dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2. 2 and resulting in Eq. (  2 ) returning significant wave height H s,SeaVision estimates based on the radar calibration coefficients A and B along with estimates for the wind wave period Eq. (3) derived from the zeroth and first moments of the spectrum. The mean wave direction D s,SeaVision is estimated with the centroid method: where arg is the argument of the complex number and D n is the zeroth moment of the spectrum S n,ω (f ) in the direction n. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. We thank Editor Giuseppe M. R. Manzella, anonymous Reviewer 1, and Alamgir Hossan (Reviewer 2) for careful examination of the manuscript and useful comments and suggestions that allowed us to improve the manuscript significantly.
We thank Takaya Uchida at the Université Grenoble Alpes for final proofreading of the manuscript. Financial support. This study was funded by the Russian Foundation for Basic Research, project no. 20-35-70025. VS and AG were also supported with grant no. 17-77-20112-P from the Russian Science Foundation (WW3 setting for the period of the research cruises). SG was supported by the Ministry of Science and Higher Education of the Russian Federation (agreement 075-15-2021-577, interpretation and analysis of observational biases). https://doi. org/10. 5194/essd-14-3615-2022 Earth Syst. Sci. Data, 14, 3615-3633, 2022.
NT, AG, DI, VS, AS, LS, VS, and PS participated in research cruises and data collection. The leading role in fieldwork programme development and implementation belongs to AG and VS. DI did pre-processing, post-processing, and analysis of the radar (SeaVision) dataset, VS developed the configuration and ran the WW3 model for the period of cruises, and AG analysed all Spotter buoy data. EE, AG, and VS carried out validation with satellite missions. VF, BT, and SB provided hardware development and mounting of the SeaVision onto the research vessels. VT, OR, and AS provided operational support for the research cruises. NT had a leading role in the project set-up and manuscript writing. The initial idea of the research was SG's. The scoping of the manuscript was developed by NT, SG, and KPK. All the authors contributed to the discussion, interpretation of the results, and writing.
Box 1. The Spectral Species Algorithm at Work The spectral species concept is grounded in an algorithm which is now readily available under a free and open source R package in GitHub (https://github. com/jbferet/biodivMapR) named biodivMapR  (Féret & De Boissieu, 2020) , which is able to produce α-and β-diversity maps starting from the detection of spectral species based on the optical properties of vegetation in the field. Starting from a multi-or hyper-spectral image, a spectral transformation like a Principal Component Analysis (PCA) is performed to reduce the dimensionality for further calculations (Figure  2 ). Based on those principal components explaining most of the variance, k-means clustering is applied to a random set of pixels in order to detect group of pixel with the same spectral reflectance, possibly related to single groups of individuals/canopies in the field that are sharing similar traits and thus that are likely to be phylogenetically related (spectral species concept).
Including seasonal changes in the analysis may improve detection of understory species when the deciduous overstory is dormant. Multitemporal datasets can form multi-seasonal spectral signatures to account for seasonal changes in a species (e. g. ,  Somers & Asner, 2014)  and multitemporal spectral libraries are under development  (Dudley et al. , 2015). The spectral species concept has shown consistency in identifying taxonomic species and traits from other taxonomic species and their traits. Detecting species in mixed pixels depends on the sensor traits listed above and the magnitude of the differences among the spectral signatures of the species in the pixel. Species with different functional strategies that are distinguishable in optically active traits will be easier to identify than species with more similar trait assemblies, but which are not optically active.
From this point of view, in situ data would greatly improve the classification, validate its accuracy, and provide a basis to investigate and identify sites that do not match a priori expectations  (Foody et al. , 2016). The R package (biodivMapR) dedicated to applications of the spectral species algorithm  (Féret and de Boissieu, 2020)  on raster data requires the number of spectral species be defined by the user. The method is not intended to estimate the absolute number of species from remote sensing, as this problem is highly scale and context dependent as explained earlier. The definition of the optimal number of clusters in a dataset is a specific problem that the spectral species framework was also not intended to solve. Adding a method to determine the number of clusters corresponding to species richness over a full dataset would definitely contribute to the generalization of the spectral species framework over various ecosystems.
