dataesr/scientific-paragraphs-categorization huggingface dataset:
When viewed from a distance of 2. 04 m, the images were slightly stretched and measured 1808 3 32. 788 of visual angle. To avoid occluding peripheral vision with the lateral bars of a chin rest, we measured head position with a Polhemus head tracker (Polhemus, Colchester, VT). The images were projected on a large curved screen 6. 4 3 2 m (image size: 6. 4 3 1. 2 m) using three Optoma HD83 projectors (resolution: 1,920 3 1,080, luminance: 68 cd/m 2 ) in an otherwise dark room (Optima, Coretronic Corp, New Taipei, Taiwan). Experimental software was written in MATLAB (MathWorks, Natick, MA). Window images showed the central portion of the scene that was visible within a circular region, with everything outside of the circle replaced by neutral gray (a luminance value of 127). Scotoma images were the inverse of the window images-the central portion of the scene was blocked from view within a circular region filled with neutral gray, with the remainder of the original image shown outside the circle.
Data analysis Sensitivity was measured using signal-detection theory. To derive d 0 values for each condition, we used a generalized multilevel linear probit regression-modeling approach  (DeCarlo, 1998). Because a probit link function was specified, the estimated slope for a predictor coded as þ0. 5 for the correct response and À0. 5 for the incorrect response assesses the d 0 for a twochoice task. Because this technique derives d 0 values for two-choice tasks and not eight-alternative forcedchoice (8-AFC) tasks, the d 0 values were converted to probabilities and then converted back to the correct d 0 for 8-AFC tasks under the assumption of no bias (DeCarlo, 2012) using the computations that produced the Hacker and Ratcliff (1979) d 0 table. Random effects were the predictor slope and intercept within participant and an item random effect of intercept within scene category.
1/ Materials a/ Imaging of the scapula Forty-three CT scans of normal glenohumeral joints were used in this project. The scans were preselected based on patient age and CT indication. Thus the CTs were from patients having an average age of 43. 3 years (28-55 years). The CTs were initially ordered during the work-up of a polytrauma patient or a proximal humerus fracture. Each chosen CT was viewed and analyzed in its entirety to ensure the glenoid side of the joint was normal (no osteoarthritis, dysplasia or traumatic sequelae) and that the entire scapula was visible. These images were initially in DICOM format. They were imported into ImageJ® software to extract only the scapula in its entirety. The resulting files were in TIFF format. Lastly, the files were converted into OBJ format, so they could be used in the 3D imaging software chosen for all the portions of this research project: Blender®.
b/ 3D imaging software Blender® (Blender Foundation, the Netherlands) is an open-source, 3D modeling, animation and rendering software. It has advanced modeling, 3D sculpting, texture mapping, special effects, 3D animation and rendering functions. Version 2. 68a was used in this project. With the Blender® software, the coordinates and center of an object were critical during the various experiments to ensure the same working coordinate system was used for each of the 43 manipulated scapula. c/ Data processing and analysis software We used the R® software in this project. This is open-source data processing and statistical analysis software implemented with the R programming language, which was itself inspired by Scheme (The R Foundation). The objective of modeling a native glenoid required several steps to be completed.
The first was to construct a database with the set of 43 healthy scapulas. Linear regression techniques were then applied to model the native normal glenoid. a/ Database Precise manual pointing of each of the 43 normal scapulas reconstructed in 3D and placed in a common reference system was carried out with Blender®. Twenty points were captured by the surgeon. These points were chosen to be easily identifiable when they are selected on each scapula. A comparison of the pointing done by two, independent, nonsurgeon raters was done beforehand to validate the reproducibility and precision of the process that will be repeated on the 43 scapula. The points were distributed between the blade of the scapula, which was considered as normal over time and thus served as a reference, and the glenoid, which was considered as becoming pathological over time, thus a candidate for reconstruction.
We constructed a matrix of distances Y i, j , ∀j such that P j was outside the glenoid. A new script selected the best four statistically significant equations based on the values of the coefficient of determination, R 2. This method was applied to characterize each point on the glenoid (Figure  1 ), except for points P 8 and P 20 that we excluded because they were deemed redundant relative to neighboring points. In the first scenario, the glenoid modeling was done assuming the glenoid was completely destroyed. In practice, glenoid wear is most often localized, as evidenced by the classifications schemes of Walch et al. (11)  and Favard et al. (12). Thus in the second scenario, the glenoid modeling was done assuming only the inferior portion of the glenoid was worn. The same regression methods were applied to only the points on the inferior portion of the glenoid, assumed to be missing because of inferior wear.
