dataesr/scientific-paragraphs-categorization huggingface dataset:
This will enable INCEFA-SCALE to investigate one of the largest fatigue databases in the world. However, a database compiled from a range of sources with differing objectives creates an issue for analyists as the data will not necessarily be balanced. The impact that this issue may have on an analysis can be mititgated through good data screening and selection practice that must be informed by data visualization. One of the objectives of the INCEFA-SCALE data mining workpackage is to develop and provide a set of tools to aid in this mitigation as well as enabling the statistical analysis of the broader dataset. During the INCEFA-SCALE project, a data mining tool was developed to statistically analyze all data available to date. In this paper, these tools are presented and, as a proof of concept, used to analyse the available using similar concepts to that of INCEFA-PLUS.
To aid in the evaluation of the database and tools expressions for predicting fatigue life of stainless steel specimens in air and ligh water reactor primary coolant conditions are provided. A key requirement of these data exploration and analysis tools is that they are generally accessible and available to the INCEFA-SCALE community to support collaborative data analysis. The conclusions and findings extracted from this data mining analysis are shown and compared with the current procedures most used in environmental fatigue analyses of nuclear components and systems.
To address these problems, we designed the Spatio-Temporal Information Compositing Algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatio-temporal correlation, and original retrieval, and thus enables both "reprocessing" and "data value-added" of the existing MODIS LAI products, leading to the development of the High-Quality LAI (HiQ-LAI) dataset. Compared to ground measurements, HiQ-LAI shows better performance than the original MODIS product with Root-Mean-Square Error (RMSE) / Bias decreased from 0. 87 / -0. 17 to 0. 78 / -0. 06. This is due to the improvement of HiQ-LAI in capturing the seasonality of vegetation phenology and in reducing time-series abnormal fluctuations. The Time-series Stability (TSS) index which represents temporal stability, indicated that the area with smooth LAI time-series expanded from 31.
, 2012). With advancements in remote sensing technology (e. g. , large-scale and continuous observation) and improved data acquisition capabilities, LAI estimation and spatiotemporal dynamic monitoring have become more accurate and comprehensive  (Fang et al. , 2019; Ganguly et al. , 2010). Among the various time-series LAI products with global coverage, Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product possess irreplaceable characteristics. MODIS LAI offers clear theoretical foundations, extensive historical time series, satisfactory validation results outcomes, and open access policy  (Yan et al. , 2016b (Yan et al. , , 2021a)). Additionally, it does not rely on other LAI products as input data, ensuring complete independence  (Myneni et al. , 2002). It is often utilized as a training dataset by other products  (Baret et al.
, 2013; Ma and Liang, 2022)  and as reference data for comparison  (Xiao et al. , 2013; Yan et al. , 2016b). The long time series MODIS LAI dataset has made significant contributions to landmark studies on "Greening the Earth" phenomena, the possible causes of large-scale vegetation dynamics, and the relationship between vegetation dynamics and global climate change or human activities  (Mao et al. , 2013; Chen and Dirmeyer, 2016; Zhu et al. , 2016; Chen et al. , 2019). From this perspective, the high quality of MODIS LAI product is of utmost importance. The MODIS LAI operational algorithms comprise the main algorithm based on the three dimension radiative transfer theory and the backup algorithm relies on the empirical relationship between Normalized Difference Vegetation Index (NDVI) and canopy LAI  (Myneni et al.
Consequently, inconsistency and excessive noise pose limitations on its practical applications. Many methods have been proposed to reprocess MODIS LAI products (ranging from C4 to C6) to improve their quality. A pressing need exists for continuous, high-quality, and easily accessible LAI datasets to better facilitate investigations in land surface process simulation, climate modelling, and global change research,  Fang et al. (2006)  proposed a Spatio Temporal Filtering (TSF) method that integrates multi-seasonal average trend (background) and seasonal observations to generate spatially and temporally continuous MODIS LAI C4 products for the North American region to fill in and improve the gaps and poor quality values caused by cloud cover, seasonal snow cover, and instrument problems. Gao et al.
