dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding information European Cooperation in Science and Technology , Grant/Award Number:  COST Action   FP1304   PROFOUND ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-10-LABX-25-01 ; Evropské strukturální a investiční fondy, Operační MG acknowledges funding from the  German Federal Ministry of Agriculture and Food  and the  Federal Ministry for Environment, Nature Conservation and Nuclear Safety  through the project " DENDROKLIMA ", funded within the  German Waldklimafonds ,  28W-C-4-077-01. that there is no conflict of interest.
Advantages, limitations, and challenges of three different approaches to model forests: species distribution models (SDMs), individual-based models (IBMs), and dynamic global vegetation models (DGVMs) Advantages Examples (references and links) Bussotti et al. , (2018); FACE experiment (Free-Air CO2 enrichments), Norby et al. , (2016); rainfall manipulation, SDMs allow a quick assessment of potential climate-change vulnerability can serve as a coarse filter for more detailed/process-based approaches are easily applicable to many taxa due to low data and computational demands, as well as available R-packages and methods Grossiord et al. , (2018); Meir et al. , (2015) tree-ring databases, Treydte et al. , (2007), https://www. ncdc. noaa. gov/data-acces s/paleo clima tolog y-data/datas ets/tree-ring; tree sapflow database, Poyatos et al.
, (2016); seed production database, Ascoli et al. , (2017); Muller-Landau et al. , (2008) TRY, Kattge et al. , (2011); sPlot, Bruelheide et al. , (2019) Global Biodiversity Information Facilities, GBIF, https://www. IBMs simulate the growth and demography of every tree in a forest from decades to centuries can easily integrate field data since forest monitoring is mainly done at the tree level are able to simulate dynamics of forest structure and project changes in species composition by including important ecological processes (e. g. , competition between species) can integrate disturbances, climate change and forest management are mostly process-based and therefore useful for extrapolations to new conditions gbif. org/ German national inventory, https://bwi. info/start. aspx?lang=eng; CTFS-ForestGeo, https://fores tgeo.
Diabetes mellitus 2] [43] [44]  Early data reported a higher prevalence of diabetes in patients with severe disease as compared to those with mild to moderate disease (16. 2% vs. 5. 7%). 41, 45 Moreover, the unadjusted case fatality rate of COVID-19 was higher among diabetic patients than non-diabetic patients (7. 3% vs. 2. 3%). 46 As the global COVID-19 pandemic progressed, a similar pattern of worse prognosis in patients with diabetes was reported across European and USA studies. 24, 29, 42, 47 The OpenSafely 29 study showed a linear relationship between measured glycated haemoglobin (HbA1c) level of > _58 mmol/mol (> _7. 5%), recorded in primary care, and risk of COVID-19-related mortality, suggesting an association with hyperglycaemia. 8] [49]  Another UK population-based study using the QResearch database (QCOVID, n = 6 083 102) reported 4.
The experimental result reported in this chapter review the application of (high resolution) Synthetic Aperture Radar (SAR) data to extract valuable information for monitoring urban environments in space and time. Full polarimetry is particularly useful for classification, as it allows the detection of built-up areas and to discriminate among their different types exploiting the variation of the polarimetric backscatter with the orientation, shape, and distribution of buildings and houses, and street patterns. On the other hand, polarimetric SAR data acquired in interferometric configuration can be combined for 3-D rendering through coherence optimization techniques. If multiple baselines are available, direct tomographic imaging can be employed, and polarimetry both increases separation performance and characterizes the response of each scatterer.
Indeed, remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their changes over time, at various spatial and temporal scales. Synthetic Aperture Radar (SAR) is an active remote sensing technique capable to gather data independently of time and weather conditions. Urban scenes are composed of a variety of natural and artificial scatterers. In this sense, polarimetric information is useful for classification, because the polarimetric backscatter from man-made targets varies highly with orientation, shape and distribution of buildings and houses, and street patterns. The first application linked to polarimetric data is then the classification methods for detecting built-up areas and discriminating their different types. Several classification schemes can effectively extract the urban structures by mapping urban related classes with better accuracy than with single-polarimetric data.
