dataesr/scientific-paragraphs-categorization huggingface dataset:
These methods were assessed on a subset of ISIIS data collected in the Mediterranean Sea, from which a ground truth dataset of > 3,000 manually delineated organisms is extracted. The naive thresholding method captured 97. 3% of those but produced ~340,000 segments, 99. 1% of which were therefore not plankton (i. e. recall = 97. 3%, precision = 0. 9%). Combining thresholding with a CNN missed a few more planktonic organisms (recall = 91. 8%) but the number of segments decreased 18-fold (precision increased to 16. 3%). The MSER detector produced four times fewer segments than thresholding (precision = 3. 5%), missed more organisms (recall = 85. 4%), but was considerably faster. Because naive thresholding produces ~525,000 objects from 1 minute of ISIIS deployment, the more advanced segmentation methods significantly improve ISIIS data handling and ease the subsequent taxonomic classification of segmented objects.
The regions identified in this way were then extracted using a mask and subsequently re-segmented using the MSER approach. MSER detects stable connected regions in images, which are areas that stay nearly unchanged over a wide range of grayscale thresholds. MSER can be tuned to allow for varying degrees of stable region area and the range of pixel gray values tested in the dynamic thresholding. High SNR frames are directly segmented using the MSER approach (Figure  2  skip from step B to step D). Going from a pure MSER approach to the threshold+MSER (T-MSER) on low SNR (< 50) frames increased the recall on the test data from 65% to 85%, while also substantially increasing precision. This SNR and MSER method is written in C++17. The OpenCV and OpenMP Python packages were used for general computer vision and parallel processing for high processing efficiency, respectively.
Above 400 pixels, the quantile-based threshold approach, with dilation and erosion, was used because it was simple and did not generate too many non-plankton segments. In Detectron2, we used Mask R-CNN  (He et al. , 2017) , which allows simultaneous bounding box detection and instance segmentation. The model was initialized with weights trained on the COCO reference dataset 1 but, for it to detect planktonic organisms on ISIIS images, it has to be fine-tuned on a dataset of ground truth bounding boxes and masks of such organisms. This dataset was generated by manually delineating all recognizable planktonic organisms in a set of ISIIS images, using a digital pen on a tablet computer. This produced 23,197 ground truth masks, from which bounding boxes were computed. Among those, 10,878 object were in the 50-400 pixels area range and usable.
Furthermore, focusing on segmentation only is also more comparable with the two other methods described above. The model was trained for 30,000 iterations, and evaluation was run on the validation set every 1,000 iterations to ensure that the validation loss reached a plateau. The learning rate was set to 0. 0005 initially and decreased 10 fold after 10,000 and 20,000 iterations. To increase the generality of the detector, data augmentation was used in the form of random resizing of the 524 pixels crops (to 640, 672, 704, 736, 768 or 800 pixels) and random horizontal flipping. The test set was used to assess theoretical performance after training and guide the choice of model settings; the actual performance was assessed on a separate, real-world dataset (presented below). To apply the trained model to new images, a tiling of 524×524 pixels crops (the size used during model training) was generated over each input image, resulting in an overlap of 143 pixels vertically and 135 pixels horizontally.
For each bounding box proposal, the model computes a confidence score. We retained all boxes with a score over 0. 1, which is a quite low confidence threshold designed to increase the chance of detecting all objects of interest (i. e. favor recall) at the cost of some false positive detections (i. e. lower precision). Those false positives (i. e. segmented objects that are not plankton) will have the opportunity to be eliminated later, when segments are classified taxonomically. The CNN was coded in Python with PyTorch, the original implementation library for Detectron2. Training was conducted on an Nvidia Quadro RTX 8000 GPU and the code is available at https://github. com/ThelmaPana/Detectron2_plankton_training. The combined CNN and threshold segmentation pipeline is implemented in https://github.