Outlook In this manuscript we focused on the need to model biodiversity from hyperspectral remote sensing and field data to calibrate such models. A huge amount of field (free) data is actually available (e. g. , sPlotOpen,  Sabatini et al. , 2021, GBIF) , but creativity for mapping species diversity from hyperspectral imagery should be reinforced. From this point of view, the spectral species concept is expected to make a major contribution in mapping ROCCHINI ET AL. 10. 1029/2022JG007026 10 of 13 and analysis of hyperspectral satellite data to produce remote sensing based essential biodiversity variables (RS-EBVs, see  Skidmore et al. , 2015 Skidmore et al. , , 2021)). Even if the predicted spectral species do not precisely identify the number and identity of taxonomic species in an image, it will provide a first-order exploratory tool to detect areas of low to high species diversity.
Currently, advances in modern MR sequences allow acquiring high-resolution images making possible to divide the hippocampus into its constituent parts. In the last years, several delineation protocols have been proposed (some of these protocols have been used to create manually labeled MRI datasets). However, there is still little consensus between the different HC subfield protocols as shown in Ref. 11  where 21 delineation protocols were compared. For example, in 2013, Winterburn presented a new in-vivo high-resolution atlas  12  to divide the hippocampus in five different sub-regions: CA1, CA2-3, CA4/DG, Stratum and Subiculum. Later, in 2015, Kulaga-Yoskovitz developed another segmentation protocol  13  consisting of three structures: CA1-3, CA4/DG and Subiculum. Several automatic methods for HC subfield segmentation have been developed in the last years  [14] [15] [16].
Materials and methods Training data. In this work, we have used two different datasets including two manual labeling hippocampus subfield segmentation protocols, both with high-resolution (HR) T1w and T2w MR images (see Fig. 1 ). Details of these datasets are given below: Kulaga-Yoskovitz dataset. This dataset includes 25 subjects from a public repository (http:// www. nitrc. org/ proje cts/ mni-hisub 25) (31 ± 7 years, 12 males, 13 females) with manually segmented labels dividing the HC in three parts (CA1-3, DG-CA4 and Subiculum). The Ethics Committee of the Montreal Neurological Institute and Hospital approved the study and written informed consent was obtained from all participants in accordance with the standards of the Declaration of Helsinki. Participants gave their written informed consent prior to scanning and received a monetary compensation.
MR data from each subject consist of an isotropic 3D-MPRAGE T1-weighted (0. 6 mm 3 ) and anisotropic 2D T2-weighted TSE images (0. 4 × 0. 4 × 2 mm 3 ). Images underwent automated correction for intensity non-uniformity, intensity standardization and were linearly registered to the MNI152 space. T1w and T2w images were resampled to a resolution of 0. 4 mm 3. To reduce interpolation artifacts, the T2w data was upsampled using a non-local super-resolution method  28. For more details about the labeling protocol see the original paper  13. Winterburn dataset. This dataset contains 5 subjects with 0. 3 × 0. 3 × 0. 3 mm 3 high resolution T1-weighted and T2-weighted images obtained by 2 × interpolation of 0. 6 × 0. 6 × 0. 6 mm 3 acquisitions and their corresponding manual segmentations. The HR images are publicly available at the CoBrALab website (http:// cobra lab.
ca/ atlas es). These MR images were taken from 5 healthy volunteers (2 males, 3 females, aged 29-57). The study was conducted in keeping with the Declaration of Helsinki, was approved by the Centre for Addiction and Mental Health Research Ethics Board, and all subjects provided written, informed consent for data acquisition and sharing. High-resolution T1-weighted images were acquired using the 3D inversion-prepared fast spoiled gradientrecalled echo acquisition (TE/TR = 4. 3 ms/9. 2 ms, TI = 650 ms, α = 8°, 2-NEX and isotropic resolution of 0. 6 mm 3 ). High-resolution T2-weighted images were acquired using the 3D fast spin echo acquisition, FSE-CUBE (TE/TR = 95. 3 ms/2500 ms, ETL = 100 ms, 2NEX, and isotropic resolution of 0. 6 mm 3 ). Reconstruction filters, ZIPX2 and ZIP512, were also used resulting in a final isotropic 0.
