dataesr/scientific-paragraphs-categorization huggingface dataset:
A9, page 2 of 17 Lesur, G. R. J. , et al. : A&A proofs, Acknowledgements. We wish to thank our referee,  James Stone , for his valuable comments and suggestions that significantly improved the manuscript. We 7 https://github. com/idefix-code/idefixalso thank  Philipp Grete ,  Jeffrey Kelling ,  Rémi Lacroix ,  Thomas Padioleau  and  Simplice Donfack  who mentored and helped us to improve IDEFIX during the 2021 hackathon organised by IDRIS and  NVidia. Finally , we acknowledge the inspiring work of  Pierre Kestener  (CEA) on Kokkos-based Godunov methods. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 815559  (MHDiscs)). This work was supported by the  "Programme National de Physique Stellaire" (PNPS) ,  "Programme National Soleil-Terre" (PNST) ,  "Programme National de Hautes Energies" (PNHE)  and  "Programme National de Planétologie" (PNP)  of  CNRS/INSU  co-funded by  CEA  and  CNES.
The polarization degree increases from ∼20% between 2 and 3 keV to ∼80% in the 6-8 keV energy range. The polarization angle remains roughly constant in all energy bins. We performed a spectropolarimetric analysis making use of the IXPE, Swift-XRT, and NICER observations (the latter performed within a month; see Appendix A). We extracted Swift and NICER energy spectra with the standard data analysis tools and used IXPEOBSSIM to derive the IXPE I, Q, and U energy spectra. We use XSPEC (version 12. 13. 0, Arnaud 1996) for a joint spectropolarimetric fit of the I spectra from all three observatories and the Q and U spectra from IXPE. We tested two models commonly used to interpret magnetar soft X-ray spectra, i. e. , an absorbed BB+PL and a BB+BB. 58 e started with the BB+PL model, multiplying the BB and PL components with a polarization factor constant in energy; the corresponding XSPEC model is TBABS * (BBODY * POLCONST + POWERLAW * POLCONST), with abundances from  Wilms et al.
We tested various configurations and found that there exist at least two geometrical setups, within the scenario outlined above, that match well most of the observed properties of 1RXS J1708. The first one (model (A), or "belt plus cap") is a variation of the geometry envisaged for 4U 0142 + 61 Note that data points in the phase-resolved analysis are obtained with IXPEOBSSIM, and therefore independent on the spectral decomposition. (Taverna et al. 2022) , in which the warm (condensed) region corresponds to an equatorial belt, and the hot (atmospheric) region corresponds to a circular, polar cap; the belt, however, needs to be limited in azimuth in order to reproduce the X-ray pulse profile. In this case, calculations have been performed using the general relativistic ray-tracing code discussed in  Zane & Turolla (2006;  see also  Taverna et al.
g. , RCS (saturated RCS only predicts a ∼30% level of polarization). However, it is worth noticing that 1RXS J1708 exhibits a strong nonthermal emission at high energies, as detected in the INTEGRAL band, with extreme variations in phase and energies, whose origin is still unexplained in terms of detailed physical models  (Kuiper et al. 2006 ). It could therefore be possible that this nonthermal component and that observed in the IXPE band could be similar in origin (but with varying degrees of manifestation). We thank K. C. Gendreau and Z. Arzoumanian for their help in scheduling NICER observations of the source. We acknowledge the use of public data from the Swift data archive, and we thank the Swift team for promptly scheduling a target of opportunity (ToO) observation. R. LV2) data, processed to produce suitable inputs for science analysis, were downloaded from the IXPE archive at HEASARC.
5%, which is deemed negligible for the subsequent analysis (see A. Di Marco et al. 2023, in preparation, for a more detailed description). The source region is then defined with SAOImage Deep Space 9 (DS9; Joye & Mandel 2003) as a circle with radius of 1 5. The background is extracted from an annulus centered on 1RXS J1708 with inner and outer radii of 2 5 and 4 0, respectively. The response to polarization is extracted from LV2 data with the IXPEOBSSIM package  (Baldini et al. 2022) , which implements the algorithms in  Kislat et al. (2015)  and is publicly available. 62 The spectro-polarimetric analysis is carried out by feeding the Stokes spectra, generated with the IXPEOBSSIM tool XPBIN, into XSPEC  (Arnaud 1996)  and exploiting the appropriate response matrices available at HEASARC for modeling the polarization as suggested by  Strohmayer (2017).
