dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the ratio of the reflectance) between the bands impacted by saturation and its closest reference channel(s). Reference channels are taken as those least affected by saturation, i. e. , Oa01 (412 nm), Oa06 (560 nm), Oa09 (674 nm), Oa18 (885 nm), and Oa19 (900 nm). When DCC pixels are selected from their original context and tagged with respect to saturation, a very large database is gathered from which our methodology is deployed as presented in the next section. DCCs are among the brightest targets on earth, as shown above it is not uncommon to spot reflectance higher than unity induced by optical effects. Consequently, saturation may occur at detector level when the input radiance exceeds the energy flux allowed by the detector sensitivity. It is worth mentioning that such radiance levels can be considered for optical sensors as upper limits for the dynamic range allowed in sensors design.
In OLCI, most differences in the sensitivity to high radiances are found between the spectral bands as each band is designed with a sensitivity that depends on its dedicated application. One reference band is considered at 412 nm (band Oa02) where saturation very seldom occurs. Saturation shows up with very abnormal values (see Figure  2 ) which are traced within the OLCI processing chain from raw data packets up to the next levels. Figure  2  highlights the difference between OLCI-A and OLCI-B, the latter being much less affected by saturation. Indeed, a statistics of the saturated pixels is provided below in Figure  3  per spectral channel and detector index from a large number of DCC observations for OLCI-A (left) and OLCI-B (right).
F 0 (λ) is taken from Thuillier  [16]  and its convolution with the instrument spectral response function (ISRF) is performed from the spectral characterisation per detector and included in each L1B product. The conversion to reflectance is coherent with the absolute calibration methodology which uses a reflectance reference. The gaseous correction is performed in a way similar as in  [12] : radiative transfer simulations are performed within the ARTDECO (Atmospheric Radiative Transfer Database for Earth and Climate Observation) framework (http://www. icare. univ-lille1. fr/projects/artdeco). Gaseous transmissions are simulated every 1 nm across the complete OLCI spectrum using the standard tropical atmosphere profile of  McClatchey [17]  between TOA and a cloud whose top altitude is taken at 16 km to represent a mean DCC.
For Oa20 and Oa21, we choose Oa18 (885 nm) as the reference instead of Oa19 (900 nm) as the latter is impacted by water vapour absorption (although much attenuated considering DCC altitude and opacity) and the interband relationships gets slightly less precise (not shown). Spectral distance is also mentioned, the larger this latter is the noisier may be the interband relationship. Indeed, in  [9]  larger discrepancies were found in such relationships for channels farther from the considered reference channel. Density plots of I λ, λ re f are first displayed in Figures  4  and 5  from statistics over the tandem phase dataset, individually for OLCI-A (left) and OLCI-B (right). Black lines indicate the median in each OLCI detector bin. Between cameras, gaps stand for the detector indices that correspond to measurements made by two adjacent cameras and which are not used in the nominal products, the average relationship is however used to fill the gaps visually.
Completing results are displayed in Supplement Figures  S1 to S3  showing very stable interband relationships ACT both for OLCI-A and OLCI-B. As the sampled DCCs, by nature, exhibit microphysical and macrophysical variability, variability in their reflectance spectrum produces noise in these figures, whose amplitude qualitatively depends on the spectral distance between the two considered wavebands. Density plots of  ,  are first displayed in Figures  4  and 5  from statistics over the tandem phase dataset, individually for OLCI-A (left) and OLCI-B (right). Black lines indicate the median in each OLCI detector bin. Between cameras, gaps stand for the detector indices that correspond to measurements made by two adjacent cameras and which are not used in the nominal products, the average relationship is however used to fill the gaps visually.
The benefit of using the inflexion point instead of the mode is first validated by performing a random draw on a sample of about 5000 DCC observations. Iteratively, using an increasing number of datapoints from about 500 to 5000, a random sub-sample of this dataset is selected. For each sub-sample a PDF model is fitted on the selected dataset and its mode and inflexion point are retrieved. The variation of their corresponding reflectance values against the number of datapoints is plotted below in Figure  14  in red for the mode and in green for the inflexion point, in the legend are also added the corresponding standard deviations computed from the collection of all individual results, giving an idea of the dispersion around the mean value. As we can see, the inflexion point provides about half of the dispersion of the mode, which directly translates into a factor two improvement in the precision (i.
