dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank all the affected individuals and families for their collaboration. Several authors of this publication are members of the European Reference Network for  Developmental Anomalies and Intellectual Disability (ERN-ITHACA) ITHACA (EU Framework Partnership Agreement ID :  3HP-HP-FPA ERN-01-2016/739516 ). We thank  Jette Rasmussen  for graphical assistance and  Cristina Guilera  and  Juanjo Martínez  for technical assistance.
INTEGRAL is also a successful actor in the new multi-messenger astronomy introduced by non-electromagnetic signals from gravitational waves and from neutrinos: INTEGRAL found the first prompt electromagnetic radiation in coincidence with a binary neutron star merger. Up to now more than 1750 scientific papers based on INTEGRAL data have been published in refereed journals. In this paper, we will give a comprehensive update of the satellite status after more than 18 years of operations in a harsh space environment, and an account of the successful Ground Segment.
Moreover, these characteristics can be used to distinguish extended emission from point sources and thus provide considerable power for serendipitous science: a very important feature for an observatory-class mission. Routine INTEGRAL science operations are implemented using a pre-planned sequence of observations as a baseline. The X-and gamma-ray sky is, however, highly variable and many of the mostly unpredictable ToO events are scientifically important and so often warrant modifying the pre-planned observing schedule. INTEGRAL has no on-board gamma-ray burst (GRB) detection and triggering system. However, it continuously downlinks its acquired data to Earth, allowing for constant, near-real time burst monitoring on the ground. The 'INTEGRAL Burst Alert System' (IBAS) sends out alerts with the characteristics of GRBs detected in the FoV of the main instruments.
The SPI Anti-Coincidence (veto) Subsystem (ACS), with a time resolution of 50 ms and timetagging down to 1 ms at energies above 75 keV, and the IBIS VETO system, with a time resolution of 8 s at energies above 50 keV, provide an all-sky monitor for GRBs. For instance, SPI-ACS detects about 300 GRBs per year outside the FoV of the instruments. Therefore, INTEGRAL is a key participant in the Inter-Planetary Network (IPN) that combines data from several satellites to provide accurate localisations of GRBs, crucial for follow-up observations at other wavelengths. Apart from finding GRBs, INTEGRAL is well-suited to the search for any transient electro-magnetic counterpart to, e. g. , gravitational-wave (GW) signals, ultra-high energy neutrino (UHEN) events and fast radio bursts (FRBs), owing to the large FoVs of IBIS and SPI, and their nearly omni-directional anti-coincidence shields.
INTEGRAL Announcements of Opportunities (AOs) for observing proposals are open worldwide. The provision of the Proton rocket by ROSCOSMOS led to an optimised orbit that increased the available observing time as opposed to earlier estimates. As a result, currently, 25% of the Open Time is reserved for scientists from the Russian Federation. ESA provides the opportunity to propose coordinated observations, with ESA's XMM-Newton, NASA's NuSTAR and NASA's Neil Gehrels Swift Observatory missions. INTEGRAL also has a very strong Target Of Opportunity policy which enables any observers, from different astronomical domains, to propose observations of transient events. The INTEGRAL Science Working Team (ISWT) was formally established at the time of the selection of the INTEGRAL payload, data centre and mission scientists  (1995).
A direct deconvolution of the SPI data is challenging, because the response matrix is singular and inversion is ill-defined. Background signal is modelled together with the source in the same data space of detectors and their counts per energy bin. The response to background is recorded by the active detectors, since background from all directions imprints its pattern on the detector. This pattern remains fairly constant over periods of days  (Diehl et al. , 2018) , despite changing its level continuously. This offers the possibility for broad-band spectral bins to use observations of regions without sources to produce a background model (flat fields) that can be scaled to match the background level in each science window. Exploiting only the signal modulation from the coded mask and the dithering strategy, over a sufficiently long obser-vation, it is possible to disentangle the signal of the source from the overwhelming background.
Beyond such flat-fielding, the SPI teams have developed other methods and tools to extract scientific products, allowing more elaborate background modeling. As an example, it is possible to fit the background detector pattern from data at high spectral resolution, and it is possible to use scaling from radiation monitor detectors to account for short-term background variations. These methods have been used in various works (see  Diehl et al. , 2021, and references therein)  Using these methods, it is possible to obtain the intrinsic signal both for diffuse emission in narrow lines and the continuum spectrum of point sources (see Figs. 3  and 4  for two examples). For narrow astrophysical gamma-ray lines, a sensitivity of ∼10 -5 ph cm -2 s -1 has been achieved (for 10 6 s observing time).
