dataesr/scientific-paragraphs-categorization huggingface dataset:
The CRISPY interface CRISPY is a graphical user interface for the simulation of core-level spectra using the semi-empirical multiplet approaches implemented in QUANTY, shown in Fig. 5. CRISPY is a free and open-source software which is currently developed at the European Synchrotron Radiation Facility by Marius Retegan. https://doi. org/10. 5281/zenodo. 1008184 The program is written using the PYTHON programming language, and relies on a number of additional open-source scientific libraries which are part of  the PYTHON ecosystem. The user interface is developed using the PyQt5 library which provides a comprehensive set of python bindings for the Qt5 framework. Spectra manipulations are done using a dedicated plotting widget from the SILX library. Computationally demanding tasks, such as interactive broadening of the spectra, are done using the FFT module from the NUMPY library.
Using CRISPY it is possible to perform XAS, XMCD and RIXS calculations for transition metals and lanthanides considering various level of approximations for the semi-empirical Hamiltonian and different site symmetries of the absorbing atom. In the case of non-centrosymmetric site symmetries, the inclusion in the Hamiltonian of elaborate pd mixing terms is also possible. In addition, for the simulation of L 2,3 edges in transition metals energy depended broadenings and angular dependence can be considered. More information about CRISPY, including detailed installation instructions and tutorials on how to use it, can be found at http://esrf. fr/computing/scientific/crispy. To make the interface more accessible for non-expert users, easy to use package installers for Windows and macOS operating systems are also provided.
The XCLAIM interface XCLAIM  [37]  is a graphical interface written in PYTHON that interfaces with a compiled Fortran code as described in section 3. 3 and calculates spectra using a multiplet model that includes a general crystal-field based on Wybourne parameters. The XCLAIM multiplet code can be downloaded at: https://subversion. xray. aps. anl. gov/xclaim/xclaim. html. It also possible to include charge-transfer within TMO 6 and TMO 4 clusters. The program can calculate XAS, RIXS, XPS, valence photoemission and inverse photoemission. Fig. 6  shows the main window with the controls for setting the model parameters. In addition to calculating spectroscopy, it is also possible to get information from the ground and excited states by calculating expectation values of quantum operators, like the spin and orbital momenta, and occupation of the d-orbitals that give information on crystal field excitations  [38].
The expectation values are also useful to estimate the errors made when using the sum rules. Several factors can limit the application of the sum rules to experimental spectra: the magnetic dipolar term  [25] , the mixing of spectral weight between the L 2 and L 3 edges occurring in transition metals  [12, 39]  or the fact that the isotropic intensity is approximated as the average of left and right circularly polarized absorption  [40]. For cases that are not implemented in the graphical interface, it is possible to use the PYTHON modules of XCLAIM directly from the PYTHON interpreter, together with the Numpy, Scipy and Matplotlib libraries for plotting. The code saves the Hamiltonian matrices and transition operators and includes sample python scripts to calculate XAS and RIXS that can be edited and modified by the user.
The CTM4XAS interface The THOLE multiplet program has been used in the CTM4XAS interface. CTM4XAS can calculate the XAS, XPS, XES and RIXS spectra of transition metal systems and rare earths  [41]. The XAS calculations include the 1 s, 2p and 3p core excitations of the 3d, 4d and 5d transition metal ions. In addition the 3d and 4d XAS spectra of uranium and of the rare earths can be calculated. The XPS calculations include all possible core excitations, where the interface limits the number of configurations to two. XPS calculations with more configurations must be calculated with the THOLE program. CTM4XAS also can be used to calculate the XES spectra and the matrix elements for 1 s2p, 1 s3p and 2p3d RIXS calculations, that can be performed with CTM4RIXS. The CTM4XAS6 software includes an option to switch from the THOLE to the QUANTY program  [29].
CTM4XAS6 is programmed by Mario Delgado. It can be used to calculate the 2p and 3p XAS spectra of 3d transition metal ions. The THOLE and QUANTY codes can both be used, and as far as has been tested provide the same result. The code can also be used to generate the LUA scripts for QUANTY calculations, that then can be further modified as required. CTM4XAS can be found at: http://www. anorg. chem. uu. nl/ CTM4XAS/ An additional program from Mario Delgado is CTM4DOC  [42]. This program analyses the ground state that has been used to calculate the XAS spectra. The ground state can be analyzed in terms of (a) an orbital description, (b) an atomic term symbol description or (c) a crystal field term symbol description, where the respective components that make up the total ground state are given.