3 mm 3 dimension voxels. The hippocampus and each of their subfields were segmented manually by an expert rater including 5 labels (CA1, CA2/3, CA4/ DG, (SR/SL/SM), and subiculum). For more details about the labeling protocol see the original paper  12. All methods were performed in accordance to relevant guidelines and regulations. Example images of these two protocols are shown at Fig. 1. Images and labels were visualized using ITK-SNAP v 3. 4. 0 software (http:// www. itksn ap. org). Image preprocessing. The images were preprocessed using the following steps: (1) Denoising using the Spatially Adaptive Non-Local Means Filter 29 , (2) Intensity inhomogeneity correction using the N4 bias field correction 30 , (3) Affine registration to the Montreal Neurological Institute (MNI) space by applying the Advanced Normalization Tools (ANTs) package  31.
2. Alternatively, in our proposed GJL loss we did not use the squared volume to normalize but just the volume, i. e. w c = 1/ N i=1 t ci. As the size of our training datasets is small (specially for the Winterburn dataset) we used different approaches of data augmentation. We randomly smooth and sharpen the images to simulate different image quality conditions during the training. We expanded the Winterburn dataset with automatic segmentations of the Kulaga-Yoskovitz dataset using the method HIPS. Note that to generate these segmentations only training data was used as library. Finally, we used also mixup  35  as a data agnostic method for performing data augmentation. Batch normalization is a highly effective manner to speed up the training process and to improve the results by minimizing the internal covariate shift.
Experiments and results In this section, the analysis of the different options of the proposed method and their results are presented. To evaluate the segmentation accuracy, we have used the DICE coefficient  36  measured in the linear MNI152 space. All experiments were performed using tensorflow 1. 2. 0 and keras 2. 2. 4 using Titan Xp Nvidia GPU with 12 GB RAM. To train the network, we used an Adam optimizer  37  with default parameters during 200 epochs and we test different loss functions with multiscale loss weights (0. 1, 0. 2 and 0. 7) for low, medium and high-resolution outputs respectively (see Fig. 2 ). A batch size of one was used in all our experiments. Kulaga-Yoskovitz and Winterburn datasets were preprocessed as described in "Image preprocessing". To increase the size of the training data, we put together left and right crops by left-right flipping the left crops to generate right oriented crops.
This yield 50 right crops in Kulaga-Yoskovitz dataset and 10 right crops in Winterburn dataset. Since the size of both datasets is quite small we have used a K-fold cross validation strategy to increase the relevance of our findings. Specifically, we used K = 5 in both datasets. In Kulaga-Yoskovitz dataset this let each fold with 40 training images (5 of them for validation) and 10 test images. In Winterburn dataset each fold had 8 training images (2 of them for validation) and 2 test images.
Analysis of the proposed method. There are many factors that affect the performance of deep learning methods such as the architecture, the loss function, data augmentation strategies, etc. In this section we will present some experiments that show their effects on the proposed method. The first option we tested was the loss function. We compared 5 different loss functions using the same exact network initialization. In Table  1  the validation DICE of each loss function is compared for both datasets. We also included the categorical cross entropy (CCE) in the comparison as it is a common loss used in segmentation/ classification. As can be noted, CCE performed worse than dice loss which is a common loss function used in segmentation. Curiously, the GDL failed giving a really low dice compared with the other losses.