Application to ISIIS Data from VISUFRONT Campaign We evaluated these segmentation methods on ISIIS data from the VISUFRONT campaign, which sampled the Ligurian current front (North Western Mediterranean Sea), in the 0-100 m depth range, during summer 2013. Towed at a speed of 2 m s -1 (4kts) and set for a 28 kHz scanning rate, the ISIIS sampled 108 L per second. The 2048 pixels high continuous image strip created by the line scan camera moving in the water was cut in 2048×2048 pixels frames for storage. The ISIIS captured marked volutes caused by water density variations (Figures  1D-F ), mostly driven by temperature changes around the thermocline, previously described by  Faillettaz et al. (2016). The continuous image strip was reassembled from the stored 2048×2048 pixels frames. Each line of pixels was flat-fielded by subtracting the row-wise average over a 8000 pixels moving window, hence removing streaks (Figures  1A, B, D, E ).
The cleaned image was cut into 10,240 pixels long images (5 frames, instead of 1) to reduce the probability of cutting objects across images while keeping the memory footprint of each image manageable. Finally, the image was contrasted by stretching the intensity range between percentiles 0 and 40 (Figures  1B, C, E, F ). These values were chosen by iteration, through discussions with the taxonomist in charge of delineating planktonic organisms from raw images, as to achieve the highest distinguishability for those. A ground truth dataset was generated by manually delineating all planktonic organisms (using a digital pen and tablet) in 106 10,240×2048 pixels images, regularly spread across a full transect, hence representative of different environments. This resulted in 3,356 objects that were later taxonomically sorted into 24 taxa (Figure  3 ), in the Ecotaxa web application  (Picheral et al.
, 2017). This dataset was completely independent from the one that was used to train, validate and test the Detectron2 model. Some images were checked by two independent operators to check their consistency; when this was done, no differences were found. Segments from each of the three automated methods were matched with ground truth segments of the same image. A bounding box intersection over union (IoU) score higher than 10% was considered as a match between segments. This threshold was set after manually inspecting a set of potential matches with various IoU values and was found to be the best value to discriminate between true and false matches. In case a ground truth segment matched multiple automatic segments, only one match was retained, to avoid inflating artificially the number of matches from the automated pipelines.
Number and Size Distribution of Segments On the 106 images of the segmentation benchmark dataset, 3,356 organisms were manually segmented, whereas the automated pipelines generated many more segments, especially the threshold-based one (Table  2 ). The normalized abundance size spectra (NASS) (Figure  4 ) display the expected linear decrease of abundance with size in log-log scale. For the ground truth segments, the curve dips below this linear relationship for objects of 25 pixels in diagonal and smaller (dotted vertical line on Figure  4 ). Since this dataset specifically targeted recognisable planktonic organisms, this dip highlights that not all organisms below this size could be detected by a human taxonomist upon detailed examination of the images  (Lombard et al. , 2019). The discontinuity is towards smaller diagonal sizes in the automated pipelines, but likely because many of the small segments are of non-plankton objects.
Performances Per Taxonomic Group In the ground truth dataset, half of the 24 detected taxa were represented by fewer than 18 individuals (median is 18. 5), hence inducing little resolution and large variance in the performance statistics of segmentation pipelines. Among the other half of the taxa, the recall of the T-CNN pipeline was lower than that of the threshold pipeline by more than 10% for only two taxa (Bacillaryophycea and Doliolida) and for only four in the case of the T-MSER pipeline (Bacillariophyceae, Ctenophora, Acantharea, and other Rhizaria; Figure  6 ). The lowest recall values were reached for Bacillariophyceae and Ctenophora, for all pipelines. In concordance with the consistent recall performance across size classes, taxa-wise recall performance of  the T-CNN pipeline do not seem linked to organism size: small organisms (e.
Processing Time and Cost The quantile-based thresholding pipeline ran on a single CPU core at a rate of 30 minutes of processing for 1 minute of ISIIS data (0. 03x), on an Intel Xeon  E5-2643 v3 (3. 40 GHz). Its memory requirements were limited so it was easy to run simultaneous processing of multiple batches of data on a multi-core/multiprocessor machine, but the treatment of ISIIS data as a continuous stream for flat-fielding prevented automatic multithreading. The T-CNN pipeline required a GPU with sufficient memory (48 GB, on aNvidia Quadro RTX 8000 in our case) to efficiently train the CNN portion and to fit ISIIS images in at evaluation time. It processed data at the same rate as the threshold-based pipeline (30 min processing for 1 min of data, or 0. 03x). The T-MSER pipeline was optimized for speed and utilized the 8 cores of an AMD Ryzen 3700, processing one minute of ISIIS data in 50 seconds (1.
