dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 13  illustrates the CDF of positioning error achieved by the proposed positioning scheme versus the transmit SNR. Note that the bandwidth Error (m) (0. 045,0. 9) (0. 845,0. 9) (0. 0185,0. 9) CDF of LED is set as 50 MHz and the reconstructed sampling rate is 2 GHz. Moreover, the transmitter power is fixed at a specific value in the semi-physical experimental measurement. Subsequently, different levels of additive white Gaussian noise are added to the actual signals measured by the oscilloscope during MATLAB processing to simulate the desired SNR. It can be seen that the positioning performance increases with the increasing transmit SNR. Specifically, positioning performance corresponding to 50 dB, 30 dB, and 15 dB transmit SNR is 1. 7 cm, 4. 5 cm, and 16. 8 cm, respectively. The experimental results further demonstrate that the proposed positioning scheme achieves high positioning performance even under low bandwidth conditions.
This work aims to assess the effectiveness of machine learning (ML) algorithms and semi-empirical models for surface soil moisture (SSM) retrieval by exploring the Sentinel-1 backscatter and interferometric coherence data. Firstly, three commonly used categories of ML algorithms are evaluated using data gathered from diverse rainfed and irrigated wheat fields located in Morocco and Tunisia. Specifically, these algorithms include: artificial neural network (ANN), deep neural network (DNN), three support vector regression (SVR) models (radial basis function (SVR rbf), linear (SVR linear) and polynomial (SVR quad) kernels) and two tree-based methods (Random Forest (RF) and XGBoost). The comparison between predicted and measured SSM showed that the best retrieval results were obtained using Sentinel-1 data at VV polarization with R ranging between 0.
68 and 0. 76 and RMSE of 0. 05m 3 /m 3 and 0. 06m 3 /m 3. Secondly, to further assess their transferability, the ANN, SVR rbf and XGBoost that demonstrated the most favorable results from each category were evaluated and compared against the coupled Water Cloud and Oh models (WCM), using a second dataset collected over a drip-irrigated wheat field in Morocco. Overall, the best retrieval results were achieved by ANN and SVR rbf with R and RMSE of 0. 81 and 0. 034m 3 /m 3 , respectively. In addition, their performances were consistent with that of WCM which yielded R and RMSE values of 0. 81 and 0. 04m 3 /m 3 , respectively. Finally, due to its good compromise between retrieval accuracy of SSM, processing time and simplicity, SVR rbf was chosen to generate high-resolution SSM maps from Sentinel-1 data over irrigated wheat fields.
Most of the previous studies focused on the joint use of radar data and optical data (as a descriptor of vegetation) as input features for training their ML algorithms and thus retrieving SSM  [19] ,  [40] -  [43]. However, a severe lack of optical data observations may occur in the event of prolonged cloud cover, which is quite frequent when crops, particularly cereals, are cultivated during the wet season, as in the South Mediterranean regions. To deal with this issue,  [44]  have proposed framework for unsupervised deep domain adaptation for radar and optical satellite imagery with cycle-consistent adversarial network (cycleGANs). In the same vein, using a large in-situ database collected from several irrigated and rainfed wheat in Morocco and Tunisia countries,  [45]  have developed a new approach to predict SSM, based only on two complementary and relatively independent information extracted from Sentinel-1 radar including backscattering coefficient and interferometric coherence (ρ).
A. Experimental data In order to test the potential of the ML algorithms and their transferability to retrieve SSM, several rainfed and irrigated wheat sites located in the Haouz plain (center of Morocco) and Kairouan plain (center of Tunisia), were used in this study (Figure  1 ). Both sites are characterized by a semi-arid Mediterranean climate with high potential evaporation (about 1600 mm per year), greatly exceeding the annual rainfall which ranged between 250 and 300mm. Descriptive details of both sites are detailed in  [31]  and  [45]. SSM measurements were conducted using time domain reflectometer (TDR Campbell Scientific CS616) and handheld theta probe sensors at different wheat fields in Haouz and Kairouan sites, respectively. In addition, these measurements were properly calibrated using the gravimetric method.
