dataesr/scientific-paragraphs-categorization huggingface dataset:
[32]  which exhibits a PSD that is quite similar to those obtained by Dana and Skoczylas  [33]  and a Callovo-Oxfordian argilite tested by Andra  [34]  (Fig. 4 ). The parameters in the hierarchical model are A = 1000, B = 8750  [28]. The intrinsic permeabilities obtained for each material are reported, along with experimental data in table  1. The model stands rather well across a wide range of porous materials spanning six orders   of magnitude in permeability, keeping in mind that there is no fitting parameter. Predictions for the concrete used by Chen et al. [30]  are less accurate. This discrepancy could come from the fact that the PSD datasets provided by Chen et al. were not obtained using the same MIP apparatus. Low-pressure measurement quality depends on the specific apparatus and is related to the probability distribution in Eq.
Evolution of the relative permeability to gas and to liquid upon damage In the context of ageing concrete structures, it should be informative to determine how the material's relative permeability evolves with damage due to micro-cracking. Upon microcracking, an intrinsic permeability increase is expected, as observed experimentally e. g. in Refs (  [6] ,  [36] ) and modelled by the hierarchical model where micro-cracks are treated as voids. Here we shall assume, same as in Ref. [6] , that micro-cracks act as voids and that their effect on the relative permeability is of the same nature as their effect on intrinsic permeability. Hence, given the PSD for a damaged material, the relative permeability to vapour and liquid water can be computed. A complete dataset on pore size distribution evolution upon micro-cracking of mortar in uniaxial compression is available in the work of Khaddour et al.
[6]. The dataset provides the PSD evolution along with intrinsic permeability and 20  Klinkenberg coefficient predictions upon damage. Evolutions of the latter two quantities are recalled in Fig. (10). Fig. (11)  shows the evolution of relative permeability to vapour and to liquid water that has been computed from the various PSDs, according to the extended model. In these figures, micro-cracking is described as the degradation of the material's Young's modulus, which goes up to 10% and remains in the pre-peak regime under uniaxial compression. Several observations can be made: first, the evolution of the relative permeability to vapour seems to be quite insensitive to the state of damage. This had to be expected since the relative permeability to vapour depends on the pores of small diameter and the effect of damage on pores of these diameters is negligible on the PSD.
Funding statement The authors would like to thank The  Islamia University Bahawalpur, Pakistan, National Research Program for University (NRPU) for Higher Education Commission  ( 9458 ),  Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China  for providing lab facilities. The authors would like to acknowledge the  Vice-Chancellor fellowship  scheme at  RMIT University , the  RMIT Micro Nano Research Facility (MNRF)  in the Victorian node of the  Australian National Fabrication Facility (ANFF) , the  RMIT Microscopy and Microanalysis Facility (RMMF)  to support this work. S. Loomba  would like to acknowledge the  School of Engineering  for financial support.
As comet 67P approached perihelion, the size of the magnetosphere grew and a solar wind ion cavity formed  (Behar et al. 2017; Nilsson et al. 2017). Closer to the nucleus a diamagnetic cavity formed, just as for the larger comet ionospheres  (Goetz et al. 2016a, b). When Rosetta was located inside the solar wind ion cavity a full scale bow shock may have formed further away from the nucleus. Some traces of this have been observed. An asymmetric smaller scale shock-like structure, an infant shock, was seen intermittently  (Gunell et al. 2018). Cometary ion data observed inside the solar wind ion cavity can be interpreted as a remote detection of a shock  (Nilsson et al. 2018) , in agreement with hybrid simulations  (Alho et al. 2019). The general flow pattern of ions in a small-scale ionosphere, a plasma cloud, is governed by the local electric fields  (Nilsson et al.
