dataesr/scientific-paragraphs-categorization huggingface dataset:
■ ACKNOWLEDGMENTS G. D. M. and A. J. were supported by a Ph. D. and a Chancellor's Fellowship from Strathclyde University, respectively, G. T. and U. Z. acknowledge funding from the  Scottish Universities' Physics Alliance  (SUPA). P. A. H. acknowledges the support of the  Natural Environment Research Council  ( NE/M001415/1 ) and A. J. the support of  Tenovus Scotland  (Project  S17-07 ). M. T. I. , F. M. S. , and J. S. H. acknowledge support by the  Deutsche Forschungsgemeinschaft  ( HU 1971-1/1 ,  HU 1971-3/1 ,  HU 1971-4/1 ). We thank the  ILL Block Allocation Group (BAG)  system for SANS beamtime at D22 and  Dr A. Martel  for help with the setup of the instrument. We acknowledge  Diamond Light Source  for time on Beamline  B21. We thanks  Dr. P. Soule  ( NanoTemper Technologies GmbH ) and  Dr.
Acknowledgements This publication has emanated from research by M. D. S. supported in part by a research grant from  Science Foundation Ireland (SFI)  under Grant Number  13/SIRG/2137. T. J. S is grateful to the  European Commission  for a  H2020-MSCA-IF grant , project number nanoOIPC-DLV-708814. P. P. acknowledges the financial support from the  Swiss National Science Foundation  under Grant  Ambizione Energy 160553.
Acknowledgements We gratefully acknowledge the  Région Normandie  for nancial support to A. C. and A. A. This work was supported by  Normandie Université ,  INSA Rouen ,  Rouen University ,  CNRS ,  European Union   EFRD, Labex SynOrg  ( ANR-11-LABX-0029 ). We are grateful to  Patricia Martel  ( University of Rouen ) for IR analyses, and to  Albert Marcual  ( CNRS ) for HRMS analyses. We gratefully thank the  Erasmus Mundus Battuta program  for nancial support to  A. O. Pr. Richard C. Brown  ( University of Southampton ) is gratefully acknowledged for language polishing.
Fungal identification Samples of olive pruning wastes were surface sterilized by incubating one minute in ethanol 96%, two minutes in NaOCl 10%, then 30 s in ethanol 96% and finally rinsed in sterile water. Sterilized leaves and small twigs were deposited onto potato dextrose agar (PDA) Petri plates and incubated for five days at 27 C. Fungal mycelia originating from the samples were collected and inoculated onto fresh PDA plates. Any one sample yielded from one up to six different fungal strains. DNA from the samples was then extracted using the protocol of  Lecellier and Silar (1994). The ITS regions were amplified using the ITS1 and ITS4 primers  (White et al. , 1990)  and send for sequencing by Genewiz UK (Takeley, UK). Identification was made by comparing the sequences with the GenBank (http://blast.
Precise identification of all the molecules would require the injection in the GC of standard compounds. Indeed, the similarity level with NIST database varied from high values (97% for acetic acid, dodecene, phenol, 2,4-bis (1,1dimethylethyl)-) to intermediate (88% for eicosanol) and low (75% for benzoin). From a quantitative perspective, extractives are often present in small amounts (approximately 5 wt% in lignocellulosic materials). Here, 10 wt% extractives were recovered using acetone as extraction solvent and 3. 3 wt% with diethyl ether. More products were detected in acetone extracts than in diethyl ether. Py-GCMS experimental device enabled to identify the decomposition products of the three major polymers in lignocelluloses, lignin, cellulose and hemicelluloses  (Chen, 2014). Additional Py-GCMS data of olive leaves have not been found in the literature.
Acknowledgments IdEx Sorbonne Paris Cit e is acknowledged for the financial support through the grant « Olizero » included in the project « Energies, territoire, soci et e: enjeux et approches crois ees » -Programmes InterDisclipinaires of  COMUE Sorbonne Paris Cit e. Philippe Silar  and  Moussa Dicko  are grateful to  R egion Ile-de-France  ( R2DS ) for Grants  P3AMB  and « Valorization de la biomasse lignocellulosique sur le territoire de la RdBF ». Pôle image and  Coumba Doucour  e ( Universit e Paris 13 ) are acknowledged for their technical support,  Miguel Yanes  and the locals of Sierra M agina for their time and support during fieldwork and  Marianne Cohen  for the scientific coordination of Olizero.
