqua combined Deep Blue/Dark Target dataset (AOD_550_Dark_Target_Deep_Blue_Combined_Mean _Mean) from the latest collection (6. 1)  (Sayer et al. , 2014)  and the Multi-angle Imaging SpectroRadiometer (MISR) (MIL3MAE monthly mean data at 0. 5 • resolution;  Kahn and Gaitley, 2015)  (see Table  2 ).
OMI dataset The OMI sensor, which has been operating since October 2004 aboard the EOS Aura satellite, is a spectrometer with a high spectral resolution  (Levelt et al. , 2006). OMI offers nearly the daily global coverage with a spatial resolution for the UV-2 and VIS (UV-1) channels ranging from 13 × 24 km 2 at nadir. The OMAERUV_v003 product contains retrievals from the OMI near-UV algorithm  (Torres et al. , 2007). This algorithm derives a variety of aerosol radiative properties, such as an aerosol index (AI), AOD and ACAOD (uncertainty of ± (0. 05 + 30 %)) for clear-sky conditions. For this study, we have used ACAOD  (Jethva et al. , 2018)  retrieved at 500 nm (Table  2 ). An above-cloud aerosol retrieval technique was also applied to the multi-year record of OMI observations to deduce a global product of ACAOD on a daily scale  (Jethva et al.
SEVIRI dataset Spatiotemporally highly resolved geostationary satellite observations are taken here from the CLoud property dAtAset based on SEVIRI edition 2 (CLAAS-2;  Benas et al. , 2017). The CLAAS-2 dataset is based on measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and was generated and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) as the successor of CLAAS  (Stengel et al. , 2014). CLAAS-2 includes a variety of cloud properties, of which LWP, COD and CER were used in this study. CLAAS-2 COD and CER are retrieved, similarly to the widely used cloud retrieval method described in  Nakajima and King (1990) , under the assumption of plane-parallel cloud layers. Lookup tables are precalculated and used to map SEVIRI reflectance at 0.
Reanalyses of atmospheric composition Two different reanalyses are used for aerosols and clouds (Table  2 ). The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of global atmospheric composition since 2003 includes five main aerosol species. The first generation of ECMWF reanalysis  (Morcrette et al. , 2009) , issued by the GEMS (Global and regional Earth-System Monitoring using Satellite and in situ data) project, covers the period 2003-2008. MACC is the secondgeneration product and provides improvements in sulfate distributions and has been extended to the 2003-2011 period  (Benedetti et al. , 2009). Here, we use MACC near-realtime (NRT) daily datasets at 1. 125 • resolution of the anthropogenic SW direct effect at TOA in all-sky conditions (Table 2). In addition, we use MERRA-2, generated with version 5.
Sc properties The different properties of Sc clouds are analyzed over the box at 10-20 • S, 0-10 • E, defined by Klein and  Hartmann (1993) , referenced in the following as box_O. In contrast, an important negative bias of approximately ∼ -20 % is detected in the simulated low cloud fraction compared to ERA-INT values (Fig. 2b ). This result is related to a well-known bias (underestimates of low cloud fraction) detected in most GCMs over the Sc regions  (Nam et al. , 2012). The mean modeled LCF is 57 %, compared to 80 % in the ERA-INT dataset. The poor representation of LCF over Sc regions remains an open issue outside of the scope of this work. Nevertheless, the analysis and discussions of the following results take into account this underestimate, notably for SW heating rate and the DRE exerted by BBA at TOA.
AOD over biomass burning source Figure  3  compares the (monthly mean) total (clearsky, not above-cloud) AOD simulated by ALADIN-Climate to those derived from MODIS Terra and Aqua combined Deep Blue/Dark Target dataset (AOD_550_Dark_Target_Deep_Blue_Combined_Mean _Mean) from the latest collection (6. 1)  (Sayer et al. , 2014)  and MISR. The total AOD obtained from the MERRA-2 and CAMS reanalyses is also indicated. All AODs are at 550 nm. Over Angola and Zambia, the model is able to simulate a regional pattern of AOD consistent with the MODIS and MISR retrievals, with AODs of ∼ 0. 7-0. 9, even if BBAs are located too far south compared to satellite data. A general good agreement is also observed compared to the CAMS reanalyses, even if the model underestimates AOD over eastern Gabon. Over the continent, significant differences (overestimates) appear clearly compared to MERRA-2.
