dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Our study investigated the use of non-invasive medical imaging, specifically regional strain analysis using multiparametric speckle-tracking, to identify individuals at risk of unfavorable post-infarction heart repair as early as 5 days after a myocardial infarction. We found that this method directly correlates mechanical stress with biological function and offers a more accurate and sensitive classification for clinical cardiology. While a multi-parametric approach has not yet been widely adopted due to technological limitations and the computationally demanding nature of analyzing complex datasets, we anticipate that as technology and software continue to improve, more studies exploring multi-parametric approaches will emerge in the near future, ultimately improving the diagnosis and prognosis of cardiopathies.
We provide a proof of concept that early multidimensional stretch imaging can predict the outcome of heart failure, as assessed by ejection fraction, fractional area change, and fibrosis extent. Using this approach, an exhaustive inter-sample characterization using dynamic imaging could help define patient profiles with important prognostic value. The expression regulation of mRNA encoding for clinically targeted receptors, as well as the modification of their downstream signaling pathways, suggest that dynamic imaging could be a valuable tool in predicting patient response to treatments. However, larger experimental cohorts will be necessary to draw more definitive conclusions. A multi-centered database of patient information could be investigated to establish classification and learning models that could aid in predicting outcomes.
Through a repeatable petrographic characterization method, we show that feldspar-like absorption bands are present in all our rock samples containing plagioclase feldspar in varying amounts and compositions. We additionally discuss the possible implications of our findings for the interpretations of Mars hyperspectral observations. The composition of the rocks involved in the detections of feldspar plagioclase on the surface of Mars is still discussed  (Carter & Poulet, 2013; Farrand et al. , 2021; Flahaut et al. , 2022; Payré et al. , 2022; Phillips et al. , 2022; Rogers & Farrand, 2022; Rogers & Nekvasil, 2015; Sautter et al. , 2015). The data collected in our study provide clues to determine the primitive, secondary, or tertiary nature of the rocks involved in these Martian detections.
Additional chemical maps were acquired using a SEM JEOL 6510 at CRPG Nancy (France). Thin sections of our samples were coated with carbon prior to analysis. An electron beam obtained by heating a tungsten filament is then focused on the thin section of the sample. The interaction between the incident electrons and the sample causes the formation of lower energy secondary electrons. The microscope detects secondary electrons at each point of the thin section by scanning it. Whole thin sections of the five samples described above were mapped with a resolution of about 1. 4 µm/pixel. To obtain this resolution, it was necessary to analyze the thin section in several parts. About 400 images were stitched for each sample using a custom-made Python code to get a global map of the thin sections. The elements analyzed are Al, Si, Ca, S, Fe, K, Mg, Mn, Na, Ni, O, P, Rb, Sr, and Ti.
The ENVI image processing software (L3Harris Geospatial) was used to process these chemical maps. By superimposing three chemical maps as red, green, and blue, false color images were created. To highlight the chemical composition diversity of feldspars, false color maps were created with calcium as red, sodium as green,   Chile; De Silva et al. , 1994; Flahaut et al. , 2021) , granite NJ11 (Vosges Mountains, France;  Simmler, 1962) , anorthosite NM6 (Kiglapait, Canada;  Morse, 2015) , basalts NR1 and NR2 (Afar, Ethiopia;  Pik et al. , 1998). and potassium as blue (Figure  2 ). One Region Of Interest was created for each mineral contained in each sample to be used as an endmember. Using the Minimum Distance (MD) algorithm, pixels of these images were classified according to their color, and therefore their chemical composition.
This continuum is a convex hull that fits the spectrum using straight-line segments that connect local maxima. The first and last reflectance values are equal to 1. We further used hyperspectral cameras to resolve finer spatial scales and acquire spectra of each mineral contained in these rocks. As opposed to the one-dimensional data of ASD Fieldspec 4 point-spectrometer (spectra), data from hyperspectral cameras are three dimensional, consisting of a two-dimensional image plus the spectral data as the third dimension (hyperspectral cubes). We acquired hyperspectral images of the samples with the HySpex VNIR-3000-N and SWIR-640 cameras that operate from 0. 40 to 1. 00 μm and from 0. 96 to 2. 50 μm, respectively. At a distance of 30 cm from the sample, the resulting pixel size is about 39 and 130 μm in the VNIR and SWIR, respectively.