We feel that description of such a framework may lead to more successful routine exploitation in the future of monolayer data including systems that involve interactions with species such as synthetic polymers,  40  proteins,  41  DNA,  42  nanoparticles  43  or drugs. 44 xperimental Section Materials SDS (Sigma; 99. 9%) was recrystallized twice from ethanol and C12TAB (Sigma; 99. 9%) was recrystallized twice from acetone; each time solutions were cooled over several hours to maximize the purity. Chaindeuterated SDS (d25-SDS) and C12TAB (d25-C12TAB) were used as supplied by the ISIS Oxford Isotope Facility (Didcot, UK). DPPC, DMPS, chain-deuterated DPPC (d62-DPPC) and chain-deuterated DMPS (d54-DMPS) were used as received from Avanti Polar Lipids (> 99%). Pure water was generated by passing deionized water through a Milli-Q unit (total organic content = 4 ppb; resistivity = 18 mΩ.
The barrier speed during compression was 10 mm/min. The reported surface pressure/area isotherms were performed using a Langmuir trough (250 cm 2 maximum area; KSV-Nima, Finland) equipped with hydrophilic barriers. The same experimental approach as above was used. The compression modulus, CS -1 ,  46  is defined as: = -. ( ) 1 where A is area per molecule, and π is surface pressure. This parameter affords information on the physical state of a monolayer, such as its phase, at a given surface pressure. The data in this study were recorded at 20-22 °C.
NR Data Acquisition Neutron reflectivity measurements were recorded on the FIGARO reflectometer at the Institut Laue-Langevin (Grenoble, France). 3  A chopper pair with a wavelength resolution of 7% dλ/λ was used at incident angles of θ = 0. 62° and 3. 8°. Measurements were recorded in 4 isotopic contrasts: hydrogenous surfactant/phospholipid (hsurf) or deuterated surfactant/phospholipid (d-surf) with a subphase of pure D2O or a mixture of 8. 1% v/v D2O in H2O called air contrast matched water (ACMW) that has a scattering length density of zero. Generous measurement times on the order of 1 h were used to optimize the data quality. Reflectivity profiles comprise absolute measures of the specular neutron reflectivity, R, with respect to Qz, which is defined as: = ( 2 ) where π is the numerical value (not surface pressure as in eq.
NR Data Modeling The data analysis was performed using the Aurore software. 48 The interfacial material was modeled as one or more uniform stratified layers separated by bulk media (air and solvent). The variables for any given layer are the scattering length density (ρ), thickness (d) and roughness (σ), where for a given species, ρ is equal to its scattering length (b) divided by its molecular volume (Mv). Table  S1  in part 1 of the Supporting Information reports the values of b, Mv and ρ of the species featured in the present work. The surface excess (Γ) in moles per unit area of a species in a given layer may then be expressed as: 2 Γ =. ( 3 ) where vf is its volume fraction in the layer and NA is Avogadro's number. The change in ρ across an ideal interface located at z0 is described as a step function, and the associated roughness is zero.
52 Reported values of the uncertainties account for any correlation between parameters. Note that χ 2 is a measure of distance between the model curve and the error bars, not the points. Since the values are weighted by the experimental errors, datasets with high counting statistics (i. e. with generous acquisition times and/or on a high flux instru-ment) will have commensurately low error bars that fail to take into account all of the systematic and random errors in the measurement. In this case, relatively large absolute values of χ 2 will result compared with the application of the same model to datasets with poorer counting statistics. As such, the relative χ 2 values are compared to evaluate the application of different models to the same datasets while we do not discuss the absolute values.
0, 6. 0 and 10. 0 Å, respectively. Fixed layer roughness values from capillary waves are again used as above. The molecular constraint is implemented by the application of eq. 3 to both the chain and solvated headgroup layers, which we denote with subscripts 1 and 2, respectively, through: Γ =. =. ( 6 ) where by approximation vf,1 = 1, and by rearrangement this gives an equation to calculate the volume fraction of layer 2: =. (7)   The hydration of the headgroups layer is therefore inversely proportional to the surface excess, as calculated directly from the fitted parameter d1. The standard version of the Aurore software has been developed as part of this work to apply this constraint during a global fit of neutron reflectivity data recorded in multiple isotopic contrasts. 48 In this approach, data in different isotopic contrasts are fitted simultaneously to minimize the overall χ 2 value.
