dataesr/scientific-paragraphs-categorization huggingface dataset:
VII. CONCLUSIONS In summary: (i) We have reasonable confidence that low-impulse glitches are due to rare transients in the interferometer readout and can be kept well under control in ordinary operating conditions. (ii) The fastest force glitches with less-than-a-minute duration may have different explanations, including electronics events, eddy current transients and outgassing events. For all these possibilities, proper ground testing is possible to both consolidate the understanding and reducing the risk that their rate impacts on LISA data quality. (iii) For the long, minute-to-hour force glitches, the only credible explanation that seems to match most of the observational evidence is outgassing. The kind of outgassing events that would explain the observation are somewhat different from the outgassing spikes observed in the vacuum system under transient conditions (Fig.
16 ). Thus, to consolidate this hypothesis, a dedicated study is needed, and will be performed, also including appropriate experiments. In conclusion, we are confident that the rate of glitches in LISA will be kept to a manageable level, as supported by the indicated tests and studies. The work presented here demonstrates how glitches can be subtracted in the LPF case, which is admittedly simpler than LISA. As with other gravitational wave detectors, identification and mitigation of the remaining glitches will be part of the analysis pipelines that yield LISA's astrophysical data products. Compared with ground-based gravitational wave instruments, in which both the astrophysical sources and glitches have similar duration, many of LISA's sources are present in the detector band for much longer timescales than typical glitches, which should aid in distinguishing the two types of signals.
ACKNOWLEDGMENTS This work has been made possible by the  LISA Pathfinder mission , which is part of the  space-science program  of the  European Space Agency. We thank  Paolo Chiggiato  and the vacuum, surfaces and coatings group from  Conseil Européen pour la Recherche Nucléaire (CERN) , for very helpful discussions about the LPF outgassing environment. The Italian contribution has been supported by  Istituto Nazionale di Fisica Nucleare (INFN)  and  Agenzia Spaziale Italiana (ASI) , Project No. 2017-29-H. 1-2020 " Attività per la fase A della missione LISA ". The UK groups wish to acknowledge support from the  United Kingdom Space Agency (UKSA) , the  Scottish Universities Physics Alliance (SUPA) , the  University of Glasgow , the  University of Birmingham , and  Imperial College London.
The Swiss contribution acknowledges the support of the Swiss Space Office via the  PRODEX Programme of ESA , the support of the  ETH Research Grant  No. ETH-05 16-2 TRANSIENT ACCELERATION EVENTS IN LISA PATHFINDER … PHYS. REV. D 106 ,  062001 (2022 )  062001-21  and the support of the  Swiss National Science Foundation  (Projects No. 162449  and No. 185051 ). The Albert Einstein Institute acknowledges the support of the  German Space Agency, DLR. The work is supported by the  Federal Ministry for Economic Affairs and Energy  based on a resolution of the  German Bundestag  (No. FKZ 50OQ0501 , No. FKZ 50OQ1601 , and No. FKZ 50OQ1801 ). J. I. T. and J. S. acknowledge the support of the  U. S. National Aeronautics and Space Administration (NASA). Spanish contribution has been supported by Contracts No.
AYA2010-15709  ( Ministerio de Ciencia e Innovación, MICINN ), No. ESP2013-47637-P , No. ESP2015-67234-P , No. ESP2017-90084-P  ( Ministerio de Asuntos Económicos y Transformación Digital, MINECO ), and No. PID2019-106515GB-I00  (MICINN). Support from  AGAUR  ( Generalitat de Catalunya)  Contract No. 2017-SGR-1469  is also acknowledged. M. N. acknowledges support from  Fundacion General CSIC (Programa ComFuturo ). F. R. acknowledges an  FPI  contract from  MINECO. The French contribution has been supported by the  CNES  ( Accord Specific  de projet No. CNES 1316634/CNRS 103747 ), the  CNRS , the  Observatoire de Paris  and the  University Paris-Diderot. E. P. and H. I. would also like to acknowledge the financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  (No. ANR-10-LABX-0023  and No.
