dataesr/scientific-paragraphs-categorization huggingface dataset:
5 • around the AERONET sites. For AERONET AOD and AE, the averaging temporal window was set to ±1 h around the time of the POLDER-3 passage. For AERONET AOD F , AOD C , and shape retrieval, this temporal window produces an insufficient number of data, for springs and summers in the period 2005-2011, due to the temporal time shift of the POLDER-3 passage towards the afternoon. For these two variables, the averaging temporal window was extended to the whole afternoon (that is, all data points later than 12:00 UTC) in order to allow for a significant dataset for comparison. Table  1  reports the number of available observational days for POLDER-3 and AERONET aerosol parameters at each station in the period March 2005-October 2013, as well as the number of coincident days obtained between POLDER-3 and AERONET.
g. Alboran, Lampedusa or Gozo). Between 1 pixel in the case of inland stations of Rome and Burjassot, and up to 29 pixels in the case of the small remote island of Alboran were considered. Overall, the number of available AERONET observation days is important both for AOD and AE (18 223), and AOD F and AOD C  (11 228). The number of days with AERONET-derived f NS was less significant (4976 data points), due to additional constrains in the inversion necessary to derive this parameter. The number of available AERONET observations per site varied from 158 to 2059 for AOD and AE, and from 43 to 1333 for AOD F and AOD C , mainly due to partial functioning of the instruments or maintenance of the sites. At some stations, measurements started years after the beginning of POLDER-3 mission (e. g. 2011 for Alboran, 2013 for Gozo).
Coincidence with airborne observations The comparison between POLDER-3 and airborne measurements was conducted for profile ascents or descents of the ATR-42 close in time with POLDER-3 overpasses. Flight tracks and profiles locations are shown in Fig. 2 , whereas additional details (dates, geographical coordinates, altitude span and duration) are given in Table  2. Data from the PLASMA sun-photometer, operated only during ADRIMED, were available only on 8 profiles (also indicated in Table  2 ) for which the minimum flight altitude was as close as possible to the surface. The dataset was limited to ATR-42 profiles extending as much as possible over the column. To evaluate whether the aircraft profile sampled entirely or only partially the aerosol layers, we compared the AOD measured by PLASMA to that obtained by integrating the extinction profile of the CAPS-PMex instrument (not shown).
Statistical indicators The agreement between the POLDER-3, AERONET and airborne datasets was quantified by several evaluation metrics, including the number of matchups (N), the linear correlation coefficient (R), the slope (S) and intercept (I ) of the linear regression, the root mean square error (RMSE), and the bias (B), representing their mean difference. RMSE = 1 n n i=1 (y i -x i ) 2 , (5) B = 1 n n i=1 (y i -x i ) , (6) where x and y are generic datasets, and n the number of pairs of compared values. Additional metrics are provided by the "fraction of accurate retrievals" (G frac ) defined by  Bréon et al. (2011). This quantity is defined as G frac = #obs ( < EE) #obs , ( 7 ) and quantifies the fraction of POLDER-3 data points for which the absolute difference ( ) between reference and evaluated data is lower than the estimated error (EE).
In accordance to  Bréon et al. (2011) , EE was calculated as EE = ± (0. 03 + 0. 05 × AOD) , (8) and applied to all the AOD advanced products. Because G frac is only appropriate for large datasets whose number of data points exceeds 100  (Bréon et al. , 2011) , it was calculated only for comparisons with AERONET data. Lampedusa observed on 25 April 2011. This is the only event coincident with an AERONET measurement (1. 50 ± 0. 06) with POLDER-3 AOD > 1.
003, G frac = 91 %, RMSE = 0. 02, R = 0. 60). Data corresponding to D cut-off ≥ 1. 0 µm were less numerous (1442 days). Whereas the cor-   relation improved slightly (R = 0. 69 vs. R = 0. 60), the dispersion increased (B = -0. 01, RMSE = 0. 03) due to the appearance of points for which AERONET AOD F almost doubled that of POLDER-3. Colouring the data points by AE showed that the data points with D cut-off below 1. 0 µm mostly corresponded to aerosols with a weak-to-moderate spectral dependence (low AE), whereas cases with D cut-off above 1. 0 µm mostly (but not exclusively) corresponded to aerosols with a moderate-to-strong spectral dependence (high AE). The size cut-off definition also affects the comparison for AOD C. For D cut-off < 1. 0 µm, AOD C values were high and the correlation was significant.