(2008)  utilized TIMESAT to process MODIS LAI C4 product, aiming to fit the LAI time series profile by incorporating sufficient high-quality data and replacing low-quality or missing observations, thus obtaining high-quality spatiotemporal continuous LAI time series for the North America region to produce temporally smoothed and spatially continuous biophysical data for the North American Carbon Program. Aiming to generate continuous input dataset for global climate models,  Yuan et al. (2011)  improved Fang's method and proposed the modified TSF (mTSF) method to conduct simple data assimilation for relatively low-quality data and used post-processing TIMESAT and SG filtering to obtain the final improved MODIS LAI C5 product. Recently, Yuan's group  (Lin et al. , 2023)  reprocessed MODIS LAI C6 products using a similar procedure.
We anticipate that the High-Quality LAI (HiQ-LAI) product effectively addresses regions with quality issues  establish an accurate relationship between satellite observations and ground parameters  (Knyazikhin, 1999). The MODIS land cover type product (MCD12Q1) provides global land cover maps at annual time steps and 500 m spatial resolution from 2001 to the present. The product was created using supervised classification of MODIS reflectance data for a total of 13 scientific datasets. The MCD12Q1 adopts LAI legacy classification scheme, including B1 (grass and cereal crops); B2 (shrub); B3 (broadleaf crops); B4 (savanna); B5 (evergreen broadleaf forest, EBF); B6 (deciduous broadleaf forest, DBF); B7: (evergreen coniferous forest, ENF); B8 (deciduous coniferous forest, DNF)  (Yan et al. , 2016a; Sulla-Menashe and Friedl, 2018).
GBOV The Copernicus Ground-Based Observations for Validation (GBOV) service, which is part of the Copernicus Global Land Service (CGLS), is dedicated to the development and distribution of robust in situ datasets from various ground monitoring sites for the systematic and quantitative validation of land products  (Bai et al. , 2019; Brown et al. , 2020). A comprehensive GBOV reference measurement database has been established through quality control and raw measurements reprocessing obtained from existing in situ sites. This database includes canopy reflectance, surface albedo, LAI, FPAR, cover area, 5 cm soil moisture, and surface temperature. Currently, 29 available sites provide LAI references from 2013 to 2022. The data from this database are freely accessible to the scientific community through the GBOV portal (https://gbov.
The updated BELMANIP V2. 1 dataset (Fig. 1 ) added 25 sites corresponding to bare land (desert) and tropical forests, resulting in a total of 445 sites (https://calvalportal. ceos. org/web/olive/site-description). It is important to note that most of these sites do not have ground reference measurements. Consequently, the network is primarily used for comparative analysis among sites rather than direct validation.
DIRECT V2. 1 We also employed the DIRECT V2. 1 ground measurement reference to validate satellite-based products. This validation dataset was curated from a collection of sites that followed the CEOS/WGCV-LPV guidelines for collecting and processing ground survey data  (Morisette et al. , 2006)   (Garrigues et al. , 2008). Mean values of LAI, the maximum fraction of photosynthetically active radiation absorbed by vegetation (FPAR), and vegetation cover (FCover) over a 3km × 3km area were compiled from the DIRECT V2. 1 database. By the CEOS WGCV LPV LAI good practices  (Weiss et al. , 2014) , the ground data were upscaled using an empirical "transfer function" between high spatial resolution radiation data and biophysical measurements to appropriately account for the spatial heterogeneity of the site.
The DIRECT V2. 1 database constitutes a major effort of the international community to provide ground references for the validation of LAI and FAPAR ECVs. It currently encompasses 176 sites across seven major biome types around the world, with 280 LAI values, 128 FPAR, and 122 FCOVER values spanning from 2000 to 2021 (https://calvalportal. ceos. org/lpv-direct-v2. 1).
e. , everything is related to everything else, but near things are more related than distant things. The final step of the algorithm is to take a weighted average of the original MODIS LAI and the LAI calculated using spatial/temporal correlation, with their respective weights quantified using an indicator (Time-series Stability, TSS) that represents the temporal fluctuation of the time series  (Zou et al. , 2022). All processes of the method are implemented using the GEE cloud computing platform. The reprocessed LAI dataset, namely the HiQ-LAI product, has been generated with the help of the powerful cloud computing capability of GEE, covering the period from 2000 to 2022.