Data set used We have used the publicly available Rosetta data from the PSA. At the time of submission not all data have been ingested. For RPC-ICA, we use the L5 moment data, which is based on the L4 PHYS MASS data set, which contains data separated into the same physical mass ranges used in this paper, i. e. H + , He 2+ , He + , and H 2 O +. Note that the H 2 O + mass range may contain heavier ions (mainly CO + 2 and other ions resulting from chemical reactions). The moment calculations have been done assuming a mass per charge of 18. In the later part of the mission, with summer in the Southern hemisphere, this may be an underestimate, as there was significant CO + 2 outgassing  (Hansen et al. 2016; Hoang et al. 2019 ) which close to the nucleus would result in a higher mean ion mass of up to about 35 a.
m. u. per charge  (Nicalaou et al. , in preparation). Thus the velocities close to the nucleus during the post-perihelion part of the mission may be overestimated with up to a factor 2. The full moment data set consists of about 160 000 data points of 192 s length obtained over a period of about 2 yr, from 2014 August to end of 2016 September. RPC-ICA was regularly off during and just after wheel off-loadings, and sometimes operated in a twodimensional low energy high time resolution mode  (Stenberg Wieser et al. 2017)  which is not included in the moment data set. The moment data are a numerical integration over the available field of view. No fitting or extrapolation to uncovered viewing directions has been done. We define the moments and provide equations on how they are calculated from the data in Appendix A.
The moment calculations are described in more detail in  Behar (2018)  and a general description of moment calculations for an almost identical instrument on Mars Express can be found in  Fränz et al. (2006). The cometary ion data have been divided into two energy ranges, above and below 60 eV. This is because the ions exhibit different behaviour above and below this approximate limit as shown in  Berčič et al. (2018)  and further discussed later in this study. As discussed in  Berčič et al. (2018) , the incomplete field of view of the RPC-ICA instrument may affect the results. Most of the time Rosetta was in a nadir-pointing terminator orbit. For such orbits RPC-ICA has a free 180 • × 90 • field of view centred on the nucleus. The field of view with a component towards the Sun is partly obscured.
2017). The lowenergy (<60 eV) cometary ions have a significant antisunward component, but most prominent is a systematic variation of the Y CSEq and Z CSEq components. Closer inspection shows that the Y and Z components varies with the same period but a phase shift. A comparison with panel (e) shows that the velocity is correlated with the position of Rosetta relative to the nucleus. We show in Section 3. 2 that this is due to a radial outflow of the low-energy cometary ions in the Y-Z plane, giving rise to the consistent behaviour of the Y and Z components of the velocity and position of the spacecraft. In order to see the effect of the solar wind electric and magnetic field, the same data are shown in Fig. 2  in the CSE reference frame. X CSE points towards the Sun, Z CSE is orthogonal to X CSE and along the solar wind electric field.
This does not provide data in the solar wind ion cavity, but outside it provides a consistent pattern regarding the pick up ion flow. It is still towards negative Z CSE in the region of sunward solar wind flow just outside the solar wind ion cavity, and otherwise towards positive Z CSE. The regular pattern in the low energy ion (<60 eV) velocity Y and Z components is now gone. This can be expected, as the position of Rosetta in the CSE reference frame does not vary slowly in a consistent manner, the way it did for the CSEq coordinate system. Finally, we note that there is sunward flow in the pick up ions very early and late in the mission. This is mostly a signal from sectors 0 and 15 which at times are noisy. Some of this signal may be real, most of it is believed to be noise or cross-talk.
In particular sector 5 and elevation 10 was such a centre (see  Nilsson et al. (2007)  for definition of sectors and elevation indices). Sector 5 is around where most of the cometary ions are observed  (Nilsson et al. 2015b) , with an elevation typically between 8 and 11. To fully understand the effect of the spacecraft potential requires making use of the simulations performed by  Bergman et al. (2020a) , which is beyond the scope of this study. The moments used here are calculated as measured by the instrument with no attempt to compensate for the spacecraft potential. This is thus representative also of the results obtained using any lower level RPC-ICA data. ICA low-energy data also suffers from less elevation angle coverage, see the RPC-ICA User Guide on the PSA archive. For energies below 20 eV coverage is very poor (1-3 elevation bins) while above 50 eV the elevation range is close to nominal but coarse, and above 100 eV it is nominal.