, 2003). ISGRI consists of 128x128 pixels of semiconductor detectors. Photons with energy above 100 keV can interact in the full depth range of each pixel. When the interaction happens deep in the detector, it takes longer for the charge carriers to reach the electrodes and the registered pulse peaks at a later time: the delay is dominated by the transport of the holes. Due to charge-trapping in the detector medium and the increase of the ballistic deficit when the pulse duration increases, deeper interactions are also registered with smaller pulse heights. If not Figure  4 : Astrophysical result from SPI event data analysis for point source emission. Cygnus X-1 stacked SPI spectra obtained using standard analysis (single events; 22 keV-2 MeV, red points) and multiple events (130 keV-8 MeV, blue points).
This correction did not take into account the evolution of the energy resolution and low-energy threshold. It was not possible to predict changes in the instrument detection efficiency, so the spectral response normalization was adjusted to make the Crab spectrum consistent with the INTEGRAL/SPI measurements. In the last few years, a more complete time-dependent physical model for the charge loss in the detector has been developed  (Lebrun et al. 2021, in preparation). The charge loss due to finite carrier mobility, ballistic deficit and resolution of the measuring electronics was adjusted using ground calibration data and in-flight line properties. This model predicts the dependency of the line position on the rise time throughout the mission with good accuracy. The two-dimensional model line profiles are fitted to the bi-parametric data for each INTEGRAL revolution.
OMC data and scientific results Since the telemetry rate allocated to OMC does not allow to download one full image per integration, a number of around 100 CCD sections of 11×11 pixels around pre-defined objects of interest (including the target of the main instruments) are extracted from each observation and downloaded to Earth. The CCD sections are processed at the INTEGRAL Science Data Centre (ISDC), being corrected for bias, dark current and flatfield and being photometrically calibrated. The processed and fully calibrated data can be accessed through the INTEGRAL archive interface at the ISDC, by using the standard ISDC query tools, or classified by object identification from the OMC database at the Centro de Astrobiología Spanish Virtual Observatory repository (see  Gutiérrez et al. 2004 and Domingo et al.
IREM's role as a payload is to support instruments on board the INTEGRAL satellite and continuously measure particle fluxes along its orbit. It enables the payload instruments to promptly react to elevated radiation levels, in the belts and during Solar events. Since its switch-on shortly after the launch, IREM has been operating continuously up to the present day. With the total mission duration to date, IREM observations covered half of the very dynamic Solar cycle No. 23 and the whole quiet cycle No. 24. The IREM data are publicly available for the wide user community; they are part of the satellite house-keeping data and made available at the INTEGRAL Science Data Centre (ISDC) in user-friendly format (https://www. isdc. unige. ch/heavens/). A database with all the SREM data in CDF format is accessible from PSI from http://srem.
Occasional crashes of a Data Processing Electronics (DPE) of an instrument require a full restart of that instrument. In July 2019 error messages indicating a software overrun on the central on-board computer were received followed by rejection of certain command classes by INTEGRAL travels in a geosynchronous highly eccentric orbit with high perigee in order to provide long periods of uninterrupted observation with nearly constant background and away from the radiation belts. Over time, the perigee and apogee have changed, as has the plane of the orbit. The closest approach to Earth was on 25 October 2011, at 2756 km (1); the furthest away from Earth was on 27 October 2011, at 159967 km (2). In 2015, four thruster burns were conducted that were carefully designed to ensure that the satellite's eventual entry into the atmosphere in 2029 will meet the ESA's guidelines for minimising space debris.
Acknowledgements Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Switzerland, Spain) and with the participation of Russia and the USA. The OMC team has been funded by different Spanish grants, including Spanish State Research Agency grants PID2019-107061GB-C61 and MDM-2017-0737 (Unidad de Excelencia María de Maeztu -CAB). The INTEGRAL French teams acknowledge partial funding from the French Space Agency (CNES). The Danish JEM-X gratefully acknowledges support from the Danish PRODEX delegation through contract C90057.
The INTEGRAL Italian team acknowledges support form the Italian Space Agency, ASI, along these years via different agreements, last of which is 2019-35-HH. 0. LH acknowledges sup-port from SFI under grant 19/FFP/6777 and the EU AHEAD2020 project (grant agreement 871158). TS is supported by the German Research Society (DFG-Forschungsstipendium SI 2502/1-1 & SI 2502/3-1). "If I have seen further, it is by standing upon the shoulders of giants" (Sir Isaac Newton in a letter to written to Robert Hooke in February 1675). General appraisal goes to the many colleagues (consult, e. g. , the author lists of the special Astronomy & Astrophysics letters issue in 2003 on: "First science with INTEGRAL": see https://www. aanda. org/component/ toc/?task=topic&id=652) who conceived, built, launched and operated INTEGRAL in the beginning; without them IN-TEGRAL would not be where it is now.