2x), or 6 min 40 s of processing for 1 min of ISIIS data (0. 15x) when considering running on one core. The MSER implementation followed  Matas et al. (2004)  closely. The optimization of the T-MSER approach stems from adding the SNR switch, which leads to the pre-processing of high-noise images with naive thresholding, while going straight to the MSER-based detection in low noise images. Adding these changes increased segmentation recall from 65% to 85%. Further optimization included making the code multi-thread ready for deployment on High Performance Computing infrastructures. Using the specialized CPUs of these infrastructures, such as the AMD EPYC 7742 (64 cores, 128 threads) performance could improve well above 1. 2x. At current data collection rates of 75-100 h of ISIIS data per scientific cruise, a real time or faster than real time segmentation approach constitutes a substantial benefit.
, 2022)  and manual delineation of each organism is much more time consuming than manual classification. A second obstacle is the size range of organisms imaged by ISIIS. Although Detectron2 does produce multi-scale feature maps through a Feature Pyramid Network in order to apply receptive fields of multiple size, the ratio between the largest and the smallest feature maps is only 16. Here, the ratio between the smallest and largest bounding box diagonals of manually segmented organisms is 65 and can reach > 180 in more exhaustive ISIIS datasets. To tackle this span, one could theoretically set up an ensemble of detectors, fed with crops of different sizes, each one targeting a restricted size range. Yet, this would be a particularly computationally demanding and complex set up, for a gain yet to be determined since, for larger sizes, the proportion of non-plankton objects, and therefore the advantage of a CNN-based segmentation, diminishes.
Although at the lower limit of the detection capabilities of CNNs, our content-aware approach was able to detect planktonic organisms among an overwhelming number of marine snow and noise images, exhibiting the best recall of the three methods. Therefore, the ideal segmentation approach depends on the study objectives and operational constraints. These approaches seem relevant for imaging studies focused on living planktonic organisms, since they reduce the number of objects from non-plankton classes that are extracted. In turn, this dampens the imbalance towards these classes, laying the foundations for easier, faster, and more accurate subsequent object classification by (i) reducing the amount of work needed to generate a training set with similar class distribution, which is essential to avoid the caveat of dataset shift  (Moreno-Torres et al.
FUNDING This study is part of project " World Wide Web of Plankton Image Curation ", funded by the  Belmont Forum  through the  Agence Nationale de la Recherche   ANR-18-BELM-0003-01  and the  National Science Foundation  (NSF) # ICER1927710. Funding also came from  NSF  # OCE2125407. The  Extreme Science and Engineering Discovery Environment (XSEDE)  provided computing resources to the  US  team through grant # TG-OCE170012. Data acquisition during the VISUFRONT cruise was funded by the  Partner University Fund  and supported by the  French Oceanographic Fleet  through ship time. TP's doctoral fellowship was granted by the  French Ministry of Higher Education, Research and Innovation  (# 3500/2019 ).
  where m dim is the mass of Dimorphos, m DART is the mass of the DART spacecraft at impact, and ¥ V n is the impact velocity component in the normal direction ( ¥ V n • ˆ). Here, ¥ V n is the velocity component orthogonal to the normal direction ( - ¥ ¥ V n V n ˆ), e T ˆis the unit vector of Dimorphos's orbital velocity at impact, and ò is the the offset vector between the surface normal direction and the ejecta velocity vector. Equation (3) provides a convenient framework for estimating the β factor in the Didymos-Dimorphos system given the data expected from the DART mission. For a head-on collision, Equation where p ejecta is the momentum of the ejecta in the vertical direction, and p DART is the vertical component of the momentum of the DART spacecraft at impact. In contrast, this work focuses on the heliocentric changes to the entire Didymos system as a result of the DART impact, with particular emphasis on the contribution of possible ejecta leaving the system.
