dataesr/scientific-paragraphs-categorization huggingface dataset:
CROSS calibration system In order to perform in-situ calibrations of the electromagnetic energy response and of the neutron capture efficiency, a calibration robot, CROSS, is mounted on top of the SoLid detector inside the container, as shown in Fig. 7. First, each of the modules is mounted on a trolley, which is itself mechanically connected by a pivot link to a linear actuator (SKF -CAHB10  [53] ). This actuator allows to move the module carriage on the rails by a few centimeters, which is needed to insert small radioactive sources between modules during calibration. These displacements are monitored to an accuracy of better than 5 mm by mechanical position sensors mounted on the ground rail of the detector. As such a total of six calibration air gaps of 30 ± 5 mm can be created sequentially on both sides of each module.
In order to capture these slow neutrons, thin cadmium sheets with a thickness of 2 mm are sandwiched between the passive shielding and the container housing of the detector. The capture efficiency of these cadmium sheets for slow neutron is about 88%. The cadmium sheets, cover the entire back side of the experiment container and most of its top and bottom surface, amounting to roughly 45% coverage of the experiment. ). The SoLid position system is based on three Cartesian coordinates along perpendicular axes in a right-handed system. The Z-axis is perpendicular to the detector planes and its direction points away from the nominal center of the BR2 reactor core. The Y-axis points upward towards the zenith, and the X-axis points to the right side of the detector, when facing the reactor. The environment of the BR2 containment building is continuously monitored and registered by the BR2 Integrated Data Acquisition System for Survey and Experiments (BIDASSE).
During SoLid operation, environmental parameters, such as temperature, humidity and pressure, outside and inside the containment building, are constantly monitored. Also the background radiation is monitored using gamma and beta detectors placed in the vicinity of the SoLid container. So far, these variables are used as a cross check of the data coming from the container instrumentation, i. e. environmental sensors, NaI scintillator and airborne radon detector mentioned in section 2. 5. The thermal power is determined by measuring the flow rates and temperatures at the entrance and inlet of the primary cooling circuit and the reactor pool circuit. The flow of the cooling water is measured using Dall tubes and the temperature is measured using resistor thermometers. The main uncertainty in this measurement originates from the calibration of the Dall tubes.
Backgrounds The SoLid detector is also subjected to various background processes that contaminate the IBD samples for final analysis. Because the primary physics trigger is set to detect thermal neutrons interacting in the neutron detection screens, most backgrounds are related to either the production of neutrons via processes other than IBD interactions, or processes that excite the ZnS(Ag) scintillator embedded in the neutron detection screens. Some background processes exhibit a clear time structure between the triggered NS time and preceding ES signals and are called correlated. Others have a random time structure and are called accidental. Reactor independent backgrounds dominate our data sample and can be extracted from data collected during reactor OFF periods. We quantify our understanding of these background components by comparing background simulations with data in specific control regions that are enriched in one specific background component.
In addition, also the integrated energy of the NS signal can be used to discriminate neutrons and alphas from the 214 Po decay. Other backgrounds can be broadly categorized as accidentals and consist of random coincidences of ES signals that are typically induced by gamma rays and thermal neutrons in the surroundings of the detector. The accidental distribution can vary with reactor power, but can be easily extracted from data itself, using negative time differences between the ES and the NS signals. Accidentals contribute only marginally to the selected IBD events sample.
Detector operation and data quality monitoring Run operations are controlled via a dedicated Python-driven web application, the "SoLid Data Quality Monitor" (SDQM). It automatically processes a small fraction of each run (first GB) using the SoLid reconstruction and analysis software. Output measurements and distributions of the  detector as well as in-situ environmental sensors are read out periodically, as show in Fig. 17  and stored in an online database and is continuously inspected via a web application. The rates obtained from the monitoring database are shown in Fig. 18. The NS trigger rate stays stable irrespective to the reactor operation. Once the muon contamination has been removed, the NS rate is around 18 Hz and is strongly correlated to the airborne radon concentration which is monitored by a Rn detector (see section 2.