Evaluation of the Ångström exponent Figure  7  shows the comparison of AE retrieved by POLDER-3 with values obtained by AERONET, PLASMA and the optical calculations. The comparison with AERONET was restricted to days when the POLDER-3 AOD exceeds 0. 1 (2031 out of the 6421 data points), in order to take into account only those values with relative uncertainties within 50 %. The comparison showed a significant spread and a moderate correlation coefficient (R = 0. 70). However, POLDER-3 tends to underestimate values of AE larger than 1 with respect to AERONET, and overestimate values smaller than 0. 5, yielding a significant bias  (-0. 11). The values obtained by POLDER-3 compare well with the airborne observations of PLASMA (R = 0. 84), but less well to the optical calculations (R = 0. 42).
08 µm for AE = 3. 3). Figure  8  compares the scatter plots of AE and AOD obtained for the coincident POLDER-3 and AERONET datasets. The tendency of POLDER-3 to underestimate AE shows up clearly by the absence of values of AE larger than 2. 5, which, conversely, are retrieved by AERONET. On the other end of the spectrum, values down to -0. 5 are found in the AERONET dataset when POLDER-3 hardly retrieves negative values. Both POLDER-3 and AERONET show a trend with the largest AOD values at lower AE values. However, high AOD values (> 0. 9) are found with POLDER but not AERONET, and are all except one associated to relatively low AE (< 1). Because the cloud screening of AERONET is relatively robust thanks to triplet measurements  (Smirnov et al. , 2000) , these outliers may result from undetected cloud contamination in the POLDER algorithm.
In the operational ocean algorithm, f CNS can only take discrete values equal to 0, 0. 25, 0. 50, 0. 75, and 1. The averaging process produces intermediate values when there is local variability between the pixels around a given AERONET station In general, the POLDER-3 f CNS and the AERONET f NS are poorly correlated. The correlation coefficient R is 0. 29 for the coincident data points of all the 17 stations (N = 730, Table  1 ). At individual coastal and insular stations (Lampedusa and Malaga), notably impacted by mineral dust, the correlation between POLDER-3 f CNS and AERONET f NS is more significant (R = 0. 73 for N = 54 and R = 0. 59 for N = 53, respectively). This is also seen when restricting the dataset of Ersa and Lampedusa to the summers of 2012 and 2013 (R = 0. 55 at Ersa, N = 11; R = 0.
70 at Lampedusa, N = 10). The robustness of the comparison can be increased by further constraining the dataset to POLDER-3 and AERONET AOD values larger than 0. 10 and limiting the comparison to AERONET data for which AOD C is at least 30 % of the total AOD. By applying these thresholds (Fig. 9a ), the correlation   between f CNS and f NS is R = 0. 56 (N = 274 for the 17 stations). Overall, 80 % of the POLDER-3 f CNS agrees within 25 % with the AERONET values. The largest differences occur when AERONET retrieves f NS values lower than 50 %. In this case, only 40 % of the POLDER-3 f CNS are in the ±25 % agreement interval with AERONET. Conversely, for AERONET f NS > 50 %, 88 % of the POLDER-3 f CNS agree within ±25 % with the AERONET estimate of f NS. Figure  10  shows that a relatively good agreement is obtained by comparing broad classes 25 % wide, providing confidence to the classification of non-sphericity by POLDER-3.
Finally, Fig. 10  shows the implication on those results on the evaluation of the POLDER-3 AOD CNS and AERONET AOD NS. With the previous thresholds (POLDER 3 and AERONET AOD values larger than 0. 10 and AERONET AOD C /AOD larger than 30 %), the correlation obtained between coincident POLDER-3 AOD CNS and AERONET AOD NS at 865 nm is significant (R = 0. 87). The two datasets are very consistent. However, the POLDER-3 AOD CNS is almost systematically lower than the AERONET AOD NS , regardless of the percent that it represents with respect to the AOD (not shown). The physical reasons behind this evident discrepancy are beyond the scope of this paper and we recommend addressing them in future research.
