dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012). In DEM, the rock-like material can be simulated as a dense assembly of rigid or deformable particles that interact at their contact points. Also, the discrete bodies can detach and new contacts can automatically be detected  (Itasca, 2013a)  and therefore fractures can be simulated at the grain (or block) boundary opening. In the Bonded-Particle Model (BPM), which falls within the DEM formulation, the particles are represented by rigid disks in 2D and rigid spheres in 3D  (Potyondy and Cundall, 2004). The breakage of bonds between the spheres in the BPM is comparable to fracturing in rock. The BPM which is implemented in Particle Flow Code (PFC)  (Itasca, 2008)  has been used widely for crack damage simulation in rock. The concerns and possible solutions for the accuracy of PFC simulations have been documented in the past by many researchers, including  Diederichs (2003) ,  Potyondy and Cundall (2004) ,  Cho et al.
The Neper software All algorithms described previously have been implemented in a free (open-source) software: Neper  (Quey et al. , 2011; Quey, 2014). The software offers support for both single-scale and two-scale Voronoi tessellations, in 2D and 3D, however the focus of this paper is on single-scale 3D Voronoi tessellated models. Thanks to efficient and robust algorithms, very large tessellations can be easily created (typically 105 cells). Any convex domain shape can be used; cubes (or any parallelepiped) and cylinders are directly supported. The tessellations can also be stretched to get elongated cells. Rotation and cropping are also straightforward operations. Finally, tessellations can be written at various formats, including 3DEC for DEM simulations, and meshed for FEM simulations.
Performance parameters We evaluate three key performance metrics. The network lifetime can be defined in three ways  [38] : the time taken to exhaust the battery of the first network node, the time taken to exhaust the battery for N network nodes, and the time when the battery of the last network node is exhausted. We choose the second way. Energy consumption is the average of the energy consumed by nodes participating in packets transfer from the source node to the destination node during the whole simulation. End-to-end delay is the average transmission delay of data packets that are delivered successfully over the total duration of the simulation.
Mobile laboratory "MoLa" The Mobile Laboratory "MoLa" developed by the Max Planck Institute for Chemistry in Mainz consists of a regular Ford Transit delivery vehicle as the platform with a fully equipped mobile aerosol and trace gas laboratory. The aerosol inlet system was optimized and characterized for minimum particle losses and sampling artifacts using the software tool Particle Loss Calculator  (von der Weiden et al. , 2009). An extensive description of MoLa including technical features, inlet system characterization and instrumental equipment is presented in  Drewnick et al. (2012). Instrumentation: for the MEGAPOLI field campaigns MoLa was equipped with the instrumentation for the investigation of Additionally, meteorological parameters as well as the GPS vehicle position were logged and events in the surrounding were filmed using a webcam.
Otherwise it is not possible to clearly distinguish between the Paris emission plume and local contamination contribution, and the characterization of the urban plume would be significantly biased. Several methods for the removal of local pollution contamination were tested  (Drewnick et al. , 2012). Finally, a "video analysis method" was applied to the MEGAPOLI mobile data sets. Here the videos recorded by a webcam looking through the windshield during the mobile trips were analyzed to identify time periods with potential local contamination. These time periods were excluded from the mobile data sets. Further details about the applied removal procedure can be found in von der  Weiden-Reinmüller et al. (2014). The analysis results presented in this paper are based exclusively on the "decontaminated" data sets.
High-resolution mass spectra analysis One advanced analysis method applied in this work is the analysis of high-resolution mass spectra using "Peak Integration by Key Analysis" (PIKA; ToF-AMS Analysis Software Homepage, 2013). The algorithm implementing this method is generally applied for in-depth analysis of high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) data. While the standard AMS data analysis yields unit-mass resolution information only, PIKA is used to obtain information on individual fragment ions from the high-resolution mass spectra. In combination with APES (Analytical Procedure for Elemental Separation; ToF-AMS Analysis Software Homepage, 2013) it is possible to quantify the overall contribution of different elements (H, C, O, N, S) to the highresolution AMS signals, enabling elemental analysis with the HR-ToF-AMS.
Combining these tools the O / C ratio of the organic aerosol was calculated for the MEGAPOLI data sets. Only the peaks up to m/z 100 were considered to ensure sufficient resolution for separation of individual peaks. Since m/z larger than 100 contain only a negligible fraction of the total mass (typically < 1 % of the total signal and about 5 % of the organic signal during the MEGAPOLI MoLa summer and winter measurements), this is an acceptable limitation. The O / C ratio is an important marker for the oxidation level and therewith the age of the particulate organic matter  (Aiken et al. , 2007 (Aiken et al. , , 2008)). In this study we used low O / C ratios as a marker for fresh pollution in the Paris emission plume opposed to aged pollution in background air masses characterized by high O / C ratios.