This approach relies on the assumption that data in different contrasts have identical chemical structures. It is not currently possible to do this in the standard versions of other programs like RasCAL (https://sourceforge. net/projects/rscl/) or Motofit. 54 his limitation can be circumvented through application of a laborious iterative procedure, however, and a suggested possible workaround to the problem is described in part 2 of the Supporting Information. In spite of the software development to apply the above constraint during a global fit of data recorded in multiple isotopic contrasts, still it has not yet been possible to perform this procedure while also simultaneously fitting the scattering length density of the acyl chains layer and applying a constraint for the layer to have the same physical density in the different isotopic contrasts.
It is clear therefore why if data of a new system are measured in only this isotopic contrast, different types of model could appear to be satisfactory. Even so, Model 1 fails for the data recorded of DPPC across the whole Qz-range while Model 2 is also poor. These differences demonstrate that the compensation effect between thickness and roughness does not work for the phospholipid with its longer chains, a fact which is related to the position of the minimum of the first Keissig fringe being closer to the accessible measured Qz-range. We go on now to examine the application of these models to data recorded in multiple isotopic contrasts in order to evaluate the validity of extracting structural information. As such, the 3 models are applied to data of the 3 systems recorded in 4 isotopic contrasts (d-surf and h-surf each in ACMW and D2O).
Models were applied for a discrete range of physical densities and the minimum value of the χ 2 , reported in part 3 the Supporting Information, was used to determine the optimum fit. Fig. 6B  shows the result in which the model matches the data recorded in all 4 contrasts. The value of %C is 90 ± 11, which is accompanied by a global χ 2 value that is better by a factor of > 4. The high value of %C is supported qualitatively by measured values of CS -1 , which although still in the LC phase are very close to the border value of the solid phase at 250 mN m - 1. 56 The observation is also backed up by data from polarization modulation-infrared reflection absorption spectroscopy (PM-IRRAS) obtained for DPPC monolayers at 30 mN m -1 where the relative low wavenumbers of the CH2 stretches indicate an increase in trans conformations.
In the present work, even though the maximum value of Qz was resolved only to around half of 2π/d, we have shown that it is necessary to take into account the extent of acyl chain compaction in models of neutron reflectivity data of monolayers at the air/water interface, and further that it is in fact possible to quantify to a precision of ~ 10% the density of the acyl chains (i. e. ~ 15 Å 3 in volume) independently of the thickness. Information on the compaction of acyl chains of phospholipid layers may also be obtained by spectroscopic techniques such as PM-IRRAS. 65 It has been shown that the tilt angles are smaller for DMPS compared to DMPC, which suggests that DMPS molecules are more closely packed due to different headgroups interactions. Information has also been provided on the chain ordering and the phase of phospholipid bilayers as the position of CH2 stretching bands can be related to the presence of gauche defects and chain melting.
69 n the application of the robust model to neutron reflectivity data recorded in 4 isotopic contrasts validated in the present work, we have found no evidence for this effect. Although the thickness of hydrated headgroups layer was fixed at 10 Å according to the molecular dimensions for the data both at 5 and 35 mN m -1 , free fitting of this parameter as well resulted in minimal changes (< 1 Å), and the thickness of the layer was in fact very slightly higher (~1 Å) at the higher surface pressure. The lack of significant changes in the structure of the headgroup region was also discussed in a Fourier transform infrared spectroscopy study of oriented phospholipid multilayers, as the spectra corresponding to C=O bands recorded for hydrated films both below and above the phase transition temperature were similar.
ACKNOWLEDGMENTS We thank the  Institut Laue-Langevin  for allocations of neutron beam time on FIGARO (DOIs:  10. 5291/ILL-DATA. 9-10-1348 , 10. 5291/ILL-DATA. 9-10-1465and 10. 5291/ILL-DATA. 9-13-683 ),the  ISIS Oxford Isotope Facili-ty  for provision of the deuterated surfactants,  Jian Lu ,  Andrew Nelson ,  Emanuel Schneck ,  Ernesto Scoppola ,  Giovanna Fragneto  and  Tommy Nylander  for helpful discussions,  Imre Varga  and  Bence Feher  for sample preparation in preliminary experiments,  Elżbieta Jabłonowska  for recording complementary surface pressure isotherms, and  Reneta Bilewicz  for involvement in some neutron reflectometry experiments. YSa is thankful for the GETFund research grant. EN and DM thank the support from a  SNF Sinergia  grant No. CRSII2 154451.
a b s t r a c t The Multisensor Time-Frequency Signal Processing (MTFSP) Matlab package is an analysis tool for multichannel non-stationary signals collected from an array of sensors. By combining array signal processing for non-stationary signals and multichannel high resolution time-frequency methods, MTFSP enables applications such as cross-channel causality relationships, automated component separation and direction of arrival estimation, using multisensor time-frequency distributions (MTFDs). MTFSP can address old and new applications such as: abnormality detection in biomedical signals, source localization in wireless communications or condition monitoring and fault detection in industrial plants. It allows e. g. the reproduction of the results presented in Boashash and Aïssa-El-Bey (in press) [2].
