dataesr/scientific-paragraphs-categorization huggingface dataset:
All species of the genus are numbered in the circular tree from 1 to 60 (clockwise, starting from top-left end of the tree) in reference to species as numbered in S1 Table. This tree was built using 808 DNA barcode records of more than 400bp in the public BOLD dataset DS-LONO2; branch lengths are proportional to genetic divergences (uncorrected p-distances). Colored background areas of the tree represent the three species-groups recognized in this work: the electra-group (blue), obliqua-group (yellow), and achelous-group (green). Left side picture: Lonomia sp. adult (live unidentified specimen from Costa Rica; courtesy of Armin Dett). Right side picture: Lonomia camox 6th instar caterpillar (photo by RR). A traditional vertical representation of this tree is also available at https://itol.
, 2012)  instruments onboard the SDO spacecraft  (Pesnell et al. , 2012)  continuously monitor coronal holes in unprecedented detail. A common strategy to detect coronal holes in SDO mission data is intensity-based thresholding on AIA EUV images. The challenge is to specify an intensity threshold that best separates coronal holes from the surrounding closed-field regions. ASSA, for instance, sets this threshold equal to a fraction of the median intensity on the solar disk  (Hong et al. , 2012). Similarly, SPoCA-CH uses fuzzy C-means clustering, an iterative clustering algorithm, that associates the class with the lowest intensity with coronal holes  (Delouille et al. , 2018). ACWE  (Boucheron et al. , 2016)  approaches coronal hole detection based on homogeneity of intensities rather than absolute intensity values.
Another widespread strategy is partitioning full-disk images into sub-frames. Examples that rely on this strategy are CHARM  (Krista and Gallagher, 2009)  and CHOR-TLE  (Lowder et al. , 2014). SYNCH, in contrast, also partitions images into sub-frames but uses three wavelengths  (17. 1 nm, 19. 3 nm, 30. 4 nm)  simultaneously to detect coronal holes  (Hamada et al. , 2018). CHIMERA also uses three passbands but deduces a differentiation rule in the intensity space to compute coronal hole maps  (Garton et al. , 2018). A unique scheme was proposed in  Caplan et al. (2016) , where the coronal hole maps are calculated from simultaneous multi-instrument EUV images. These synchronic EUV maps are valuable for studying the evolution of coronal holes. Recently, data science applications expanded into coronal hole research, where the goal is to surrogate a human observer.
Research tools and applications will be easily accessible and made available online alongside research results. We expect a holistic view of solar phenomena by multispacecraft observations supported by new data science methods will shape the research landscape. 3. S2-02: Solar indices and irradiance working team 3. 1. What is (are) the key science question(s) that the team wants to answer? In addressing them, what will be the broader impact(s)? Variability in solar spectral irradiance (SSI) is an important driver in the heating of the terrestrial upper atmosphere by influencing the atmosphere's chemistry, dynamics, and ionization state. The Solar Indices and Irradiance (SII) Team's general area of interest is how best to model and predict the observed variability in SSI. Specifically, the team's primary objective is to nowcast and forecast the short-term variability in the solar soft X-ray (XUV, 0.
How does the coronal plasma get heated to multi-million Kelvin temperatures? How does this vary between and among solar features? How do these variations impact the resulting XUV/VUV emission? Combined with flux transport model forecasts of the photospheric magnetic field  (Schrijver and De Rosa, 2003; Arge et al. , 2013; Upton and Hathaway, 2014; Hickmann et al. , 2015)  and atomic physics databases of plasma emission (e. g. CHIANTI,  Dere et al. , 1997) , improvements in data-driven models of the solar atmosphere will lead to long-term forecasts of SSI throughout the heliosphere. 7. 2. What is the current understanding of the research field? What are the most important studies that define the frontier in your field? It is well established that there are two primary mechanisms that convert magnetic to thermal energy in the solar atmosphere -magnetic reconnection and waves -but their relative importance is actively debated.
". The solar magnetic field is changing on multiple temporal scales. Pevtsov et al. (2023)  focus on "Cluster S1: Longterm Solar Variability" and discuss the importance of studying and understanding solar variability. The S1 review will specifically focus on observations such as historical sunspot time series, solar spectral irradiance time series (which tie into our S2-02), and large-scale heliospheric properties such as the open magnetic flux (tying into S2-01 and S2-03). Building the foundation for all activities in Cluster S2,  Pevtsov et al. (2022)  will go deeper into the origins of the solar magnetic field -the solar dynamo -and discuss dynamo modeling with a focus on its long-term variability. Current challenges for historical data preservation, curation, and ways forward are also covered.
