dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the models Table  1  represents the RMS error in K for the regular and the structured databases, for the NN and the CNN retrievals, over the training and the testing sets. The training and testing scores are identical in all these experiments confirming that we were successful in controlling the overtraining on over-parameterised architectures such as CNNs. For the regular dataset, the pixel scale NN has scores equivalent to those of the CNN version. It is even slightly better (0. 31 K compared to 0. 33 K) but these differences are not significative and result more on the numerical instabilities of the CNN training. Fig. 7  shows examples of retrieval, and it can clearly be noted that the CNN has the tendency to smooth out the SST images, and that extreme cases are dampened. Isolated extreme pixels are suppressed.
This is natural since it uses the spatial filters that use the information of the neighbourhood pixels to estimate the SST in the central pixel. It will be seen that this smoothing can be positive in some cases (higher pixel noise or in the presence of stronger spatial features) but it is not the case in the regular database. On the contrary, over the structured database, the CNN offers a strong improvement compared to the NN at the pixel scale (0. 24 K instead of 0. 32 K STD error). This can be understood by the fact that the CNN exploits the spatial features in the database to better retrieve the SST. Therefore CNNs are pertinent at this noise level when spatial features are present in the SST images. Therefore, the use of a CNN using spatial filters can be negative (eroding extreme cases), neutral, or positive.
The training of a regression tool intends to find the best overall compromise over the learning database. This means that the smoothing of the CNN can be positive or negative depending if spatial features are present or not. The challenge would be to obtain a CNN that could easily detect the presence of this type of spatial features and adapt to it. This will be investigated in the future. Fig. 8  represents the overall processing scheme of the CNN retrieval that will be tested in the following section. The PCA preprocessing is performed outside of the CNN architecture, this is a choice as this could had been added inside the CNN. At the end of the processing chain, the diagnostics (bias, STD and maximum errors) are estimated over a testing databases not used for the training of the CNN.
Global statistics The SST retrieval error statistics (bias and STD errors) are provided in Fig. 9 , for the CNN retrieval, over the regular database. Results would be similar for the NN retrieval. The error patterns would be similar over the structured dataset. These maps are based on the compositing of the retrievals over each 1 ∘ ×1 ∘ oceanic pixels. In regions with more clouds, the SST seems to be underestimated (too cold) contrarily to the low latitudes where an overestimation can be observed. Furthermore, retrieval errors are overestimated on west of African coast, with a pattern similar to the seasonal mineral dust emitted by the Sahara (see Fig. 2  in  Bellouin et al. (2013) ). The pattern includes plumes from the Sahara mineral dust in summer and from biomass fires in the spring. These mineral dust have a seasonality (?), with more aerosols in summer, but also a varying altitude.
The mineral dust absorb the infrared radiation emitted by the Earth surface (?) so it is natural that IASI observations would be colder in this case. Further analysis should be conducted to know if these are errors due to aerosol contamination in the IASI observations used for the retrieval, or if EUMETSAT SST estimates used here (Section 2) were already contaminated by the aerosols. Since retrieved SST seem too warm, it is probably a aerosol contamination on the EUMETSAT SST database.
Fig. 10  represents the retrieval errors of the NN and CNN retrievals when the noice level increases, over the original and the structured databases. With the original noise, as mentioned before, the NN and CNN retrieval are equivalent over the regular database, but the CNN outperforms the NN over the structured dataset. What is interesting is that retrieval errors increase significantly for higher TBnoise for the NN (on both the regular and structured dataset). The CNN appears to be much less sensitive to this TB noise increases. So pixel noise can be compensated in the CNN by the exploitation of the spatial features of the SST map.
Sensitivity to missing data Several sources of missing data can be listed. Clouds cover about 60% of the globe at any time  (Rossow and Schiffer, 1999) , this number depends on the spatial resolution. LST can be retrieved from IR observations for about 60% of the locations with a spatial resolution of 1 km 2 twice a day from polar orbiters  (Prata et al. , 1995) , and with a spatial resolution of 2 km 2 every 15 min from geostationary satellites  (Schmit et al. , 2017). In some regions, the number of missing data due to clouds can be extremely high. IASI observations are highly sensitive to the presence of clouds, so missing data due to clouds is a true difficulty for such infrared instruments. Instrument can also have a technical problem or a recalibration step, so missing data can add up in the image.
