dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification in the EODHaM 1st stage The EODHaM 1st stage involves classification at the pixel and small object level and, in its current implementation, utilizes a sequence of decision rules, with these minimized to include only a narrow range of spectral indices that allow discrimination of LCCS Level 1 and Level 2 categories (i. e. vegetated and not vegetated terrestrial and aquatic). Whilst the sequence of rules and data layers used is set, the thresholds applied are subject to change depending upon the users' a priori knowledge and interpretation of the scene although they are generally similar within and often between environments (e. g. , Temperate or Mediterranean). At the pixel level, simple binary masks representing, for example, the extent of vegetation cover are generated. However, at the small object level, the raster attribute table (RAT) associated with the KEA format is accessed for classification  (Bunting and Gillingham, 2013).
). The assigned codes are then combined and translated to a meaningful description; for example, A12. A3. A11. B5. C1. D1. E1 translates to semi-natural open continuous high broadleaved evergreen forest. An example of the classification for the Veluwe in Fig. 7. Areas of the grassland (GRA), which can be differentiated where these occur adjacent (with a border) to urban buildings (URB). the Netherlands is provided in Fig. 6. In this case, the classification benefits from the inclusion of LIDAR data as the LCCS describes vegetated areas on the basis of their height (with divisions at 0. 03, 0. 3, 0. 5, 0. 8, 3, 5, 7, 14, and 30 m) and cover (with divisions at 1, 4, 15, 40 and 65%). The LIDAR point cloud data can also be binned on a per unit area basis to differentiate vegetation with one or two or more layers, with this describing their stratification  (Miura and Jones, 2010; Bunting et al.
Change detection modules A wide range of techniques have been developed for detecting change using EO data  (Singh, 1989; Bovolo et al. , 2012). Common amongst these are image and class differencing, change vector analysis, and cross correlation analysis  (Koeln and Bissonnette, 2000; Blaschke, 2010; Chen et al. , 2012). Within the EODHaM system, six types of change assessment are considered, with these based on the use of the RAT matrix of the KEA file format: changes in (i) LCCS classes (or GHCs), (ii) LCCS component codes, (iii) the number of extracted objects belonging to the same category (e. g. , buildings), (iv) object size and geometry (splitting or merging), (v) EO data and derived metrics (e. g. , LIDAR-derived height or the NDVI), and (vi) calculated landscape indicators. Change detection is possible, even when some information on habitats or indicators is missing.
Use of the LCCS and GHCs taxonomies When classifying land covers from EO data, a common approach has been to take training areas for pre-defined classes (e. g. , broadleaved deciduous forest) and use the statistics from these to provide a classification result (e. g. ,  Muchoney et al. , 2000; Xie et al. , 2008). The EODHaM system takes a different approach by classifying the components of each LCCS class (e. g. , broadleaved, deciduous and forest) based on a range of input data (airborne, spaceborne, thematic) and then combining these to provide a meaningful description (i. e. , broadleaved deciduous forest). The main benefit is that the classes, which can be numerous, are relevant to any location worldwide (by virtue of using the LCCS and GHC taxonomies) and the classification can be applied at any spatial scale provided that features within the landscape can be adequately resolved and appropriate spectral data (in terms of wavelength regions) are available.
Hence, sensors such as the Worldview-2, Quickbird and/or Landsat data can be used to classify sites ranging in size from several to hundreds of km 2. Where available, LIDAR data have also been integrated primarily to classify vegetation height and cover. Where only coarser spatial resolution data are available, such as provided by the Landsat sensors, the classification of some elements of the landscape (e. g. , buildings, hedgerows) may not be achieved. In general, the sequence of rules used for classification remains the same but the threshold values may vary depending upon the image data used, prevailing environmental conditions and local knowledge. Thresholds are generally transferable to other regions when using data from the same sensors, with preference given to imagery acquired prior to and during the peak vegetation flush period.
Detection of change Most change detection techniques typically focus on just one element of the change process, whether it be a change in class (e. g. , to indicate processes of deforestation) or reflectance values and derived measures. The change detection method is also often applied to the entire image, and the change process is rarely placed in context. Within the EODHaM system, the change process considers differences in the LCCS class codes (including their components), the numbers, sizes and dimensions of objects in the landscape, and differences in image data values and derived measures. As such, the EODHaM system provides users with a more complete insight into the changes that are occurring within protected areas and their surrounds and also results that are easier to interpret. Rather than stating only that a change has taken place, the change itself is described in detail.