2006; Colin-Koeniguer and Trouve 2014)  or a phase scatter separation methods such as  ESPRIT (Guillaso et al. 2005). Then the potential of polarimetry for this application is double: it can first be used for pre-segmentation algorithms and then for improving the estimation of the heights or separating phase centers. Another technique dedicated to 3-D rendering is the tomographic approach that is the extension of the conventional two-dimensional SAR imaging principle to three dimensions. Full three-dimensional imaging of a scene is achieved by the formation of an additional synthetic aperture in elevation by a coherent combination of images acquired from several parallel flight tracks. Once again, the polarimetric extension of this technique has been applied only to airborne data  (Huang et al.
Introduction and Motivation An urban area is characterized by complex man-made structures with heterogeneous scattering objects. When sensed by radar, it exhibits strong backscattering if the radar illumination is orthogonal to buildings. The scattering magnitude from an urban area, in general, is much larger than those from other areas such as rural, agricultural, vegetation, or forest region. It is rather easy to recognize urban areas using the backscattering coefficient even with a single-polarimetric radar. If the detailed information is desired for urban area application, we need to use fully polarimetric data  (Lee and Pottier 2009; Yamaguchi et al. 2005 ). Fully polarimetric data, i. e. , the scattering matrix S, can be expressed in the horizontal (H) and vertical (V) polarization basis.
Polarimetric data analyses on urban areas, up to now, have revealed the following results as shown in Fig. 5. 1. If the polarimetric radar illumination is orthogonal to buildings or building blocks, the scattering mechanism is characterized by the double bounce scattering caused by the vertical building wall surface and horizontal road surface. The co-polarized backscattering (|S hh | 2 and |S vv | 2 ) are strong enough compared to the cross-polarized (|S hv | 2 ) component. On the other hand, if the radar illumination is not orthogonal or parallel to building or building blocks, i. e. , in the case of oblique incidence to building facets, the scattering magnitude significantly reduces, and the scattering mechanism changes from double bounce to single bounce with the generation of the cross-polarized component.
Comparison with Single/ Dual-Polarization Data Since the correlation coefficient in the circular polarization basis and the four-component scattering power decomposition are based on fully polarimetric data, they do not apply to single-/dual-polarimetric data sets. These methods make use of fully polarimetric data and bring full color image by polarization. If single-/dual-polarimetric data are provided, the backscattering information only will be available, resulting in mono-color image.
Thus, particularly in case of regional and national surveys within a short period of time, disaster management, or when data have to be acquired at specific dates, radar systems are more valuable. Western Niigata city, Japan Google photo images PiSAR-X airborne data, X-band, 1. 5 m resolution Thus, the new generation of civil space borne Synthetic Aperture Radar (SAR)-Systems with short revisit can serve as a valuable resource. Promising approaches toward the classification of urban areas include the analysis of multipolarized image analysis. However, to ensure a detailed mapping of urban structures, we need high ground resolution. Indeed, the emergence and recognition of urban remote sensing appear to be linked to the continuous improvement of the spatial resolution offered by generation sensors  (Boehm and Schenkel 2003).
Unfortunately, satellite sensors using the polarimetric mode are often in a degraded resolution. That is why to evaluate the benefits of polarimetry in the urban areas, we try to use here the satellite data with the best achievable resolution, which are achieved today by the TerraSAR-X system. Among the algorithms to detect buildings in polarimetric SAR image, we must distinguish between what is purely statistical  (Pellizzeri et al. 2003; Cao et al. 2011; Deledalle Fig. 5. 10 Four-component power decomposition to Pi-SAR-X data set. The effect of imaging window size can be seen and compared in the decomposition image  et al. 2015)  or imaging techniques  (Wang et al. 2010; Liu et al. 2010)  from what is of interest of polarimetry. Some works are only effective exploitation of spatial characteristics of the image such as line detection, analysis of speckle, region-based and edge-based information, etc.