The hyperspectral cubes are converted to reflectance using reference panels of 10% or 50% reflectance depending on the darkness level of each sample. The analyses of the cubes were performed with the ENVI image processing software to classify the image pixels according to their spectral signature. We used the Spectral Angle Mapper (SAM) method, which is one of the supervised classification methods available in ENVI  (Kruse et al. , 1993) ; this algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra of the hyperspectral cube and a given reference spectrum (known as an "endmember"). Endmembers were handpicked to map as many mineral classes as expected from the petrographic analyses of the samples. This classification then provides mineralogical maps and associated statistics that give the mineral surface abundances according to the class created with SAM based on the endmembers.
Both the total rock spectra or powder spectra acquired with the ASD Fieldspec 4, and the spectra of minerals acquired with hyperspectral cameras were then compared to the USGS reference spectral library  (Kokaly et al. , 2017)  to identify mineral signatures. The plagioclase absorption band, observed in the spectra of total rocks, powders, and plagioclases in each sample themselves, is located in the 0. 8-1. 7 μm region. The plagioclase band centers were then determined numerically with a custom-made python routine. First, the slight gaps in spectra arising from the three separate detectors (in the case of ASD Fieldspec 4 spectra) were realigned in order to avoid interpreting them as real spectral features. Next, plots of spectra were visually inspected for narrow bands that typically arise from the hydration of minerals or other bands due to associated minerals present in the rock (e.
The granite NJ11 contains partially altered plagioclase crystals, which are not taken into account in the total amount of plagioclase. Hence, the plagioclase content determined with the SEM data of these three samples is likely underestimate of the total amount of plagioclase feldspars. Similar petrographic observations were made from μ-XRF data, which are even less resolved than SEM data, and are presented in Figure  S7  (Supporting Information S1). Following these analyses, measurements with the EPMA were performed in order to obtain quantitative compositions. The location of the EPMA analysis points is shown in Figure  2  on the SEM chemical maps. Several feldspars of different sizes, both plagioclase and alkaline, were measured in each sample. The results obtained are plotted in Figure  3a  according to their calcium, sodium, and potassium content in the classical feldspar ternary diagram.
VNIR Spectral Signature The macroscopic samples and their corresponding powders were spectrally characterized in the VNIR with the ASD Fieldspec 4 instrument. Figure  4a  shows the rock spectra (solid lines) and powder spectra (dotted lines) of the five samples NJ2, NJ11, NM6, NR1, and NR2, as well as a reference spectrum of anorthite powder from the USGS Spectral Library  (Kokaly et al. , 2017). Figure  4b  shows the same spectra with the continuum removed using the convex hull method available in the ENVI software. In both Figures  4a  and 4b , spectra are plotted with an offset for clarity. All macroscopic samples show a broad absorption band centered around 1. 3 μm. This band center varies from one sample to another: 1. 16 for NJ2 (dacite) and NJ11 (granite), 1. 19 for NR1 (basalt), 1.
The diagnostic feldspar BARTHEZ ET AL. 10. 1029/2022JE007680 8 of 16 absorption band is not visible on the powder spectra of samples NJ2 (dacite) and NJ11 (granite). The exception is the NM6 (anorthosite) sample, which is an anorthosite and contains between 80% and 90% of plagioclase feldspars (Table  1a  and 1b ). Both the powder and the total rock spectra of NM6 display the diagnostic plagioclase absorption band. It should be noted that the band depth is generally lower on the powder spectra than that of the macroscopic samples (Figure  4 ). Grain to grain analyses were performed using the VNIR-3000-N and SWIR-640 hyperspectral cameras. The mineral locations and types are known from optical microscopy and SEM data (Figure  2  and Figures S8-S12 in Supporting Information S1). The average spectrum of each mineral contained in each mineral group from each sample was obtained using a SAM classification from the ENVI software.