This difference with reanalysis product can be due to the scaling factors applied for biomass burning emissions in the different models. Over the ocean, ALADIN-Climate is found to be very consistent with MERRA-2 (AOD ∼ 0. 7 near the Angola coast) but the differences are large compared to the satellite retrievals. The latter AODs are higher (∼ 0. 7-1. 0), especially for MODIS. It should be mentioned that 50 % of MODIS AOD over the SAO is due to coarse mode according the level 2 retrievals. In addition, the land-ocean contrast in AOD detected by MODIS and MISR, with lower AODs over the continent and higher AODs over the ocean, is not observed in ALADIN-Climate. This contrast in AOD is still detected in MERRA-2 data, but it is not as large as in the satellite retrievals. It is likely that some of the land-ocean contrast in the satellite data comes from different factors.
The first is that the over-land and over-water algorithms are different and may have different biases. The second is that cloud fraction is also significantly higher over the water than over the land, meaning that typically more days of data contribute to the monthly mean over land than over water. Both of these effects could suppress or enhance any real land-ocean contrast in the AOD. Finally, part of the contrast could be due to the presence of sea-spray aerosols, certainly about ∼ 0. 1. As well as continued refinement of AOD retrieval algorithms, it is recommended that future work attempts to quantify the potential magnitude of these sampling effects on land-ocean contrast, which has received comparatively little attention to date. Finally, the satellite data also indicate a larger spatial extent to the aerosol loading over the ocean compared to ALADIN-Climate.
04 is found in the model over smoke sources. In terms of temporal correlation, the score is worse compared to MODIS. As mentioned previously, this could be due to the time frequency of biomass burning emission (monthly mean emission) imposed in the ALADIN-Climate model. The absence of spectral nudging in the ALADIN-Climate simulation can also explain part of the low temporal correlation, which is clearly higher (0. 73) for MERRA-2 data. Data from three AERONET stations -Mongu Inn (Zambia), Lubango (Angola) and Ascension Island -provide additional AOD evaluation. Figure  5  indicates the daily mean AERONET and ALADIN-Climate (only daytime values) AOD at each station. At Lubango, the model is able to correctly simulate the AOD, except in the beginning of September, when it is overestimated. The maxima (AOD of 0.
6) of 20 and 27 September are also well represented by the model. The total monthly mean AOD simulated by ALADIN-Climate (0. 41) is consistent with AERONET (0. 43), if with a small mean negative bias. Over the Mongu Inn station, the comparisons indicate a more pronounced negative bias (mean value of -0. 14). This is due to a nearly constant underestimate of total AOD throughout the period of the simulation, leading to a simulated monthly mean of 0. 47, lower than the one observed (0. 61). In parallel, the two maxima detected in AERONET data are well captured by the model. The first one, occurring between 18 and 21 September, is simulated too early and its magnitude is overestimated by about ∼ 0. 2. The second (23-29 September) is better reproduced in terms of magnitude (∼ 1. 0) but a significant underestimate in its duration is observed.
A second important aspect concerns the possible errors in the actual parameterization used to estimate the changes in BBA extinction with RH in the model. Indeed, our results indicate a possible overestimate of the increase in smoke extinction due to RH when compared to ORACLES-1 observations. Nudged simulations, associated with in situ observations, would certainly provide a unique dataset to test and constrain the hygroscopic properties of BBA over the SAO. All of these points have possible implications for DRE considerations and future works will extend significantly the number of cases studied to test the robustness of the results. For September 2016, the important negative surface dimming due to BBA (around -5 to -15 W m -2 ) over the subcontinent significantly modifies the surface energy budget over much of southern Africa.
