dataesr/scientific-paragraphs-categorization huggingface dataset:
During the characterization in pure argon of a helicon configuration source designed to study chemical etching  [15] , tunable diode-laser induced fluorescence (TD-LIF) on the 3d 4 F 7/2 Ar + metastable level showed a process of metastable depopulation when increasing confinement magnetic field in inductive mode. This phenomenon was assumed to be due to metastable state electron-impact excitation towards higher radiative levels. In the 1990s, several research teams published results on electron-impact excitation cross sections (Bretagne et al. [16] , Vlcek  [17] , and were modified later by Bogaerts et al. [18]  and Bultel et al. [19] ). These databases were also complemented in the last decade  [20] [21] [22] [23]. The latter cross sections have enabled to deepen the knowledge of argon metastable kinetics in various conditions, such as in microwave discharges  [24, 25] , ICP  [2] , or helicon plasmas  [26].
A Scientific Instruments Langmuir probe was placed in the centre of the diffusion chamber as pictured in Fig. (1). Its working principle and the underlying theory can be found in  [36]. To limit the perturbations due to B diff , the tip was aligned to the magnetic field lines  [37]. Measured I(V ) curves are treated by the Smartsoft software provided with the probe to obtain the electron energy distribution function (EEDF) f e (E) with: 0 f e (E)dE = n e (1) Since Langmuir probe measurements are performed in the centre of the diffusion chamber, i. e. far from the source, EEDF was assumed to be maxwellian. Thus the values for electron density n e and temperature kT e can be deduced from f e (E) (in cm 3 eV 1 ): f e (E) = 2n e E (kT e ) 3/2 exp E kT e. (2) Fig. (5)  presents the experimental EEDFs and their corresponding fit for P RF = 900 W and B diff = 10 , 30 , and 90 G.
ACKNOWLEDGEMENTS This work, supported by the  European Community  under the contract of Association between EURATOM,  CEA , and the  French Research Federation  for fusion studies, was carried out within the framework of the European Fusion The  Open Plasma Physics Journal ,  2013 ,  Volume 6 43 Development Agreement. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. Financial support was also received from the  French National Research Agency  through contract  ANR-08-JCJC-0068-01.
Large circular holes The full dispersion curves are computed numerically using COMSOL (finite element code (COMSOL 2012)) and then asymptotically with the HFH using the equations developed in section 2. Fig. 4  illustrates the typical dispersion curves versus the asymptotics for wavenumbers, on the specified path of Fig. 3 , for the example of a hole with radius 0. 4. There is, as expected, a zero-frequency stop-band and the lowest branch is isolated with a full stop-band also lying above it. At some standing wave frequencies two modes share the same frequency, for instance the third and fourth modes at Γ; these modes are asymptotically quadratic in the local wavenumber. Table  2  shows the T 11 , T 22 values for point Γ of the Brillouin zone. Note how T 11 = T 22 for all single eigenfrequencies, but that symmetry breaks for the double roots.
Acknowledgements The authors thank  Julius Kaplunov ,  Kirill Cherednichenko ,  Aleksey Pichugin  and  Evgeniya Nolde  for many enjoyable and useful conversations. R. V. C. thanks the  EPSRC (UK)  for support through research grant nunmber  EP/J009636/1  and the  University of Aix-Marseille  for support via a Visiting Professorship. S. G. is thankful for an  ERC starting grant  ( ANAMORPHISM ) that facilitates collaboration with  Imperial College London.
To investigate the transformation of inorganics in ash, samples were heat-treated for one hour at 1000ºC in a laboratory furnace (Nabertherm 30-30-3000ºC, N7/H, heating rate 5ºC/min, stationary air). The chemical composition was determined by energy dispersive spectroscopy (SEM-EDS, F. E. I. , XL 40 TMP ESEM) with cobalt reference. The data were obtained with software developed by Oxford Instruments. The crystalline phases of ash samples were examined by X-ray diffraction (XRD, Brucker D8 Advance (cathode Cu, Kα1=1. 5406 Å, 12-70º, detector open at 5º, step 0. 0164º, scan speed=2s/scan, 30kV, 40mA, div split 0. 7, rotation 15mps, sample holder: Si hollow).