Acknowledgments The authors thank all contributors to the  COSPAR ISWAT initiative , which supported this research effort. C. N. A. and J. A. K. are supported by the  NASA competed Internal Scientist Funding Model (ISFM). K. M. acknowledges support by the  NASA   HGI program  (#  80HQTR19T0028 ), the  NASA  cooperative agreement  NNG11PL10A  and  80NSSC21M0180 ; J. A. L. was supported by  NASA   LWS  grant  80NSSC20K0217  to  Lockheed Martin  under a subcontract to PSI, the  NSF   PREEVENTS program  (grant  ICER1854790 ), and the  NASA STEREO SECCHI  and  PSP   WISPR  contracts to NRL (under subcontract  N00173-19-C-2003  to PSI). The  National Solar Observatory  (NSO) is operated by the  Association of Universities for Research in Astronomy (AURA), Inc. , under a cooperative agreement with the  National Science Foundation.
Individual tree segmentation from airborne laser scanning data is a longstanding and important challenge in forest remote sensing. Tree segmentation algorithms are widely available, but robust intercomparison studies are rare due to the difficulty of obtaining reliable reference data. Here we provide a benchmark data set for temperate and tropical broadleaf forests generated from labelled terrestrial laser scanning data. We compared the performance of four widely used tree segmentation algorithms against this benchmark data set. All algorithms performed reasonably well on the canopy trees. The point cloud-based algorithm AMS3D (Adaptive Mean Shift 3D) had the highest overall accuracy, closely followed by the 2D raster based region growing algorithm Dal-ponte2016 +. However, all algorithms failed to accurately segment the understory trees.
Introduction Aerial laser scanning (ALS) is widely used in forest ecology, but automatic individual tree segmentation (ITS) in dense broadleaf forests remains a key outstanding challenge  (Qin et al. , 2022). Accurate ITS algorithms would enable researchers to study tree growth, mortality, leaf phenology and carbon dynamics remotely, providing opportunities to track changes at landscape scales. These remote sensing methods complement existing field-based methods, which are essential but limited in scale  (Ke et al. , 2011). Many ITS algorithms have been developed, but robust comparisons of their accuracy are lacking, particularly for broadleaf forests. In this study we address this knowledge gap by generating an ALS tree segmentation benchmark data set for temperate and tropical broadleaf forests and using it to assess the accuracy of leading ITS algorithms.
ALS data In Sepilok, ALS data was acquired in February 2020 using a RIEGL LMSQ560 with the flight height of 200 m. the instrument has a beam divergence of<0. 5 mrad and ± 30-degree scanning angle. For the focal area, the ALS data has a point density of about 139 per m-2. In Wytham Woods, ALS data was collected in summer 2014 using a Leica ALS50-II with the altitude of 2000 m. It has a beam divergence of<0. 22 mrad and ± 14-degree scanning angle. Over the area of interest, it has a point density of around 6. 26 per m-2 (see Table  1 ).
TLS data At both locations terrestrial laser scanning (TLS) data was captured using a RIEGL VZ-400 (RIEGL, Horn, Austra) following standard protocols described in  (Wilkes et al. , 2017). The instrument has a beam divergence of 0. 35 mrad and operates in the infrared (wavelength nm) with a range up to 350 m, a pulse repetition rate of 300 kHz and the angular sampling resolution was 0. 04 •. At each scan position, two scans were acquired where the scanner rotation axis was perpendicular and parallel to the ground surface respectively. In both sites, reflective targets were located between scan positions to aid with co-registration  (Wilkes et al. , 2017). Scans were registered in RiSCAN Pro in a twostep process where (1) the reflective targets were used to generate a coarse registration (2) a set of planes generated from the point cloud were used in a multi-station adjustment to improve the registration.