5 and 1. 5 in steps of 0. 1 (with seed = 0). The resulting 51 (for seed variation) and 31 (for fpeak variation) PMF factor mass spectra and time series were used to estimate the uncertainty of each PMF factor by means of standard deviation and mean values  (Freutel et al. , 2013). In general, the uncertainty due to the fpeak variation is larger (2 to 32 % for the different factors) than that due to the seed variation (< 1 to 7 %), regarding the 5 factor solution in summer and the 3 factor solution in winter (see below) for the MoLa MEGAPOLI data set. The 6 factor solution in winter (see below) shows higher uncertainties (8 to 52 % for seed variation and 12 to 52 % for fpeak variation). This is likely due to the larger sensitivity of the solution when separating one factor into two that have very similar properties (i.
This threshold has nothing to do with local contamination events at the first place. Local pollution contamination, as defined above, was not removed from the AMS data before PMF application but afterwards from the resulting factor time series (after the PMF calculations were finished). The results of the investigation of PMF factor uncertainties can be summarized as follows: the relative uncertainties of the AMS mass concentrations are significantly lower than those of the absolute values and thus do not affect the significance of the results presented in this work. MEGAPOLI summer data: the number of PMF factors for the MoLa MEGAPOLI summer data set was varied between 2 and 10, with the 3 to 6 factor solutions producing explainable results that were examined in detail. The 3 factor solution consists of one factor describing a more oxidized organic aerosol (oxygenated organic aerosol, OOA) and two factors that are both mainly a mixture of hydrocarbon-like organic aerosol (HOA) and cooking-related organic aerosol (COA).
We decided that the 5 factor solution provides the best view on the organic aerosol during the MEGAPOLI summer campaign measured with MoLa. In summary, the factors of the 5 factor solution are -HOA: fresh pollution marker, associated with traffic emissions; -COA: fresh pollution marker, associated with cooking emissions; -SVOOA1: medium-aged pollution marker, associated with a mixture of various sources including biomass burning; -SVOOA2: medium-aged pollution marker, associated with a mixture of various sources including biogenic emitters of precursor gases for secondary organic aerosol; -LV-OOA: aged pollution marker, associated with longrange transported pollution. In the Supplement (Sect. S1) the factors of the 5 factor solution and their identification are described in more detail. MEGAPOLI winter data: for the MEGAPOLI winter data set also the 2 to 10 factor solutions were calculated, while the 3 to 6 factor solutions were examined in detail.
To fulfill this requirement the mobile measurement routes were chosen to cover a sufficiently large area so that both, Paris influenced and not influenced air masses were measured during the same trip. The identification of the emission plume was performed in several steps: 1. The expected direction of the emission plumes was determined from meteorological forecasts and Prev'Air pollution forecasts. Based on this information the measurement route was chosen (for details see von der  Weiden-Reinmüller et al. , 2014). 2. All data sets from all measured variables (validated, cleaned from local pollution influence) were searched for fingerprints from the emission plume in the region where approximately -according to pollution re-analysis maps (for details see von der  Weiden-Reinmüller et al. , 2014)  and meteorological data -the plume was expected.
This step was made without any preference for certain types of variables like concentrations of fresh pollution markers. 3. The emission plume was identified by the combination of meteorological data, pollution re-analysis maps and a simultaneous increase of concentration levels in several measurement parameters. In this process it turned out, that the emission plume was mainly visible in the data sets of fresh pollution markers. Therefore, these data were finally used to determine the location of the plume and of the background without plume influence. Only after this determination average plume and background concentrations were calculated for all variables. So the definition of plume and background air masses is based on the combination of information. In the following, the procedures applied to identify time periods of plume and background measurements in the data sets are exemplified for the different types of measurements performed.
g. , favoring district heating) in the city. In rural areas usually wood burning is a more common way of domestic heating. Particle number size distributions: in Fig. 4  averaged particle number size distributions for emission plume and background air masses during summer and winter are presented. These data were measured by the FMPS (fast mobility particle sizer) instrument (size range 5. 6 to 560 nm). Again, the particle size distributions were calculated as average values for the two categories "background" (B) and "emission plume" (P ) as described in Sect. 3. 1. , analog to the average pollution concentrations shown in Figs. 2  and 3. The contribution of the Paris emission plume (on top of the average background particle size distribution) is depicted as gray shaded area. In summer the background aerosol shows mainly one broad particle number distribution mode between a few nm and 200 nm.
S8  in Sect. S3). During the winter campaign the axial trips were much shorter (up to 100 km) than in summer (up to 180 km) and the wind direction was not as stable as in summer. However, the database of 3 axial trips in summer and in winter is too small for a comprehensive comparison of model and measurement data. For more measurement examples showing the exponential axial decrease of the emission plume, and the corresponding model results, see Figs. S8 and S9 in Sect. S3.
