dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability statement The datasets generated for this study can be found on the web interface (http://www. eo4sibs. uliege. be/) and on Zenodo under data doi: 10. 5281/zenodo. 6397223 with a full documentation that include Products User Manuals (PUM) and Algorithm Theoretical Basis Document (ATBD). All these products are distributed in netCDF files Gre ́goire et al. (2022). SMOS SSS and CDM products are also available at https://bec. icm. csic. es/bec-ftp-service/.
3) and a 2 × eyepiece. To facilitate fast read out, we operated the camera in frame-transfer mode (see  [39]  for more details). Phase retrieval was performed using Paganin's algorithm  [40]  with a δ/ β of 3/1. 3D reconstructions were performed us-ing pyHST (python High Speed Tomography), an ESRF specific implementation of a filtered back projection  [41]. Ring artefacts were corrected using a custom-made MATLAB (MathWorks, Massachusetts) software  [42]  in two steps (Table  S1 ). Reconstruction was performed using 4 GPU Tesla C1060 cards with 240 processors per GPU. Datasets of 2048 ×2048 ×1024 were reconstructed in approximately 240 s. The resulting isotropic voxel size in the images was 670 nm.
Digital volume correlation We performed DVC analysis (DaVis v8. 4, LaVision, Germany) on the SRμCT images after masking out the indenter tip and air to investigate the displacement and strain fields underneath the indenter tip at increasing indentation depth. We used the image acquired at step 0 as a reference. The DaVis software is based on a local approach of deformable registration, and details on the operating principles of the algorithm have been extensively reported elsewhere  [ 47 , 48 ]. The image volumes were correlated using a multipass scheme that used the displacement gradient from the previous pass to deform the subvolumes on the subsequent passes. A Fourier-fast-transform pre-shift window of 112 voxels size preceded a direct correlation DVC computation with decreasing subvolume sizes (i.
Irradiation-induced damage We evaluated the force-time signal during beam exposure. Visual inspection of the force-time data suggested a linear decay of the force signal after beam start ( Fig. 1 a ). Therefore, a moving regression with a window width of 300 s was used and the slope with a maximum Pearson's correlation coefficient was recorded as beam relaxation rate, K, in N/s. We performed Raman spectroscopy on six irradiated samples post-test and six untested specimens not exposed to SRμCT to investigate a possible influence of the beam on sample composition. Raman spectra were collected using a commercial confocal system (Nova Spectra, NT-MDT, Russia), equipped with a 100X objective (Olympus, NA 0. 95) and 632 nm HeNe laser excitation source. We made ten separate measurements on each irradiated and nonirradiated sample using a 60 s integration time (single acquisition) and a 60 0/60 0 grating with approximately 2.
Statistical analysis Indentation and imaging data were grouped in Berkovich axial, Berkovich transverse, spherical axial, and spherical transverse. Median, minimum, and maximum values for all samples within each group were computed to allow a boxplot-like representation, Data evaluation was performed with Gnu R (R Development Core Team, 2008) or python. Non-parametric tests to a significance level of p = 0. 05 were chosen due to the small sample number.
Acknowledgments The project was supported by a  Leverhulme Research Project Grant  to U. W. (  RPG-2020-215  ), the  AO Foundation  (  AO S-12-13W  ) and the  LabEx PRIMES framework  ( ANR-11-LABX-006 ) of  Université de Lyon. We would like to thank the  ESRF  for providing beam-time within LTP MD431,  James Best  for his assistance with Raman analysis, and  Max Langer  and  Loriane Weber  for help during the experiment. Finally, we would like to thank the team of the  ARTORG workshop  around  Urs Rohrer  for manufacturing the devices for the project.
The treatment of continuum foregrounds has mainly been studied in the context of 21 cm LIM. Most of the techniques rely on both its spectral smoothness and its strong fluctuations to reconstruct and subtract it  (McQuinn et al. 2006; Furlanetto et al. 2006; Morales et al. 2006; Chang et al. 2010; Parsons et al. 2012; Liu & Tegmark 2012; Switzer et al. 2015; Yue et al. 2015)  or used to avoid the contaminated angular scales in the (3D) Fourier space  (Chapman et al. 2016). Regarding the interlopers, the cross correlation between the data and an alternative tracer probing the same cosmic volume can be used to disentangle the signal of interest from the interlopers. This option has been used for popular lines in intensity mapping  (Lidz et al. 2009; Visbal & Loeb 2010; Masui et al. 2013; Croft et al.
