dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation of local losses by electromagnetic simulations To calculate the losses and distribution of the electromagnetic field the simulation program Opera was applied, considering the machine working as a synchronous generator. An external motor drives the shaft at 5000 rpm. The stator phases are connected to load resistances for getting a stator current of 4 A at 833 Hz. First the 2D simulations were performed for computing core losses with a reasonable computing time. The time-stepping analysis was performed over approximately 20 electrical periods for waiting the steady state; then, the induction components Bx and By were recorded at regular short intervals (800 times per period). A macro was developed for calculating losses in the stator and rotor soft core using Discrete Fourier Transform (DFT) of the recorded data  [16, 17].
Related Work WSN have been greatly utilized for deploying smart systems for precision agriculture. Marco Mancuso et al. presented in  [7]  a WSN for environmental monitoring in a tomato greenhouse. Sensicast devices were utilized as the nodes. Their proposed system considered air and soil temperature and humidity. For data transmission, the proprietary SensiNetTM protocol was utilized over ZigBee. Results showed a relation between temperature and humidity and determined that both of them are non-linear. Another WSN for smart agriculture that utilizes ZigBee is proposed in  [8]. Authors addressed the necessity of precision agriculture systems so as to reduce water consumption. The monitored variables were soil temperature and moisture. Several modules with ZigBee radios such as TelosB, ATmega256RFR2 and OpenMote-CC2538 were considered.
The data was stored each 15 minutes on a SD card for further analysis. Due to some data loss, the resulting efficiency of the system was 94. 79%. Several architectures have also been designed for precision agriculture. J. A. López-Riquelme et al. presented in  [9]  a FIWARE cloud-based software architecture for precision agriculture. Authors discussed the advantages of FIWARE and its open-source modules and enablers for agriculture. The hardware architecture was performed utilizing the MEWiN Mainboard with a SIM900 Module. Experiments were performed both on the laboratory and on real crops. The average power consumption of the node was 4. 29 mA. Another architecture for precision monitoring intended for potato crops and based on Cyber-Physical Systems (CPS) is presented by Ciprian-Radu RAD et al.
in  [10]. Their proposed architecture was divided in four layers being application, analyzing, networking and sensing. The data obtained from the system has then to be processed so as to perform the decision making and control the system on real-time. The aims of the proposed architecture were the optimization of water and chemical usage, the increase in production and sustainability and the reduction of costs. H. Navarro-Hellín et al. presented in  [11]  an architecture for WSN intended for water management. The system utilized ES-2 sensors for water temperature and conductivity, GS3 and HP II sensors for moisture, temperature and conductivity, 10HS sensors for soil moisture, LMK sensors for pressure and MPS-2 sensors for temperature and soil matric potential. The communication was performed over GSM/GPRS and the data was stored both in a database and in SD cards.
A web application allowed monitoring the data remotely. Furthermore, implementations of monitoring systems employing IoT protocols such as CoAP are starting to be performed. Adarsh B. U. et al. performed in  [12]  an implementation of a water quality monitoring system utilizing CoAP over 6LoWPAN. The Contiki OS cooja simulation environment was utilized to design the system. The monitored parameters were conductivity, pH, temperature and ORP (Oxidation Reduction Potential). A TelosB mote was utilized so as to gather the data from the sensors and transmit it to the database. Actuators were utilized so as to add acid or basic solutions to the water according to the sensed data. The data could be accessed through a web interface. An environmental monitoring system that utilized CoAP as the communication protocol was developed by Tomislav Dimčić et al.
in  [13]. Authors utilized a Telit GSM-GPRS modem with a Python engine and implemented CoAP on the application layer of the M2M (Machine-to-Machine) system. Comparisons were performed with CoAP and HTTP were CoAP obtained a considerably lower transmission time. Lastly, a comparison of application protocols for IoT systems was performed by Nitin Naik in  [14]. CoAP, MQTT, HTTP and AMQP were compared according to message size and overhead, power consumption and resource requirements, bandwidth and latency, contrasting reliability vs. interoperability, security vs. provisioning and IoT usage vs. standardization. CoAP presented the lowest size, power consumption and bandwidth usage followed by MQTT, AMQP and HTTP. However, MQTT was the most reliable and HTTP and CoAP were the ones that presented more interoperability.