2. 4. 3. A necessary requirement for this analysis method is a connected flow between both measurement locations. We confirmed this situation by analysis of the measured wind direction at several stationary sites and additional backward trajectories utilizing HYSPLIT  (Draxler and Rolph, 2013; Rolph, 2013)  for one measurement day in summer and one in winter. The estimated transport time of the emission plume between the two locations (3 h for both measurements) was used to identify the corresponding time periods in both data sets that have to be compared for analysis. Average organic mass spectra were calculated for the two locations during the respective time periods, which comprise several hours of measurement time. Average organic mass spectra of the night before and the night after the actual measurement day, obtained during simultaneous measurements of MoLa at the suburban northeastern site, allow direct comparison of both applied AMS instruments.
The positive and the negative signals of this differential mass spectrum were separated into two single mass spectra. The mass spectrum resulting from the positive signals describes the fraction of the total organic aerosol that increases during transport while the other mass spectrum (m/z with negative signals) describes the decreasing fraction (see Fig. S4 ). These two mass spectra were then compared to the MoLa PMF mass spectra derived for the respective field campaigns. The results of this comparison are presented in Table 3. The described method has the advantage of comparing data of the same air mass measured almost simultaneously at two different locations. A second advantage is that MoLa's stationary measurement locations were always chosen to be not contaminated by any local pollution, which is not possible during mobile (axial) measurements (von der  Weiden-Reinmüller et al.
Summary We present a detailed investigation of a mid-latitude European megacity emission plume based on mobile aerosol and gas phase measurements. The measurements were carried out in the framework of the MEGAPOLI project during two major field campaigns in summer 2009 and winter 2010 in the greater Paris region. The data obtained by the mobile laboratory MoLa were selectively combined with data measured at a fixed suburban site and model results to obtain extended information on plume properties and processes. Three mobile measurement strategies were applied depending on meteorological conditions. Quasi-Lagrangian axial measurements were used to explore the spatial extent of the emission plume, while cross section measurements allowed the investigation of plume shape and width and the direct comparison of plume and background conditions.
Flexible stationary measurements in emission plume as well as background air masses complement the mobile data set. In the advanced data preparation for analysis local pollution contamination was removed from the data set. Positive matrix factorization was applied to AMS organic mass spectra to obtain additional chemical information about the particulate organic aerosol by separation of the organic aerosol into fractions (factors) related to different oxidation levels (ages) and source origins. For the summer data set five different factors were used to describe the organic particulate matter: traffic-and cooking-related organic aerosol and three fractions representing organic aerosol of different ages and mixtures of sources. In winter six factors describe best the organic aerosol: hydrocarbon-like organic aerosol related to cooking and traffic activities, respectively, organic aerosol related to biomass burning with two different oxidation levels and two factors associated with more oxidized mixtures of organic aerosol.
Also dilution processes in the simulated plume seemed to occur much slower/weaker in the model than reflected in our measurements. However, a detailed model evaluation including more than a few "snapshots" is beyond the scope of this publication. The combination of MoLa stationary measurement data sets with results from a fixed suburban measurement location showed significant organic aerosol aging during summer. In winter this transformation seemed to occur more slowly due to the different environmental conditions. Our analysis results add interesting new aspects about near-ground emission plume characteristics, extending the knowledge so far obtained by research aircraft, ship and stationary measurements as well as model studies. The large amount of valuable data obtained with the MoLa measurements allowed a quantification of properties of the Paris megacity emission plume.
We choose to focus on the problem of simulating the inspiratory effort delivered by the patient, that is, the dynamics of the breathing patterns as it can be produced with the active mechanical lung ASL 5000 (IngMar Medical, Pittsburgh, USA), one of the most flexible active mechanical lungs available in the market. The choice for this device results from the fact that it becomes very often used for testing performances of domiciliary ventilators  [12] [13] [14] [15] [16] [17]. It consists of a piston chamber driven by a software and whose dynamics can be adjusted by realistic parameters such as the resistance of the airways and the lung compliance. With such a device, it is quite easy to simulate the inter-patient variability clinically observed since the muscular pressure responsible for the inspiratory effort can be parametrized using predefined waveforms or designed by the operator (in the latter case, the curve is downloaded to the device).
It is therefore convenient to simulate the corresponding muscular pressure time series for driving a mechanical lung as the ASL 5000: ventilators are thus tested as if they were connected to a large number of different patients. Such a procedure allows to make the study reproducible by any group having an ASL 5000 (something which is impossible to achieve with a database of measurements in patients). Our objective is therefore to construct time series of the respiratory muscle pressure in agreement with physiological evidences for investigating ventilator performances under quite realistic conditions. For that purpose, we started from the equation of motion for the respiratory system, used to determine the muscle pressure P mus. Based on an electrical analogy, the resulting mathematical model is then presented and some examples of inspiratory efforts are discussed.