Assessment of LAI Datasets In this study, we utilized the GBOV LAI measurements from a total of 29 sites spanning from 2013 to 2021 as our ground reference LAI. A 3 km × 3 km square centered on the site location was selected as the study area (Fig. 1 ) so that the corresponding LAI product of each site was 36 (6 × 6) pixels. To enhance the credibility of the ground truth LAI, we filtered the ground LAI reference of these 29 sites based on the criterion that the "effective pixel" exceeded 90% and the input and output of land product value in the data aggregation process were within the specified range. This filtering process yielded a total of 818 reliable verification data points. Furthermore, we compared MODIS LAI and HiQ-LAI in 2021 using the BELMANIP V2. 1 sites (445 in total). Employing these sites not only reduced the computational burden on a global scale but also mitigated additional uncertainties arising from geometric registration bias and land cover misclassification.
, 2013; Richardson et al. , 2013; Tillack et al. , 2014)  global water regulation, carbon and energy cycles  (Sellers et al. , 1997a; Chen et al. , 2020). HiQ-LAI is expected to facilitate the advancement of these models by providing more accurate and reliable data sources. Additionally, due to the algorithm's high adaptability, it can also be applied to improve the data quality of other vegetation state parameters such as FPAR, NDVI, EVI, etc. The HiQ-LAI product maintains consistency with MODIS LAI in terms of data format, still being stored in HDF format according to tile convention. This reduces the learning curve for users when working with the data. Moreover, we implemented the algorithm on the GEE cloud computing platform. Leveraging the powerful computing capabilities of the platform, users can easily conduct long-term time series and large-scale, even global-scale research and applications using this dataset.
Data availability The High-quality Leaf Area Index (HiQ-LAI) is now accessible as an open dataset, providing two different spatial resolutions. Datasets with a spatial resolution of 5km and a temporal resolution of 8 days can be found on Zenodo (https://doi. org/10. 5281/zenodo. 8296768)  (Yan et al. , 2023)  Including this dataset on the GEE platform will greatly benefit the GEE community, simplifying access to and utilization of this valuable resource. The 5km spatial resolution was derived by upscaling the original 500m data using the nearest-neighbour method, which maximally preserves the pixel-level details including binary and discrete Quality Control (QC) flags from the original scale. The HiQ-LAI product encompasses 5-layer scientific datasets, containing LAI, original quality control information, relative-TSS of MODIS and HiQ LAI, and the absolute difference between HiQ-LAI and MODIS LAI.
Acknowledgments. We are grateful to the editors and anonymous reviewers for their constructive comments and suggestions for improving the manuscript. We gratefully acknowledge the Google Earth Engine (https://earthengine. google. com/). Financial Support. This work was supported in part by  National Natural Science Foundation of China  under Grant  42271356  and the  National Natural Science Foundation of China   Major Program  under Grant  42192580. https://doi. org /10. 5194/essd-2023-410  Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0  License.
https://doi. org/10. 5194/essd-2023-410 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0 License. while maintaining good consistency with the original MODIS product, holding significant implications for other LAI/FPAR products development. We implemented the entire algorithm process using the Google Earth Engine (GEE) cloud computing platform to reprocess MODIS C6. 1 LAI, resulting in the production of the HiQ-LAI dataset covering the years 2000 to 2022 on a global scale. The accuracy of HiQ-LAI was evaluated through ground-based validation and compared with MODIS LAI at a global scale and across different biome types. The temporal consistency and trends of global LAI products were analysed, with indepth comparative assessments conducted for regions exhibiting significant quality issues.
original values have been adjusted to integers considering the data storage size, and all layers are stored as uint8 data types. The LAI layer has a valid range from 0 to 100, where each value represents a tenth of the original value. The relative-TSS of MODIS LAI and https://doi. org/10. 5194/essd-2023-410 Preprint. Discussion started: 26 October 2023 c Author(s) 2023. CC BY 4. 0 License. HiQ-LAI have valid ranges spanning from 0 to 10, with each value corresponding to 0. 001 units. Lastly, the LAI difference layer (LAI_Diff) applies a scale factor of -100. More details about HiQ-LAI can be accessed at https://github. com/tiramisu18/HiQ-LAI. Users are encouraged to refer to the data description document for guidance on value restoration.