Architecture Overview The proposed system uses the information collected by the sensors of a WSN. The data from the sensor network will be sent through a Wireless Mesh Network (WMN) formed by Access Points (AP) located in environments where irrigation canals exist in urban and rural areas. In addition, in a centralized location, we will install the network devices that allow the Wide Area Network (WAN) connection to send the collected dataset to an external location, where it will be stored and processed. Also, in that location, we will have equipment to automatically respond to alarms that have been previously detected and where the action that must be carried out in response is known. The communication technology we use is that of device-to-device (D2D) and device-to-machine (D2M). We define a model that can be used by different technologies and work environments.
Our system includes features such as the following ones: • Independent of communication technology. • Supports different manufacturers. • Adaptable to different physical environments. • Low power consumption. We avoid the need of the nodes of an external power supply. • Low bandwidth consumption. • Scalable, supporting the aggregation of new nodes. The WSN nodes collect information from the sensors connected to each one of them. The collected data will be stored in a SD card that will be included in the node hardware. In addition, the data will be sent periodically, through a WAN connection, to the place where they will be stored and processed, using Artificial Intelligence (AI). The AI uses the processed information to know what is happening and to use it in the decision algorithms. The actions that must be taken, as a result of the application of AI, are sent back to the WSN so that they receive nodes with actuators and carry out the appropriate actions.
In our architecture, each sensor node gathers measurements from each one of the sensors that it has connected. All these data will be used later in the decision algorithms. The sensor nodes are installed in two areas, mainly in the water canals for irrigation and in the crop fields. We have grouped the sensors located in the water canals for irrigation. Each group is formed by three sensor nodes for water control. This is done so as to avoid false positive problems. If a variation of the parameters is not detected in more than one node of a group, it is considered to be an error in the measurement. This redundant implementation allows us to provide high reliability for the taken measures.
Firstly, the data acquisition from all the distributed sensor nodes is performed. Periodically, the data acquired in the previous phase is stored on the remote storing servers. Then, the different characteristics of the data such as location, date, time, humidity, temperature and turbidity are extracted so as to be processed by applying the decision rules designed by experts. The processed information will be utilized to obtain models and predictions and its results will be stored increasing our database. Lastly, the corresponding actions are performed on the actuator nodes according to the obtained predictions. Figure  3  shows the flow of the data. Initially, the data is collected by the physical sensors. The algorithms will process the data and make the appropriate decisions based on the previously created rules.
Acknowledgement This work has been partially supported by the  "Ministerio de Economía y Competitividad" in the "Programa Estatal de Fomento de la Investigación Científica y Network Protocols  and Algorithms ISSN 1943-3581 2018, Vol. 10, No. 4 www. macrothink. org/npa51  Técnica de Excelencia ,  Subprograma Estatal de Generación de Conocimiento " within the project under Grant  TIN2017-84802-C2-1-P. This work has also been partially supported by  European Union  through the  ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond)  project  ERANETMED3-227 SMARTWATIR.
it be possible to evaluate the roles that these phage play in the functioning and evolution of ecosystems. Sequencing of whole community DNA can uncover phagederived DNA fragments, yet large phage genomes can still escape detection due to genome fragmentation  (Shkoporov and Hill, 2019). However, a new clade of human and animalassociated megaphage was recently described based on phage genomes manually curated to completion from metagenomic datasets  (Devoto et al. , 2019). This finding motivated more comprehensive analysis of microbial communities to evaluate the prevalence, diversity and ecosystem distribution of phage with large genomes. Here, we present hundreds of phage sequences >200 kbp in length that we reconstructed from microbiome datasets generated from a wide variety of ecosystems.
Ecosystem sampling Metagenomic datasets were acquired from human fecal and oral samples, fecal samples from other animals, freshwater lakes and rivers, marine ecosystems, sediments, hot springs, soils, deep subsurface habitats and the built environment ( Table  1  ). Genome sequences that were clearly not bacterial, archaeal, archaeal virus, eukaryotic or eukaryotic virus were classified as either phage or plasmidlike based on their gene inventories. De novo assembled fragments close to or >200 kbp in length were tested for circularization and a subset selected for manual verification and curation to completion (see Methods). https://docs. google. com/document/d/1ZV8fmYx0e8oQT1UmuGD3a03pU_not9Y61tjsYPR6Gcg/edit# 4/31.