The QUANTY first principle multiplet code In section 2. 4 we described how to use QUANTY to calculate core-level spectroscopy using empirical models. Models including crystal field multiplet theory, charge transfer multiplet theory, multiple site clusters or Anderson impurity models have been used for many years with success and QUANTY allows for an implementation of each of these. For high symmetry crystals, i. e. those where the transition metal ion is on a site with cubic point-group symmetry, the number of parameters needed in these calculations is moderate, especially if the Coulomb interaction is taken from atomic Hartree-Fock or Density Functional Theory calculations. For low symmetry systems determining all crystal field or ligand field parameters becomes cumbersome. The bonding within a crystal is a result of a gain of kinetic energy and a loss of potential energy with respect to an assembly of neutral atoms.
The RSPt interface to QUANTY QUANTY can also be used in conjunction with the full-potential linear muffin-tin orbital (FP-LMTO) code RSPt (http://fplmto-rspt. org). Here the impurity problem is formulated in the language of dynamical mean field theory (DMFT), which operates with several sets of effective ligand orbitals  [49]. The first step is to construct a set of localized wavefunctions describing the 3d impurity states. There is no unique way of obtaining these states and a possible strategy is to perform Wannierization of the band structure by selecting the bands belonging to a certain energy range and possessing particular atomic character  [29]. RSPt adopts another procedure and defines more general projection schemes, which are independent of the band structure details and do not require any a priori knowledge about the system.
There are two projection schemes  [50]  muffin-tin-heads and Löwdin-orthogonalized LMTO's. The former projection takes the density within the muffin-tin sphere of a certain l-character and neglects the interstitial part. The latter one mixes the orbitals centered on different sites, making it reminiscent to Wannier functions. Once the projection is defined, one can calculate the local Hamiltonian. The projected Hamiltonian will contain all information about the crystal field and spin-orbit coupling directly from the DMFT calculation. An impurity problem with several sets of bath orbitals is then efficiently solved using QUANTY or an alternative impurity solver, which also uses Krylov projection scheme [https://github. com/JohanSchott/impu rityModel]. It is worth mentioning that for a long time the number of bath states (N b ) was restricted to just a few orbitals, but recent development allowed to consider even hundreds of bath states.
BSE spectra tend to agree well with measurements of early 3d transition metal oxides, but show discrepancies for oxides toward the middle of the 3d series. The BSE for both core-and valence-level excitations is implemented within the OCEAN code  [76, 77]. Due to the localized nature of the core-hole, computational effort in evaluating the screening response may be limited to the neighborhood of the absorbing site by working in real-space  [78]. Finally, rather than solving explicitly for the final states by full diagonalization, the spectrum is generated by Lanczos iteration. An all-electron version of the BSE formalism  [79, 80]  is implemented within the all-electron full-potential code package EXCITING  [81] , which has recently been applied to calculations of core-spectroscopy  [82]. Kohn-Sham energies and wavefunctions are obtained from all-electron full-potential DFT calculations employing the linearized plane-wave basis set.
The MULTIX multiplet code The MULTIX multiplet method  [104]  is a self-contained, accessible ligand-field/crystal-field approach to calculate XAS, RIXS and inelastic neutron spectra (INS), as well as ground state properties including anisotropic magnetic properties of the ion. The MULTIX website http://multiplets. web. psi. ch/ provides downloads and documentation for MULTIX and auxiliary codes for converting crystal structures to ligand cluster files for the application of the crystal-field point-charge model. MULTIX works in the active space of the core-hole shell and the local orbital valence shell as a self-contained first principles code. This aspect calculates the key matrix elements for electron-electron interaction, relativistic effects including spin-orbit coupling and single-electron orbitals with minimal input.
