dataesr/scientific-paragraphs-categorization huggingface dataset:
4 and 150 mM NaCl. The reaction was stopped by adding 100 µL of acidic phenol. Nucleotides in the aqueous phase were recovered after centrifugation and analyzed by high-performance liquid chromatography or NMR. Matrix-assisted laser desorption/ionization MS analyses were performed directly on the aqueous phase after phenol treatment of the samples using an UltrafleXtreme spectrometer (Bruker Daltonique, France). The instrument is equipped with an Nd:YAG laser (operating at 355 nm wavelength of <500 ps pulse and 200 Hz repetition rate). Acquisitions were performed in a negative ion mode. MS data were processed using DataExplorer 4. 4 (Applied Biosystems). Reconstitution of apo-ThyX with synthetic flavin carbinolamine. Flavin carbinolamine was synthesized in anaerobic conditions in a glove box by reducing FAD with 2 molar equivalents of dithionite freshly prepared, followed by the addition of 100 molar equivalent of formaldehyde (CH 2 O).
Stop-flow experiments between CH 2 O and reduced ThyX-dUMP complex. All stopped-flow experiments were performed in 0. 1 M Tris-HCl (pH 8) at 25 °C using a TgK Scientific SF-61DX2 KinetAsyst stopped-flow instrument that had been previously equilibrated with a glucose/glucose oxidase solution to make the internal components of the system anaerobic. A solution containing ThyX (4 µM tetramer) and dUMP was made anaerobic in a glass tonometer by cycling with vacuum and argon. The ThyX-dUMP complex was stoichiometrically reduced by titration with dithionite, loaded onto the instrument, and mixed with buffer containing formaldehyde that had been bubbled with argon to make the solution anaerobic. Reaction traces were monitored at 450 nm and were fit to sums of exponentials using KaleidaGraph (Synergy Software) to obtain observed rate constants.
5 and 150 mM NaCl. One microliter of 7 mg/mL reduced ThyX was mixed with 1 µL of reservoir solution composed of 40% PEG 200. Colorless crystals grew overnight and were soaked for 30 min in 20 mM formaldehyde in 40% PEG 200 before flash freezing in liquid propane. All diffraction data were collected on single crystals at the microfocused PROXIMA-2 beamline at the SOLEIL synchrotron (Saint-Aubin, France) at 100 K using an Eiger X-9M. Data were indexed, processed, and scaled using XDS  45. Both structures were solved by molecular replacement using a monomer from PDB 4gt9 as a search template and further refined with autoBUSTER  46. A clear density for the FAD moieties was observed in both structures with additional density on the N5 modeled as CH 2 OH adduct. Chemical restraints for the carbinolamine flavin adduct were generated using JLigand and grade  47.
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique ,  Université Pierre et Marie Curie  as well as the " Initiative d'Excellence" program  from the  French State  ( ANR-11-IDEX-0004-02 ,  ANR-15-CE11-0004-01 , Grant " DYNAMO ,"  ANR-11-LABX-0011-01 ), and the  United States National Science Foundation  ( CHE 1905267 ). This work used the icOS platform of the Grenoble Instruct-ERIC Center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). We also acknowledge SOLEIL for the provision of synchrotron radiation facilities (proposal ID 20170872).
Data availability The raw kinetic traces used in this study are provided in the Source data file. The atomic coordinates and structure factor amplitudes for the crystal structures of ThyX FADH - soaked with CH 2 O and apo-ThyX/synthetic compound 4′ have been deposited in the Protein Data Bank (https://www. rcsb. org) under accession codes 7NDW and 7NDZ, respectively. Source data are provided with this paper.
The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments.
In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743-758 nm window. A secondary SIF dataset derived from an extended fitting window (735-758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3 nm windows devoid of atmospheric absorption within the 665-785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work.