25% to an outlying 0. 5% between Oa01 and (e. g. ) Oa02. Also, the result for Oa21 (1020 nm) is nearly always outlying for all cameras, we suspect that this is rather due to the lack of precision in the reconstruction of the saturated pixels of OLCI-A. The results for 2019/11 (Figure  21 , top-left) provide a remarkably good accuracy with comparisons to tandem analysis lower than 0. 5%. Results from the other months of observations appear to be less accurate but also slightly less precise. This may be due to the amount of observations that were found available within the datasets, as shown below in Table  2. For instance, the amount of observations for 2019/11 is more than twice the one of 2020/05. The fact that such amounts differ so much between the collected months is rather due to the availability of the data (missing products, temporary unavailability etc.
The results for 2019/11 (Figure  21 , top-left) provide a remarkably good accuracy with comparisons to tandem analysis lower than 0. 5%. Results from the other months of observations appear to be less accurate but also slightly less precise. This may be due to the amount of observations that were found available within the datasets, as shown below in Table  2. For instance, the amount of observations for 2019/11 is more than twice the one of 2020/05. The fact that such amounts differ so much between the collected months is rather due to the availability of the data (missing products, temporary unavailability etc. ,) than due to the actual variability in the DCC natural geophysical occurrence. It is to be noted that the figures in Table  2  are representative of the complete OLCI FOV. As this latter is decomposed in 185 bins of 20 detectors, the figures of Table 2 must be interpreted with an average minimum of 3367 datapoints for 2020/05 and an average maximum of 7580 datapoints for 2019/11.
Conclusions Throughout this article we have developed and validated a methodology for assessing the cross-calibration of the OLCI sensors (presently OLCI-A and OLCI-B) based on observations of deep convective clouds. The OLCI-A/OLCI-B tandem phase has provided a unique and extremely useful framework for the development and validation of the method, notably regarding the careful handling of saturated pixels impacting more OLCI-A than OLCI-B. Adjustments have been performed from the analysis of interband relationships between bands affected by saturation and reference bands which are not impacted (or are much less impacted) by saturation. Our method is based on a statistical analysis over monthly to multi-monthly DCC observations datasets with a sampling along the OLCI FOV as this latter provides the largest geometrical variability.
Acknowledgments: We thank the three anonymous reviewers for their interest in reviewing this work. We are especially grateful to two of them for their expert and very constructive comments allowing many clarifications and improvements of both the presentation and the quality of the results. We thank the S3TC team, the  S3-Mission Performance Center and OLCI/SYN  expert support laboratories. We thank  EUMETSAT  for the provision of OLCI data. We Thank  Philippe Garnesson  and  Julien Demaria  from  ACRI-ST  for their support with the reprojection tools. The first author thanks  Corinne  for her support during the writing of this article, and dedicates this work to Clarisse, on the day of her seventh birthday.
In addition, the new systems will be used to quantify emission reduction signals that will not be larger than the ones induced by Covid-19 in the first half of 2020 (~20%; Le  Quéré et al. , 2020). Country-wide changes in 5-year intervals between two emissions censuses of the PA  (Pinty et al. , 2017)  will at best share the same order of magnitude as the 14-year-equivalent pulse signal triggered by Covid-19 worldwide. Here, we study the changes in the column-average dry air-mole fraction of CO 2 (XCO 2 ) retrieved from the second Orbiting Carbon Observatory (OCO-2) during the first months of the coronavirus recession, from February to May 2020. After the presentation of our data and the methods in section 2, section 3 begins with a global simulation of the transport of CO 2 in the atmosphere which allows us to estimate the order of magnitude and the location of the Covid-19-induced XCO 2 signal.
10. 1029/2020GL090244 We now try to zoom into the regions of interest with the OCO-2 XCO 2 retrievals that reach kilometer-scale resolution. A first basic requirement for this task is the acquisition of data on the few regions where the reduction signal manifested itself: eastern China, eastern United States and, to a lesser extent, the western coast of the United States, Europe, Western Russia, and some urban areas (Denver, Calcutta, and the Siberian power station). From an instrument like OCO-2, which observes the Earth with a swath of 10 km only, we do not expect dense local sampling, but examining the locations of the OCO-2 data shows additional complications. Low solar elevations reduce the signal and prevent the retrieval of XCO 2 in the mid to high latitudes of the winter hemisphere.
