dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Continuous data After the conclusion of SR1, the XENON collaboration conducted an R&D campaign, referred to as SR2. This campaign focused on implementation and testing of a number of new systems planned for the successor experiment XENONnT  [19]. One key improvement implemented during SR2 in XENON1T was a data acquisition system without a software trigger. During SR2, a number of dedicated datasets were collected, using only the custom self-triggering firmware developed together with CAEN  [20] , in which the independent trigger is implemented on each channel once the signal on that channel exceeds a configurable threshold. Data is then continuously read out and stored. Data was collected in this mode during three time periods: a one week period in February 2018, two days in April 2018 (during which data was collected under varying extraction field conditions), and one week in July 2018 (under the highest LXe purity conditions, ∼1 ms electron lifetime, achieved with XENON1T).
1. Using emulated PMT signals from a waveform simulator, we estimate the data processor to be >99% efficient at reconstructing S2 signals above 10 PE. This guarantees a high acceptance for the detection of single electrons which have a gain of (28. 8 AE 0. 1) PE measured for the science data used in this work. We apply a lower threshold of 14 PE, where the acceptance is 99. 3% in this analysis for both datasets.
Therefore, we select interactions from three separate regions in the detector using the continuous dataset. Interactions in the GXe region are selected by the fraction of light seen by the top PMT array. Interactions in the drift region are selected such that the time separation between the S1 and the S2 does not exceed the known maximum drift time. In the region below the cathode no S2 is observed, thus we select S1s greater than 150 PE without an S2 within the maximum drift time. We investigate the rate of subsequent single electron emission, normalized by livetime, in the aforementioned regions, shown in Fig. 2. Additionally, we require that no other interaction may have occurred in the detector within 200 ms of the most recent interaction. We observe that for the first 750 μs (the maximum drift time) following an interaction anywhere in the detector the rate of single electron signals is high, due to photoionization of impurities within the drift region from scintillation light from either the S1 or the S2 if it is produced  [15].
B. x-y position dependence After applying the S2 shadow parameter and requiring the primary interaction to have occurred within the drift region, we investigate the spatial separation between the primary S2 and its subsequent delayed electrons, shown in Fig. 3. Our distribution of primary S2 and delayed electron signals is not uniform across the liquid-gas interface, in both cases being strongly biased to large radii. We therefore calculate the expected displacement from random correlation of the primary S2s and delayed electrons in the triggered dataset, shown in the lower panel of Fig. 3  in the orange dashed line. At small values of the displacement (Δx or Δy), we observe a clear excess in data indicating position correlation between the primary S2 and the majority of the delayed electron emission.
C. Dependence on interaction size The spectrum of primary S2 interactions, as defined in this work, spans many orders of magnitude, from five electrons, at the chosen threshold of the analysis, to several hundred thousand electrons for the most energetic interactions. The origin of delayed electrons has been hypothesized to originate either from delayed extraction of electrons at the liquid-gas interface or from impurities with the LXe. Therefore, we investigate the number of delayed electrons observed as a function of the size of the primary S2 (Fig. 4 ). We use the continuous dataset and select all delayed electrons that occur within 2-200 ms after a primary S2. We find that as the size of the primary S2 increases so does the number of delayed electrons. The relationship between the primary S2 size and the number of delayed electrons is however not observed to be directly proportional.
D. Temporal dependence As shown in Fig. 2 , the rate of delayed electron emission approximately follows a power law behavior. The observed emission rate of each of the 1 electron, 2 electron, and 3-5 electron populations are shown in Fig. 5  for the correlated (uncorrelated) electrons in the top (bottom) panel using the continuous dataset. Additionally, we normalize for the number of electrons in the primary S2, as we observe a dependence of the delayed electron emission rate on the size of the primary S2, discussed in Sec. III C. We fit a power law to the rate of delayed electron emissions TABLE I. Percentage of delayed electron signals that occur at a radial distance Δr or greater from the preceding primary S2 that are attributed to the correlated electron population. The percentage is calculated by comparing the integrated value of the Cauchy-Lorentz model (shown by the green dashed line in Fig.