Systematic Numerical Studies on the Influence of Physical Parameters on β One of the most important target properties that influences the evolution of the crater and the production of ejecta is the target yield strength at zero pressure (i. e. , cohesion). The influence of the effects of target cohesion on cratering has been studied in several numerical studies (e. g. ,  Bruck Syal et al. 2016; Raducan et al. 2019). Raducan et al. (2019)  used the iSALE-2D shock physics code  (Wünnemann et al. 2006; Collins et al. 2011 ) to numerically simulate impacts on low-gravity, strength-dominated asteroid surfaces with varying target cohesion, coefficient of internal friction, and porosity. Using this, they proposed a revision to the Housen & Holsapple (2011) ejecta scaling relations to better approximate the non-power-law behavior of the high-speed ejecta close to the impact site.
Momentum Distribution in Ejecta The data from the studies described in Section 3. 1 were used to quantify the amount of ejected mass and momentum that leaves the Didymos system compared to the amount of mass and momentum that remains within the system, depending on Dimorphos's target properties. The  Raducan et al. (2019)  study suggested that the influence of the target porosity on impact ejecta production is overcome by the influence of the target strength parameters (i. e. , cohesion and coefficient of internal friction). Therefore, in this section, we further focus on data from impacts on targets with fixed porosity and varying cohesion and coefficient of internal friction. In Figure  1 (a), the solid lines show the total mass of ejecta, and in Figure  1 (b), the solid lines show the momentum enhancement, β, from impact simulations using the Bern smoothed particle hydrodynamics (SPH) and iSALE-2D codes for targets with constant porosity (40%) and varying cohesion and coefficient of internal friction.
Additionally, the gravitational forces near Didymos and Dimorphos cannot simply be calculated by approximating them as point masses due to their irregular shapes. To mitigate these challenges, we used an open-source software package called GUBAS 7  that solves the full two-body problem presented by the binary asteroid system (Davis & Scheeres 2020). Both Didymos and Dimorphos are simulated using shape models that are discretized into polyhedra. Computing the mutual gravitational forces on these polyhedra allows for a high-fidelity dynamical model of the binary asteroid system. The ejecta particles are then initialized in this environment and propagated without any interparticle interaction. Thus, each particle forms the third body in the RF3BP. The SRP model used in this scenario is a simple cannonball approximation with shadowing.
Then, the estimates for both β and β e could be used together to reduce the uncertainties about the mechanical properties of Dimorphos. This would be especially helpful for future modeling of the DART impact. It could also aid a future scenario in which additional scouting spacecraft cannot be sent before the impactor spacecraft. In this case, this information would be used to reduce the epistemic uncertainties surrounding a target asteroid's physical properties and allow for accurate predictions of the post-impact heliocentric state of the asteroid. This work was supported in part by the DART mission, NASA contract No. 80MSFC20D0004 to JHU/APL. S. D. R. acknowledges funding support from the European Unionʼs Horizon 2020 research and innovation program under grant agreement No. 870377 (project NEO-MAPP).
9 m and the peak storm tide was at 4. 4 m  (Hsu, 2013). 3. DATA U. S. Geological Survey (USGS) aimed to characterize beach topography following substantial erosion that occurred during Hurricane Sandy. USACE-FRF team collected the highresolution LiDAR data using the CLARIS platform. CLARIS is a vehicle-mounted system that integrates a terrestrial LiDAR scanner and X-band radar with precise motion and position information. In this study, we used point cloud data which was used to create the DTMs. The point cloud data was derived as ASCII format, which needed to convert to las format for the subsequent data processing.
Parameters in Point cloud data processing: There are many parameters in LAStools model making a difference to the outcome. Here, the parameter "No bulge" in LASground model is discussed as followed. We notice this parameter can contribute a lot when classifying the Lidar points into Unassigned or Ground points. When the parameter "No bulge" is applied in LASground tool, nearly 10 times as point clouds are classified into Unassigned as Figure  11. Therefore, the parameter "No bulge" is significant when using the terrestrial data because this tool is designed for airborne-LiDAR. The coastline is extracted by ground and water binary images. In the experimental results, the confusion matrix comes from the experimental classifier. In the meanwhile, the accuracy of water and land division is verified, therefore, the coastline can be extracted successfully.