Temporal Estimation We have seen that for oriented districts, and for some resolution ranges, entropy is high when estimated spatially, and therefore, it cannot be used efficiently for built-up detection or land classification. In this case, we can estimate entropy temporally. If a few years ago, access to polarimetric SAR data on the same site was rare, today the number of revisits over the same site increases and can be used for statistical estimation of second-order parameters. The potential of a temporal estimation has been shown on part of SAR images of San Francisco. We calculated the entropy of parts of the image for three types of estimation: • A spatial average over 3 pixels • A spatial average using a classical 5Â5 pixels • A temporal average using 3 passes The resulting estimation of entropy is presented over an extract of a TerraSAR-X image in Fig.
Comparison with Single-/Dual-Pol Data The contribution of polarimetric data in comparison with single-pol data has been demonstrated in the previous section. As regards the contribution of HV versus dual-pol mode HH /VV, the situation is less clear. The ROC curves comparing the benefits of full polarimetric optimization compared to dual-pol mode presented in Fig. 5. 21 show that • For small false-positive rates (<0. 5), the dual-pol optimization seems to outperform slightly the full polarimetric optimization. • For high false-positive rates higher than 0. 5, the full polarization outperforms the dual-pol mode. However, the contribution of the HV should be highlighted differently. Indeed, it is clear that the contribution can be shown only in cases where the SNR is sufficient. The ROC curves estimated over Toulouse in dualpolarimetric mode are presented in Fig.
Discussion on the Role of Polarimetry and on the Maturity of the Application and Conclusions The detection of man-made targets using polarimetry has a few years now. The contribution of polarimetry to distinguish natural areas from artificial targets is well-known in classification. This application at X-band using satellite data is far more recent with the launch of TerraSAR-X. In this context, polarimetry seems to become less effective for discriminating built-up areas. Indeed, the roughness of roofs and walls, sensitive to this scale, the different materials, and different elements that compose them, all seem to contribute to the depolarization effects or mixtures of mechanisms. In this context, it becomes difficult to predict and interpret the polarimetric answer of the urban areas. The processing of these data at this wavelength is indeed relatively recent.
Methodology To perform a 3-D rendering over the entire image using a PolInSAR data set in a single pass mode, we use the following method: • First, a prior segmentation method is proposed. It is a method of using hierarchical segmentation criteria form (essentially linear edge detection and size criteria). • Then, an optimization method is applied to the whole image, using the calculation of coherence matrices of all pixels belonging to the same segment. We use the coherence optimization described in  (Colin et al. 2006 ) • Locally, for building elevation estimation, we use a scattering phase center separation method, based on the assumption that the resolution cell contains only two main scatterers at two different elevations. The algorithm is described in (Colin-Koeniguer and Trouve 2014).
Experimental Results The 3-D rendering applications are conditioned mainly by two parameters: • The single-pass or multipass mode of acquisitions • The resolution In terms of resolution, it is unlikely to achieve satisfactory results with images of resolutions higher than 3 meters. Thus we will restrict satellite data in case of the dual-pol mode of TerraSAR-X. In this context, we can evaluate the contribution of this partial polarization mode. This data will also allow us to quantify the impact of temporal decorrelation on this type of applications. We will also use data from RAMSES airborne single-pass mode and will be able to quantify the impact of multipass or single-pass mode. In both cases, we select the site of Toulouse city in southwestern France. The Toulouse metropolitan area is the fifthlargest in France, one of the bases of the European aerospace industry.
