dataesr/scientific-paragraphs-categorization huggingface dataset:
A4 Model #3 Oslo-DISORT (G. Myhre) The Oslo-DISORT code uses the discrete-ordinate method (DISORT)  (Stamnes et al. , 1988)     (4 bands), with the main emphasis on wavelengths below 1. 5 µm. The spectral regions are 0. 3-0. 5 µm, 0. 5-0. 85 µm, 0. 85-1. 5 µm, and 1. 5-4. 0 µm. The absorption by water vapor and ozone is taken into account by the exponential-sum fitting method (ESFT,  Wiscombe and Evans, 1977). The number of exponential-sum fitting terms for each spectral region is two or three. Higher accuracy can be obtained with a higher number of exponential-sum fitting terms, but this increases the computational time. The GENLN2 line-by-line model  (Edwards, 1992)  is used to calculate the transmission data for water vapor with spectroscopic data from the HITRAN92 database  (Rothman et al.
aer. com/rrtm frame. html) is a solar radiative transfer model that utilizes the correlated-k (ck-D) approach to treat gaseous absorption and to calculate shortwave fluxes and heating rates efficiently and accurately in a large-scale model environment  (Clough et al. , 2005; Iacono et al. , 2008). Modeled sources of extinction are water vapor, carbon dioxide, ozone, methane, oxygen, nitrogen, aerosols, clouds, and Rayleigh scattering. The solar spectrum, 0. 2-12 µm, is divided into 14 bands and spectral extinction integration within each band is accomplished using a variable number of g-points that add to 112 g-points for the entire solar spectrum. Absorption coefficient data for ck-D are obtained directly from the line-by-line radiative transfer model, LBLRTM, which has been extensively validated against observations, principally at the ARM SGP site.
00 µm). Water vapor absorption is accounted for in the 0. 7-4. 0 µm spectral interval; ozone is accounted for in the 0. 2-0. 4 µm (UV) and in 0. 5-0. 6 µm (VIS) spectral intervals. For water vapor and water vapor continuum, we use the k-distribution method proposed by  Chou and Lee (1996)  and further advanced by  Tarasova and Fomin (2000). Reference transmission database is HITRAN96. Ozone parameterization follows  Lacis and Hansen (1974). The model is configured with variable number of layers (>31), depending on presence of aerosol and/or clouds. More details can be found in  Wang and Pinker (2009). For this study 1 km resolution was used below 25 km; above this level, the vertical resolution for ozone and water vapor profiles is 5 km. atmosphere using a delta-two-stream algorithm. The solar spectrum is divided into 19 discrete spectral and pseudospectral intervals: 7 for O 3 , 1 for the visible, 7 for H 2 O including water-vapor continuum, 3 for CO 2 , and 1 for the near-infrared following  Collins (1998).
The solar absorption by water vapor between 1000 and 18 000 cm -1 is treated using seven pseudo-spectral intervals with a constant specific extinction specified for each interval. These extinctions have been adjusted to minimize errors in heating rates and flux divergences relative to line-by-line (LBL) calculations for reference atmospheres  (Anderson et al. , 1986)  using GENLN3  (Edwards, 1992)  combined with the radiative transfer solver DISORT2  (Stamnes et al. , 1988). This parameterization is essentially an exponential sum fit (e. g. ,  Wiscombe and Evans, 1977). LBL calculations are performed with the HITRAN 2000 line database  (Rothman et al. , 2003)  and the Clough, Kneizys, and Davies (CKD) model version 2. 4. 1  (Clough et al. , 1989). The Rayleigh scattering optical depths in the seven pseudo-spectral intervals have been changed for consistency with LBL calculations of the variation of water-vapor absorption with wavelength.
A24 Modle #27 ULAQ (G. Pitari, G. Di Genova) The University of L'Aquila radiative transfer module, operating on-line in the climate-chemistry coupled model ULAQ-CCM, is a two-stream delta-Eddington approximation model  (Toon et al. , 1989)  used for chemical species photolysis rate calculation in UV-visible wavelengths and for solar heating rates and radiative forcing in UV-VIS-NIR bands. Species cross sections are updated using JPL (2011) recommendations from the MPI-MAINZ database, while water vapor absorption data are derived from HITRAN92. Schumann-Runge bands are treated following the parameterization of  Minschwaner et al. (1993)  based on (fixed-T) ODF formulation. Diurnal averages are calculated with a 5 point Gaussian quadrature. Top-of-atmosphere solar fluxes are taken from SUSIM-SL2 and LOWTRAN7 and are carefully integrated on the wavelength bins used in the model: they are in total 150 in the UV and visible range and 100 in the NIR, covering the solar spectrum from Lyman-alpha up to 7 µm.
