dataesr/scientific-paragraphs-categorization huggingface dataset:
With our control device, complicated profiles can be followed by the anvils. For example, the RoToPEc has been recently extended to forced-oscillation method at HP and HT. These torsional forced-oscillation experiments provide measurements of shear modulus G and the associated strain energy dissipation Q -1 G. For that, the sample and an elastic standard of know modulus and negligible dissipation, connected mechanically in series, are subjected to a low-frequency sinusoidally time varying torque. Our software allows to configure this desired profile (sine), its amplitude, period, number of cycle and synchronisation step. After defined synchronization step, a 2D array is generated and downloaded to the memory of our controllers. Motors, and therefore the anvils then follow perfectly the complicated profile set by the operator.
Heating configuration The heating system for the RoToPEc module is different from the standard PE press, as illustrated in Figure  9 (a). The electrical feed-through is provided by an external copper  piece which is compressed against the anvils housing using a spring. The specific shape of this copper piece allows the housing to rotate without interference during anvils rotation. Also, because the HT experiments can cause multiple damage to our system (anvils, encoders, bearings, etc. ), it is necessary to cool the anvils and the press during the HT runs. For that, a Coldstream Air Gun (from Meech) is used to rapidly cool (with a cold airflow) the various components. Its size and simple magnetic base permit easy installation on the RoToPEc (Figure  9 (b)). The cold air produced by the Vortex Tube is easily directed on to the anvils to be cooled via a knuckle trunking.
The rotational axis of the press was aligned with the X-ray beam and camera (PCO 4000) using a small (0. 5 mm) Pt sphere placed within the centre of a dummy assembly within the press frame. The PCO camera (beamline module 4) is a CCD detector with a 4008 × 2672 array of 6. 5 μm × 6. 5 μm pixels, with 3. 3 mm × 2. 8 mm field of view, giving a pixel scale of approximately 1. 3 μm. Pressure and temperature were monitored and controlled during experiments using control systems within the experimental hutch. The press drive system was controlled remotely from outside the hutch. Due to limitations in machine time available, RoToPEc motor rotation and beamline camera shutter software were controlled separately rather than being fully integrated into the same control system. However, subsequent experiments performed at ID27, ESRF have demonstrated that RoToPEc control software is easily integrated with beamline software to facilitate data collection.
In order to obtain tomographic scans at DIAMOND, RoToPEc anvils were continuously rotated through an angle of 180°at a constant speed, and the X-ray camera programmed to take projections at the correct time interval to ensure that 1501 projections were taken for every scan. The benefit of this approach was that anvils could readily be left to rotate during sample annealing, and data later binned to provide a time-resolved series of scans of the sample. Exposure times of 1. 73 s proved to be adequate to ensure good data quality for the sample investigated; movement of the motors over this exposure time produced no noticeable effect on projection quality. Before and after each scan, 40 flat field and 40 dark current images were taken to normalise projections. Flat field images were obtained through air by driving the sample stage so that no parts of the assembly or press frame were in the field of view of the camera.
Dark field images were obtained by then closing the experimental shutter. Relative intensities in processed images were then self-corrected using beamline software. The majority of tomographic scans were collected using a monochromatic beam energy of 53 kV. Samples were chosen for initial tomography scans to test the capability of the RoToPEc for microtomographic imaging under HP, HT, high stress conditions. Synthetic, polycrystalline forsterite (Mg 2 SiO 4 ) capsules (Figure  13 ) were prepared by hot-pressing cylinders of powdered forsterite (pre-synthesised in a piston-cylinder press) for several days at 1200°C in air. Cylinders were then shaped and drilled to produce 3 mm outer diameter, 1 mm inner diameter capsules with lids. Capsules were loaded with a mixture of natural olivine (Fo 92 composition) and finely powdered Fe-S, with the approximate volume proportion 20% Fe-S.
All scans were reconstructed using DIAMOND in-house software using a standard, filtered back-projection algorithm. Final scans were produced with a calculated voxol resolution of approximately 2 µm, which is comparable to the resolution routinely available with commercial, large-volume lab-based microtomographic devices. Tomographic scans were taken of a series of samples at (1) room temperature and pressure, (2) room temperature and HP during various stages of annealing (to provide time-resolved snapshots of textural equilibrium), (3) HT and HP, and (4) simultaneous HT, HP during ongoing deformation, to provide an assessment of resolution and data quality under different conditions. A final tomographic scan was also taken of all quenched samples in situ. Samples were removed and later scanned (within full gasket assemblies) using an in-house built computed tomography system at the University of Edinburgh, both to compare data quality and allow full examination of volumes of the sample obscured during in situ scans by the RoToPEc anvils.