Test sites and corresponding radar and validation data sets selected for the generation of showcases on 3-D urban rendering are summarized in Table  5. 4 and further described in the Appendix. To validate our estimations over Toulouse with the singlepass mode, we have used a file describing building footprints and their elevation as ground truth for this application. It is a file shapefile, organized as a structure containing a list of polygonal elements. These polygons define the footprint of each building on the ground, and for each element, the minimum and maximum elevation data are given. We select the buildings of our ground truth over Toulouse that are in our PolInSAR image and that are high (> 6 m) and big (> 10 m 2 ) enough. That gives us 140 buildings whose elevation is given with a precision of 1 m, represented in Fig.
Comparison with Single/ Dual-Polarization Data Results indicate that estimation using a single polarization is better for HV polarization whose level is high at this frequency over a wide variety of roof surfaces. When using the partial coherence matrices obtained from HH and VV polarization only, we still can follow the same algorithm and obtain the results reported in Table  5. 6. Results obtained show that the mean error is equal for dual-pol and full-pol case; however, the root mean square error is higher in the dual-pol case.
3-D imaging of a scene is achieved by the formation of an additional synthetic aperture in elevation and the coherent combination of images acquired from several parallel flight tracks using tomographic imaging. This technique directly retrieves the distribution of the backscattered power in the vertical direction and may be applied to estimate forest structure, building height, or layover areas induced by strong terrain slopes or discontinuities in the imaged scene. 3-D SAR focusing using tomographic processing of multibaseline interferometric data sets may be considered as a spectral estimation problem. A wide variety of spectral analysis techniques can be used to perform tomography, ranging from classical Fourier-based methods to highresolution (HR) approaches. A recent study by  (Sauer et al.
Comparison with Single/ Dual-Polarization Data The VV reflectivity tomogram in Fig. 5. 34 (left) shows an incomplete building shape especially on the top of the buildings, leading to an inaccurate estimation of the building height due to missed patterns. The HH tomogram is affected by spurious sidelobes which degrade building height estimation too. However, the tomographic profile obtained from fully polarimetric data set permits to guess correctly building shapes with a consistent height estimation compared to the lidar profile. This fact reveals that fully polarimetric dual-baseline configuration improves significantly the tomographic accuracy, compared with single-polarization ones, and provides additional information, related to scattering mechanisms, which helps to better characterize building features, like geometrical shapes as well as dielectrical properties, etc.
2003; Berardino et al. 2002). In the second approach, the phase quality is associated with the amplitude dispersion index DA of the images  (Ferretti et al. 1999) , often used in urban environments, where it is common to find point-like, deterministic scatterers (called Persistent Scatterers, PS) associated with strong and stable backscattering from man-made structures. The higher the interferometric coherence or, accordingly, the lower the DA, the better the phase quality and, thus, the most reliable the deformation process estimation. Due to the lack of polarimetric SAR (PolSAR) data sets, the use of DInSAR techniques has been traditionally limited to the single-polarization approach. However, the scenario has changed with the launch of new satellites with polarimetric capabilities, such as TerraSAR-X, RADARSAT-2, ALOS-PALSAR, and the upcoming launch of Sentinel-1, ALOS-2, and RADARSAT Constellation Mission.
Improvement of Differential SAR Interferometry for Subsidence Monitoring with Polarimetric Optimization Techniques 5. 4. 1. 1 Introduction, Motivation, and Literature Review The application of polarimetric optimization methods has led to an improvement in the density but also in the quality of the deformation process retrieval  (Pipia et al. 2009; Navarro-Sanchez et al. 2010; Navarro-Sanchez and Lopez-Sanchez 2011a, b, 2012; Iglesias et al. 2012; Monells et al. 2012). This section describes the three polarimetric optimization methods available in the literature when PolSAR data sets are available. The methods are referred as Best  (Pipia et al. 2009) , Equal Scattering Mechanism (ESM)  (Colin et al. 2006)  and Sub-Optimum Scattering Mechanism (SOM)  (Sagues et al. 2000). Their exploitation for DInSAR applications is addressed with both the coherence stability and DA pixel selection criteria.