Acknowledgements. This work has been conducted in the frame of the ORACLES investigation under the  National Aeronautics and Space Administration's Earth Venture program. The LASIC extinction and SSA values are available through https://doi. org/10. 5439/1369240 ,obtained from the  Atmospheric Radiation Measurement (ARM) User Facility , a  US Department of Energy (DOE) Office of Science user  facility managed by the  Office of Biological and Environmental Research. Paquita Zuidema  acknowledges support from  DOE ASR  grant  DE-SC0018272. AERONET data are available from https://aeronet. gsfc. nasa. gov(last access: November 2018). Marc Mallet ,  Pierre Nabat  and  Paola Formenti  acknowledge the  AErosols, Radiation and CLOuds in southern Africa (AEROCLO-sA ) project, supported by the  French National Research Agency  under grant agreement no.
ANR-15-CE01-0014-01 , the  French national program LEFE/INSU , the  Programme national de Télédetection Spatiale  (PNTS; http://www. insu. cnrs. fr/pnts(last access:  2018 ), grant nos. PNTS-2016-02  and  PNTS-2016-14 , the  French National Agency for Space Studies (CNES) , the  Centre National de la Recherche Scientifique (CNRS) , the  South African National Research Foundation (NRF)  under grant  UID 105958  and the  European Union 's  Seventh Framework Programme  ( FP7/2014-2018 ) under EUFAR2 contract no. 312609. We thank the  CNRM  climate model development team. Review statement. This paper was edited by  Joshua Schwarz  and reviewed by  Jeffrey Reid  and one anonymous referee.
Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge for highly-artifacted signals. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection under noisy conditions. The MFPD method calculates a binary Bayesian probability for each derived feature and makes a centralized fusion, using the Kullback-Leibler divergence. The method is evaluated on two ECG databases: 1) the MIT-BIH arrhythmia database from Physionet containing clean ECG signals, 2) a benchmark noisy database created by adding noise recordings of the MIT-BIH noise stress test database, also from Physionet, to the MIT-BIH arrhythmia database. Results are compared with a well-known wavelet-based detector, and two recently published detectors: one based on spatiotemporal characteristic of the QRS complex and the second, as the MFDP, based on feature calculations from the University of New South Wales detector (UNSW).
For both benchmark Physionet databases, the proposed MFPD method achieves the lowest standard deviation in sensitivity and positive predictivity (+P) despite its online algorithm architecture. While the statistics are comparable for low-to mildly artifactual ECG signals, the MFPD outperforms reference methods for artifacted ECG with low SNR levels reaching 87. 48 ± 14. 21% in sensitivity and 89. 39 ± 14. 67% in +P as compared to 88. 30 ± 17. 66% and 86. 06 ± 19. 67% respectively from UNSW, the best performing reference method. With demonstrations on the extensively studied QRS detection problem, we consider that the proposed generic structure of the multi-feature probabilistic detector should offer promising perspectives for long-term monitoring applications for highly-artifacted signals.
Section 2 presents the general architecture of the proposed Multi-Feature Probabilistic Detector (MFPD) and a specific implementation adapted to robust QRS detection. Section 4 evaluates its performances on two ECG databases: 1) the benchmark MIT-BIH  [25]  to validate the proposed method on clean ECG signals with consensus annotations, 2) the benchmark noisy database (created by adding noise recordings to the MIT-BIH) with known artifact types and levels, to validate the proposed method on highly artifactual ECG signals including various artifacts.
Databases Two databases were used: • Benchmark database: we first performed a comparative study using the first lead of the MIT-BIH Arrhythmia Database with consensus annotations to show the detection performances on clean ECG signals even though the main objective of the paper is to evaluate detector's robustness under artifact conditions. • Benchmark noisy database: we then created a benchmark simulated database by adding to the MIT-BIH Arrhythmia Database three noise sources (baseline wander, muscle and electrode motion artifact) extracted from the MIT-BIH noise stress test database. Noises were recorded from physically active volunteers  [25]  and with SNR levels from -6dB to 24dB by 6dB increments. It is composed of 864 noisy signals for which the reference annotations are simply copies of those for the MIT-BIH Arrhythmia Database.