Conclusions MTFSP, a software tool for the analysis of multichannel nonstationary signals, extends conventional stationary array processing to the non-stationary case using high-resolution TF methods. The codes included in MTFSP can be used for a wide range of applications such as multisensors non-stationary signals analysis, ACS using MTFDs, DOAs based on MTFDs, blind system identification and equalization, source denoising or tracking of moving sources, and EEG source reconstruction.
Acknowledgment This research study was funded by  Prof. Boashash 's  QNRF NPRP  grants  6-885-2-364  and  NPRP 6-680-2-282. The 2nd and 3rd authors,  Dr. Aïssa-El-Bey  and  Mr. Mohammad Fathi Al-Sa 'd were employed respectively as postdoc and research assistant on the grants during 2016-2017. Dr. Aïssa-El-Bey  was on leave from  Loire-Atlantique University in France.
Introduction About 60 % of the solar radiation absorbed by the Earth's atmosphere is due to water vapour absorption. Water vapour absorption involves two contributions: (i) a succession of vi-brational bands separated by about 1500-2000 cm -1 with decreasing intensity from the far infrared to the visible. These are formed by multitudes of narrow rovibrational absorption lines that are tabulated in spectroscopic databases like HI-TRAN  (Gordon et al. , 2017)  and GEISA  (Jacquinet-Husson et al. , 2016) ; (ii) a weak broadband absorption continuum with slow frequency dependence roughly following the contour of vibrational bands. In the regions of low absorption between the vibrational bands -the water vapour transparency windows -the continuum has a major contribution and may dominate the cumulative effect of the weak rovibrational lines.
4 ± 0. 5 MHz. A spectrum over 105 cavity modes (or FSR) corresponding 0. 7 cm -1 is acquired within about 100 ms by modulation of the laser current at 10 Hz. The laser current scans and operating temperature, the cavity temperature and pressure, and data acquisition were controlled by an electronic card (from AP2E). The optical assembly is composed of the diode laser, an infrared polariser (colorPol MIR), two stirring aluminum mirrors, two photodiodes (PCI-2TE-4), and the optical cavity (7. 75 mm apex diameter). This is made of a stainless-steel single piece which contains a V-shaped (6 mm diameter) channel with gas inlet and outlet for the sample flow, and also carries the three high reflectivity mirrors permanently aligned on removable vacuum-tight metal holders. The laser ramp is provided by the software, and the frequency acquisition rate is maximised.
The gas pressure was measured upstream of the cavity by a high accuracy CPG2500 pressure gauge (from Mensor, 0-1000 mbar with an accuracy better than 0. 5 mbar). A second sensor was directly connected to the cavity. During the data acquisition, we noted significant differences between the pressure values provided by the two sensors, especially for low pressure values indicating the presence of a pressure gradient and different Venturi depression between the two measurement points. These pressure artefacts depend on the configuration of gas lines and can become significant when pressure is low and rapidly varying. At the end, we decided to deduce the actual water vapour pressure (or molecular density) by directly using two relatively strong absorption lines present in the recorded spectra (see below).
The calibration of the frequency axis relies on the known FSR value (187. 5 MHz) and on the absolute frequency of water absorption lines as given in the HITRAN2016 database  (Gordon et al. , 2017). Figure  1  shows typical spectra obtained with the OFCEAS system for a temperature of 50 • C and pressure of 5, 15, and 20 mbar. The increase of the baseline level and of the absorption lines with pressure is clearly apparent. The baseline is not completely flat because of optical interference pattern which is produced between the output cavity mirror and the photodiode. Several efforts have been made in order to minimise these effects, but it was not possible to remove them completely. However, these optical fringes are taken into account in the spectral fit and therefore removed for the spectral analysis.