(2017 Béthermin et al. ( , 2022) )  and  Gkogkou et al. (2023) , hereafter B17, B22, and G22, respectively. The component separation methods are applied directly to these mock observations, as they would be applied to the real CONCERTO observations. For the CIB contamination (continuum emission), our component separation is based on two different approaches. The first is principal component analysis (PCA) adapted to LIM data (e. g. ,  Bigot-Sazy et al. 2015; Yohana et al. 2021) , and the second is a baseline estimator called asymmetric reweighed penalized least squares (arPLS;  Baek et al. 2015).
SIDES simulation The SIDES simulation, which mimics the CONCERTO extragalactic sky, is a central element of our study. SIDES accurately replicates a series of statistical properties, spanning from mid-infrared to millimetre wavelengths, including the redshift distributions and number counts of galaxies, line luminosity functions, and CIB fluctuations (see B17, B22, and G22 for a complete description of the simulation and for comparisons with observational constraints). It produces realistic LIM mock data constituting our input sky used to evaluate the component separation method efficiency. In Sect. 2. 1, we briefly summarize the important SIDES characteristics for our use. We further review the map and cubemaking process in Sect. 2. 2.
Cube making From the SIDES catalogue, we create spatial-spectral data cubes for each component: continuum (CIB), CO, [C I ] and [C II ]. The adopted pixel size is 5 arcseconds and the frequency axis ranges from 125 to 305 GHz with an absolute spectral resolution of 1 GHz. Voxels (the spatial-spectral elements of a cube) are populated with the corresponding intensities. Lastly, the beam smearing is applied to each frequency channel by convolution in Fourier space with a Gaussian beam kernel and periodic boundary conditions. The Gaussian beam profile in one frequency channel is G beam = 1 2πσ 2 e -r 2 2σ 2 , ( 1 ) where σ is the standard deviation of the Gaussian beam profile linked to the frequency ν of the channel by σ = FWHM 2 √ 2 ln(2) = 1 2 √ 2 ln(2) 1. 22c νD , ( 2 ) with FWHM is the full with at half maximum of the instrument beam, c is the celerity of light and D = 12 m is the diameter of the APEX telescope where CONCERTO is installed.
We first assessed the continuum removal on SIDES. SIDES contains both the CIB (mean continuum level) and its spectral fluctuations, which are about 10-20% of the CIB. But for CONCERTO, even if in theory it would be sensitive to the CIB, the data processing removes the mean levels. We thus also performed the continuum removal after subtracting the CIB at each frequency of the SIDES simulation (i. e. removing the mean level).
Principal component analysis Principal component analysis is a non-parametric method of dimensionality reduction, based on constructing linear combinations of variables in the dataset. Geometrically, these new variables, the so-called principal components, represent the directions of maximum variance in the dataset. The principal components do not have a direct physical interpretation. They are only linked to the variance, not to an underlying physical origin. Most of the variance in the dataset is described by the first principal components, ranked by the amount of variance they carry. Dimensionality reduction corresponds to discarding principal components of lower significance, when projecting the dataset onto this new basis. As mentioned above, the CIB produces the strongest fluctuations at all frequencies in CONCERTO.
It is therefore likely that its contamination is condensed in the first few principal components and can be accurately removed. We use for this work the PCA implementation from the package scikit-learn  (Pedregosa et al. 2011). We create a matrix, X, that contains the collection of spectra from the foreground-contaminated mock cube of Sect. 2. 2. The dimensions of X are N ν × N p , where N ν is the number of frequency channels and N p is the number of pixels. For clarity, we use Greek symbols for the frequency indices and a Latin symbol for the spatial indices (p for pixel). The covariance matrix, C, of the spectral data reads C νν = Xνp Xpν , ( 3 ) where X is defined as Xνp = X νp -µ p , (4) and µ p is the average in the pixel p over all frequencies. The covariance matrix, C, is square, symmetric, and has dimension N ν.