Readout simulation After modelling the energy deposits or the creation of secondary particles in the detector and its surroundings with SoLidSim, the energies deposited in the sensitive volume of the detector are translated in detected pixel avalanches in the MPPCs connected to fibres surrounding the energy deposit. This is modelled in a standalone readout software library, ROSim, which is specific to SoLid, and supported by dedicated lab bench measurements of various parts of the optical system, as described in  [46]. For the optical part, it includes the modelling of the scintillation photon production in the PVT and in the ZnS scintillator of the neutron detection screens, in particular the non-linearity corrections to the energy response using Birks' law, the loss of scintillation photons due to scattering and absorption in the PVT cubes, the neutron detection screens and the wrapping material, the attenuation of the wavelength shifted photons in the optical fibres, the reflectivity and absorption losses in the mirrored fibre ends.
8 V above its breakdown voltage. The pulses are finally sampled with the same frequency and resolution as the SoLid ADCs and the data is stored in the same format as real data for processing by the reconstruction software. An example of an IBD event generated with the SoLidSim software and processed by the readout simulation is shown in Fig. 22  and compared to an observed IBD candidate. Since the IBD trigger operates on themalised neutrons, a proper modeling of the neutron waveforms is essential. We assume that neutrons, once thermalised, are indistinguishable with respect to the process they were produced by. As such, thermalised neutrons from the IBD process, or from a neutron calibration source, should behave identical. Consequently, a trigger bias on a neutron waveform from IBD events should behave the same as one on a neutron waveform from a calibration source.
Data taking and calibration The SoLid detector was commissioned between February and June in 2018, after which it entered stable physics operations. Since then the experiment has been in continuous operation during all subsequent BR2 reactor cycles and refuelling periods (see section 4. 1). We thus collect approximately as much reactor ON data as reactor OFF data. The data taking is guaranteed to last until the end of 2021, but can possibly still be extended if needed, in order to accumulate enough statistics and achieve a relative measurement almost solely limited by the systematics of the detector. On a regular basis, in between reactor ON cycles, two to five days are reserved for in-situ detector calibrations using the CROSS system, described in section 2, with several neutron and gamma sources, as described below.
Neutron calibration The neutron detection efficiency drives directly the IBD detection efficiency. In SoLid, we aim to perform an absolute flux measurement as well as an oscillation analysis based on the relative flux distortion across the detector. To this end, we aim to determine the neutron detection efficiency per module with an accuracy of around 5% in absolute and at the % level in relative. Due to the fact that the coverage of the neutron sources are not uniform within the detector, the calibration is done individually for each of the 12800 detection cells before being averaged. The neutron detection efficiency, hereafter denoted as   , can be expressed as the product   =    ×. In the first place, the probability that the neutron gets captured on the neutron detection screens, denoted    , depends on the position and initial energy of the neutron, as well as its transport in the experimental set-up.
Year The NS trigger was optimized during the detector commissioning to ensure the largest possible neutron trigger efficiency, while keeping the data rate sustainable (see section 3). However, this come with a relatively low NS event purity of about 20%. The first step consists in removing the muons contribution (see section 5. 2). In order to reject remaining background, the second requirement is based on an offline pulse shape discrimination using the integral over amplitude ratio. The results are displayed in Fig. 24. For the standard data taking in physics mode at BR2, the NS signals, whose rate does not depend on the reactor operation, can be well separated from the tagged ES events, with a purity above 99% after the selection requirements are applied. During calibration runs, two neutron sources are used, AmBe and 252 Cf, for which activities have been calibrated at the 2% precision level at the National Physical Laboratory (UK).
𝛾-ray During the quality assurance process, two methods to tackle the latter issue were developed  [50]. The first method consists of fitting the Compton edge profile of the spectrum by an analytical function based on the Klein-Nishina cross section and the result is compared to the predicted value (see Fig. 27 ). The second method employs a Kolmogorov-Smirnov test and compares the measured energy spectrum to a G 4 simulated sample varying the energy scale and energy resolution (see Fig. 27 ). Although the two methods rely on different assumptions and different event selections, the obtained results are consistent within 2% and meet the required energy scale precision  [50]. For the standard data taking in physics mode at BR2, an average of 94 PA/MeV/cell was measured without MPPC cross-talk subtraction, which is estimated to be around 20%.