In the case of the Covid-19 reduction of emissions, this could affect the observation of Europe, but the reduction signal in Europe starts late enough (April) to be observable by OCO-2 in principle. However, the dominance of cloudy conditions in China (see, e. g. , the climatology of total visible cloud cover of  Warren et al. , 2015) , systematically disrupts the retrieval of XCO 2 along satellite orbits and leaves much of this country unobserved for most of the study period, particularly around the Hubei province (Figure  2a ). Around Beijing, only a few short segments of orbits with valid data points are available for each month, which makes it difficult to describe the XCO 2 emission plumes in their environment. Likewise, the lockdown in India began in late March, just before pre-monsoon conditions increased the appearance of cirrus clouds and gradually hampered the retrievals.
Acknowledgments The present work was granted access to the HPC resources of TGCC under the allocation A0070102201. OCO-2 retrievals were produced by the OCO-2 project at the Jet Propulsion Laboratory, California Institute of Technology and obtained from the OCO-2 data archive maintained at the NASA Goddard Earth Science Data and Information Services Center (https:// disc. gsfc. nasa. gov/). David Crisp and two anonymous reviewers provided useful comments on an earlier version of the text.
Materials and Methods A literature search was performed in the time period between January 2010 and September 2020, including PubMed, Scopus, Embase and Google Scholar databases, in accordance to the Preferred Reporting Items for Systematic Review and Metanalysis (PRISMA) guidelines  [25]. The following search terms were applied: ("PRRT" OR "peptide receptor radionuclide therapy") AND ("177Lu" OR "90Y") AND ("NET" OR "neuroendocrine tumor*") AND ("therapy response" OR "assessment" OR "response" OR "efficacy") AND ("RECIST1. 1" OR "PERCIST" OR "positron emission tomography" OR "computed tomography" OR "magnetic resonance" OR "PET" OR "CT" OR "MR" OR "DOTATOC" OR "DOTANOC" OR "DOTATATE" OR "FDG" OR "DOPA" OR "biomarker" OR "liquid biopsy" OR "NETest" OR "PPQ" OR "dosimetry*"). Additional filters, such as English language, original article and/or research article, and studies including only humans and with 10 or more subjects, were used.
IV. COMPARISON OF ANTENNA PATTERNS There have been many attempts to define objective quantitative metrics of comparison between radiation patterns that can be used to assess the accuracy, the sensitivity and also the repeatability associated with the manufacturing of antennas  [27]. None is perfect because they are all suffering from drawbacks. Many of these figures of merit are only suited for the comparison of power patterns because they are not sensitive to the phase pattern. Moreover, none of them has been specially designed for large datasets  [28]  like those involved in imaging radiometry by aperture synthesis (SMOS is equipped with 69 antennas  [7] , preliminary design studies of SMOShr are using 231 antennas  [9] ). There is a broad consensus that such a comparison is not an easy task.
g. Herzberg, 2004; Walter, 2003). Here we provide the first comprehensive set of petrologic, geochemical and age data for main types of the most refractory xenolith materials in kimberlites from the Siberian craton: dunites (including their "megacrystalline" variety) and olivine-rich harzburgites, as well as large olivine megacrysts reported in earlier studies  (Pearson et al. , 1995b; Pernet-Fisher et al. , 2019). We report highly siderophile element (HSE) concentration data and Os isotopic compositions for 24 new, olivine-rich peridotites, as well as three opx-rich xenoliths, from the Udachnaya kimberlite together with modal and petrographic data, bulk-rock and mineral major and trace element compositions. The major objectives of this paper are to: (a) establish the Os isotope and HSE distribution in these materials; (b) better constrain their formation (melt extraction) ages, and (c) examine relations between the mantle and crustal components of different ages during the formation and assembly of the central Siberian craton.
Sample selection and preparation Olivine-rich xenoliths, including olivine megacrysts and "megacrystalline dunites" ≥5 cm in size were targeted in the field in recent years, but they are very scarce and usually hard to recover from massive kimberlites in Udachnaya. In addition, hundreds of xenoliths collected by AVG and AVK since 2003 were re-examined to select olivine-rich (≥80%) samples, regardless of their grain size. Twenty-four such xenoliths (including U220 earlier reported by  Ionov et al. (2010) ) were chosen for this study based on their size, low alteration degrees and high modal olivine (from visual inspection) without preference for any rock type. Three opxrich rocks were included as well for comparison. The samples are listed in Table  1 , which provides a summary of essential data for each xenolith; the full dataset is given in Electronic Supplement 1 (ES1).