Furthermore, the retrieval developed here is for SST only, so the presence of land in the 22×22 images brings missing pixels in the image too. In current IASI processing chains, missing data has not been a true difficulty since retrievals are performed at the pixel scale: Data is processed in the available pixels, and missing data is reported with no values. For the CNN retrieval scheme, this is obviously a problem because the input images to the network are supposed to be complete. The default setting in the DL toolbox of Matlab is to fill these missing values with zero values. This solution is not satisfactory as these artificial zeros values can mislead the CNN. This shows that this missing data issue is still not solved. Dedicated missing value detection and filling could be implemented inside a deep CNN but this is not a trivial task and it will be investigated in the future.
A simpler approach is tested here, based on simple spatial interpolation. In order to test this missing data filling approach, a missing data model is first defined. Several holes are randomly placed inside the input TB-images. These holes are chosen as circles of radius 1 to 5 pixels. Another constraint is that these holes do not cross the image limits so that the interpolation can be done in an easier way. This missing data model is rather simple, and in most cloud-contaminated IASI images, a large part of the image is actually missing with no possibility of spatial interpolation. A more sophisticated extrapolation technique would then be required and this will be investigated in the future. Fig. 11  provides some examples of such missing data structures. These holes are introduced in the TB images simultaneously for the ten selected input channels.
To perform the SST retrieval, the TB-images are first filled using a  bilinear interpolation. Table  2  represents the retrieval errors on the location of the missing pixels, when using the NN or CNN retrievals, over the regular and the structured datasets, when no pixels are missing or when the missing pixels were interpolated. For comparison purpose, the statistics are given only over the missing pixels location, even for the "no missing pixels" statistics. First comment is that the missing data deteriorates the retrieval, from 0. 123 to 0. 137 K standard deviation error over the regular database for the NN retrieval for instance. As mentioned earlier, the CNN and NN approaches are equivalent in the regular database for the no-missing and the missing experiments, so CNN do not help much (but do not degrade either) the interpolation retrieval on the regular database.
Conclusion and perspectives The NN retrieval at the pixel scale and the CNN retrieval at the image scale have been compared in the context of SST retrieval using IASI observations. It was shown that for this application, the noise level and spatial coherency of the SST images is such that the retrieval at the pixel scale is very efficient (about 0. 3 K of STD error). In these conditions, the image processing approach of the CNN has equivalent scores. However, when the instrumental noise is increased, CNNs outperform NNs because it becomes interesting to use spatial features. It is shown also that in datasets with more prominent spatial features, CNNs outperform traditional NNs. The compromise between pixel and image scale retrievals is dependent on the spatial resolution of the pixels. It was shown that CNNs can be useful for coarse resolution instruments such as IASI.
, 2002a Aires et al. , , 2002b Aires et al. , , 2002c Aires et al. , , 2002d;; Schlussel (2005) ;  Collard (2007) ;  Paul et al. (2012) ;  Pellet and Aires (2018) ;  Safieddine et al. (2020). Using more channels would automatically reduce the impact of the IASI instrument noise on the retrieval. Therefore, a spectral dimension will be added to the 2D dimension of the CNN inputs, in a forthcoming version of our retrieval scheme. It will be investigated if PCA should be done independently from the CNN, to feed the CNN, or if the raw channels can be given directly to the CNN that should then implement its own compression. A nonlinear PCA using a NN structure should also be investigated. A spatial interpolation of the CNN inputs was proposed here to limit the problem of missing data in input images.
Acknowledgements The authors would like to thank the  Centre National  d' ÕÉ tudes Spatiales and, in particular,  C. Deniel  for supporting our analysis of the IASI observations by partially funding this study under the  TOSCA program. We would like to thank  Samuel Favrichon  too for interesting discussions, and  Cathy Boonne  for helping accessing the EUMETSAT and IASI data on AERIS data center.