Introduction The sun-induced fluorescence (SIF) signal emitted by the chlorophyll a of terrestrial vegetation has been shown to be a closer indicator of vegetation functioning than other variables traditionally derived from optical remote sensing data  (Mohammed et al. , 2019). Global retrievals of SIF from space were first achieved in late 2011 from the GOSAT mission  (Frankenberg et al. , 2011b; Joiner et al. , 2011; Guanter et al. , 2012). Since then, a number of global SIF datasets have been produced from spaceborne spectrometers originally intended for atmospheric research, such as GOME-2 (e. g. ,  Joiner et al. , 2013; Köhler et al. , 2015a; van Schaik et al. , 2020) , SCIAMACHY  (Köhler et al. , 2015a; Khosravi et al. , 2015; Joiner et al. , 2016) , OCO-2 (e. g. ,  Frankenberg et al.
, 2014; Sun et al. , 2018), and TanSat (e. g. , Du et al. , 2018; Yao et al. , 2021). The SIF data from those missions have been typically used to investigate the spatial and temporal patterns of the gross primary production (GPP) of different ecosystems (e. g. ,  Frankenberg et al. , 2011b; Guanter et al. , 2014; Yoshida et al. , 2015; Sun et al. , 2017; Luus et al. , 2017; Walther et al. , 2017; Jeong et al. , 2017; Smith et al. , 2018; Zuromski et al. , 2018; Wu et al. , 2018)  and also ecosystem transpiration and water limitation  (Pagán et al. , 2019; Maes et al. , 2020; Shan et al. , 2021). The main limitations of those SIF datasets for scientific work are the high precision errors and the low spatial resolution and/or sparse spatial sampling. ESA's FLuorescence EXplorer mission, scheduled for launch after 2025, will enable a breakthrough in the spatial resolution of space-based SIF datasets  (Drusch et al.
SIF retrieval approach The different SIF retrieval schemes implemented for spaceborne measurements in the last years have relied on the Fraunhofer line in-filling principle proposed by  Plascyk and Gabriel (1975). This principle establishes that the fractional depth of solar Fraunhofer lines, which are included in the reflected solar light spectrum, decreases when it is combined with an additive signal such as SIF. The feasibility of this concept for satellite-based SIF retrieval was first shown by simulations  (Sioris et al. , 2003; Frankenberg et al. , 2011a)  and then applied to all satellite missions for which global SIF data products have been derived. We can group SIF retrieval strategies into physically based and data-driven methods. The first group of methods are based on a physical formulation of the radiative transfer and the instrument's response for the modeling of top-ofatmosphere (TOA) radiance measurements.
The 1σ retrieval errors were first calculated by means of Eq. (  2 ) for several orbits and grouped into TOA radiance bins of 5 mW m -2 sr -1 nm -1. A third-order polynomial was then fitted to the binned radiance-σ (SIF) data. The result of this process is shown in Fig. 3. The resulting polynomial coefficients are used to predict σ (SIF) as a function of average radiance on a per-pixel basis. With this strategy we avoid the inversion of the measurement error covariance matrix in Eq. (  2 ), which had proved to be unstable and computationally expensive. It must be mentioned that the measurement noise data provided in TROPOMI's Level 1B (L1B) files suffer from float number truncation errors from the storage as byte data. This means that the σ (SIF) that we calculate from the measurement noise data can affected by the same effect.
Day-length scaling The variability in SIF retrievals due to instantaneous illumination conditions must be accounted for when SIF retrievals are to be compared with other SIF datasets acquired with a different satellite overpass time or with daily GPP estimates  (Zhang et al. , 2018). We have followed the approach proposed by  Frankenberg et al. (2011b)  to generate daily-equivalent SIF (SIF d ) estimates in TROPOSIF. This is based on the calculation of a day-length scaling factor DL as DL(t 0 ) = t ss t sr µ s (t) dt µ s (t 0 ) , (3) where t 0 is the time of the measurement, t sr is the time of sunrise, t ss is the time of sunset, and µ s (t) is the cosine of the sun zenith angle. This approach assumes that SIF scales linearly with incoming solar radiation and that the entire day is cloud-free.