Limitations of approach The greatest difficulty in the mapping of the LCCS classes is the differentiation of aquatic vegetation as well as cultivated and managed areas. This can be partly overcome by developing contextsensitive rules or referring to existing ancillary data layers or hydrological models. In the case of ancillary layers, cadastral information can be used to identify vegetation occurring, for example, in rice or cotton fields. Whilst the classification is currently rule-based, outputs from other classification procedures (e. g. , supervised, support vector machines) can be used. For example, components of these classifications (e. g. , maps of forest extent, water or urban areas) can be used as direct input to the EODHaM system. A further limitation is that subpixel proportions (e.
Summary and conclusions The EODHaM system (which is outlined in Fig. 2 ) is comprised of software that allows for classification within the framework of the FAO LCCS and subsequent translation of the LCCS classes (Level 3 and beyond) to GHCs and Annex I habitats (taking into account ambiguities). These taxonomies are recognized for their global application. The EODHaM system was developed using VHR data but data from moderate resolution optical sensors can also be used as the same LCCS taxonomy is applied. However, there is a scale dependency on the number and types of classes that can be discriminated. Classifications can also be undertaken based on knowledge of the user without the need for extensive ground truth datasets. Change detection modules are available with these based on changes in LCCS codes and classes, spectral data and indices and counts of extracted objects (e.
g. , trees, buildings). The system is largely automated, with user interaction required primarily to fine-tune the thresholds used in the rule-based system. The system benefits from interactions by the end users in refining the rules used for classification and selecting ancillary data (e. g. , tidal levels) relevant to the discrimination of land cover types. Overall, the EODHaM system provides a framework and potentially operational approach to the monitoring of protected areas and their surrounds from EO data. Based on EO data availability, the methods have been successfully applied to Natura 2000 sites and their surrounds in Wales, the Netherlands and Italy and have been evaluated at other locations in Greece, Portugal and India, thereby covering a range of environmental and biogeographic contexts.
Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modellingderived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patientspecific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets.
2005; Chabiniok et al. 2012; Chandrashekara et al. 2004; Gao et al. 2015; Göktepe et al. 2011; Imperiale et al. 2011; Mojsejenko et al. 2014 ). However, model complexity often limits the ability to identify patient-specific parameter values uniquely  (Hadjicharalambous et al. 2014b; Xi et al. 2013) , thus adding uncertainty to the quantitative characterisation the model can provide. The aim of this work is to develop a relatively simple model characterising full-cycle left-ventricular (LV) mechanics with sufficient accuracy, and an associated pipeline for reliable estimation of a limited number of patient-specific parameters, based on a non-invasively acquired dataset including 3D tagged MRI. Successful implementation of this pipeline would provide a basis for linking the modelderived values, such as passive and active parameters, to the underlying pathophysiology.
In passive parameter estimation, optimal values are selected based on the L 2 (Ω 0 )-norm difference between 3D motion fields extracted from the data and model predictions. Active tension through the cardiac cycle is estimated based on a combined functional incorporating relative L 2 -norm displacement errors and cavity pressure errors. Parameter identifiability and model sensitivity are verified in a series of tests based on in silico data emulating acquired data and processing errors. These tests provide a useful tool for evaluating the estimation pipeline under perfect or nearperfect model fidelity and allow us to assess the errors in the estimated quantities. With the view of using the process in patient-specific modelling, we then apply the model and the parameterisation protocol to three in vivo datasets including MR images and pressure data as an important verification step.
Model fidelity is reduced for in vivo data, and the feasibility of the estimation process, as well as the reliability of computed parameter values, needs to be re-evaluated for potential use in clinical cases. Parameter identifiability is verified in the in vivo cases, and good agreement between the data and the model can be observed. Section 2 gives the details of the full-cycle LV mechanics model used in the pipeline and describes the datasets and the process for model personalisation. The results for all of the in silico and the in vivo tests are presented in Sect. 3. The outcomes of the testing, along with limitations and future work, are discussed in Sect. 4.
Non-invasive datasets For a given set of patient-specific parameter values a, a f and α(t) the data required for simulating cardiac mechanics using the above model includes the following: 1. reference geometry Ω 0 , 2. fibre orientations f , 3. LV cavity volumes over the cycle V data (t), 4. displacements of the LV base plane u base. The estimation of a, a f and α(t) relies on additional information: 5. local displacements throughout the entire LV and 6. LV cavity pressure estimates: a single value or a full-cycle trace, as discussed below. Obtaining the reference geometry from image data is a non-trivial task, since the myocardium is not observed in its unloaded state during the cardiac cycle. In practice, endsystolic  (Wang et al. 2009 ) and early  (Mojsejenko et al. 2014; Xi et al. 2013) , or even end-diastolic  (Dokos et al.