In 2D studies, our novel PROjection for Jointly lEarning non-Cartesian Trajectories while Optimizing Reconstructor (PROJeCTOR) trajectories present an improved image reconstruction quality at a 20-fold acceleration factor on the fastMRI data set with SSIM scores of nearly 0. 92-0. 95 in our retrospective studies as compared to the corresponding Cartesian reference and also see a 3-4 dB gain in PSNR as compared to earlier state-of-the-art methods. Finally, we extend the algorithm to 3D and by comparing optimization as learning-based projection schemes, we show that data-driven joint learning-based PROJeCTOR trajectories outperform model-based methods such as SPARKLING through a 2 dB gain in PSNR and 0. 02 gain in SSIM.
Further, as such disjointedness between training a TSD and testing on different non-Cartesian trajectories and image reconstruction neural nets could lead to suboptimal results, there is a need to jointly learn both the TSD and the image reconstruction deep learning architecture in a non-Cartesian setting. Recently, new methods  [18] [19] [20]  have been developed to overcome the need for estimating a TSD, through direct joint learning of the non-Cartesian k-space sampling trajectories and MR image reconstruction in a data-driven manner on the fastMRI dataset  [21]. In  [18, 20] , the authors jointly learned Physics-informed learned optimal trajectories (PILOT) trajectories along with U-net parameters as a reconstruction model to denoise the basic image yielded by the adjoint of the Non-Uniform Fast Fourier Transform (NUFFT) operator.
Materials and Methods In this section, we present a generic and modular framework (Figure  1 ) for learning non-Cartesian k-space trajectories and deep neural networks for MR image reconstruction. Particularly, we discuss 2 sub-models; namely, (1) an acquisition model parameterized by k-space trajectory, and (2) a reconstruction model parameterized by a deep neural network. Later, we discuss in detail how to handle the MR hardware constraints and which approach seems the most efficient within the sampling pattern optimization process to end up with hardware compliant k-space trajectories. θ parameterized by θ. The input training data consists of emulated single coil complex images, from which simulated k-space data are obtained through F S(K). The loss L is calculated between the reconstructed image and the ground truth.
Retrospective Study Next, we compared the results of image reconstruction from retrospectively undersampled k-space data using PILOT (Figure  3 ) and BJORK (Figure  4 ) trajectories. To this end, we used 512 slices from the fastMRI validation data set. We observe that both SPARKLING with a learned density and PROJeCTOR outperform PILOT and BJORK, with PROJeCTOR yielding the best scores with a gain of nearly 0. 06 in SSIM and 3-4 dB in PSNR values as compared to PILOT and BJORK. We computed paired t-tests on Structural Similarity Index (SSIM)/Peak Signal-to-Noise Ratio (PSNR) scores between PILOT and PROJeC-TOR on one hand and BJORK and PROJeCTOR on the other hand and obtained p-values p < 10 -4 , thus confirming that the improvements we observed visually and quantitatively are statistically significant.
This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  800945-NUMERICS-H2020-MSCA-COFUND-2017. This work was granted access to the  CCRT High-Performance Computing (HPC) facility at CEA  under the Grant  CCRT2022-gilirach. This work was granted access to the  HPC resources of IDRIS  under the allocation 2021-AD011011153 made by  GENCI. Institutional Review Board Statement: Not applicable.
A degradation in performance was observed when the ASIC was connected to the LGAD array. This is attributed to digital couplings at the entrance of the preamplifiers. When the ASIC is connected to the LGAD array, the lowest detectable charge increased from 1. 5 fC to 3. 4 fC. As a consequence, the jitter increased for an injected charge of 4 fC. Despite this increase, ALTIROC1 still satisfies the maximum jitter specification (below 65 ps) for the HGTD project. This coupling issue also affects the time over threshold measurements and the time-walk correction can only be performed with transimpedance preamplifiers. Beam test measurements with a pion beam at CERN were also undertaken to evaluate the performance of the module. The best time resolution obtained using only ALTIROC TDC data was 46.