6. The average flow direction is from the direction where RPC-ICA has the largest unobstructed field of view. This could thus be an artefact of the instrument field of view. The data were frequently seen in a relatively narrow angular range well within the field of view  (Nilsson et al. 2015b) , showing that this general feature is not an artefact of the instrument. For flow from the nucleus, the sunward and antisunward flow directions are equally sampled. As part of the ESA enhanced archive effort we also attempted to create a flag indicating when the maximum signal was at the edge of the instrument field of view. The predictive power for the solar wind density estimate was low and the flag was not used. It did however show that for 92 per cent of the cases the peak of the cometary ion signal was not at the edge of the field of view.
The transport of these electrons is apparently not inhibited across the magnetic field, and does follow the pick up ions. The Rosetta spacecraft potential was often significantly negative. Positive ions are accelerated towards the spacecraft. Ions with an energy (prior to acceleration by the spacecraft potential) less than about twice the energy gained from the spacecraft potential can have their trajectories significantly distorted  (Bergman et al. 2020a ). What we can clearly say from the data we presented is that the bulk drift speed estimate is not a function of the spacecraft potential (Fig. 5  lower left panel). Ions in a range where the spacecraft potential should be less important (30-60 eV) also shows a radial expansion, as did the cometary ions during the nightside excursion, where ions reached much higher energy as Rosetta went far away from the nucleus, out to about 1000-km distance  (Behar et al.
C O N C L U S I O N S The RPC-ICA moment data base which we have used reproduces the main features of many previous observations, notably the deflection of the solar wind, acceleration of pick-up ions along the solar wind electric field for the low-activity case, dominating antisunward flow of pick up ions and a radial expansion of low-energy cometary ions in the Y-Z plane (X towards the Sun). We also find a number of new features. The radial expansion of the low-energy ions in the Y-Z plane is seen throughout the mission at all positions. The expansion is apparently not strongly affected by the electric and magnetic fields of the solar wind. A somewhat more sustained gradual acceleration out to large distances can be seen along the solar wind electric field direction (positive Z in CSE coordinates).
Transformation from a Cartesian coordinate system to the instrument coordinate system is given by n(r) = ∞ -∞ ∞ -∞ ∞ -∞ f (v x , v y , v z ) dv x dv y dv z (A3) = E ϕ θ f (r, E, ϕ, θ) 2E m 3 cos(θ) dEdϕdθ. ( A4 ) The data are obtained in discrete energy -angle bins, so that the numerical integration is in practice performed through sums given below for the zeroth-order (density) and first-order (velocity) moments. In these equations, c is the number of counts registered by the instrument in time τ for energy level E, with an instrument geometric factor of G, for a particle of mass m and Q elementary charges per particle. The energy is given in eV, which is why the elementary charge e is included in the equation. The instrument geometric factor is provided with the data in PSA, and in the units of cm -2 sr -1 s -1 eV/eV.
We formalize the coupled criterion (CC) in the framework of Finite Fracture Mechanics (FFM) to take into account the material nonlinear behavior. Once the true stress-strain curve is recorded, we estimate the Ramberg-Osgood (RO) parameters. We then exploit these values in the FFM numerical implementation, considering both an average stress requirement and the energy balance. As a case study, we consider experimental data on Brazilian disks containing a circular hole and involving two materials -PMMA and GPPS. The computational effort of the analysis increases with respect to the linear elastic case, but keeps reasonable. On the other hand, the nonlinear FFM investigation allows a significant improvement of the failure stress predictions, especially those related to smaller holes corresponding to a more pronounced nonlinear behavior detected during tests.
The discrepancy between both cases remains lower than 10% for PMMA and 15% for GPPS. Taking into account the material nonlinear behavior allows reducing the discrepancy with respect to the mean value of experimental data (Tables  2  and 3 ), especially for the smallest holes. For instance, the difference between predictions and mean experimental failure stress is reduced from 27. 9% to 9. 6% for PMMA and from 18. 5% to.
ACKNOWLEDGMENTS We kindly acknowledge the  INGV-OE seismic network  staff who ensure the regular working of seismic stations and the " Gruppo Analisi Dati Sismici " for providing earthquake data. AB is particularly indebted to the technical staff of the  Ground Deformation Group of INGV-OE  for the regular operation of the tilt and GPS monitoring networks, which enabled the previous studies over the last 3 decades. We thank  S. Conway  for revising the English language of the manuscript.