Hosts, diversity, and distribution An intriguing question relates to the evolutionary history of phage with huge genomes. Are they the result of recent genome expansion within clades of normal sized phage or is a large inventory of genes an established, persistent strategy? To investigate this, we constructed phylogenetic trees for large terminase subunit ( Figure  2  ) and major capsid ( Figure  S5A ) proteins using sequences in public databases as context (Methods). Many of the sequences from our large phage genomes cluster together with high bootstrap support, defining clades. Analysis of the genome size information for database sequences shows that the public sequences that fall into these clades are from phage with genomes of at least 120 kbp in length. here as Mahaphage (Maha being Sanskrit for huge), includes all of our largest genomes as well as the 540 552 kbp Lak genomes from human and animal microbiomes  (Devoto et al.
, 2019). We identified nine other clusters of large phage, and refer to them using the words for "huge" in languages of some authors of this publication. We acknowledge that the detailed tree topologies for different genes and datasets vary somewhat, but the clustering is broadly supported by protein family and capsid analyses ( SI and Figure  S5A, B  ). The consistent grouping together of large phage into clades establishes that large genome size is a relatively stable trait (SI). Within each clade, phage were sampled from a wide variety of environmental types ( Figure  2  ), indicating diversification of these large phage and their hosts across ecosystems. We also examined the environmental distribution of phage that are so closely related that their genomes can be aligned and found 19 cases where they occur in at least two distinct cohorts or habitat types ( Table  S2  ).
The S1 binds mRNA to bring it into the site on the ribosome where it is decoded  (Subramanian, 1983). Phage ribosomal protein S21 might promote translation initiation of phage mRNAs, and many sequences have Nterminal extensions that may be involved in binding RNA (dashed blue line in ribosome insert, PDB: 6BU8  (Loveland and Korostelev, 2018)  , analyzed with UCSF Chimera  (Pettersen et al. , 2004). Many other proteins of the translational apparatus are encoded by huge phage, belonging to all steps of the translation cycle.
Phage and plasmid genome identification Datasets generated in the current study, those from prior research conducted by our team, the Tara Oceans microbiomes  (Karsenti et al. , 2011)  , and the Global Oceans Virome (GOV)  (Roux et al. , 2016)  were searched for sequence assemblies that could have derived from phage with genomes of >200 kbp in length. Read assembly, gene prediction, and initial gene annotation followed standard methods reported previously  (Joshi and Fass, 2011; Peng et al. , 2012; Edgar, 2015; Bushnell, 2016; Nurk et al. , 2017). Phage candidates were initially found by retrieving sequences that were not assigned to a genome and had no clear taxonomic profile at the domain level. Taxonomic profiles were determined through a voting scheme, where there had to be a winner taxonomy >50% votes at each taxonomic rank based on UniProt and ggKbase ( ggkbase.
berkeley. edu ) database protein annotations  (RavehSadka et al. , 2015). Phage were further narrowed down by identifying sequences with a high number of hypothetical protein annotations and/or the presence of phage structural genes, e. g. , terminase, capsid, tail, holin. All candidate phage sequences were checked throughout to distinguish putative prophage from phage. Prophage were identified based on a clear transition into genome with a high fraction of confident functional predictions, often associated with core metabolic functions, and much higher similarity to bacterial genomes. Plasmids were distinguished from phage based on matches to plasmid partitioning and conjugative transfer genes.
Curation generally followed methods described previously  (Devoto et al. , 2019). In brief, reads from the appropriate dataset were mapped using Bowtie2 v2. 3. 4. 1  (Langmead and Salzberg, 2012)  to the de novo assembled sequences. Unplaced mate pairs of mapped reads were retained with shrinksam (github. com/bcthomas/shrinksam). Mappings were manually checked throughout to identify local misassemblies using Geneious v9. Nfilled gaps or misassembly corrections made use of unplaced paired reads, in some cases using reads relocated from sites where they were mismapped. In such cases, mismappings were identified based on much larger than expected paired read distances, high polymorphism densities, backwards mapping of one read pair, or any combination of these. Similarly, ends were extended using unplaced or incorrectly placed paired reads until circularization could be established.