If we are lucky we might be able to find experimental conditions that remove these saturations. Nevertheless, we must not lose sight of the fact that it is not always possible to reproduce the experiment a second time, for example when the technique is destructive as in LIBS. In general, we can say that we always try to avoid the saturation phenomenon as much as possible. Unfortunately, it is observed in many cases and it is necessary to deal with these data as they are. The question that then arises is the following: what should we do with saturated values that we know to be systematically erroneous? Fig. 1a  gives a schematic representation of a dataset with six spectra of which three contain saturations highlighted in red. We can notice first that it is not always the same bands that are saturated in this dataset used as a toy example.
Second, the number of saturations in a given spectrum is quite variable. Fig. 1b  presents the two strategies typically used to manage potential spectral saturation in a dataset. The idea is finally very simple since knowing that saturated values do not represent the true values, it seems logical to remove them from the acquired dataset. We then have a first possibility which is to remove all the spectra as soon as they contain at least one saturated spectral variable also known as rowwise deletion. This strategy might seem satisfactory because it is simple to implement, but it is not flawless. Indeed, we could then remove a spectrum made up of several hundreds of spectral variables and thus potentially a very large amount of molecular/atomic information just because a single variable would be saturated, for example.
We would then have a significant loss of chemical information as in the present case study where only 50% of the spectra would be kept for multivariate analysis. In the specific case of spectroscopic imaging, we would then end up with areas of the image without defined chemical information. From a more statistical point of view, we would also have a biased analysis since we would no longer have the initial population of acquired spectra. In a second strategy known as column-wise deletion, we could suppress a spectral variable in the data set as soon as at least one of the spectra of the dataset presents a saturation on this same variable. This strategy is no more satisfactory because a significant loss of information would still be observed. In the case of the presented example, we notice that such a strategy would remove almost all the spectral information from the dataset.
e. with the initial number of spectra and spectral variables resulting from the acquisition. Thus in this work, three different spectroscopic imaging datasets will first be used to show the need to manage saturations present in the spectra at the risk of seeing many artifacts during multivariate analyses generating biased chemical images and extracted spectral profiles. The principle of imputation will of course be explained and the analysis of the corrected datasets will allow us to demonstrate the benefits of this concept to find chemical images and corresponding spectroscopic information representative of the analytical reality of complex samples.
As the JM approach is often more constraining from a statistical point of view to be applied, the MICE method has been considered in this work. MICE specifies the multivariate imputation model on a variable-byvariable basis by a set of conditional densities, one for each incomplete variable (i. e. containing missing values). In this work, a regression model is developed using the complete variables of the matrix as input and a given incomplete variable as output. Once this model is established, we can then use it to predict missing values of spectra at this specific spectral variable based on known values in the matrix. We will thus have as many regression models developed as spectral variables containing missing values in the considered dataset. At the end of this imputation procedure, we then find a full matrix free of missing values that we can then explore with usual multivariate methods.
Multivariate data analysis Principal component analysis (PCA) is one of the most flexible and effective chemometric method for exploratory data analysis applied to hyperspectral imaging  [7]. It is indeed very sensitive and thus allows the detection of very low variance levels. Its use will first of all allow to see artifacts generated during a direct use of saturated spectral datasets but also to estimate in a second time the efficiency of the corrections brought by our imputation strategy. All PCA calculations were performed under the Matlab 2016b environment.
Dataset #1: a simulated sample The first hyperspectral imaging dataset consists of synthetic spectra that could have been acquired using LIBS. The advantage of using such simulations lies in the fact that all the parameters potentially influencing a given problem are under controlled. In this way, we often have a less biased view of the phenomena and a real generalization is possible. As we will see further on, it will also be a way to vary the importance of the saturation phenomenon. On the basis of the spectroscopic information given by the Kurucz database  [8] , we first simulated the emission spectra of silver, aluminum and arsenic by considering a typical plasma temperature and electron density (9000 K and 5. 1016 cm À3 respectively). In order to be the most faithful with the spectral reality, we then applied a Lorentzian profile with a linewidth of 0.
Dataset #2: a lung biopsy The second dataset used in this work corresponds to a LIBS imaging experiment conducted on a lung biopsy of a patient with severe emphysema  [10]. Note that the patient signed informed consent, and the clinical procedure was approved by the local ethics committee. The LIBS imaging has been conducted with a protocol dedicated to paraffin-embedded tissues, as described in a previous work  [11]. The aim of such application was to characterize the distribution of metallic particles (from nanometric to micrometric size) in tissue biopsies, which represent a precious help for clinicians to diagnose the cause of the exposition (i. e. environmental and/or occupational). This spectroscopic experiment is a good example of a case where saturated spectra cannot be avoided. Since the concentration, composition, location and size of the particles are not known prior the experiment, the measurement system requires an extremely large dynamic in term of detection, typically from few ppm to a few percent in mass.