5%, 1%, 2% and 10% of biochar (Figure  1 ). The first three doses of biochar used were such that they could be applied in a realistic agronomic application  [8, 16] , while the 10% dose was used for modeling and exploratory purposes. After filling the columns, they remained intact for 45 days to achieve homogenization between the soil and the biochar. It was noted that the pH of the biochar-soil mix did not significantly change among different applications. The mean pH value of the mixtures was 7. 13 ± 0. 3. The concentration of the main exchangeable cations and the cation exchange capacity (CEC, cmol/kg) in the biochar-amended soils mixtures are given in Table  3. The release of nutrients (Ca, Mg, K, Na) from biochar-amended soils was modelled using PHREEQC v3. 3. geochemical code employed with phreeqc.
dat database  [32]. The conceptual model assumed that irrigation water was applied in a column of agricultural soil amended with biochar (0-20 cm) and subsequently mobilized the water-soluble and the exchangeable fraction of nutrients from the solids, which are transferred within the soil column (Figure  1 ). ESP = exchangableNa CEC × 100 (1) The one-dimensional transport of each element within the soil column was modelled using the advection-reaction-dispersion equation (ARD): ∂C ∂t = -u ∂C ∂x + D L ∂ 2 C ∂x 2 - ∂q ∂t ( 2 ) where C is the soluble elemental concentration (mol/kg of water), t is the time (s), x is the distance (m), u is the linear water velocity (m/s), D L is the hydrodynamic dispersion coefficient (m 2 /s) and q the elemental concentration per mass of solid (mol/kg). The ARD equation was implemented using the TRANSPORT subroutine, assuming 5 cells each having 0.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/agronomy12020480/s1. Figure  S1 : Example of the TRANSPORT subroutine used in the simulations; Figure  S2 : Script example for E. E. model (input parameters for soil with 0% biochar); Figure  S3 : Script example for K. E. model (input parameters for soil with 0% biochar); Figure  S4 : Exchanger composition (KX, CaX2, NaX, MgX2) during the simulation time of E. E. model; Figure  S5 : Exchanger composition (KX, CaX2, NaX, MgX2) during the simulation time of K. E. model; Table  S1 : Total amount of moles in the exchangeable sites used in Model E. E. ; Table  S2 : Ion-exchange coefficients relative to Na, following the Gaines-Thomas convention and implemented in PHREEQC database (EXCHANGE_SPECIES).
Funding: This work was funded by FERTICHAR project, through the ARIMNet2 Joint Call by the following funding agencies: MHESRT (Tunisia), ANR (France), and HAO332 DEMETER (Greece). ARIMNet2 (ERA-NET) has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 618127. The authors gratefully acknowledge the funding agencies for their support.
Finally, a semi-supervised graph convolutional network is trained based on thee fused latent representation space to perform the hyperspectral image classification. The main advantage of the proposed approach is to allow the automatic extraction of relevant information while preserving the spatial and spectral features of data, and improve the classification of hyperspectral images even when the number of labeled samples is low. Experiments are conducted on three real hyperspectral images respectively Indian Pines, Salinas, and Pavia University datasets. Results show that the proposed approach is competitive in classification performances compared to state-of-the-art.
The remainder of the paper is organized as follows. In Section II, we present some works related to the spectral-spatial classification of HSI. In Section III, we detail the proposed methodology called Multi-View Deep Neural Networks (MV-DNNet) which includes the multi-view deep autoencoder (MVDAE) that allows fusing spectral-and spatial-features, and the semi-supervised graph convolutional network (SSGCN) model. In Section IV, we first describe the HSI dataset; then we detail our experimental protocol. Quantitative evaluations demonstrate the good performances of the MV-DNNet for classification tasks and we conclude in Section V.
Semi-supervised classification with graph convolutional network (SSGCN) In this section, we present our proposed semi-supervised graph convolutional network (SSGCN). The main goal of this model is to perform the spectral-spatial classification by considering the constructed graph G. For semi-supervised learning, let T D l = { z i , y i } L i =1 be a set of labeled training dataset of size L, where z i indicates a feature vector of the i th labeled pixel, and y i is its corresponding label. Moreover, let T D nl = { z i } L + NL i = L +1 be a set of unlabeled training samples of size NL ( L + NL = N). The aim of semi-supervised learning is to predict the labels of unlabeled training samples T D nl , using a non linear function f (Z , W ) such as ReLu  [43].
Analysis of the reconstruction error In this section, we present the reconstruction errors obtained by different representation learning models, including, PCA, ICA, AE( linear,linear ), AE( relu,linear ), and AE( relu,sigmoid ). We opted for the MSE loss function to compute the reconstruction error between the initial HSI X and the reconstructed input ˆ X and we reported then the average MSE versus to the encoding dimension for Indian Pines, Salinas, and Pavia University (see Fig. 8  ). Thus, we can interpret that the AE( relu,linear ) is the appropriate one for representa-  tion learning for three HSIs data with an MSE value equal to 0. 069 and an encoding dimension set to 20 for Indian Pines, 0. 064 for an encoding dimension of 30 for Salinas, and 0. 058 for a dimension defined to 20 for Pavia University.