Modeling We performed thermodynamic calculations with Factsage 6. 3® based on the ultimate analysis of the plant. Factsage 6. 3® contains evaluated compound and software databases. The equilibrium state is calculated by minimizing the Gibbs energy. The combustion of 1 kg of miscanthus with stoichiometric amount of oxygen at 1 atm between 400 and 1000ºC was calculated using FactPS, FToxid, and FTsalt. The compounds selected were C, H, O, N, S, Cl, Si, Ca, Mg, K, P and Na. Minor elements in miscanthus ash (Mn, Fe, Ti and Al) were excluded. The chosen model consists of liquid (FToxid-slagB) and solid solution phases (FToxid-WOLLA, FToxid-CPyrA, FTsalt-CSOB).
Conclusion Carbonation has great importance for water transport properties in cement based materials. In the context of long-lived intermediate level radioactive waste geological disposal, that is a major issue for the durability of reinforced concrete structures. An experimental program is defined to investigate the carbonation effect on water transport properties. Note that literature data are limited and often conflicting. By using the simplified approach, water transport properties are determined. Practically, four hardened cement pastes of interest for Andra (the French National Agency for Nuclear Waste Management) are carbonated at 3% CO 2 content (25°C, 55% RH) over a time span of about one year (until complete carbonation). All data needed to describe water transport are then determined.
Two to eight images were acquired from different areas in a given sample, depending on the size of the specimen and the complexities observed in the tissue architecture. The images were processed in real time with a digital imaging and communication in medicine viewer and saved. They were read and further processed with Image J software (National institutes of Health, Bethesda, MD), if necessary. Speckle noise was minimized using Gaussian filtering in Adobe Photoshop CS5 (San Jose, CA).
Additionally, we found that a certain amount of image post-processing significantly improved the diagnostic quality of the images. Some of the processing was routine for any microscopic image, i. e. the adjustment of brightness and contrast. This is mostly done automatically by the light-CT TM software, but manual tweaking at certain times helped highlight areas of interest (e. g. , regions where there was confusion between adenocarcinoma with a lepidic pattern, vs. alveoli filled with histiocytes). Furthermore, FFOCT images are subject to speckle artifacts, similar to other imaging modalities involving interference of two images (e. g. , ultrasound, OCT). [13, 14] These speckles could sometimes lead to misdiagnosis, especially in situations where a pathologist is trying to distinguish between stroma and dense cellularity of solid tumor.
ACKNOWLEDGMENT We acknowledge that the Light-CT TM instrument was installed as a long-term loan in the  Mukherjee laboratory by LLTech, Inc. LLTech also paid part-time salary for a technician,  Mr. Salamoon. None of the other authors at  Weill Cornell Medical College  received any compensation, monetary or otherwise; from  LLTech ,  Inc. Claude Boccara  is one of the founders of LLTech and is a part-time LLTech employee.
Acknowledgments Authors would like to thank the  ANR  for financial support for this work ( Progelec program ). C. L. , B. D. , R. L. , P. L. T. and P. S. thank the  THALES company (Research & Technology department)  for supporting this work. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Data analysis and uncertainties The rBC mass in individual particles is determined from the peak intensity of the incandescence signal applying the fullerene soot calibration described in Sect. 2. 1. 2. Mass equivalent rBC core diameters, D MEV , are calculated from the measured rBC mass assuming a void-free density of 1800 kg m -3. The rBC core mass size distributions, derived from 30-min intervals of single particle data, were fitted with a lognormal function in order to estimate the rBC mass below the SP2's detection limit. All rBC mass concentrations presented here are corrected for this missing mass, which accounted for less than 10 % of the total estimated rBC mass on average. In addition to the rBC mass, the measurement of the light scattered by the particle allows the determination of its scattering cross section.
Details on the data inversion procedures for HTDMA data are provided in  Gysel et al. (2009). Briefly, sizing differences between the first and second DMA are corrected for with dry measurements of ambient air. Accurate operation of the HTDMA was verified with pure ammonium sulphate aerosol particles. The RH and growth factor accuracy are typically better than ± 1. 2 % and ± 2 %, respectively. The growth factor probability density function (GF-PDF) was retrieved from the raw data using the TDMAinv inversion algorithm  (Gysel et al. , 2009). The HTDMA was operated at a nominal RH of 90 %. Only measurements taken between 88 and 92 % RH were considered in the results and all inverted GF-PDFs were recalculated from the measured RH to the nominal RH of 90 %, following the approach described in  Gysel et al.
