dataesr/scientific-paragraphs-categorization huggingface dataset:
Ground Truths Reference Data for Training and Validation Reference data for flood damages were gathered from various field sources and from VHR imagery photo-interpretation. For the Aude 1 event, previous works  [32]  already assembled a training/validation dataset from geo-referenced agricultural disaster claims at the plot scale (more than 300 plots on the official land cadastre, source: Aude's Direction Départementale des Territoires et de la Mer, i. e. ,  DDTM 11). In Cerbelaud et al. [27] , information on closed-off road segments was used for Aude 2 (around 180 plots, source: DDTM 11), and field mission ground truths (observations of shallow landslides and debris flows) were acquired for Alpes-Mar (435 plots in the Tinée, Vésubie and Roya valleys). From these affected plots, vector-based annotations were performed manually on VHR post event images to assemble a training/validation dataset.
The number of epochs was limited to 50 for each training run. The batch size, i. e. , the number of randomly chosen patches used for each iteration inside an epoch, was set to 32 for Aude 1 and 16 for Aude 2 and Alpes-Mar. Data augmentation was implemented on all training patches at each iteration with horizontal flips, width and height shifts and zooms and rotations. Such a process enables the creation of "new" images at each iteration run so as to prevent excessive fitting to the original training data. The optimizer was the Adam algorithm (a stochastic gradient descent method), and cross-entropy was chosen as the loss function between true labels and predicted labels. Design and implementations were carried out under Python 3 using TensorFlow and Keras.
Detection Probability Threshold (P d ) and Area Detection Ratio (ADR) In this section, both the RF and U-net classifiers were trained only on the Aude 1 learning sample (800 patches). This dataset was first used as reference for training because it has the highest number of annotated shapes and the most trustworthy sources of PFs (agricultural damage claims from regional authorities). Detection rates were computed depending on different values for P d and ADR and are provided on all annotated shapes of each case study. Results related to Aude 2 and Alpes-Mar bore significant values to showcase the method's generalizability as the classifiers were not trained on these events. Alpes-Mar's performance was even more meaningful as IGN airborne orthophotos (instead of Pléiades images) acted as VHR data to test the usability of the method on other types of VHR images.
Even after histogram matching, spectral signatures might not be sufficiently similar between the two datasets. However, good results from the RF algorithm on Alpes-Mar essentially ruled out this second explanation. As with the SPCD method  [27] , greater uncertainty in change detection was also observed over the spring season landscape, with Aude 2 s FPR being the largest based on the RF classifier (5. 2% against 2. 6% for Aude 1 and 1. 3% for Alpes-Mar, see Table  2 ). With blooming and rapidly developing crops, it is understandably more difficult to distinguish land cover changes in mid-May due to surface runoff from those due to natural evolutions solely from multispectral data. Setting P d to 0. 75 for RF and 0. 35 for U-net, classification performances were then assessed depending on the choice of input predictors.
4% vs. ~2. 5% when S-2 was used). When transferring the trained RF to the other study cases, detection rates were good, but only because FPRs were too high (e. g. , 6. 6% vs. ~1. 5% for Alpes-Mar when S-2 was used). For U-net, the network obtained through and adjusted to the Aude 1 dataset regardless of the combination of predictors (~65-77% detection rates). However, the CNN trained only on VHR data (Table  2  row (a) for U-net) was not generalizable at all (~15% FPRs in Aude 2 and Alpes-Mar), which pointed to systematic overfitting of the U-net to the Aude 1 training data. Only the addition of S-2 change predictors allowed for good transferability with lower FPRs, but with still rather low detection rates (~60-70% in Table  2  rows (b) and (c) for U-net). For combinations (b) and (c) in Table  2  (i.