Structural and functional annotation Following identification and curation of phage genomes, coding sequences (CDS) and ShineDalgarno ribosomal binding site (RBS) motifs were predicted with prodigal using genetic code 11 (m g 11 p single). The resulting CDS were annotated as previously described by searching against UniProt, UniRef100, and KEGG  (Wrighton et al. , 2014). Functional annotations were further assigned by searching proteins against PFAM r32  (Finn et al. , 2014)  , TIGRFAMS r15  (Haft et al. , 2013)  , and Virus Orthologous Groups r90 (vogdb. org). tRNAs were identified with tRNAscanSE 2. 0 (Lowe and Eddy, 1997) using the bacterial model. tmRNAs were assigned using ARAGORN v1. 2. 38  (Laslett and Canback, 2004)  with the bacterial/plant genetic code. Clustering of the CDS into families was achieved using a twostep procedure.
A first protein clustering was done using the fast and sensitive protein sequence searching software MMseqs  (Hauser et al. , 2016 ). An allvs. all sequences search was performed using evalue: 1 x 10 3 , sensitivity: 7. 5 and coverage: 0. 5. A sequence similarity network was built based on the pairwise similarities and the greedy set cover algorithm from MMseqs was performed to define protein subclusters. The resulting subclusters were defined as subfamilies. In order to test for distant homology, we grouped subfamilies into protein families using an HMMHMM comparison. The proteins of each subfamily with at least two protein members were aligned using the result2msa parameter of mmseqs2, and from the multiple sequence alignments HMM profiles were built using the HHpred suite. The subfamilies were then compared to each other using HHblits  (Remmert et al.
, 2011)  from the HHpred suite (with parameters v 0 p 50 z 4 Z 32000 B 0 b 0). For subfamilies with probability scores of ≥ 95% and coverage ≥ 0. 50, a similarity score (probability ⨉ coverage) was used as weights of the input network in the final clustering using the Markov Clustering algorithm, with 2. 0 as the inflation parameter. These clusters were defined as the protein families. Protein sequences were functionally annotated based on the accession of their best Hmmsearch match (version 3. 1) (evalue cutoff 1 x 10 3 ) against an HMM database constructed based on ortholog groups defined by the KEGG database  (Kanehisa et al. , 2016)  (downloaded on June 10, 2015). Domains were predicted using the same hmmsearch procedure against the PFAM r31 database  (Finn et al. , 2014). The domain architecture of each protein sequence was predicted using the DAMA software  (Bernardes et al.
, 2016)  (default parameters). SIGNALP  (Petersen et al. , 2011)  (version 4. 1) (parameters: f short t gram+) and PSORT (version 3. 0)  (Peabody et al. , 2016)  (parameters: long positive) were used to predict the putative cellular localization of the proteins. Prediction of transmembrane helices in proteins was performed using TMHMM  (Krogh et al. , 2001 ) (version 2. 0) (default parameters). Hairpins (palindromes, based on identical overlapping repeats in the forward and reverse directions) were identified using the Geneious Repeat Finder and located datasetwide using Vmatch  (Kurtz, 2003). Repeats >25 bp with 100% similarity were tabulated.
Alternative genetic codes In cases where gene prediction using the standard bacterial code (code 11) resulted in seemingly anomalously low coding densities, potential alternative genetic codes were investigated. In addition to making a prediction using Fast and Accurate genetic Code Inference and Logo (FACIL)  (Dutilh et al. , 2011)  , we identified genes with well defined functions ( e. g. , polymerase, nuclease) and determined the stop codons terminating genes that were shorter than expected. We then repredicted genes using GLIMMER3 v1. 5  (Delcher et al. , 1999).
CRISPRCas Locus and target detection Phage and host encoded CRISPR loci (repeats and spacers) were identified using a combination of MinCED (github. com/ctSkennerton/minced) and CRISPRDetect  (Biswas et al. , 2016). A custom database of Cas genes was built by collecting Cas gene sequences from  (Makarova et al. , 2015; Shmakov et al. , 2015; Burstein et al. , 2017; Smargon et al. , 2017; Harrington et al. , 2018; Yan et al. , 2018 , 2019)  and built with MAFFT (localpair maxiterate 1000) and hmmbuild. CDS from this study were searched against the HMM database using hmmsearch with evalue < 1 x 10 5. Matches were checked using a combination of hmmscan and BLAST searches against the NCBI nr database and manually verified by identifying colocated CRISPR arrays and Cas genes. Spacers extracted from between repeats of the CRISPR locus were compared to sequences assemblies from the same site using BLASTNshort  (Altschul et al.
