dataesr/scientific-paragraphs-categorization huggingface dataset:
Once time/depth converted, 2D linear features (fault traces and top/base reflectors) picked all along the seismic dataset (Supplementary Figure  3B ) were modeled as 2½D features using common interpolation methods (e. g. , kriging and IDW, Supplementary Figure  3C ) operated into the Move 2019. 1 geomodelling software package (Petex Ltd. ). Bathymetric fault expression allowed us to further constrain the along-strike fault geometry (see Supplementary Material). The local stress field orientation was derived by structural measurements performed on-land in the adjacent coastal sector. Structural data consist of mesofaults and fracture planes attitude and kinematic indicators mainly collected along the outcropping Miocene carbonate and Quaternary top sequences (see Supplementary Table  2 ). Even if the weak lithology (coarse sandstones, gravels, etc.
) of Quaternary deposits does not usually favor the development or preservation of kinematic indicators, movement directions along faults planes have been obtained by tectogrooves, Riedel fractures, and rare slickenlines. All data were digitally stored on field by using Field Move Mobile App and finally analyzed and plotted using the FaultKin eight software  (Allmendinger et al. , 2012; Cardozo and Allmendinger, 2013)  by adopting Schmidt's lower hemisphere projection. Obtained stress field orientation was compared to other data concerning the modern deformation field at local and regional scales and finally applied on the 2½ D modeled fault surfaces to predict their tendency to the tectonic reactivation.
Deformation Pattern and Fault Activity Displaced reflectors observed throughout the entire seismic dataset (Supplementary Figure  1B ) and the analysis of the available bathymetric data (i. e. ,  Gutscher et al. , 2017  and Emodnet database) have allowed depicting the tectonic pattern that characterizes the northernmost sector of the Malta Escarpment. The recognized tectonic structures form an array of sea-dipping, NNW-SSE trending, dip-slip faults extending offshore from Catania (in the north) to Siracusa (in the south) for a total length exceeding 60 km (Figures  4 e-f ). Within the system, three major faults segments (F1, F2, and F3 in Figure  4A ) and minor faults (F4 and F5) have been structurally and geometrically characterized following their offsets in seismic lines and their bathymetric expression (see Supplementary Material).
Diffuse minor splays associated with the major bounding faults internally deform the down-faulted block. The interpreted seismic dataset (see Supplementary Figures  1  and 2 ) revealed the continuity of the detected faults all along the offshore E Sicily, allowing to map accurately their spatial distribution (Figure  4E ) and to follow their bathymetric expression at the seafloor (Figure  4F ). It is worth noting that a prominent structural culmination occurs to the east of the analyzed fault system, bounding the turbiditic valley (the uplifted area of  Argnani and Bonazzi, 2005). It consists of ∼10 km-wide folded sectors interpreted as a positive flower structure related to the recent activity of the North Alfeo Fault System by  Gutscher et al. (2016)  or as a forced fold related to the upraise of a mantle-derived serpentinite diapirs by  Polonia et al.
Fault Displacement Analysis and Deformation Rate With the aim of estimating the deformation rate of tectonic structures characterizing the MESC, a fault displacement analysis was performed on the time/depth-converted seismic dataset (see Supplementary Figure  2 ). The displacement produced by the activity of the main detected faults (F1, F2, and F3) was measured considering the vertical component (throw) and using faultdisplaced reference timelines (S2, S3, and S4-seafloor, see Supplementary Table  3 ). Throws measured along each fault for the S2 (top-MES), S3 (top-PQ1), and the seafloor describe in general an along-strike variable pattern with displacements decreasing from the fault center to the tips (Figure  5 ). Longitudinal trends for F1 and F2 faults define a double bellshaped pattern for the three displaced timelines with relative maxima of throw (about 460 and 300 m, respectively) recorded by the older F1-displaced S2 surface, whereas the younger S3 is less deformed by the same fault as it reaches the maximum offset of about 260 m (Figures  5A, B ).