Stimulated emission depletion fluorescence scanning microscopy (STED) creates super-resolution image by reducing the diffraction spot with the help of an additional STED pulse. The scanning process strongly limits the data acquisition time for large size object, even with parallelizing STED  [5]. Penghuan Liu got the PhD degree in École Centrale de Nantes at the Laboratoire des Sciences du Numérique de Nantes (LS2N), France. He is now an independent researcher in Foliu village, 061802 Wuqiao county, Hebei province, China. E-mail: liu. penghuan@outlook. com Structured illumination microscopy (SIM) retrieves superresolution by illuminating the object ρ with a few structured patterns I m. In the linear regime, the measured dataset {y m } M m=1 has relation with the sample ρ by  [6] : y m = h * (ρI m ) + m ( 1 ) where h is the point spread function (PSF) of the system and * denotes convolution operator.
G. Reconstructions from experimental data To provide a more convincing illustration of the superresolution capacity of blind-speckleSIM, the processing of experimental datasets is presented in this section. The raw images are obtained with an objective of N A = 1. 49 and 100× magnification. The PSF used is simulated using a ICY plug-in called PSF Generator with Gibson & Lanni 3D Optical Model. The spatial sampling rate is set to be equal or slightly above the Nyquist rate λ e. ) FFT of (b) f. ) FFT of (c) slightly adapt the objective function introduced in Ref. [13]  and reconstruct ρ with only the second-order statistics (that is, the mean values are abandoned) by minimizing: D R (ρ) = D KL N (0, Γy ) N (0, Γ y ) = 1 2 log|Γ y | + 1 2 Tr Γ -1 y Γy + K (39) where Γ y indicates the theoretical covariance of y m (respectively Γy the empirical covariance) and K is a constant number.
Observations Data from the Passy-2015 field experiment are used to analyse the flow structure within the basin during both episodes, to highlight the main differences and to initialize numerical simulations. A summary of the sensors used in this paper is given in Table  1  while the complete list can be found in  Paci et al. (2016). The flow structure in the basin centre (SITE1) is characterized by a combination of four sonic anemometers deployed on a 10 m mast and a profiler Doppler wind lidar (WLS8-5) which provides measurements from 40 m up to 500 m above ground level (a. g. l. ). In addition, a scanning Doppler wind lidar deployed at SITE2 provides spatialized measurements of line-of-sight velocity (V los ) with an atmospheric volume documented every hour. This database has been investigated in depth by  Sabatier et al.
The fine resolution of the first levels is chosen according to  Blein (2016)  who recommended 2 m as the resolution required to ensure the realism of slope wind systems. An absorbing layer is activated at the domain upper boundary located at 20 km a. s. l. in order to prevent the reflection of gravity waves. Lateral boundary conditions are open, letting the short-scale waves out of the domain with minimal reflection at the boundaries. To ensure the realism of the flows developed in the vicinity of the basin, the domain is extended significantly beyond the domain of interest. The orography is issued from the 90 m resolution Shuttle Radar Topography Mission (SRTM) database. A local smoothing is applied to the slopes with an inclination above 45 in order to ensure the convergence of the pressure solver due to the anelastic approximation.
PRELIMINARY RESULTS FROM OBSERVATIONS This first section of results provides an overview of the main differences between the along-valley circulation observed during a typical day of the IOP1 and the March episodes using the Passy-2015 field experiment database. The strategy for the numerical experiments presented in the following sections is based on these observations. These observations, along with those presented in  Sabatier et al. (2018) , are also important for supporting the relevance of the numerical simulations for studying the situations behind these two pollution episodes.