Evaluation of uncertainties on the advanced POLDER-3 oceanic aerosol products In this paper we provide a first comprehensive evaluation of the advanced POLDER-3 aerosol products over ocean by the latest operational algorithm, based on ground-based remote sensing (AERONET) but also airborne remote sensing and in situ observations (TRAQA and ADRIMED campaigns) over the western Mediterranean sea. Table  4  summarises it by presenting the absolute errors ( ) derived from the RMSE (representing the precision) and the bias (B) as a measure of accuracy. For consistency with previous similar analyses and as an acknowledgment of the large size of the dataset, only the RMSE and bias of the linear regressions with the AERONET data have been reported. The uncertainties in AOD CS and AOD CNS were calculated as the square-root of the quadratic sum of the errors in AOD C and f CNS.
Our estimate of AOD indicates that, for the western Mediterranean basin, the accuracy and the precision of the POLDER-3 are better than those derived by the error analysis of  Tanré et al. (2011) , also reported in Table  4 , based on a global comparison with AERONET of the POLDER-1 instrument. It is noteworthy that the POLDER-1 retrieval algorithm was using a single mode spherical particle size distribution  (Goloub et al. , 1999)  instead of the current two modes allowing, in addition, an aspherical component. Furthermore, from our regional evaluation of the whole latest collection 3 of the POLDER-3 dataset, G frac value for AOD (73 %) is much better than that reported by  Bréon et al. (2011)  (G frac = 45 %), based on previous collection of POLDER-3 retrievals at a global scale. Evaluation of the fine and coarse aerosol optical depth Table  4  reports the uncertainties in AOD F and AOD C based on estimates RMSE and bias.
8 µm. This suggests that AOD F measured by POLDER-3 could be used for predicting the submicron column concentrations for air quality studies, and for evaluating the radiative effect of fine aerosols. Data availability. POLDER-3 data extraction was performed with the program PARASOLASCII (http://loa-www. univ-lille1. fr/~ducos/public/parasolascii/, last access: 6 December 2018). This version is made available from the AERIS Data and Service Center (http://www. icare. univ-lille1. fr/parasol/overview/, last access: 6 December 2018). Technical details are described at http://www. icare. univ-lille1. fr/projects_data/parasol/docs/Parasol_ Level-2_format_latest. pdf, last access: 6 December 2018. The definition of the flag index is detailed on page 18 (parameter: quality of the fit). The AERONET version 2.
0 aerosol products at the Level-2 quality (cloud screened and quality assured with up-to-date calibration) were obtained from the official website at https://aeronet. gsfc. nasa. gov/, last access: 6 December 2018. The SAFIRE ATR-42 aircraft data are available at the Mistrals/ChArMex database maintained by the AERIS Data and Service Center (http://mistrals. sedoo. fr/ChArMEx/, last access: 6 December 2018). Single-particle Mie scattering calculations were performed with the Mie_single. pro routine under IDL available at http://eodg. atm. ox. ac. uk/MIE/mie_single. html, last access: 6 December 2018. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-11-6761-2018-supplement. Author contributions. PF and IC designed the data analysis. LMK, PF, and IC carried out the data analysis with significant contributions from FD and DT.
Acknowledgements. This work is part of the  ChArMEx  project, supported by  CNRS-INSU ,  ADEME ,  Météo-France , and  CEA  in the framework of the multidisciplinary program  MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ; http://mistrals-home. org/). It has also been supported by the  French National Research Agency (ANR)  through the  ADRIMED program  (contract  ANR-11-BS56-0006 ) and by the  French National Program of Spatial Teledetection  (PNTS, http://www. insu. cnrs. fr/pnts,project no. PNTS-2015-03 ). TRAQA was also supported by the  ADEME/PRIMEQUAL programme. Lydie Mbemba Kabuiku  was supported by the  French Environment and Energy Management Agency (ADEME)  and  National Center of Space Studies (CNES). Airborne data were obtained using the ATR-42 atmospheric research aircraft managed by SAFIRE, which is a joint facility of the  French national centre for scientific research (CNRS) ,  Météo-France , and  CNES.
