dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion During pilot observations of the Virgo cluster carried out with the MeerKAT radio telescope we discovered an extended (10 kpc projected distance) low column density atomic gas tail (N(HI) 2. 5 × 10 20 cm -2 ) in a dwarf galaxy (NGC 4523, M star = 1. 6 × 10 9 M ) at the northern periphery of the cluster (R 0. 85 × r 200 ). The multifrequency analysis of the data consistently suggests that NGC 4523 is suffering a ram pressure stripping event which has already removed part of the atomic gas. The overall star formation activity of the galaxy, estimated using new Hα narrow-band imaging gathered during the VESTIGE survey, is not significantly reduced if compared to that of similar unperturbed objects. A few compact H ii regions, whose age has been estimated comparing the prediction of spectral energy distribution (SED) models to their observed Hα, ASTROSAT/UVIT FUV, GALEX NUV, and NGVS u colours ( 30 Myr), have been formed within the tail of stripped gas.
NGC 4523 is a further example indicating that ram pressure stripping is efficient in intermediate mass clusters such as Virgo (M 200 10 14 M ) up to the virial radius, confirming once again the main role of this hydrodynamic process in shaping galaxy evolution in rich environments. The results presented in this work are a further demonstration of the power of untargeted surveys of nearby clusters in identifying galaxies undergoing a perturbation. Once completed, the MeerKAT survey of the Virgo cluster will provide us with a unique view of the effects of the environment on the cold atomic gas of infalling galaxies. Combined with similar information gathered thanks to the VESTIGE survey for the ionised gas component, the H i data will be crucial to understand the effects induced by the perturbations on the process of star formation.
acknowledge financial support from 'Programme National de Cosmologie and Galaxies' (PNCG) funded by CNRS/INSU-IN2P3-INP, CEA and CNES, France, and from "Projet International de Coopération Scientifique" (PICS) with Canada funded by the CNRS, France. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration and of the GOLDMine database (http: //goldmine. mib. infn. it/) (Gavazzi et al. 2003). Part of the data published here have been reduced using the CARACal pipeline, partially supported by ERC Starting grant number 679627 FORNAX, MAECI Grant Number ZA18GR02, DST-NRF Grant Number 113121 as part of the ISARP Joint Research Scheme, and BMBF project 05A17PC2 for D-MeerKAT.
Acknowledgements. We thank the anonymous referee for constructive comments which helped improving the quality of the manuscript. The MeerKAT telescope is operated by the  South African Radio Astronomy Observatory , which is a facility of the  National Research Foundation , an agency of the  Department of Science and Innovation. We are grateful to the whole CFHT team who assisted us in the preparation and in the execution of the observations and in the calibration and data reduction:  Todd Burdullis ,  Daniel Devost ,  Bill Mahoney ,  Nadine Manset ,  Andreea Petric ,  Simon Prunet ,  Kanoa Withington. We.
PCR products were resolved by electrophoresis on a 1. 5% agarose gel stained with ethidium bromide and visualized under UV light. Both strands of PCR products were sequenced using Sanger sequencing on an ABI 3730 automatic sequencer at the Centre National de Séquençage (Genoscope) in Evry (France) and ABI 3730xl DNA Analyzer at the Infectious Disease Surveillance Center, Japan. The sequences were edited and assembled using CodonCode Aligner Version 3. 7. 1 (CodonCode Corporation) and Genetyx v11 software (Genetyx Corporation, Japan). Sequences generated for this study were deposited in the EMBL/DDBJ/GenBank database under the accession numbers OQ659403-OQ659410 and OQ658596-OQ658598 (Table  S1 ). Our sequences were aligned with 83 COI and 37 cyt b sequences of R. r. rex and R. r. paradoxolophus acquired from GenBank using AliView 1.
Bacula were extracted from two specimens of Vietnamese R. r. paradoxolophus and preserved follow ing  Friley (1947). Digital 2D images of these bacula were taken using the same acquisition parameters on a Leica M80 stereomicroscope (Leica Microsystems Ltd, Switzerland). The baculum of one intact specimen of Vietnamese R. r. paradoxolophus (VN11-0442) was also examined using an X-ray microtomography device (inspeXio SMX-90CT Plus, Shimadzu Corp. , Kyoto, Japan) and its genitals were scanned with 18 μm voxels and a 90 kv source voltage with 100 mA source current. An osseous 3D model of the baculum was manually segmented using Amira 5. 2 software (Visage Imaging, San Diego, USA)  (Sohn et al. 2021). This can be obtained from the Figshare repository (https://doi. org/10. 6084/m9. figshare. 14784504.