ACKNOWLEDGEMENT Authors would like to thank  Dr. Jonathan Resop  for his thoughtful guidance and anonymous reviewers for their constructive comments. This study was financially supported by the geological data related programs of  China Geological Survey   DD20190428 ,  National Key R&D Program of China  ( 2016YFC0600600 ),  China Geological Survey Project  ( DD20190450 ,  DD20190451 ),  National Nonprofit Institute Research Grant of IGGE  ( AS2022P03 ).
As in ecoacoustic studies, vibroscape recordings may generate a large amount of raw data reaching several TB very quickly  (Šturm et al. , 2019). Manual identification is highly timeconsuming, so that large datasets cannot be thoroughly processed by a small number of observers and automated species identification and diversity assessment is needed. At present, manual identification and assignation of vibrational signals by listening and visualization of spectrograms is still challenging due to unpredictable changes in signal structure during transmission, high levels of vibrational noise overlapping the frequency range of vibrational signals, and the lack of reference libraries. The great majority of vibrational signals are unknown, so it can be challenging for an inexperienced listener to distinguish signals from incidental vibrations caused by locomotion or feeding  (Šturm et al.
, 2019). Computational methods for automatic classification and identification of vibrational signals have not yet been tested on field recordings  (Korinšek et al. , 2019; Šturm et al. , 2019 , 2021). Nevertheless, even taking into account specific challenges encountered in vibroscape recordings, automatic identification of signals should be possible using recent AI techniques such as convolutional neural networks applied to spectrogram images  (Stowell et al. , 2019). This option will be possible only if the identification models can be trained with annotated datasets. This again underlines the need of expert and shared libraries. Seismology is a well-established discipline (e. g. ,  Lecocq et al. , 2020)  and therefore it is not surprising that it has already provided the first applications of ecotremology in monitoring elephants either by vibrational component of their infra-sound vocalizations or by incidental vibrations induced by locomotion  (Wood et al.
Design Fires In a companion paper  [14] , the authors present a two-zone model that was developed to predict fire development in a compartment where single-or multi-fuel combustion occurs. This model was validated using data from full-scale experiments involving different combinations of solid and liquid fuels and varying the confinement level of the compartment. In this phase of validation, the pyrolysis or combustion rate, and therefore the HRR, was deduced from the fuel mass loss rate measured over time. Unfortunately, for the present study cases, the HRR time curve in each compartment is unknown. To overcome this issue, the time evolution of the HRR is assumed to follow three phases: a growth phase.
Design Fires In a companion paper  [14] , the authors present a two-zone model that was developed to predict fire development in a compartment where single-or multi-fuel combustion occurs. This model was validated using data from full-scale experiments involving different combinations of solid and liquid fuels and varying the confinement level of the compartment. In this phase of validation, the pyrolysis or combustion rate, and therefore the HRR, was deduced from the fuel mass loss rate measured over time. Unfortunately, for the present study cases, the HRR time curve in each compartment is unknown. To overcome this issue, the time evolution of the HRR is assumed to follow three phases: a growth phase where it increases as the square of time  [18, 19] , a steady phase, and a decay phase where it decreases as the square of time: HRR =        α gr t 2 si t < t max HRR max si t max ≤ t ≤ t de α de (t ex -t) 2 si t de < t ≤ t ex 0 si t > t ex ( 1 ) where t is the time from established ignition (in s), α gr and α de are the respective fire growth and decay rates (in W/s 2 ), t max is the time at which the HRR is maximum (HRR max in W), t de is the time when decay starts, and t ex is the time when fire ends.
The CFD 1D Model A one-dimensional version of the software SAFIR, a three-dimensional CFD fire compartment model developed jointly by DGA and the University of Aix-Marseille (for example  [10] ), is used to model the gas flow and heat transfer in a ventilation duct. It is based on the conservation laws for mass, momentum, and energy. Two different duct flow configurations are considered. In the first configuration, ventilation air is flowing through the duct, which is heated along its circumference in the fire compartment, while in the second, the hot combustion products produced in the fire compartment enter the duct and propagate heat. In the second configuration, 2D axisymmetric gas radiation (including the contribution of radiatively participating gaseous species and soot) is considered.