the retrieval algorithm lead to MODIS LAI products having poor spatiotemporal consistency and accompanied by abnormally high noise. To address these limitations and improve the quality and spatiotemporal consistency of existing MODIS LAI product on a global scale, we utilize the Spatiotemporal Information Compositing Algorithm (STICA) that leverages pixel quality information, spatiotemporal correlation, and original observation information to improve the MODIS LAI retrievals with poor quality. By considering multiple dimensions of information to compensate for the deficiency of using only temporal information, High-Quality Reprocessed MODIS LAI Dataset (HiQ-LAI) achieve better spatio-temporal consistency and provide global coverage from 2000 to 2022. Direct ground verification demonstrates that HiQ-LAI exhibits higher accuracy and is closer to the ground references compared to the original MODIS LAI.
For example,  [37]  proposes an end-to-end model involving structural output alignment for distribution shift. However, these methods only use adversarial loss and ignore the voxel-level alignment which makes the model ignore details of segmentations while adapting, thus leading to the model's segmentation performance degradation on the multiple-domain target site. Based on preserving domain uncertainty minimization, the proposed HUL adds voxel-level uncertainty minimization concurrently. Therefore, the segmentation model minimizes the distribution differences at the level of the entire dataset in a global view and pays attention to details when adapting.
Problem formulation A labeled source site dataset D s = {(x s , y s )|x s ∈ R H×W ×D×1 , y s ∈ (1, C ) H×W ×D } and an unlabeled target site dataset D t = {x t |x t ∈ R H×W ×D×1 } are given, where x s and x t correspond to the source and target site 3D images, respectively; y s is the label for the corresponding source image which provides the annotation of the voxel at (H, W , D) in the form of a one-hot vector; and H, W , and D are the height, width, and depth of the images, respectively. There exist n s image-label pairs in D s and n t images in D t. The problem can be formulated as follows: min θ G ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ 1 ns ∑ xs L S (G(x s ), y s ), 1 nt ∑ xt L T (G(x t )), (1) where L S is the loss of the source site image and L T is the loss of the target site image. The proposed DCSS is used to minimize the among-target differences.
log(p ( ) 8 (2) Domain-level Uncertainty Reduction to minimize the entire distribution differences: Minimizing L ent alone takes the generalization boundary of the model to the low-density area at a voxel-wise level. To manage the domain-level distribution differences of the entire dataset, the proposed HUL introduces adversarial learning at the output level to unify the distribution. Adversarial learning consists of two networks: the generator network G g and the discriminator network D. In this study, the generator network takes the incredible image x i as input and produces its pseudo-label p i = G(x i ). Therefore, the generator network G g is also the segmentation network G. Then, D takes the entropy map I i as input and produces its classification results ∈ {0, 1} to judge whether the input image comes from the credible set or the incredible set, where I i is the entropy map of p i after entropy calculation I (h,w,d) i = -p (h,w,d) i • log p (h,w,d) i.
Dataset We evaluate the proposed DyDA framework on two important one-to-multiple medical image segmentation tasks with different image modalities: (1) Kidney&Tumor Segmentation on JSPH → KiTS19 evaluates the proposed DyDA in kidney&tumor segmentation of abdominal CT images using JSPH as the one-domain source dataset and KiTS19 as the multiple-domain target site dataset. Onedomain source site JSPH is a collection of abdominal CT scans from Jiangsu People's Hospital. All images were acquired by a Siemens dual-source 64-slice CT scanner. The contrast media was injected during all the CT image acquisition and all the images were scanned in the early-artery phase. The slices of all the images are fixed at 0. 5 mm with annotation. Because all the images were collected from the same hospital and there was no difference in acquisition parameters and equipment, the data distribution of these images varies little, making JSPH a onedomain dataset.