Despite our efforts to set up the experimental parameters as optimized as possible, it is not uncommon to have a significant number of spectra showing saturations on a LIBS image as in this case. The size of the analyzed area of the biopsy was 5. 42 mm long by 3. 18 mm wide with a spatial resolution of 20 mm. We have thus acquired 43,089 spectra over a spectral range from 282. 01 to 310. 03 nm and an approximate spectral resolution of 0. 04 nm. The hyperspectral data cube was therefore defined by 271 pixels Â 159 pixels x 644 wavelengths.
Dataset #3: a rock section The third dataset was also acquired using a LIBS imaging instrument on a banded iron formation rock consisting of alternating layers of iron oxides and silicates. We selected this sample because it somehow allowed us to find approximately the same chemical distributions for two contiguous zones of the sample on which we could set different acquisition parameters. This procedure of selection of analysis area was necessary because we must not forget that LIBS is a destructive technique. Therefore, we could not analyze the same area several times. As a consequence, two successive zones each having a size of 20 mm long and 2 mm wide were analyzed following the protocol already used for other work  [12]. A spatial resolution of 20 mm and a spectral range from 245. 85 to 334.
03 nm were considered for these acquisitions generating two hyperspectral datasets defined each by 1000 pixels Â 100 pixels x 2048 wavelengths. The laser pulse energy and the detection gate were adjusted for these two sub-zones in order to control the saturation level. Indeed, our aim was to obtain no saturated lines on the first zone of the sample (gate: 1 ms; energy: 1. 2 mJ) and saturation of Si emission lines on the second zone (gate: 5 ms; energy: 1. 2 mJ). All the other acquisition parameters such as the delay and detector gain was kept constant.
Results and discussion Before tackling the problem of correcting saturation in the spectra, it is important to understand how this is necessary to manage it, at the risk of giving a completely biased vision of the analyzed sample. For this purpose, we will use the first dataset of simulated spectra. Fig. 2a  thus presents the results of a first principal component analysis applied to the raw data (i. e. without saturated signals). Unsurprisingly, we note first of all that there are three significant eigenvalues in the scree plot that correspond to three spectral contributions. More specifically, the first three principal components respectively extract the spectra of the three pure elements Ag, Al and As. This is quite logical since there is no correlation between these elements in the considered dataset.
The consequences are not long in coming since a fourth significant component is already detected in the eigenvalues scree plot. Of course, this is not normal because we know that only three elements are present. Compared to the initial results (in Fig. 2a ), both the first principal component and the first scores map are no different. Nevertheless, there is a small decrease in the expressed variance from 21. 42 to 20. 44%. As far as the second and third scores maps are concerned, they are quite comparable to those observed from the non-saturated dataset. On the other hand, the corresponding principal components show small artifacts in the spectral region of the saturated Ag band (highlighted by red boxes in the corresponding figure). Finally, the fourth principal component specifically reflects the saturation phenomenon observed on silver for an expressed variance of 0.
08%. We see many structures in the corresponding scores map but we know that they do not reflect any analytical reality. We observe even more the typical W-shaped artifact in this principal component. This shape can be explained quite well. Indeed, when saturations are potentially present at a given wavelength, principal components have to express the variance precisely at this wavelength for the unsaturated spectra in the dataset but other ones also have to express variances specifically localized on the feet of this same peak. In other words, a clipped band at a certain wavelength of a given spectrum is no longer homothetic to an unsaturated band of another spectrum. So if we do not have any prior information about this dataset, we see that even a limited saturation level can induce the extraction of erroneous information at the spatial and spectral levels about the sample being explored.
It is then interesting to amplify the saturation effect by considering this time a saturation level equal to 20. Under these new conditions, 10,119 spectra are saturated, i. e. 16% of all the spectra. Once again, saturations are present in areas where Ag is the most concentrated, but this time for values higher than 50% (SI Figure  3S ). We are also starting to see saturated spectra for pure Al pixels. Fig. 2c  show PCA results of this new dataset. Four significant contributions are still observed, but with an even greater influence of artifacts on all components. We notice thus on the first principal component which should be specific to Ag that contributions from Al and As are now easily observed. We note here that the presence of saturations can also create spurious correlations. The second and third components have even greater expressed variances and ever more pronounced 'W-shapes'.
While the scores maps are relatively little changed under these new conditions for the first three principal components, this is not at all the case for the fourth one. This high-contrast, low-noise scores image could indeed lead us to believe that real chemical compounds are present, which is of course not the case. In a final step, the saturation is further increased by considering this time a level equal to 10. This situation is extreme since 27,870 spectra are now saturated, i. e. 44% of the dataset. What is more, the saturated pixel location map shows that this percentage is even underestimated since almost all of the areas that should contain the three elements are almost all saturated, the unsaturated areas being mainly the background (SI Figure  4S ). At the same time, we observe that almost all emission lines show saturation over the entire spectral range.