dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this remains a challenge because the amount of data needed exceeds what is readily available for the underwater environment. For example, NetVLAD is trained on Google Street View Time Machine. Another approach would be to minimize the changes due to physical phenomena induced by the underwater environment to get closer to terrestrial images, using for example algorithms like Sea-thru  (Akkaynak and Treibitz 2019)  or SUCRe  (Boittiaux et al. 2023).
Conclusion This paper presented a novel dataset to evaluate visual localization methods in deep-sea environments. Unlike preexisting datasets, Eiffel Tower presents long-term changes in underwater scenarios, e. g. , topography, population and species distribution, backscatter and color attenuation. We analyzed these changes and evaluated several localization pipelines on the proposed dataset. The obtained results can be used as a baseline for future work on underwater visual localization systems. Besides its use for visual localization, this dataset can also be employed to detect changes in the scene's geometry in deep-sea environments. More generally, it may also be useful to study the effects of water on various computer vision algorithms.
Acknowledgements All data acquisitions were conducted by Ifremer. The authors would like to thank the crews of the research vessels, Pourquoi Pas? and  L'Atalante , the pilots of the ROV Victor6000, as well as all the personnel who helped in acquiring these data. MM and LVA were supported by the  European Union 's  Horizon 2020 research and innovation project iAtlantic  under Grant Agreement No. 818123.
To some extent, the advances of marker-assisted selection (MAS) enable faster development of new varieties but are limited to the introgression favorable alleles of major genes or quantitative trait loci (QTLs) with large effects mainly related to abiotic (e. g. submergence, salinity) or biotic (e. g. blast, bacterial leaf blight) stress tolerance into elite backgrounds  (Gregorio et al. 2013; Jena and Mackill 2008). MAS is not tailored to enhance the effectiveness of breeding strategies for quantitative traits like grain yield which are governed by a large number of genes or QTLs with small effects  (Jena and Mackill 2008). With the reduction in genotyping costs, genomic selection (GS) has arisen as a more efficient option for breeding program optimization  (Ahmadi et al. 2020; Heffner et al.
2018; Monteverde et al. 2018 Monteverde et al. , 2019;; Morais Júnior et al. 2017). The conclusions arising from these works based on a relatively small number of environments are that multi-environment models tend to give higher prediction accuracies. This study aimed to assess the efficiency of multienvironment genomic prediction models in the context of an applied breeding program. We used an elite core panel that represents the elite diversity managed by the irrigated rice breeding program at the International Rice Research Institute (IRRI). This panel was phenotyped in 15 environments in Asia and Africa regions from 2018 to 2020. This information from multi-environment trials (phenotypic data and environmental covariates) was used to characterize the level of G × E interaction and to cluster the environments.
The quality of gDNA was visually checked on 1% agarose gel. The quantity of gDNA was then evaluated using PicoGreen ® (https:// www. biotek. com) fluorometric kits and adjusted to obtain a concentration close to 10 ng/ µl gDNA for the library preparation. Illumina ® 's TruSeq Custom Amplicon chemistry was used to create the libraries and the sequencing was performed using the MiSeq Sequencing-by-Synthesis Technology System. A custom SNP-calling pipeline was used to align sequence data on the Nipponbare rice genome MSU7  (Kawahara et al. 2013 ). The sequences with non-alignment and multiple positions were then removed. SNP data was saved in a HapMap format  (Gibbs and et al. 2003). The raw SNP data was then filtered with TASSEL 5  (Bradbury et al. 2007 ). The SNPs with more than 20% of missing data, a minor allele frequency (MAF) lower than 5%, and a percentage of heterozygous calls greater than 10 were removed.
Consequently, four out of 111 ECP lines have been removed from the list. A final set of 107 lines and 882 SNP markers distributed along the rice genome was used for the analyses (Additional file 2: Fig. S1 ). The genotypic information for the ECP is available in HapMap format (Additional file 3). The genotypic characterization of the ECP in relation to O. sativa subgroups was performed by combining the genotypic data of the ECP from the 1K-RiCA assay above with the 3000 rice genomes (3 K-RG) data  (Wang et al. 2018). The physical positions of the 882 SNPs were used to extract a dataset of filtered SNPs for the entire 3 K-RG using the rice SNP-seek database  (Mansueto et al. 2017 ). As a result, a total of 837 SNPs in common in both data sets were used for downstream analysis. The SNPs were then encoded from nucleotide alleles into numeric genotypes as 0, 0.
