dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, in all conventional petroleum systems, hydrocarbon migration is controlled by gradients of fluid overpressure and impacted by natural fracturing of cap rocks  [9, 10]. Lastly, assessment of present-day pore pressure appears as a crucial step to ensure drilling safety  [11, 12]. In the present study, our objective is to assess the stress states undergone by siliciclastic sediments in passive margins and the development of weakness zones as favorable locations for rock failure. To do so, our simulation strategy is rooted on an iterative hydro-mechanical coupling between a basin simulator, ArcTem  [13] , and a finite element mechanical solver, Code Aster  [14]. The hydro-mechanical coupling was applied on a set of synthetic basin models, designed with the target to be representative of siliciclastic sedimentation in passive margins and of operational models in the hydrocarbon industry.
Iterative Hydro-Mechanical Coupling In this work, we aim at conciliating the geological realism and dynamic aspect of basin simulations with the integration of lateral mechanical effects. To do so, we use a coupling between two codes: ArcTem, a basin simulator  [13] , and Code Aster, a finite element mechanical solver  [14]. ArcTem relies on the classical 1D stress simplification while Code Aster computes a full stress tensor. The coupling is implemented in a prototype software named A 2 [71]. Following an iterative procedure, the two codes exchange inputs and outputs at each simulation step, until providing a consistent set of porosity, permeability, fluid pressure, and stress tensor in each cell of the basin model. This differs from the integrated numerical model of  [72]  but shares similar objectives.
Past Geometries The restoration of past geometries for all models was performed with a backstripping method  [83, 84] , implemented in the TemisFlow TM software (2016 version, Beicip-Franlab, Rueil-Malmaison, France)  [85]. The uncompacting used a different Porosity vs. Depth table for each lithological type. Shalier facies have larger deposit porosities but compact more quickly with burial than the sandier ones (plots available in Figure  A1  in the Appendix A). The model bottom was used as a reference horizon. Its shape was arbitrarily input for each deposition event in order to model a progressive subsidence and tilting for the margin basement, following Table  2. The paleo-bathymetry profiles were then computed by the algorithm from the compaction tables and the basement shape. Lastly, absolute sea level variations were input by translating the full model upward or downward from the 0 mark on the vertical axis.
Readout Software and Communication Figure  7  shows the SoLid data flow, beginning with the digital board ADCs, through to data recorded on disk at the on-site DAQ server. Each digital board can be read out over a 1 Gb/s optical ethernet port. During normal data taking operations, the data rate is considerably lower, at around 2 Mb/s. During source runs, planes near the source can reach the maximum limit. The 2 Tb/s total data rate out of the ADCs is reduced down to ∼200 Mb/s recorded to disk. The FPGAs are configured and read out using the IPbus protocol, which operates on top of UDP/IP and is designed for reliability and high performance  [6]. The readout software is written in the Golang programming language, to take advantage of the language's intrinsic multi-threading and memory management tools.
As a result, a Golang implementation of the IPbus software is now available: goIPbus. The software can configure all readout electronics components for data taking, and performs continuous retrieval of data from all fifty detector planes in parallel. A minimal parsing of the data is performed to check the format is as expected, and data from all planes are concatenated into a single data file, in the order of retrieval from the FPGAs. The readout software has been tested up to 1 Gb/s -test results are shown in section 5. 1.
1% in the range 1-3 V over-voltage, and precision around 0. 5 ADC counts. The variation in breakdown voltage and the over-voltage sensitivity necessitated an iterative voltage scanning procedure, with the appropriate parameters for a given SiPM channel successively refined between scans. Figure  11  shows the resulting mappings for the channels with extremal cases of gain differentials (i. e max and min gradient of gain v. s voltage). A linear fit is performed for each In the extreme cases, the amplification rate variation can cause a change in gain of around 10 % at 1. 8 V over-voltage. channel, and the output parameters stored in a database. Upon setting the bias voltage, the database is queried to look up the required bias voltage for a desired gain. Figure  10  shows that the spread in gain across channels after this procedure is 1.
