dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Five focus groups were conducted with between five and eight occupational therapists attending each. Four members of the research team facilitated the focus groups, two facilitating each, with the exception of one smaller group with just three attendees that was facilitated by one person. The groups were audio recorded. Facilitators completed observational notes and reflexive analysis during and immediately following focus groups to document additional key information, such as participants' facial expressions, gestures and researchers' thoughts and interpretations. Audio recordings were independently transcribed verbatim and anonymised. Transcripts were checked for accuracy and missing data by focus group facilitators. As it was not possible to identify all focus group participants from the recorded transcript, each was identified only as a facilitator or participant.
1999; Liu and Zipser 2009) ; do such clouds persist when deep convection is aggregated? d Anvil clouds are a direct result of deep convection detraining near the tropopause, but are the anvil characteristics such as height, thickness, and relative frequency per deep convective cloud related to the degree of aggregation? To address such questions, we have analyzed individual cloud layers identified over nearly 5 years of data (July 2006-April 2011) from two A-Train satellites, CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)  (Stephens et al. 2002). The cloud profiling radar aboard CloudSat and the CALIPSO lidar observe the same cloud scenes, and the strengths of the two instruments are combined to provide near-complete cloud profiles, from thin cirrus through deep convection down to shallow cumulus.
Analysis of cloud-type occurrence has highlighted the prominence of congestus and midlevel detrainment in the West African monsoon  (Stein et al. 2011b) , and the identification of individual convective cloud features in CloudSat data has related deep convective characteristics to SSTs  (Igel et al. 2014). In this paper, we seek to explore the dependence of vertical cloud structure and cloud type on convective aggregation in greater detail using CloudSat-CALIPSO data. We summarize the methodology from  Tobin et al. (2012)  and the design of the Simple Convective Aggregation Index (SCAI) in section 2. The compositing techniques and cloud-type classification from CloudSat-CALIPSO data are described in section 3. In section 4, we show our results for the vertical distribution of cloud fraction.
The lidar detects liquid clouds, allowing the identification of cumulus and stratus as well as supercooled liquid layers. The analysis presented here uses the radar-lidar (DARDAR) mask product  (Delanoë et al. 2011) , in which CloudSat and CALIPSO data are interpolated and averaged, respectively, on to a common grid with 60-m vertical resolution and 1. 5 km horizontal footprint. All available orbits between June 2006 and April 2011 inclusive were used. To composite cloud properties on SCAI and other convective indices, each CloudSat-CALIPSO orbit was matched with the nearest 3-hourly time, t i. For each 108 3 108 grid box, the part of the satellite orbit that passed through it was included and stratified by the convective indices of that grid box at t i. The further restrictions mentioned in the previous section also applied to the compositing analysis.
Low-level cloud cover Estimating low-level cloud cover from satellite data is not straightforward, as observations of low-level clouds are incomplete. The CloudSat radar sensitivity threshold means that it only detects low-level clouds with high liquid water contents or when drizzle is present  (Christensen et al. 2013b ). In our analysis, for profiles where CALIPSO detects low-level cloud layers, only 40% of these profiles have low-level cloud layers that are also detected by CloudSat, so the low-level cloud cover estimated from radar alone will be an underestimate. The CALIPSO lidar, on the other hand, gets attenuated by thick cloudstypically optical thickness greater than 3  (Chepfer et al. 2008 )-so that for cloudy conditions, the probability of detecting low-level clouds using CloudSat-CALIPSO is severely reduced.
In Fig. 11  we show the RLH fraction for rainfall rates between 4. 5 and 5. 5 mm day 21 and for SST ranges of 299. 5-300. 5 K, 301. 5-302. 5 K, and 302. 5-303. 5 K, for comparison with Figs. 3a  and 3d. The increase in cloudiness when SCAI increases is universal across the different SST ranges, while for a given SCAI there is an obvious dependence of cloudiness on SST as well: the height of maximum cloud fraction increases from 12-13 km at 300 K to 14-15 km at 303 K. Assuming that the ''fixed-anvil temperature'' hypothesis  (Hartmann and Larson 2002)  or the ''proportionally higher anvil temperature'' hypothesis  (Zelinka and Hartmann 2010)  applies, the rise of high clouds when SST increases is associated with a nearly unchanged cloud-top temperature or a slight warming. Igel et al. (2014) , however, studied individual contiguous cloud objects identified in CloudSat data and found cooler cloud-top temperatures with warmer SSTs.