Obviously, the best results were obtained for each study event when training was conducted on the site itself (diagonals in Table  3 ). Better Oas on test samples were obtained with RF compared to U-net, in line with RF's overall better behavior concerning detecting damages with reasonable ADR and low FPR. Aude 2 trained classifiers yielded the worst results, echoing previous findings on the complexity of the spectral and spatial data for this event. FPRs on that same event were still rather high for both RF and U-net. On the contrary, with the Alpes-Mar training dataset, outstanding results were obtained on Alpes-Mar (which was to be expected), but also on the two other study cases. Looking at U-net, the CNN performed on Aude 1 and Aude 2 similarly to when Aude 1 was used for training. Hence, training the U-net on Alpes-Mar wherein annotated shapes were smaller in size enabled better identification of damages in this same site while still performing well on the Aude events.
This result additionally proved the operational potential of FuSVIPR to automatically provide future impact maps of PFs without requiring further training. Moreover, this exhibits fine capacity to derive damage maps in lightly populated periurban areas where interpretation of remote sensing images still poses major challenges. Optimal detection capacity of FuSVIPR is illustrated with close-up examples in Figure  8 , in the video provided as part of the Supplementary Data and over all the study sites in Figure  9. Overflowing damages are also detected, but were not accounted for in accuracy assessments. Blue lines and red pixels depict the ground truths and PFs detected with FuSVIPR, respectively. Overflowing damages are also detected, but were not accounted for in accuracy assessments. Blue lines and red pixels depict the ground truths and PFs detected with FuSVIPR, respectively.
Detection Extent and False Alarms In order to further discuss the detection capacity of both classifiers in the FuSVIPR method, the distributions of ADRs among all annotated shapes in the final detection maps (metrics exposed in Table  4 ) are displayed in the violin plots in Figure  10. 4 ). ADRmed and ADR25th correspond to the median and first quartile value of ADR. Violin plots represent the distribution of values among a dataset similarly to boxplots, with the addition of a rotated smoothed density plot on each side of the boxplot. In the RF case, half of all annotated shapes were detected with an ADR greater or equal to 53%, 66%, 45% and 35% (=ADRmed) in Aude 1, Aude 2, Alpes-Mar and Durban, respectively. Looking at the first quartile of ADR, 75% of ground truths were detected with an ADR of at least 24%, 20%, 26% and 15% in the study events.
, how many pixels were classified as "damaged" everywhere outside annotations), it is important to insist that all FPRs given in this work constitute maximum values. Indeed, some areas that were missed during the photointerpretation process were sometimes rightfully detected with the method (see Figure  11 ), inducing spurious rises in the FPR. This also demonstrates the performance of the FuSVIPR method to automatically identify relevant PF-induced damages that operators can miss while photo-interpreting post event imagery. Either way, FPRs below 2% were   4 ). ADR med and ADR 25th correspond to the median and first quartile value of ADR. Violin plots represent the distribution of values among a dataset similarly to boxplots, with the addition of a rotated smoothed density plot on each side of the boxplot.
[38]  implemented an S-2-based landslide detection method and reached precisions mostly under 70%. However, they tested their networks on three different areas of interest with cross-validation and site-specific training datasets as well. Interestingly, they found no improved generalized performance when collectively merging all their case studies into one global training dataset. Meena et al. [39]  compared several ML methods to U-net for landslide inventory using Rapid-Eye satellite imagery (5 m resolution) and found U-net performed slightly better with precisions around 70% as well. Cerbelaud et al. [27]  evaluated the SPCD repeatable method on extended datasets of Aude 1, Aude 2 and Alpes-Mar events with S-2 change images (no VHR data) and found similar accuracies at plot scale, but with globally higher FPRs (see Table  5  for a comparison between SPCD and FuSVIPR).
Random Forest Classifier or U-Net Semantic Segmentation? FuSVIPR was developed to function with two famous methods for classification of remote sensing images. If training was significantly longer for U-net (~2. 5 h for 800 256 × 256 patches and 50 epochs on a 16 GB 11th Gen. Intel Core i7 @ 3. 00 GHz processor, vs. a few seconds for RF), both are fast when applied on large-scale VHR data and S-2 change images (a few minutes) once trained and provide accurate pattern detection of PF-induced damages (Table  4 ). In the literature, Du et al. [64]  found that, for dynamic classification of paddy rice, U-nets outperformed RF when relying solely on pixel-based OA and F1-Score for their unique case study test sample. Yoo et al. [65]  compared CNN to RF for local climate zone (LCZ) classification using bi-temporal Landsat 8 data of four urban areas with a transferability assessment study.