A27 Model #30 MPI-MOM (S. Kinne) The Max Plank Institute for Meteorology MPI-MOM scheme combines the matrix-operator  (Plass et al. , 1973)  and adding doubling method. The method was prepared for atmospheric broadband sold radiative transfer calculation by  Grassl (1978). The trace gas absorption (including water vapor) in the near-IR is based on LOWTRAN-5 data and ozone absorption data are based on  Vigroux (1953). Trace-gases were interpolated to the model resolution in the vertical using the log of pressure. A28 Model #31 Matrix-Operator Model (MOMO; J. Fischer, L. Doppler) MOMO is a radiation transfer code for radiance and irradiance computations in the ocean and atmosphere  (Fell and Fischer, 2001; Fischer and Grassl, 1984). Its spectral range is 0. 2-100 µm. MOMO combines the matrix-operator  (Plass et al.
, 1973)  and adding doubling method. The gas transmission is computed using a code CGASA, derived from XTRA  (Rathke and Fischer, 2000). CGASA combines the water-vapor continuum model of  Clough et al. (1992)  with Voigt line computations. Line properties are taken from the HITRAN-2008 spectral database  (Rothman et al. , 2009). A k-distribution method is used, following  Bennartz and Fischer (2000). This k-distribution is exact (we do not make the correlated approximation). For this study, we computed MOMO simulations within 55 UV bands and 12 VIS bands, in order to consider the high variation of Rayleigh optical depth. 30 bands have been defined to model the near-IR radiation. 3000 k-intervals have been needed to model water vapor, ozone and mixed gas absorption lines with accuracy. The vertical resolution was 1 km from 0 to 26 km and 2 km from 26 to 100 km.
Matrix-Operator adding-doubling, ck-D (LOWTRAN5+Vigroux), 8 (4/4), µm, 20-layers 31 MOMO Doppler, Fischer Matrix-Operator adding-doubling, non-correlated k (HITRAN-2008), 97 (67/30), 0. 2-100 µm, AFGL a See Appendix A for further model details. Appendix Table A1 provides additional information on gaseous transmission. b Format: #-streams and multiple-scattering scheme, gaseous transmittance scheme (transmission database), total # bands (# UV-Vis/# Near-IR), full λ-range [µm], vertical resolution. c Abbreviations: RT = radiative transfer, LBL = line-by-line, DISORT = discrete-ordinate method, PIFM = Practical Improved Flux Method, Ed = Eddington, δ-Ed = delta Eddington, ES96 = Edwards and Slingo (1996), SES = Sun-Edwards-Slingo, CS = Chou and Suarez, FL = Fu-Liou, FLG = Fu-Liou-Gu, ESFT = exponential sum fit transmission, ck-D = correlated k-distribution, AFGL = Air Force Geophysical Laboratory vertical resolution.
Acknowledgements. C. Randles was supported by the  NASA Atmospheric Composition Modeling and Analysis (ACMAP) Program  Grant  NNX11AK20G  under  R. Eckman. G. Myhre  was supported by the projects  SLAC  ( Short Lived Atmospheric Components ) and EarthClim funded by the  Norwegian Research Council. M. Schulz  acknowledges support under the  EU   ECLIPSE  project, contract No. 282688. J. Huttunen  was supported by  Graduate school in Physics, Chemistry, Biology and Meteorology of Atmospheric Composition and Climate Change :  From Molecular Processes  to  Global Observations  and  Models. D. Neubauer  and  R. Hitzenberger  acknowledge financial funding from the  University of Vienna , FPF 234 and thank  Warren Wiscombe  ( Goddard Space Flight Center ) for the  ESFT program. D. Neubauer  gratefully acknowledges the support by research fellowship F-369,  University of Vienna.