Acknowledgments. Official CloudSat products '' 2B-GEOPROF'' and ''ECMWF-AUX '' are available at cloudsat. cira. colostate. edu and are provided by the  NASA   CloudSat  project; CALIPSO lidar data are available at eosweb. larc. nasa. gov. The CloudSat and CALIPSO data were analyzed using the DARDAR merged products, which were accessed through the  ICARE Data and Services Center , available from icare. univ-lille1. fr/projects/dardar. Sandrine Bony  was supported by the  European Research Council  from the  European Union  (Grant Agreement 694768). Chris Holloway was funded by  UK NERC  Grant  NE/ I021012/1. The combined rainfall-CCA-SCAI dataset will be made available through the  NERC Centre for Environmental Data Archival (CEDA)  at catalogue. ceda. ac. uk.
Geological processes (e. g. , aeolian, volcanic, impact, fluvial, lacustrine, and periglacial) often produce deposits whose stratigraphy, bedding geometry, fracture orientation, and embedded block size are diagnostic of their origin and timing. Terrestrial field investigations, with roverdeployable GPR prototypes, have demonstrated that a GPR can aid in the identification and determination of the orientation and spatial variability of the above features (e. g. ,  Annan and Davis, 1976; Arcone et al. , 1998; Hinkel et al. , 2001; Yoshikawa et al. , 2004). Our team's preparation work has contributed to a growing database of GPR observations that can be used to help establish the geologic setting and history of the rover environment and select the locations of greatest interest for further investigation (e.
The crossed antenna elements are rotated by 45 degrees with respect to the Rover path (Fig. 2 ) not only to save space but also to detect whether scattering objects in the subsurface are located to the left or right of the rover. Both the surrounding support structure and the substrate for the antenna elements are made of a single type of honeycomb glass fiber reinforced plastic. The baseplate is also honeycomb, but is made of aluminum for its increased stiffness. The conductor used for the Vivaldi antennas is a thin layer of copper, the surface of which is treated with a gold layer. The baseplate accommodates both the mechanical and electrical interfaces to the rover. All materials used are listed in the outgassing databases of ESA and NASA and are used in other space applications, such as the TROPOspheric Monitoring Instrument spectrometer on the ESA Sentinel-5 Precursor satellite  (Veefkind et al.
As explained before, WISDOM collects a data set of N measurements, performed at different frequencies, to retrieve the desired response in the time domain, using an IFT. As a consequence, it is the SNR obtained in the time domain that is used for data interpretation. For a given A Att and S T , we use Eq. 4-accounting for the gain resulting from the number of coherent additions and the IFT-to identify, for a given target depth, the frequencies that will significantly contribute to the target detection. The achievable range resolution for the considered depth is estimated from the frequency bandwidth actually received by the instrument. To illustrate the performance capabilities of WISDOM, we consider two kinds of targets: a smooth interface and a moderately rough one, for which the area contributing to the reflection is assumed to be limited to the first Fresnel zone  (Cook, 1975; Grimm et al.
How will the instrument be used? Terrestrial experience has demonstrated that 3D techniques can significantly reduce the interpretive ambiguities associated with identifying subsurface structures and discriminating between lithologic and volatile units. WISDOM will contribute to understanding the 3D geology of the landing site in two important ways: (1) By conducting radar soundings at frequent intervals along the rover's traverse to construct 2D profiles of the subsurface over distances ranging from a few tens of meters up to several hundreds of meters, depending on the length of the EC. The along-track spatial resolution of such profiles is enhanced when the interval between successive soundings, which is controlled by navigation commands given to the rover, is reduced. In areas where the rover traverses cross or pass within close proximity of each other, the GPR sounding data can be used to provide a coarse 3D understanding of the local geological context, which can be used to investigate how the stratigraphy exposed in nearby outcrops and the interior of impact craters compares with the subsurface stratigraphy of the rover environment.