Methods This qualitative study used data from 20 in-depth interviews conducted between June and October 2020 to examine the social, economic, and health implications of COVID-19 for undocumented immigrants. We used snowball sampling to recruit a purposive sample of 20 undocumented immigrants representing equal proportions of male and female Asian and Latinx people, who selfreported their race and ethnicity. All participants provided verbal informed consent and were virtually interviewed using a semistructured interview guide codeveloped by a community advisory board that included undocumented immigrants and subject matter experts. Specific questions regarding contact tracing were integrated after the fourth interview based on community reports about challenges engaging immigrants in contact tracing.
2017). LC can reduce sample complexity by separation molecules into fractions of different physical or chemical properties. In addition, MS/ MS fragmentation provides additional information for structural elucidation and allows comparison with databanks, if available  (Wang et al. 2016). When aligned LC-MS/MS spectra are used with molecular networking approaches  (Aron et al. 2020; Petras et al. 2021) , hundreds of unique molecular features can be classified at the compound level  (Kujawinski et al. 2017; Petras et al. 2017). The online coupling of LC to HR-MS has also been introduced as an alternative to direct infusion HR-MS or to offline LC-HR-MS techniques, which have been the traditional biogeochemical approaches  (Patriarca et al. 2018). Untargeted LC-MS/MS has been used to evaluate influences of microbial activities on the DOM composition.
Complex datasets generated in targeted and untargeted metabolomics require novel tools for data analysis  (Steen et al. 2020). Molecular networking and fragmentation tree algorithms have become key methods to visualize and annotate the chemical space in non-targeted mass spectrometry data. Metabolomics analysis software infrastructures, such as METLIN  (Smith et al. 2005) , mzCloud (https:// www. mzclo ud. org/), MetaboLights  (Haug et al. 2020) , or Metabo-Analyst  (Chong et al. 2018) , focus on search, annotation, and store of MS/MS spectra. However, neither of those allows free download of its reference library and enables searching a single MS/MS spectrum for identical or analogous MS/ MS spectra in public data repositories  (Wang et al. 2020). MZMine 2  (Pluskal et al. 2010 ) and XCMS  (Smith et al.
2016). GNPS provides public dataset deposition and/or retrieval through the Mass Spectrometry Interactive Virtual Environment (MassIVE) data repository  (Wang et al. 2016) , the feature-based molecular networking (FBMN) as an analysis method  (Nothias et al. 2020) , and the web-based system MASST to search the public data repository part of the GNPS/MassIVE  (Wang et al. 2020). Recently, the web-based GNPS Dashboard was also integrated to facilitate inspection, visualization, analysis, and sharing of private and public mass spectrometry data remotely  (Aron et al. 2020; Petras et al. 2021). A first large-scale application of FBMN for the analysis of marine organic matter composition has been recently done  (Petras et al. 2021). Other studies have used GNPS to get access to the complexity of DOM molecules at a more precise level of molecular annotation  (Petras et al.
2017)  or to create a molecular network and to search against GNPS spectral libraries  (Stephens et al. 2020). These approaches consider the annotated features to be "putative" identifications that have not yet been verified by reference standards, but are based on spectral similarity to data from public or commercial libraries  (Longnecker et al. 2015; Kujawinski et al. 2017). Even though molecular networking, spectral and structural databases, statistical significance estimation, and in silico and community-based annotation criteria have significantly improved the annotation of MS/MS spectra  (Horai et al. 2010; Wang et al. 2016; Watrous et al. 2012) , the annotation of compounds that are not covered by spectral libraries remains as major challenges  (Petras et al. 2021). This is particularly evident in marine DOM, as its molecular diversity exceeds that of any characterizable metabolome.
Acknowledgements SLS thanks  Institut Pasteur  for support and is grateful to colleagues at the  UTechS-PBI/Imagopole (Paris)  and the  Screening Discovery Platform (Institut Pasteur Korea). TSC thanks  Maite Méndez  (www. maite mendez. com) for the graphic support of Fig. 1 and  Dr. María Calleja  for the support with the representative structures of marine DOM of Fig. 1.