24% , AA is 98. 17% , and k is 98. 29% (see Table  6  ). Also, we can notice that the MV-DNNet method gives better classification rates for 13 out of 16 classes, with a number of features d = 30. Most classification rates are greater than 97%. Again, for very few classes our model is slightly less. Indeed, the GF method gives the best classification rates for the class 'Grapes-U', with an OA of 97. 80%. Also, the 3DCNN model is better for the class 'Fallow-R-P', with an OA of 96 , 99% , and the F-3DCNN is better for the class 'Brocoli-G-W-2', with an OA of 96. 68%. Fig. 11  shows the thematic maps of different classification methods for Salinas Dataset. Overall, we obtained a higher performance in classification with the proposed MV-DNNet model. This prove the effectiveness of the incorporation of the deep multi-view representation in the classification task.
However, in the rest of classification methods, there are still some mistakes in different thematic maps. Table  7  reports the obtained classification results for the Pavia University HSI. From this table, we can see that the OA with MV-DNNet is 99. 16% , AA is 99. 04% , and k is 99. 07%. The MV-DNNet model gives better classification rates for all the 9 classes of Pavia University dataset. Also, most classification rates OAs are greater than 98%. In Fig. 12  , we compare also the obtained classification maps with MV-DNNet based on multi-view latent representation, i. e. , fused both spectral and spatial features with those only relying on spectral and spatial features, and average or concatenated spectral and spatial features. We can notice that the MV-DNNet can extract effective f eatures to perform the classification.
TreeTuner: A pipeline for minimizing redundancy and complexity in large phylogenetic datasets Various bioinformatics protocols have been developed for trimming the number of operational taxonomic units (OTUs) in phylogenetic datasets, but they typically require significant manual intervention. Here we present TreeTuner, a semi-automated pipeline that allows both coarse and fine-scale tuning of large protein sequence phylogenetic datasets via the minimization of OTU redundancy. TreeTuner facilitates preliminary investigation of such datasets as well as more rigorous downstream analysis of specific subsets of OTUs.
SUMMARY Various bioinformatics protocols have been developed for trimming the number of operational taxonomic units (OTUs) in phylogenetic datasets, but they typically require significant manual intervention. Here we present TreeTuner, a semiautomated pipeline that allows both coarse and fine-scale tuning of large protein sequence phylogenetic datasets via the minimization of OTU redundancy. TreeTuner facilitates preliminary investigation of such datasets as well as more rigorous downstream analysis of specific subsets of OTUs. For complete details on the use and execution of this protocol, please refer to  Maruyama et al. (2013)  and  Sibbald et al. (2019).
BEFORE YOU BEGIN Rapidly expanding genomic databases make it possible to generate sequence alignments with thousands of operational taxonomic units (OTUs) from a wide range of organisms  (Yandell and Ence, 2012). Many bioinformatics tools developed for trimming large phylogenetic datasets aim to optimize the diversity of OTUs in a given tree relative to the phylogenetic question at hand  (Krishnamoorthy et al. , 2011; Menardo et al. , 2018; Emms and Kelly, 2021). However, little attention has been given to the development of automated and semi-automated bioinformatic tools capable of both coarse-and fine-grained tuning of OTU complexity in large phylogenetic datasets. Here we describe TreeTuner, a bioinformatics pipeline for researchers wanting to combine the ability to explore species tree diversity with rigorous downstream analysis of specific OTUs.
Data collection and methods overview We demonstrate the utility of the TreeTuner pipeline using a selected set of ClpS protein coding sequences (i. e. , ATP-dependent Clp protease adaptor protein)  (Nishimura et al. , 2013; Bouchnak and van Wijk, 2019)  from the highly curated genome of A. thaliana as a query for the retrieval of homologs from the NCBI-nr database. Prior to assembly of a 'master' alignment capturing the full breadth of known sequence diversity, we first build preliminary phylogenies to identify general patterns in the data ('coarse tuning'), and then proceed to curated, in-depth analyses using more sophisticated tree-building methods and optimized taxon sampling ('fine tuning'). The goal of this protocol is to provide a step-by-step guide to the TreeTuner workflow (https:// github.