A default ZS threshold is always applied, but the trigger sequencer can change the threshold depending on the trigger type. The duration stored in this buffer depends on its size and the ZS compression rate -a compression factor of 50, due to ZS at 0. 5 PA, allows up to 2 ms to be stored. In practice, to avoid buffer overflow, the buffer is limited to 500 µs, which is suitably large for the IBD buffer for each neutron trigger. Each plane has a readout sequencer that determines which data are recorded for each trigger (either local or remote triggers). This sequencer sets the duration of data readout from both before the trigger (i. e stored in the ZS buffer), and after the trigger (i. e not yet entered the ZS buffer) in units of blocks. The smallest amount of data that can be read out is therefore a single block, corresponding to 256 waveform samples (6.
Online Monitoring Run control operations are controlled via a dedicated python based web application, the SoLid Data Quality Monitor (SDQM), which also provides an interface to inspect data quality. A small fraction of each run is processed online using the SoLid reconstruction and analysis program (Saffron2), and the output measurements and distributions can be inspected via the web application. Long term trends of certain measurements, such as SiPM gains and detector deadtime, are also stored in an online database, and input to an automated alarm system. In addition to detector measurements, several in situ environmental sensors are placed in the detector container and immediate vicinity, which are read out periodically via the SDQM and stored in the online database.
Acknowledgements This work was supported by the following funding agencies:  Agence Nationale de la Recherche  grant  ANR-16CE31001803 ,  Institut Carnot Mines ,  CNRS/IN2P3  et  Region Pays de Loire, France ;  FWO-Vlaanderen  and the  Vlaamse Herculesstichting, Belgium ; The U. K. groups acknowledge the support of the  Science & Technology Facilities Council (STFC) ,  United Kingdom ; We are grateful for the early support given by the  sub-department of Particle Physics at Oxford and High Energy Physics at Imperial College London. We thank also our colleagues, the administrative and technical staffs of the SCK•CEN for their invaluable support for this project. Individuals have received support from the  FWO-Vlaanderen  and the  Belgian Federal Science Policy Office (BelSpo)  under the IUAP network programme; The  STFC Rutherford Fellowship program  and the  European Research Council  under the European Union's  Horizon 2020 Programme  ( H2020-CoG)/ERC Grant  Agreement n.
B. 3. Simulation of kinetics studied by Raman spectroscopy under Python 2000 Raman spectra of 2000 points each were calculated in silico using four Gaussian lines at 450, 510, 750 and 900 cm -1 , respectively. Full Widths at Half Maximum (FWHM) ranged from 118 to 212 cm -1. Such high FWHM are typical of amorphous materials like glasses  (45). In order to reflect a kinetic evolution, the amplitude of peaks at 510 and 750 cm -1 were linearly decreased across the series while amplitude of peaks at 450 and 900 cm -1 were linearly increased. This series of spectra may mimic ageing of a material for instance. Homoscedastic white Gaussian noise was added on each spectrum. SVD was applied using Principal Component Analysis (PCA) function from Python Scikit-learn package  (92). Computation took only a few seconds under Python Anaconda 3.
B. 4. Simulation of NMR spectra with known noise under Matlab NMR complex FID were simulated in silico under Matlab (The MathWorks, Inc. , Natick, MA, USA) with a complex exponential at the expected frequency ν and either an exponential decay (Equation  1 ) or a Gaussian decay (Equation  2 ), leading to a Lorentzian or a Gaussian peak on spectra after Fourier transform, respectively. While the former is typical of a relaxation-driven shape, the latter highlights a distribution of chemical environments  (94)  or more complex relaxation phenomena, e. g. , strong dipoar coupling. To obtain a Gaussian peak with the same FWHM as a Lorentzian peak, the shape is defined according to Equation  3. y=e i 2 πν t ⋅e -t T 2 (1) y=e i 2 πν t ⋅e -t 2 2 σ 2 (2) σ=T 2 √ 2 ln (2) (3) 7380 NMR FID were simulated, grouped as follows: • 2 shapes for decay: exponential and Gaussian; • 3 T 2 values of 10, 1.