Quality control In order to gain additional insights on the reliability of the multiparameter subsurface models, we compare the recorded data with the synthetic ones computed in each FWI models both in the frequency and time domains. Frequency-domain data fit  Operto et al. (2015)  hypothesized a significant footprint of attenuation in this dataset after noting that the amplitudes of the modelled 7 Hz monochromatic gathers were overestimated compared to the recorded ones at long offsets along the parallel profile intersecting the gas cloud. We check now this hypothesis by comparing the recorded and modelled amplitude for the different FWI tests (Figs  17  and 18a ). Fig. 17  shows the recorded 7 Hz common-receiver gather 1 (Fig. 2 , white circle) as well as the modelled gathers computed in the FWI models of tests FWI1, FWI3 and FWI4 obtained after the 7 Hz frequency group inversion (Table  1 ).
Time-domain data fit We now show the data fit in the time domain to assess more precisely which parts of the wavefields have been matched during the different FWI tests. We compute time-domain seismograms with the same frequency-domain forward modelling engine as the one used to perform FWI to prevent any bias in this assessment. For a simulation length of 8 s and a cut-off frequency of 10 Hz, this required to perform 81 frequency-domain modelling (LU decomposition + solve phases) on the 35 m grid before inverse Fourier transform. This number of modelling could have been, however, decreased by a factor two if a reduced time scale implemented with complex-valued frequencies would have been used as illustrated in Fig. 3 , bottom panels, with the additional difficulty to control wraparound effects  (Mallick & Frazer 1987).
Face recognition system is proven to be vulnerable to face spoofing attack. Many approaches have been proposed in the literature to resolve this vulnerability. This paper proposes a novel method dedicated to mobile systems. The approach asks users to capture a video by moving the device around their face. Thanks to a 3D reconstruction process, the shape of the object is estimated from the video. By evaluating this 3D shape, we can rapidly eliminate attacks in which a photo of a legitimate face is used. Then, the camera's poses estimated from the 3D reconstruction is used to be compared to the data captured from the device's motion sensors. Experimental results on a real database show the efficiency of the proposed approach.
g. photo printed on paper or displayed on an LCD screen). The construction of PAD classifier is described in the section 3. 2. For the ones which pass through the PAD classifier, they will be after that classified thanks to the Video Attack Detection (VAD) classifier, described in the section 3. 3. The VAD classifier permits to detect video-replay attacks. The ones which finally pass through the VAD classifier would be considered as legitimate authentications. Apart from the necessary movement of smartphone (Figure  2 ) which requires the collaboration of Face Spoofing Detection for Smartphones using a 3D Reconstruction and the Motion Sensors user, all other process can be automatic. In this study, the video and sensors data collector, 3D reconstructor and classifier are regrouped inside a unique android application.
d 2 x(t) dx 2 = a x (t) (6) d 2 θ x (t) dx 2 = g x (t) (7) Where t ∈ R + is the index of time, g x , g y , g z are data from gyroscope and a x , a y , a z are data from acceleration. By using integration method, the position and orientation of thecamera can be calculated from the acceleration as the initial speed and initial position are both zeros. Let's t max is the length of video, one frame is taken each ∆t = t max n-1 second. Another sequence of the camera state can be obtained from the sensor data: X i = x(t i ) (8) θ x i = θ x (t i ) (9) where i = 1,. , n, t 1 = 0, t n = t max , t i = ∆t. (i -1) The camera poses are compared to the information captured from movement sensors (e. g. accelerometer, gyroscope). Both information sources (3D reconstruction and movement sensors) represent the movement of the camera according to the user's face.
Thus, for a legit authentication case, there will be, a priori, a high similarity between these two sources. Meanwhile, when attackers use a video to authenticate, the camera poses estimated from the camera output will not represent the real poses of the camera. Therefore, the similarity between the two sources would not be assured. To estimate this similarity, a simple correlation can be applied for each couple of data as: (θ x i , θ x i ), (θ y i , θ y i ), (X i , X i ) and (Y i , Y i ). All these features (correlation results) are fed to an SVM classifier to form the VAD classifier mentioned previously.
EXPERIMENTAL RESULTS The proposed video-replay attack detection process requires the data of motion sensors integrated within the smartphone. Actually, there are no public face spoofing datasets responding to this requirement. Therefore, we tested the proposed method in a specific database constructed in our laboratory. The database includes 201 videos of 3 people including sensor data, therein: 99 cases of legitimate authentication, 63 cases of video-replay attack and 39 cases of photo attack. The videos are captured in different light conditions and movement speed by three devices: 2 instances of Samsung Galaxy Alpha and a Samsung Galaxy Tab. The database is divided into 2 sets. Each set has all three types of videos (legitimate, photo-attack and video-replay attack authentication). One set is used for training the PAD and VAD classifiers and the other set is used for testing.