The point cloud was then downsampled using a voxel size of 0. 026 m and 0. 02 m for Wytham and Sepilok respectively. At Wytham, scans were done within a larger 6 ha area to ensure the best possible data quality within our 1 ha study area  (Calders et al. 2022). TLS data were collected throughout December 2015 and January 2016 (in leaf-off conditions) on a 20 m × 20 m grid  (Calders et al. ,2018). Individual trees were segmented from the larger point cloud using treeseg  (Burt et al. , 2019)  and manually checked for quality assurance. In Sepilok, the study area was in the tall alluvial forest and the data were collected in 2017. The local 1 ha plot reference is RP292/1 and SEP12 in the forestplots. net data base. TLS data was captured across a ha plot from 121 scan positions on a 10 m × 10 m grid.
Trees were segmented from the point cloud using TLS2trees  (Wilkes et al. , 2022). Briefly, trees are segmented using a 2-step process where a semantic segmentation is used to classify points into ground, leaf, wood, and coarse woody debris. Using the wood and leaf classes only, a graphbased instance segmentation is used where woody stems are first identified, leaf points were subsequently added to individual stems. Resulting segmented trees were manually checked for quality assurance. Note that TLS scanning parameters and tree segmentation workflow differed between the two sites. However, in both cases the TLS data contains more than enough detail to accurately label the individual trees in the ALS data.
Creating ALS benchmark data sets We aligned the ALS and TLS data using a geo-coordinate transformation with 9 manually selected feature points. These features were selected as the intersection of two branches in the upper canopy, so they could be easily identified in both ALS and TLS. The root mean square error in alignment for Sepilok was 0. 192 m and for Wytham was 0. 625 m. We created a polygon to outline the core area in which the TLS (1 ha) overlaps with the ALS (>100 ha). A kdTree was used for fast 3D data retrieval. We labelled the individual trees in the ALS data using the segmented TLS data to produce a benchmark ALS data. Here, we used an iterative nearest neighbour voting strategy to label the ALS data. The labelling strategy consisted of the following steps (see Fig. 2  for workflow and Fig.
3  for resulting benchmark data set). (1) For each ALS point, we selected a maximum of 900 (Sepilok) or 300 (Wytham) surrounding points within a radius of 1. 4 m (Sepilok) or 2. 0 m (Wytham). (2) The label of each ALS point was then assigned as the label of majority of these neighbour points. This process was repeated until it converged (i. e. the number of newly labelled ALS points was<5). The pseudo-code demo can be found in Supplementary L.
Manually rating ALS benchmark trees with confidence score A confidence score system was introduced to control the data quality of the labelled ALS benchmark tree (see Fig. 2 , Supplementary C, D). We categorized the trees as high, medium, and low confidence. The scores were defined in terms of the number of points contributing to a crown   polygon, the distribution of ALS point clouds with comparison to corresponding TLS data and bias of individual tree feature representation. In the benchmark data set, all points within a tree were assigned the tree-level confidence score. Importantly, ITS algorithms segmented trees on the raw ALS data, but only trees belonging to high and medium classes were used in our assessment. The confidence score of the predicted trees was assigned as the most common confidence score of the points within it.
ITS algorithm selection We reviewed all highly cited ITS algorithms and summarized our findings in Table  A1  of Supplementary A. We noted the algorithm type (2D or 3D), key methods, assessment method, forest type and overall accuracy and the number of citations. We chose to focus on four of the most highly cited algorithms, which are representative of different algorithm structures. Dalponte2016 and Dalponte2016þ. Dalponte2016 is the 2D-raster based ITS algorithm, which is widely used as it is integrated in the lidR package  (Dalponte and Coomes. , 2016). The algorithm firstly finds treetops with local maxima filtering on a top of canopy height raster and determines the tree crown boundary with a region growing method. During the region growing, TH_CR and TH_SEED are key parameters determining the edges of the crown.
TH_SEED controls the height difference between the treetop and neighbouring pixels. A neighbouring pixel would be included in the region if its height is greater than the product of TH_SEED and the height of the treetop. While TH_CR controls the difference between the mean height of the regions and their surrounding pixels, and a neighbouring pixel would be view as part of the region if its height is greater than the product of TH_CR and the mean height of the growing region (see Table  2 ). Therefore, the two parameters range from 0 to 1. The code for Dalponte2016 used in the study can be found in  (Roussel et al. , 2020). We also assessed an improved version of Dalponte2016 (hereafter Dalponte2016 + )  (Coomes et al. , 2017b) , which can find more realistic treetops before the region growing stage.