As peak was not at the middle of the spectrum, signal region was defined as the 512 points centred at peak frequency. Noise region corresponded to the other 512 points. Baseline zero-order offset was preliminary corrected by subtracting the mean value of noise region. This step was essential to avoid spectrum aliasing due to Fourier transform, especially for broad peaks. The source codes of SVD automatic thresholding and FID simulations are available in files sfa. m and Figure_I. 7_I. 8_I. S2. m of (93), respectively. Computation of the full set of 6 × 7380 = 44280 spectra took 30 minutes with an overclocked Intel Core i5 4670K @ 4. 4 GHz processor with Matlab R2016b.
D. 1. Denoising of NMR and Raman spectra Images are already matrices and SVD can directly be applied on them. Twodimensional (2D) spectra can be treated similarly. However one-dimensional (1D) spectra are not directly suitable for SVD. If a series of spectra is available, one just need to stack the successive spectra to obtain a 2D data set. Figure  4a  shows such a stack of spectra simulating a reaction kinetics studied by Raman spectroscopy, as described in Section B. 3. A similar stack can be obtained when a surface is mapped to analyse species in a sample region  (31). The spectrum consisted of four overlapped peaks with varying intensities. Without SVD, the two peaks at 510 and 900 cm -1 were difficult to detect due to the amount of noise (red arrows). However, after SVD, these components were identified as evidenced by green arrows.
One should note the very low residual noise. It is also possible to use SVD on a 1D data set, by way of a Hankel or a Toeplitz matrix, as described in Section C. 2. This feature was used in the context of 29 Si MAS solid-state NMR (Figure  4b ) using the GPU Java application  (83). The 29  It should be noted that this spectrum already displays a very good SNR. The signal has been enhanced using CP, allowing a non-quantitative spectrum to be acquired in 40 minutes. The second spectrum shows the same sample analysed using CPMG echoes  (39) , which led to numerous spikelets. The overall shape of the CPMG spectrum is qualitatively similar to the above spectrum. Such an approach was proved to be a mean to increase SNR during acquisition step by discretising broad peaks  (104). The original shape with improved SNR can be recovered by summing echoes, but nevertheless this leads to relaxation distortions.
D. 4. Time and frequency domains In this section, spectra resulting from a same FID with SVD denoising applied either before (Figure  6 , Toep_FID, in cyan) or after Fourier transform (Toep_SPC, in green) are compared. Both were simulated under Matlab and converted into a square Toeplitz matrix, according to Section D. 2, before applying SVD. Additionally, multiple similar spectra with identical signal and random noise were stacked (mult_SPC, in red), to simulate a mapping of a homogeneous region in MRI or in Raman. For mult_SPC, we chose a number of spectra identical to the number of points per spectrum, again to get a square matrix. Mult_SPC was also useful to compare the influence of multiple sampling vs. a single sampling converted to a semi-circulant matrix. Singular values are presented in Figure  6a.
Above the second threshold of PSNR rms dB = 36 dB, RMSD exhibited a further decrease downto the level obtained for Lorentzian peaks. This confirms the significance of the second singular value. A similar trend was observed on truncated data (Figures  S2e  and S2f ). The next step that was investigated concerns the possibility to use denoised spectra for the sake of quantification. For each peak width and shape at PSNR rms dB = 20 dB, i. e. at quantification limit, the spectrum with the worst RMSD is presented in Figure  8. These spectra were fitted with a Voigt function with error estimation implemented in Matlab  (111). Amplitude, position, shape and width were automatically adjusted. Results are reported in Table  2  and 3 for Lorentzian and Gaussian peaks, respectively. A very large uncertainty occurred on fitting parameters derived from noisy spectra (top traces).
e. with PSNR max = 2. 0 and PSNR rms = 6. 6, whatever the peak width. A systematic shape modification has been highlighted for Gaussian peaks with an overestimation of peak area by 20 %. This overestimation for Gaussian peaks is a major result as peak shape is often neglected when denoising, which can give misinterpreted data. A correction step is thus needed if Gaussian / Lorentzian ratio of denoised peak is around 0. 5. When used carefully, SVD can lead to similar results between denoised spectra at PSNR rms = 6. 6 and noisy spectra at quantification limit (PSNR rms  = 10). As PSNR rms is increasing with the square root of time, this difference is equivalent to a considerable gain on acquisition time of 2. 3, which is of paramount importance for low sensitivity experiments. In a second part (II) (86), we will focus on the computation time needed for SVD treatment under Java, Matlab and Python, using both processors and graphic cards.
