dataesr/scientific-paragraphs-categorization huggingface dataset:
This method is automated and will be implemented in the Hydroweb database. This also proves that radar altimeters, even on very small lakes of few ha, allows reaching accuracy as good as laser altimetry (ICESAT2) which has been evaluated in  [55]. The demonstration has been made with Sentinel-3 SAR data. The authors plan to extend this work to the Sentinel-6 mission, and also to the LRM acquisitions. A specific work has to be conducted on the echoes for which the LPP fails. The waveforms are really perturbed and distorted. The roughness evolution due to natural events (wind) is very local and rapid. Comparisons with SAR images tend to prove a very high heterogeneity of the lake surfaces. The LPP should take this better into account, at least with a more elaborate editing. It has also been observed that when the surface is frozen, LPP performances are impacted since the echo modeling does account for double peaks shape of the waveforms.
This work is licensed under a Creative Commons Attribution 4. 0 License. For more information, see https://creativecommons. org/licenses/by/4. 0/This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information:  DOI 10. 1109/TGRS. 2021. 3137034 , IEEE Transactions on Geoscience and Remote Sensing > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < ACKNOWLEDGMENT  The authors thank  Claire Pottier  ( CNES ) for her support on lake database and  Sophie Le Gac  ( CNES ) for her support on OLTC database. They also thank  EDF (France) ,  CTOH (LEGOS, France)  and  OFEV (Switzerland)  for providing in-situ data, and  ESA/ESRIN  for the GPOD processing facility.
This research is a development research that aims to produce learning innovations in the psychomotor domain for practice in archival subjects. The research design adapts the Borg and Gall model which consists of eight stages, namely, (1) potential and problems, (2) data collection, (3) product design, (4) design validation, (  5 ) design revision, (  6 ) products testing, (  7 ) product revision, (8) mass production. This research was conducted at the Office Administration Vocational School in Malang City. Data collection is done through filling out a questionnaire to determine the feasibility of the product being developed. The result of this research and development is a learning media called STELAR which is feasible and valid to be applied in the learning process, especially in the Office Administration Department.
Data Presentation and Analysis The process of validating the feasibility of the product that has been developed, namely a website-based digital archive application called the STELAR application, was assessed by a media expert and a material expert. The validator provides an assessment to determine whether the STELAR Application product that has been developed is valid or not to be used as a learning medium in the Archives Subject of the Office Administration Vocational School. Indicators of achievement in this study include the relevance of learning theory and practicum media, completeness of media presentations, and providing an easy understanding of theory through practice using this simulator media. Then the feasibility of the media is reviewed in terms of media validation based on the feasibility of display, practicum media, ease of access, navigation, and operation as well as suitability to the abilities and understanding of students.
The following in Table  5  is the result of validation by material experts. Based on Table  5 , the expert validators assess three aspects of the assessment. The determination of this aspect of the assessment is adjusted to the needs of each assessor for the product being developed. Based on the results of the calculation of the material expert's questionnaire assessment of the STELAR learning media that has been developed, the results are 92. 85%. In addition to the assessment by material experts, this research and development product was also assessed by media experts which are presented in Table  6  below. 11. Ease of entering data 12. Ease of printing letters 13. Ease of storing letters 14. Data security Based on the results of the calculation of the questionnaire assessment by media experts, in this case, lecturers who are experts in the development of learning media.
Binning is a critical step to establish an accurate genome from a metagenome assembly. Binning tools cluster contigs based on compositional properties (e. g. GC content, tetranucleotide frequencies) or sequence coverage across multiple samples  (Albertsen et al. , 2013; Imelfort et al. , 2014). Some of them additionally use marker genes from a reference database and scoring strategies  (Parks et al. , 2015; Simao et al. , 2015). Yet, recent publications showed that no single binning approach such as ABAWACA (https://github. com/CK7/abawaca), CON-COCT  (Alneberg et al. , 2014) , MaxBin  (Wu et al. , 2016)  and MetaBAT  (Kang et al. , 2019)  is superior for all samples/environment types or even for all populations within one sample. Therefore, bin consolidation tools such as Binning_refiner  (Song and Thomas, 2017)  and DAS Tool  (Sieber et al.