Interloper separation at z [CII] = 5. 2, 6. 5, and 7 In this section we assess the efficiency of masking at retrieving the underlying [C II ] (DL14) APS in the interloper-contaminated mock LIM data (see Sect. 4. 2). As mentioned above, the correction of the mask impact is taken care of by POKER, so we focus here on the residual contribution of CO and [C I ] left after masking on the COSMOS field. When using an APS measurement to derive cosmological parameters, one must also account for the FtF variance, namely the overall uncertainty on the relative contribution of CO and [C I ] to [C II ], in different parts of the sky. This particular point is addressed in Sect. 5. 2, and we focus here on the measurement on COSMOS only. In the following, power amplitudes and power ratios are given for the same k range (as in Table  4 ), where the clustering Table  4.
Summary and conclusion We tested a complete foreground deconfusion process for the [C II ] LIM survey conducted with CONCERTO using realistic mock data of the sub-millimetre sky provided by SIDES-Bolshoi (B17, B22). To build the SIDES mock data, a galaxy catalogue complete down to 10 7 M and z = 7 was first obtained using abundance matching between stellar mass and DM halo mass in a simulated DM light cone. Galaxy properties were then derived from their type (MS or SB), redshift, and stellar mass using empirically calibrated relations with appropriate scatters. Finally, mock cubes for CIB, CO, [C I ], and [C II ] were produced. A foreground-contaminating mock cube was created by adding the separate foreground cubes to the [C II ] cube. We first assessed the contamination by CIB (continuum) fluctuations.
We show that M * is the deepest CO and [C I ] proxy available in the COSMOS field. We also investigated its use as a CO proxy. The most contaminating sources (M * > 10 8. 1 M ) are the least numerous and are above the L16 completeness relation, allowing us to efficiently remove the interloper contamination with a limited loss of the surveyed area. The spatial extent of the masks is defined with a (frequency-evolving) beam width criterion. To test the masking on our mock data in a realistic way, we masked only sources above the COSMOS (deep) completeness relation described in L16. For the SIDES-Bolshoi simulation, the power amplitude from bright CO and [C I ] sources was lowered by four orders of magnitudes. Masking is also more efficient than expected in B22 as it also removes a fraction of the faint source signal at the same time, thanks to the clustering.
2022) , in which the [C II ] power spectrum is predicted with a much lower amplitude than in earlier models (e. g. ,  Serra et al. 2016). For the first generation of LIM experiments, such as CONCERTO, and given such a low-amplitude [C II ] power spectrum, we expect the instrument noise to dominate. Van Cuyck et al. (in prep. )  will introduce the noise generator of SIDES and investigate the effects of instrument white noise on different LIM observables, such as auto-power spectra, cross-power spectra between lines and galaxies, and cross-power spectra between different lines within the LIM dataset. We have shown that our method for removing the continuum emission does not create any systematic residuals, but this will have to be tested with instrument noise. On the other hand, we expect masking to be robust to noise contamination because the interlopers' signal is removed regardless of the noise level.
The robustness of cross-correlation measurements between different lines within the LIM dataset in the presence of instrument noise is also worth investigating in the context of component separation. Such a cross correlation could allow for a direct estimate of interlopers' residual power amplitude after masking. The last effect that would need further checks for the masking technique is the inclusion of the catalogues' redshift uncertainties, reported to be δz/(1 + z) = 0. 025 below z ≤ 3 for the COSMOS field  (Weaver et al. 2022). Finally, the CO-line emission, while being a strong foreground for the [C II ] survey, is of great importance. The CO part of the CONCERTO survey can be used to probe the CO SLED and the gas content of galaxies at z ≤ 3, including at cosmic noon (2 ≤ z ≤ 3), when star formation in galaxies statistically reaches its peak.
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir " programme. M. A. acknowledges support from  FONDECYT  grant  1211951 ,  CONICYT + PCI + Max Planck Institute for Astronomy MPG 190030 ,  CONICYT + PCI + REDES 190194  and  ANID   BASAL  project  FB210003.
Data acquisition The dynamometer used was the K-Force Grip (Kinvent Biomecanique SAS, Montpellier, France) with a sampling rate of 1000 Hz and accuracy of 100 g. It is fixed to a specially made support that ensures that the hand is correctly placed. The sEMG activity was recorded using Trigno® Wireless Biofeedback System (Delsys Inc. , MA, USA). Sensors are composed of two pairs of silver bar contacts with 10-mm inter-electrode distance and a sampling rate of 1926 Hz. Before electrode placement, the skin was shaved and wiped clean with 70°alcohol. Four sensors were placed on the Flexor Digitorum Superficialis and hold with a double-sided tape and kinesiology taping while following the SENIAM recommendations and the previous anatomical location described. 26 he participant gets direct visual feedback of the force and the sEMG by means of an interface created through LabVIEW v19.