L. Oreopoulos  and  D. Lee  acknowledge support from  NASA 's  Modeling Analysis and Prediction program  managed by  D. Considine. The work of  R. T. Pinker  and  Y. Ma  was supported under grant  NNX11AF54G  through the  NASA Science of Terra and Aqua Program  and grant  NNX08AN40A  from the  NASA Science Mission Directorate-Division of Earth Science. S. T. Rumbold  was supported by the  Joint DECC  and  Defra Integrated Climate Programme  ( GA01101 ). H. Yu was supported by  NASA ACMAP , managed by  R. Eckman. F. Zhang  was supported by  US DOE office of Biological and Environmental Research (BER)   DE-SC0001683. H. Zhang  was funded and supported by the " Strategic Priority Research Program -Climate Change: Carbon Budget and Relevant Issues " ( XDA05040200 ) and " National High-tech Research and Development (863) Project  ( 2011AA12A104 )".
We used 0 -8-d-old adults for the electrophysiological recordings. There are essentially no differences in nature of responses to various stimulants among sensilla prepared from adults of varying in age (data not shown). A foreleg (tarsus, tibia, and femur) was removed from the body using forceps and then fixed on a polystyrene block with fine strips of sticky paper tape. A glass capillary electrode filled with Ringer's solution (CaCl 2 •H 2 O; 3 mM, KCl; 182 mM, NaCl; 46 mM, Tris base; 10 mM, pH 7. 2) was inserted into the leg and connected to the ground. The leg was oriented such that sensilla from the ventral side of the fifth tarsomere were accessible to stimulation by glass capillary electrodes (diameter ϳ20 m) filled with a solution of a given compound. Nerve responses were recorded using the tip-recording method  (Hodgson et al.
, 1955; Calas et al. , 2006). The electrical signal was amplified by a TastePROBE preamplifier DTP-02 (Syntech) (Marion-Poll and van der Pers, 1996) and further amplified and filtered (10 -2800 Hz) by a CyberAmp 320 amplifier (Molecular Devices). Data were recorded and stored on a computer with a 16-bit A/D conversion card DT9803 USB A/D (Data Translation) under the control of the custom software dbWave  (Marion-Poll, 1995 , 1996). Recordings were analyzed using dbWave (http://taste. versailles. inra. fr/deterrents/tk/dbwave/) to detect and sort spikes according to their amplitude and shape. We paid particular attention to correlate our recordings to L1, L2, and S sensilla types. Within the L1 sensilla, we counted the number of responsive and nonresponsive sensilla for a given stimulus. For the "responsive" sensilla, we evaluated the number of active cells based on the spike shape and the presence of doublet spikes, which indicate that at least 2 cells are active  (Meunier et al.
, 2003). All electrophysiological data for L2 and S sensilla were combined, because these sensilla showed almost identical electrophysiological responses to all tested compounds (data not shown). Assay for oviposition behavior. Females were hand-paired on the day of (or 1 d after) eclosion. For the assay, we used mated 3-6 d-old females, D, L sensillum on fifth tarsomere with single pore at the apex (arrow). Scale bar, 20 m. E, Numbers of sensilla on first to fifth tarsomeres (mean Ϯ SD, n ϭ 6 for first-fourth tarsomere, n ϭ 21-42 for fifth tarsomere). *p Ͻ 0. 0001 female versus male (Student's t test). The second to fifth tarsomeres housed all three types of sensilla, whereas the first tarsomere had S type sensilla only. Sexual dimorphism was observed only in the number of L1 sensilla on the second to fifth tarsomeres.
We presented the wetted artificial leaf to each female for 2 min and then examined it for eggs to assess the oviposition responses to each solution. Scanning-electron microscopy. Tarsi of female butterflies were removed from body by using forceps, dehydrated in an acetone series, dried in air. Mounted legs were sputter-coated with gold and observed by a JSM-5300 LV scanning electron microscope (JEOL). Statistical analysis. Paired comparison and group comparison were made with the Student's t test and the one-way ANOVA followed by Tukey-Kramer post hoc test, respectively, using KaleidaGraph Version 4. 01 (Synergy Software). A correlation coefficient between the number of spikes and oviposition activities was calculated by Microsoft Excel for Mac, 2011 Version 14. 2. 4 (Microsoft).