The detector is capable of operating at very close proximity to a compact research reactor with practically no overburden. Its design is simple and very modular and some of its parameters were improved after a measurement campaign with a single module prototype in 2015. As of the spring of 2018 the full size SoLid detector is in continuous operation at the BR2 research reactor of the SCK ⋅ CEN in Belgium. The BR2 reactor is operated with highly enriched 235 U fuel arranged in a very compact geometry, which reduces the uncertainties in the calculation of the incoming electron antineutrino flux and its energy spectrum. The detector has proven to run very stably over long periods of time and can be routinely calibrated with dedicated gamma and neutron sources with an in-situ system. The statistical energy resolution, the energy scale precision and the level of inter-channel response calibration all adhere to or surpass the initial SoLid design specifications.
Acknowledgements This work was supported by the following funding agencies:  Agence Nationale de la Recherche  grant  ANR-16CE31001803 ,  Institut Carnot Mines ,  CNRS/IN2P3  and  Region Pays de Loire, France ;  FWO-Vlaanderen  and the  Vlaamse Herculesstichting, Belgium ; The U. K. groups acknowledge the support of the  Science & Technology Facilities Council (STFC) ,  United Kingdom ; We are grateful for the early support given by the  sub-department of Particle Physics at Oxford and High Energy Physics at Imperial College London. We thank also our colleagues, the administrative and technical staffs of the SCK ⋅ CEN for their invaluable support for this project. Individuals have received support from the  FWO-Vlaanderen  and the  Belgian Federal Science Policy Office (BelSpo)  under the IUAP network programme; The  STFC Rutherford Fellowship program  and the  European Research Council  under the European Union's  Horizon 2020 Programme  ( H2020-CoG)/ERC Grant  Agreement n.
Although little developed before the advent of DNA sequencing techniques, this change in the study of life was greatly stimulated when it became apparent that knowledge of genomic sequences would rapidly become ubiquitous. The need to develop this endeavour was highlighted for the first time at a meeting of the European Biotechnology Action Programme in Tutzing, Germany, in 1989: 'From the information technologies perspective, we are faced with the need to create databases where we will recognize relevant features in response to learning processes. The creation of descriptors, the genesis of exploratory hypotheses will add to the traditional methods of biology, in vivo and in vitro, experiments in silico'. Although this emphasis is already three decades old, it can be safely anticipated that in silico approaches will gain in importance in the coming decades, especially for developments in microbial biotechnology.
In the meantime, less dramatic changes continue to be designed. Since the beginnings of SynBio research, researchers attempted to reassign codons  (Robertson et al. , 2021) , often to non-natural proteinogenic amino acids, as for example illustrated early on  (D€ oring and Marli ere, 1998). The most efficient approaches attempt to device orthogonal constructs, for the time being devoted to a limited fraction of the translation machinery  (Schmied et al. , 2018). Recent attempts have created an artificial genetic code of 68 codons that could allow the incorporation of four non-canonical amino acids into synthetic proteins  (Dunkelmann et al. , 2021). In other advances, the synthesis of proteins containing d-amino acids, b-amino acids, phosphorylated amino acids as well as long-chain and cyclic amino acids in which the nucleophilic amino group is not in the a-position has been achieved using modified peptidyl transferase regions in the 23 ribosomal RNA.