A. 2. NICER In order to complement the IXPE data, we requested a set of new NICER observations, which have been awarded and performed a week after the end of the last IXPE observation. However, all these observations suffered from high optical loading (resulting from a low Sun angle), and thus were unusable for our analysis. We therefore resorted to the use of the NICER archival data, using the most recent available observations (closest in time to the IXPE observation). Specifically, for the contemporaneous spectral fitting with IXPE and Swift spectra, we made use of the observations from 2022 August 21 (observation ID 5593051301). The data were processed using version 9 of the NICER Data Analysis Software (NICERDAS) on version 6. 30. 1 of HEA-Soft. We applied standard filtering criteria as per the default cuts with nicerl2, resulting in 972 s of filtered exposure.
A. 3. Swift A Swift-XRT target of opportunity observation was performed on 2022 October 20 for a total of 1020 s in windowed timing mode. The data were processed using the standard HEASoft tools, extracting the spectra using xselect, after generating ancillary response file using xrtmkarf and the latest calibration files available in the Swift-XRT CALDB. The source was extracted from the cleaned event file using a centered circular region with a radius of 17 pixels. The background region was extracted using an offcentered circular region of the same radius. The extracted source spectra were binned, requiring each spectral channel to have at least 25 counts.
T. and R. T. acknowledge financial support from the Italian Ministry of University and Research (MUR) through grant PRIN 2017LJ39LM. D. G. -C. , J. H. , and I. C. acknowledge support from the Natural Sciences and Engineer Council of Canada and the Canadian Space Agency. M. N. acknowledges the support by NASA under award number 80GSFC21M0002. T. T. was supported by grant JSPS KAKENHI JP19H05609. H. K. and E. G. acknowledge NASA support under grants 80NSSC18K0264, 80NSSC22K1291, 80NSSC21K1817, and NNX16AC42G. We thank an anonymous referee for a careful reading of the paper. Facilities: IXPE, Swift (XRT), NICER.
T. and R. T. acknowledge financial support from the Italian Ministry of University and Research (MUR) through grant PRIN 2017LJ39LM. D. G. -C. , J. H. , and I. C. acknowledge support from the Natural Sciences and Engineer Council of Canada and the Canadian Space Agency. M. N. acknowledges the support by NASA under award number 80GSFC21M0002. T. T. was supported by grant JSPS KAKENHI JP19H05609. H. K. and E. G. acknowledge NASA support under grants 80NSSC18K0264, 80NSSC22K1291, 80NSSC21K1817, and NNX16AC42G. We thank an anonymous referee for a careful reading of the paper. Facilities: IXPE, Swift (XRT), NICER. Appendix A Observational Data and Data Processing A. 1. IXPE IXPE observed 1RXS J1708 in two segments close in time, from 2022 September 19 05:08 UTC to 2022 September 29 12:00 UTC and from 2022 September 30 12:52:02 UTC to 2022 October 8 11:17, for a total livetime of ∼837 ks.
Visual localization plays an important role in the positioning and navigation of robotics systems within previously visited environments. When visits occur over long periods of time, changes in the environment related to seasons or daynight cycles present a major challenge. Under water, the sources of variability are due to other factors such as water conditions or growth of marine organisms. Yet it remains a major obstacle and a much less studied one, partly due to the lack of data. This paper presents a new deep-sea dataset to benchmark underwater long-term visual localization. The dataset is composed of images from four visits to the same hydrothermal vent edifice over the course of five years. Camera poses and a common geometry of the scene were estimated using navigation data and Structure-from-Motion.
Introduction With the advent of Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), there is a need to enable these vehicles to localize themselves accurately in an underwater environment. This paper addresses the problem of visual localization which consists in estimating the 6 degrees-of-freedom (6DOF) pose of a camera given its image and previous observations made in the area. It has received a lot of attention over the last decade with the rise of self-driving cars. This task becomes more difficult when the environment is subject to important changes, as it is the case for images acquired during successive visits in deep ocean. Long-term localization methods aim to deal with major changes in the environment, e. g. , snow during winter  (Sattler et al. 2018). Most of the databases used to evaluate these methods are made up of terrestrial data.