5, and 1. A principal component analysis (PCA) was carried out using the function dudi. pca within the R package ade4  (Dray and Dufour 2007) , the PCs were then visualized using the R package ggplot2  (Wickham 2016 ). An unweighted neighbor-joining tree between ECP and 3 K-RG's subgroups was constructed using TASSEL 5 software  (Bradbury et al. 2007).
The factors of fixed, random, and residual effects for statistical models of each trial were described in detail in Additional file 1: Table  S3. The analyses were performed using the asreml() function of the R package asreml (version 4. 1. 0. 143)  (Butler et al. 2017). Broad-sense heritability (H 2 ) was estimated for each trait using the following formula: y = Xb + Zu + e H 2 = σ 2 g / σ 2 g + σ 2 e where σ 2 g is the genotypic variance obtained from the experimental data and σ 2 e is the residual variance obtained from the model. H 2 and the associated standard error were estimated with the function predict(). The best linear unbiased predictors (BLUPs) for all genotypes were extracted for each trial and each trait and were used as adjusted phenotypes for further analysis. For the two trials without the replications (Nizmawna-Bangladesh and Hyderabad-India), we used the phenotypic data directly.
The phenotypic information of the ECP is available in Additional file 4. We considered an environment as the combination of location, year, and season. Analysis of correlation between the three traits within single environments, and between environments was performed using the Pearson correlation method within the ggpairs() function in the GGally R package  (Schloerke and et al. 2020). The hierarchical clustering analysis of the environments was carried out using the pvclust package in R  (Suzuki and Shimodaira 2006). A simple analysis of variance (ANOVA) for genotype by environment interaction (G × E) upon the phenotypic performance of ECP was also carried out using the metan R package  (Olivoto and Lúcio 2020).
Weather Data and Environmental Covariates The weather data of each environment were obtained from the NASA POWER database (https:// power. larc. nasa. gov/). This database was queried using the R package nasapower  (Sparks 2018 ) via the R packages EnvRtype  (Costa-Neto et al. 2021). The get_weather function was used to retrieve daily weather data based on the geographical coordinates (N latitude and E longitude) of each trial. The following daily weather variable from the transplanting date to the harvesting date was obtained for all the trials: the total precipitation (PP, mm), the dew-point temperature at two meters (DPT, °C d -1 ), the minimum, maximum and mean temperature at two meters (TMIN, TMAX and TM, °C d -1 ), the relative humidity at two meters (RH, %), the all-sky surface photosynthetically active radiation total (APAR, W m 2 ) and the clear sky surface photosynthetically active radiation total (CPAR, W m 2 ).
The processWTH function from the R package EnvRtype was then used to compute the temperature range (TR, °C), the potential evapotranspiration (PET, mm d -1 ) and the vapor pressure deficit (VPD, kPa d -1 ) (Costa-Neto et al. 2021). Finally, eight environmental covariates (ECs) were selected for further analysis: PP, DPT, PET, VPD, TM, TR, APAR, and CPAR (Additional file 1: Table  S4 ). To assess the effects of ECs through different developmental phases of the ECP on the genomic predictive ability, the phenology of the crop was identified for each environment consisting of: the vegetative phase (from the transplanting date to the earliest line); the reproductive phase (the interval between the earliest and latest date of flowering); and the ripening phase (from the latest date of flowering up to the latest harvest date) (Additional file 2: Fig.
The genomic heritability ( h 2 g ) of the studied traits was estimated based on the seven models described above. The different estimates of h 2 g were obtained with the following formula (de los Campos et al. 2015): (2) Model GE : y ij = µ + g i + e j + ε ij (3) Model GW : y ij = µ + g i + w ij + ε ij (4) Model GEW : y ij = µ + g i + e j + w ij + ε ij (5) Model GE -G × E : y ij = µ + g i + e j + ge ij + ε ij (6) Model GW -G × W : y ij = µ + g i + w ij + gw ij + ε ij (7) Model GEW -G × E -G × W : y ij = µ + g i + e j + w ij + ge ij + gw ij + ε ij where σ 2 g is the additive genetic variance obtained with the genomic relationship matrix (G) and σ 2 ε is the residual error variance as defined previously. The analyses of genomic prediction were performed in R (R Core Team 2022) using the R statistical package BGLR (Pérez and de los Campos 2014).