Intelligent storage and analysis of nucleic acid and also protein sequence data began four decades ago  (Blumenthal et al. , 1982). Subsequently, an explosion of applications resulted in the creation of a new domain of biology, bioinformatics, with a slight change in the initial meaning of the word, now often restricted to genome-based studies  (Hogeweg, 2011 ). Yet, while this restriction is commonplace, the advent of genomics, with its related 'omics' studies  (Yadav, 2007) , has considerably expanded the domain, with only fuzzy boundaries with recently fashionable domains such as AI and image analysis  (Burian et al. , 2021). Gene, genome and some transcriptomics data are collected, managed and released in a highly standardized way by the International Nucleotide Sequence Database Collaboration (DNA Database of Japan, Mishima, Japan; European Nucleotide Archive, Hinxton, UK; and Gen-Bank, Bethesda, USA), which has developed standards for more than three decades  (Arita et al.
Also, the data structure of specialized databases is still extremely disparate, which prevents interoperability  (Danchin et al. , 2018). When dealing with SynBio, the situation is possibly less disappointing. The iGEM repository of Biobricks (https://biobricks. org) and, in Europe, the SEVA effort (Mart ınez-Garc ıa et al. , 2020) are pushing hard for widespread standardization. A further incentive for an international coordinated effort in this direction would be more than welcome to support the development of SynBio in the next decades. All the same, three major domains of in silico research for SynBio develop in parallel: 'omics-based' SynBio, whole-cell models and in silico integration of metabolic pathways.
'Omics-based' synthetic biology Besides the domain of genomics, born and diversifying into a number of subdomains, the fields of transcriptomics, proteomics and metabolomics that progressively sprouted out of this original domain  (Tyo et al. , 2010; P€ uhler, 2012)  are now key for SynBio developments. The high-dimensional datasets generated by these experimental approaches require considerable storage capacity and widespread sharing. A large number of analysis methods must also be made available. Sometimes it is possible to combine both storage and availability of analysis methods. In the case of bacterial genome data, this is the case, for example, with the MicroScope platform and its implementation of the MaGe methods  (Vallenet et al. , 2020). Repositories such as GitHub with its community of open-source developers are progressively gaining more and more impact (see, e.
g. its SynBio entry: https://github. com/topics/syntheticbiology). Cloud computing provides generally low-cost and highly flexible solutions in the domain of molecular modelling, omics data analytics (e. g. RNA sequencing, metabolomics or proteomics datasets) and for the integration, analysis and interpretation of phenomes  (Koppad et al. , 2021). Merging highly heterogeneous datasets has been a prime goal early on  (Arakawa and Tomita, 2013). This is critical because multi-omics may be used to improve systematic genome manipulation for novel synthetic constructs  (Fontana et al. , 2020)  or improve industrial processes. Among the many illustrations of the approaches for SynBio applications, we may notice fine cellulose biosynthesis  (Ryngajłło et al. , 2020) , treatment of natural biomass, in particular lignocellulose  (Xie et al.
, 2014; Chen and Dou, 2016) , or in the present context of general concern triggered by climate change, genome manipulation carried out for the metabolic engineering of Cyanobacteria  (Hagemann and Hess, 2018; Ng et al. , 2020). The data introduced in modern omics analyses allow for efficient characterization of the genetic, regulatory and metabolic phenotypes of engineered microbes. Yet, designing genetic interventions to achieve the desired phenotype remains a challenge. With recent developments in genetic engineering techniques, the time frames associated with constructing and testing strain designs have been greatly reduced through in silico modelling. This has created an efficient design-buildtest-learn loop iteration cycle between experiment and analysis. However, the scale and complexity associated with multi-omics datasets still require manual biological reasoning about the mechanisms driving phenotypic changes.
As a consequence, the use of traditional statistical approaches can still reduce the dimensionality of these datasets and help compare strains of interest and improve metabolic engineering  (Danchin et al. , 2018; St John and Bomble, 2019). Besides modelling individual cells, modelling is also used to understand how microbial communities and their synthetic counterparts may form stable ensembles. Models allow researchers to better monitor the changes in microbiome composition and, for example, how this affects disease onset and development. Construction of models that integrate genomics, transcriptomics, proteomics and metabolomics allows researchers to visualize the meta-metabolism of the community. Constraint programming  (Brown and Miguel, 2006 ) is used to couple meta-metabolism and multi-omics analyses  (Ebrahim et al.