They cover a wide range of environmental changes such as day-night, seasons and weather conditions  (Griffith et al. 2017; Sattler et al. 2018; Burnett et al. 2023). Because underwater images have different sources of technical and environmental variability, existing datasets are not suitable for evaluating long-term localization performance in such scenarios. Indeed, the characteristics of the underwater medium cause many visual perturbations related to light and color absorption, turbidity and backscattering. Furthermore, in deep-sea scenarios, underwater vehicles must be equipped with artificial lighting systems in order to illuminate the absolute darkness of the environment. While this allows the use of cameras to record what lies on the seafloor, it also creates strong differences in illumination depending on the distance between the robot and the scene.
In addition to these short-term perturbations, long-term changes occur in these environments, e. g. , changes in the distribution of microbial and animal communities or topographical changes. Some localization methods rely on deep retrieval and feature matching networks to be robust to large sources of variability. However, these networks, e. g. , NetVLAD  (Arandjelovic et al. 2016) , have only been pre-trained on terrestrial data. Applications of these models in underwater environments may not be straightforward due to the domain shift associated to the specificity of underwater imaging. Previously published underwater datasets targeting online localization  (Ferrera et al. 2019; Mallios et al. 2017 ) span over very short temporal ranges, i. e. , data were acquired during the same day. Thus, they do not cover the long-term changes that can appear in these environments, leaving a gap in methods and datasets available to treat multiannual deepsea image sequences.
This paper presents a new dataset for long-term visual localization in a deep-sea environment. It is composed of four different visits of the same hydrothermal edifice over five years  (Girard et al. 2020 ) (Figure  1 ). More specifically, it provides the following data: • Images of the vent for all four visits. • Navigation data in the form of latitude, longitude and altitude information. • 3D models of the scene estimated using Structurefrom-Motion (SfM) for each visit year. • A global 3D model including all images in a common reference frame. The data presents changes over time related to all the aforementioned underwater imaging factors. It also presents some peculiar characteristics, like the occurrence of black and white smokers that emit hot hydrothermal fluid. Moreover, due to the numerous sources of variation present in this dataset, it may also be used for detecting longterm changes that take place in deep-sea environments.
Related work Datasets used for benchmarking visual localization algorithms are mostly terrestrial, including Aachen Day-Night, RobotCar Seasons and CMU Seasons introduced by  Sattler et al. (2018)  as well as Cambridge  (Kendall et al. 2015) , 7-Scenes  (Shotton et al. 2013 ) and 12-Scenes  (Valentin et al. 2016). 7-Scenes and 12-Scenes are collected in an indoor setting, while the others are composed of outdoor environments. Sattler et al. (2018)  datasets exhibit some difficult localization scenarios, like day-night observations. Similar datasets to benchmark the visual localization task under water are rare due to the cost of data collection. Existing underwater datasets  (Mallios et al. 2017; Ferrera et al. 2019)  focus on providing data for the development of underwater SLAM algorithms.
AQUALOC dataset  (Ferrera et al. 2019)  provides underwater monochromatic images synchronized with inertial and depth data for three different sites off Corsica. One of the sites is a harbor lying at a depth of 3 to 4 m and the other two are archaeological sites that lie at depths of 270 m and 380 m. While sequences follow different trajectories, all different visits occurred during the same day, not covering all the possible changes that can happen in this environment, e. g. , salinity variation that can alter the pinhole model, increased turbidity, sedimentation or marine population changes. Nielsen et al. (2019)  evaluated PoseNet  (Kendall et al. 2015) , an end-toend visual localization neural network, on an underwater dataset acquired in a pool where camera poses were obtained with an underwater motion capture system.
Other underwater datasets that focus on different tasks, e. g. , dehazing  (Akkaynak and Treibitz 2019; Li et al. 2019; Berman et al. 2021) , do not provide images of the same site over long periods of time. Campos et al. (2015)  already published image data of the 2015 visit of the Eiffel Tower vent. The authors presented a novel method for surface reconstruction of underwater structures. To benchmark their algorithm, they used a point cloud resulting from a SfM on the images of the 2015 dive. The current work also exploits SfM to produce the reference poses and scene geometry on this collection, and completes it with similar data from three other dives of the same site in different years, capturing long-term changes. Visual localization benchmarking datasets require reference camera poses for each image, which can be constructed in different ways.