Acknowledgements HE, FF, RW and FdG were supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  340279 ). MDJ was financially supported by the  National Council of Science and Technology of Mexico (CONACyT) , under grant  A1-S-8384. MvV and JFR were supported by the  US Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering  under Award No. DE-FG02-03ER46097. Work at  Argonne National Laboratory  was supported by the  U. S. DOE, Office of Science, Office of Basic Energy Sciences , under contract No. DE-AC02-06CH11357. JFR has received funding from the  European Union   Horizon 2020 research and innovation programme  under the  Marie Sklodowska -Curie  grant agreement No.
665593. RJG was supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC). HI was supported by the  JST PRESTO , Grant No. JPMJPR16N1 16815006 , and  JSPS KAKENHI , Grant No. JP20H05180. HR acknowledged financial support by  Swissnuclear during his stay in Switzerland (Paul Scherrer Institute ). MO was supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  860553  and the  Carl Tryggers Foundation  (contract  CTS18:285 ). ML acknowledges financial support from the  Knut and Alice Wallenberg Foundation  (Grant No. KAW-2013. 0020 ). SIB was supported by  Deutsche Forschungsgemeinschaft  (Grant No. BO 4915/1-1 ). OK was supported by  Deutsche Forschungsgemeinschaft  (Grant No. OK952/10-2 ).
Observations and Data Reduction 2. 1. GRAVITY data HD 163296 was observed with GRAVITY  (Eisenhauer et al. 2008 (Eisenhauer et al. , 2011;; Gravity Collaboration et al. 2017 ) during 2018 and 2019 as part of the Young Stellar Object (YSO) Guaranteed Time Observations (GTO). The log of the observations is reported in Table  A. 1. The observations were done using the highest spectral resolution (R ∼ 4000) of the instrument, deploying the science source as Fringe Tracker (R ∼ 22, sampling ∼1 kHz;  Gillessen et al. 2010). This allows us to stabilize the fringes of the science beam combiner for up to several tens of seconds. All the data were recorded using the 1. 8 m Auxiliary Telescopes (ATs) with the intermediate array (D0-G2-J3-K0) 1. The interferometric observables (squared visibilities and closure phases) were obtained using the instrument's data reduction software provided by ESO  (Lapeyrere et al.
Ancillary Data To complement the analysis and results obtained from our GRAVITY data, we used the available PIONIER-VLTI data sets of HD 163296 taken during 2013, and 2017, which are included into the Optical Interferometry Data Base (OIDB 2 ) hosted by the Jean-Marie Mariotti Center (JMMC). These data are reduced and calibrated in a consistent way, which allows us to compare and merge them directly. PIONIER data are of low-spectral resolution, sampling three and six spectral channels across the H-band (λ 0 = 1. 65 µm) in 2013, and 2017, respectively. The log of these observations are also listed in Table  A. 1 in the Appendix. A&A proofs: manuscript no. aanda22  2. 12 2. 9 2. 17 3. 0 2. 9.
We explicitly constrain the model to have F s + F c + F h = 1 For the minimization, we used the Gradient Descent Least-Squares algorithm implemented in lmfit. The starting points of the parameters use values close to the position angle, inclination, size and flux ratios reported in  Lazareff et al. (2017). The c 1 and s 1 variables, which define the degree of asymmetry in the ring, were initially set to zero. Initial values were the same for all epochs and instruments. Each spectral channel was fitted independently. The 2013 datasets were combined into a single epoch. Since this corresponds to the same dataset used by  Lazareff et al. (2017)  and  Kluska et al. (2020) , this serves us to compare our best-fit models directly with those previous estimates. For the PIONIER data sets taken in 2017, we only combined data taken with a maximum separation of three days and with a minimum of four snapshots.
Constraints on the geometry of the target Our parametric model reproduces the observables for all the epochs. The morphological characteristics of the source obtained with our best-fit model are the following ones: 1. Geometry: The radii of the ring vary from 1. 75 -2. 7 mas (0. 175 -0. 27 au) for the 2013, 2018 and 2019 epochs, and of 0. 65 -1. 2 mas (0. 065 -0. 12 au) for both 2017 epochs. The 2013 PIONIER data helped us to compare our results with previous estimates in the literature. In this regard, the estimated radius of the ring, a r , is in agreement with the 1. 81 mas reported by  Lazareff et al. (2017)  using the same dataset. We expect to observe a change in the radius of the ring between the H and K bands due to a radial gradient in the temperature profile of the dust. However, we observed that there is an important difference in the obtained values, even within the PIONIER epochs (a 2013,June/July r ∼ 1.