Data analysis The ALTIROC1 sensor board was exposed to 120 GeV pions at the H6B beamline at the CERN-SPS North Area during two weeks in Autumn 2021. The setup was similar to the one used in previous HGTD beam tests  [6, [9] [10] [11]  and the differences are mentioned below. The setup included a EUDET telescope  [12]  based on six MIMOSA pixel planes, combined with an FE-I4  [13]  readout chip-based module. The trigger logic was handled by a programmable Trigger Logic Unit (TLU)  [14]. The telescope was only used for the alignment. The timing reference was provided by two Cherenkov counters consisting of a 6×6 mm 2 quartz bar coupled to a 5×5 mm 2 silicon photomultiplier (SiPM). analog ASIC probe were sampled by a Lecroy oscilloscope with sampling rate of 20 GSamples/s and a bandwidth of 2.
5 GHz. The 40 MHz clock was also sampled to be able to associate the digital information to the oscilloscope pulses. A trigger was issued by ALTIROC when a hit was recorded, initiating the data acquisition of the oscilloscope. While the digital data acquisition was done continuously, the oscilloscope buffered the data until its memory was full. At this point it was necessary to pause the data acquisition and transmit the oscilloscope data. Therefore, a "busy" logic was implemented in the FPGA to stop the ASIC acquisition while the oscilloscope was read out. A common event number provided a unique mapping between the two data streams. A complete description of the oscilloscope waveform processing can be found in Ref. [10]. The maximum of the pulse amplitude was estimated as the sample with the maximum amplitude.
Finally, the time resolution was also measured for a restricted range of the TOA values (greater than 1600 ps). In this window of 900 ps, the TOT has a weak dependence on the TOA and in this case the time resolution was measured as 46. 3±0. 7 ps (see figure15) which is the best time resolution obtained using only TDC data from ALTIROC. The residual time-walk contribution is equal to 23 ps and remains the dominant electronic contribution to the time resolution, since the jitter contribution is 15 ps at 15 fC as shown in figure  10a. A summary of the time resolution obtained for various time-walk correction methods can be found in table  3.
Acknowledgments The authors gratefully acknowledge CERN and the  SPS  staff for successfully operating the  North Experimental Area  and for continuous supports to the users. The  USP  group acknowledges support from  FAPESP  grant  2020/04867-2  and  CAPES. This work was partially funded by  MINECO ,  Spanish Government , under grant  RTI2018-094906-B-C21. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 754510.
NWP relies on our ability to assimilate observations and in particular weather extremes observations. Satellite remote sensing is the ideal tool to monitor the atmosphere and the land/ocean surfaces at a global scale and with a high revisit time (e. g. geostationary satellites such as Meteosat Second Generation have a revisit time of 15 min). To do so, the satellite community has increasingly relied on statistical methods, including neural networks (NNs), to process Earth observations  [9] [10] [11] [12] [13]. However the very nature of such statistical regression methods is to make compromises as they are trained on a large database of 'pooled' samples. Data pooling here refers to the process of combining data samples from either several sources or, in the case of remote sensing, different locations around the globe.
Thus they have the tendency to better perform on average conditions and less so on more extreme conditions. A regression model provides a trivial example, because it always explains less than 100% of the true variance of a dataset  [14] , meaning that extremes cannot be perfectly estimated. This is what we call the 'dampening' of the retrieval ranges of variability. In parallel, to simplify the implementation of the models, data from different locations are often pooled together to train a unique and global model, meaning that the same NN is used to retrieve variables over somewhat different environments and is required to make compromises. This leads, although less frequently, to what we refer to as the 'inflating' of the retrieval ranges of variability. It is crucial to understand and control these two types of errors (i.
Datasets For the application part of this study, we use a database of collocated brightness temperature (TB) observations from IASI (Metop A) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis of LST  [24, 25]. A surface emissivities climatology will be used as an ancillary database. Both the TB observations and the emissivities are regridded onto a 0. 25 • regular grid. Regridding is done by averaging the TB (or emissivity) values of IASI pixels whose centers fall in each gridpoint. The ERA5 LST is downloadable on the same regular grid. We use data ranging from July 2007 to December 2018 over a fixed domain covering France and its surroundings only, offering a rich variety of surfaces (including coasts, lowland and mountains). The domain ranges from 41 • N to 52 • N in latitude and 5 • W to 10 • E in longitude.