5 6 Households in such countries experience financial hardship and are often pushed below the poverty line as a result of their spending on healthcare. Industrial workers face significant health risks due to poor workplace conditions and limited access to healthcare. 7 The International Labour Organization (ILO) estimates 2. 3 million deaths annually for reasons attributed to work, 2. 0 million linked to work-related diseases and 0. 3 million linked to occupational injuries around the world. Economic costs of strengths and limitations of the study ► First study of its kind in a challenging environment such as that of a large-scale industry in the country. ► Validation of self-reported data on expenditures was done by verifying documents such as prescriptions, bills, drug receipts, etc. ► Study was conducted in one large industry, so the findings may not be generalisable to the entire industrial workforce in Nepal.
dAtA COlleCtIOn The employee register was obtained from the management. All those who were eligible for the study and currently employed in the industry were identified and self-administered a brief screening questionnaire in local language. Workers not found to be present on the day of the survey were traced back to their residences. We excluded those who could not be tracked after two attempts. If the response to any of the two questions, whether hospitalised within the last year or received OPD care within the last 30 days of the survey, is 'yes', they were asked to appear for a face-to-face interview on another date using a structured questionnaire. Prior to conducting the interview, consent was taken, study was explained and, once they agreed, the participants were asked to bring documents related to their hospitalisation/OPD care such as bills, reports, receipts, prescriptions, etc, if available.
Five postgraduate students from B. P. Koirala Institute of Health Sciences, Dharan, Nepal, were trained to conduct the interviews. The investigators were trained for 2 days about the study protocol, data collection process and interviewing skills. The principal investigator also accompanied the interviewers and checked the completeness of the questionnaires at the end of each day. The OOP expenditure on health for each episode of hospitalisation (in the last 365 days) or OPD care (in the last 30 days) was estimated by asking the respondents about their expenses on medical, otherwise known as direct costs (consultation, diagnosis, surgery, drugs, bed, other medical supplies), and non-medical items, otherwise known as indirect costs (transportation, lodging/ boarding, loss of wages, food, etc), separately.
Analysis and statistics Data were double-entered into Epi-Data Entry V. 3. 1 (Epi-Data Association, Odense, Denmark) and imported into STATA V. 12. 0 for analysis (StataCorp LP, USA). Rates of hospitalisation and OPD service utilisation were summarised in the form of numbers per 1000 employees/ year. The key outcome of the study was cost of hospitalisation and OPD care which was expressed in terms of median (IQR). Other outcomes such as prevalence of CHE and distress financing due to hospitalisation and OPD care were presented as percentages. Krusal-Wallis test was done to compare median OOP expenditure across income quartiles and type of health facility (public and private). US$1=NRs 103. 68 was used as conversion rate on 9 December 2017 to calculate the various costs. 32 ogistic regression was carried out to identify the social and treatment-related factors associated with distress financing and CHE due to hospitalisation after adjusting for potential confounders.
strengths and limitations of the study There were some strengths in this study. This is the first study of its kind in a challenging environment such as that of a large-scale industry in the country. Validation of self-reported data on expenditures was done by verifying documents such as prescriptions, bills, drug receipts, etc. Double data entry and validation was done using EpiData to minimise data entry errors. The study also adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines in reporting the study findings. 44 owever, this study is not without any limitations. First, under-reporting of household income is common in surveys. This could have led to overestimating the proportion of households experiencing CHE. Second, because the study was conducted in one large industry, the findings may not be generalisable to the entire industrial workforce in Nepal.
Acknowledgements This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Program for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). The model is based on a course developed jointly by the  International Union Against Tuberculosis and Lung Disease  ( The Union ) and Medécins sans Frontières (MSF/Doctors Without Borders). The specific SORT IT programme which resulted in this publication was jointly developed and implemented by: The  Union South-East Asia Office, New Delhi, India ; the  Center for Operational Research, The Union, Paris, France ; The Union, Mandalay, Myanmar;  MSF Luxembourg Operational Research (LuxOR) ;  MSF Operational Center Brussels (MSF OCB) ;  Institute of Medicine, University of Chester, UK ; and  Department of Medical Research, Ministry of Health and Sports, The Republic of The Union of Myanmar.