Experimental Results The PolSAR data set used in this work consists of 34 fully polarimetric RADARSAT-2 images, from January 2010 to May 2012, that correspond to the metropolitan area of Barcelona, Northeastern Spain. RADARSAT-2 works at C-Band, with a resolution of 5 meters in both range and azimuth directions and a revisiting time of 24 days. Selected test sites and data sets are summarized in Table  5. 8 and further described in the Appendix. This section shows the PolDInSAR results obtained using the different polarization optimization techniques described previously with both the coherence and amplitude approaches. Once the phase optimization is performed in the pixel selection step, the classical DInSAR processing can be applied to the new stack of optimized interferograms, since there are no differences from the single-polarimetric case.
10. The ESM approach increases in a factor of seven the number of pixels from the singlepolarimetric case. As in the coherence stability case, this pixels' density increase justifies the use of PolSAR data in the DInSAR framework. Finally, Fig. 5. 36 shows the linear deformation maps using each optimization approach in the area within the city of Barcelona commented before. All approaches are able to determine the deformation bowls with similar values of terrain deformation. The main differences come from the larger pixels' densities obtained when using the advanced polarimetric optimization methods. ESM provides the larger improvement in number of pixels, and consequently the deformation map allows precisely determining the extension of the deformation bowl along the new underground line.
Comparison with Single/ Dual-Polarization Data The usage of fully polarimetric data imposes some limitations in the sensors regarding the swath extension, basically reduced by a factor of two if compared with the single or dual-pol cases. This section presents a comparison between the performances of full and dual-pol data in terms of pixels' density. Fully polarimetric data entails covering the entire polarimetric space; hence the optimum value in terms of the phase quality can be reached. If only dual-pol data are available, the result is obviously suboptimum, but the complexity of the optimization process is significantly reduced. Moreover, dual-pol configurations are in general more efficient in terms of coverage. To simulate the performance of dual-polarimetric products, the fully polarimetric data set available has been narrowed down selecting either the two direct channels (HH/VV) or a direct channel and a cross-polar channel (HH/HV or VV/VH).
The first option presents the same swath restrictions than the full-pol case. The comparison is based on the optimization process performance, so swath differences are omitted. The ESM approach, under the DA pixel selection criterion, is selected for the comparison since it provides the best performance. Table  5. 11 shows the results after the pixel selection step. Results reveal that both fully polarimetric and dual-polarimetric data are suitable for phase quality optimization techniques since they clearly outperform the single-polarimetric case. As expected, the dual-pol optimization does not produce the same improvement in pixels density than the full-pol case. For this particular data set,   there is roughly a factor of two between the dual-pol and the full-pol densities. On the other hand, the performance among the dual-polarimetric configurations is rather similar, being the HH-VV combination slightly better than the ones involving the cross-polar channel.
For this reason, despite the major computational load and the higher complexity in the algorithms, the use of fully polarimetric data is strongly recommended. 5. 4. 1. 5 Discussion on the Role of Polarization and on the Maturity of the Application and Conclusions General polarimetric optimization methods for its application in DInSAR processing have been evaluated. The three different optimization techniques have been put forward using RADARSAT-2 fully polarimetric data and working with both the coherence stability and the amplitude-based pixel selection criteria. With the proper combination of the available polarimetric channels, the proposed polarimetric optimization methods demonstrate their capability to enhance the quality of DInSAR results. First, it is possible to obtain a higher density of pixels compared with the single-polarization case.
Acknowledgment This research work received partial support from the SafeLand project funded by the Commission of the European Communities, grant agreement 226479, from the Big Risk project, contract number BIA2008-06614, the project TEC2011-28201-C02-01 and the grant BES-2009-015990 associated with the project TEC2008-06764-C02-01, both funded by the Spanish MICINN and FEDER funds. The RADARSAT-2 images were provided by MDA in the framework of the scientific project SOAR-EU 6779.