Field tests, with a remotecontrolled rover in terrestrial analogue environments, have shown that WISDOM can be operated continuously while the rover is in motion during a traverse (Gunes-Lasnet et al. , 2014) at a reduced speed of around 20 m/h. (2) By conducting targeted, high-resolution polarimetric surveys of areas of scientific interest, which are necessary for the identification and evaluation of potential sampling sites for the rover's drill. Such surveys (informally called a WISDOM grid) will be conducted by acquiring either three 10-m-long or five 5-m-long parallel profiles, *1 m apart, from which an interpolated 3D map of the local radar stratigraphy can be constructed. To detect buried rocks and make the best use of the polarimetric capability of the instrument, the spacing between consecutive soundings has been set to 10 cm, which is the maximum permissible distance for a correct 2D migration of data along track.
WISDOM ground data processing pipeline 4. 2. 1. WISDOM data processing. With two communication passes per sol through the ExoMars orbiter, all the data necessary to plan the activities for the next sol need to be transmitted to the Rover Operations and Control Center (ROCC) during the evening pass and must be processed rapidly so that telecommands can be transmitted to the rover during the following morning pass. All WISDOM data processing will be performed on Earth. The data volume for a WISDOM pattern is about 20 Mbits, which will be stored in a temporary memory onboard before being downloaded to Earth. One key element in the WISDOM data analysis is the fast and reliable classification of geological features and correct localization of subsurface scatterers and layers. To get the best out of the instrument, data processing must be adapted to the sounded environment, but the elements remain the same.
05 (black); e † = 0. 1 (red); and e † = 0. 25 (blue). The gray area corresponds to range resolutions better than 3 cm that cannot be achieved with the WISDOM frequency bandwidth. WISDOM, Water Ice Subsurface Deposit Observation on Mars. 2016). Then, the median, which is computed on a number of soundings, is subtracted from the whole set of data to remove any instrumental artifacts that might occur at constant delay (such as reflections on a part of the rover structure or inside the instrument). A first image is built and available for a first global environment description. The measured delays can be converted into distances once permittivity of the medium is known. The permittivity at depth is estimated from radar signatures of embedded scatterers (if any). More sophisticated instrument-specific, tool-based migration techniques and compressed sensing have been developed  (Lu et al.
, 2013; Zhang et al. , 2013)  and will be used in the future to process the WISDOM data. A detailed analysis of the data over two or more frequency intervals that span the entire bandwidth allows for the study of frequency-dependent physical processes (scattering, losses) to better characterize the subsurface. The fully polarimetric nature of the WISDOM measurements allows the use of modern methods for polarimetric data processing and a better characterization of the shape and orientation of the reflecting structures.
WISDOM data products. The different types of data products that will be delivered are shown in Table  4  with their estimated production/delivery time. The 2D radargrams converted to depth will be essential to plan the drilling operation. As a consequence, the time available for the processing is limited by the need to upload tele-commands for operation during the next sol. These 2D radargrams will undergo progressive refinement between T1 + 45 min and T1 + 6 h (T1 being the time when the antenna system location and orientation are made available for the WISDOM team) to meet this deadline. The mission science team members will discuss the scientific interest of each area. WISDOM and ADRON's contribution to this discussion will be to provide information about the nature of the subsurface. For WISDOM, this will include a tentative identification of potential targets and estimate of their potential depth, which must lie within the maximum 2-m depth that can be achieved by the drill.
Data Interpretation In a simple environment made of homogeneous units separated by smooth interfaces, detection of interfaces and retrieval of electrical properties are reasonably easy. In complex environments, wave propagation is modified by the roughness at interfaces and the volume scattering at spatial scales commensurate with the instrument wavelengths. As a result, quantitative interpretation of radargrams is not straightforward. This can only be efficiently achieved through three complementary tools and approaches that are currently being developed or adapted for use by WISDOM: (1) dielectric characterization of analogue samples in the laboratory, at ambient martian temperatures, over the frequency bandwidth employed by WISDOM for comparison with the future Mars data obtained by WISDOM; (2) numerical modeling of WISDOM radar propagation in realistic martian environments that will provide a data base of simulated radargrams that can be used to study the effect of subsurface characteristics (such as the size distribution of rocks, the roughness of interfaces) on the WISDOM data; and (3) field testing in a variety of martian analogue environments to validate WISDOM data processing techniques and interpretation as well as the instrument's end-to-end performance.