One important difference between the ring model presented by  Setterholm et al. (2018)  and ours is that those authors use an infinitesimal ring, which produces more pronounced rebounds in the visibilities after the first minimum. In our case, the wire-frame of our azimuthally modulated ring is convolved with a Lorentzian kernel which smooths the profile of the ring and produces flatter rebounds after the first minimum in the visibility trend. Additionally, the uncertainties in the K-band data presented by those authors have values Comparison with reconstructed images: Our geometrical models reproduce the observed profiles in our data. However, those geometrical models can only explore a limited degree of asymmetries in the data. In order to better explore the asymmetries traced by the closure phases, we performed image reconstruction on our datasets.
T d was computed considering a power-law dust size distribution  Mathis et al. (1977) , for dust sizes ranging from 10 -2 to 1 µm. To calculate the distribution of temperatures per epoch, we extracted 10 4 different samples of the peak's positions, dust grain sizes, and we set L * ,38 = 6. 38×10 34 erg s -1 (Acke & van den Ancker 2004). The deprojected peak's positions were obtained from the Ring model, assuming an inclination i = 40 •. This produces the temperatures reported in Table  3. These values ( 1240 K < T d < 1600 K) are in agreement with temperatures between the sublimation point of the silicate (T s = 1500 K) and carbon (T c = 1800 K) dust grains. However, we should notice that the reported range of temperatures only traces the material observed with the different interferometric datasets.
Acknowledgements. The authors thank the anonymous referee for his/her valuable comments. JSB acknowledges the full support from the  UNAM   PAPIIT  project  IA 101220. This research has made use of the  Jean-Marie Mariotti Center OiDB  service available at http://oidb. jmmc. fr. ACG acknowledges funding from the  European Research Council  under Advanced Grant No. 743029 ,  Ejection ,  Accretion Structures in YSOs (EASY). A. A. , M. F. and P. G. were supported by  Fundação para a Ciência e a Tecnologia , with grants reference  UIDB/00099/2020  and  SFRH/BSAB/142940/2018. RGL acknowledges support from  Science Foundation Ireland  under Grant No. 18/SIRG/5597.
The only Sentinel 2 ship dataset publicly available is  [16]  but it only includes small size image chips and weak annotations for precise localization, i. e. , a single point for each ship, obtained by geo-referencing AIS GPS coordinates to pixel coordinates in the chips. Although large datasets exist for VHR images, for Sentinel-2 none are available with pixel level annotations while usually thousands of examples are needed to train deep learning object detectors. Few-shot learning based approaches can bring interesting perspectives for remote sensing in general and in our setting in particular. Few-shot learning consists in training a neural network with few labeled samples, most often thanks to quality feature extractors upon which transfer learning is performed. One recent method for unsupervised learning of features extractors that enable few-shot learning is contrastive self-supervised learning  [6, 17].
Contributions In this work, we make two contributions: (1) A deep learning pipeline for ship detection with few training examples. We take advantage of self-supervised learning to learn features on large non-annotated datasets of Sentinel 2 images and we learn a ship detector using few-shot transfer learning; (2) A novel Sentinel 2 ship detection dataset, with 16 images of harbours with a total of 1053 ship annotations at the pixel level.
The fully connected layer has 512 output neurons and produces the embedding. The encoder's parameters θ q are updated with SGD while the momentum encoder's parameters θ k are updated using Equation (  2 ) where m = 0. 99 is the momentum value: θ k ← mθ k + (1 -m)θ q (2) Patches in SSL are pre-processed by first clipping to the 3rd and 97th percentile computed, for each band, over the pre-training dataset. Then the patches are standardized by subtracting the mean and dividing by the standard deviation computed on a part of the EuroSAT dataset  [25]. encoder momentum encoder x q ,x k+ q k + , k - q. k contrastive loss gradient x k- i momentum encoder i-1 momentum encoder i-2 k - k - queue Figure 2. MoCo training algorithm  [6] : negative embeddings from momentum encoders at previous iterations (i -1, i -2, etc.