Black-box models The black-box models are based on statistical data-driven approach. The relationships between the system inputs and outputs are not based on physical equations as for analytical models, but are deduced through suitable experimental databases. The experimental data are split in two different sets, one dedicated to the training procedure for the identification of the input/output correlations and one used for the model validation. Implementation for black-box models is well suited for complex non-linear systems such as PEMFCs, where the identification of physical parameters of grey-box models may require high numerical efforts  [17]. On the other hand, a large amount of experiments is required for model identification. The models available in the literature for PEMFC blackbox modelling are introduced in the next sections.
NN models mentioned in this section are further summarized in Table  2. Compared with the analytical methods, ANN has the advantage of an excellent non-linear approximation ability and fewer assumptions for model construction  [45]. Furthermore, it has a low sensitivity to noise and can be built based on incomplete database  [46]. However, usually a large amount of dataset under a wide range of operating conditions is needed. When using MLPNN, the determination of the number of hidden layers and the number of neurons of each layer is also a critical issue.
Most relevant and non-redundant input variables for each module are firstly selected through utilizing mutual information based technique. Then, a Gas-tofaneKessel (GK) clustering algorithm, which has an adaptive distance norm and is suitable for detecting clusters of different geometric shapes in the dataset, is applied for extracting fuzzy rules from data. An efficient prediction is finally provided at different load conditions, evaluated by two performance indices, the variance accounted for (VAF) and the root mean square error (RMSE). However, dataset for training and testing is obtained from an analytical model of a 50 kW PEMFC system constructed in MATLAB/SIMULINK, not from a real operational system. Fennie et al. [50]  aims to predict the state of health (SOH) of PEMFC stacks by developing a fuzzy logic model.
Data from two 5 W PEMFC stacks are used for training, and data from the other two are for testing. Both EIS measurements and I/V data are applied for building the fuzzy logic model. By applying a subtractive clustering to find initial membership functions and rules, a three-input and one-output model is developed. An encouraging correct rate of 87% is finally obtained on the test data for detecting three types of health states-drying, flooding and healthy. To further improve the accuracy and the robustness, more complete dataset should be collected and a fine tuning of the model using supervised learning algorithm is needed. In Ref. [17] , a fuzzy model with two inputs, the stack voltage and the stack current, and one output-satisfaction rate (SR) is built for detecting two types of faults in a 500 W PEMFC system.
4. 3. Adaptive neuro-fuzzy inference systems (ANFIS) Compared with the previous artificial intelligence methods, ANFIS is still not so popular in PEMFC diagnosis domain. However, numerous models based on it can be found and have demonstrated obvious advantages in the literature [51e53], which could be useful for further developments of model-based diagnosis methodologies. ANFIS model, as an effective combination of neural network and fuzzy logic, has gained more and more acceptance in the field of non-linear system modelling. As already mentioned, neural network has the limitation that a wide range of dataset under different operational conditions is needed, while fuzzy logic depends completely on human expert knowledge. In an ANFIS model, the membership functions and rules of the fuzzy system are defined and optimized by ANN, thus not requiring any prior knowledge of the system  [52].
Neural networks, fuzzy logic, adaptive neuro-fuzzy inference systems, and support vector machines methods applications have been reported in this paper. Artificial neural networks are mostly used in non-linear dynamic modelling  [38,40,43e45]. Starting from an input/ output dataset, ANN learning process allows the system nonlinear mapping. Residuals are directly generated comparing model outputs and experimental results with a high accuracy (less than 2. 9%)  [43]. ANN guarantees an excellent non-linear approximation with a low sensitivity to noise. The main drawback is that the training process needs of a large amount of dataset under a wide operating condition range which collection might be costly and time consuming. Some authors  [17, 49, 50]  introduce successfully the fuzzy logic techniques for on-line PEMFC monitoring, especially for flooding detection.
In literature non-model based approaches are also available. These methodologies can be knowledge-based or signal-based. In the non-model based approaches, FDI is performed through fault classification and no residuals are generated. Available experimental data are therefore processed and normalized. Then the different features, which are relevant for fault detecting are extracted. These features are analysed in a proper low-dimensional space. Several techniques such as NN, fuzzy logic, ANFIS, and SVM are employed as fault classifier. This is the main difference between blackbox models and clustering techniques. While in the first approach, artificial intelligence and statistical techniques are adopted to model the system and generate residuals, in the second one they are used to classify the fault in a feature space.