Tauro-Brucoli horst, structural measurements were performed on the eastern border fault. The structure is well exposed along the coastline where it forms a subvertical sea cliff (Figures  6E-L ) and consists of an ENE-dipping, NNW-SSE trending fault. Slickensides on fault plane dipping at 70 °toward the N65E direction (Figures  6E-2 ) indicate an extensional dip-slip sense of movement according to an ENE trending extension (see diagram in Figure  6E-3 ). Further south, data were collected along the Magnisi Peninsula (station-5, Figure  6A ), a narrow carbonate horst bounded by NNW-SSE trending faults (Figure  6F ). Slickensides on mesostructures planes were collected on the landward side of the peninsula where strikeslip faults with related pull-apart basins and associated fractures occur (Figure  6F ).
FAULT MODELING AND SEISMOTECTONIC POTENTIAL Structural interpretation and interpolation of 2D seismic profiles and time/depth conversion of seismic dataset along with faults bathymetric expression (see Deformation Pattern and Fault Activity and Supplementary Table 1 for adopted velocity model) allowed us to build up a simplified but consistent 2½D model of the faults occurring in the study area (Figure  7A , see also Supplementary Figure  3  for the workflow). Although depth-limited, due to the penetration of the seismic signal in the subseafloor, the model provides an estimate of the geometric parameters (i. e. , length, width, dipping, and strike, see Figure  7B  and Table  1 ) for the shallow portion of the investigated faults. According to the reconstructed model and to the fault displacement analysis, the F1 consists of a roughly 45 kmlong, two-branched structure-oriented N345E with a fault plane dipping toward the ENE at about 45 °.
Geometric parameters F1 F2 F3 Mean  roughly 60 km-long fault with a N352E-oriented fault plane dipping toward the east at 55 °(see pole/attitude diagrams in Figure  7B  and Table  1 ). Derived fault geometric parameters were then used as input data to estimate the maximum expected earthquake magnitude for each fault assuming a reactivation where there is a seismic slip along their entire length. Based on the surface rupture length (SRL) and the underestimated rupture area (RA) and considering fault segmentation (i. e. , for F1 and F2, see section 4. 3), empirical scaling relationships  (Wells and Coppersmith, 1994; Leonard, 2010)  put forward a high seismic potential for the most continuous F3 faults capable of generating events exceeding magnitude 7 (see also  Trippetta et al. , 2019). (Table  1 ).
, 2006). Finally, our data provide additional information to consider reactivation of the Malta Escarpment as one of the potentially causative processes from which destructive seismic events in the area nucleated (see also  Bianca et al. , 1999; Azzaro and Barbano, 2000; Argnani and Bonazzi, 2005; Argnani et al. , 2012). Assuming a prevailing tectonic component of deformation during the Quaternary, our data could provide useful information to improve databases concerning potential seismogenic sources in Italy (e. g. , DISS or ITHACA) and contribute to a better assessment of the seismic hazard in the densely populated SE Sicily.
Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMAbased studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies.
03; 95% CI, 2. 48-3. 69). When we introduced batch-specific differences in both means and in variances (Figure  5C ), the observed hazard ratio without batch effect mitigation decreased to 1. 90 (95% CI, 1. 66-2. 16) compared to the expected hazard ratio of 3. 0 (Figure  5F ; Supplementary file 1c). Batch effect mitigation methods that only focus on means (approaches 2-4) reduced but did not fully eliminate bias, with hazard ratios ranging between 2. 67 and 2. 70. Methods that address differences in both mean and variance resulted in less bias, with an observed hazard ratio of 3. 11 (95% CI, 2. 54-3. 81) for approach 6 (quantile normalization). We also included two stratification-based approaches. Fitting survival models separately by batch, followed by inverse-variance pooling (approach 8), resulted in approximately unbiased estimates but was less efficient than other approaches, comes with a risk of sparse-data bias, and resulted in considerably wider CIs in our simulation.
