dataesr/scientific-paragraphs-categorization huggingface dataset:
Figs. 3-10. In Fig. 3, where the dataset has the largest beam momentum, both LO and NLO CEM results calculated with SMRS, GRV, xFitter, and JAM pion PDFs are shown, whereas only NLO results are shown in the other figures. The hadronization factor F , as an overall normalization parameter, is determined by the best χ 2 fit to the x F distributions of cross sections, shown as the black lines in Figs. 3-10. The experimental normalization uncertainties listed in Table II are not included in the error estimation, since they.
Periods when these detectors were above ground have been tracked in a database  [12]. Thus, for the well-monitored BEGe detectors we expect impurities of 5 nuclei/kg of 68 Ge and 21 nuclei/kg of 60 Co as of September 2014  [12]. Extrapolating the expected impurities to the whole Phase II data taking period we expect on average 0. 03 cts/day from 68 Ge and 0. 1 cts/day due to 60 Co. From background modeling in Phase I  [14]  the contribution for the coaxial detectors formerly used in the Heidelberg-Moscow (HdM)  [28]  and Igex  [29]  experiments is expected to be even smaller due to their long storage underground. Simulating the expected detector bulk impurities we find background contributions around Q ββ of less than 10 -4 cts/(keV•kg•yr) in both cases. Hence, we conclude that 68 Ge as well as 60 Co can be neglected in the following analysis.
Statistical analysis The multivariate statistical analysis, which is used to model and disentangle the background in its components, runs on the three binned data sets M1-enrBEGe, M1-enrCoax and M2-enrGe. It is based on the reconstructed energy with the zero area cusp (ZAC) filter algorithm which is close to optimal and provides an excellent low-frequency rejection  [16]. The single-detector data sets M1-enrBEGe and M1-enrCoax contain the reconstructed ZAC energy of all M1 events whereas for the two-detector events the sum of the two reconstructed energies is put in the M2-enrGe data set. Moreover, the count rate per detector is used for the two potassium γ-lines. The spatial event distribution is a collection of the number of events per detector for M1 events and expressed in a matrix of pairs of detectors for all M2 events.
Assuming that the number of events in each bin follows the Poisson probability distribution Pois(n; ν), where ν is the expected mean and n is the experimentally measured number of counts, the likelihood function for a binned data set reads N bins i=1 Pois(n i ; ν i ). Here ν i = Ncom k=1 ν (k) i is the expected number of events in the i-th bin, calculated as the sum of the contributions from each background component k; ν i (λ 1 ,. , λ Ncom ) is a function of the parameters of interests λ j (isotope activities, 2νββ half-life, etc. ). The complete likelihood function adopted for the present analysis combines the three data sets M1-enrBEGe, M1-enrCoax and M2-enrGe: L(λ 1 ,. , λ m | data) = N dat d=1 N bins i=1 Pois(n d,i ; ν d,i ). (1) The statistical inference is made within a Bayesian framework.
For the twodetector events (M2-enrGe data set) the fit range starts at 520 keV and extends up to 3500 keV. Possible additional components outside of this range (e. g. 39 Ar) do neither add information to the background decomposition in the ROI around Q ββ nor to the analysis of 2νββ decay. Furthermore, at energies lower than ∼100 keV the shape of the PDFs is dominated by uncertainties on the detector transition layer model, which describes the charge-carrier collection at the interface between the n + contact and the detector active volume. The exact nature of this transition region is different for each detector and prone to systematic uncertainties  [39]. GD91A GD35B GD02B GD00B GD61A GD89B GD91C ANG5 RG1 ANG3 GD02A GD32B GD32A GD32C GD89C GD61C GD76B GD00C GD35C GD76C GD89D GD00D GD79C GD35A GD91B GD61B ANG2 RG2 ANG4 GD00A GD02C GD79B GD91D GD32D GD89A ANG1 6 - With an energy resolution which is typically 3-4 keV at Q ββ (FWHM)  [8, 9]  and better at lower energies, a fixed bin size of 1 keV was chosen for all data sets.