Phage encoded tRNA synthetase trees Phylogenetic trees were constructed for phage encoded tRNA synthetase, ribosomal, and initiation factor protein sequences using a set of the closest reference sequences from NCBI and bacterial genomes from the current study. The tRNA synthetases were identified based on annotation of genes via the standard ggKbase pipeline (see above), and confirmed by HMMs with datasets from TIGRFAMs. For each type of tRNA synthetase, references were selected by comparing all the corresponding genes of this type against NCBI nr using DIAMOND v0. 9. 24  (Buchfink et al. , 2015)  , their top 100 hits were clustered by CDHIT with 90% similarity threshold  (Huang et al. , 2010). The phylogenetic tree of each tRNA synthetase was constructed using RAxML v8. 0. 26  (Stamatakis, 2014)  with the PROTGAMMALG model.
Figure2: Phylogenetic reconstruction of huge phage evolutionary history using large terminase sequences from this study and similar matches from all RefSeq r92 proteins. The tree also includes large terminase sequences from complete and incomplete RefSeq phage, the Lak phage clade (Devoto et al. , 2019)  and nonartifactual phage genomes that are >200 kbp from (PaezEspino et al. , 2016). Huge phage clades identified in this study were independently corroborated with a phylogenetic reconstruction of major capsid genes (Fig S5A)and protein clustering (FigS5B). The tree was rooted using 13 eukaryotic Herpesvirus terminases. The inner to outer rings display the presence of CRISPRCas from this study, host phylum, environmental sampling type, and genome size. Host phylum and genome were not included for RefSeq protein database matches because the sequence may be integrated prophage or part of organismal genome projects.
Figure3: Model for how phageencoded capacities could function to redirect the host's translational system to produce phage proteins (bacterial components in blue, phage in red). No huge phage has all of these genes, but many have tRNAs and tRNA synthetases (see TableS6). Phage proteins with up to 6 ribosomal protein S1 domains occur in a few genomes. The S1 binds mRNA to bring it into the site on the ribosome where it is decoded (Subramanian, 1983). Phage ribosomal protein S21 might promote translation initiation of phage mRNAs, and many sequences have Nterminal extensions that may be involved in binding RNA (dashed blue line in ribosome insert, PDB: 6BU8 (Loveland and Korostelev, 2018)  , analyzed with UCSF Chimera (Pettersen et al. , 2004). Many other proteins of the translational apparatus are encoded by huge phage, belonging to all steps of the translation cycle.
Acknowledgments Funding for this project was provided by  National Institutes of Health (NIH)  under awards  RAI092531A  and  R01GM109454  and the  Alfred P. Sloan Foundation  under grant  APSF20121005  to JFB and MM;  National Science Foundation  grant ( NSF) Sustainable Chemistry grant   1349278  to JFB,  NSF  Graduate Research Fellowships to BAS ( DGE 1752814 ) and  MRO  ( DGE 1106400 ); the  Paul Allen Foundation Frontiers Group; Chan Zuckerberg Biohub; Innovative Genomics Institute. The  Novo Nordisk Foundation  ( NNF16OC0021856 ) to P. M;  NASA 13R0043  and  CA AES  to KRA and KF;  German Science Foundation postdoctoral scholarship  to  A. J. P  ( DFG PR 1603/11 );  Camille & Henry Dreyfus Postdoctoral Fellowship in Environmental Chemistry  to C. H. ; Watershed Function Scientific Focus Area funded by the  U.
S. Department of Energy, Office of Science, Office of Biological and Environmental Research  ( DEAC0205CH11231 ) to A. L. , P. M. C, and A. T; NSF grants ( GRT00048468  and  1342701 ) to KCW and MB and ( CHE1740549 ) to JHDC; the  March of Dimes Prematurity Research Center at Stanford University School of Medicine , the Thomas C. and  Joan M. Merigan Endowment  at  Stanford University  to DAR; the  National Research Foundation of South Africa  ( UID 64877 ) to STLH and NSF CZO funding. We acknowledge the scientists who generated public database sequences and thank  Allison Sharrar ,  Jenny Tung ,  Elizabeth Archie ,  Frank Aarestrup  and  Ricardo Kruger  who contributed data. We thank  Patrick Pausch  and  Gavin Knott  for helpful discussions.