We investigated the potential for joint estimation of passive parameters and the reference state in "Appendix 1" and were unable to obtain satisfactory results for the available dataset. The observed lack of identifiability was indicative of strong coupling between the estimated values. In the present pipeline, the end-systolic geometry was used as the reference state, ensuring consistent selection of the reference state between cases and balancing the accuracy of approximation with resulting parameter identifiability. A commonly used linear fibre angle distribution between 60 • on the endocardial and -60 • on the epicardial surface  (Spotnitz 2000; Streeter et al. 1969)  was used in the model. In practice, it is possible to generate personalised fibres based on diffusion tensor MRI data  (Rohmer et al.
2006; Nagler et al. 2013; Stoeck et al. 2014; Toussaint et al. 2013). However, at the moment scan duration as well as spatial resolution and accuracy of the acquired data is still prohibitive for clinical use. The reference displacements of the computational mesh through the cardiac cycle were obtained by tracking the motion of the myocardium in 3D tagged images. The process is based on a non-rigid registration algorithm  (Chandrashekara et al. 2004; Rueckert et al. 1999; Shi et al. 2012 Shi et al. , 2013) )  and carried out with the Image Registration Toolkit 1 (IRTK). The tracking procedure provides a transformation for each voxel of the image. This can be applied to the vertices of a computational mesh positioned in the physical coordinates, producing the deformed states at the time points in the cardiac cycle where 3D tagged frames were available.
(2012) , shown to be consistent across a range of cardiac disorders. Even though the accuracy of such pressure data, and hence its use as a driving force in the model, is limited (compared to the accuracy of image-derived cavity volumes), matching pressures along with displacements in the active estimation process proves beneficial (see Sect. 4). The types of clinical data used in the simulation and estimation process in the in vivo tests in Sect. 3. 2 were: 1 http://www. doc. ic. ac. uk/~dr/software/. 1. short-axis cine to produce the computational mesh, 2. 3D tags to extract mesh deformations, 3. pressure cuff measurement to estimate the SP, 4. mitral flow velocity to estimate the EDP. The datasets were acquired as part of the BHF New Horizons project Integrated Mathematical Modelling and Imaging for Dilated Cardiomyopathy (DCM).
Specifically, we processed one dataset from a healthy volunteer and two datasets from patients being treated for moderate DCMrelated heart failure. The cine, 3D tagged and flow images were acquired on a 1. 5T Philips Achieva system with the following specifications: - An end-diastolic LV mesh was created using the CGAL library 3  (The CGAL Project 2015) from a CardioViz3D 4   (Toussaint et al. 2008 ) segmentation of the end-diastolic frame of the short-axis cine image. The mesh was deformed using the results of IRTK motion tracking  (Shi et al. 2013)  from the 3D tagged image set covering the majority of the cardiac cycle. The mesh with the lowest LV cavity volume was used as the reference state for the simulations. The distances between the deformed meshes and this reference mesh at all time steps served as reference displacement data.
The end-diastolic pressure was estimated using the E/E a ratio: λ 0 = 1. 9 + 1. 24E/E a  (Nagueh et al. 1997) , with the peak early-diastolic flow velocity through the mitral plane E estimated using GyroTools GTFlow software 5  from 4D flow images, and the early-diastolic velocity E a computed at the lateral side of the base from reference displacements (assumed to be sufficiently close to the mitral annulus). The peak systolic pressure was estimated from cuff measurements using a Centron cBP301 device 6   (Brett et al. 2012). The fullcycle pressure curve was obtained by scaling the normalised pressure curve between λ max at maximum and λ 0 at the time corresponding to the first frame of the 3D tagged sequence. The time scaling was based on valve events extracted from the mesh volume curve, so as to obtain a physiological shape of the reference P-V loop.
In silico datasets The evaluation of the proposed pipeline using typical clinical datasets is the ultimate test of its applicability. However, such evaluation is often obscured by variable data quality. The lack of ground truth for most computed variables as well as model parameters means that validation using medical images is somewhat limited in scope. In silico testing, whereby the datasets are generated using the model, provides a useful tool for bridging the gap between model and clinical measurement. In this case, when all of the reference parameters are known and the model has perfect fidelity, we can investigate the accuracy of the estimation procedure as well as its sensitivity, specifically the effects of noise in the data and motion tracking errors on the quality of parameter estimates.
An idealised left ventricle was modelled as an ellipsoid cropped at an angle at the base, as shown in Fig. 1a. This geometry can be easily generated for testing purposes and provides a straightforward point for comparison. The idealised fibre field was produced as described for the in vivo datasets. The displacement data were produced in three stages, as illustrated in Fig. 2. First, we obtained clean displacements (Fig. 2a ) by running a full-cycle simulation using a given volume curve with given reference parameters a ref , a ref f and a given active scaling function α ref (t). The input data were chosen so that the model produced physiological cavity pressures, P-V loop and LV deformations. The mesh underwent characteristic twist and shortening (Fig. 1d ) in systole before relaxing (Fig.