Additional dataset used The chemical composition of the non-refractory (at T = 600 • C) components of particles (such as organics, sulfate, nitrate, ammonium and chloride) with diameters D p ≤ 1 µm (NR-PM 1 ) was determined using a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS; collection efficiency 0. 5;  DeCarlo et al. , 2006; Aerodyne Research Inc. ). Positive matrix factorisation (PMF;  Lanz et al. , 2007; Ulbrich et al. , 2009)  was applied to the dataset and the following four factors were found to explain most of the organic concentration's variability: biomass burning organic aerosol (BBOA), oxidised organic aerosol (OOA), hydrocarbon-like organic aerosol (HOA) and organic aerosol from cooking origin (COA). In this study, the PMF factors are only used to identify periods with a dominant influence from different aerosol sources, while complete results from the HR-ToF-AMS dataset can be found elsewhere  (Crippa et al.
, 1998 (Stohl et al. , , 2005)). FLEXPART was driven with data from the European centre for mediumrange weather forecasts (ECMWF) with 0. 18 • resolution over Europe (1 • over the rest of the globe). Retroplumes were calculated every three hours for the measurement site (site instrumental de recherche par télédetection atmosphérique, SIRTA) by releasing 60 000 particles in a small box, centred at the measurement location and tracking them back in time for 20 days. The primary model output is an emission sensitivity, which is proportional to the particle residence time. Emission sensitivity was calculated for a passive tracer, not taking into account wet and dry deposition. The emission sensitivity value (s kg -1 ) is a measure for the simulated mixing ratio at the measurement site that a source of unit strength (kg s -1 ) would produce.
1 ). The histogram (normalised to the area) of the measured MAC (10-min averages) is shown for the four air mass types (Fig. 12a ) and the entire dataset (Fig. 12c ). The average for each air mass type is additionally reported in Fig. 12b. The MAC observed for the different air mass types are very similar, but subtle differences exist (see also Table  1 ). The traffic influenced air mass exhibits the smallest average MAC (∼ 7. 3 m 2 g -1 ), which is consistent with the fact that the rBC cores are mainly uncoated and small  (Figs. 10 and 11). Biomass burning influenced air mass is characterised by a somewhat larger average MAC (∼ 7. 8 m 2 g -1 ) than traffic, which is consistent with somewhat thicker coating and a bigger rBC core size. The aged aerosol shows the highest average MAC (∼ 8. 8 m 2 g -1 ), which is consistent with thicker coating compared to traffic and biomass burning.
g. ,  Cappa et al. , 2008; Lack et al. , 2008). The observed 20% difference between the MAC values under the influence of traffic emissions and aged air mass may, therefore, be under-or overestimated. The average MAC of the entire dataset is ∼ 8. 6 m 2 g -1 at λ = 880 nm (Fig. 12c  and Table  1 ). This value is in agreement with previous measurements of the MAC in wintertime Paris by  Sciare et al. (2011) , who reported a value of ∼ 7. 3 m 2 g -1 at λ = 950 nm, which translates to ∼ 7. 9 m 2 g -1 at λ = 880 nm (using Eq. 5 and assuming AAC = 1). On the other hand  Healy et al. (2012)  reported a substantially lower MAC value for the measurements that took place in the city centre of Paris (LHVP site) during the same time period (5. 1 m 2 g -1 at λ = 950nm), which translates to ∼ 5. 5 m 2 g -1 at λ = 880nm).
The mixing state of the BC-containing particles is further investigated by analysing the mean coating thickness as a function of the hygroscopic growth factor (Fig. 15e, f ). The coloured lines indicate the theoretical relationship between coating thickness and hygroscopic factor for two-component particles with a total diameter of 265 nm that contain an insoluble rBC core of variable size and a soluble coating with a certain κ value (see Sect. 2. 5). The measured coating thickness (round markers) was derived from the SP2 data as   explained in Sect. 2. 1. 3. The non-hygroscopic particles with GF ≤ 1. 1 have a negligible coating, which is consistent with fresh BC-containing particles from traffic emissions (see also Figs. 11  and 13c ). The coating thickness sharply increases up to ∼ 40-50 nm at GF = 1.
DATA PROCESSING AND ANALYSIS The initiation of the movement was defined as the time that the participant's hand released from the pressure-sensitive switch. The termination of movement was defined as the time point at which the maximal value of the distance between the wrist and the starting point (i. e. , the point in which direction of the wrist movement was changed) was recorded  (Zaal and Bootsma, 1993; Bootsma et al. , 1994; Fukui and Inui, 2006). The positional data given by Cartesian coordinates in three dimensions from the receiver were recorded and filtered offline by a second-order dual-pass Butterworth filter with a cut-off frequency of 10 Hz. Further offline analysis included computation of wrist velocity from the filtered position signal. We also calculated two grasp component values; specifically, PGA and TGA (the aperture between thumb and middle finger at the point in time when the changes in grasp configuration were stable).