No attempt was made to post-process reconstructions and remove artefacts, as it was found that the extent of these had minimal influence on 3-D volume reconstructions. However, in future the extent of ring artefacts could be significantly reduced by increasing beam energy during progressive scans, and to adopt commonly used post-processing techniques (e. g. in  [23] ). Figure  17  shows higher resolution CT 3-D reconstructions of the central Fe-S rich volume from experiment DLS3. Micron-scale resolution in the reconstructed dataset is sufficient to show 3-D distribution of the Fe-S phase, which can be readily imaged by segmenting data to highlight only the most attenuated portion of the sample. Optical examination of recovered sectioned samples by Scanning Electron Microscope ground truths these reconstructions.
It is evident from the data that minimal aggregation of Fe-S occurs during progressive annealing and deformation, although CT data and SEM images do indicate some textural development of the Fe-S particles. Quality of reconstructed tomographic data means that more quantitative textural analysis of the samples can also be readily performed. Quant3D  [24]  analysis was conducted on binarised images in order to determine any development of preferential fabrics within the samples during annealing and deformation. Results of analysis on sample DLS3 (the sample shown in Figure  15 ) is summarised in Figure  18. The Star Distribution Length calculates the mean length of an object (i. e. segmented particle) in various orientations from a number of arbitrarily generated points within the dataset. Lengths from 2000 points were analysed over 513 orientations, or uniform distribution.
Acknowledgements We acknowledge the  European Synchrotron Radiation Facility , the  Synchrotrons DIAMOND  and SOLEIL for the allocation of beamtime. We are grateful to  S. Bauchau ,  G. Garbarino ,  J. P. Itié ,  N. Guignot ,  J. L. Hodeau Y. Wang  and  S. Redfern  for valuable inputs and discussions, and to  Kate Dobson  for assistance with DIAMOND experiments. We thank  Diamond Light Source  for access to beamline i12 ( EE9185 ).
Funding This work was financially supported by the  French 'Réseau Hautes Pressions' , the  Agence Nationale de la Recherche  [ ANR-2011-BS08-018 ], a grant 'Instrumentation aux limites 2013', the  'Institut National des Sciences de l'Univers' of CNRS , and the  UK National Environmental Research Council  [ NE/I016333/1  and support of  Ph. D. student M. Berg]. We thank  Diamond Light Source  for access to beamline i12 ( EE9185 ) that contributed to the results presented here.
In addition, a lot of progress has been made recently regarding the design of the detector. Prototypes of sub-detectors have been built. A series of performance tests has been carried out, which provided data for improvements in the design. The Technical Design Reports of most of the detectors are available. Although the development of the simulation and analysis software is still ongoing in parallel with the detector construction, a realistic description of the sub-detectors and new algorithms for the tracking, digitization, and particle identification (PID) has been implemented. A realistic magnetic field map, calculated with TOSCA software  [19] , is part of the simulation. In the PandaRoot version 1 used for this work, the description of most of the sub-detectors has been complemented with the passive materials, beam pipe, magnet yoke, etc.
Knowing the total cross section, one can define an effective FF as: |F p | 2 = 3βsσ tot 2πα 2 2 + 1 τ , (8) or from the integrated cross section, as: |F p | 2 = βs πα 2 σ int c 1 + c2 3 + 1 τ 1 - c2 3 , (9) which is equivalent to the value extracted from cross section measurements, assuming |G E | = |G M |. Literature offers several parameterizations of the proton FFs  [26] [27] [28] [29] [30] [31]. The world data are illustrated in Fig. 2. In Ref. [17]  two parameterizations were considered. Cross section parameters are extracted from experimental data of the integrated cross section. BABAR data  [32, 33]  suggest a steeper decrease with s, and show a strong energy dependence near threshold  [34]. [35, 36] , Fenice  [37] , PS170  [12] , E760  [38] , DM1  [39] , DM2  [40, 41] , BES  [42] , BESIII  [14] , CLEO  [43] , BABAR  [32, 33].