Results First, we describe test specifications (including geometry, input data, parameters and generation of clean reference data) and present results for the in silico cases. Model outputs were compared to reference data, and the effects of noise and tags processing were assessed. Next, we describe the in vivo datasets processed and present pipeline outputs for these cases. All simulations were performed using CHeart  (McCormick et al. 2011 (McCormick et al. , 2013 ), 8  a multiphysics solver package developed at the Biomedical Engineering Department, King's College London.
In silico cases The computational mesh was composed of 56 quadratic hexahedral elements. 9 The cycle was taken to last 1000 ms, driven by the cavity volume curve shown in Fig. 3a  and the active tension curve shown in Fig. 3b. The reference parameters were chosen to be a ref = 0. 16 kPa and a ref f = 1. 6 kPa, with γ ref = 0. 1. For simplicity, the base of the ventricle was assumed to be fixed. The cavity pressures produced by the reference simulation are shown in Fig. 3a , and the reference P-V loop in Fig. 3c. Reference simulation results were obtained at 5-ms intervals to achieve convergence of the nonlinear solver. However, only solutions at 30, 70,. , 830 ms were taken as data points, making a total of 21 "frames" to emulate a 3D tagged image sequence typically obtained using the in vivo imaging protocol by means of temporal sampling and initial offset given by the prospective ECG triggering.
Estimation of the passive parameters All diastolic frames (t = 510, 550,. , 830 ms) were used for the estimation of the passive parameter ratio. In addi-  tion, the first frame (t 0 = 30 ms) was assumed to be sufficiently close to the end-diastolic state to provide a data point at t = 1000 ms. Since the reference volume changes between frames were too large to apply in single simulation steps, additional linearly interpolated volumes were used. Even though the exact reference volumes were known at intermediate steps between frames, they were not used in estimation, so that the dataset resembled the in vivo case. For each of the six datasets, a parameter sweep consisting of ∼25 simulations was carried out in order to assess the behaviour of the objective function J p over the γ = a/a f space (see Fig.
Estimation of the active tension The estimated passive parameters for each dataset were used in active scaling estimation. We performed parameter sweeps for each time frame in order to map the behaviour of the active objective function. In the systolic frames with nonzero reference active tension α ref. , 830 ms, parameter sweeps consisting of 21 simulations were carried out between -1 and 1 kPa, and the variation of the objective function is shown in the α space in Fig. 5b. In this case, the minimum was always expected at or near α = 0. The mean and maximum errors in the active scaling estimated using the combined objective function J a are given in Table  2  for the systole and Table  3  for the diastole. Sample comparison plots (worst case, processed displacements with 20 % noise) for the reference and estimated active scalings and P-V loops are shown in Fig.
In vivo cases We processed three datasets acquired in a healthy volunteer (V) and two patients with dilated cardiomyopathy (P1 and P2). Case details are presented in Table  4. The meshes were composed of 7795 (V), 10740 (P1) and 17047 (P2) linear tetrahedral elements. The reference volumes were computed from meshes deformed according to 3D tagged image tracking, and reference pressure curves were scaled between estimated end-diastolic and peak systolic pressures, and in time based on volume-derived valve events in order to obtain physiological shapes of the reference P-V loops (see Fig. 8 ). Weak adherence to reference base displacements was required in all simulations (ε = 10 -7 -10 -8 ), and the reference mesh volumes were used in the endocardial boundary condition.
Discussion Patient-specific cardiac modelling holds significant promise for providing details on the performance, function and properties of the heart. However, the process of personalising mechanical models needs to be reliable and robust in order to become a viable tool for model-based analysis of patient cases. The modelling pipeline proposed in this work is based entirely on a non-invasive dataset, which includes a shortaxis cine MRI stack (used to construct the LV mesh) and a 3D tagged MRI sequence (providing mesh deformations over time) as well as end-diastolic and end-systolic pressure estimates derived from flow MRI and a cuff pressure measurement. The main goal of the study was to ensure the existence of unique sets of parameters that provide a good fit to the three-dimensional cardiac motion over the cardiac cycle.
In silico cases Testing with in silico datasets provided a useful insight into how the proposed model and parameterisation performed. The in silico tests (see Fig. 2 ) enabled a decoupling between the issues of model fidelity and parameter identifiability, since the model was tested under the circumstances where it was able to replicate reference deformations. The knowledge of the reference parameters used for generating the data allowed the assessment of the sensitivity of the model parameters to different types of errors (in addition to the sensitivity of the problem variables). In active estimation, the identification of active tension scaling throughout the cardiac cycle was uniformly achieved (see Fig. 5 ). The inclusion of random displacement noise of 10-20 % resulted in a minimal increase in error, while errors in pressure values translated directly to active parameter errors.