Plasmode simulation We evaluated the impact of batch effect mitigation approaches on known, investigator-determined biomarker-outcome associations using plasmode simulation, an approach used, for example, for evaluating confounding control for binary exposures in pharmacoepidemiology  (Franklin et al. , 2014). We used observed data from all tumors included on the 14 TMAs to determine covariates (Gleason grade, pathological stage) and outcome (lethal disease), preserving the observed correlation structure (e. g. , joint distribution of clinical characteristics across TMAs). The only simulated elements were the biomarker levels and the strengths of biomarker-outcome associations (hazard ratios ranging from 1/3 to 3) that we fixed by simulating event times with flexible parametric survival models  (Crowther and Lambert, 2013).
Acknowledgements The authors thank the participants and staff of the HPFS and the  PHS  for their valuable contributions. In particular, the authors would like to recognize the contributions of  Liza Gazeeva ,  Siobhan Saint-Surin ,  Robert Sheahan ,  Betsy Frost-Hawes , and  Eleni Konstantis. The authors would like to thank the following state cancer registries for their help: AL , AZ, AR, CA, CO, CT, DE, FL, GA , ID, IL, IN, IA, KY, LA,   ME, MD, MA , MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI , SC, TN, TX, VA, WA, and WY. The authors assume full responsibility for analyses and interpretation of these data.
Funder Grant reference number Author  DOD Prostate Cancer  Research  W81XWH-18-1-0330   Konrad H Stopsack Prostate Cancer Foundation Young Investigator  Award  Konrad H Stopsack Stephen P Finn Tamara L Lotan Lorelei A Mucci Kathryn L Penney The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Data availability The batchtma R package is available at https://stopsack. github. io/batchtma and Comprehensive R Archive Network (CRAN). Code used to produce results this manuscript is at https://github. com/ stopsack/batchtma_manuscript (copy archived at swh:1:rev:a588f10906f8685b055e5a6f0a487f5f850 d13bc,  Stopsack, 2022). Data are available for analysis on the Harvard FAS computing cluster through a project proposal for the Health Professionals Follow-up Study (https://sites. sph. harvard. edu/hpfs/ for-collaborators).
Data availability The batchtma R package is available at https://stopsack. github. io/batchtma and the Comprehensive R Archive Network (CRAN). Code used to produce results this manuscript is at https://github. com/ stopsack/batchtma_manuscript (copy archived at swh:1:rev:a588f10906f8685b055e5a6f0a487f5f850d 13bc). Data are available for analysis on the Harvard FAS computing cluster through a project proposal for the Health Professionals Follow-up Study (https://sites. sph. harvard. edu/hpfs/for-collaborators).
Kumar et al. [22]  were the first to use SSM for irrigation mapping by exploiting the differences between coarse-resolution microwave SSM products (ASCAT, AMSR-E, SMOS, WindSat and ESA CCI) and SSM modeled by a land surface model that does not account for irrigation. Based on a similar approach, Malbéteau et al. [23]  assimilated coarse-scale SSM products at 1 km disaggregated from SMOS (Soil Moisture and Ocean Salinity) products into a simple land surface model forced by precipitation only. Irrigated areas were thus detected using the cumulative analysis increment (difference between the analyzed and the background value of SSM) during the season. Some other studies combined both optical and SAR observation  [24] , in particular since the launch of radar Sentinel-1 (S1) and optical Sentinel-2 (S2)  [25] [26] [27] ; see also, for instance, the study carried out in Tunisia by  [26]  that showed a better irrigation mapping accuracy when using SSM derived from S1 than when using S2 data only.
Irrigation and Meteorological Data An automatic weather station is installed near the fields of study. The weather station measures continuously the air temperature, solar radiation, relative humidity, wind speed and precipitation amounts with a time step of 30 min. For the flooded irrigation fields, water is brought to the fields by a network of concrete channels with different sizes and flow rates. The amount of irrigation over each plot is determined from the flow rate of the canal knowing the opening duration of the channel through surveys in the field. For the drip-irrigated fields, irrigation amounts are measured by flowmeters. The owner of D1 and D2 uses the FAO-56  [40]  approach to determine the irrigation amount and timing. A flowmeter was installed downstream of a valve that controls the distribution of water in the drip pipes  [43].