, 2018)  attempt to combine the strengths and minimize the weaknesses of different binning tools. A similar strategy has been used in a modular pipeline software called metaWRAP  (Uritskiy et al. , 2018)  combining Binning_refiner and a subsequent refinement module. This module uses the completion and contamination metrics estimated with CheckM, which is based on collocated sets of nearly ubiquitous and single-copy genes  (Parks et al. , 2015). Despite the success in extracting reconstructed genomes from a variety of environmental metagenomes, significant challenges still exist. Notably, microdiversity inherent to many bacterial species leads to major difficulties in the accurate resolution of metagenome-assembled genomes (MAGs), as analysis techniques are insufficient for distinguishing many closely related genomes  (Sharon et al.
In order to introduce additional abundance differences among the extracted metagenomic DNAs, two different extraction methods were used, extraction A, a gentle extraction preserving long DNA fragments, and extraction B, a standard method. The DNA was sequenced with a short-read (Illumina HiSeq; extractions A and B) and a long-read (PacBio Sequel; extraction A) sequencing technology. This produced 39. 6 Gbp of paired-end short-read data and 26. 5 Gbp of long-read data.
Some of them were found in multiple samples and/or in multiple contigs of the same sample. ACC001 was found in sample d71 and d322. It is phylogenetically close to the 16S rRNA genes of Ca. Accumulibacter UW1  (Martin et al. , 2014). ACC003 and ACC005 were found in the assemblies of the three first samples. ACC005 is identical to the 16S rRNA gene of the MAG Ca. Accumulibacter BA93  (Skennerton et al. , 2015). ACC007 and ACC012 were found only in sample d740. ACC004, found in the assemblies of samples d322 and d427, was easier to assemble than the other Ca. Accumulibacter sequences included in this study. The best match for its 16S rRNA gene was a Ca. Accumulibacter with 99. 0% of identity in MiDAS database vS123_2. 1. 3 (McIlroy et al. , 2017). This affiliation was confirmed with online 16S rRNA databases MiDAS v4.
Multi-distance long-read binning with silhouette coefficient optimization The long-read assembly was achieved in winter 2019, before the release of MetaBAT2  (Adler, 2019; Kang et al. , 2019). The popular binning tools tested at the time were not successful at separating the different Ca. Accumulibacter MAGs. These tools, designed for shortread assemblies, have shown good results when combining the coverage information of numerous datasets  (Alneberg et al. , 2014). They were therefore not adapted to our experimental design consisting in grouping MAGs from long-read assemblies without combining the coverage information from other samples. With long-read assemblies, the scaffolding was not possible since all the non-ambiguous information provided by the long reads is used to build the contigs.
Our in-house binning tool MuLoBiSC was therefore developed in order to group the contigs into bins by taking advantage of the length of long-read contigs. In addition to tetranucleotide frequency and mapping information of short and long reads from the same sample, it includes a metric based on the complementarity of benchmarking single copy orthologous genes (BUSCO;  Seppey et al. , 2019). The clustering of the contigs was optimized by maximizing the sum of silhouette coefficients weighted by the length of the contigs (WSC). It enabled to reduce MAG contamination, discard unreliable contigs, and recover more robust bins. This multi-distance binning strategy allowed to recover 19 high-quality MAGs (>90% completeness, <5% contamination) from the four samples, including taxa lacking a complete reference genome from the same genus in public databases (e.
7 Mbp. For the combination of MetaBAT2 and MuLoBiSC, called metaWRAP_BM, the results were identical to metaWRAP_BXM for seven out of eight bins. Only the bin ACC012 from day 740 comprised two extra contigs. From tests with the integrative binner DAS Tool, we concluded that it was not adapted to investigate microdiversity of Ca. Accumulibacter from our initial contigs dataset. Overall, the results showed that the MAGs provided by metaWRAP and the combination metaWRAP_BXM had a higher quality than those Only the MAGs with a completeness >80% and a contamination <10% are shown here. More details, including the lower quality bins are in the Supporting Information Table  S8. The completeness and contamination after the correction of the chimeral contig are 98. 1% and 4. 4%, respectively. obtained with the combination metaWRAP_BXC or with individual binning tools.