Results As described in Sec. 3. 2 the α-event background and potassium γ-lines are studied individually and the results are incorporated in the full-range fit as prior distributions. The latter combines a simultaneous fit of the M1 and the M2 data sets. For the final combination of parameters, outlined in this section, components with a posterior distribution peaked at zero were eliminated from the fit. The stability of the results with respect to the bin size and prior distributions was verified. Changing the prior distribution for fit parameters for which no screening measurement is available from a flat to an exponential one does not significantly impact the final posterior distributions. The compatibility of the final model, which includes 34 free fit parameters, with data is supported by a p-value of ∼ 0.
Inside the mini-shrouds a homogeneous distribution is compatible with the data as well as 42 K attached to the detectors contact surfaces. In the fit model given here, a possible scenario is chosen where all 42 K is located on the n + surfaces. However, we note that 42 K on the p + appears to partly substitute the energy-degraded α-component in the M1-enrCoax data set if introduced in the fit and predicts a higher total BI around Q ββ. The extracted 42 K activity on the enr Coax p + contact in this case is 22 ± 4 µBq corresponding to a contribution to the BI around Q ββ of (7 ± 1) • 10 -3 cts/(keV•kg•yr). For the M1-enrBEGe data set the posterior distribution of a possible 42 K component on the p + contact is compatible with zero. Outside the mini-shrouds an inhomogeneous distribution of the 42 K decays explains the observations better.
A potentially asymmetric 42 K distribution is, thus, not further followed in the main analysis. Nevertheless, some considerations can be found in Appendix A. An explanation for 42 K on the p + contact being rejected for the M1-enrBEGe data set but potentially present in the M1-enrCoax data can be the specific bore-hole geometry of the semi-coaxial detectors. 42 K produced inside the hole can not easily escape and is trapped close to the p + contact. For each source of background the contribution to the BI at Q ββ prior to active background reduction is listed in Tab. 2. The statistical uncertainties on the single contributions to the BI are generally of the order of 10% or lower, with the exception of 42 K and energy-degraded α-events, for which the uncertainty is roughly double. The two contributions are affected by a higher uncertainty because they are not bound by screening measurements.
The estimate of the FCCD used in this analysis is based on measurements using an 241 Am source with characteristic γlines at 60 keV, 99 keV and 103 keV. However, the FCCD was also measured using a 60 Co source with characteristic γ energies of 1173 keV and 1332 keV. The latter FCCD Co is systematically higher (about 3%) with respect to the FCCD Am. The discrepancy could be explained by an energy dependence of the initial charge-carrier cloud size inside the detector but the actual impact on the active volume is still under investigation. For the enr Coax detectors only FCCD values determined with a 60 Co source are available. Considering the systematic uncertainties affecting the determined active 76 Ge exposures of the M1-enrBEGe and M1-enrCoax data sets (1. 8% and 5% respectively, see Tab.
Acknowledgments The Gerda experiment is supported financially by the German Federal Ministry for Education and Research (BMBF), the German Research Foundation (DFG) via the Excellence Cluster Universe and the SFB1258, the Italian Istituto Nazionale di Fisica Nucleare (INFN), the Max Planck Society (MPG), the Polish National Science Centre (NCN), the Foundation for Polish Science (TEAM/2016-2/2017), the Russian Foundation for Basic Research (RFBR), and the Swiss National Science Foundation (SNF). The institutions acknowledge also internal financial support. This project has received funding or support from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No. 690575 and No. 674896, respectively. The Gerda collaboration thanks the directors and the staff of the LNGS for their continuous strong support of the Gerda experiment.
A. 1 Data Two windows around the potassium γ-lines are projected in detector index space, such that, for single-detector data, each data point n i represents the total counts in detector i in the respective energy window. For two-detector data the detector space is two-dimensional, and each data point n ij represents the number of events for which energy is deposited in detector i and detector j. The events in the potassium lines (denoted with K40 and K42 in the following) are selected in a ±3σ energy interval around the respective line, rounded up to an integer number of keV to match the specific energy windows in the energy distributions with 1 keV binning. σ is the energy resolution in the respective energy window. Additionally, three side-bands (SB1, SB2 and SB3 in the following) are used to estimate the continuum below and above the γ-lines.