More recently, 3D reconstruction and automatic segmentation software packages have been developed and made available to surgeons. These aim to allow accurate preoperative planning of a total shoulder arthroplasty procedure  (4, 5, 17) , and also to design PSGs used to transfer and carry out intraoperatively the previously established plan. One of the most welldesigned packages is the one developed by Imascap (Plouzané, France). This software, called Glénosys®, uses a least-square method to automatically created new scapular and glenoid planes, using all the 3D points on the scapula, no matter where the joint is damaged  (17) (18) (19). In fact, it is based on these observations that this research project was implemented to develop a predictive statistical model for the premorbid glenoid. Beyond the description of the pathological glenoid, as is commonly done, our goal was to model the native glenoid.
Our study has several limitations. The first is related to its methods. All of the preliminary steps needed to develop the statistical model require manual imaging processing procedures (3D reconstruction, segmentation, capturing of points) with different software packages (ImageJ®, Blender®). Learning the various operations and mastering them may have contributed to imprecisions when putting together the initial database. An inter-rater reproducibility study was conducted to make sure the anatomical points captured on each scapula were relevant and precise. Another limitation is the fact the accuracy of our statistical model was evaluated on only the 43 scapulas used to create the regression equations. In the end, this population served as its own control to evaluate the predictive method developed.
In our knowledge, this kind of study is the first one in the literature. Our work is based on the Monte Carlo ray-tracing methodology, which provides an accurate description of the optical propagation within the specific aircraft cockpit environment. From the 3D representation of the studied environment, we calculate the received power for Line-Of-Sight (LOS) and non-LOS (NLOS) paths, i. e. reflected paths over all the elements inside the cockpit. Furthermore, we take into account blockage effects because of body along with head movements of pilots. Based on the channel analysis, we then determine the maximal data rate to guaranty a given link reliability. The paper is organized as follows. The system description and the optical channel analysis are presented in section 2. Section 3 focuses on the performance analysis in terms of data rates and reliability.
B. Channel modelling To determine the impulse response for the different considered channels, we adopt a modeling approach based on a stochastic Monte Carlo method, associated with the ray-tracing algorithm. Our research laboratory has developed the RaPSor software (Ray Propagation Simulator), which is an open source and extensible tool, based on the Netbeans platform for modelling IR and visible links  [12, 13]. It allows determination of the impulse response h(t) for a defined link. The following inputs are required for the simulation: The detailed 3D geometric model of the cockpit, composed of 3. 3e6 faces, is first imported into RaPSor. We set the cockpit surface reflection coefficients equal to 0. 5, which is a mean value between absorbent and perfectly reflective materials  [13]. In addition to the cockpit itself, important parameters affecting optical propagation are the body and the changes of the transceivers orientation resulting from head movements.
The body model representing pilot and co-pilot is a 3D realistic human mesh of 1. 8m height and composed of 1004 faces (see Fig. 4 ). It was fully articulated and animated by using the Blender software in order to take into account a realistic mobility scenario in our channel simulations. As for the cockpit and for the same reason, the reflection coefficient of the body is fixed to 0. 5. All the surfaces in the scene are considered as perfectly diffuse and are consequently modeled by a Lambertian Bidirectionnal Reflectance Distribution Function (BRDF)  [12]. The optical characteristics of the transceivers are: type of radiation pattern of the optical source, power spatial distribution of the source defined by the half-power angle φ1/2, surface area of the photodetector and FOV. In the study, we consider Lambertian optical sources with different half-power angles.
We evaluated the accuracy of phylogenetic methods for determining the direction of HIV transmission by analyzing next-generation sequencing (NGS) data from index-partner pairs enrolled in the HIV Prevention Trials Network (HPTN) 052 trial. HIV-infected index and HIV-uninfected partner participants were enrolled as serodiscordant couples; samples were analyzed from couples with index-to-partner HIV transmission that was confirmed by genetic linkage studies. NGS for HIV gp41 (HXB2 coordinates: 7691-8374) was performed using plasma samples from thirty-nine index-partner pairs (seventyeight samples collected within 3 months of partner seroconversion). Maximum likelihood trees were generated using the entire dataset using FastTree v. 2. Topological patterns of HIV from each index-partner pair were analyzed.
Material and Methods The system overview is shown in figure  1. A fundamental desktop application of the system was developed in MATLAB language and basic functions of contouring education were implemented as described below. It is considered that the system is used by experts(teachers) and beginners(students). Experts prepare educational data with their treatment planning systems (TPSs) at their institution and send them to the cloud system and then beginners can access these data according to their privilege. Educational image resources such as treatment planning CT, MR and PET/CT are imported, and image fusion are performed based on a DICOM image registration object exported from a TPS to ensure same image registration results as those of TPS. Data of structure sets which experts prepared are also imported to the system.