Data used in this study were recorded from three sites (see table I): 1) Kairouan site: 18 fields of irrigated (Sprinkler technic) and rainfed wheat located in the Kairouan plain were selected  [31]. Twenty handheld theta probe samples were recorded from different locations in each field and were averaged per field. These measurements were performed at topsoil (i. e. a depth of 5cm) and taken at the same time of Sentinel-1 acquisitions over the chosen fields. 2) Sidi Rahal site: One rainfed wheat field situated approximately 40 km east of Marrakech city (Haouz plain) was selected  [50]. Automated SSM measurements were obtained through the use of two TDR sensors positioned at a depth of 5cm and subsequently averaged. 3) Chichaoua site: Two irrigated wheat fields (referred to hereafter as F1 and F2) within a private farm were selected.
This farm is situated near the province of Chichaoua at 65 Km west of Marrakesh city (Haouz plain). Data was recorded in these fields during three growing seasons. The first field was monitored during the two growing seasons 2016-2017 and 2017-2018, while the second one was monitored during the 2018-2019 growing season. In both fields, drip irrigation was applied, with a flow rate of 7. 14 mm/h for each dripper. Automated SSM measurements were collected using two TDR sensors positioned between and beneath the drippers at a depth of 5cm. These measurements were then averaged to offer a more representative value for the Sentinel-1 pixel. Note that in this work, SSM data collected over Kairouan and Sidi Rahal fields were used for the training and validation process of the ML algorithms, while that collected over Chichaoua site (i.
B. Satellite data This study used the Sentinel-1 radar data for retrieving SSM including the backsacttering coefficient and the coherence at VV and VH polarizations. Sentinel-1 consists of two identical satellites, Sentinel-1A and Sentinel-1B. Sentinel-1A was launched on April 3, 2014, while Sentinel-1B was launched on April 25, 2016. Both satellites are equipped with a Cband synthetic aperture radar (SAR) system in three different imaging modes, providing all-weather and day-and-night imaging capability with a revisit time of six days  [20]. The Interferometric Wide-swath mode (IW) is the main operational imaging mode over land surfaces, realizing acquisitions in VV and VH polarizations over a wide-area coverage with a swath of up to 250 km and a resolution of up to 5x20 meters. Please note that, level-1 products, which can be directly downloaded from the Sentinel-1 mission's official data hub, are typically distributed in the Single Look Complex (SLC) and Ground Range Detected (GRD) formats.
Herein, a brief description of the GRD and SLC images processing is given, more details are given in  [51]. GDR images were processed at the original 10 m resolution with the Orfeo ToolBox (OTB) software  [52]  in order to compute the backscattering coefficient σ 0. The main processing steps included in this software are: (i) thermal noise removal, (ii) radiometric calibration  [53] , (iii) terrain correction  [54]  using DEM (Digital Elevation Model) SRTM (Shuttle Radar Topography Mission) at 30 m resolution  [55]. Also, SLC images were processed using Sentinel application platform SNAP to derive the coherence which typically ranges between 0 (incoherence) and 1 (perfect coherence). This process includes the following 5 steps: (1) the "Apply-Orbit-file" module to more accurately estimate the satellite's location and speed;  (2)  The orbital data and the DEM SRTM were used to co-register ("Back-geocode") the two consecutive images;  (3)  The application of the coherence module; (4) The TOPSAR-Deburst module was applied to remove the bursts (the black band in the SLC products); (5) terrain correction was utilized to project the images on the earth's surface using the SRTM DEM.
All of these algorithms were trained and validated using data collected from a variety of rainfed and irrigated wheat fields situated in the Sidi Rahal and Kairouan sites. Following a comparison between the retrieved and measured SSM, a model was selected from each category based on its statistical performance metrics (RMSE, R², and BIAS). In the second phase, the selected models were examined for their transferability to improve their usability across diverse conditions, employing different datasets gathered from drip wheat fields situated in the Chichaoua site. Simultaneously, these models were assessed in comparison to Water Cloud Model (WCM), which employs the same inputs, including backscatter coefficient and coherence, along with additional parameters like fractional cover and roughness.
III. RESULTS AND DISCUSSION In this study, the collected data over Morocco and Tunisia rainfed and irrigated winter wheat sites were combined and used to train and validate all machine learning methods. To this end, the data were subdivided into a training (calibration dataset) and a validation sets. The backscatter coefficient and interferometric coherence at both polarization V V and V H were used as input and the measured soil moisture as a target. Using multiple data sites has allowed us to build robust ML algorithms able to generate the simulated SSM from Sentinel-1 data over different regions. Subsequently, to enhance their applicability across various conditions, the most successful algorithms from the three categories were further tested on winter wheat sites with drip irrigation in Morocco.