Accumulibacter BA93 (clade IA) have a pkk1 sequence similar to Ca. Accumulibacter BA93, placing the ACC005 MAG in clade IA. These three MAGs have different values of completeness and contamination, yet, they are highly similar to one another (Supporting Information Fig. S12 ), with an ANI from 98. 6% to 99. 6%. The percentage of aligned bases between two ACC005 MAGs goes from 68. 8% between sample d71 and d322 to 89. 8% between sample d322 and d427, respectively (Supporting Information Table  S10 ). The three ACC003 MAGs also share a unique pkk1 sequence. This sequence places ACC003 in type I but no specific clade could be assigned since no other sequence with an identity percentage higher than 86% was found in the NCBI database for this pkk1. Again, these three MAGs have different completeness and contamination values, but they have an ANI above 98.
5% (Supporting Information Table  S11  and Fig. S13 ). The pkk1 sequence of MAG ACC004 is found on the same contig as the two ACC004 16S rRNA gene sequences; therefore, it provides information about the consistence of the assembly but not about the quality of the binning. As in the 16S rRNA gene tree, the ACC004 pkk1 is placed apart from the other Ca. Accumulibacter pkk1 genes from other MAGs and references. For this pkk1, the best match on NCBI database is with Ca. Accumulibacter UW1 but with an identity of 80. 6% on 95% of the sequence. The ANI between ACC004 and the other Ca. Accumulibacter MAGs and chosen references, assessed by the percentage of aligned bases, is between 77. 9% and 78. 6% (Supporting Information Table  S12 ). The similarity of this MAG with the (Ca. ) Propionivibrio MAGs and references is slightly higher, with ANIs ranging from 78.
Accumulibacter populations and that the first third of the contig belongs to the ACC007 MAG and the other two thirds belong to the ACC012 MAG. The manually corrected MAGs ACC003 from sample d322 (ERS6225847), ACC004 (ERS6225849) and ACC005 (ERS6225848) from sample d427, ACC007 (ERS6225850) and ACC012 (ERS6225851) from sample d740 and DEC001 (ERS6225852) from sample d71 are deposited in the ENA under the project accession number PRJEB38840 with the PacBio and Illumina runs. All the submitted MAGs meet the standards of high-quality draft MAGs  (Bowers et al. , 2017).
Binning strategy Binning tools were optimized for short-read or hybrid assemblies. Our in-house binning tool MuLoBiSC was developed in order to take advantage of the big length of long-read contigs. It was specifically designed to our data consisting for each sample of one dataset of long-reads and two of short-reads and would need to be further adapted in order to be used with other data configurations. The particularity of MuLoBiSC is that it initiates the binning with the longest contigs, giving more weight to the information they contain and use single-copy genes (BUSCO) complementarity to reconstruct the metagenomic puzzle. To objectively assess the classification of each contig within each bin, we used Silhouette coefficient (SC). SC has been successfully used after clustering as a cluster validity measure for gene expression data  (Gat-Viks et al.
, 2003)  or SNP genotypes  (Lovmar et al. , 2005). Here, it helped to refine the binning of the contigs by increasing the consistency of the global clustering. It also provides a metric on how 'well' a contig is classified within a bin. If the performance of MuLoBiSC with our dataset was similar to the one of MetaBAT2, the combination of the two binning tools and MaxBin2 with metaWRAP (metaWRAP_BXM) resulted in MAGs of higher quality (good estimated completeness and contamination, plausible genome size) than the default combination proposed in metaWRAP (MetaBAT2, MaxBin2 and CONCOCT). metaWRAP_BXM provided a good resolution of the different Ca. Accumulibacter MAGs, and the 16S rRNA genes and pkk1 information was consistent. Even so, a careful examination of the MAGs obtained with metaWRAP_BXM detected remaining contamination.
The elusive microdiversity of Ca. Accumulibacter From the four Ca. Accumulibacter reconstructed from our metagenomes, ACC005 is very similar to Ca. Accumulibacter BA93, with an identical 16S rRNA gene sequence, similar pkk1 sequences (97. 9%) and a global ANI value of 98. 9%. ACC007, found in the sample collected when the reactor was treating complex polymeric wastewater, is classified in clade IC. Based on the comparison of pkk1 and 16S rRNA gene, ACC012 can be classified in type I. The pkk1 sequence of ACC003 also places this MAG outside of the actual clade classification. Comparing ACC003 ppk1 sequence to the NCBI nucleotide database, the highest identity (93. 4%) is obtained for Ca. Accumulibacter phosphatis clone PPK8H07, with a query cover of 47%. This ACC003 represents therefore a valuable new reference in the genus Ca.