1 , the monomer wing contribution in this region is in fact the dominant contribution to the baseline increase. According to the usual definition of the water continuum, lines located in the [ν -25, ν + 25 cm -1 ] wavenumber interval have to be considered and simulated using the HITRAN2016 database with a Voigt profile truncated at ±25 cm -1 from the line centre. For each spectrum, the baseline is determined by effectively subtracting the four absorption lines using a spectral fit L. Lechevallier et al. :  The water vapour self-continuum absorption in the infrared atmospheric windows procedure. A Rautian profile was used to fit the line profile by fixing the Doppler widths to the theoretical values and adjusting the Lorentzian collisional broadening and Dicke narrowing for each individual absorption line.
The baseline is fitted by a third order polynomial in the basis of Legendre Gauss polynomials. Optical fringes due to parasite reflections are taken into account by adding three sine waves at fixed frequency and amplitude with free phase (frequencies and amplitudes were determined by FFT analysis of the fit residuals before inclusion of these terms). The quality of the spectrum simulation is illustrated by the residuals displayed in the bottom panel of Fig. 1 , showing an rms of the residuals of 1. 7 × 10 -8 cm -1 at 20 mbar for a single scan (100 ms). As mentioned above, the pressure value of each spectrum was calculated using the water absorption lines. The integrated absorption coefficient of a given line is simply the product of the molecular density (in molecule cm -3 ) by the line intensity (in cm molecule -1 ) as tabulated in the HITRAN2016 database.
, 2006)  and  Schwenke and Partridge (2000)  theoretical databases of water vapour transitions gives a ratio of 1. 048 and 1. 061, respectively. As a result, the pressure of each spectrum was obtained as the average value of the two determinations with an associated error bar estimated to 3 %. Figure  2  presents the dependence of the zero order term of the baseline fit versus the squared pressure, during a pressure ramp at 50 • C. A convincing quadratic dependence is obtained. After subtraction of the constant term due to the cavity losses, the plotted quantity results as the sum of the water vapour continuum (α WCS ) and the monomer wing contributions, which have both a quadratic pressure dependence. Let us note that the wing contribution, calculated as indicated above, is more than twice the water vapour continuum.
Comparison to literature The different experimental determinations of the selfcontinuum cross-sections at room temperature in the 4 µm window are gathered in Fig. 3  together with some of the last versions of the MT_CKD continuum up to the current 3. 2 version. In addition to the present study at 3007 cm -1 located at the high energy edge of the window, previous OFCEAS studies were performed at 2283 cm -1  (Campargue et al. , 2016)  and 2491 cm -1  (Richard et al. , 2017). Three datasets were obtained by FTS with long-path absorption cells and cover most of the transparency window: (i) CAVIAR consortium dataset obtained with a 512. 7 m pathlength at 293 K  (Ptashnik et al. , 2011a) ; (ii) two series of FTS determinations from IAO in Tomsk  (Ptashnik et al. , 2013 (Ptashnik et al. , , 2015)) , obtained with a pathlength of 612 m at 289.
5 and 287 K, respectively, (iii) the FTS dataset obtained at NIST by  Baranov and Lafferty (2011)  with a 100 m pathlength between 311 and 363 K. Finally,  Burch and Alt (1984)  reported continuum measurements in the centre of the window using a grating spectrograph and an optical configuration aiming to minimise the spectra baseline drifts. At 3007 cm -1 , the present OFCEAS and the MT_CKD3. 2 values are in excellent agreement. The comparison to the FTS values is similar to that encountered in most of the transparency windows: the FTS values are over-estimated compared to both MT_CKD and the laser measurements. The CAVIAR C S value at 296 K and Tomsk 2015 value at 287 K were reported with a 50 % error bar. A rough extrapolation at 300 K leads to C S = 6. 3 and 4. 53 × 10 -23 cm 2 molec -1 atm -1 , respectively, which should be compared to an OFCEAS value of 1.
CRDS near 5000 cm Figure  4  shows an overview of the water vapour CRDS spectrum near 5002 cm -1 and the location of one of the four spectral points selected for the water vapour absorption continuum retrieval. From the spectrum recorded with a sensitivity of α min ∼ 3 × 10 -10 cm -1 , water vapour transitions with intensity values below 1 × 10 -28 cm molecule -1 are measured. The recorded spectrum was found in very good agreement with simulations performed on the basis of the HI-TRAN2016 database (see Fig. 4 ). The CRDS cell was filled with water vapour after purification by liquid nitrogen cooling and pumping on the residual vapour phase. The pressure in the cell was continuously monitored with a capacitive transducer (model ATM. 1ST from STS; 50 mbar full range, accuracy of ±0. 1 % of the full range).