Furthermore, the positive outcomes of our algorithms provide additional support to recent research conducted by  [45] , which highlighted the capacity of backscatter coefficients and interferometric coherence at VV polarization for soil moisture retrieval through the inversion of the combined Oh and Water Cloud model, utilizing the same dataset. Their obtained values of R, RMSE and Bias ranged between 0. 7-0. 75, 0. 06-0. 08 m 3 /m 3 and 0. 01-0. 03 m 3 /m 3 , respectively. In contrary, the use of the backscatter coefficient and the interferometric coherence at V H polarization showed more dispersion for borh algorithms ANN and DNN compared to V V polarization as seen in Figure  3b. The values of R, RMSE and Bias were about 0. 54, 0. 07 m 3 /m 3 and 0. 00 m 3 /m 3 for DNN simulations and 0.
By the same token, by exploring Sentinel-1 and Landsat 8 data,  [58]  have reported also that the backscattering at VV is more appropriate for soil moisture retrieval with an improvement of about 10% and 21% in R and RMSE compared to VH polarization. Overall, in our specific study, we have noticed that the performance of single-layer ANN mimics the highly complex multi-layers DNN in terms of statistical results at V V polarization. May be this behavior can be related to the fact that multi-layers DNN needs more data and is devoted to more complex problem, particularly when the estimated target depends on several inputs variables. Interestingly, as demonstrated by  [59]  the predictive performance of multilayers DNN increases strongly with the use of a large dataset. In the same vein, as stated in  [60] , the use of single-layer ANN can be considered as potential algorithm for providing satisfactory soil moisture simulations from soil suction rather than the use of highly complex multi-layers DNN, peculiarly under wetting conditions where it overfits the soil moisture.
A large in situ database gathered from several irrigated and rainfed wheat fields in Morocco and Tunisia is used in this work. Specifically, ML algorithms that were evaluated include single-layer artificial neural network (ANN), deep neural network (DNN), three support vector regression (SVR) models (Viz. , radial basis function (rbf), linear and polynomial kernels), and two tree-based methods (Viz. Random Forest(RF) and XGBoost). In a first step, all ML algorithms were trained and validated using data collected over several irrigated and rainfed wheat fields located in Morocco and Tunisia. Based on the comparison between estimated and measured SSM, it was found that ANN, DNN, SVR rbf and XGBoost were the best from each category algorithms to retrieve SSM with a good performance for VV polarization (0.
75 < R < 0. 76 and RM SE = 0. 05cm 3 /cm 3 ). In the second step, an investigation of the transferability of these algorithms is made using a second in situ dataset collected over a drip irrigated wheat in Morocco. The investigation only focused on ANN, SVR rbf, and XG-Boost, as the highly highly complex DNN yielded comparable results. Overall, the ANN and rbf-based SVR algorithms showed to reasonably estimate SSM at VV polarization (R and RMSE of 0. 81 and 0. 034m 2 /m 2 ) and performed slightly well than XGBoost (R and RMSE of 0. 76 and 0. 038m 2 /m 2 ). Likewise, it was found that their performance is comparable to that revealed by the WCM model which demonstrate their potential for retrieving SSM from only radar data. More precisely, due its good compromise between retrieval accuracy of SSM, processing time and simplicity, it can be concluded that SVR rbf can be fairly used in SSM inversion process instead of backscattering modeling even semi-emprical ones, particularly for SSM mapping purposes.
ACKNOWLEDGMENTS The authors wish to thank the projects: IRRIWELL (A novel plant-based approach to esti-mate irrigation water needs and the application of optimal deficit strategy, grant number 01DH21016, 2021-2024), Rise-H2020-ACCWA (grant agreement no: 823965), PRIMA-S2-ALTOS-2018 "Managing water resources within Mediterranean agrosystems by accounting for spatial structures and connectivities" , ASSIWAT, IDEWA, GEANTech.