Ship Detection Dataset: S2-SHIPS To the best of our knowledge, no ship detection Sentinel-2 datasets for both moving and static ships with pixel level annotations has yet to be published. We introduce a novel ship detection dataset made up of littoral and harbor regions images. This dataset includes 16 L2A images of coastline, ports, and the Suez canal. Figure  4  shows the geographical distribution of the data. The images are of size 1783 × 938, cover 167 sq. km each, and are annotated at the pixel level with a total of 1053 distinct ships. We also provide earth/water masks for these images, rasterized from OpenStreetMap layers. The ships are from various size, with areas ranging from 100 m 2 (e. g. , pleasure boats, small fishing ships) to more than 5000 m 2 (e. g. , cargo ships). Since the Sentinel 2 mission does not provide high sea tiles, the images are taken near coasts and we annotated moored ships and ships at sea separately.
Our dataset also provides images taken under different weather conditions, including turbulent seas, clouds, or sun glint. Thus, the complex environment surrounding ships in our dataset makes it challenging for ship detection. Several samples are shown in Figure  5. We rasterize OpenStreetMap water layers (ocean, major rivers, canals) on the 16 georeferenced images to produce binary masks of water. These layers sometimes have the contours of peers, jetties, but the annotation of these entities as land is not insured.
Backbone Pre-Training Datasets For backbone pre-training with SSL, we look at existing large scale Sentinel 2 datasets. Several have been published in recent years and usually focus on land cover classification or segmentation. Since we do not use labels for pre-training we can use these types of data easily and in large quantities. Some well-known datasets are: • EuroSAT  [25] , which is a 10 class land-cover classification dataset containing 27,000 multispectral patches of size 64 × 64 pixels. We apply the (A)ugmentation pretext task on this dataset; • BigEarthNet  [28] , which is a very large scale multi-label land-cover classification dataset. It contains 590,326 multi-spectral image patches of size 120 × 120 pixels. We randomly crop 320,000 64 × 64 pixel patches from the original dataset and we apply the (A)ugmentation pretext task for BigEarthNet; • SEN12MS  [29] , which is a very large curated land cover segmentation dataset, made of Sentinel 1, 2 and MODIS images.
It contains 180,662 Sentinel 2 multi-spectral patches of size 256 × 256. For this dataset, we apply both the (A)ugmentation and the region-wise and augmentation (RA) pretext task. For the first one we sample 1,337,360 patches 64 × 64 patches from the 256 × 256 patches in the dataset. For the (RA) and (RA-lo) tasks we sample patches x q and x k + of size 64 × 64 pixels randomly from the same 256 × 256 patch. The distance between these two "positive" patches can thus be at most 1. 2 km for (RA) and 640 m for (RA-lo), while sometimes there can be an overlap.
Experimental Settings and Parameters We run the pre-training SSL pipeline for 100 epochs with a learning rate of 0. 001 and a cosine annealing schedule. For the augmentation (A) task, we used a batch size of 500 for the EuroSAT and BigEarthNet pretraining and a batch size of 900 for SEN12MS. We trained the pretext task on one GPU with 12 Go of memory for the EuroSAT and BigEarthNet datasets, and on a multi-gpu machine for SEN12MS dataset to accelerate the learning process. The region based pretext (RA) task was applied to SEN12MS using the same hyperparameters as for (A), with a batch size of 500. Next, we copied the parameters of the ResNet-50 backbone trained with SSL into the corresponding layers of the U-Net. We train the network in two ways: fine-tuning (FT) and transfer learning (TL).
We use the NDWI spectral index NDW I = B03-B08 B03+B08 and we threshold its value to segment water and nonwater pixels. The threshold for the NDWI segmentation is chosen to obtain the best performance on the whole dataset, which may lead to suboptimal choices for some images. Next, we eliminate land pixels using the water/land segmentation (CO) (Section 2. 5. 1) map, giving a ship proposal map. We consider non-water pixels in what are normally water regions to potentially be ships. We extract connected components and we eliminate those that have a width and height greater than 50 pixels (500 m) since no ships larger than this size exist. These are due to islands or sandbanks not correctly mapped in OpenStreetMap layers or thin water banks where the coastline annotation in OpenStreetMap is imprecise.