JL performed similar than dice loss. The proposed GJL was the best performing loss in both datasets and therefore was selected a loss function of the proposed method. To study the impact of the proposed architecture, we run the proposed network with deep supervision and compared it with the classic UNET. In Table  2  the results of the comparison are shown. As can be seen, the proposed architecture was able to improve the results in both datasets. (2) It is well-known that in deep learning the amount of training data plays a major role (probably the biggest) in the quality of the network results. Unfortunately, manually labeled cases of hippocampal subfields is a rare resource due to the difficulty of generating such data. Automatic data augmentation has been traditional used to artificially increase the number of training cases.
This has been usually done applying random transformations on the available training data (rotation, scale, etc. ). In this project, we have used a combination of different methods to augment the number of training cases. In the case of Kulaga-Yuskevitz, we randomly smooth and sharpen the cropped images to generate low and high-quality images to improve generalization capabilities of the network. We also used mixup  35  to linearly combine inputs and outputs (alfa = 0. 3). Mixup is a data-agnostic data augmentation method that has been proven beneficial specially when using a small training dataset  38. In the case of Winterburn dataset, we used the same approach but in addition we increased the training dataset using automatic segmentations of the Kulaga-Yuskevitz dataset with the HIPS method  18  which is a patch-based multiatlas label fusion based method (using as atlases the training cases of each fold).
In the Table  3  the results of the proposed method with and without data augmentations are shown. As expected, data augmentation strategies helped to improve the results in both datasets. The improvement in Winterburn dataset was more important given the small size of the training set (N = 6). GDL p, t = 1 -2 NC c=1 w c N i=1 p ci t ci NC c=1 w c N i=1 p ci + t ci , (3) JL p, t = 1 - 1 NC NC c=1 N i=1 p ci t ci NC c=1 ( N i=1 p ci + t ci -N i=1 p ci t ci ) , (4) A last experiment was performed to evaluate the effect of the TTBN technique. In Table  4  the results of both datasets are shown. As can be seen, TTBN helped in both data sets, but the improvement in the Winterburn dataset was relatively greater. For the final results of both datasets, we estimated each structure dice, average dice among structures and whole hippocampus dice.
Standard resolution vs high resolution. The proposed method uses high resolution MR images but these sequences are not always available either in research or in clinical environments. However, it would be desirable to be able to analyze legacy data. For this reason, we evaluated the proposed method using standard resolution (1 × 1 × 1 mm 3 ) images upsampled to 0. 5 × 0. 5 × 0. 5 mm 3 using B-spline interpolation and a super-   28. To do it, we reduced the resolution of the HR images by a factor 2 and later we upsampled them using the described methods. Tables  6  and 7  show the results for both datasets. The results confirm that the proposed method can produce competitive results when using standard resolution images. Note that the results using LASR are better than using B-spline interpolation for both datasets and closely resembles those obtained using the original HR images.
Method comparison. The proposed method was compared with state-of-the-art related methods. Specifically, for the Kulaga-Yoskovitz dataset we compared with HIPS method  18  and a recent deep learning-based method named ResDUnet dedicated to hippocampus subfields segmentation  27. In both cases we used published results in their papers for the comparison. In Table  8  we show the results of the comparison. We included also the inter and intra-rater accuracy for comparison purposes. As can be noticed, the proposed method outperformed previous state-of-the-art methods. It is also worth to note that the proposed method improved the inter-rater accuracy and got very close to the intra-rater accuracy. For the Winterburn dataset, we compared with HIPS method  18  that represents the state of the art in this dataset.