The main advantage of the frequency domain approach is that it allows computation of real and imaginary parts of permittivity and magnetic permeability of the sample from circuit parameters, assuming a specific equivalent circuit. In the time domain approach, the data analysis is more complicated and time-consuming. To obtain the same EM parameters, the time domain data should be transformed into frequency domain and fitted using some dielectric model  (Chen et al. , 2004; Kaatze, 2013). At the frequencies of interest for WISDOM, the most suitable method with which to estimate the simulants' EM properties is based on the measurement of wave propagation parameters in a transmission line filled with test material. Velocity and attenuation of EM signals can be measured in the time domain by means of the so-called time domain reflectivity technique using a cable tester equipped with a pulse generator and an oscilloscope connected to the open end transmission line.
Such buried landforms are expected in Oxia Planum, where orbital imaging of eroded sedimentary deposits has revealed inverted channels that confirm fluvial erosional and sedimentary processes occurred at the same time  (Quantin et al. , 2016). The ability of GPR to detect buried channels on Mars has already been demonstrated by the SHARAD orbital sounder on the Mars Reconnaissance Orbiter, which revealed kilometer-scale evidence of buried channels in the Elysium volcanic region  (Morgan et al. , 2013). Small channels are indicative of surface runoff and indicate that conditions were once favorable for atmospheric precipitation. The identification of such landforms by WISDOM will provide unique insights into environmental conditions that prevailed during the time the channels were formed. To test the ability of WISDOM to detect and characterize buried channels, we modeled the radar profile it would acquire over a small-sized buried channel, using the EM modeling software, TEMSI-FD.
Field tests in natural environments A prototype that is representative of the WISDOM Flight Model (same design, mass, volume, power consumption, and function) is currently being used for field testing. Because liquid water is highly conductive and a strong reflector, its presence in soil greatly attenuates the propagation of radar waves. Therefore, we have conducted our field tests in either dry (Atacama Desert in Chile, Etna in Italy) or cold (glacier in the Alps, Dachstein ice caves in Austria) environments. This testing, in well-characterized environments, is critically important for validating the data processing and analysis tools to be used and for a better understanding of the instrument's likely performances on Mars. The data collected in various environments (Fig. 8 ) have demonstrated WISDOM's ability to resolve shallow finescale structures and thus provide clues for understanding the geology and geological evolution of the site.
During the SAFER experiment in Atacama, WISDOM was used to sound an area where a buried channel bed was exposed at the surface. WISDOM succeeded in detecting and following the bottom of the channel beneath *50 cm of sediment (Fig. 8b ). Colocalization with surface data allowed for a successful 3D representation of the site. The radargrams in Fig. 8  were obtained by a simple time-to-depth conversion assuming a uniform permittivity value the subsurface (corresponding to data product #6bis of Table  4 ). The permittivity value used was estimated from the surface echo, namely 5. 7 for the Etna (a) and 3. 7 for Atacama (b). Field testing in the ice caves of Dachstein, Austria, and volcanic ash deposits at Mt. Etna demonstrated that by acquiring data in a grid pattern, WISDOM soundings can be used to construct a 3D representation of buried interfaces, whether beneath ice or sediments.
Because the radiation pattern of the antennas is directional, the soundings performed with the two copolarization configurations can be used to constrain the 3D location of buried blocks  (Dorizon et al. , 2016). An example of a 3D reconstruction of bedrock beneath a layer of ice and an example of a 3D localization of individual small rocks embedded inside the ice are presented in Fig. 9. In addition to testing WISDOM's capabilities, field campaigns are also underway to refine team, instrument, and rover operations (rover navigation, payload operations, fast data processing, and interpretation) in preparation for landing on Mars in 2020. During the SAFER experiment in 2013, a preliminary version of ESA's ExoMars Rover with onboard prototypes of three ExoMars instruments-WISDOM, PanCam, and Close-UP Imager (CLUPI)-was operated in Chile from a remote control center at the Satellite Applications Catapult facility in Harwell, United Kingdom.