Acoustic analyses In the field, echolocation calls of bats held in the hand or resting in a flight tent were recorded with Pettersson D240x, D980 and D1000X bat detectors (Pettersson Elektronik, Sweden) or an Echo Meter Touch detector (Wildlife Acoustics, USA). These call recordings were deposited in the bat call library of the ChiroVox project  (Görföl et al. 2022)  under the ChiroVoxIDs or accession numbers indicated in Table  S2. All recordings were analysed using BatSound Pro 4. 1 (Pettersson Elektronik, AB) and for each bat, the mean value of the frequency of maximum energy (FmaxE) was calculated from 5-10 calls. We also tabulated the same metrics reported for R. r. rex, R. r. paradoxolophus and R. schnitzleri in the literature to determine inter-and intraspecific variation in their echolocation calls.
Acknowledgements We would like to thank numerous agencies and individuals for their research permits and assistance during the present study: in China (the management board of  Mu Lun Nature Reserve ); in Laos (the  Department of Forest Resource Management of the Lao Ministry of Natural Resources and Environment , the directorate of the  National University of Laos  and the management board of the  Hin Nam No National Protected Area) ; in Vietnam (the  Vietnam Administration of Forest of the Ministry of Agriculture and Rural Development , the management board of  Dong Van Karst   Plateau Global Geopark , and the directorate of the  Institute of Ecology and Biological Resources) ; and  Nguyen Truong Son  ( IEBR, Hanoi, Vietnam ),  Vilakhan Xayaphet  ( NUoL, Laos ), and  Sohn Joonhyuk  ( SNU, Seoul, South Korea ).
This research was supported by the  AAP   RA-COVID-19 , grant number  ANR-21-CO12-0002 , the  CNRS , the  MNHN , the  INRA  and the  CEA (Genoscope)  to A. H and V. T. T, the  National Research, Development, and Innovation Fund of Hungary  ( NKFIH FK137778 ,  RRF-2. 3. 1-21-2022-00010 ), the  Hungar-ian-Vietnamese bilateral mobility grant  ( NKM-2021-39 ) to TG and GC, the  János Bolyai Research Scholarship  of the  Hungarian Academy of Sciences  ( BO/00825/21 ) to TG, the  JSPS   24405045  ( Kakenhi ) and the  Research Program on Emerging and Re-emerging   Infectious Diseases, Japan Agency for Medical Research and Development (AMED)  ( JP-20fk0108097  and  JP22fk0108634 ) to S. A,  JSPS   22KK010  (Kakenhi) to D. K. , and the  Vietnam Academy of Science and Technology  (Project No:  QTHU01.
2017). Sky emission sampled at the start and end of each scan allowed for subtraction of sky emission from the measured spectra of the target. The wavelength-dependent absolute calibration for each spectrum was performed using corresponding exposures of a blackbody card of known temperature mounted above the instrument's window. Custom IDL software and the NAIF (the Navigation and Ancillary Information Facility) SPICY toolkit  (Acton 1996)  were used perform a limb fitting procedure in order to calculate the latitude, longitude, emission angle and velocity (combining Jupiter's radial and rotational velocity) of each pixel. In this work, we use planetocentric latitude and the System III longitude system. Each individual spectrum was corrected for Doppler shift. show the distributions of individual observations sorted by setting, hemisphere and in chronological order, with each spectrum colored according to the radiance of an emission line of the relevant species.
1 mbar, then continue to rise towards lower pressures. At pressures lower than 0. 1 mbar, atmospheric heating is inferred to result from processes directly related to the deposition of energy from the magnetosphere and external space environment, including chemical heating, H 2 dissociation from excited states  (Grodent et al. 2001) , joule heating from Pedersen currents (e. g. Badman et al. 2015 ) and ion drag. These processes essentially move the base of the thermosphere to lower altitudes, as has been shown using general circulation modelling of Jupiter's thermosphere  (Bougher et al. 2005). A localised maximum in temperature at ∼1 mbar has been observed in previous work, using different datasets and appears to be a persistent feature of both the northern and southern auroral regions  (Kostiuk et al.