The purpose of this database test is to provide a ground truth for detection performance comparison with different levels and types of noises. A quick access to this benchmark database is available at: https://github. com/dge996/MIT_NoiseStress/ though it can also be constructed using the method described in  [25]. We choose to report results on two databases to prove the design concept of the MFPD robustness for clean and noisy databases with consensus annotations. For the second one, both artifact type and level classification annotations are provided, they allow differentiated comparisons.
Performance criterion In this study, we used the bxb program (as part of the WFDB applications available on physionet) to obtain the beat-by-beat performance statistics for QRS complex detection according to the American national standard for ambulatory ECG analyzers (ANSI/AAMI EC38). We report for the two databases the total number of TP, FP and FN as well as the sensitivity (Se) and positive predictivity (+P) metrics calculated per record and a mean and standard deviation alongside the overall result. As in recent publications  [24]  [34], we chose a match window of 50 ms. Only one detection is considered as true positive (TP) inside the match window centered at each QRS annotation while the rest are considered as false positive (FP) detections. A false negative (FN) is counted when no detection is found inside the match window.
On the other hand, that the SFPD using the s-feature performs the worst (with multiple FP detections) is inline with its lowest KLD measure, or the smallest distances between the related H 0 and H 1 pdfs. Therefore, depending on the noise onset, the KLD measure of the distribution distances evolve dynamically to quantify the pertinence of each feature and to adapt their relative weights in the decision fusion. The power of the proposed method lies on this engineered flexibility since KLD are not learned from a particular database, but from the past observations of the features labelled by the detection results. Furthermore, the detection statistics of the whole record MIT-231 are given in Fig 5 : the MFPD outperforms the SCD method in both sensitivity and +P, has lower sensitivity than UNSW and WBD but highest in +P (see Table  1 ), which is consistant with the illustration of the 60s segment detection results in Fig  5.
Performance evaluation 4. 4. 1 Benchmark MIT-BIH arrhythmia database. In Table  2 , we first present the performance comparison on the MIT-BIH database, containing mainly clean ECG recordings, and high Se and +P results for all tested methods. We simply note that the MFPD has the lowest  standard deviation for both Se and +P statistics, a phenomenon also observed in the next tests to illustrate its stability. On the whole, the results indicate a trade-off in favor of the +P as compared with the reference methods while the overall score DER also shows high detection accuracy in both mean and standard deviation terms. To illustrate the method's inter-subject stability, 2. 08% of signals had a DER higher than 60% with MFPD versus 6. 25% for UNSW, 2. 08% for SCD and 8. 33% for WBD. This test validates the MFPD on a clean ECG database with consensus annotations and peer methods.
No manual annotations are needed since the Pan-Tompkins detector  [1]  results are used for feature extraction and classification labeling (H 0 /H 1 ). Prior laws of the Beta distribution for the correlation feature are fixed and not database dependent. Among the wide variety of pdf families, the GND seems well-suited for uncentered features with long tail distributions, but to our best knowledge, no analytic expression of the KLD can be found in the general case. Existing solutions  [36]  are limited to cases of equal means (see 2. 2. 4). We proposed in this paper an innovative estimation method of the KLD in the general case. With a reasonable computational cost, it can be used in real-time context. The proposed MFPD QRS detector has been evaluated using two different databases and compared with two most recent state of the art detectors and one reference detector from the literature.