, 2019). Yet, the mechanisms of these rearrangements and their dynamic evolution warrant further investigations. More generally, the synthetic mock communities are probably too simple to mimic the true diversity of environmental metagenomes  (Nicholls et al. , 2019; Sevim et al. , 2019). Adding some intra-strain genomic transferts and rearrangements would probably increase the relevance of the mock datasets and help increasing the fitting of MAGs reconstruction pipelines to real metagenomic data. Since sequencing technologies are evolving fast  (Frank et al. , 2016) , we can expect that the length and accuracy of sequences will continue to increase in the future. The use of innovative techniques such as chromosome conformation capture may help solving the challenges brought by microdiversity and mobile genetic elements  (Marbouty and Koszul, 2015).
Short-read and hybrid assemblies The trimming and quality filtering of Illumina short-reads were performed with trimmomatic v0. 36  (Bolger et al. , 2014)  with a sliding window of 10 bp, a minimal quality score threshold (phred33) of 15 and a minimal sequence length of 50 bp. A de novo assembly of each dataset was performed with the trimmed sequences using Spades v3. 12. 0  (Nurk et al. , 2013)  and the script metaspades. py with increasing kmer sizes of 21, 33, 55, 77. For each day, an hybrid assembly the long-reads (extraction A) and short-reads (extraction A) of the corresponding sample was performed using Spades and the command metaspades. py with kmer lengths 21, 33, 55 and 77 and the option --pacbio.
Taxonomic affiliation of contigs A first taxonomic classification of long-read contigs was performed by  CAT v5. 2 (von Meijenfeldt et al. , 2019). CAT uses DIAMOND  (Buchfink et al. , 2015)  to align predicted open reading frames to the NCBI nonredundant protein database  (Sayers et al. , 2021)  (released 23 November 2020) and computes the most likely probable ancestor. The 16S rRNA genes on the contigs were used to confirm or refine the taxonomic classification. A new tool for taxonomic classification based on ANI, relative evolutionary divergence (RED) and AF, named GTDB-tk v1. 4. 1, proposed by  (Chaumeil et al. , 2020)  was used to propose an alternative classification for ACC004.
Multi-distance long-read binning with silhouette coefficient optimization The long-read contigs were grouped into MAGs with an in-house script inspired from binning techniques used with Illumina data  (Albertsen et al. , 2013; Alneberg et al. , 2014) , but taking advantage of the large size of the contigs. Four different distance measures were averaged with weighting to obtain a global distance between contigs: a distance based on the tetranucleotide signatures of the DNA sequences, a distance based on the coverage of the short-reads from the same sample, a distance based on the coverage of long-reads and a distance based on the overlapping of BUSCO v4 (Betaproteobacteria BUSCO set). Finally, the binning was optimized using silhouette coefficients. In the following, we use the acronym MuLoBiSC for multi-distance long-read binning with silhouette coefficient optimization, to refer to this in-house binning method.
The pairwise distance based on tetranucleotide characteristic frequencies vector between two DNA sequences was computed as follows: a tetranucleotide median frequency vector was computed on sliding windows of 2000 bp and a resolution of 200 bp over the whole DNA sequences. The euclidean distance between the median frequency vectors of two DNA sequences was computed. The pairwise distance based on BUSCO was computed as the number of common BUSCO (complete or duplicated) over their total in the two DNA sequences. The presence of BUSCO in the sequences was assessed by BUSCO v4  (Seppey et al. , 2019)  with the bacteria BUSCO set 'betaproteobacteria_odb10' containing consensus hidden Markov model (HMM) profiles of 569 BUSCO and the option -m geno. The pairwise distance based on the short-reads coverage between two sequences was computed as follows: the two short-reads datasets corresponding to two different extraction methods (A and B) were aligned separately on the PacBio contigs from the same sample with Bowtie v2.
Comparison with recently published binning tools Automatic binning of the long-read contigs of each sampling day was performed using independently MaxBin2 v2. 2. 4, MetaBAT2 v2. 15, and CONCOCT v1. 1. 0 through the metaWRAP v1. 2 pipeline  (Uritskiy et al. , 2018)  with the two short-read datasets from the corresponding day. MetaWRAP's integrative approach enables the refining of bins generated with different combinations of the three published binning tools and the in-house binning tool MuLoBiSC. All tested combinations are presented in the Supporting Information, but in the Results section, we will focus on the comparison of the original combination of MetaBAT2, MaxBin2 and CONCOCT (referred to as met-aWRAP_BXC), and the one of MetaBAT2, MaxBin2 and MuLoBiSC (metaWRAP_BXM). Metrics related to completeness and contamination of bins were compared by using CheckM v1.