By training the model on three cities and evaluating the results on the remaining one, they concluded that the DL classifier systematically resulted in 7-18% higher OAs. In two subtropical forest fragments of Southern Brazil, tree species were classified using hyperspectral data from unmanned aerial vehicles, with the CNN approach being 22-26% more accurate than the SVM and RF  [66]. ML methods were faster but less stable to changes in datasets and depended on prior segmentation and hand-engineered features to reach similar accuracies to those of the CNN. In other works related to earth observation and geosciences, Taghizadeh-Mehrjardi et al. [67]  analyzed the spatial distribution of soil particle size fractions (PSF) in digital soil mapping. They found that CNN consistently outperformed RF with, for example, around 20% lower RMSE values for clay, sand, and silt contents at the top layer when using CNN compared to RF.
Making the most of some of the best spatial resolution (0. 5 m) and freely accessible frequent revisit (5 days) today in satellite optical imagery, FuSVIPR can generate unprecedented large-scale pixel-based observational data on pluvial floods with satisfactory detection rates (~75%) and low false positive rates (mostly < 2%). Its major contribution lies in the great transferability of the trained U-net and Random Forest classifiers, which was displayed across four diversified events and landscapes, making FuSVIPR potentially operational in various regions and all year round. Both classifiers presented good detection capacity, and neither of them stood out as the best one. RF is recommended for more comprehensive segmentation, especially for small, isolated deteriorations, but U-net does a better job dealing with complex shapes in periurban landscapes.
Further tests should be carried out to confirm high generalizability in other environments. Once two cloudless and closely timed Sentinel-2 images are retrievable before and after a flood event, the FuSVIPR method can be applied with different types of VHR post event visible data, i. e. , from satellites, airborne sensors or even probably UAVs (unmanned Aerial Vehicles). Final segmented maps of PF-induced damages provide valuable, reliable and unbiased observational data for evaluation and improvement of intense surface runoff susceptibility models at very high spatial resolution. Applications to natural hazard mapping in alpine environments for instance could also be conceived for the study of mountain torrents and rapid mass movements. Furthermore, this method shows great potential for helping extreme precipitation downscaling from radar ground measurements using stochastic simulators or geostatistical techniques such as kriging through informing on the distribution of PF-affected areas in similar ways rain gauge networks inform on local precipitation.
Acknowledgments: The authors would like to thank  Emilie Bronner  ( CNES ) for the retrieval of Pléiades images in the context of the International Charter Space and Major Disasters as well as  Mathilde Caspard  and  Hervé Yésou  ( ICube-SERTIT ) for their expertise in the mapping of damages caused by natural disasters using VHR optical imagery, especially in the Durban case. We are also grateful to DDTM 11 for providing precious ground truths information in the Aude territory.
Structure of the ToO software The ToO software builds a list of observable sources for each observation period, and proposes an observation schedule, comprised of a sub-sample of these observable sources. From a transient and steady source database, observable sources are determined using criteria 1, 2 and 3 (described in section 1). In the following we detail how we build the source database, how we account for the balloon motion, the impact of the Sun and the Moon on the observation window, observability constraints from the field of view, and the prioritization strategy. The user interface is also described. Transient and steady source database: The first module of the ToO software is a listening module designed to filter alerts from various alert networks, such as the Gamma-ray Coordination Network (GCN) and the Transient Name Server (TNS).
GCN and TNS alerts are available in machine-readable formats  [12]. Several criteria determine which alerts are added to a source database: we select alerts corresponding to potential sources of VHE neutrinos. The software updates the source database on a continuous basis. In addition, we incorporate Astronomer's Telegram (ATel) alerts. These are not machine readable, thus are hand-processed. A catalog of steady sources is also included, and additional sources can be added by the user. With these various components, a combined source list is produced in a generic database format (db). Balloon trajectory: In order to determine the observing schedule, the software must account for the trajectory of EUSO-SPB2. During the mission, a KML file is provided by the Columbia Scientific Balloon Facility (CSBF) that provides the balloon's projected latitude, longitude, and altitude for the next three days in 6 h bins.