Requirements for setting up the pipeline The TreeTuner pipeline combines the software and tools required for both coarse-and fine-scale tuning. For coarse tuning, to run TreeTrimmer  (Maruyama et al. , 2013)  locally, pre-installed Ruby (e. g. , Ruby v2. 5. 1 and BioRuby v2. 0. 3) is required. For fine tuning, the necessary custom Perl scripts (rm_inparal_rank. pl and trim2untrim. pl) and Python script (rename_ncbi_blastdb. py) can be found at the following GitHub website: https://github. com/zx0223winner/TreeTuner. Pre-installed Perl (e. g. , Perl 5 and BioPerl v1. 7. 2) is required to run these scripts. To color the OTUs on the Newick tree based on taxonomic rank, the Environment for Tree Exploration (ETE3) toolkit  (Huerta-Cepas et al. , 2016)  and associated Python scripts (e. g. , color_coarse_tuning_tree.
py and color_fine_tuning_tree. py) are needed. TreeTuner users will also need to run their BLAST searches  (Altschul et al. , 1997)  against the NCBInr database. Subsequently, the initial raw alignments, trimmed alignments and preliminary tree files can be created by different tools, such as MAFFT v7  (Katoh and Standley, 2013) , BMGE v1. 12  (Criscuolo and Gribaldo, 2010), trimAl v1. 4 (Capella-Gutierrez et al. , 2009) , FastTree v2. 1  (Price et al. , 2010)  and IQ-TREE v1. 6. 12  (Nguyen et al. , 2015)  software packages, which can be accessed via the links in the key resources table. A conda environment definition file (TreeTuner-ENV. yaml) is also provided at the GitHub page to assist users with installing all the required dependencies. Note: At a minimum, a computer with 2 cores, 4 GB of RAM and 256 GB of storage with at least 100 GB of free hard disk space is required; this will allow the OTUs of the phylogenetic tree to be trimmed within a few minutes.
MATERIALS AND EQUIPMENT The TreeTuner pipeline implementation was written in Ruby v2. 5. 1, Perl 5 and Python 3 using the following software and custom scripts: treetrimmer. rb, which enables large phylogenetic datasets to be coarsely trimmed in terms of redundancy. The representative OTUs can be referenced for more rigorous downstream analysis; rm_inparal_rank. pl and trim2untrim. pl are custom scripts to more finely remove redundancy in the selected OTUs and retain the sequences of interest to build a more robust tree; rename_ncbi_blastdb. py, color_coarse_tuning_tree. py and color_fine_tuning_ tree. py are custom Python scripts to parse the input protein sequences, format the header of NCBI sequence IDs and color the phylogenetic tree files based on taxonomic ranks of the OTUs. A full list of the utilized scripts and database, including links, can be found in the key resources table.
The full TreeTuner source code can be found in the GitHub repository. A useful hands-on tutorial (Online_TreeTuner Tutorial. pdf) can also be accessed under the tutorial directory of GitHub. Our test input data consisted of BLAST search results against the NCBI-nr database, the initial raw alignments, trimmed alignments and preliminary tree files created by different tools from the key resources table. Note that five documents are required before the TreeTuner pipeline can be run. The NCBI-nr database BLAST results are obtained first in a 13-column table  (Table 1 ). The second document contains the query sequences retrieved from NCBI (Figure  1A ). The third document is a reference list of OTU names and taxonomic information (Figure  1B ), and the fourth document is the untrimmed alignment and trimmed alignment (Figure  1C ).
STEP-BY-STEP METHOD DETAILS Preparing the online NCBI-nr protein BLAST-search result files Timing: 2 min (Depending on file sizes and Internet speed) For the purposes of demonstration, we select the ClpS protein from A. thaliana in this protocol, which is applicable to any other proteins and species. The NCBI-nr protein BLAST-search result file is the first document required for the TreeTuner pipeline (Table  1 ). What follows is a description of how to acquire both online and local BLAST-search results using an example FASTA file (Figure  2A ). The sample files can be acquired from GitHub under the tutorial directory. Users will acquire the online BLAST-search results (e. g. , FASTA file and Hit b. Select the NCBI-nr database (Figure  2A ). c. Choose the parameters (e. g. , the max target sequences: 1000; expect threshold: 1e-10) (Figure  2B ).
d. Click the BLAST button at the bottom. 2. After a few seconds, users can see the BLAST result interface (Figure  2C ). Users can then download the FASTA (complete sequence) and Hit Table (CSV) via the Download option tab. The FASTA file will contain the ClpS homologs retrieved from the NCBI-nr database. The Hit Table  summarizes  the matched hits in a 13-column tabular file (Table  1 ). These two documents are necessary input files for subsequent steps. Before moving on to the tree building step, an alternate approach (i. e. , local BLAST) to acquiring these input files is described at step 3. Preparing the local NCBI-nr protein BLAST-search result files Timing: 24 h (Depending on file sizes, computing power, and Internet speed) Users can also use the local BLAST approach to acquire the significant hits to the protein query from a customized database.