WISDOM will also help to ensure the safety of drilling operations by identifying potential hazards and successful retrieval of subsurface samples. To ensure accurate interpretation of WISDOM data to be returned from Mars, field tests are currently being conducted with a fully functional instrument prototype in a wide variety of arid and cold climate environments. The resulting database of well-characterized terrestrial analogue environments will improve our ability to characterize similar environments on Mars and provide valuable experience in procedures and data analysis for the team. Ongoing field tests with other instruments of the Pasteur payload (including PanCam, CLUPI, ADRON, and Ma_MISS) will continue to be conducted to enhance their operational and scientific synergies. would like to recognize the support of the ExoMars project, ESA, Roscosmos, and the European states and agencies participating in the ExoMars program.
Acknowledgments  J. J-M. acknowledges support by the  DOE Office of Nuclear Physics  through Grant No. DE-FG02-09ER41620  and by the  Idex Paris-Saclay  though a Jean d'Alembert grant and would like to thank  L. Dixon  for helpful correspondence on spinor helicity methods. Support has been received in part by  UNAM-DGAPA-PAPIIT  grant number  IN101515  and by  Consejo Nacional de Ciencia y Tecnología  grant number  256494. M. H. acknowledges support by  CONACyT-Mexico  grant numbers  CB-2014-22117  and  Proy. No. 241408 as well as the Red-FAE. M. E. T. -Y. acknowledges support from  CONACyT-México  sabbatical grant number  232946  and the kind hospitality of ICN-UNAM provided in the initial stages of this collaboration.
For processing the HTAP inventory, population was not used as a parameter for spatially distributing the emissions. For the NA domain, the 2008 National Emission Inventory was used as the basis for the 2010 emissions, providing the inputs and datasets for processing with the Sparse Matrix Operator Kernel Emissions (SMOKE) processing system  (Mason et al. , 2012). Specific updates for the year of 2010 were made for several sectors, including mobile sources, power plants, wildfires, and biogenic emissions. Details are given in  Im et al. (2015a, b)  and  Pouliot et al. (2015). Typically, emission processors use annual emission total, while AQ models require hourly input values. Therefore, proxy variables and surrogate fields are used to spatially disaggregate the annual total and to allocate them temporally.
6 (https://www. cmascenter. org/smoke) or as implemented directly into CMAQ, while CMAQ1, CMAQ2, and CMAQ3 calculated biogenic emissions through the MEGAN model  (Guenther et al. , 2012). Moreover, the CCLM-CMAQ model does not include the dust module, while the other CMAQ instances use the inline calculation  (Appel et al. , 2013)  and CMAQ1 uses the dust calculation previously calculated for AQMEII Phase 2. Finally, all runs were carried out using CMAQ version 5. 0. 2 except for CMAQ1, which is based on the 4. 7. 1 version. A series of known shortcomings of CMAQ v. 5. 0. 2 are discussed in  Appel et al. (2016)  (and partially addressed in the new version 5. 1 of the model), among which is the tendency to underestimate the vertical mixing during transition periods, with the net result of increasing the concentration of primary pollutants and reducing that of ozone as a consequence of more available NO x.
, 1993) , the Yonsei University (YSU) PBL scheme  (Hong et al. , 2006) , the NOAH land-surface model, and the updated version of the Grell-Devenyi scheme  (Grell and Devenyi, 2002)  with radiative feedback. Chemical options include RADM2 chemical mechanism  (Stockwell et al. , 1990) , MADE/SORGAM aerosol module  (Schell et al. , 2001)  including some aqueous reactions, and Fast-J photolysis scheme. The modeling domain covers Europe and a portion of North Africa. Simulations of WRF-CAMx over EU were performed by RSE (Italy) using CAMx version 6. 10 (Environ, 2014) with Carbon Bond 2005 (CB05) gas-phase chemistry  (Yarwood et al. , 2005)  and the coarse-fine (CF) aerosol module. Input meteorological data were generated by WRF model version 3. 4. 1  (Skamarock and Klemp, 2008; Skamarock et al.