Furthermore, MFPD method is more compact and easy to implement than  [2]  or  [21]. Quantitative comparisons results are particularly encouraging for challenging monitoring situations, in which the heterogeneity and levels of noise may be particularly high. For the two databases under evaluation, the ms-level jitter for all tested methods should be acceptable for further HRV parameter analysis and is thus not regarded as a distinguishing factor for comparison. Even though this method was implemented for single-lead ECG signals, it can be extended to the multi-lead and multi-source cases. Indeed, further improvements in detection robustness are expected by combining multiple ECG leads, but also by integrating other physiological signals (pulse oximetry, phonocardiography, etc) or other sensors sensitive to noise (accelerometers for movement noise, etc).
Conclusion We proposed an original multi-feature probabilistic detector working in real-time and applicable to different physiological signal applications. The method, illustrated on QRS complex detection, has been compared to two latest detectors in the literature, using the MIT-BIH arrhythmia benchmark database and a benchmark noisy database by adding noise recordings  [25]  to the MIT-BIH database. The proposed MFPD has achieved significant performance improvements on artifacted signals and comparable (in mean) but more stable (in std) performances for low-to mildly artifacted signals. These performance improvements are mainly due to the multi-feature probabilistic model and the KLD-based decision fusion that adaptively adjust the relative contribution of each feature's decision in real-time.
Data of the MIT-BIH Arrhythmia Database -PhysioNet used in this paper are available at: https://physionet. org/ physiobank/database/mitdb/. The noisy benchmark database is available at: https://github. com/ dge996/MIT_NoiseStress/ and can also be constructed using the method described at: https:// physionet. org/physiobank/database/nstdb/".
16±11. 46 96. 81±9. 03 9. 86 ± 18. 96 7. 91±4. 62 UNSW 103010 6484 6439 94. 01±16. 44 94. 01±16. 45 11. 97 ± 32. 86 7. 04±4. 77 SCD 104942 4552 4631 95. 88±13. 62 95. 79±13. 60 8. 32 ± 27. 20 5. 46±3. 69 WBD 103125 6369 5684 93. 88±13. 77 94. 33±13. 77 11. 77 ± 27. 45 3. 08±3. 33 Performances on the benchmark MIT-BIH arrhythmia database including: total number of TP, FN, FP, Se and +P (mean and standard deviation in %) and detection jitter (mean and std in ms) for 48 recordings. https://doi. org/10. 1371/journal. pone. 0223785. t002 Fig 6. Se and +P (in %) of MFPD, UNSW, SCD and WBD on the benchmark noisy database, with different noise types and SNR levels. https://doi. org/10. 1371/journal. pone. 0223785. g006 the.
Bioinformatic Analyses The generated FastQ files were first quality-checked though the FastQC software (https: //www. bioinformatics. babraham. ac. uk/projects/fastqc) and then analysed using the Quantitative Insights into Microbial Ecology pipeline (QIIME) v1. 9. 1  [37]  by choosing the open-reference OTU calling approach  [38]. The Illumina adapters were removed trough the "cutadapt" function  [39]. Forward and reverse paired-end sequence reads were collapsed into a single continuous sequence according to the "fastq-join" option of the "join_paired_ends. py" command in QIIME. Therefore, the "split_libraries_fastq. py" command was used to demultiplex and filter (at Phred ≥ Q20) the fastq sequence data  [40]. Subsequently, the sequences were clustered into an Operational Taxonomic Unit (OTU) against the GreenGenes database (release 2013-08: gg_13_8_otus)  [41]  by using the uclust algorithm  [42]  method at a 97% similarity cutoff.
Biostatistical Analyses Performance data (BW, ADG, FCR) were analyzed through a repeated measure ANOVA procedure using the general linear model (GLM) procedure of SAS  [45]. Sampling time and dietary treatment were included as fixed factors in the model and their interaction was evaluated using replicates as experimental units. Replicates were also included in the model to verify concordance within groups and showed no differences when nested within dietary treatment. All CFU data were transformed to respective log 10 values before being analyzed with the same ANOVA model used for performance. For the microbiota composition analysis, the biom OTU table was imported into R (v. 3. 6. 0; www. r-project. org) using Phyloseq package (v. 1. 28. 0)  [46]. Feature abundance matrices were calculated at the OTU levels.