Abstract. Owing to increase in snowfall, the Antarctic Ice Sheet surface mass balance is expected to increase by the end of the current century. Assuming no associated response of ice dynamics, this will be a negative contribution to sea-level rise. However, the assessment of these changes using dynamical downscaling of coupled climate model projections still bears considerable uncertainties due to poorly represented high-southern-latitude atmospheric circulation and sea surface conditions (SSCs), that is sea surface temperature and sea ice concentration. This study evaluates the Antarctic surface climate simulated using a global high-resolution atmospheric model and assesses the effects on the simulated Antarctic surface climate of two different SSC data sets obtained from two coupled climate model projections.
Introduction Projected 21st century increase in the Antarctic surface mass balance (SMB) due to higher snowfall rates is expected to partly compensate for eustatic sea-level rise (SLR) due to opposite changes in almost all other components affecting global sea level  (Agosta et al. , 2013; Ligtenberg et al. , 2013; Lenaerts et al. , 2016). However, the acceleration of ice flow and the interactions between oceans and ice shelves are expected to lead to an overall positive Antarctic contribution to SLR  (Pollard et al. , 2015; Ritz et al. , 2015). Uncertainties in ice dynamics and surface mass balance trends are large and influence each other (e. g. Winkelmann et al. , 2012; Barrand et al. , 2013). It is therefore crucial to produce highquality Antarctic climate projections for the end of the current century with reduced uncertainties, yielding trustworthy estimates of the contribution of the Antarctic Ice Sheet (AIS) SMB and useful driving data for ice dynamics and ocean-ice shelf interaction model studies.
Model evaluation The ability of the ARPEGE model to reproduce atmospheric general circulation of the Southern Hemisphere is assessed by comparing sea-level pressure (SLP) and 500 hPa geopotential height (Z500) poleward of 20 • S to that of the ERA-Interim reanalysis (ERA-I). For surface climate of the Antarctic continent, several studies have shown that (near-)surface temperatures from ERA-I are not reliable  (Bracegirdle and Marshall, 2012; Jones and Harpham, 2013; Fréville et al. , 2014) , as the reanalysis is not constrained by a sufficient number of observations and because the boundary layer physics of the model fail to successfully reproduce strong temperature inversions near the surface that characterise the climate of the EAP. As a consequence, near-surface temperatures in Antarctica from ARPEGE simulations are evaluated using observations from the SCAR READER database  (Turner et al.
Near-surface temperatures Screen level (2 m) air temperatures (T 2 m ) from the ARP-AMIP simulation are compared to those from the MAR-ERA-I simulation and READER database in winter (JJA) and summer (DJF) for the reference period 1981-2010 (Fig. 4 ). In this analysis, stations from the READER database for which fewer than 80 % of valid observations were recorded for the reference period were not used. Altitude differences between corresponding ARPEGE grid point and stations www. the-cryosphere. net/13/3023/2019/ The Cryosphere, 13, 3023-3043, 2019 errors in the ARP-AMIP simulation are very similar in the comparisons with MAR-ERA-I and the READER database. Considering errors on near-surface temperatures of the Antarctic Plateau as large as 3 to 6 K for ERA-I reanalysis in all seasons  (Fréville et al.
Acknowledgements. This study was funded by the  Agence Nationale de la Recherche  and by the  ESA Snow_cci initiative. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups participating in CMIP5 for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. The  Centre National de Recherches Météorologique (Mété-France, CNRS ) and associated colleagues are warmly thanked for providing resources and help to run the ARPEGE model. We also thank the  Scientific Committee on Antarctic Research, SCAR  and the  British Antarctic Survey  for the availability of the MET READER database.
We also want to warmly thank  Michiel van den Broeke  for providing access to the latest ERA-Interimdriven RACMO2 outputs for Antarctica. We thank  Jan Lenaerts  and the anonymous referee for reviews and comments aiming at the improvement of the paper. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (project  ASUMA , grant no. ANR-14-CE01-0001-01 , and project  APRES3 , grant no. ANR-15-CE01-0003 ) and by the  European Space Agency (ESA)  as part of the  Snow_cci  project of the  Climate Change Initiative (CCI)  ( ESA ES-RIN). Review statement. This paper was edited by  Christian Haas  and reviewed by  Jan Lenaerts  and one anonymous referee.