Most common methods to access such information as well as the scene's geometry rely on SfM or depth-based SLAM  (Brachmann et al. 2021). In most deepsea missions, the use of motion capture is discarded due to the difficulties in deploying such a system. Standard RGB-D sensors cannot directly be used underwater because of the absorption of infrared light in water, making depth-based SLAM harder to set up. Thereby, SfM offers a practical solution for estimating camera poses and scene geometry in the underwater environment. Nevertheless, Brachmann et al. (2021) showed that the performance of a localization method on a given dataset is greatly affected by the method used to build the "ground truth" of this dataset. Indeed, methods that minimize the same error as the algorithm used for estimating the ground truth poses have the advantage of leading to the same local minima.
2020). During the different dives, synchronized videos and navigation data were acquired using the ROV sensors. Videos were captured using two different cameras, whose characteristics are presented in Table  1. Images were acquired through a specifically designed dome port with corrective lenses to account for underwater refraction. As these lenses largely compensate the distortion induced by the air-glass-water mediums, a second order radial distortion model with k1 and k2 distortion coefficient was used to calibrate the cameras underwater. The ROV incorporated an artificial lighting system consisting of 12 LED panels delivering 20,000 lumens each. It also embedded an Ultra-Short Baseline acoustic positioning system (USBL), an Inertial Navigation System (INS), a Doppler Velocity Log (DVL) and a depth sensor that were fused similarly to  Guerrero-Font et al.
Camera poses and scene geometry This visual localization dataset offers images and their 6DOF poses expressed in a common frame of reference. COLMAP SfM  (Schönberger and Frahm 2016)  was used to obtain camera poses and intrinsic parameters, as well as 3D scene geometry. From the videos captured each year, one image was extracted every 3 seconds to create the input data of the dataset. Some images were polluted on their border with small navigation overlays that were removed using an inpainting technique  (Telea 2004 ). In addition, images were inspected manually to discard irrelevant ones, e. g. , images only capturing the water column. Several issues were encountered when attempting to use COLMAP directly on images from all years. Firstly, to create image pairs, COLMAP relies on image retrieval methods like vocabulary tree  (Schönberger et al.
2016)  or NetVLAD  (Arandjelovic et al. 2016). While these methods successfully match images of the same year, they show poor performance at pairing images across different years. Secondly, to perform feature matching between image pairs, COLMAP uses SIFT descriptors. However, as shown on Figure  3 , hand-crafted descriptors fail to produce satisfactory matches between images of different years. To overcome these issues, we adopted the SfM pipeline described on Figure  4  *. We first built a model for each year independently. For each individual model, image retrieval was performed using navigation data. However, navigation data are missing for 3,178 images of the 2015 dive. In this case, image retrieval was achieved using NetVLAD  (Arandjelovic et al. 2016). Within the same visit, NetVLAD proved to be efficient to retrieve similar images.
Instead of hand-crafted descriptors, we used SuperPoint (DeTone et al. 2018) and SuperGlue et al. 2020) for feature matching. We then registered the resulting 3D point clouds of each individual model to the 2020 point cloud using TEASER++  (Yang et al. 2021 ) and refined the result with ICP  (Zhou et al. 2018). This way, we obtained a coarse estimation of the camera intrinsics and poses of each year in the same reference frame. We then paired cross-year images using these poses and matched these images with SuperPoint and SuperGlue. Finally, we used COLMAP to obtain a global model embedding all images. The scale of each model was retrieved by aligning camera poses in Sim(3) with available navigation data using Umeyama's algorithm  (Umeyama 1991). For the global model that includes all observations, the alignment was performed using only 2020 navigation data.