On 2009 March 11, the LAT computer that interfaces to the spacecraft suffered a software error and stopped operating normally. Several minutes later, this led the other two operating event processing computers in the LAT to also stop functioning correctly from a separate software error, as they attempted to process very high event rates during a transit of Fermi through the SAA (see § 6. 3), a region of high density of geomagnetically trapped charged particles. During the LAT recovery, the spacecraft went into its safe-mode because of an issue with extended collection of diagnostic data, which then resulted in the LAT being automatically powered off by the spacecraft. Science data collection with the LAT was restarted by ground commanding on March 14. The errors in the LAT computers were determined through captured diagnostic data and fixed in a subsequent LAT flight software update to prevent recurrence.
On 2018 March 16, the Fermi spacecraft went into safe-mode and powered off the LAT, because the -Y solar panel rotation drive on Fermi stopped moving. The LAT remained powered off for over 17 days, as the solar panel problem was investigated. The LAT's temperature was kept at survival levels by its spacecraft-powered heaters. Ground commands to power up proceeded without problems on April 2. The first science data were collected the same day, but the first science data flagged as suitable for routine science analysis were collected on April 8, because of the slow thermal response of the LAT calorimeter, which has temperature-dependent performance. Figure  4   With one solar panel on Fermi stuck since March 2018, the rocking profile enabling the LAT all-sky survey was replaced with periods of various alternating rocking angles.
3), configuration verification errors (see § 2. 2) and infrequent commanding errors. Almost 600 billion triggered readouts of the LAT detectors occurred in the first 10 years, corresponding to a daily average trigger rate of about 1900 triggers per second (Figure  5 ). Each event readout is then passed through flight software event filters running in the two Event Processing Unit (EPU) computers in the LAT. The EPUs perform simple filtering to discriminate between gamma-ray photons and charged particles, see §3. 1. 2 in  Ackermann et al. (2012). The EPUs discard about 80% of the events, and losslessly compress the raw data for the remaining 20% of the events into a data stream with a rate of about 1. 5 Mbps, corresponding to an average rate of about 400 triggers per second. These compressed event data are continuously transferred from the LAT to the Fermi spacecraft and stored in a solid-state data recorder.
About once per orbit, a radio data link is opened by command between Fermi and the ground, via the TDRSS geostationary data-relay satellite network, to the TDRSS ground stations in White Sands, New Mexico. The data are transmitted at about 40 Mbps via TDRSS, and then transferred to the Fermi MOC at the GSFC (see also § 2. 3). The MOC then transfers the raw LAT event data, plus engineering housekeeping data for the LAT and related spacecraft data, to the LAT Instrument Science Operations Center (LISOC) at the SLAC National Accelerator Laboratory (SLAC) at Stanford University. The downlinked data are checked for gaps at both the MOC and the LISOC, and any missing data are re-transmitted from Fermi to the ground. There has been negligible loss of downlinked data during the mission. About 120 billion LAT events have been received at the LISOC and processed there during 10 years.
After delivery to the LISOC, reconstruction algorithms process each LAT event to search for and reconstruct charged particle tracks and electron-positron pair-production vertices in the tracker, and to find associated energy signals in the calorimeter. Multi-parameter selection filters applied to the reconstructed data distinguish gamma-ray photon events from charged-particle events. Multiple classes of assessed purity of photon selections are used to classify events. The summary data for the reconstructed filtered photons are then delivered back to NASA for immediate public release. LAT photon data are publicly available at NASA's Fermi Science Support Center (FSSC) 3 at the GSFC. About 1. 13 billion "source" class gamma-ray photons have been detected by the LAT in 10 years and are publicly available through the FSSC.