Simulation and analysis software The offline software for the PANDA detector simulation and event reconstruction is PandaRoot, which is developed within the framework for the future FAIR experiments, FairRoot  [69]. It is mainly based on the object oriented data analysis framework ROOT  [70] , and utilizes different transport models such as Geant4  [20] , which is used in the present simulations. Different reconstruction algorithms for tracking and PID are under development and optimization in order to achieve the requirements of the experiment. A schematic view of the simulation and data analysis chain is shown in Fig. 6.
Competitiveness of the PANDA experiment The moduli of the individual FFs, |G E | and |G M |, will be measured for the first time at BESIII using the data collected at 20 different q 2 values between 4. 0 and 9. 5 (GeV/c) 2  [79]. A statistical precision on the FF ratio between 9% and 35% is expected. Based on these numbers, it is clear that PANDA will extend these measurements up to about q 2 = 14 (GeV/c) 2 , with a precision better than that expected at BESIII or comparable in the case of the reduced luminosity mode. The modulus of the proton FFs ratio can also be measured at Belle  [80] , using the initial state radiation (ISR) technique, with a comparable accuracy to the BABAR data. The Belle detector was operating on the KEKB e + e -collider  [81]. An integrated luminosity of about 1040 fb -1 was collected at KEKB between 1999 and 2010.
Acknowledgments The success of this work relies critically on the expertise and dedication of the computing organizations that support PANDA. We acknowledge financial support from the  Science and Technology Facilities Council (STFC) ,  British funding agency ,  Great Britain ; the  Bhabha Atomic Research Center (BARC)  and the  Indian Institute of Technology, Mumbai, India ; the  Bundesministerium für Bildung und Forschung (BMBF) ,  Germany ; the  Carl-Zeiss-Stiftung   21-0563-2. 8/122/1and 21-0563-2. 8/131/1 , Mainz , Germany; the  Center for Advanced Radiation Technology (KVI-CART) ,  Groningen , the  Netherlands ; the  CNRS/IN2P3  and the  Université Paris-Sud , France; the  Deutsche Forschungsgemeinschaft (DFG) ,  Germany ; the  Deutscher Akademischer Austauschdienst (DAAD) , Germany; the  Forschungszentrum Jülich GmbH, Jülich, Germany ; the  FP7   HP3 GA283286 ,  European Commission  funding; the  Gesellschaft für Schwerionenforschung GmbH (GSI) ,  Darmstadt, Germany ; the  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Germany ; the  INTAS, European Commission  funding; the  Institute of High Energy Physics (IHEP)  and the  Chinese Academy of Sciences, Beijing, China ; the  Istituto Nazionale di Fisica Nucleare (INFN), Italy ; the  Ministerio de Educación y Ciencia (MEC)  under grant  FPA2006-12120-C03-02 , Spain; the  Polish Ministry of Science and Higher Education (MNiSW)  grant No.
Conclusion We presented a multi-overlay multi-level hybrid P2P live video streaming system for MMOGs. The system uses mul-tiple trees and rateless codes to stream screen-captured video of the game. A P2P server is responsible for the overlay construction and dynamically adapts trees to peer arrival and departure. The main problem addressed by the paper is how to construct trees such that the transmission rate at the source is minimized, the number of served peers is maximized, and all peers receive enough encoded symbols to decode the video on time. We proposed algorithms to dynamically build such trees and provided extensive experimental results with the ns-2 network simulator to study the performance of our system with respect to scalability, bandwidth heterogeneity, packet loss, and peer churn.
Numerical validation In this section, a numerical model based on the finite elements method is developed in order to validate the analytical model. This axisymmetrical model was developed under the finite elements software Samcef. The same as for the analytical model, fictitious cracks are originally created by adding fictitious material on the circumferential boundary of the plate (Fig. 2 ). These cracks are used to calculate the restitution energy along the crack corresponding to a given external load. Moreover, the model predicts the decomposition of the restitution energy with respect to different opening cracks modes. The same material and the same stacking sequences used in experimentation are considered in this section. In this model, modes II and III corresponding to opening cracks are neglected.