Acknowledgement The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) for the  Fuel Cells and Hydrogen Joint Technology Initiative  under grant agreement n 256673 e project  D-CODE (DC/DC COnverter-based Diagnostics  for PEM systems). The support of  University of Salerno (FARB projects)  is also acknowledged. references.
and Zahedi A. (2011) [57] (1) Stack current (1) Oxygen excess ratio Air flow control of a PEMFC system (2) Compressor voltage at pressure clusters of different geometric shapes in the dataset in the preceding layers. The neural network Li, X. et al. (2006) [59] (1) Hydrogen flow rate (1) Operating temperature Stack temperature control of (2) Cooling water flow rate a 1 kW PEMFC stack (3) Air flow rate.
A search for highly ionising, penetrating particles with electric charges from |q| = 2e to 6e is performed using the ATLAS detector at the CERN Large Hadron Collider. Proton-proton collision data taken at √ s = 7 TeV during the 2011 running period, corresponding to an integrated luminosity of 4. 4 fb -1 , are analysed. No signal candidates are observed, and 95% confidence level cross-section upper limits are interpreted as mass-exclusion lower limits for a simplified Drell-Yan production model. In this model, masses are excluded from 50 GeV up to 430, 480, 490, 470 and 420 GeV for charges 2e, 3e, 4e, 5e and 6e, respectively.
The data sample corresponds to an integrated luminosity of 4. 4 fb -1. Multi-charged particles will be highly ionising, and thus leave an abnormally large specific ionisation signal, dE/dx. In this Letter, a search 1 The term long-lived in this paper refers to a particle that does not decay within the ATLAS detector. for such particles traversing the ATLAS detector leaving a track in the inner tracking detector, and producing a signal in the muon spectrometer, is reported. A SM-like coupling proportional to the electric charge is assumed as the production model of the multi-charged particles. Therefore, the main production mode is Drell-Yan (DY) with no weak coupling. Multi-charged particles can also be pair-produced from radiated photons resulting in a larger production cross section, and in some cases non-perturbative effects  [20]  can also enhance the production rate.
Summary A search for long-lived, multi-charged particles has been performed using an integrated luminosity of 4. 4 fb -1 of pp colli- : Upper limits on the production cross section of multi-charged highly ionising particles from pair-production as a function of particle mass. The dotted line shows the expected limit with the ±1σ and ±2σ uncertainty bands. The observed limit is compared with the predicted rapidly falling cross section from the DY model. The plots are shown separately for charges from |q| = 2e to |q| = 6e. In the |q| = 2e case, the observed limit lies on top of the expected limit. sions recorded by the ATLAS detector at the LHC. No candidates are found in the 2011 data set, consistent with the background expectation. The results presented here are the first mass limits from ATLAS for charges of 2e to 6e, filling the missing range of charges between the searches for slow singly charged long-lived particles  [10]  and searches for particles with charges from 6e to 17e  [8].
Acknowledgements Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
Qualitative data were collected from physicians, nurses, other service providers, administrative staff, and medical students. Personnel from seven primary health centers (two in Bolivia, three in Ecuador, and two in Nicaragua) that are involved in the CERCA project were invited to take part in the study. This study was the introductory component of future interventions that aimed to involve all health center personnel, taking into account that the transfer of knowledge to colleagues is rather low  (25). Thus, all personnel, including auxiliary administrative staff, and medical students who were in training at health centers during the study, were invited to take part in the study. Discussion meetings were scheduled in all of the seven centers; in two centers two discussions were performed.
Limitations of the study This study had several limitations. First, the groups of study participants were not similar across the countries: medical students were present only in Bolivia. In Nicaragua, groups consisted nearly exclusively of healthcare providers: physicians and nurses. Physicians dominated in Nicaragua, nurses, auxiliary medical staff, and medical students in Bolivia. However, the categories and themes, except the last theme (supporting policies) consistently represented the data from all three countries, indicating similarities in the perspective of health center personnel. The views and experiences of the participants may not represent those of the larger primary healthcare providers' community since the study included only providers from those primary healthcare facilities that took part in the CERCA project.
On the other hand, we believe that participation of all personnel in the development of interventional strategies favorably affected the selection of the most appropriate initiatives for each center and increased the commitment of healthcare providers to project activities that were in line with the idea of communitybased participatory research. The individuals collecting the data (moderator and note-taker) were CERCA staff members. This may have influenced study participants' opinions. However, as participants identified the factors in written form before the discussions were held, the influence of CERCA staff was likely to be limited.