The window size increases with canopy height so that taller trees have larger crowns. The rate at which the window size increases is defined by the user-defined parameter tau. To find the optimal tau for different forest structures, the global allometry database  (Jucker et al. , 2022)  was used. The crown size and tree height relations were described using quantile regression. The parameter tau refers to the percentile of the quantile regression, which represents different crown size and tree height relations. The remaining parameters (TH_SEED and TH_CR) are defined in the same way in both Dalponte2016 and Dalponte2016 +. Comparing these algorithms is therefore an ideal way to isolate the effect of the variable window size on tree segmentation accuracy. Li2012  (Li et al. , 2012 ) is a rule-based point ITS algorithm, which has been integrated in the LiDAR processing packages  (Roussel and Auty.
, 2022)  and commercial software. This ITS algorithm starts with finding the global highest point in the data space. Then the highest point and a dummy point far away from the highest point are used to create two sets. The rest of points are assigned to the two sets following a topdown order, relative spacing, shape index and point density distribution. In the procedure, DT1 and DT2 are the key parameters (see Table  2 ), which present the minimal distance to tree set and non-tree set respectively. The range of the two parameters is from 1 to 10. Point clouds of individual trees can be obtained by repeating above procedure till no unlabelled point left. The code for Li2012 used in our experiment can be found in LidR package. Adaptive Meanshift 3D (AMS3D) was implemented according to the descriptions by  (Ferraz et al.
4 ). The ITS predictions were done on the raw ALS data covering an area larger than the core plot to avoid edge effects. Predicted tree crown polygons with more than half their area inside the core plot area were selected as candidates. These predictions cover all trees in the plot, but we want to assess the accuracy only on those trees whose labels have 'good' or 'medium' confidence. We therefore filtered out predictions which mostly cover trees with low confidence labels. Next, the Intersection over Union (IoU) was calculated for each predicted tree crown polygon. the IoU of a predicted tree crown polygon can be determined by selecting the maximum IoU among its corresponding reference tree crown polygons. For those predicted tree crown polygons with its max IoU ≥ 0. 5, their tree crown score would be 1, which means these tree crown polygons are correctly segmented, otherwise 0.
Novel benchmark data set We produced a novel benchmark data set for ITS algorithm assessment using TLS to label individual trees in ALS data (Fig. 2 ). We manually assigned confidence scores to 419 trees in Sepilok and 467 trees in Wytham. Overall, 249 (59. 4%) trees in Sepilok and 225 (48. 1%) trees in Wytham had a'high' confidence score. Many canopy trees showed a visually perfect match between the TLS and ALS labels and taller trees generally higher confidence scores, mainly due to better ALS coverage. Importantly, our data set also contains many understory trees with'good' or'medium' confidence scores (75 trees in Sepilok Forests and 11 in Wytham Woods, see Fig. B1  in Supplementary B). Understory trees were defined by tree heights (<25 m for Sepilok and < 15 m for Wytham). This enables us to assess ITS algorithm performance for understory trees.
All ITS algorithms fail to segment understory trees We tested the range of input parameters for each algorithm, and we report the results for the parameter set with the highest accuracy. All ITS algorithms performed poorly for understory trees in both Sepilok (<25 m, see Fig. 7 ) and Wytham (<15 m, see Fig. 7 ). All of the algorithms had precision and recall scores below 0. 1 for understory trees, with the exception of Dalponte2016 in Sepilok, which had a moderate precision (0. 33) but very low recall (0. 01) and F1 score (0. 01). Importantly, the Wytham benchmark data set has a low point density, so understory trees have fewer ALS points, meaning that they are very challenging to accurately segment. This is representative of many ALS data sets. The Sepilok benchmark data set has a high point density and many understory trees with good coverage, which were nevertheless poorly segmented.
Tree segmentation algorithms are only accurate for tall trees We compared the accuracy of four widely used ITS algorithms for ALS data in 1 ha of temperate and 1 ha of tropical forest. Our most striking result was that all four ITS algorithms, including 3D-point-cloud algorithms, failed to accurately segment understory trees. Tree segmentation accuracy increased dramatically with tree height across both sites. This is likely because the top of the forest canopy is clearly visible to aerial surveys, while understory trees are often obscured. Note that we tested a wide range of input parameter combinations for all algorithms (see Supplementary E and F), and reported results from the parameter combination with highest accuracy. Also, we defined understory trees purely by their height, so some of the understory trees with better ALS coverage are found in canopy gaps.