The restitution energy is calculated using the virtual crack extension method (VCE) integrated in Samcef software. This restitution energy for mode I is determined for each node in front of the circumferential crack. The maximum value of the calculated restitution energy is selected. In the analytical model, constant restitution energy was taken. In reality for an orthotropic material, the resistance of one ply under the drill against delamination is not constant. The fibres resist in tension in the longitudinal direction more than in transverse direction. The calculation of the maximum restitution energy is found for a number of plies non-drilled remaining under the drill varying from 1 to 6 and in function of the applied external load. A program under Linux and using the script TCL Shell is developed in order to run the calculation under Samcef several time.
Evaluations We implemented the hidden Markov model using the Matlab. We tested performances of this model from the viewpoint of the recognition of behavior and human activity. Figure  2  shows the hidden states for each room (the first three curve) and the observation of our model (the fourth curve). Each targeted state has a specific scenario. The scenario kitchen is the activities realized in the kitchen according to exact times (the first curves), the scenario Living room is the activities realized in the kitchen according to exact times (the second curves), the scenario Bathroom is the activities realized in the kitchen according to exact times (the third curves). The fourth curves show the observation of these hidden states. Using the observation sequence O, the activities of the person can be estimated as shown in figure  2  using the transition matrix A and the initial matrix Π.
CONCLUSION In this work, we were interested in the events recognition in smart environments for elderly and dependent persons. Our objective was to identify and experiment an efficient recognition model. We evaluated the likelihood rate of the hidden Markov model based on our grammar "Home By Room Activities language" in a smart home with a monitored person. Finally, we evaluated the efficient of this model using Matlab-based simulation tool. The results reveal that the proposed model is efficient for activities recognition with an observation error rate that is not very large compared to our hidden states. In the next steps of this work, we will explore the enrichment of our approach by investigating the learning-based systems (such as neural networks with a new learning algorithm) in order to recognize the events in a smart home and improve the learning phase such by using the differential evolution algorithm  (Chengyao et al.
Results sPAINT enables single-molecule spectroscopy. In sPAINT, the spatial and spectral information of the fluorescent molecules were simultaneously collected to generate multi-dimensional datasets (x,y,l). sPAINT was easily implemented using a single-molecule fluorescence imaging microscope with the addition of a blazed transmission diffraction grating placed before the image plane (Fig. 1a ; Equations 1 and 2). The two major diffraction orders (zeroth order and first order, Fig. 1a ) were projected onto two non-overlapping areas of the same detector and recorded simultaneously. The zeroth order was used to super-localize spatially (x,y coordinates; m 0 ) the diffraction-limited puncta (Fig. 1b , left), while the first order diffraction was used to collect the spectral information (l; m 1 ) of the fluorescence signal (Fig.
1 mm pore size (Avanti Polar lipids). This generated monodisperse LUVs with a mean diameter centred at 0. 11±0. 01 mm as measured with a Malvern Zetamaster 300 (Malvern, UK). LUVs were extruded and imaged immediately. Data are representative of three experiments conducted on separate days. Protein expression and purification. For the aS protein, BL21(DE3) Gold cells (Stratagene) were transformed with wild-type (WT) human aS. Starters were diluted into Overnight Express Instant TB Medium (Novagen) supplemented with 1% glycerol and left to grow for 16-18 h at 30 °C. Cells were then harvested and the protein was purified as previously published  45. Human amyloid-b 1-42 protein (ab 1-42 ) was purchased from Anaspec (USA). The protein was re-suspended in 10 mM NaOH in MQ water and purified by high-performance-liquid-chromatography (Agilent) using a BIoSep_SEC-s2000 column (OOH-214-K0) 300 Â 7.
The ab 42 protein solutions were prepared from monomeric solutions of human ab 42 (Anaspec, San Jose, CA) and prepared by dissolving the lyophilized peptides in SSPE buffer (150 mM NaCl, 10 mM Na 2 H 2 PO 4 Â H 2 O, and 10 mM Na 2 EDTA, adjusted to pH 12 using NaOH), followed by sonication over ice for 30 min, and subsequently flash freezing into 5 mL aliquots. Before each of the incubations, aliquots of each peptide were diluted into SSPE buffer (pH adjusted to 7. 4 using HCl) to 500 nM (monomer concentration) and placed under conditions for aggregation (37 °C, agitation). Samples were collected after 5 h incubation (for early aggregates/oligomers) or at 20-24 h incubation (for fibrils and small fibrils). The samples were stored on ice and imaged immediately. Data are representative of two aggregation experiments conducted on separate days.