Garnet and spinel were run on a JXA-8900 instrument at 20 kV accelerating voltage, 40 nA beam current, 1-3 mm beam diameter, counting times of 20-40 s for peaks and 20-40 s for background  (Ziberna et al. , 2016)  with ZAF data reduction procedure. Olivine and pyroxenes (homogeneous cores delimited using BSE imaging) were analyzed on Cameca SXFive FE. Olivine was run at 20 kV, 20-60 s peak counting time, and the current of 20 nA and 100 nA. Data obtained at 20nA are reported for Si, Fe, Mn and Ni, and data at 100 nA for Cr, Ca and Na; concentrations of Al and Ti are below detection for nearly all samples. Machine drift and accuracy were monitored with the MongOl reference sample  (Batanova et al. , 2019) , see ES3. Pyroxenes were run at 20 kV, 40 nA, 1-3 mm beam, and 40 s counting times for peaks and background; the PAP procedure was used for matrix correction.
Os isotope and HSE analyses of whole-rocks at the GIG-CAS Os isotope ratios and Re and Os abundances were determined in ~1 g of powder aliquots. The samples were mixed with a 185 Re-190 Os spike, sealed into Pyrex Carius tubes with 10 ml of inverse aqua regia (3:1 HNO 3 :HCl) and kept at 240°C for 2 days. Osmium was extracted from the aqua regia to CCl 4 , then to HBr, micro-distilled using CrO 3 -4N H 2 SO 4 , then loaded onto Pt filaments and followed when dry with a Ba(OH) 2 activator. Os isotopic ratios were measured on a Triton thermal ionization MS (TIMS) via peak hopping on single electron multiplier. Data were fractionation-corrected to 192 Os/ 188 Os = 3. 08271. Total Os blank was 0. 46 ± 0. 42 (2, n=5) pg. A mean 187 Os/ 188 Os of 0. 12042 ± 0. 00027 (2 , n=6) was obtained for Merck Chemical AA standard solution for the period of analysis.
These results are in good agreement with a value of 0. 12022 ± 0. 00020 (2, n=14) measured on the same mass spectrometer in Faraday cup mode  (Li et al. , 2010). Zhang et al. (2017)  found no systematic differences in Os isotope ratios for USGS BIR-1a digested in Carius tubes either with or without de-silicification with HF prior to Carius tube digestion, indicating that an HF dissolution step is not required to obtain reliable Re-Os isotope results. Our mean 187 Os/ 188 Os value of 0. 13373±0. 00081 (2, n=5) for BIR-1a analyzed with the same procedure is in good agreement with published data (0. 13372 ± 0. 00080 and 0. 13371± 0. 00092) reported by  Ishikawa et al. (2014)  and  Zhang et al. (2017) , respectively. The aqua regia was dried and re-dissolved in 1N HCl after Os extraction; Re was separated by anion chromatography (see ES3), with a cleanup column to exclude interferences.
Constraints on the use of Re-Os isotope data for melt-depletion age estimates Constraining the age of the xenoliths involves several types of uncertainties. One of them relates to ubiquitous post-melting Re-enrichments, which are commonly linked to processes coeval with the eruption of host magma. This is a problem common to Re-Os studies of xenoliths that led to the definition of the Re-depletion model age (T RD  : Walker et al. (1989) ). T RD ages are calculated by first correcting the 187 Os/ 188 Os ratios measured in each sample back to the age of the host volcanic rock using each sample's Re/Os ratio to account for any Re-addition that presumably occurred at the time of the eruption. The calculated initial peridotites: 187 Os/ 188 Os = 0. 1296 ± 0. 0008  (Meisel et al. , 2001 ) and 187 Re/ 188 Os = 0.
1281 ± 0. 0009;  (Walker et al. , 2002b) ). If the PUM composition was set via addition of a late veneer of planetesimals, it appears that the veneer was dominated by ordinary and enstatite chondrites. Incorrect use of the model parameters may lead to erroneous T RD and T MA values and considerable confusion in comparison of mantle formation ages, e. g. as shown recently for xenoliths from Obnazhennaya in the NE Siberian craton  (Ionov et al. , 2018a). The differences in T RD ages based on contrasting BSE models for ≥3 Ga old cratonic peridotites may be as high as 0. 3 Ga (e. g. Carlson et al. , 1999). BSE reference values in some previous Re-Os work on Udachnaya xenoliths are different from those in this study. Pearson et al. (1995b)   The model ages from these papers were recalculated using the PUM model (Plate 6 of ES1) for comparison with data in this study.