A. 2 Analysis The statistical approach of factorizing the likelihood is described in Sec. 3. 2. The part of the likelihood we are analyzing here runs simultaneously on the 5 × 2 energy ranges presented above. Following the naming convention L K (λ 1 ,. , λ m | n) = N dat d=1    N det i=1 Pois(n M1 d,i ; ν M1 d,i ) × N det j<k Pois(n M2 d,jk ; ν M2 d,jk )    , where the index i runs over the bins (i. e. detectors) and the index d over the 5 considered energy windows, namely the three side-bands SB1, SB2, SB3 and the two line-bands K40 and K42. The M2-data sets are two-dimensional in detector space and run over the two indices j and k. Gaussian prior probability distributions for the 40 K activity are built from radio-purity screening measurements (see reference  [5]  Sec. 5). For 42 K, for which no screening information is available, uniform priors are adopted, with the exception of the two 42 K components located on the n + contact surface of enr BEGe and enr Coax detectors.
5 MeV almost all registered events are due to α-emitting isotopes. The respective part of the full likelihood can be approximately factorized and studied separately. α-particles have a very short range in LAr as well as in germanium (continuous slowing down approximation, CSDA, range of 50 µm and 20 µm, respectively  [47] ) and are able to reach a detector's active volume only through the very thin (of the order of 500 nm) p + contact surface. Therefore, the α-emitter contamination is detector-specific and depends only on the p + surface contaminations. Therefore, we analyze the enr BEGe and enr Coax detector data separately in energy space; the projection in detector space bares no correlation between detectors and hence contains no further useful information. The number of events in a single detector is not sufficient to further split the data on a detector-by-detector basis.
5 MeV to 5. 25 MeV. Events with an energy higher than 5. 25 MeV are put in a single overflow bin: L α (λ 1 ,. , λ m | n) = N bins i=1 Pois(n i ; ν i ) (2) A flat prior probability is assigned to each of the fit parameters λ i. Both data sets are fit separately with a fixed bin size of 10 keV 10  as the α-contamination is detector individual and the two single-detector data sets are uncorrelated in the respective energy window. The fit results are shown in Fig. 9  and listed in Tab. 8. The 210 Po component is modeled with a combination of p + contact thicknesses from 400 to 600 nm for the M1-enrBEGe data set and from 300 to 700 nm for the M1-enrCoax data set in steps of 100 nm. Further 210 Po components are rejected by a Bayes factor analysis. Impurities belonging to the 226 Ra chain are mostly located on ANG2 and thus a fit of the M1-enrCoax data set using a single p + thickness describes this component well.
For the M1-enrBEGe data set we observe a very small number of counts for the 226 Ra chain, therefore, also in this case a single component is sufficient. We determine a best-fit value of 100 nm and 500 nm, respectively. The estimated p-value for M1-enrBEGe is 0. 2 whereas the p-value for M1-enrCoax is 0. 3. The dominant spectral component below 4. 5 MeV is due to degraded α-events which extends down to the ROI.
B. 1 Time distribution of α-events The time distribution of 210 Po decays is well known to be exponential, however, in the presence of a 210 Pb contamination a constant contribution can also be observed. 210 Pb, decaying to 210 Po, feeds a constant 210 Po component once their decay rates stabilize in a secular equilibrium. To disentangle the two we fit the time distribution of events with energies between 3. 5 MeV and 5. 25 MeV with a constant C and an exponential function: where T 1/2 = (138. 4 ± 0. 2) days is the half-life of 210 Po. We use a Poisson likelihood function corrected for data acquisition dead time  [50]  and model the time bin content as follows f (t) = C + N exp - log 2 T 1/2 t 1 - ν i = f LT i Cδt + N τ exp - t 0 + iδt τ -exp - t 0 + (i + 1)δt τ C and N are the amplitudes of the constant and the exponentially decaying components and are the only two free fit parameters.
InSAR Data Processing Using available synthetic aperture radar (SAR) data from ascending and descending passes of the ALOS satellite (Table  1 ), we analyze the surface deformation on Leyte during the 2007 to 2011 interseismic period. Because there has not been any large earthquake in the area in the 70 yr prior to the 2017 earthquake, we do not expect significant postseismic transient motion, and we can be confident that the ALOS measurements capture only interseismic motion. ALOS L-band SAR data (wavelength,  = 23. 6 cm) has the advantage of being able to produce coherent interferograms in vegetated areas despite long temporal baselines (e. g. , Pousse  Beltran et al. , 2016; Sandwell et al. , 2008) , unlike SAR data at shorter wavelengths (e. g. , C and X bands). We used the GAMMA Software to produce unwrapped interferograms from single-look complex images (SLC).