The scheduler accounts for this priority ranking in setting observation times and telescope azimuthal pointing directions for a given night. In the prioritization algorithm, we also account for the maximum time during which sources can cross the field of view of the detector. These times can vary between 20 mins and 1 h 20 mins. In each ranking category, we favor the sources that can be observed the longest, considering that observation time is a good proxy for the acceptance. This assumption will be validated by detailed simulation computing the acceptance as a function of energy and nadir angle. Jonatan Posligua User interface: In order to create a software accessible for a variety of users, we developed a user-friendly interface. The software modules used to collect alerts, generate source catalogs, and generate the observation schedules, are incorporated in a GitLab distribution.
Acknowledgements -The authors acknowledge support by  NASA  awards  11-APRA-0058 ,  16-APROBES16-0023 ,  17-APRA17-0066 , and  NNX17AJ82G ,  NNX13AH54G ,  80NSSC18K0246 ,  80NSSC18K0473 ,  80NSSC19K0626 , and  80NSSC18K0464 , the  French space agency CNES, National Science Centre in Poland  grant n. 2017/27/B/ST9/02162 , and by  ASI-INFN  agreement n. 2021-8-HH. 0and its amendments. This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231. We acknowledge the  NASA   Balloon Program Office  and the  Columbia Scientific Balloon Facility  and staff for extensive support. We also acknowledge the invaluable contributions of the administrative and technical staffs at our home institutions.
Plant material For this study, we used 1,141 accessions from the field and in vitro copies taken from the clonal CCC of potatoes genebank managed by AGROSAVIA and 20 commercial varieties and advanced breeding lines obtained from different sources. This CCC contains mainly landraces and a small proportion of released varieties. Based on  Spooner et al. (2007)  taxonomy, most accessions belong to the species S. tuberosum group Andigenum, and few accessions to S. tuberosum group Chilotanum. However, the classification of the material in the potato collection follows  Hawkes (1990)  previously accepted taxonomic classification. According to this classification, 143 accessions are S. phureja, 681 S. tuberosum subsp. andigenum, 67 S. tuberosum subsp. tuberosum, one (1) S. rybinii (older classification), while for 269 accessions we currently do not have the information because it is in the curation process and digitalization of data from hard copy records.
We obtained the raw values from the fluorescent signals for all samples using GenomeStudio 2. 0 software (Illumina, San Diego, CA). Clustercall package  (Schmitz Carley et al. , 2017)  in the R platform (R Core Team, 2021) transformed these signal values to obtain the tetraploid genotype calls (AAAA, AAAB, AABB, ABBB, BBBB coded as 0, 1, 2, 3, 4).
Population structure and genetic diversity The final SNP genotype matrix was obtained from the ClusterCall output matrix, filtered to remove markers with minor allele frequency of less than 0. 03 and missing data greater than 5 percent at the genotype and marker level. We assessed population structure using this dataset with tetraploid genotype calls through two strategies: i) a Bayesian model implemented in the software STRUCTURE 2. 3  (Pritchard et al. , 2000)  without a priori population information, and ii) a Principal Component Analysis (PCA) calculated using the R-Adegenet package  (Jombart and Ahmed, 2011). For the Bayesian model, the number of populations (in this study referred to as genetic groups) K was estimated from one to ten using an admixture model with frequencies of correlated alleles, a burn-in set to 150.
000 interactions, and ten independent repetitions per run. Then, we found the best number of populations in the Structure Harvester program  (Earl and vonHoldt, 2012)  using the Evanno et al. method  (Evanno et al. , 2005). The NbClust R-package  (Charrad et al. , 2014)  was used to determine the K for the PCA loading as input the information of the first three components. We tested if there was population differentiation by calculating the F ST and the percentages of differentiation between and within identified subpopulations using the analysis of molecular variance (AMOVA) in the R-packages dartR  (Mijangos et al. , 2022)  and Poppr  (Kamvar et al. , 2014) , respectively. Finally, we manually estimated the genetic diversity by calculating the observed heterozygosity (Ho) per genotype and genetic group, using the methodology described by  Berdugo-Cely et al.