This is especially useful for users to explore the full breadth of homolog diversity, since some of homologs might not be present in, or retrievable from, NCBI-nr. Users will acquire two necessary documents (e. g. , BLASTP_clps. tsv and clps_hits. fasta) for the TreeTuner pipeline in steps 3-8. 3. Although it is easy to acquire NCBI-nr protein BLAST results online (step 1-2), we also recommend users perform their BLAST searches locally (steps 3-7 below) for more robust analyses. This is because users can more freely adjust the parameters such as max target sequences. The NCBI BLAST server (https://blast. ncbi. nlm. nih. gov/Blast. cgi) limits the number of retrievable sequences to 5,000, which might not capture all of the sequence diversity in the NCBI-nr database. We recommend choosing a maximum (max) target sequence parameter value between 100,000 and 300,000.
A max target sequence parameter of 1,000-5,000 is fine if one wants to quickly test the pipeline, and users can skip steps from 3 to 7 and proceed to the tree building analysis starting at Step 9 directly. Note: Local NCBI-nr protein BLAST allows users to conveniently adjust the parameters of thresholds, such as E-value cut-off and maximum target sequences, and to perform thousands of queries against NCBI-nr and other environment-or taxon-specific databases, such as the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMSTSP)  (Keeling et al. , 2014; Caron et al. , 2017). It should also be noted that there is a need for users to have a basic knowledge of bioinformatics in order to take full advantage of TreeTuner, in particular, the ability to use the basic BLAST package and dash shell in a Linux/Unix environment.
4. To set up the local BLAST, users will need to download the BLAST Tool Package and the FASTA sequences corresponding to the custom database. Also, the ''blastdbv5-user-guide. pdf'' document in the GitHub ''tutorial'' directory contains complementary vignettes to help guide users, which has been proved useful in our previously published protocol  (Zhang et al. , 2021a (Zhang et al. , , 2021b   b. To simplify the pipeline, we will still use the A. thaliana ClpS protein as the example file (clps. fasta). c. d. The makeblastdb command will construct a protein database by taking in the FASTA file with the parameter (-in), setting up the database type (e. g. , protein) with the parameter (-dbtype protein), and naming the database (e. g. , MMETSP_database) with parameter (-title databa-se_name).
The '-out' option will yield the database output name (e. g. , MMETSP_db). e. To download the NCBI-nr v5 databases, use the Perl script update_blastdb. pl, which is in the downloaded BLAST+ package (https://ftp. ncbi. nlm. nih. gov/blast/executables/blast+/ LATEST/). This command will download the NCBI-nr database (https://ftp. ncbi. nlm. nih. gov/blast/db/v5) with the name nr without using the makeblastdb command to recreate the database files. It could take hours for processing, depending on the Internet connection. i. Users can first check all available databases via the command below. ii. Users can then run the command below to automatically download the NCBI-nr database which includes 55 volumes of data (>100 Gb). Or users can manually download these 110 files (i. e. , nr. 00. tar. gz, nr.
00. tar. gz. md5, etc. ) from the link: https://ftp. ncbi. nlm. nih. gov/ blast/db/v5. 5. Use the BLASTP search option to blast the amino acid sequences against NCBI-nr databases. The BLASTP command can carry out the protein similarity search by searching the query file (e. g. , clps. fasta) against the protein database using parameters, such as '-evalue' (indicating the significance of the BLAST hits), '-outfmt 6' (meaning the tabular format of the BLAST result), '-out' (telling the file name of the output file; e. g. , BLASTP_UWO241_uniprot. xml) and '-max_target_ seqs' (potential number of hits). CRITICAL: The parameters after the option '-outfmt' will yield the desired columns in a tab-delimited output file. For example, 'qseqid' refers to query sequence ID and 'qlen' refers to query length (https://www.