Further, the elongated elliptical shape of the subsidence suggests a northwest trending source. We therefore inverted for a horizontal rectangular source (sill) in an elastic half-space  (Okada, 1985)  (Figure  6b  and Table  2 ). We constrained the source parameters using the Geodetic Bayesian Inversion Software (GBIS;  Bagnardi & Hooper, 2018). We determined the optimal source parameters, including length, width, depth, strike, position, and rate of opening. GBIS uses a Markov chain Monte Carlo (MCMC) sampling, controlled by a Metropolis-Hastings algorithm, to approximate the posterior PDF from the parameter space based on a priori assumptions of the source  (Bagnardi & Hooper, 2018, and references therein). The prior PDFs are expressed as uniform probability density functions. As the subsidence source in Tongonan is unknown, we set broad bounds for the prior PDFs to allow exploration of different sill geometries between 0 and 5 km depth (a priori bounds are summarized in Table  2 ).
However, only one Sentinel-1 acquisition was realized between the two events, so information is available for only one line-of-sight in constraining the separate slip distributions of each event. ALOS-2, on the other hand, has only premainshock and postaftershock acquisitions, but data are available on both ascending and descending tracks. The modeling strategy we have developed to invert for the mainshock and aftershock slip distributions using all available interferograms (similar to  Ragon et al. , 2019)  is described in the following section. This approach exploits the advantage of using different look geometries from distinct satellite tracks, providing more constraints to infer fault displacements.
InSAR Data Processing We used Sentinel-1 interferograms generated by the LiCSAR semiautomated processing chain  (González et al. , 2016). LiCSAR automatically combines bursts and subswaths of Sentinel-1 SLC images within predefined frames to create interferograms using GAMMA Software with minimal manual input. The interferograms were multilooked by 20 and 4 in azimuth and range, respectively, roughly equivalent to pixels of around 60 m × 60 m pixels. We took the unfiltered interferograms from LiCSAR frame 061D_07906_131313 (derived from Sentinel-1 descending Track 61 data) that covers Leyte. In the processing, we included the NAMRIA IfSAR DEM (the same as in section 3. 1) for topographic corrections and geocoding. The ALOS-2 interferograms, as in  Fukushima et al. (2019) , were processed using the RINC software  (Ozawa et al.
Inverting for Mainshock and Aftershock Slip In order to obtain models of the mainshock and aftershock slip, we set up the inverse problem considering the different data sets (Table  3 ). The long temporal baseline of the ALOS-2 interferograms means that they include some interseismic (preseismic) and postseismic slip. For example, the premainshock acquisition of the ALOS-2 ascending Track 24 is almost 1. 5 yr prior to the M w 6. 5 event, which means that there could be up to almost 6 cm of left-lateral displacement on sections that slip aseismically. To separate the mainshock, aftershock, and preseismic and postseismic slips, we write the direct problem as On the left-hand side, the d vector contains the five LOS displacement observations including only one or both of the mainshock-and aftershock-related signals (superscripts M and A, respectively), along either the ascending (subscript a) or descending (d) LOS from the ALOS-2 and Sentinel-1 satellites.
The G matrix is a combination of Green's functions that relate slip on the fault from different source patches of the fault (m) to the observations in d. m M is the distribution of the mainshock coseismic slip, m A is the distribution of the aftershock coseismic slip, and m P is the distribution of the sum of preseismic and postseismic slip recorded by the ALOS-2 interferograms. We also include terms in G and m that allow us to fit a residual ramp for each individual data set. We used the same fault parametrization of the Northern Leyte Segment as in the interseismic model (section 3. 4. 2), but use only the onshore patches. Our prior PDF is uniform for all strike-slip parameters, between -5 and 300 cm, and Gaussian for the dip-slip component (mean at 0 cm and the 3 probability range within -200 and 200 cm).