Tree-Based Algorithms: Random forest regression & XGboost regression Random forest regression (RFR) algorithm consists of creating each tree from a different sample of training dataset. At each tree node, RFR selects a different sample of features for splitting and then runs the decision trees in parallel with no interaction among them. The predictions of all decision trees are averaged to increase the performance of the model. Given an input vector x, RFR algorithm independently generates N regression trees h n (x), (n = 1,. N ), and the final model prediction (FMP) is given by the following equation: F M P = 1 N N n=1 h n (x) (6) XGBoost (eXtreme Gradient Boosting) is an ensemble technique that is also a tree-based algorithm and works through the use of gradient boosting method. The main difference between the two algorithms is that, as mentioned before RFR builds several decision trees independently in parallel form at the training phase while XGboost creates decision trees in sequential form by selecting firstly one decision tree as its base learner.
In-situ TEM and STEM characterization In-situ TEM straining was carried out in a JEOL 2010HC microscope operated at 200 kV, using a Gatan roomtemperature straining holder. MEGAVIEW III CCD camera was used to record microstructural evolution (Supplementary video 6). The TEM results were analyzed using a Pycotem package  [53]. STEM and STEM-EDXS characterizations were performed in an FEI F20 transmission electron microscope equipped with a Bruker XFlash 6TI30 EDX detector. The STEM-EDXS data were analyzed using the software Esprit 2. 0. The samples were prepared by electric discharge cutting, followed by mechanical grinding to 50 μm thick using 4000 grid SiC sandpaper. The samples were then ion-polished using Gatan Precision Ion Polishing System (voltage 4. 8 kV and angle 4 °) at -100 °C until a small hole formed in the specimen.
10 m ). To reveal the deformation mode of the short-processed AZ31 sample, slip trace analysis was performed. Based on the EBSD result, the three Euler angles of particular grains selected from EBSD map can be obtained and input in the MATLAB code for slip trace analysis. By comparing the orientation of a selected trace with the 12 potential traces, which correspond to different slip systems. A detailed introduction to this method has been provided in Appendix 2 and the literature  [73 , 74]. In the in-situ tensile test, slip traces are predominantly found in the grains with a relatively high m basal value ( > 0. 3), and slip traces observed in these grains are identified to be basal slip traces ( Fig. 11  ). The non-basal-oriented grains, where slip traces can be observed, however, only take a small fraction of total grains.
ARTICLE IN PRESS The dislocation nucleation, motion, and annihilation shown in Fig. 12  a-h repeatedly happen with deformation. Given that the particles have an interaction with dislocations and thus affect the plastic deformation of the shortprocessed AZ31 sample, they are further characterized using STEM and EDX ( Fig. 15  ). These particles have a spherical shape. Measured by ImageJ software, the size of particles is about 17. 8 ± 6. 3 nm in diameter. The EDX mappings show that these particles are predominantly Al-Mn particles. The existence of Al 8 Mn 5 particles has been widely reported in Mg-Al-(Zn)-Mn alloys  [77] [78] [79]. The volume fraction of Al-Mn nano-particles, obtained from thermodynamic (PAN-DAT® version 2020) calculation, is about 0. 25%. This value is consistent with the previous publication  [80].
Acknowledgements The authors are thankful for the helpful discussion with  Dr Huizhi Peng ,  Dr Yuman Zhu  and  Prof. Nick Birbilis. This work is the financial supported by the  Science and Technology Innovation Program of Hunan Province  ( 2020RC4013  and  2021JC0005 ), " Technology Innovation 2025" Major Special Project   of Ningbo City , the  Joint Funds of the National Natural Science Foundation of China  (  U22A20187  ), the  Future Fellowship of the Australian National University.
Software, hardware, implementation details In the following numerical experiments, the iteration counts assess the control of the condition number via the adaptive coarse spaces. We also report timings along with iteration counts. We also showcase the fact that the size of the GenEO coarse space adapts automatically to the difficulty of the problem at hand, for example when going from a homogeneous to a heterogeneous problem. We illustrate the efficiency of the method by performing weak and strong scalability tests, using the automatic graph partitioner Metis  [22]  for the subdomain partitioning. The problem is discretized and solved with the open-source parallel finite element software FreeFEM  [17]. FreeFEM is domain specific language (DSL) where the problem to be solved is defined in terms of its variational formulation.