We found a similar trend with a smaller but balanced data set suggesting that the poor estimation of the G × E was related to other factors such as the use of reaction norms to model the interactions  (Cuevas et al. 2016 ). In rice, two studies reported the use of multienvironment models to predict the performances of genotypes in untested environments and obtained similar results  (Monteverde et al. 2019; Morais Júnior et al. 2017). Morais Júnior et al. (2017)  used historical data from three cycles of a breeding program with a total of 10 environments to assess the predictive ability of a single-step reaction norm model. They obtained high accuracies for the prediction of untested environment for the three traits evaluated: DTF (0. 5-0. 9), HT (0. 25-0. 7), and YLD    2017 ) also evaluated the effect of GxE modeling in the context of the prediction of untested lines but did not find important differences with the models including only the main effects.
Acknowledgements We thank all the staff from the  Rice Breeding Team at IRRI  in the Philippines and in the regions: South and  Southeast Asia  ( Parth Sarothi Saha ,  Pranesh K J) , Est and  Southern Africa  ( Oliver Nyongesa ,  Simon Njau Kariuki ,  Arlindo Matsinhe ,  Rehema Kwayu ) for their support in the conduct, management, and collection of phenotypic data. We also thank  Juan David Arbelaez  and the members of the  IRRI Genotyping Service Lab  who helped with the genotyping. Many thanks to  Diego Jarquin  and  Eliana Monteverde  for sharing references for genomic prediction analysis.
Funding This study was funded by the  Bill and Melinda Gates Foundation  through the  Accelerated Genetic Gain in Rice (AGGRi)   Alliance  project (Grant no. OPP1194925 ). Agropolis Foundation  (http:// www. agrop olis-fonda tion. fr/) and SEARCA (http:// www. searca. org/) funded the PhD fellowship of  Nguyen Van Hieu  via the  DivBreed  project, Grant no. 1803-007.
Spectroscopy The LDSS3 mask (see Section 2 ) targeted one of the three known images of the source, image 1. 1, among other objects of interest in the field (see  Mainali 2019 ). These data were reduced and presented in  Mainali ( 2019 ) , who reported the detection of several broad UV emission lines, which enabled the identification of this object as a quasar at redshift z spec 2. 069. As can be seen in Fig. 4  , the spectrum features several prominent emission lines. The two brightest ones are the C IV λ1550 Å and C III ] λ1909 Å (unresolved) doublets at z 2. 1, and there is also a somewhat weaker He II λ1640 Å emission. We use the specutils package ( v1. 9. 1 ;  Earl et al. 2022 )  to compute the equi v alent widths (EWs) and perform a Gaussian fit to the detected lines in order to measure line centres and full-width-half-maxima (FWHM).
D. E. acknowledges support from a  Beatriz Galindo senior fellowship  ( BG20/00224 ) from the  Spanish Ministry of Science and Innovation , projects  PID2020-114414GB-100  and  PID2020-113689GB-I00  financed by  MCIN/AEI/10. 13039/501100011033 , project  P20-00334  financed by the  Junta de Andaluc ía , and project  A-FQM-510-UGR20  of the  FEDER/Junta de Andaluc ía-Consejer ía de Transformaci ón Econ ómica ,  Industria ,  Conocimiento y Universidades. G. E. M. acknowledges financial support from the  Villum Young Investigator  grant  37440  and  13160  and the The  Cosmic Dawn   Center (DAWN) , funded by the  Danish National Research Foundation  under grant No. 140. F. E. B. acknowledges support from  ANID-Chile BASAL CATA   FB210003 ,  FONDECYT Regular 1200495  and  1190818 , and  Millennium Science Initiative Program -ICN12 009.