Spectral reflectance Spectral reflectance spectra at seven spectral points in the 665-785 nm range covered by TROPOMI's band 5 and 6  (namely, 665, 680, 712, 741, 755, 773, and 781 nm)  are included in the TROPOSIF product as an ancillary dataset. Spectral reflectance spectra are calculated as ρ TOA,i = π L TOA i µ s I sc i , (4) where L TOA refers to the TOA radiance measurement (L1B radiance spectra); I sc is an extraterrestrial solar irradiance spectrum corrected by the Sun-to-Earth distance at the day of the overpass; and i refers to the spectral convolution operator, which in this case corresponds to a boxcar spectral response function with a 3 nm width, applied at the ith spectral point. The Kurucz 2005 high-resolution irradiance spectrum 2 (from 300 to 1000 nm, with ∼ 0. 001 nm sampling at 740 nm) resampled at the seven spectral points with the L.
Processing flow TROPOSIF's processing chain starts by reading in TOA radiance data from TROPOMI band 5 and 6 L1B orbit files as well as cloud fraction data from the TROPOMI L2 Cloud product. Data acquired over water bodies (identified using the MODIS MCD12C1 2018 land cover product) with a high cloud fraction (> 0. 8) or with a lower quality according to the quality_level flag attached to the L1B data (threshold of 80) are screened out from the processing. Spectral band no. 179 in TROPOMI's channel 6 is also removed from the processing, since spectral spikes were found for that band in pixels in the vicinity of clouds. The first step in the processing is the generation of the singular vectors. For that, spectra over barren areas worldwide as identified in the MODIS MCD12C1 2018 dataset are used.
Next, TOA radiance data from either a continuous fitting window (for SIF retrieval) or at certain "macrochannels" at atmospheric windows within the far-red region (for reflectance retrieval) are read in. SZA and latitude and longitude fields are also extracted for the computation of the DL factor. SIF and reflectance retrievals are run separately for each column in order to account for column-dependent changes in TROPOMI's spectral and radiometric responses. The output SIF, spectral reflectance, and DL are written to a NetCDF file together with other variables of interest for later data visualization and processing. This NetCDF file has the same structure as the official TROPOMI L2 products (see Appendix A).
L2B processing In order to facilitate data download and processing, a final processing step extracts all valid retrievals (qa_value > 0. 5) from the L2 orbit files in a given day and combines them into single daily files, referred to as L2B files. The application of the qa_value filter substantially reduces the volume of data in the L2B file with respect to the combined L2 dataset. In the case of spectral reflectance data, only spectra acquired under a cloud fraction smaller than 0. 2 are included. For the sake of reducing the data volume, some information fields in the L2 files are not included in L2B files, such as the χ 2 r values, the qa_value, the day-length factor, and the illumination and observation azimuth angles (which are converted into a single relative azimuth angle).
Consistency of SIF retrievals Maps from spatial subsets of different variables derived from the processing of one TROPOMI orbit dataset are displayed in Fig. 4. The different panels show the spatial distribution of TOA radiance at 743 nm, the cloud fraction in TROPOMI's L2 Cloud product, qa_value, the TOA reflectance at 665 and 781 nm, the normalized difference vegetation index (NDVI) derived from those two channels, and the retrieved SIF, 1σ error, and the χ 2 r of the fit for both 743-758 and 735-758 nm fitting windows. χ 2 r values larger (smaller) than 1 indicate an underestimation (overestimation) of the model-data error variance. The following specific points can be highlighted from Fig. 4. -The areas with the highest abundance of green vegetation (e. g. , the northern Iberian Peninsula, South West France, Brittany, and Normandy) are clearly visible in the SIF and NDVI maps.
In the case of SIF, the range of values is within the expectation for both fitting windows. Slightly higher values are found for the 743-758 nm fitting window in clear-sky regions. -Single-retrieval uncertainties are also within the expected range (0. 3-0. 6 mW m -2 sr -1 nm -1 ). They are substantially lower for the 735-758 nm window. -For both fitting windows, the χ 2 r is typically around 0. 8-0. 9. This deviation from an ideal case of χ 2 r = 1 would indicate that the model is slightly over-fitting the data (χ 2 r > 1 implies that the model does not model the full variance in the spectrum). However, the 0 %-15 % truncation errors in the measurement noise input data discussed in Sect. 2. 3 could also explain that χ 2 r is in general smaller than 1. On the other hand, the sensitivity to clouds of the 735-758 nm retrieval can also be noticed in the χ 2 r maps.