Then the local matrices pA N eu i q 1ďiďN (see eq. (  4 )) and p Ci q 1ďiďN (see eq. (  11 )) are easily obtained by restricting the corresponding variational formulations to adequate local subdomains. Note that these matrices are different from the restriction of the global matrices A and C to the local degrees of freedom. The domain decomposition algorithm presented in this paper is implemented on top of the ffddm framework, a set of parallel FreeFEM scripts implementing Schwarz domain decomposition methods. ffddm already implements the GenEO method  [32]  for SPD problems, and its building blocks are designed to simplify the implementation and prototyping of new domain decomposition methods such as the saddle point solver presented in this paper. The ffddm documentation is available on the FreeFem.
Conclusion and outlook Under the assumption that the diagonal block matrices of a saddle point problem are spectrally equivalent to a sum of positive semi definite matrices, we have introduced an adaptive domain decomposition (DD) method. For this, two coarse spaces are built by solving generalized eigenvalue problems, one for the primal unknowns and the second one for the dual unknowns. The robustness of the method was assessed on a notoriously difficult three dimensional elasticity problem for a steel-rubber structure discretized with continuous pressure. Several issues deserve further investigations. First a multilevel method with more than two levels would enable even larger and possibly faster simulations. Also, the tests were performed with FreeFem scripts using the standalone ffddm  [35]  framework.
The range of GMM median or standard deviation values for the contrasting model is described by a shaded region beneath the GMMs for the model in question. The GMM logic tree adopted for FR2020 is the simplest of the three, using four ground motion models each assigned an equal weight of 0. 25 (Ameri, 2014 1  ;  Abrahamson et al. , 2014; Cauzzi et al. , 2015  [with variable reference 𝑉 !"# ]; Drouet and Cotton, 2015 [using rupture distance and with 10 MPa stress drop for large magnitude events]). Two of these models  (Ameri, 2014; Drouet and Cotton, 2015)  are based exclusively on French seismological data, while  Abrahamson et al. (2014)  is fit to records from the NGA West 2 dataset (global in scope but with most records originating from California), and  Cauzzi et al. (2015)  is fit predominantly to Japanese strong motion data (supplemented by some records from other regions of the globe).
The DE2016 GMM logic tree combines both a standard multi-model approach with elements of a scaled backbone approach to capture some of the uncertainty in the underlying seismological properties of the target region; hence, we refer to it as a hybrid multi-model and backbone GMM logic tree. ground motion prediction for small-to-moderate magnitude events, and iii) the observation of several earthquakes with higher than average stress drop in stable regions of France, Germany and the UK. The multi-model approach and the choice of models selected largely addresses the first two of these issues. Three different datasets (RESORCE, NGA West 2 and Japan) are represented, which also implicitly incorporate GMM source-region to source-region variability (i. e. , Europe, Western US, Japan). The highest weight [0.
The full explanation of the logic tree, including both its motivation and calibration, is given in  Weatherill et al. (2020). This change in approach is motivated in large part by the development of the Engineering Strong Motion (ESM) database and flatfile  (Lanzano et al. , 2019) , which increases by nearly an order of magnitude the number of ground motion records available in Europe, particularly those of small-to-moderate magnitude earthquakes including many more from France and Switzerland than in RESORCE. The backbone GMM is fit to this data set  (Kotha et al. , 2020) , but with such a large volume of data additional random effects are included to capture region-to-region variability in the stress parameter scaling of the model (𝛿𝐿2𝐿 B ) and in the attenuation (𝛿𝑐 " -where 𝑐 " is the coefficient of the anelastic attenuation term of the model).
Acknowledgements The work presented here has benefitted from discussion with, and data provided by,  Emmanuel Viallet ,  Stephane Drouet ,  David Baumont  and  Gabriele Ameri  (FR2020),  Gottfried Grünthal  and  Dietrich Stromeyer  (DE2020), and  Laurentiu Danciu  (ESHM20) and members of the European Facility for Earthquake Hazard and Risk (EFEHR). We thank  Sum Mak  (formerly of  GFZ ) for his proposal and explanation of the Overlap Index. Seismic hazard calculations have been undertaken using the OpenQuake-engine, for which we thank the  GEM Foundation  for their ongoing development and support. Other quantitative analysis used tools from the Python Scientific Stack (including Numpy,  Scipy ,  Pandas ,  Matplotlib , H5Py and Geopandas), and some maps have been prepared using QGIS. This work has been supported by the  SIGMA2 research program  and partially funded by  Électricité de France.