In creating our benchmark data set, we found that many understory trees were poorly sampled by the ALS data and therefore had lower confidence scores (see supplementary B). This was particularly important in Wytham, which had lower ALS point density, making segmentation more challenging. The understory trees in Sepilok also had far fewer points and lower confidence scores, however, there remained over 100 understory trees with high confidence scores on which we assess the ITS algorithms.
Value of ALS benchmark data sets It is critical that tree segmentation accuracy is assessed directly, rather than by comparing with ecological indices such as biomass or the number and size distribution of trees. Benchmark data are needed to achieve this, but they are rare in broadleaf forests (see  (Weiser et al. , 2022) ), especially in the tropics. Our Sepilok benchmark dataset is, to the best of our knowledge, the only available ITS benchmark data set in a tropical forest. The benchmark data sets used in this study (and provided online) are particularly valuable as they include many understory trees. This was possible because we labelled the individual trees in the ALS data using TLS data  (Xin, 2021) , which contains detailed information on understory trees. We controlled for data quality issues by manually assigning a confidence score to each tree.
What next for tree segmentation algorithms? Understory trees are not visible in most remote sensing data as they only detect the top of the canopy. Laser scanning penetrates through the canopy and so can potentially be used to segmented understory trees. However, we show that the accuracy of understory tree segmentation is currently very low for typical airborne laser scanning data sets in broadleaf forests. This challenge may be overcome using extremely high point density laser scanning data collected using Unoccupied Aerial Vehicles  (Hamraz et al. , 2017). The processing power required to apply existing ALS segmentation algorithms to these massive data sets may be prohibitive, so we expect efficient algorithms to be adapted for this specific purpose. Machine learning methods are proving helpful for distinguishing trees with studies ranging from object detection  (Sun Y et al.
Acknowledgements YC was funded by  China Scholarship Council. TJ and DC were funded by  NE/S010750/1. The  Sepilok  ALS data collection was funded by  NE/ S010750/1. We thank  Ground Data Solutions R & D Sdn Bhd  for collecting the ALS data in Sepilok. We thank the  Natural Environment Research Council Air-borne Research Facility  for collecting the Wytham ALS data. The Wytham TLS fieldwork was funded through the  Metrology for Earth Observation and Climate project (MetEOC-2) , grant number  ENV55  within the  European Metrology Research Programme (EMRP). The  EMRP  is jointly funded by the  EMRP  participating countries within  EURAMET  and the  European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the  UK NERC National Centre for Earth Observation (NCEO)  and  UCL Geography.
Data and code availability. • Benchmark data for Sepilok Forest and Wytham Woods: https://zenodo. org/record/7181101#. Y6W4XuzP0ea. • Raw ALS data collected in Sepilok in 2020: http://dx. https://doi. org/10. 5285/dd4d20c8626f4b9d99bc143 58b1b50fe. • Raw ALS data collected in Sepilok in 2014: https://data. ceda. ac. uk/neodc/arsf/2014/GB13_10. • Segmented TLS data collected in Wytham in 2015: https://doi. org/10. 5281/zenodo. 7307956. • Segmented TLS data collected in Sepilok in 2017 will be available on acceptance. • Labelling ALS with TLS: https://github. com/Elephant-C/ALS-labelling. • Tree crown polygon-based assessment: https://github. com/Elephant-C/tree-crown-based-assessment. • AMS3D: https://opus4. kobv. de/opus4-hnee/frontdoor/index/index/docId/ 372.
In this Gaia-coordinated Focus Product Release (Gaia FPR), the astrometry of 156 792 minor planets and 31 natural satellites is provided. However, this paper focuses only on the orbit computation of minor planets, and only this category of Solar System bodies is investigated. Likewise, photometric and spectral data are also beyond the scope of this work and have been largely presented with the Gaia DR3 results in  Tanga et al. (2023). Gaia DR2 has shown the quality of Gaia observations and their huge value for asteroids. Based on the high-precision astrometry and because the time span covered by the observations was long enough (at least ≃ 1000 days), it was possible to derive accurate orbits for the observed asteroids, as was shown in Gaia DR3  (Tanga et al. 2023) , who reported a summary of the observational technique and the epoch astrometric accuracy.