0 mM for 30 min at 37 °C, respectively. The cholesterol manipulation was confirmed using a cholesterol assay kit (ab133116, abcam, UK). Cells were rinsed twice with warmed PBS and imaged immediately using imaging buffer. Data are representative of at least two experiments conducted on separate days. Instrumentation. Fluorescence imaging was performed using a home-built, bespoke inverted optical microscope (Olympus, IX73) coupled to an electron multiplied charged coupled device (EMCCD) camera (Evolve II 512, Photometrics, Tuscon, AZ). The microscope was configured to operate in objective-type total internal reflection fluorescence (TIRF) mode (for LUVs and protein aggregates) or using highly inclined and laminated optical sheet (HILO) microscopy  46  (for the cell membrane experiments), with the circularly polarized beam of either: 1.
For each of these steps, accompanying sample code and data are provided in Supplementary Data 1-5 (see below) and ref. 48. Step 1. Spectral calibration, Aberration Correction and Absolute Wavelength Calibration. The location of the spectral domain on the EMCCD chip is strongly dependent on the orientation and position of the diffraction grating, and so to ensure that the wavelengths determined were correct and therefore comparable, the instrument was calibrated before imaging each day, and following re-alignment. To calibrate the instrument, TetraSpeck beads were imaged by exciting at 405 nm and the emission collected from 480 to 760 nm (Supplementary Fig. 1a , Supplementary Data 1) (100 frames were collected with a frame rate of 35 ms). The positions of the beads within the spatial domain of the image were determined using the PeakFit plugin (an imageJ/Fiji plugin of the GDSC Single Molecule Light Microscopy (SMLM) package (http://www.
sussex. ac. uk/gdsc/intranet/microscopy/ imagej/gdsc_plugins) for imageJ (ref. 49) using a typical 'signal strength' threshold of B30. The fit results values are then saved as a text file in the directory containing the image for use the subsequent spectral analysis below. The spectral component of the image (Supplementary Fig. 1b ) was analysed using a custom written macros. For non-linear least-squares data fitting the analysis was performed in either; (A) Igor Pro (Wavemetrics) using the Levenberg-Marquardt algorithm to search for the minimum value of chi-square or (B) In the case of the included ImageJ Plugin using the inbuilt fit (Fit. doFit) function (see Supplementary Data 2). With the filter set allowing collection of wavelengths between 480 and 760 nm, there are three clear peaks in the spectral region of the image for each bead (Supplementary Fig.
Each intensity profile was fit to a Gaussian distribution to determine the center positions of the peaks (Supplementary Fig. 2 ). Fits that gave negative amplitudes, centers outside of the range, widths o1. 5 pixels or 420 pixels were discarded. The output data were then clustered using R (https://cran. r-project. org). Emission wavelengths at peak maximum (l) were set as 512. 7, 581. 5 and 676. 5 nm (Supplementary fig. 3a ). Z 0 Z 1 distances were fitted to a multiple linear regression model: y ¼ b 0 þ b 1 l þ b 2 x þ b 3 y þ e ð3Þ where y is the Z 0 Z 1 distance (in pixels), b 0 is the intercept and b 1 to b 3 are the regression parameters associated to l, x and y dimensions, respectively. These parameters are required for the spectral analysis of datasets. The corrected wavelength can finally be calculated from Z 0 Z 1 distance according to: l ¼ 1 b 1 y À b 0 À b 2 x À b 3 y ð Þ ð 4Þ R code is available as a script in Supplementary Data 3.
Step 2. Image acquisition, processing and spectral analysis. Images were acquired at a frame rate typically o50 ms and 1,000 frames were acquired for LUVs and protein aggregates, 1,000-3,000 frames for the mammalian cell membrane. (Example 100 nm LUV data are available in ref. 48). Analogous to the spectral calibration (see above), the co-ordinates corresponding to the localizations within the spatial part of the images were determined using the peak fit plugin for imageJ. For the aggregates and LUVs, the localizations corresponding to each individual unit were clustered using the DBSCAN (ref. 50) algorithm in R (fpc package https://CRAN. R-project. org/ package=fpc) using a size of the epsilon neighbourhood (eps) of 0. 5 pixels, and a minimum points threshold in the eps of 10. Only clustered localizations were analysed further.