Data Availability Statement The ALOS data set for the interseismic period is ©JAXA, METI and includes data accessed through ASF DAAC (https://www. asf. alaska. edu). Sentinel-1 coseismic interferograms contain modified Copernicus Sentinel data (2017), freely available at https://scihub. copernicus. eu/, with LiCSAR data processing done using CEDA/JASMIN cluster facilities. ALOS-2 interferograms are from the PALSAR-2 data shared among the PALSAR Interferometry Consortium to Study our Evolving Land Surface (PIXEL). These data were provided by the Japan Aerospace Exploration Agency (JAXA) under a cooperative research contract with the Earthquake Research Institute of the University of Tokyo. The 5-m resolution DEM of Leyte island is courtesy of the National Mapping and Resource Information Agency (NAMRIA), Philippines.
Methodological Approach and Objectives Starting from the in-depth knowledge of the pilot sites, the methodological approach (Figure  1 ) planned within the project will lead to the improvement of the already existing management practices for UCH sites, giving as main results innovative tools and materials for promoting conservation strategies of underwater sites  [25] [26] [27] , as well as their knowledge, promotion, and fruition in a sustainable way. This approach will be driven by several steps which can be summarized as follows: (a) the realization of a network of knowledge and exchanges of skills through training activities; (b) the analysis and study of the three pilot sites (from historical/archaeological, environmental, geological, biological, etc. point of view) along with the recording of data useful for 3D reconstruction and mapping; (c) the implementation of monitoring systems for the underwater environment through the realization of a platform by using artificial intelligence (AI); (d) the testing of antifouling/protective materials/coatings suitable to limit decay phenomena (according to the type of selected objects within the pilot sites); and (e) the implementation of robotic solutions for the inspection, documentation, and monitoring of UCH.
Investigation actions on materials will focus on stone and metals which are the heritage materials prevailing at the three sites. The latter ones will be first evaluated from a historical and archaeological point of view; then environmental, physical, chemical, and biological conditions will also be taken into consideration, sharing and comparing data and protocols used for data collection. Additionally, there will be an exchange of best practices and regulations related to the study of UCH in Europe and worldwide, with the final aim to share a common methodology. Data will be used in order to create underwater historical/archaeological maps to be used as additional tools for UCH management and will be drawn up and improved also on the basis of existing studies and recent fieldwork, covering all the latest data and research.
The creation of such a detailed map will increase the knowledge of selected submerged sites and will also have a strong socio-economic and tourism impact. Finally, thanks to the development of innovative opto-acoustic multi-resolution techniques focused on 3D reconstruction of underwater archaeological sites, additional data for site mapping will be made available, providing in this way a cost-and time-effective, accurate, and fully-detailed means of surveying  [28] [29] [30] [31] [32] [33] [34] [35]. The second objective concerns the development of a decision-support tool for UCH risk assessment in changing environments. Monitoring of historical/archaeological sites is necessary in order to know their state of conservation and assess causes of deterioration processes in terms of driving environmental/pollution parameters.
Nowadays, the use of sensors and probes is one of the most cost-effective methods for monitoring environmental changes as well as for assessing the state of preservation of archaeological items and sites in underwater environments  [32]. Within this objective, a decision-support tool for risk assessment of UCH that will integrate environmental monitoring by the application of existing tools and models will be developed. A platform to integrate data monitored by different commercial probes and sensors will develop, also by using other open data sources. This platform will be able to support heritage managers in the definition of appropriate monitoring approaches and solutions for the sustainable preservation of underwater archaeological remains. Further, the acquired data will be elaborated in order to propose possible models of deterioration of stones and metals in underwater conditions due to major environmental changes (e.
g. , temperature, pH, water salinity). The platform developed will collect all these data in order to build a knowledge base with information about the decay evolution of the samples and the environmental conditions. This data will be processed by the means of AI techniques to infer subjacent relations among the parameters registered and to perform a comparative data analysis able to define which environmental aspects have a deeper impact on the preservation of the underwater heritage. With this information, the decay process can be modeled, and an accurate preservation protocol can be incorporated into the platform, as an intelligent tool (decision-support system)  [36]. The third objective is related to conservation studies, protocols, and suitable procedures for preservation/conservation activities.