Overview We used a computer tool called floodam  (Grelot and Richert, 2019)  and developed in R language (R Core Team, 2017) to assess the efficacy of the strategies aimed at reducing the vulnerability of existing dwellings. floodam requires as input a numerical model of building and produces a function which associates a level of damage to several combinations of flood depth and duration. We call damage function this type of outputs. The efficacy and the cost of the strategies aimed at reducing the vulnerability of existing dwellings were assessed for several numerical models of dwellings. The cost of each strategy depends on the geometry of the dwellings and, in some cases, on whether the strategy is taken on an intact or damaged building and on the building materials. Its efficacy is assessed by comparing the damage functions of the dwellings obtained with and without the strategy.
Overview of floodam We define the damage suffered by a good as the expected cost of the actions that must be performed after a flood in order to bring it back to its pre-flood state. Using this definition, floodam relies on two main assumptions: 1) the damage suffered by a building is the sum of the levels of damage suffered by its components, and 2) the levels of damage of the components are independent. floodam relies on a database of 431 elementary damage function. An elementary damage function associates a level of damage suffered by an elementary component to several combinations of flood depth and duration. A partition wall in plaster or a washing machine are examples of elementary components. The elementary damage functions come from interviews with insurance and construction experts.
We used elementary damage functions with data points at immersion depth values from 0 to 500 cm (included) in 10 cm increment, and immersion duration values from 0 to 144 hours (included) in 12 hours increment. For each numerical model of dwelling, floodam computes the height above the ground of all elementary components and the size of the elementary components of the building. Then, for each combination of depth and duration of immersion of the dwelling, it 1) computes the depth of immersion of each elementary component, 2) retrieves the corresponding levels of damage by measurement unit for the duration of immersion considered, 3) multiplies each level of damage by the size/quantity of the corresponding elementary component, 4) sums the obtained values to obtain the level of damage at the scale of the dwelling.
Copernicus is the current European Union programme for the establishment of a European capability for Earth Observation (http://www. copernicus. eu). It includes a set of services such as the Copernicus Atmosphere Monitoring Service CAMS (https://atmosphere. copernicus. eu/), aimed at providing consistent and quality-controlled information related to air pollution and health, solar energy, greenhouse gases and climate forcing, across the globe. The CAMS service encompasses a global 10 and regional air quality forecast and analyses service. At the base of the Copernicus services lie data from satellite Earth Observation systems and in situ (non-space) networks. The Copernicus programme also encompasses a space component dedicated to new spaceborne missions developed and managed by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).
2 The OSSE set-up In this study, we follow the different OSSE steps and requirements as identified in  Timmermans et al. (2015). Figure  1  provides a schematic overview of the work flow in this study. As the observations under investigation were not available yet, we first had to produce a set of synthetic observations. We based production of the simulated observations on a so-called 25 nature run (NR), which acts as the representation of reality. We converted the output from the NR into synthetic observations using information on the S4 and S5P instrument characteristics, as well as influencing parameters for the observations such as clouds and surface albedo. Next, the synthetic observations were ingested into the chemistry transport model (LOTOS-EUROS) using an ensemble Kalman Filter data assimilation system.
Observation simulator We generate NO2 tropospheric column synthetic observations using model profiles extracted from the NR model datasets. The observations are generated for the TROPOMI instrument on board the S5P satellite and the UVN instrument on board 5 the S4 satellite. The TROPOMI instrument is a spectrometer based on its predecessors OMI and SCIAMACHY  (Veefkind et al. , 2012). It measures in the ultraviolet (UV) -visible (270-500 nm), near infrared (NIR, 675-775 nm) and short-wave infrared (SWIR, 2305-2385 nm) wavelengths ranges to enable the retrieval of several air quality data products, including ozone, NO2, formaldehyde, SO2, methane, and CO. The instrument has a wide swath of 2600 km allowing daily global coverage and was designed for a spatial resolution of 7 by 7 km. After our study and the launch of the instrument, the spatial 10 resolution was further improved to 7 by 3.
Based on these reflectances, we simulate the effective cloud fraction and effective cloud top height similar to the procedure in the O2-O2 cloud retrieval. The distribution of effective 15 cloud fractions obtained in this way was compared with OMI O2-O2 cloud observations for the year 2006 and a reasonable qualitative agreement of the histograms was found for summer and winter months, with somewhat less cloud free days and more intermediate cloud fractions in the OMI dataset, which could in part be due to the above-average number of sunny days in 2003  (Williams et al. , 2013). We use the simulated effective cloud fraction and cloud pressure in the synthetic retrievals. We convert the cloud fraction into a cloud radiance fraction (the fraction of the top-of-atmosphere radiance coming from the 20 cloud-covered part of the scene) by computing radiances using the surface albedo map and assuming a cloud albedo of 0.