The resulting 8-15 July 2019 SIF map shows the expected SIF global maximum at the US Corn Belt  (Guanter et al. , 2014). High SIF values are also found in Central Amer-ica, Central Asia, and China. The data gap in the southern part of Greenland is due to the incoming radiance being higher than the 200 mW m -2 sr -1 nm -1 threshold used for quality filtering, and the one in southern Argentina is due to SZA > 70 •. The effect of cloud filtering on the standard errors of SIF composites is shown in the σ (SIF) map. It is especially noticeable in regions with persistent cloud cover such as the tropics and the northern part of Europe and America. Overall, typical standard errors are in the range 0. 1-0. 2 mW m -2 sr -1 nm -1 for this weekly composite. The impact of cloud fraction thresholds on SIF composites (systematic and random errors) has been further investigated.
For the same region, the change of the standard error due to the lower number of retrievals in the stricter cloud filtering is about 0. 25 mW m -2 sr -1 nm -1. This illustrates that the selection of the optimal cloud fraction threshold depends on the particular use of the data. A strict cloud filtering (e. g. , cloud fraction lower than 0. 2) should be applied if an accurate SIF mean value is needed, whereas a more relaxed cloud filter (e. g. , cloud fraction lower than 0. 8) could be applied if smooth spatial and/or temporal signals are needed. The second case is often preferred for SIF, but it must be taken into account that unfiltered clouds can also introduce noise-like changes in SIF time series. Including cloud fraction data in the analysis would be needed for a proper interpretation of the SIF signals if a relaxed cloud filtering was applied.
The seasonal variation of TROPOSIF SIF data is illustrated in Fig. 8 , which shows daily and 0. 1 • composites over a set of instrumented SIF sites  (Parazoo et al. , 2019)  and the entire time period covered by the TROPOSIF product. Two configurations of fitting window and cloud fraction thresholds are used: 743-758 nm with cloud fraction < 0. 8 ("all sky") and 735-758 nm with a cloud fraction < 0. 2 ("clear sky"). The higher number of daily estimates available for the all-sky case is a result of less strict cloud filtering. Figure  8  illustrates the lower retrieval uncertainty associated with the clear-sky case (lower error bars and less scattering) compared to the all-sky SIF. SIF data derived from the 743-758 nm fitting window are usually greater than those derived from the broader 735-758 nm window, as indicated by the slope  Removing negative SIF retrievals from the processing should be avoided, as it would lead in positively biased averages.
Comparison to Caltech's TROPOMI and OCO-2 SIF products The quality of the TROPOSIF SIF product has been investigated through the comparison to the Caltech TROPOMI SIF product  (Köhler et al. , 2018 ) (hereinafter, "Caltech") and SIF retrievals derived from OCO-2  (Sun et al. , 2018). Using the latter products as benchmark datasets, TROPOSIF SIF data have been evaluated in terms of absolute SIF values, systematic errors (biases), and random errors (noise). Comparisons between the TROPOSIF (743-758 nm), Caltech TROPOMI, and OCO-2 SIF estimates were performed at the global scale for the evaluation of SIF absolute values. The instantaneous SIF retrievals were binned at 0. 1 • and daily resolutions, with a cloud fraction below 0. 2 for those derived from TROPOMI observations. The pixel-to-pixel comparison between each pair of products shown in Fig.
029 mW m -2 sr -1 nm -1 , which is lower than the mean bias between Caltech and OCO-2 (0. 036 mW m -2 sr -1 nm -1 ). The difference between the Caltech and TROPOSIF SIF estimates is due not only to the difference in the estimation algorithm, but also to the different cloud products that are used as input: concomitant cloud fraction data from TROPOMI's L2 Cloud product for TROPOSIF, when Caltech relies on cloud data derived from VIIRS  (Köhler et al. , 2018). The existence of potential biases in the TROPOSIF SIF product has firstly been investigated using retrievals over large non-vegetated areas where it can be assumed that no SIF emission is present (i. e. , the Sahara, Antarctica, and Greenland). The analysis has been conducted for the TROPOSIF products inferred from the two fitting windows, as well as for the Caltech SIF product and for SIF retrievals derived from OCO-2, which both act as benchmark datasets.