However, we observe a second region of transient heating on the duskside of the northern oval, which we believe to be physical. The heating occurs predominantly in the upper stratosphere with a temperature increase at 9 µbar of 9. 1 ∼ 2. 1 K at 67. 5 • N, 162. 5 • W, as an example location/altitude. This location is spatially coincident with the MAE (within uncertainty). In  Sinclair et al. (2019b) , broadband 7. 8-µm images of Jupiter's CH 4 emission on January 12th, 2017, also captured a bright, elongated feature colocated with the northern duskside MAE. This feature was absent from images recorded less than 24 hours later, which demonstrated its transience. However, without images recorded before January 12th, it could not be determined how long the feature had been present. From the same dataset, Jupiter's southern auroral oval exhibited a brightening, then dimming of CH 4 emission between January 11th -14th 2017 and contemporaneous with the northern, duskside feature, which suggested both phenomena were driven by a common mechanism.
248 V/SHE (SCE, Radiometer Analytical) was set between the counter and working electrodes. A fourth remaining opening was used for sampling addition. The medium was purged with nitrogen for 20 minutes to eliminate oxygen before launching the electrochemical techniques. The working electrode potential was controlled under the operation of a multichannel potentiostat (Biologic SA) operated by a data acquisition software that permits the control of the potentiostat (Ec-Lab). Stainless steel microelectrodes were constantly polarized at 0. 1 V/SCE. 40 mM of sodium acetate was used as a substrate. COD (Chemical Oxygen Demand) levels were regularly monitored and kept at a value of 2560 mg O 2 L À 1 to correspond to the equivalent of 40 mM of sodium acetate. LCK 514 COD kits (Hach Lange, range of measurement 100-2000 mgO 2 L À 1 ) were used for COD measurements.
Epifluorescence microscopy Biofilms stained with acridine orange were imaged with a Carl Zeiss Axio Imager-M2 microscope (Carl Zeiss) equipped for epifluorescence with an HXP 200C light source and the Zeiss 09 filter (excitor HP450r HP450200 C light source). Biofilms were observed with the objective EC Plan-Neofluar 10x/0. 30. Images were acquired with a digital camera (Zeiss AxioCam MRm) along the Z-axis and the set of images was processed with the Zen (Carl Zeiss) software. For each sample, two observations were made. The Zen Zeiss) software optimized the z-step of the stack. Thickness was measured with the toolbox of the Zen (Carl Zeiss) software, taking nine points per image. Biofilm growth rate was calculated as the increase of thickness divided by the time interval. Biofilm thickness was calculated as the diameter of the colonized microelectrode (; total Þ minus the diameter of the blank microelectrode (; microelectrode Þ obtained from microscopic images, as in in [Eq.
Confocal laser scanning microscopy and image analysis The exopolymeric matrix of the biofilm and biofilm viability was imaged with a Leica SP8-2017 microscope (Leica Microsystems) with the Leica Application Suite X: LAS-X software (Leica Microsystems). The image acquisition was made by using the HC PC FLUOTAR 10x/0. 30 objective in dry immersion. For each sample, two stacks of horizontal plane images (1024 × 1024 pixels) were taken from two randomly chosen areas. The LAS-X software optimized the z-step of the stack depending on the sample. Once the acquisition was completed, the LAS-X software represented the projection of the stack in a 3D view. For image analysis, 3D images were treated with the Image J software. In the case of viability analysis, since a two-color image was obtained, the color threshold tool was used.
The primers were designed to contain overhang compatible sequences with Nextera XT index (Illumina). The purified amplicons were sequenced using the MiSeq platform (Illumina). Microbiome bioinformatics were performed by the open-source software QIIME2, version 2021. 11 (https://qiime2. org). [58]  Raw reads were demultiplexed, quality-filtered, denoised and chimera-checked using DADA2. [59]  DADA2 uses a parametric model to infer true biological sequences from reads. The model relies on input read abundances (true reads are likely to be more abundant) and the pairwise similarity between sequences. Sequences were aligned using MAFFT,  [60]  and were used to construct a phylogeny using FastTree. [61]  The taxonomic annotation of the resulting amplicon sequence variants (ASVs) was assigned using the feature-classifier command with default parameters in QIIME2 and sequences were matched against the Greengenes 13_8 database.