E. Drift time dependence Given the observed dependence of delayed electron emissions on primary S2 size (Sec. III C) and position in the x-y plane (Sec. III B), we further study any dependence on the drift time of the cloud of ionization electrons produced by the original interaction, using the continuous dataset. We split our data into 14 bins based on the drift time of the primary S2, ranging from 50 μs (just below the gate) to 700 μs (just above the cathode). The intensity of delayed electron emission between 2-200 ms after the primary S2 is shown in Fig. 6  for position correlated (uncorrelated) electrons in red (blue), subdivided into 1, 2, and 3-5 electron populations. As the size of the primary S2 is reduced due to electronegative impurities within the LXe capturing electrons along the drift track, we use the electron lifetime corrected S2 (cS2) when calculating the percentage of delayed electron emission.
The expected number of delayed electrons is determined by accounting for the known electron lifetime in the continuous data used here. We also account for the possibility that delayed electrons that are emitted several to hundreds of millisecond after the primary S2 can be recaptured by impurities as they drift towards the liquid-gas interface. The expected number of delayed electrons are normalized to the value of the lowest drift time bin, where the impact of the secondary losses are minimized due to the shorter drift time. For the data used in Fig. 6 , the electron lifetime was measured to be 660 μs. We observe good agreement between data and expectation for all three populations. Contrary to correlated electrons, the intensity of position-uncorrelated electrons shows no equivalent dependence on the drift time.
F. Extraction field dependence The source of delayed electron emission could be electrons with insufficient kinetic energy to cross the barrier at the liquid-gas interface on their first attempt  [25]. These electrons are trapped at the interface but are subject to the strong extraction field in the LXe of 4. 1 kV=cm between the gate and anode electrodes in standard data taking conditions. They could consequently continue to attempt to tunnel through the barrier in the Oð10Þ ms following the primary S2. Therefore, we study the effect of the extraction field on the intensity of delayed electrons. The intensity is expressed as a percentage of delayed electrons observed from 2-200 ms after the primary S2, relative to the number of electrons in the primary S2. We use data from the continuous dataset taken at different anode voltages.
8   (uncorrelated) delayed single electron emission as a function of date using the triggered dataset. The behavior for 2 and 3-5 electrons is observed to be the same as for single electrons. The intensity of the delayed emission is given as a percentage of electrons in the primary cS2. We observe that the intensity of position correlated electron emissions decreases gradually throughout the first two science runs with increasing LXe purity. The intensity of delayed electron emission is more variable in SR2 due to frequent interventions on the detector, but the overall decreasing trend in the intensity of delayed electron emission with time is still observed. The intensity of position uncorrelated delayed electron emission displays a much smaller decrease throughout the time period during which XENON1T collected data.
We additionally plot the intensity in Fig. 8  as a function of the average electron lifetime measured in each period using the triggered dataset, shown in Fig. 9. We observe that data taken with equally pure LXe, but separated in time, do not produce the same intensity of delayed electron emission. Specifically, data collected during SR2 show lower intensity of position correlated single electron emission while obtaining the highest electron lifetime. In fact, the greatest change in LXe purity in XENON1T, occurring in July 2018, shows very little change in the intensity of the delayed electron emission. We conclude that both the correlated and uncorrelated electrons display a weak dependence on the measured electron lifetime. Note, that the apparent rapid decrease in the fraction of delayed electrons extracted between 600 and 660 μs is an artifact of our electron lifetime improving in time as shown by the gray data points in Fig.