This AFM/MALDI-MS coupled analysis is truly original, since, without the need for labeling, it provides complementary qualitative information beyond that obtained by photoluminescence  [5, 40]  or labeled techniques like fluorescence  [32, 37, 40]. The surface preparation, consisting of spraying TCEP, trypsin and HCCA matrix on the GaAs modified surfaces, was done on the whole surface, corresponding to a square area of 10 mm width. Afterwards, we screened the whole surface pitch by pitch with MS measurements to verify the homogeneity of protein coverage. Each pitch of this test is defined by a 120 µm wide square, composed of 2000 shots randomly distributed at steps of 20 µm. Among these measurements, we chose one position on each chip that showed the highest signal intensity. The protein generated trypsin fragments were matched to a database sequence.
A home-made MALDI target was used to analyze samples in the chamber. The chips were processed with the automatic imageprep standard protocol. This procedure includes three steps: TCEP reduction (Tris(2-carboxyethylphosphine) 20 mM in NH 4 HCO 3 buffer, 10 min at 37 °C), trypsin digestion (Trypsin Gold Mass Spectrometry Grade, Promega ® , 10 ng/µL, 37 °C) and HCCA matrix deposition (α-cyano-4-hydroxycinnamic acid, Bruker Daltonics ® , 1. 5 mg/mL in 50/50 acetonitrile/TFA 0. 25%). The automatic mode, FlexImaging, was used with constant parameters (number of shots 2000, random walk 20 µm and pitch 120 µm) to allow comparison of each sample. The data from mass spectrometry images were exported and treated with home-made software. Additional tandem mass spectrometry (MS 2 ) analysis, consisting in a second fragmentation of one specific peptide (here, the 1960 Da peptide) was performed to identify the protein unambiguously.
Several algorithms allow for the optimization of the reconstruction by correcting ring, edge or streak artifacts in the image. Post-treatment (segmentation, quantifying, etc. ) necessitates highly specialized software, for instance VGSTUDIO MAX (Volume Graphics), MIMICS (Materialise), AVIZO (Visualization Science Group) or ARTECORE (Nespos). 3D pre-rendering and rapid segmentation of reconstructed data are possible on the AST-RX Platform.
Each odorant was presented once. For each trial, subjects received task instructions generated by a digitally recorded voice, via speakers. Each trial began with an auditory primer ''Please prepare to sniff,'' followed by a countdown (''3, 2, 1, sniff''). Odorants were randomly presented by the experimenter 1 cm below the subject's nose for 3 seconds. Subjects were asked to sniff for the full duration of odorant presentation. Inter-trial interval was 45 seconds. In order to habituate the subject to the experimental setting, a training session with a sequence of 1 to 3 empty flasks was carried out. After presentation of each odorant, subjects were instructed to rate hedonic valence and intensity on a scale from 1 (not at all intense/ pleasant) to 9 (very intense/pleasant). Data analysis. To examine the influence of aging on hedonic appreciation of pleasant and unpleasant odors, odorants were grouped into two classes as a function of their hedonic valence: 12 odorant molecules were classified as pleasant (SAN, ION, XOL, DEC, ACE, ETB, MAN, ALC, ISO, CAR, HOL, PEA) and 12 odorant molecules were classed as unpleasant (IVA, HEX, BEA, TMA, DOD, APA, THA, GUA, DPO, GER, ABU, HAL).
Odorants were put in one of the two groups on the basis of young adult subject's hedonic ratings (one odorant, EUG, which had the most neutral hedonic rating, was not taken into account in the analysis). For each subject, ratings of pleasantness and intensity were then averaged for all odorants of a given category (pleasant and unpleasant) and served as a dependent variable in an analysis of variance (ANOVA) with ''age'' as a between-subjects factor (2: Young, Older) and ''Hedonic valence'' as a within-subject factor (2: pleasant, unpleasant). The same statistical analysis was done for intensity ratings. Significant level was set at p,. 05 and all statistical analyses were performed using StatViewH software.