In Situ Surface Soil Moisture Measurements of surface soil moisture (SSM) were recorded using Time Domain Reflectometry (TDR) sensors located at a depth of 5 cm in each field. The sensors collected data every 30 min. D1 and D2 are equipped with a sensor under the drippers and a second is installed between two rows. The average of the two sensors is considered as the SSM of the field. Within the other fields (F1-F3 and R1), only one sensor is used. The calibration of these sensors has been carried out using the gravimetric technique (see  [17, 35, [44] [45] [46]  for details). It should be mentioned that even if in situ SSM measurements are expected to be of better quality than C-band satellite estimates in particular when the vegetation cover is dense  [17, 47] , in situ observations are also associated with errors, mainly of spatial representativeness.
The approach was validated using a large in situ database including the drip fields D1 from Morocco and other fields in Tunisia. Reasonable metrics were obtained with R = 0. 74, RMSE = 0. 06 m 3 /m 3 and bias = 0. 01 m 3 /m 3. For more details about the approach, the reader is referred to the original paper  [17]. Among the two incidence angles available on D1 (35. 2 • and 45. 6 • ), SSM used in this study is derived from descending orbit #52 (35. 2 • incidence angle) since the lower incidence angles are more suitable for SSM retrieval  [17, 49]. D2 is not considered because the special growing conditions affected the geometry of the field and therefore affect the modeling of the radar signal leading to inaccuracy in the SSM retrieval (see  [17]  for more details). SSM for F2 and F3 is retrieved from orbit #45 (~40 • of incidence angle) and from orbit #154 at 40 • of incidence angle for R1.
Sentinel-2 Data Sentinel-2 is an optical satellite composed of two constellations S2A and S2B launched by ESA in 2015 and 2017, respectively. With a revisit time of 5 days (using S2A and S2B), Sentinel-2 provides images composed of 13 bands among which the bands 4 and 8 are used in the computation of the Normalized Difference Vegetation Index (NDVI). The products corrected from atmospheric effects are processed by the CESBIO's MAJA processor  [51]  and distributed as Level 2A products by the National Centre for Space Studies (CNES) via the open access platform (https://peps. cnes. fr/, accessed on 3 July 2021) or directly from the THEIA website (https://theia. cnes. fr/, accessed on 3 July 2021). The NDVI is computed for D1, D2, F2, F3 and R1 (seasons 2016-2017 and 2017-2018) from S2. For 2014-2015 season on R1, the MOD13A2 products used in  [52]  are also used here.
Methodology Daily to seasonal irrigation timing and amounts are estimated at the field scale based on SSM data assimilated into the FAO-56 through a particle filter technique. The approach is implemented in three steps:  (1)  synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM, and the a priori constraints on the irrigation scenarios for different irrigation technics (flooding and drip); (2) the method is evaluated using in situ SSM using three assimilation window lengths to mimic the revisit time of the available soil moisture products derived from existing and potentially future remote sensing sensors on polar orbiting satellites (3, 6 and 12 days); (3) the approach is evaluated using SSM products derived from S1 data.
In this study, the algorithm is run on assimilation windows of a duration equal to the data revisit time (6 days or 12 days for S1 SSM products depending on the site). A sensitivity analysis to the revisit time is conducted using in situ measurements by varying the windows length from 3 days (revisit time of SMOS product) to 12 days (revisit time of S1 when S1A only was available before 2016). An ensemble of N particles is stochastically generated at the beginning of each assimilation windows. Each particle is associated to an irrigation scenario generated stochastically. The amounts and the day of irrigation (timing) are generated stochastically using a uniform distribution as no information are available on both variables. For flood irrigation, the minimum time between two irrigations is defined to a value of n days (a sensitivity analysis to this constraint is carried out in the Section 4).
Synthetic Experiments The objective of the synthetic experiments is to investigate the sensitivity of the algorithm to some important parameters of the approach including (i) errors on the SSM measurements, (ii) window length or observation frequency and, (iii) irrigation timing constraints. For this purpose, a set of synthetic observations of SSM were generated using the FAO-56 model by perturbing the model's parameters. Two experiments were carried out, one for flood and a second for drip irrigation. For the flood experiment, 6 irrigation events of 60 mm were applied throughout the season with an interval of 15 days between two successive events, which corresponds to the typical frequency of the classical flooding technique. For the drip experiments, irrigation data recorded on D1 during the 2016-2017 growing season were used.