The multiple-domain target site KiTS19  [55]  is a segmented CT image dataset collected from more than 60 medical centers and scanned at different phases. Manual segmentation results of training data are provided for the kidneys and tumors in these images. The KiTS19 test set consists of 90 CT scans and the JSPH test set consists of 70 CT scans. The test set results of KiTS19 are uploaded for online evaluation. (2) RV&Myo&LV Segmentation on ACDC → MyoEmidec evaluates the proposed DyDA in RV&Myo&LV segmentation of cardiac magnetic resonance (CMR) images using ACDC as the one-domain source dataset and MyoEmidec as the multiple-domain target site dataset. One-domain source site ACDC  [56]  was provided by MICCAI'17 Automatic Cardiac Diagnosis Challenge. This dataset consists of shot-axis cardiac cine-MRIs of 100 patients for training, and of 50 patients for testing.
Manual segmentation results of training data are provided for the right ventricle (RV), left ventricle (LV), and Myo during the end-diastolic and end-systolic phases (Myo). The multiple-domain target site MyoEmidec is composed of MyoPS2020  [57]  and EMIDEC  [58]. This dataset consists of 375 CMR images from five domains for training. My-oPS2020 provides 225 CMR images from three domains: balanced steady-state free precession (bSSFP), late gadolinium enhancement (LGE), and T2-weighted CMR (T2). EMIDEC contains 150 CMR images from two domains: normal MRI after injection of a contrast agent (normal) and myocardial infarction with a hyperenhanced on DE-MRI (pathological). Manual segmentation results are available for all images of the left ventricle (LV), right ventricle (RV), and myocardium (Myo).
8. This framework is implemented on PyTorch and runs on NVIDIA TESLA V100. In the JSPH → KiTS19 experiment, random rotation (-20 • to +20 • ), mirror flip, scaling (0. 7 to 1. 4), and random cropping (128*128*128) are used to augment the image online. To have a better average segmentation performance on the overall medical images, we set the ratio of weights to different organs and lesions as the inverse ratio of their voxels in the source labels of the training dataset. For the convenience of calculation, we expand them to the minimum integral multiple. Specifically, the voxel ratio of tumor and kidney in the JSPH training dataset is 1:3, and the voxel ratio of RV, Myo, and LV in the ACDC training dataset is 3:1:1. Therefore, when calculating the weighted credibility entropy in the JSPH → KiTS19 experiment, the weights of the tumor and kidney were initially 0.
Comparison settings To explain the superiority of the proposed framework, we compared the proposed DyDA with three existing UDA methods on the KiTS19 test set and MyoEmidec test set: MinEnt  [59] , AdvEnt  [15] , IntraDA  [10]. MinEnt focuses on reducing the voxellevel uncertainty by minimizing entropy loss. AdvEnt focuses on reducing domain-level uncertainty by minimizing adversarial loss. For a fair comparison, nnU-Net  [46]  is applied as the benchmark for all methods. All these methods were written into 3D for comparison in the proposed 3D datasets.
Dataset distribution analysis As shown in Fig. 6 , the datasets used in the two one-tomultiple image segmentation experiments have marked sourcetarget differences and among-target differences. The primary features of the data including their mean, standard deviation, spacing, and signal-to-noise ratio are extracted and dimensionally reduced using PCA  [60]. As shown in Fig. 6 (a) , four different data distributions in KiTS19 and one data distribution in JSPH are shown, indicating that there are among-target differences within KiTS19 and source-target differences between JSPH and KiTS19. Although the data distribution differences shown in Fig. 6 (b)  are not as large as those in Fig. 6 (a), source-target differences between ACDC and MyoEmidec can be observed. Among-target differences within MyoEmidec are clearly observed.
Qualitative evaluation As shown in Fig. 7 , the proposed DyDA has extraordinary visual superiority in different segmentation tasks and different datasets, which will provide visual guidance in clinical surgery. As shown in Fig. 7  (a) and Fig. 7  (b), the proposed DyDA obtains a segmentation result close to the ground truth compared with IntraDA both in kidney&tumor segmentation and RV&Myo&LV segmentation. This result likely occurs because the proposed HUL reduces the voxel-level uncertainty and the domain-level uncertainty simultaneously, thus improving the segmentation performance on the target site from the details and the global view. This result also proves that the proposed DyDA is applicable and effective to different segmentation tasks and different datasets for domain adaptation. The proposed DyDA can improve the segmentation model's generalization ability and avoid underfitting by gradually expanding the generalization boundary.