, 2018) , which allows a direct comparison between the two datasets. In addition, we have decided to include retrievals from the 735-758 nm fitting window as well. Retrievals from this fitting window have shown a higher sensitivity to cloud contamination but also lower precision errors because of the higher number of spectral points in the fitting window. Retrievals from the 735-758 nm fitting window can thus be advantageous for those applications using only clearsky data. The first assessment of the consistency of the TROPOSIF SIF data has relied on the comparison with the accurate SIF retrievals from the OCO-2 mission and with the well-established TROPOMI SIF product from Caltech. A very high similarity has been found between the two TROPOMI SIF datasets (R usually greater than 0. 93, 0.
Data availability The dataset is available through the following digital object identifier (DOI): https://doi. org/10. 5270/esa-s5p_ innovation-sif-20180501_20210320-v2. 1-202104 (Guanter et al. , 2021). Additional informative materials are also provided on the project website at https://s5p-troposif. noveltis. fr/ (last access: 19 November 2021).
Conclusions The TROPOSIF product presented in this document is intended to become a reference data stream for scientific work dealing with the global monitoring of vegetation, especially in those studies focused on the dynamics and productivity of terrestrial ecosystems. Our evaluation of the TROPOSIF product in this work has shown that it has an overall similar quality to the well-established Caltech TROPOMI SIF product  (Köhler et al. , 2018). Two main additional features are included in TROPOSIF: a secondary SIF dataset from the 735-758 nm fitting window with an enhanced signal-to-noise ratio and spectral reflectance spectra. In the case of 735-758 nm SIF retrievals, however, the first analysis of the data has found potential difficulties of the retrieval to deal with spectrally steep radiance spectra, such as in the case of some crops.
Reparaz and Gierlichs  [18]  demonstrated a chosen-input DPA attack in the third round of a DES software implementation. The above-mentioned chosen-input techniques were used for keeping the inputs (or outputs) partially constant to estimate the intermediate values in the middle rounds more easily. Note that the previous attacks estimate the secret data from the intermediate values and side-channel leakage of the same (middle) rounds. Attacks that can target the inner rounds of block ciphers, such as algebraic side-channel analysis (ASCA)  [19]  and soft analytical side-channel analysis (SASCA)  [20] , have also been reported. These techniques exploit the input/output value distributions of round functions (e. g. , S-boxes), and can be used to attack the inner rounds, where the derivation of the distinguisher function is generally computationally difficult.
ASCA/SASCA attacks are reported for software implementations on microcontrollers processing intermediate values one byte at a time. In particular, ASCA assumes that only one (8-bit) S-box is processed at a time, which effectively eliminates algorithmic noise in the observed leakage signal. Such attacks are not applicable to our scenario (i. e. , unrolled hardware implementation), where all the internal operations are performed in parallel using the entire data block width (i. e. , 64 bits for PRINCE). This implementation does not allow the attacker to obtain the distributions of any single byte-wise function trivially. Note also that SASCA is a profiled attack, which makes it difficult to compare the presented unprofiled attack with SASCA in a fair manner. Previous attacks, such as blind SCA  [21] , improved blind SCA,  [22] , and quadrivariate improved blind SCA  [23] , improve upon classical side-channel analysis methods by relaxing the condition that the input (or output) data of the cipher has to be known by attackers.
e. , Hamming weight H W (X (1)  i,  1 )). In addition, Figure  9  shows the Measurements to Disclosure (MTD) results using the switching bias corresponding to that observed in Fig. 8  with j denoting the index of the attacked key nibble. Note that MTD is widely used in this type of evaluation to determine the number of sub-keys that is recovered (or not recovered) when the number of traces is increased. We confirmed that the ranks of the correct key did not show any clear upward trend with the input dataset that was used. These results suggest that it would not be possible to recover additional sub-keys even if more traces were to be used.