The presentation order of the type of trials, presentation times and type of emotions was randomized. At the beginning of the task, participants practised for eight trials (4 valid, 2 nonvalid and 2 noncued) with pictures that were not used for the rest of the task. A break was added at the end of the 112th trial to avoid fatigue. Anticipative answers (i. e. , in which participants answered in less than 250 ms) were excluded before data processing. The outcomes recorded were reaction times and errors (omissions, i. e. , the participant did not answer, or commissions, i. e. , the participant chose the wrong side). Regarding the outcomes calculated, the benefit index (reaction time of noncued trialsreaction time of valid trials) reflects the decrease in reaction times due to the previous engagement of attention induced by the cue in a valid subliminal trial compared to a noncued subliminal trial.
Procedure After screening for eligibility, an appointment was proposed in the research centre to proceed with the inclusion and carry out the research visit. Sociodemographic data (age and educational level), concomitant treatments and measures for inclusion were first collected. Then, the participants completed a subset of 5 cognitive tasks, including the Posner Cueing Task, with a total duration of 2 h. The task order was randomized for each participant, but the Posner Cueing Task was always performed first (to avoid alterations in attention). The Posner Cueing Task was computer-administered. All participants were seated in a quiet room and positioned 60 cm from the screen. Computerized tasks were programmed using Superlab 5 (Cedrus Corporation, San Pedro, CA, USA), and the 7-button response pad RB-730 (Cedrus Corporation) was used to record the participants' responses.
We first tested the interaction term β 4 with a Wald test and β 5 with a joint test, before testing the other main effects. At each step, the term with the largest P-value was removed from the model, and a new model was estimated. In the final model, we estimated the variance explained by the fixed effects and by both the fixed and random effects by calculating pseudo R 2 values  (Snijders & Bosker, 1994). Statistical analyses were performed using Stata 17. 0 software. We used the mixed command to fit the linear mixed models with maximum likelihood estimation, and used the margins command to compute the adjusted predictions (estimated means) from the final model and to estimate the corresponding contrasts. All model assumptions (normality and homoscedasticity of the residuals and the BLUPs (Best Linear Unbiaised Predictions) of the random effects) were tested and satisfied.
One can note that the sub-problem (4) performs Gaussian denoising of (x i+1 + u i ) assuming a noise variance λ/ρ and under the prior defined by R. Hence, instead of learning R directly, unrolled ADMM optimization methods typically learn a deep denoiser D with trainable parameters θ, and replace Eq. (  4 ) with: y i+1 = D(x i + u i ; θ). (6) The deep denoiser D can thus be trained within the ADMM optimization algorithm so that unrolling a given number N of ADMM iterations gives the estimate xN that best reconstructs the ground truth x for a training image dataset.
IV. EXPERIMENTS We assess our framework for light field reconstruction from focal stacks containing very few shots, i. e. with 2 and 3 shots. We compare the proposed method against the most recent and efficient state-of-the-art methods for this task: the Fourier Disparity Layers by Le Pendu et al. [29] , the TV regularized sparse light field reconstruction model based on guided-filtering recently proposed by Gao et al. [11] , the light field reconstruction and depth estimation using convolutional neural networks proposed by Huang et al. [37] , and the Unrolled ADMM Fourier Disparity Layer Optimization by Le Bon et al. [31]. For fair comparisons, the methods of Huang et al. [37]  and of Le Bon et al. [31]  have been re-trained using the datasets listed in Section IV-A. Additionally, an ablation study is proposed in Section IV-E to study the importance of (i) using jointly the unrolled ADMM FDL optimization and the learned view synthesis network L_gt ← groundtruth views 6: L_recons ← 0 7: 8: for each unrolled iteration i do 9: for each frequency component q do 10: xq ← (A * q A q +ρI) -1 (A * q b q +ρ(y i q -u i q )) 11: end for 12: Y i+1 ← D(( Xi+1 + U i )Φ -1 ; θ 1 )Φ 13: U i+1 ← U i + ( Xi+1 -Y i+1 ) 14: end for 15: 16: for each view coordinate u do 17: XI u = Z ⊙ XI 18: L_recons u ← k S( XI u Φ -1 , C u ; θ 2 ) 19: end for 20: loss = ∥L_recons -L_gt∥ 2 2 21: θ 1 = θ 1 -λ∇ θ1 (loss) 22: θ 2 = θ 2 -λ∇ θ2 (loss) 23: end for compared to our previous work  [31]  (ii) using the shifted version of the FDL as well as the angular coordinate channels as network input in the view synthesis process.