Dataset format The Eiffel Tower dataset is composed of images of the dives and a global COLMAP model embedding all visits. For reproducibility purposes, we also provide individual COLMAP models for each visit and interpolated navigation data for each image when available. The dataset architecture is detailed in Figure  5. Each image is named after the date at which it was acquired in the format YYYYmmddTHHMMSS. fffZ, where YYYY is the four digit year, mm is the month, dd is the day, HH is the hour in the 24 hours format, MM are the minutes, SS the seconds and fff the milliseconds. A COLMAP model embeds scene geometry, camera intrinsics and poses. It is composed of 3 files: cameras. txt contains the camera intrinsics; images. txt provides camera poses as well as.
Characterizing changes across years This dataset contains numerous appearance changes across visits that present important challenges for the visualbased localization task. These alterations are of different nature, i. e. , topographic, environmental or modifications in the ROV's equipment. A number of changes observed and measured over the period reflect a modification of the edifice's local geomorphology over the years  (Van Audenhaege et al. 2023). Chimney collapse, outcrop/boulder detachment or slide resulted in a loss of material, while vent active areas grew through mineral accretion creating new outcrops, flanges and spires. Material build-up was twice as important as the loss, suggesting that the volume of the edifice is increasing over time. While these changes can be locally drastic and affect the registration of 3D models over the years, they represent only 5% of the total surface and † colmap.
github. io/format. html ‡ github. com/colmap/colmap/blob/dev/scripts are localized in areas of active venting. Local changes in hydrothermal activity also result in distinct mineralization processes, hence deposits, the color of which will vary depending on the temperature and chemical composition of the fluid. Figure  6         reoriented on a daily basis. Furthermore, the period from 2015 to 2020 showed an overall disappearance of white microbial mats over the whole edifice (-72. 85 m 2 ). Although these changes do not affect the general topography of the structure, they strongly modify the color and texture of the model. Figure  7  illustrates how the mussels' population evolution over the years can alter both the topography of the scene and the colors of the vent. Also, because of these organic modifications, there are no real matching 3D points between different years on most of the chimney.
Visual localization benchmark Train and test sets were separated based on the area covered by the ROV each year. As seen on Figure  9 , the total area covered in 2016, 2018 and 2020 contains almost all the area covered in 2015. As a result, we chose 2016, 2018 and 2020 as the train set and 2015 as the test set. Using the aforementioned train/test split, we benchmarked the Eiffel Tower dataset on renowned visual localization methods: PoseNet with different losses  (Kendall et al. 2015; Kendall and Cipolla 2017; Boittiaux et al. 2022) , hLoc  (Sarlin et al. 2019)  and PixLoc  (Sarlin et al. 2021). PoseNet trains a different neural network for each scene, while hLoc and PixLoc rely on deep-learning based features trained on terrestrial datasets. We detail below the parameters used for each of the methods.
PoseNet § : The network as described in  (Kendall et al. 2015 ) is re-implemented, except for replacing the GoogLeNet backbone with a more modern MobileNetV2  (Sandler et al. 2018). For PoseNet loss, we used β = 500 as suggested in  (Kendall et al. 2015)  for the outdoor Cambridge dataset. We initialized the Homoscedastic loss as suggested in  (Kendall and Cipolla 2017) , i. e. , ŝx = 0. 0 and ŝq = -3. 0. For the Homography loss, we selected local x min and x max as the 2. 5th and 97. 5th percentile, as presented in  (Boittiaux et al. 2022). hLoc : We use the pipeline presented in  (Sarlin et al. 2019) , i. e. , NetVLAD for image retrieval and SuperPoint alongside SuperGlue pre-trained on outdoor scenes for local matching. PixLoc : We used weights of the network pre-trained on the MegaDepth dataset  (Li and Snavely 2018).
hLoc+PixLoc : The pose of the camera is first retrieved using hLoc and then refined with PixLoc. This pipeline is presented by  Sarlin et al. (2021). Results on the dataset for all aforementioned methods are reported in Table  4  and can be used as a baseline for the comparison of other long-term visual localization methods. Unlike hLoc and Pixloc, PoseNet based methods are endto-end networks. Consistent with the results presented by  Sattler et al. (2019) , end-to-end networks obtain the least accurate pose estimates. Moreover, since the ground truth was constructed using SfM, methods that replicate this mode of operation, e. g. hLoc, have an advantage because they optimize the same metric  (Brachmann et al. 2021). hLoc and PixLoc are based on networks trained on terrestrial data, and we can expect better results by training these networks on aquatic data.