METHODOLOGY Spreading Rate To track the time-dependent evolution of tectonic plates and their boundaries, we use the plate reconstruction model of  Matthews et al. (2016)  spanning from 410 to 0 Ma and corrected for the model errata identified and explained in  Torsvik et al. (2019). This plate model is a synthesis of the  Domeier and Torsvik (2014)  model for the late Palaeozoic (410-250 Ma) and the  Müller et al. (2016)  model for the Mesozoic and Cenozoic (230-0 Ma). We use the Python programing language 1  with the GPlates software and python API pyGPlates 2  to interrogate the reconstruction of  Matthews et al. (2016)  and build a timedependent database of mid-ocean ridges, spreading segment lengths and spreading rates. Since the breakup of Pangea, the strongest constraint for the reconstruction of past plate motion including spreading rates is provided by the preserved oceanic lithosphere  (Hellinger, 1981; Lithgow-Bertelloni and Richards, 1998).
Statistics Statistical analyses were performed using an ANOVA test, Student's t-test or non-parametric tests. Spearman's rank-order nonparametric correlation test was used to analyze the relationship between two variables. Statistical analyses were performed with GraphPad Prism software version 5. 03 (GraphPad Software, San Diego, California, USA). All reported p values were two-tailed and considered significant at p < 0. 05(*) or p < 0. 01(**).
Interviews After a first cycle of document analysis, semi-structured individual interviews will be conducted with key informants and representatives of government departments involved in cancer research (public health, primary care, medical care, and nursing). Based on our previous studies and the literature  [86, 87] , approximately 15 participants per case and about 10 at the provincial level (total n = 70) will be required. This number will be adjusted according to the nature and redundancy of the data  [87].
Qualitative analysis The audio data recorded during the interviews and focus groups will be transcribed and stored in a formal database managed using QDA Miner Software version 4. 1. 21 [90]. Analysis of qualitative data will begin as soon as the first data become available and will follow cyclical thematic analysis techniques based on our framework: structural coding followed by interpretive analysis  [91]. The intra-case analysis will be followed by an inter-case analysis to contrast the differences and similarities in the recursive mechanisms of CG from a vertical perspective (between levels of governance) and horizontal perspective (between and within teams). Associations between critical contextual factors and collaborative governance mechanisms will be constructed according to the principles of realist evaluation  [73]  and our research objectives.
Quantitative data collection The quantitative data collected will allow the quantification of GC outcomes on care providers and PLCs as well as the financial burden of cancer in terms of expenditures incurred by PLCs and the cost to the healthcare system of the services used. The data collection will be based on a concurrent mixed methods approach to complement the qualitative data (Objectives 1, 2) and measure the outcomes associated with CG (Objective 3)  [78].
Although the ICHOM recommendations suggest a baseline at diagnosis at the physician's office, our choice of T0 is intended to capture outcomes associated with "exposure" to relational coordination (RC) and to avoid adding to the potential distress of some PLCs by asking them to participate in a study right when they are receiving their diagnosis. Sociodemographic, clinical, and organizational data characterizing the sample that are potentially confounding variables will be collected according to ICHOM recommendations: sex, age, income, education, marital status, postal code, and self-reported comorbidities. As for the size of the PLC sample, a total of 356 respondents will have to be recruited to detect an average difference of 0. 08 with a standard deviation of 0. 4 and a correlation of 0.
Administrative data The costs associated with the use of services will be calculated over a one-year period as with QALY measurements. They are in two areas: costs to the healthcare system and those incurred by PLCs  [109]. Costs to the healthcare system include: hospitalizations, treatments and outpatient oncology and emergency room visits, drugs reimbursed by the Régie d'assurance-maladie du Québec (RAMQ) (Quebec's health insurance plan agency), and physician fees. These costs will be calculated based on data from RAMQ's medical and pharmaceutical services databases and the Quebec ministry of health and social services (MSSS) (MED-ECHO database). Unit costs for each type of service will be calculated from the MSSS's annual budget and activity reports (AS-471, AS-478) submitted by each healthcare facility in Quebec.