Data processing A numeric force signal is filtered with a second-order zero-lag Butterworth lowpass filter at 40-Hz cut-off frequency with Matlab R2018a v9. 4 (The MathWorks Inc. , Natick, MA, USA). The onset of each contraction is determined by means of a second derivative method, described by Soda et al. (2010). 30 Force signal is considered from the onset within a 200-ms time window. Moreover, the integral of the force-time curve (∫ force • dt), namely, impulse (N•s), is calculated at each time point in the 200-ms force continuum. Impulse incorporates the entire time history of the contraction, making identification of time-related force parameters possible. 18 The RFD-time curve is calculated through the first derivative of the force signal (Δforce/Δtime) at each overlapping time interval in the 200-ms force continuum and lowpass filtered at 50-Hz cut-off frequency.
Matching was based on age and diabetes status using a Greedy algorithm with a 1:1 ratio and performed using the MatchIt package in RStudio (R Core Team 2021, Vienna, Austria). The scalars, or zero-dimensional data (RFD peak , NRFD peak , t-RFD peak and MVF-RFD peak , sEMG), were compared using SPSS v24 (IBM Corp. , Armonk, NY, USA) by group (CKD patients vs. controls) and gender (female vs. male), while interactions during the fatigability period were compared using two-way repeated measures analysis of variance (ANOVA). Mauchly's test was used to verify the assumption of sphericity and, according to the epsilon statistic, the Greenhouse-Geisser or Huynh-Feldt correction was applied if required. The partial eta squared (η 2 p ) with ANOVA calculation or Cohen's d with Student's tests were also reported to give an indication of effect size, considered as small (η 2 p ≥ 0.
01, Cohen's d ≥ 0. 2), medium (η 2 p ≥ 0. 06, Cohen's d ≥ 0. 5) and large (η 2 p ≥ 0. 14, Cohen's d ≥ 0. 8) effects. A post hoc with Bonferroni's correction was performed. Univariate regression analysis was performed to test the relationship between scalars and eGFR. To overcome the scalar limitation bias caused by the hypothesis definition used in this study (see Pataky et  and procedure  24  ), we performed SPM analysis to compare one-dimensional force-time curves between groups. SPM procedures were performed using Matlab with spm1d package (https://spm1d. org). Normality assumption was tested using the D'Agostino-Pearson K 2 test and, in case of normally distributed continua, two-way repeated measures ANOVA was employed to test time (from the explosive contraction performed at rest to the last explosive contraction performed in the fatigability period) and group effect (CKD patients vs.
Baseline data A total of 159 individuals were enrolled in the study (Figure  S1 ). Of those enrolled, 34 participants were excluded for the following reasons: mild cognitive impairment (n = 8), acute pain (n = 5), lack of time to finish the tests (n = 5), voluntary abandonment (n = 3), depression (n = 5), unstable clinical condition (n = 3), Covid-19 infection sequalae (n = 2), neurological sequelae (n = 2) and insufficient visual acuity (n = 1); 8 others were excluded because of inconsistent explosive contractions (5 at rest and 3 during the fatigability period). Thus, 117 participants were considered for analysis (Table  1 ). The a priori pairing of individuals in terms of age and prevalence of diabetes was verified, with no significant difference between CKD patients and controls. The biochemical data regarding CKD individuals are shown in Table  S1.
Acknowledgements The authors would like to thank all the volunteers who participated. We are grateful to the  French Ministry of Training and Research  for their partial financial support through  Convention Industrielle de Formation par la Recherche (CIFRE)  grant (No. 2018/1255 ). The authors also thank  Susan Finnel  for proofreading the text and  Nicolas Peyrot  and  Sebastien Boyas  for their advices. The authors of this manuscript certify that they comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. 46.