Vertical Structure Figure  9  represents the time-averaged vertical structure of anabatic winds developed along the northern basin sidewall (SLOn; Figure  1 ). The average is made for each simulation over the period for which anabatics are actually present. In addition, the profile envelopes are added for simulations REF and MARCH. In the REF simulation, the anabatic winds developed vertically over 80 m with a maximum intensity around 7 m a. g. l. The field experiment lacks data on the slope for comparison, but this simulated structure is coherent with those reported in the literature  (Zardi and Whiteman, 2013). In the MARCH simulation the anabatic winds intensity slightly while the opposite occurs in the SNOW simulation. There are no noticeable changes in the simulations SNOWsouth (in which there is no snow on the northern basin sidewall as in REF) and LESSstable.
ACKNOWLEDGEMENTS The Passy-2015 field experiment was supported by  ADEME  through the  French national programme LEFE/INSU  and by  METEO-FRANCE. We thank the cities of Passy and Sallanches for their kind support. The field experiment was led by  CNRM  while LEGI was the principal investigator of the  LEFE/INSU  project. Data are managed by  SEDOO at Observatoire Midi-Pyrénées  (http://passy. sedoo. fr;accessed 27 December 2019). We thank all the teams involved in the field experiment: GMEI/LISA, GMEI/4M, GMEI/MN-PCA and GMEI/TRAMM at  CNRM ;  LEGI, IGE, Atmo Auvergne-Rhone-Alpes  and  NCAS (UK). We are grateful to  Stéphanie Faroux  and  Aaron Boone  for their help in surface initialization, as well as the Meso-NH support team.
To this end, the INSPIRE program will provide a life-long measurement of aging-related locomotor activity in mice, through automated home cage monitoring. This technique enables to monitor animals over long periods of time without human intervention. The system we will use, known as Digital Ventilated Cages (DVC®), is designed to gather continuous animal activity data directly from the home cage while keeping cages into conventional Individual Ventilated Cages (IVC) racks (Supp material). It provides a reduction in animal distress thereby increasing welfare, minimization of biases and increased reproducibility of data  (47, 48). Therefore, mice belonging to the INSPIRE cohort will be housed in DVC cages so locomotor activity of all mice will be continuously and automatically monitored throughout their life.
Observational study: longitudinal vs cross-sectional Both cross-sectional and longitudinal approaches are observational studies commonly used in aging research. In cross-sectional studies, data are collected as a whole to study a mouse population at a single point in time to examine the relationship between variables of interest. Conversely, in longitudinal studies, data are gathered from the same mouse repeatedly over an extended period of time. In the case of age-related healthspan studies, data are collected at predetermined ages from multiple individuals within a population. The cross-sectional study design allows performing invasive or terminal procedures but precludes the evaluation of lifespan. In the case of the INSPIRE Mouse cohort, a major cross-sectional study will be conducted with endpoint analyses being performed in different groups of mice.
Acknowledgments: We thank  Massimiliano Bardotti ,  Rémy Burcelin  and  Sarah Gandarillas  for their help in the design of the cohort. The Inspire Program was supported by grants from the  Region Occitanie/Pyrénées-Méditerranée  (Reference number: 1901175), the  European Regional Development Fund (ERDF)  (Project number:  MP0022856 ), and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN,  Pfizer ,  Pierre-Fabre. Conflict of interest: All authors of the paper "Towards a large-scale assessment of relationship between biological and chronological aging: The  INSPIRE Mouse cohort " declare no conflict of interest related to this manuscript. Permissions: This article is distributed under the terms of the Creative Commons Attribution 4.
Smith. Instead, we discussed in the paper how data from the field may challenge or not effects benchmarks that are generally derived from data acquired in the lab or controlled field for several reasons detailed in our paper and in  Real and Garnier-Laplace (2018). Similar was the objective of the paper published from  Garnier-Laplace et al. (2008)  by comparing critical chronic radiotoxicity data sets (i. e. EDR10) estimated from studies in the Chernobyl Exclusion Zone vs. those estimated from controlled experiments (field or lab). The data used were extracted from the FREDERICA database quality-checked by the FASSET and ERICA consortia according to several criteria (see  Copplestone et al, 2008). About "Remarkable claims" as discussed by J. Smith The total dose which would have led to a 50% reduction of birds number in the Fukushima area mentioned in our paper was established by  Garnier-Laplace et al.