A. DATASETS Two-thirds of both the Stanford Lytro light field archive dataset [40] and the Kalantari dataset  [39]  were used as training datasets. Reconstruction performances are then evaluated with the remaining third of both datasets along with the Linköping Light Field dataset  [41]. The input measurements consist of focal stacks with 2 or 3 images (i. e. shots) synthesized from ground truth views with the shift-and-add method  [6]  and with focus parameters s covering the disparity range of the scene. As ground truth, a dense light field with a 7 × 7 angular resolution is considered.
C. RECONSTRUCTION PERFORMANCES To evaluate the reconstruction performances of the different methods, we measured the quality of the reconstructed light field views using the PSNR and the SSIM metrics, traditionally used by the the image processing community. Table  1  gives the average PSNR and SSIM values over the three considered testing datasets for light field reconstruction from 2 and 3 focal stack images as measurements. It shows that the proposed approach significantly outperforms all the state-of-theart methods on every dataset, with an average gain of 1 dB compared to the best approaches. Additionally, Fig. 3  shows a reconstructed central view for each evaluated method. As illustrated in the figure, the proposed joint optimization method better reconstructs finer details compared to other approaches.
Fig. 4  illustrates a transparency artifact occurring in an occluded region with the unrolled ADMM FDL method. We can visually see that the proposed joint optimization method significantly reduces this artifact. In Fig. 5 , we computed the average PSNR gain over the Kalantari testing dataset  [39]  with the end-to-end approach over the unrolled ADMM FDL optimization for several views with different angular coordinates. As shown in Fig. 5 , the proposed framework always improves the reconstruction quality compared to the unrolled ADMM FDL method, especially for the views that are distant from the central view with an average gain of over 1 dB.
Nevertheless, this method can be adapted to take into account the short length of the probes. To reduce the data downlink while keeping the most important information in the current-voltage characteristics, SLP includes an on-board adaptive sweeping capability. This functionality has been validated both in the plasma chamber and in space and it is demonstrated that, with a reduced number of data points the electron retardation and electron saturation regions can be well resolved. Finally, the effect of the contamination of the probe surface, which can be a serious issue in Langmuir probe data analysis, has been investigated. If not accounted for properly, this effect could lead to substantial errors in the estimation of the electron temperature.
As it can be seen on the measured I-V characteristic plotted in Figure  8  in blue (discrete dots), where the instrument was grounded to isolate the end effect from S/C charging effects, the  exponent is not 0. 5 (square root shape) but has been found to be 0. 69. Using this value, the plasma density was computed. The plasma parameters for this measurement were ne = 4. 2e10/m³ and Te = 650 K. For comparison, the theoretical I-V characteristics of the electron saturation region from an infinitely long cylindrical probe ( = 0. 5), a spherical probe ( = 1) and the  = 0. 69 fit are plotted in Figure  8  in red, black and magenta, respectively. The magenta curve ( = 0. 69 fit) satisfactorily overlap the blue curve (measurement data points).
Conclusions The SLP instrument, which flies on board the PICASSO nanosatellite, is a Langmuir probe instrument with four thin cylindrical probes. Using a specific EGSE, an electrically representative mock-up of PICASSO has been tested in a laboratory plasma chamber. It has been shown that the traditional OML collection theory used for Langmuir probes cannot be applied directly because of the limited dimensions of the probes with respect to the Debye length in the ionosphere. Nevertheless, the use of this theory can be adapted to take into account the short length of the probes. In addition, the onboard adaptive sweeping algorithm, which computes the inflection point of an I-V curve to define the bias steps of the next sweep has been validated both in the laboratory and in space. It is shown that with 60 data points the I-V curve can be very well resolved.
More inflight tests will be carried out to decrease the number of data points per curve in order to reduce further the data volume. Finally, the effect of the contamination of the probe surface, which can be a serious issue in Langmuir probe data analysis, has been investigated. Probe surface contamination can cause a drift of the I-V curve, resulting in substantial errors in the estimation of the electron temperature. We intend to monitor, in flight, the long term behaviour of the effect of the contamination layer and study the various effects that may affect the quality of the probe surface.