Discussion In this paper, we have presented a new deep learning-based method for HR hippocampus subfield segmentation that we called DeepHIPS. We have validated the proposed method using 2 publically available datasets (Winterburn and Kulaga-Yoskovitz). Our proposed method first preprocesses the HR T1 and T2 images to improve their quality and to locate them into a standard space (MNI152) to finally crop the region of interest to process. From the architecture point of view, our model is a 3D UNET variant that uses deep supervision and low-resolution feedback to make easier the training process. We found that this variant worked better that the classic UNET. We have also proposed a novel loss function (GJL) based on the Jaccard similarity index that enables to improve the accuracy of the network borrowing ideas from a modified version of the GDL (i.
e. using linear volume weights instead of quadratic). We further improve the results using classical data augmentation techniques such as image mirroring and intensity transformation and more modern ones such as mixup. Finally, we improved the results of the network at test time by running Batch normalization layers in training mode instead of test mode. We found that when using small batch sizes (N = 1 in our case) batch normalization layers didn't behave properly due to the use of the stored mean and standard deviation during training. Using current sample statistics systematically improved the results in all our experiments despite the simplicity of the approach (especially in the Winterburn dataset with an improvement of nearly the 9%). We called this strategy Training Time Bach Normalization (TTBN).
We compared the results of the proposed method with state-of-the-art methods in two datasets. In the Kulaga-Yoskovitz dataset we compared with HIPS method and a recent deep learning-based method named ResDUnet. The proposed method improved the results of both methods for all subfields and got closer to the intra-rater accuracy which can be considered as the upper bound of the method. For the Winterburn dataset, we compared with HIPS method and again, the proposed method improved the results for all subfields and the overall accuracy got very close to the intra-rater accuracy. We also studied the accuracy of the propose method using standard resolution images (1 mm 2 ) upsampled to HR (0. 5 mm 2 ) using superresolution method (LASR). Although the accuracy slightly dropped compared to the HR results we found it still very competitive making possible the use of legacy data.
We are aware that the training libraries of the proposed method are quite small to ensure a good generalization (especially in the case of Winterburn) and our future efforts will be directed to increase the size of these libraries by manually labeling new cases and using semi-supervised approaches to automatically extend the training dataset size. From an efficiency point of view the proposed method is not only more accurate but also more efficient than previous state of the art (HIPS) reducing by a factor 10 the total execution time.
Conclusion In this work, we have presented a new method for HR hippocampus subfield segmentation based on a deep learning approach and we have validated it with two publically available datasets (Winterburn and Kulaga-Yoskovitz) showing competitive results in both accuracy and efficiency. We plan to make fully accessible the DeepHIPS pipeline through the new release of our online image analysis service volbrain (http:// volbr ain. upv. es) so researchers around the world can use our pipeline without requiring complex pipeline installations or the use of expensive hardware (GPUs, etc. ).
Acknowledgements This research was supported by the  Spanish   DPI2017-87743-R grant  from the  Ministerio de Economia, Industria y Competitividad of Spain. This study has been also carried out with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the Investments for the future  Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project) and  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ). The authors gratefully acknowledge the support of  NVIDIA Corporation  with their donation of the TITAN X GPU used in this research.
In turn, this has an impact on the cost of the individual facilities and on the global cost of SNF management. At present, computer codes and associated nuclear data (ND) libraries are validated against available measurements or integral experiments that cover a limited space of SNF assembly characteristics. The level of detail in assembly design and operating data that is required for accurate calculations often exceeds what is available, from an industrial perspective, for the whole SNF inventory arising from reactor operations. A bias in the calculated decay heat rate that is introduced due to the unavailability of such details in the input data can be assessed by performing sensitivity studies. However, as these evaluations depend on the computational tool and model, a deeper understanding can be gained through benchmark exercises involving different computer codes and users, which might lead to improved computational methods.
Many of the participants in this exercise are also collaborating within the work package Spent Fuel Characterization and Evolution until Disposal (SFC) in the context of the European Joint Programme on Radioactive Waste Management (EURAD) and in the International Atomic Energy Agency (IAEA) Coordinated Research Project on SNF characterization. The results of this blind test benchmark exercise will provide valuable input to such collaborative work and will contribute to a better characterization of SNF. All uncertainties provided are quoted at a 68% confidence level unless otherwise stated. The notation x h i used in this paper denotes the average of x. Values including uncertainties are given in standard compact notation, such as V(U) where U indicates the uncertainty in the last digit(s) of V.