The experiment was composed of 5 sessions, separated by 5 minutes pauses. Within session, each of the 20 odorants was presented once, interspersed with 10 blank trials (no odorant) in order to reduce adaptation. Thus, in total, each subject received 100 olfactory stimuli and EEG acquisition lasted approximately 120 minutes. One important aspect of the experimental procedure was synchronization of odorant perception with EEG recording. Sampling instructions, odor presentation, sniffing and EEG recording were all time-locked via a single central computer. This was done by recording intra-nasal sniffing continuously during the experimental session. Sniffing was recorded using an airflow sensor (AWM720, Honeywell, France) connected to a nasal cannula positioned in each nostril. The sniffing signal was amplified and digitally recorded at 100 Hz using LabVIEW softwareH.
This measurement was used to define the onset of each EEG trial and as a potential measure of interest in later analysis (i. e. , comparing sniffing parameters across odorant conditions and age). Sniffs were pre-processed by removing baseline offsets and aligned in time by setting the point where the sniff entered the inspiratory phase as time 0. Inspired volume and sniff duration were calculated for each sniff and each participant. Both volume integration and sniff duration ended at the first data point at which the sniff returned to zero flow. Recording of respiratory data was linked to EEG recording by 1 TTL (transistor-transistor logic) pulse (5 V, negative to positive), ensuring accurate time-locking of all experimental trials. Psychophysics data analysis. As in Experiment 1, for each participant, pleasantness estimates were averaged for all odorants of a given category, and served as a dependent variable in an ANOVA with ''age'' as a between-subjects factor (2: Young, Older) and ''Hedonic valence'' as a within-subject factor (2: pleasant, unpleasant).
The same statistical analysis was done for intensity ratings. Significant level was set at p,. 05 and all statistical analyses were performed using StatViewH software. EEG data analysis. All EEG analyses were performed using custom scripts written in Matlab (The MathWorks) and the Eeglab toolbox  [53]. For OERSPs, the artifact-free signal was segmented from 21,000 to +1,200 ms around stimulus onset and each trial was analyzed in the time-frequency domain by convolution with complex Gaussian Morlet's wavelets. To remove ocular artefacts, an Independent Component Analysis (ICA) implemented in Eeglab was used. Moreover, each artefact-free epoch with amplitude values exceeding 650 mV was rejected, ending in a rejection rate of 26% of the total number of epochs. For each subject and for each electrode, signals were then averaged by ''Conditions'' (''no-odor'', ''pleasant odors'', ''unpleasant odors'') and ''Age'' (Young, Older).
Applications In this section, we consider the implementation of our homogenization approach on a simple problem, for which an analytical solution is available. The software used for the solution of the coupled Arlequin systems is freely available at https://github. com/cottereau/CArl. In all the simulations presented in this section, we have used κ 0 = 1 and κ 1 = 10 -3 for the definition of the coupling operator (see Eq. (  4 )). Also, we have used α 2 (x ∈ (D∩D)\D c ) = 1-η and α 1 (x ∈ (D ∩ D)\D c ) = η, with η = 10 -3 , for the weighting of the energies of the two models. The realizations of the random fields k k k ε (x) have been generated using the spectral representation method  [20] , and its Fast Fourier Transform implementation. Finally, in the implementation of the loops in algorithm 1 and 2, a relative tolerance of criterion = 10 -2 was selected for both the value and the argument of the potential function.
Electrochemical Measurements Electrochemical experiments were carried out at room temperature with an Autolab potentiostat/galvanostat 302N (Ecochemie, Utrecht, The Netherlands) coupled to a PC running the GPES software, using a single-compartment three electrode cell. The working electrode was a 2 mm diameter gold disk. Two microelectrode arrays were alternatively used as the working electrodes, comprising 816 microelectrodes with a 10 µm radius. Arranged in a 24 × 34 square lattice, they exhibit a center-to-center separation of 100 µm along the x-and y-axes. These microelectrode arrays were fabricated on silicon chips using standard microfabrication techniques, as described in previous works  [31]. An Ag/AgCl/KCl (3M) electrode was used as the reference electrode (Metrohm, Herisau, Switzerland), and platinum wire (Metrohm) as the auxiliary electrode, respectively.
Acknowledgments This work was supported by a grant of the  Romanian National Authority for Scientific Research, CNCS-UEFISCDI , Project number  PN-II-ID-PCE-2011-3-0271 (298/06. 10. 2011 ). JdC acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  through the  MICROPLATE  Project ( CTQ2009-08595 ). This work was also partly supported by the  French RENATECH network and its FEMTO-ST technological facility.