Due to its higher livetime, we select only the continuous data taken during February 2018 for setting limits on the models described in Sec. VI. We use one day of this continuous data as a training dataset in order to identify windows of time during which we do not expect any correlated electron backgrounds, by accounting for the observed power law of the delayed electron emission. The selection of small S2 signals into 1, 2, and 3-5 electron populations is the same as that used in Sec. III. We split the data into 1 electron (14-42 PE), 2 electron (42-70 PE), and 3-5 electron (70-150 PE) populations, where the bins are centered at multiples of the secondary scintillation gain of (28. 8 AE 0. 1) PE. For each electron population, we fit a power law to the training dataset to create a model of the delayed fewelectron population.
The pretrigger selection, described in Sec. II, is applied to remove S1s and any associated electron emission due to photoionization that occur before the primary S2. This reduces the exposure following each primary S2 by 1 ms in the continuous dataset. We limit ourselves to considering only time regions within the detector where the expected duration and intensity of delayed electrons emissions is well understood. Therefore we require the primary S2 event to be well reconstructed and to have occurred within the active LXe region between the gate and cathode; this reduces our overall exposure by ∼33%. In addition, we exclude data from any time period in which the data acquisition system returned a busy signal  [17] , further reducing the livetime by 5. 4%. The second category consists of criteria applied to small S2 signals observed in the selected regions in time and space.
VII. CONCLUSION We have studied the background of single and fewelectron S2 signals in XENON1T. This instrumental background has been observed in previous LXe TPCs and has presented an obstacle to push ionization-only searches for DM candidates in these detectors down to the lowest number of detected quanta. We attribute this background as originating from impurities within the LXe target volume. In doing so, we were able to develop data selection criteria to optimize the signal-to-background ratio in XENON1T for ionization signals which produce less than five electrons. Conservatively assuming that all remaining interactions observed are DM candidates, we set upper limits on the interactions for a number of DM models, excluding new parameter space for dark photons and solar dark photons. Despite our optimization, the instrumental background of 1 and 2 electron signals remains large in XENON1T.
ACKNOWLEDGMENTS We thank  Kohsaku Tobioka  and  Tien-Tien Yu  for useful discussions regarding dark photon interactions. We also thank  Timon Emken  for details regarding the calculation of signal spectra in effective theories. We gratefully acknowledge support from the  National Science Foundation ,  Swiss National Science Foundation ,  German Ministry for Education and Research ,  Max Planck Gesellschaft, Deutsche Forschungsgemeinschaft, Helmholtz Association, Dutch Research Council (NWO) ,  Weizmann Institute of Science, Israeli Science Foundation, Fundacao para a Ciencia e a Tecnologia ,  Région des Pays de la Loire, Knut and Alice Wallenberg Foundation ,  Kavli Foundation ,  JSPS Kakenhi in Japan  and  Istituto Nazionale di Fisica Nucleare. This project has received funding/ support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No.
From biofilm images, this software can extract conventional geometric descriptors and dissect the biofilm biovolume into a cubical grid, with a user-defined cube size. Based on internal parameters extraction from each cube of the segmented biofilm, BiofilmQ can extract 420 global and internal biofilm parameters from CLSM images. This article explores the production capability and structure diversity of B. thermosphacta biofilms. This is important to improve our knowledge on biofilm behaviour in the food processing environment and to better control food contamination by this bacteria. In this study, 30 B. thermosphacta strains isolated from diverse origins were investigated to evaluate biofilm initiation and formation using the cBRT and CV staining. The 3D structure of B. thermosphacta biofilms was deciphered by CLSM, combined with the BiofilmQ image analysis toolbox.
Statistical Analyses Statistical analyses were performed using R-studio software (R4. 1. 1) on the Migale platform  [48]. CV biofilm quantifications associated with OD 570 measurements were compared with a multifactor analysis of variance (ANOVA) and a post hoc pairwise test plus a boxplot representation to highlight statistical biofilm production groups. To highlight structural diversity, 37 parameters extracted from CLSM images with BiofilmQ were used for statistical analyses. The parameters were analyzed using a principal component analysis (PCA), and the strains were clustered using hierarchical clustering on principal components (HCPC, available in FactoMineR R package). Segmentation of strains into groups was represented with fviz_dendro and fviz_cluster (available in FactoExtra R package).