Ploidy-level prediction We predicted the ploidy level of each accession using the proportion of simplex and triplex genotype scores (ABBB, AAAB) across all SNPs per accession, according to  Alsahlany et al. (2019). This method sets up the parameters to predict the ploidy using a set of reference samples. In that case, when the sum of simplex and triplex proportions was close to zero, the sample would be diploid, and when the frequency was greater than 0. 2 it would be tetraploid. In this study, we validated the accuracy of this method by comparing the ploidy of 112 reference accessions from the CCC assessed with SNP genotype score frequencies and the ploidy values obtained using chloroplast and chromosome counts from three publications  (Guevara, 2011; Uribe Gaviria, 2011; Sańchez, 2017). For this analysis, we implemented a one-way analysis of variance (ANOVA) and Tukey test in R software to find significant differences among the ploidy prediction strategies.
Core collections We used the Core Hunter 3 program (de Beukelaer et al. , 2018) to define core collections representing the total genetic diversity present in the CCC of potatoes. The Modified Roger's genetic distances (MRD) were calculated and used to identify the core sets with the A-NE distance function. This function optimizes the entry selection to minimize the average distance between each accession in the whole collection and the closest-selected entry, as expected when creating a diverse core collection. We tested three collection sizes of 10, 15, and 20 percent of the total samples evaluated in this study  (i. e. , 129, 194, and 258 entries, respectively)  and one mini-core collection with 3 percent of the original size (39 entries). We assessed the quality of the A-NE method for core collection selection by calculating and comparing the average criterion distance for A-NE and two additional distance-based metrics evaluated by  Odong et al.
(2013)  for each of the core and minicore collections identified: (1) average distance between each entry and the nearest neighboring entry (E-NE), and (2) average genetic distances between entries (E-E). The EvaluateCore R package v. 0. 1. 3 (Aravind et al. , 2022)  and two additional core sizes (50 and 80 percent) were used for this comparison. To validate that the genetic diversity representation was kept across collections and select the most suitable core collection for breeding and research, we compared the average observed heterozygosity (Ho) measurements from the core and mini-core collections to the average in the whole CCC. Additionally, a PCA was carried out for each collection to show the distribution of the samples selected in each proposed core collection compared to the whole CCC.
Validation of core and mini-core collections' usefulness We validated the effectiveness of the accessions in the core and mini-core collections to represent the whole spectrum of phenotypic variation for three different traits: i) Average Tuber Weight (ATW) in grams, ii) Number of Tubers per Plant (NTP), and iii) Total Tuber Yield (TTY) in kg/plant. For this we used the phenotypic data of 846 genotyped accessions (206 for CCC_Group_A, 494 for CCC_Group_B1, and 146 for CCC_Group_B2) for which preliminary field evaluations had been conducted for ATW, NTP, and TTY. Data were collected between 2013 and 2015. Accession data collected across these years depends on the accessions evaluated during each field campaign. Due to financial and operational capacity limitations, we prioritized accessions and traits to be evaluated by year.
Andigenum and later the Phureja accessions were evaluated in three consecutive years. The evaluations were conducted in a highland area of Zipaquirámunicipality, in the department of Cundinamarca. Field data for each year was collected from an average of 20 clones planted in one tier per accession, using a complete randomized design for the entire CCC in the field. Thus, each year's data represents a repetition. We used the mean values from all-year evaluations per trait and accession for the analyzes. We expected that, independently of the size, a good core collection would show a similar phenotypic distribution as the one for the 846 accessions. Moreover, we analyzed to see if the identified entries for the different core and mini-core collections represented the phenotypic diversity of all accessions by comparing their average criterium distance for A-NE, E-NE, and E-E metrics.