Some simulations using the mechanical lung To validate our model, we chose to compare the airflow time series obtained from some simulations with the ASL 5000 to time series measured during a protocol which was conducted by Rabarimanantsoa-Jamous at the Rouen University Hospital during her Ph. D. thesis  [38] , in which written approval was obtained from the patients. In order to investigate how asynchrony events could affect the quality of noninvasive mechanical ventilation and sleep quality, airflow and airway pressure were measured during one night. The database consists of a cohort of 35 patients with respiratory diseases and daily assisted using a mechanical ventilator (VPAP III STA, ResMed, Australia). Some patients (n = 20) were affected by obesity hypoventilation syndrome (OHS) and some other (n = 15) by chronic obstructive pulmonary disease (COPD), associated or not with sleep apnea.
Mean body index (BMI) for these patients is 42 ± 10. 5 and their mean age is 62 ± 11. 7. We selected three airflow time series in this database, our aim being, first, to exemplify how different the dynamics underlying airflow time series can be different and, second, to show that we are able to reproduce these different dynamics with our model for the muscle pressure. The first case corresponds to a 45 years old patient affected by OHS and sleep apnea; the ventilator is set with a high pressure P h = 22 cmH 2 O, a low pressure P l = 5 cmH 2 O and the shortest pressure rise time. The measured airflow (Figure  10A ) presents a stiff rise up to nearly 1. 5 l. s -1 which is followed by a fast decrease. Expiration is characterized by a fast return to a quasi-null airflow, with a plateau up to the end of the breathing cycle.
S. A. ) was chosen. The transducer spatial position is measured with 1. 4 mm RMS and its inclination angle with 0. 5 degrees RMS. This is possible using a magnetic signal transmitter placed to the base of PEM detectors and a receiver fixed to the ultrasound probe. The software will follow transducer position during an ultrasound acquisition and the data collected will be computed to rotate the 3D image in the PEM reference system. Figure  3  shows the apparatus placed between the two plates. The images merging software was developed in the 3DSlicer framework  [14] , a multiplatform open source software for image visualization and manipulation. It was integrated in a unique user-friendly interface  [15]  and with just one platform the user can launch PEM image reconstruction, co-registration of both images and save the results in DICOM file format.
Conclusion ClearPEM-Sonic is a multimodal scanner developed to improve the diagnosis of breast diseases. Collaboration efforts have brought to combine a high resolution PEM scanner with an ultrasound system, the SuperSonic Imagine Aixplorer. Metabolic information can be rapidly compared with morphological ones and thanks to a custom dedicate software they can be fused in a single 3D--6 -image easily accessible by hospital staff. The prototype installed in Hopital Nord, Marseille has been already tested. With calibration phantoms a spatial resolution close to 1. 3 mm was measured for the ClearPEM device and the co-registration system was able to acquired images with both modalities. ClearPEM-Sonic has started a first clinical trial to develop an exam protocol and to evaluate the patient safety.
Acknowledgments The author would like to thank the partners of the  ClearPEM-Sonic Collaboration  for the privilege of presenting on their behalf the project at the 13 th Topical Seminar on Innovative Particle and Radiation Detectors (IPRD13). ClearPEM-Sonic is a project in the frame of CERIMED, the  European Centre for Research in Medical Imaging (Marseille, FR). This work has been partially supported by  IBA  grant  DMVF-12-15.
Brian 2 is a fundamental rewrite of the Brian  [1, 2]  simulator for spiking neural networks. It is written in the Python programming language and focuses on simplicity and extensibility: neuronal and synaptic models can be described using mathematical formulae and with the use of physical units  [3]. The same formalism can also be used to specify connectivity patterns (e. g. spatial connectivity), using mathematical expressions to define connections, probabilities of connections, number of synapses between neurons, and synaptic delays. Brian 2 offers two modes of operation: a "runtime mode", where executable code is generated from the model descriptions on the fly and executed from Python and a "standalone mode", where a set of source code files is generated that can then be compiled and executed with no dependency on Brian or Python.
The runtime mode is ideal for rapid prototyping and interactive exploration, e. g. from a Python console. The standalone mode on the other hand is designed for maximum of performance and for simulating models on a variety of hardware and platforms. We show a number of example applications for the standalone mode, generating code for a wide range of devices: • C++ code that is completely independent of Brian, Python or Python libraries. Optionally, this code can be parallelized over multiple CPU cores using the OpenMP libraries. • Java/Renderscript for Android-based devices, enabling Brian to run neural models on commodity hardware (e. g. phones) for robotic applications  [5]. • The GPU enhanced Neuronal Networks (GeNN) framework  [6, 7] , a neural simulator using GPUs to accelerate neural simulations.