ERA5 LST database The ECMWF ERA5 reanalysis  [25]  is a re-processing of an NWP model constrained with most of the available observations. It is used to estimate the state of the surface and atmosphere with an hourly timestep. The LST product over the domain is extracted from this reanalysis, and the observed clear-sky IASI TBs are collocated with the closest hour of the LST product.
TELSEI Emissivity climatology A climatology of surface emissivities derived from TELSEI [18, a Tool to Estimate Land Surface Emissivity in the Infrared] is used. This tool gives monthly global estimates of infrared emissivity with IASI spectral resolution. To stay coherent with the extracted IASI TB channels, three channels are kept for the emissivity database. As in  [15] , we choose here the 850, 900 and 1100 cm -1 wave-numbers to be representative of the overall spectrum.
Other solutions for IASI data are considered in  [16]. As previously mentioned, only clear-sky pixels can be used to retrieve LST. However, CNNs require complete images, both for the training and operational prediction phases. This is done by inter-and extrapolating the images. The remaining missing pixels are considered larger holes. These holes are filled using an algorithm relying on the empirical orthogonal function (EOF) representation of images  [27, 28, 38]. In this approach, an image X can be rewritten as X = λ 1 • F 1 + λ 2 • F 2 +. + λ m • F m. After calculating the EOFs (F 1 ,. F m ) using the covariance matrix of our database (computed using only clear pixels) the missing data are filled with a first guess: in this case we use λ 1 • F 1 with λ 1 that minimizes the root mean square error over the available pixels.
The 'dampening' effect of statistical methods vs. the 'inflating' effect of data pooling We introduce the dampening effect, defined by the range of values in the retrieved LST dataset being smaller than the range of values in the original (ERA5) LST dataset. Similarly, we introduce the inflating effect as the opposite; the range of values in the retrieved LST dataset being larger than the range of values in the original dataset. Mathematically, let us define the set of samples of extreme low and high values of LST at the pixel level (pixel i) from the 10th lower (Q ERA5 0. 1 (i)) and higher (Q ERA5 0. 9 (i)) percentiles: S L (i) = {y t | y t ERA5 (i) ⩽ Q ERA5 0. 1 (i)} and (1) S H (i) = {y t | y t ERA5 (i) ⩾ Q ERA5 0. 9 (i)}, (2) where t spans the time dimension. This definition is valid both for the original (ERA5) and retrieved dataset.
We then define an estimate of the range over a pixel i, for a particular dataset, as: R i = S H (i) -S L (i). ( 3 ) The range R i can thus be understood as the average LST value of samples in S H (i) minus the average LST value of samples in S L (i). Dampening is the process by which the retrieved range, computed from the y values, is smaller than the real range, computed from the y ERA5 values: R i,RET -R i,ERA5 < 0. ( 4 ) Because the retrieval errors are larger for these extreme values, it is often argued that the problem is due to a lack of extreme conditions in the training dataset. However, enriching the training dataset of the NN with additional extreme conditions does not solve the problem because sampling is not the real issue. Figure  2 (a) aims to illustrate what happens. We consider a NN with only one input (TB PC1) and one output (LST).
convolutional layer with one kernel is enough to capture the spatial dependencies present in the full image. This is due to the highly localized property of the layer. This results in a unique feature map. The Adam optimization algorithm  [42]  is used for the training. Training hyper-parameters for each model are shown in table  1. Please note that different optimization algorithms are used because in the case of the unique MLP, the problem is more complex: a lot of different local conditions are pooled into one big database and optimizing the MLP model with the Adam algorithm did not lead to satisfactory results. The Levenberg-Marquardt algorithm is more efficient in this case. However, this algorithm was not available to train CNNs in Python, therefore we opted for the well-known Adam algorithm in this case.