Analysis of the Biofilm 3D Structure Section projections from CLSM image series of each strain observed after 2 and 24 h were extracted using IMARIS software. At the early biofilm stage (t = 2 h), the numbers of microcolonies and their height were very heterogeneous, suggesting strain specificities. However, at the mature biofilm stage (t = 24 h), B. thermosphacta architectures were flat, with low visible diversity between strains' structural feature. To quantitatively analyze these architectures, 37 biofilm structural parameters were extracted with BiofilmQ software for each time point (74 parameters in total), and a hierarchical classification on principal components (HCPC) was used to cluster the 30 strains in 5 major structural groups (Figure  3 ).
Analysis of the Biofilm 3D Structure Section projections from CLSM image series of each strain observed after 2 and 24 were extracted using IMARIS software. At the early biofilm stage (t = 2 h), the numbers microcolonies and their height were very heterogeneous, suggesting strain specificiti However, at the mature biofilm stage (t = 24 h), B. thermosphacta architectures were fl with low visible diversity between strains' structural feature. To quantitatively analy these architectures, 37 biofilm structural parameters were extracted with BiofilmQ so ware for each time point (74 parameters in total), and a hierarchical classification on pr cipal components (HCPC) was used to cluster the 30 strains in 5 major structural grou (Figure  3 ). Each biofilm from each strain was also represented in IMARIS projection from CLS images at t = 2 h and t = 24 h, and they were grouped based on their PCA cluster orig (Figure  4 ).
Twenty-three isolates out of thirty (77%) were classified as poor or weak early biofilm producers by the cBRT and as low mature biofilm producers by CV staining. In contrast, the seven remaining isolates (23%) were classified as medium or high, early and mature biofilm producers. The correlation between the cBRT and CV methods has already been observed for other bacterial species  [33, 40, 41, 49]. A high proportion of low biofilm producers has also been observed in other microbial Figure  4. IMARIS projection from CLSM images of each strain at t = 2 h (A) and t = 24 h (B). Each cluster is framed by its clustering color code from Figure  3 : red, cluster 1; light blue, cluster 2; yellow, cluster 3; dark blue, cluster 4; gray, cluster 5. Quantitative structural information was extracted from CLSM image using BiofilmQ.
These five groups showed differences in the number of cells in each biofilm, in biofilm thickness, volume, local density, outer surface, surface local roughness, outer surface per substrate, and local substrate area at t = 24 h. At t = 2 h, those groups showed differences in their biofilm surface per substrate area, shape volume, biofilm height, biofilm local density, biofilm thickness, and shape roughness. Some of these parameters have been used to describe biofilm architecture from CLSM images with other tools, such as MATLAB, PHLIP, or FIJI  [42, 52, 53]. The large number of parameters extracted from the CLSM images with BiofilmQ allowed us to deeply characterize the differences between B. thermosphacta strains. The most contributory parameters of each dimension were used to quantify the differences between the five clusters.
The plasma background and impurity transport in the scrape-off layer (SOL) of the WEST tokamak are modeled for three discharges over a P sep power scan ranging between 1. 5 and 2. 35 MW. An extended modeling grid is used to investigate far-SOL sourcing from the WEST baffle and lower divertor. Divertor Langmuir probe data at the targets and upstream data from reciprocating Langmuir probes and interferometry/reflectometry is used to create a simulated background plasma with the OSM and EIRENE codes. W-I spectroscopic data from WEST at the lower divertor and baffle is used to estimate the sputtered W flux as an input to the impurity transport code DIVIMP. A potential mechanism for far-SOL impurities from the baffle to transport into the near-SOL is proposed and discussed in terms of the model's force balances.
The focus of this study is to investigate these far-SOL sources in the WEST tokamak, specifically the lower pumping baffle, and assess its contribution to the core contamination. This study investigates a power scan of L-mode plasma discharges using the OEDGE code suite to model the background plasmas and simulate the impurity transport for impurities launched independently from both the divertor and baffle. The resulting impurity distributions from these models are then analyzed and the core contamination factors or 'screening efficiencies' are assessed by looking at the concentrations of impurities that accumulate near the separatrix.