We assume the network of currently existing stations for ozone is representative for the locations that will be available during the upcoming satellite missions. Therefore, the observation locations of the synthetic surface ozone observations have been drawn from the existing AirBase network (version 6, Simoens (2012)). We use only stations 20 classified as 'background', since these are representative for concentrations at the resolution of the models used in this study. Additionally, we omit stations above 700 m above sea level considering the focus of the project on boundary layer concentrations and the difficulties models have reproducing conditions in mountainous areas. Figure  5  shows the location of the selected stations. During data assimilation experiments, it is common practice to leave a number of observations out of the assimilation procedure, and use these for validation.
However, in air-quality databases like AirBase, it is not common practice to provide characteristics of instrumental errors (while retrieval uncertainties are provided for individual observations in the case of satellite measurements). Therefore, the representation error of ground-based observations should be set or parameterized explicitly. 5 To ensure realistic synthetic surface ozone observations, we mask the synthetic ground-based observations if the corresponding real observations in AirBase are missing too. Secondly, we add a random number to each synthetic value to represent the instrumental error. We base the distribution of this random number on statistics obtained from the AirBase observations available for the same period. More specifically, we determine the temporal auto-covariance in a time series of ozone observations.
, 2010; Williams et al. , 2017). The same TM5 model runs are also used for the extension of the LOTOS-EUROS runs from its top at 3. 5 km to the tropopause. We base the anthropogenic emissions on the TNO-MACC II inventory  (Kuenen et al. , 2014). Note this is a different inventory version than used in the NR, supporting the requirement of significant differences between the models for the  NR 30  and the CR/AR. Biogenic emissions are calculated online as described in  Schaap et al. (2009). Fire emissions are taken from the MACC-II GFAS v1. 0 product  (Kaiser et al. , 2012). These emissions vary from day to day, and are provided on a 0. 5⁰ resolution grid. Dynamically, we force the model by meteorological data from ECMWF. We choose this different meteorological forcing from the one used in the NR to meet the requirements on the differences between the NR and the CR/AR.
Data assimilation system -Ensemble Kalman Filter The LOTOS-EUROS set-up features an active data assimilation system based on the Ensemble Kalman Filter (EnKf) technique  (Evensen, 2003). A Kalman filter computes probability density functions (pdfs) of the true state, given 1) a 10 transition model to propagate the state in time with associated uncertainties; and 2) observations with associated representation error. Starting from the initial pdf, the filter first performs a forecast step propagating the pdf in time to the first moment that observations become available. Then, during the analysis step, we replace the forecast pdf by an analysed version that takes into account the new information available. The Kalman filter is an example of sequential assimilation, since forecast and analysis steps follow each other sequentially in time and use only information from the past.
Evaluation of nature run and control run In the design of an OSSE, it is important to demonstrate that the NR exhibits the same statistical behaviour as the real atmosphere for aspects relevant to the observing system under study. To evaluate this, we compare the ozone and NO2 concentrations from the NR to observations from the AirBase network. We consider the sites falling into the first five classes as defined in the objective classification method suggested by  Joly and Peuch (2012) , as we understand them to be 20 representative for the resolution of the NR model. These first five classes mostly encompass sites classified as rural and urban background in the AirBase database but especially for ozone also a small fraction of the urban traffic stations have been assigned to one of the five lowest classes and are therefore considered representative for larger areas.
Furthermore, day-to-day variations 5 are reproduced, e. g. the maxima being higher in the second week of July than in the first week. Figure  8  shows the diurnal cycle from CR, NR and observations averaged over all selected locations for August 2003. For ozone, all three datasets show similar diurnal variation with lowest values in the early morning and an afternoon peak. The models do show an underestimation of the observed ozone values. This bias is regionally dependent (not shown), with larger negative values for Southern Germany and Central France, small biases over the Netherlands and even positive biases in the 10 MOCAGE model over some periods in Northern Germany. For NO2, the NR from the MOCAGE model is missing the early morning peak due to the morning rush hour when the boundary layer is still shallow and photolysis is still limited.
Acknowledgments This work was partly supported by the  ESA  funded project " Impact of Spaceborne Observations on Tropospheric Composition Analyses and Forecast " (ISOTROP-ESA contract number  4000105743/11/NL/AF) 20  Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-1360Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 12 March 2019 c Author(s)  2019. CC BY 4. 0 License.
Code and data availability The LOTOS-EUROS CTM is available as open source version for public use via www. lotos-euros. tno. nl. The LOTOS-EUROS data assimilation code used in this study is property of TNO and not allowed to be shared publicly. The MOCAGE model is property of Météo-France and not allowed to be shared publicly. The volume of the model and synthetic observation datasets discussed in this paper is large, but for scientific purposes 5 subsets can be made available upon request.