A first simulation software pipeline, E-Cell, was made publicly available in 1997, poised to exploit the then recently acquired genome data of Mycoplasma genitalium  (Tomita et al. , 1997). This generic software environment for simulating a cell was subsequently improved to conduct in silico experiments based on numerical data obtained from in vivo experiments involving other organisms  (Tomita et al. , 1997; Tomita, 2001; Ishii et al. , 2004). Among applications of the software was the exploration of an osmoregulatory switch in the growth properties of E. coli  (Srividhya and Krishnaswamy, 2004 ) and the deciphering of red blood cells' metabolism  (Yachie-Kinoshita et al. , 2010). The domain of mathematics involved in computation was based on differential equations. This preference is not without consequences, as it implies that biological objects are assumed to display a continuous behaviour, with their concentration represented as a real number not as a discontinuous integer.
, 2021). More fitting to SynBio approaches, whole-cell models may be used as a basis for genome design based on previous knowledge. The bacterium Mycoplasma genitalium is represented by an updated whole-cell model (https://github. com/CovertLab/WholeCell). At this time, this framework combines 28 cellular submodels, integrating all the known functions of every gene and molecule in the cell, running individual models on multiple supercomputers  (Rees-Garbutt et al. , 2021). This design allows researchers to propose knockouts of individual protein-coding gene, phasing out genes previously identified by their importance for growth and to test possible phenotypes to identify synthetic lethal associations. This semi-empiric work depends on known phenotypes, conducting whole-cell model simulations in three steps: design (the outputs of algorithms select possible gene deletions), simulation (behaviour of the genome minus those deletions) and testing (the behaviour of the resulting cell is analysed).
Comparative analysis of bacterial genomes has revealed that such sites are rapidly created at random during evolution, and then retained when they have a positive effect on the organism. Interestingly, the patterns of evolution depend on the nature of the regulator (Mr azek and Karls, 2019). A parallel software package, GeNESiS, was designed for the modelling and simulation of the evolution of gene regulatory networks  (Kratz et al. , 2008). Various models of gene regulatory networks are available  (Santib añez et al. , 2020; Chen et al. , 2021b) , in particular dynamic models will be of interest to take evolution into account  (Handzlik et al. , 2021). Analyses involving co-evolution patterns via comparative genomics  (Zhang et al. , 2021a; Guo and Amir, 2021)  will considerably benefit SynBio studies.
In a deep 1 Ms exposure, our simulations show that PHEMTO would be able to measure the X-ray spectral characteristics of the faintest detectable sources up to z 3 and down to L(2-10 keV) less than a few 10 44 erg s -1 , telling apart unabsorbed, Compton thin and CT objects with high significance, and recovering the intrinsic absorption with less than 20% uncertainty for Compton thin objects. Moderately obscured (N H ≤ 10 24 cm -2 ), high-luminosity QSOs have been discovered by previous Xray satellites. Current X-ray spectra are rather poor, and the uncertainties on the column densities are large. Some of these objects may even be CT but current data cannot tell for sure. PHEMTO spectra will easily distinguish between Compton thin and Compton thick AGN. For a moderately bright (L X = 10 44 erg s -1 ) Compton thick AGN at redshift z = 1, the absorbing column density can be determined with an error of 25 % within a 100 ks observation.
Recent studies could not resolve with the needed precision the degeneracy between electron shock heating models nor give stringent upper limits on the effective thermal conductivity in post-shock regions  (Markevitch 2006; Russell et al. 2012; Markevitch et al. 2003). The effective area at high energies and 1" spatial resolution will allow these crucial measurements of the ICM physics to be made routinely on a large sample of clusters. By 2050 ICM calorimeter data taken by XRISM and Athena will be fully exploited. It is an easy guess to predict that many new discoveries will be made and that the complex nature of the ICM will be even more revealed, making the definitive transition from an ideal gas to a complex low beta plasma description, as for example departure from a Maxwellian for the emitting electrons as commonly observed in the Sun's corona and heliosphere and Earth's magnetosphere.