Conclusions and Perspectives A new approach for estimating irrigation amount and timing at the field scale by assimilating SSM in the FAO-56 using a particle filter algorithm is developed and evaluated over wheat fields located in the center of Morocco and irrigated with different techniques including drip and flood irrigation (in addition to non-irrigated fields). This method is based on Sentinel-1 data only and can avoid the use of optical data that are prone to atmospheric perturbations. In a first step, a synthetic experiment showed the need for additional information to the retrieval algorithm such as a time constraint for the flood irrigation technics when the revisit time of SSM is short (3 days). The approach is then evaluated using in situ measurements of SSM mimicking revisit times of 3, 6 and 12 days.
Close results are finally obtained by using S1 SSM products and in situ measurements for the retrieval of 15-day cumulative amounts over the drip field D1: performance metrics are R = 0. 64 (0. 72) and RMSE = 28. 7 mm (27. 1 mm) using S1 products (in situ measurements). The assessment of the approach should be extended to other sites and climate conditions regarding the low number of data sets matching the period of Sentinel-1. Nevertheless, the databases providing with consistent irrigation water amounts at the field scale are very limited. Extending the validation fields could also allow analyzing the performance of the algorithm by growth stage as satellite SSM products are known to be of poor quality when the vegetation cover is dense. Even though reasonable results were obtained, some limitations related to the use of moisture of the upper soil layer are also highlighted.
, m), (4) K d -ω 2 ds M 􏼐 􏼑Φ d s � 0, (s � 1, 2,. , m), ( 5 ) where K, M, ω 2 hs , and Φ s of the intact structure represent the global stiffness matrix, the global mass matrix, the s th natural frequencies, and the s th mode shape, respectively. It is assumed that the change in mass before and after the defect is negligible, K d , ω 2 ds , and Φ d s still represent the global stiffness matrix, the s th natural frequencies, and the s th mode shape, but for the defective structure. In this 2D FEM of the cantilever beam, ω 2 hs and ω 2 ds can be obtained, respectively, from solving the eigenvalue problems (4) and (5) by using MATLAB eig function. en, the expression f s � (ω s / 2π) (Hz) is used to calculate the natural frequencies before and after the defect. Assuming that the element stiffness of the structure decreases evenly after the defects, the Young modulus reduction factor expressed in the following equation is used to simulate the defect degree of the structure  [25] : α j � E -E j E , ( 6 ) where E and E j are the Young modulus of the j th (j � 1, 2,.
Final linear system The coupling equation between the Composite-Rigid-Body Algorithm and the weak-scatterer approach is formed of Eq. (  26 ), (  39 ) and (  47 ) and leads to a linear system  =. The time-stepping is achieved using a fourth-order explicit Runge-Kutta scheme. The Composite-Rigid-Body Algorithm is implemented in C++ in the numerical tool InWave while the weak-scatterer approach is implemented in Fortran 90 in the solver WS_CN. Thus, a language binding is mandatory because of the two different programming languages. The co-simulation is driven in a Python environment. Cython makes the language binding between the C++ source code  (Behnel et al. , 2011)  and the Python glue code whereas f2py does the same for the Fortran 90 source code  (Peterson, 2009). The creation and the solving of the tight wave-structure coupling along with the temporal loop of the co-simulation are achieved by a Python numerical tool named InWaveS_CN.
Lowering velocity rise A lowering velocity rise is now applied to study the effect of the unsteadiness and the body nonlinearities in a lowering operation. The   respectively. As all the simulations have the same duration, the more important the lowering velocity is, the deeper the payload goes down, the more important the distance is to the equilibrium position of the payload where the hydrodynamic database was computed. It can be seen that the weak-scatterer approach and the linear potential flow theory match as long as the payload is lowered on a small distance  relative to its size. Thus, until a lowering velocity of 0. 003 m/s, which represents a lowering distance of 0. 45 time the radius of the payload, the two hydrodynamic theories agree. But, above this value, some differences appear.