The technique is evaluated on the PPG recordings collected from the publicly available DEAP dataset. E ciency of these easy-to-compute features is then assessed via a simple threshold-based classi cation technique to categorize the stressed and the relaxed conditions with an average accuracy of 98. 43%. Compared to the existing methods, the proposed algorithm not only shows improved performance but the associated simple methodology with minimum acquisition load also justi es its applicability in real-time standalone, personal healthcare applications.
Methodology The overall block diagram of the entire methodology is depicted in Fig. 1. The algorithm mainly comprises of four major parts: (1) preprocessing of PPG data from DEAP dataset to eliminate high frequency and power line artifacts; (2) extraction of respiratory induced amplitude variation signal (RIAV) and calculation of respiration rate; (3) feature extraction from the clean PPG signal; (4) application of easy-to-compute classi cation rule for identifying the stressed and the relaxed conditions.
PPG Dataset The proposed study uses PPG signal records collected from the publicly available DEAP database  [20]. The database contains eight peripheral bio-signals and 32-channel EEGs collected from thirty-two volunteers (17 male and 15 female). The PPG signal data in the database were recorded from the thumb of the subjects for one-minute duration with a sampling frequency of 512 Hz. The collected signals were then down sampled to 128 Hz for future use. Moreover, it is to be mention that the proposed method uses PPG records only for the relaxed and stressed conditions.
Classi cation Based on the feature values obtained, the overall discriminating ability of the extracted two features for the detection of mental stress condition are nally assessed via a simple threshold rule-based classi cation strategy. The feature threshold values for the chosen classi er are estimated from the feature boundary values, providing optimal partitioning of the feature space. It is to be mentioned that, to avoid the effect of classi er biasing, the classi cation technique follows a fourfold cross validation technique where the total data sets are divided into four equal partitions. Then, out of these four sets, three sets are used to train the classi er while the remaining fourth set is kept reserved for the testing purpose. This method is repeated for all possible combinations of the data sets so that each of the dataset can be used in both the training and the testing phases.
Performance Comparison The proposed methodology uses two easy-to-extract features solely obtained from the PPG signal and the same PPG derived RIAV signal. These easy-to-implement features strengthen the robustness and increase the applicability of the proposed algorithm in real time systems. However, in order to establish the superiority of the proposed method, its e ciency is compared with other state-of-the-art methods as presented in Table  3. The outcome listed in Table  3  indicates that instead of the differences in terms of database, sensor types, methodology, feature dimension and classi cation tool, the proposed algorithm actually outperforms all the other existing literatures. The superior performance exhibited by the proposed algorithm using the most simple threshold-based classi cation technique also establishes its potential for implementation in real-life applications.
Although, median lter does not distort the waveform but sometimes it is found to be inadequate beyond a particular limit during the presence of motion artifacts. Since, success of the method depends on proper extraction of the RIAV signal, so faithful implementation of the algorithm requires a much better motion artifact removal algorithm to eliminate the redundant motion artifact corrupted segments. PPG based mental stress estimation is comparatively a new area of research and the availability of the database is still rare. In the present research, the algorithm uses only twenty PPG beats of a record to compute the feature values. Considering fewer PPG beats may fail to capture the stress related changes, whereas larger number of PPG beats may enhance computational burden. In the the signal length and subsequent analysis requires proper inspection over other databases with varied signal length for better classi cation accuracy.
Instead of superior performance by other standard classi er [SVM (linear)], only a simple and linear classi er is selected for precise classi cation of the stressed and the relaxed states with decent accuracy. Although, the chosen threshold-based classi er needs to be optimized and evaluated further over a larger dataset in order to improve the overall performance. Moreover, in future the robustness of the features should also be evaluated using some advance classi ers. Evaluation of the developed algorithm is only carried out by implementing it on MATLAB software, but not on any hardware platform. The excellent performance of the adopted algorithm justi es its usefulness for high-tech personal assistive appliances and to cater to a broad range of other real-life applications.
To measure this, we computed the strength of the resulting vector (or mean resultant length), which is defined as its length divided by the number of vectors summed  (Borradaile, 2003). Thus, a value of 1 means that all the vectors have the same orientation, while a value close to 0 reflects the absence of preferential orientation. We calculated the LURSs' volumes using the sloping local base-level (SLBL) method  (Jaboyedoff et al. , 2020) , which is an iterative method considering that the volume above a sloping surface connecting the surrounding points, in this case the limits of the instability, can be eroded. A tolerance can be used to create a curved surface, and the value of the tolerance controls the curvature. We implemented this method in an ArcGIS toolbox (https://github. com/ngu/pySLBL).