The LAT operates in a high-flux environment of charged particles (cosmic rays and terrestrial sourced particles) that can cause single event upsets in the electronics. To reliably operate in such an environment, the LAT electronic configuration is regularly automatically verified. Before each new science run, the configuration is loaded, then read back and checked. A check mismatch generates an error message from the LAT, and generally data-taking does not start for that run, although that can be overridden. At the end of each run, the LAT electronic configuration is again read out, and if a mismatch is found, the configuration data are recorded for later analysis on the ground. Additionally, a command to check the electronic configuration is run on the LAT at the end of every transit through the SAA region of high density of charged particles.
Legacy data are usually stored on tape, while a copy of the data products from the current version of the reconstruction and analysis pipeline are kept on disk, for fast and easy access. However, in the future, to reduce data storage costs, only the most recent year (or so) of data will be kept on disk, with older data stored on tape only. By far the major part of the data consists of reconstructed events, which are more than 10 times more voluminous than the channel-by-channel digitized electronics data from which the reconstructed event data are derived, and 25 times larger than the tabular photon data in ROOT format 5  which are extracted from the reconstructed event data. About 200 MB of high-level photon data and ancillary data in FITS files are sent to the FSSC at GSFC each day. The 1500 cores available for LAT needs at the IN2P3/CNRS facilities in Lyon, France, are commonly used for Monte Carlo simulations of LAT performance and results, thus assuring the availability of the SLAC computer farm for event reconstruction.
Data quality monitoring and trending LAT subsystem and ground-support systems performance is continuously monitored through the LAT Data Quality Monitoring (DQM) System, to guarantee the quality of the LAT data delivered for science analysis. During each step of the ground processing pipeline, histograms of different quantities are generated and stored. The monitored quantities are related to the stability of the detectors (such as pedestals, gains, and rates of each channel), to the rates of events passing the different trigger conditions and on-board filters, and to high-level reconstruction outputs such as the average photon rate or the event energies. The performance of the timing system is also monitored (GPS lock loss and 20 MHz clock stability, see § 6. 2). Weekly duty scientists use web-based tools to check all the processed science data from the LAT.
Recording the time the strip signal remains above threshold provides an estimate of the total charge deposit (i. e. , of the total energy lost by ionizing particles traversing a strip). This Time Over Threshold (ToT) measurement is performed on the logical OR of all the strips on a TKR plane and is included in the data stream along with the digital position of the strips above threshold. The overall small energy loss in the TKR is especially important for primaries with energy 100 MeV, which deposit most of their energy before reaching the calorimeter. Atwood et al. (2007)  describe the TKR subsystem in more detail.
This global energy scale is monitored by observing the energy deposited by minimum-ionizing particles, mostly protons. The ToT values in the MIP dataset are corrected for the incidence angles on the Si planes (a 10% effect). The distribution is fitted with a Landau curve convolved with two Gaussians, and the location of the MIP peak is determined with a statistical uncertainty around 0. 2%. Systematic effects include charge sharing and a residual effect of the incidence angle (of order 1%), evaluated by Monte Carlo simulations and corrected for. A varying amount of non-MIP contamination in different runs causes the largest remaining systematic effect. In Figure  8  the uncalibrated ToT is shown for one recent run, together with the fitted curve. Figure  9  shows the location of the uncalibrated MIP peak as a function of time; each point is the average of several runs, the residual scatter, larger than the statistical uncertainties, is attributed to varying non-MIP contamination in different runs.
In response to the Trigger Acknowledge, analog signals from all 3,072 CAL front-end ASICs are digitized; thus the entire CAL is read out for every such trigger. In the nominal instrument configuration for science data acquisition, only one of the four gain channels at each crystal-end is digitized: the highest gain range that is unsaturated, which preserves the greatest energy resolution in each crystal. CAL data are sparsified with zero-suppression logic in each front-end ASIC that eliminates energy depositions below a configurable Log-Accept (LAC) threshold, nominally set to 2 MeV. See  Grove & Johnson (2010)  and  Abdo et al. (2009)  for more complete descriptions of the CAL and the LAT trigger system. 2009 ) detail the calibrated quantities and calibration procedure for the CAL. Here we briefly review the calibration constants and discuss how some have evolved during the mission.