In such a case, the difference in grain size between the megacrysts and coarse dunites may not be related to age, and possibly unrelated to the formation mode of their protoliths. Pearson et al. (1995b)  reported Re-Os data on five "megacrystalline peridotites" that have 0. 6-2. 6 ppb Os, 187 Re/ 188 Os of 0. 12-0. 97 and T RD of 1. 9-2. 8 Ga (re-calculated with the PUM model, Table  6 of ES1 ). The T RD in these samples fall in two groups: ~1. 9 Ga and 2. 8-3. 2 Ga; the latter range is only slightly higher than for dunites and megacrysts in this study (Fig. 12a ). These results are hard to compare directly with our dataset because they were obtained not on representative WR samples, like in this study, but on small amounts of material extracted from xenoliths, for which no other data were reported, except that their Mg# Ol range overlapped that for coarse peridotites from the same suite.
, 2015b; Moyen et al. , 2017). Our data give new insights into the earliest lithospheric formation stages in the central Siberian craton. Our preferred interpretation of the Re-Os model ages for the dunites and megacrysts in this study is that they record two distinct Archean events, one in the Neoarchean and the other one in the early Mesoarchean. The lower age limits for these two events are constrained by the T RD values (2. 6 ± 0. 1 Ga and 3. 0 ± 0. 1 Ga) and the upper age limits by the T MA values (3. 0 ± 0. 2 Ga and 3. 3 ± 0. 1 Ga). The mean T RD ages of ~2. 6 and ~3. 0 Ga (Fig. 12a ) may be closer to true values considering that Re in these highly refractory samples must be dominated by post-melting additions. Model ages calculated using the BSE model of  Walker et al. (2002a) , based on ordinary and enstatite chondrites, are only slightly lower with the T RD of 2.
Not all cratonic mantle is Archean Early Re-Os studies of coarse peridotites from cratons in South Africa and North America provided mainly Archean T RD ages  (Carlson, 2005; Pearson, 1999)  and were seen as evidence that cratonic lithospheric mantle only formed in the Archean. As a result, the terms 'Archean' and 'craton' are often considered essentially synonymous, i. e. the lithospheric mantle in all cratons is presumed to have formed in the Archean. However, data compilations  (Doucet et al. , 2015; Wittig et al. , 2010b)  show that a significant proportion of peridotites from the Slave and North Atlantic cratons record T RD ages of 1. 8-1. 9 Ga matching major crust generation events in those cratons. The central Siberian craton is the first proven case of a craton whose lithospheric mantle formed essentially in the Paleoproterozoic  (Ionov et al.
(5) The dunites are relict fragments of lithospheric mantle formed in two Archean events (at or soon after 2. 6 and 3. 0 Ga) and incorporated into present mantle lithosphere during the final assembly of the Siberian craton in the Paleoproterozoic. These formation ages of the mantle lithosphere are consistent with crustal basement ages from U-Pb dating of zircons. (6) The new data from Siberia and other cratons suggest that the formation of cratonic lithosphere with specific modal and chemical compositions did not stop at the Archean-Proterozoic boundary as is commonly thought, but continued in the Paleoproterozoic. McDonough and Sun (1995) , and the fields of cratonic peridotite xenoliths  (Doucet et al. , 2013) , fertile off-craton peridotite xenoliths from Vitim and Tariat in central Asia  (Ionov et al.
Acknowledgements We thank the  ALROSA joint stock company  and the open pit mine staff for access to the site and assistance with sample collection. DAI and ZL thank  P. Nimis  for assistance with P-T estimates. This study was financially supported by the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  ( XDB18000000 ) and the  National Natural Science Foundation of China  ( 41688103 ). AVG was supported by the  Russian Science Foundation  (project No. 18-77-10062 ); AVK was supported by the  Russian Federation state assignment project of IGM SB RAS. DAI acknowledges Chinese Academy of Sciences President's International Fellowship Initiative (PIFI)  for Visiting Scientists in 2017-18 and in 2019 (Grant No. 2017VCA0009 ). We appreciate detailed and constructive comments of  Brad Peters  and two anonymous reviewers as well as editorial handling by  R.