Measurement of displacement rates Displacement rates of 299 out of 509 LURSs have been used in this work. Displacement rates were extracted from the NGU database and own measurements using InSAR Norge portal. We used the displacement rates that are representative of entire LURSs, not of single scenarios. Within the framework of the hazard classification system done by NGU  (Hermanns et al. , 2012) , six classes of displacement have been defined: 1. Not significant; 2. <0. 5 cm/y; 3. 0. 5-1 cm/y; 4. 1-4 cm/y; 5. 4-10 cm/y; and 6. >10 cm/y. LURSs with unknown displacement rates are located mainly on slopes with dense vegetation coverage, are in shadow zones of the Sentinel-1 satellites or are on inaccessible slopes. The methods used for the monitoring of displacement rates are as follows: Differential Global Navigation Satellite System (dGNSS): used to measure displacement rates on 95 LURSs.
Acknowledgements The authors acknowledge the  Norwegian Water Resources and Energy Directorate  for funding most of the research done in the framework of this study. Parts of the study are based on results from the project ' CryoWALL -Permafrost slopes in Norway ' ( 243784/CLE ) funded by the  Research Council of Norway (RCN)  and the  University of Oslo, Norway. We would like to express our gratitude to the two anonymous reviewers for their valuable remarks, suggestions and interest in our work.
Most notably, a significant advance was made in defining the concept of "data preservation", that includes in fact all aspects related to a productive data analysis activity: digital data, metadata, publications, software, databases, documentation etc. A key issue was identified to be the organisation of data analysis activities in the long term, in particular by transforming and adapting the collaborations (new rules for governance, lighter procedures, flexible membership etc. ), as well as considering to open the data for reuse by enlarged communities. The ICFA panel, with a strong support from major laboratories and in particular CERN  3  initiated an international collaboration called Data Preservation in High Energy Physics (DPHEP) with the primary goal to foster the international collaboration and mutual support across HEP collaborations in enriching the scientific return of HEP data.
It is likely that the preparation for FCC-ee will benefit from the preserved LEP data sets, at least at the level of data model design and analysis frameworks preparation. -The proof that complicated data analysis frameworks can be preserved with significant gains may stimulate more communities and experiments to join the reflection and implement suitable strategies. An example is offered in this document by MINERvA, a neutrino-nucleon scattering experiment at Fermilab. -New types of analysis methods (e. g. machine learning) and data access (e. g. zenodo  [13] ) are being tested on preserved data sets, thereby leading to new results or methods for long term preservation and open access. -Open science policies implemented for recent or ongoing data sets are essential for the long term robustness of the data preservation.
2 What is data preservation? It may be useful to recall the scope and the goals of data preservation in HEP  [2]. What is data?: the short answer is "everything that was created as a result of planning, running and exploiting an experiment". Indeed, there is a persistent confusion associating "data" to an operating system files, i. e. bits on a memory support such as disks, tapes, etc. This simplistic approach is not operative for running experiments and cannot be used to plan for a functional long term data preservation. Although the file system is of course essential, it is by far insufficient to perform a data analysis. Instead, we propose to define the "data" in HEP -but also in any experimental work using digital/computing systems -as the multiverse of all specific inputs, outputs and digital tools used by a group of researchers in order to obtain a novel result: -digital data files: raw and processed, control/configuration, meta-data, environmental parameters, operational data, databases, etc.
The level of complexity differs for the various aspects of the data. The simplest examples include the digital files, the documentation etc. that need only storage and access, i. e. rather standard operations independent of the experiment complexity in general. In contrast, specific experimental software and databases are much more difficult to keep functional across technological changes (hardware, operating systems etc. ). -ensure unambiguous and permanent data validation  [14]  and results reproducibility, naturally setting the ground for data reuse and open access  [15]. -define and identify the human resources related to the research plan. 7  -oversee and manage the collaborative work and manage the preserved data analysis activity according to the DP design. -define and implement data access policies, i.