Methodology Three L-mode plasma discharges were chosen for study within the WEST C5 experimental campaign over a range of injected power. The three chosen WEST plasma discharges (shots) are displayed below in Table  1. To model the plasma in the WEST tokamak, the code suite OEDGE (OSM/EIRENE/DIVIMP) calculates the background plasma using an onion skin model (OSM) on a geometric grid created from both the geometry of the device and magnetic equilibrium of the specific plasma discharge to be modeled. To create the background plasma, density and temperature profiles are specified along the targets, correlating to specific magnetic surfaces or "flux tubes" within the grid. OSM/EIRENE then calculates the background plasma working upstream from the target conditions. This generated background plasma is then used by DIVIMP to model the transport of impurities launched with a specified erosion profile using a Monte-Carlo approach  [8].
2. A dip in upstream T e occurs in the OSM results after the initial peak rising to a small secondary peak due to a feature of the divertor Langmuir Probe data showing a similar double peak. Note that in the low power case, an inaccurate reflectometry profile was stored in IMAS database. The INTREF2ne diagnostic couples both interferometry and reflectometry to infer the density upstream as well as the homothetic compression required to correct the magnetic equilibrium, as described in  [10]. In this case interferometry and reflectometry profiles do not match, which is generating large error bars in the density profile. The overall agreement with RCP data validate the use of these profiles despite the inaccurate reflectometry profile. The inferred target conditions are shown in Fig. 3. The right sides of each plot (where the DivLP data ends) include the OSM profiles at the baffle and thus there is an increase in temperature and density in relation to Ψ N for these profiles because the baffle is upstream of the divertor targets, and is separated from the cold neutral population that recycles off the main divertor target.
In the absence of reliable data for light impurity fluxes with which to attempt calculating W sputtering using a predictive model, the eroded W profiles were directly constrained by experimental data, where available. W-I spectroscopic data near the outer lower divertor and baffle along with the temperature and density profiles (to estimate S/XB) was used to calculate an experimentally-derived W gross erosion flux directly above the divertor and baffle. [12]  These spectroscopy-based estimates of the W erosion profiles were then used as an input to DIVIMP, as shown in Fig. 4. Separate runs of DIVIMP were conducted for each impurity launching region, with the baffle and divertor impurity profiles being run separately. This was done to isolate the cases for each source region because the sourcing of impurities in the plasma was of specific interest in this study.
While the magnitude of eroded impurities is expected to increase with increasing power due to more intense plasma contact, the power (or density) scaling of the plasma behavior affecting screening is less clear, and it is critical to understand both to evaluate far-SOL sources and how they might contribute to the overall contamination of the plasma. It would be valuable to understand how these far-SOL sourced impurities might behave when reaching higher power levels in WEST, or achieving H-mode confinement. To this effort, it is a goal to push for future experimental campaigns in the WEST tokamak to include target data from the baffle and upper divertor regions, as well as more lines of sight for spectroscopy on the baffle and upper divertor regions, the two main far-SOL sources to be assessed in further modeling efforts using OEDGE.
this paper presents a new dataset of infectious disease outbreaks collected from the Disease Outbreak News and the Coronavirus Dashboard produced by the World Health Organization. the dataset contains information on 70 infectious diseases and 2227 public health events that occurred over the period from January 1996 to March 2022 in 233 countries and territories around the world. We illustrate the potential use of this dataset to the research community by analysing the spatial distribution of disease outbreaks. We find evidence of spatial clusters of high incidences ("hot spots") in Africa, america, and asia. this spatial analysis enables policymakers to identify the regions with the greatest likelihood of suffering from disease outbreaks and, taking into account their degree of preparedness and vulnerability, to develop policies that may help contain the spreading of future outbreaks.