5. 0+  (Camacho et al. , 2009)  against the MiDAS 16S rRNA gene sequence database vS123_2. 1. 3 (McIlroy et al. , 2017). A name with a three letters acronym followed by a three digits number was attributed to each rRNA sequence with a length higher than 1400 bp. The similarity of the sequences between them was assessed with blastn, the rRNA gene sequences with less than <3 bp differences were given the same name. The MAGs containing at least one 16S rRNA gene sequence were named based on the accronym of this sequences. A list of the acronyms and their corresponding taxa is provided in the Supporting Information Table  S2.
Extraction of pkk1 Sequences homologous to the polyphosphate kinase pkk1 were searched by using hmmer v3. 3. 1 (http:// hmmer. org, 2018), comparing hidden Markov models (HMM) TIGR03705, from the databases TIGRFAMs v15  (Haft et al. , 2001) , with the coding DNA sequences annotated by prokka in the long-read contigs and reference genomes. The pkk1 sequences corresponding to Ca. Accumulibacter, Propionivibrio and Dechloromonas were kept and aligned by using MAFFT v7. 271  (Katoh and Standley, 2013)  with a selection of pkk1 from different Ca. Accumulibacter clades  (He et al. , 2007; Peterson et al. , 2008; Mao et al. , 2015)  listed in the Supporting Information Table  S3. The alignment was cut in Jalview v2. 7  (Waterhouse et al. , 2009)  to fit the size of the extra pkk1 sequences.
Plasmids were purified using the QIAprep Spin Miniprep Kit (QIAGEN) following the manufacturer's instructions. Aliquots of 200 ng of plasmid DNA were sent to Fasteris (Fasteris SA, Plan-les-Ouates, Switzerland) for sequencing using T7 and SP6 standard primers. DNA sequences were analysed using the SnapGene Viewer software (from Insightful Science; available at snapgene. com).
Acknowledgements The authors thank  Vincent Jeannet  ( LBE, EPFL ), for the tests on DNA extraction. The work of  Emmy Oppliger ,  Idriss Hendaoui ,  Marie Horisberger  and  Valerie Berclaz  (LBE, EPFL), on the maintenance of the reactors is kindly acknowledged. We thank  Emmanuelle Rohrbach  and  Stephane Marquis  ( LBE, EPFL ) for the training of the apprentice and their help in the laboratory and  Marc Deront  ( LBE, EPFL ) for the installation of the computing machine. The authors thank  Emanuel Schmid-Siegert  for his precious guidance on the assembly of long reads. They thank  Gabriel E. Leventhal  and  Otto X. Cordero  ( DCEE, MIT ) for their work on individual granules. This research was financed by the  Swiss National Science Foundation (SNSF) , grant number  200021-152963. Open Access Funding provided by  Ecole Polytechnique Federale de Lausanne.
Availability of data and materials The data generated and analysed during this study are included in this published article and its supporting information files. The long-read and short-read runs and the high-quality drafts ACC003 from sample d322, ACC004 and ACC005 from sample d427, ACC007 and ACC012 from sample d740 and DEC001 from sample d71 are available in the ENA repository under the project accession number PRJEB38840. The script of the in-house binning MuLoBiSC, which was specially adapted for the experimental design of this study, can be consulted at https://github. com/Aline-Git/MuLoBiSC.
In this context, metagenomics (MG)  30, 31  , metatranscriptomics (MT)  32  and metaproteomics (MP)  33  enable the establishment of sample-specific reference databases that simultaneously resolve both compositional and functional aspects of the system. When dealing with complex and uncharacterised microbial systems, far from lab-scale experiments, empirical modelling can enable efficient representation and forecasting. To achieve this we foresee a combination of strategies to extract all the temporal information in an agnostic manner, such as through SVD, and to perform forecasting by explicitly computing the temporal cycles and link those patterns directly to the explanatory variables. SVD can decompose a matrix in two separated matrices of eigenvectors and a vector of eigenvalues. When applied to gene abundance (or expression) data over time, one of the matrices is interpreted as the set of temporal patterns underlying the data and the other as the "loadings" (i.