TALYS package TALYS code  [27]  was selected as a tool to investigate the protoninduced reaction channels for all candidates labeling elements. TALYS is a software package for the simulation of nuclear reactions in the 1 keV-200 MeV energy range, involving different projectiles (e. g. , protons, neutrons, photons, alphas, etc. ) and targets (mass equal or higher than 5). This code can be also used in applied nuclear physics to predict nuclear level density, gamma-ray strength functions, compound nucleus reaction mechanism, as well as direct and preequilibrium processes  [29]. In all TALYS calculations, we used the default values of all parameters.
5 × 5 × 5 cm 3 flask with a thin entrance wall about 88 μm thick, in order to minimize the beam energy loss. For both experimental campaigns, the data were recorded with a VME-based data acquisition system (DAQ) triggered by the LaBr 3 :Ce. The analog signals of the two detectors were processed by a CAEN 792N 12-bit resolution Charge-To-Digital Converter (QDC), using a 500 ns long integration gate for the LaBr 3 :Ce and a 100 ns long integration gate for the plastic scintillator. A CAEN V830 scaler was used to count the number of events registered by each detector, as well as the number of triggers accepted by the DAQ. These data were needed to estimate the acquisition dead time, defined as the ratio between accepted and total triggers, that we used to correct all experimental PG energy spectra.
The events produced by the LaBr 3 : Ce internal radioactivity contributed both to the number of the free triggers and accepted triggers, and generated ≈ 2 • 10 2 counts per second. In all data taking, the proton rate was set to achieve approximately 5 • 10 2 accepted triggers per second, which corresponded to an average dead time of 45%. This value was chosen as trade-off between minimizing the dead time and the acquisition time. To reduce the background contamination in the off-line analysis, we acquired the Radio-Frequency (RF) signal from the cyclotron. The RF is an analog sinusoidal signal reproducing the time periodicity of the proton bunches extracted from the cyclotron (12 ns-84 MHz for Cyrcé and 9. 4 ns-106. 3 MHz for the Trento facility). Both the LaBr 3 :Ce and the RF signals were discriminated using a CAEN N845 16-channel leading edge discriminator and plugged into the CAEN V1190B multihit Time-to-Digital Converter (TDC).
5 MeV and outside the 4-7 MeV region, where the most probable de-excitation peaks produced by 12-Carbon and 16-Oxygen are found. These requirements ensure that the PGs emitted by the label   63 Cu, and 89 Y), or 12 C and 16 O used as proxies for tissues. The PG production cross sections σ PG were evaluated with TALYS, by counting all gammas of a given energy generated in the material with a 0. 05 MeV resolution (bin width). The results are reported for two different proton energy ranges (0-25 and 0-70 MeV). The production reactions reported (where identified) are from the NUDAT database  [62]. element are different from those produced by tissues (here simulated as Carbon and Oxygen, because of their large presence in the human body  [43] , and thus that the signal-to-background ratio is favorable.
At increasing proton energy (0-70 MeV dataset), the production cross section increases independently of the gamma energy. For 12 C, 16 O, and 31 P some de-excitation channels appear to be extremely improbable at low proton energy (σ PG ≈ mb) but become dominant at higher energy (σ PG ≈ b). Although 12 C and 16 O can generate gammas in the same energy range of the candidate materials, their σ PG appear much lower, and thus we expect a clear signature when the labeling elements are irradiated with protons. From panels (b)-(d), we selected the PGs with the highest dσ PG /dE, and calculated their production σ PG as a function of the incident proton energy. The results are reported in Figure  6 , and suggest that the proton energy corresponding to the largest cross section varies significantly both with the material and gamma energy.
As for the dσ PG /dE of Figure  5 , the σ PG are calculated for gammas within a 0. 05 MeV energy interval, which reflects the LaBr 3 :Ce energy resolution (Figure  1B ). The 1. 25-1. 30 MeV gamma from 31     2 ). The fact that some datasets do not show a single peak suggests that more than one characteristic gamma is present in the considered energy range. This behavior will be reflected also in the experimental measurements because the LaBr 3 :Ce energy resolution will not allow to discriminate gammas with energies within ≈0. 05 MeV. According to TALYS calculations, the characteristic gammas which present the sharpest σ PG peak, and thus better correlate with a given proton energy, are 1. 25-1. 30 MeV for 31-Phosphorous, 1. 30-1. 35  MeV for 63-Copper and both 0. 95-1. 00 MeV and 1. 15-1.