dataesr/scientific-paragraphs-categorization huggingface dataset:
5% and 72. 8%, respectively) for the. ΔS2 = 15 case. Results show a high detection precision for M1 with a Pn reaching 100% (no false detection) over all the tested ΔS2. The accuracy was slightly better for the M2 dataset of Tarbes in 2019 (M2) for the Re, Pn and Fscore metrics, for theΔS2 = 5, 5mod 10 and 5mod 15 cases, with a 75% value for all three metrics. There was a clear decline for the ΔS2 = 10 and 15 cases, down to 54% for Re, 60% for Pn and 57. 5% for the Fscore. For the M3 plot of Tarbes in 2020, 100% of the reference irrigations (d R ) were detected (Re equal to 100%) with Pn = 60% and Fscore = 75%, respectively, for the ΔS2 = 5 and 10 cases. The performances drop down to Re= 50% and F-Score = 66. 6% for the ΔS2 = 15case. Unlike for the M1 and M2 plots, the Pn and Fscore values increased for the M3 plot for theΔS2 = 5mod 10 and 5mod 15 cases with respect to the ΔS2 = 10 and 15 cases, pointing at the relevant use of interpolated LAI values.
The tested cases for ΔS2 values were 5, 10, 15, 5mod 10 and 5mod 15 days, the latter two notations indicating the use of interpolated LAI values as a surrogate to the missing values, forming artificial ΔS2= 5 days cases. On the synthetic dataset of irrigation events, the best detection performances were obtained from the ΔS2 = 5, 5mod 10 and 5mod 15 cases with a F-score of 84. 3%. In addition, the absolute |Δt| difference between the detected and the reference irrigation dates was 0, 1 or 2 days in a large majority of cases, and never exceeding 3 days. The detection of irrigation amounts was less efficient for the synthetic dataset associated with the humid climate of Tarbes than for that associated with the (nearly) semi-arid climate of Montpellier, in which the correct irrigation amount was detected in 80% of the cases.
Acknowledgments This research received fundings from the  French Space Study Center (CNES ,  TOSCA 2022  project), the  National Research Institute for Agriculture, Food and the Environment (INRAE)  and the  RSEAU  project financed by the  French Environment and Energy Management Agency (ADEME). The authors also wish to thank  Campus France  for partially financing  M. Hamze 's PhD thesis, the  European Space Agency (ESA)  for the Sentinel-1 data, and the  French Land Data Center (Theia)  for providing Sentinel-2 data corrected from atmospheric effects.
With this technique, leakage detection was shown; however, some challenges persist. For instance, the localisation error was up to ten kilometres, some discrepancies in the flow metre were recorded, and there were limitations on the communication bandwidth and the effect of the type of fluid on this result. On the other hand, Santos et al. [21]  proposed a detection method based on the usage of Doppler ultrasonic flow metres and transit-time ultrasonic flow metres. They carried out simulations to determine the impact of air bubbles on the accuracy of the detection and to understand its effectiveness. Karray et al. [22]  also presented a leak detection technique based on the modified time of arrival difference, predictive Kalman filter, and the system-on-a-chip wireless sensor node. However, this work focused on reducing the energy consumption of LDMSs by incorporating data filtering, preprocessing, and compression in the algorithm.
Sigfox uses an ultra-narrowband signal that further reduces energy consumption and is limited by the number of daily message deliveries depending on the data rate (usually up to 100 bps). LoRaWAN also has a low data rate, and the throughput depends on multiple factors, i. e. , the size of the bandwidth and the spreading factor. Thus far, we have presented works that considered the benefits of sensor placements, such as detection time, energy consumption, sensitivity, scalability, and robustness to failures, by proposing different strategies. However, various drawbacks exist; for example, the assumption that one sensor can detect failures in multiple pipelines, or their centralised implementations (both of which make them susceptible to SPOFs). Energy consumption and the accuracy of DAL are also other concerns in the industrial application of IoT-based monitoring systems.
Mass Volume Balance MVB technique is used to detect leakages when the mass balance at the outlet exceeds the threshold defined by Equation (1)  [50] ≤ ρ in -ρ out - d dt m p (1) where is the defined threshold, ρ in is the fluid density at the inlet, ρ out is the fluid density at the outlet, d dt m p is the change in pressure and temperature of the pipeline based on the liquid density and the cross-sectional area of the pipe. The supervisory and data acquisition system (SCADA) typically uses the MVB in combination with other statistical methods, such as PPA for leakage detection and localisation in pipelines. The detection is determined using two variants of MVB, the simple volume balance and the modified volume balance. The simple volume balance is completely based on the principle of mass conservation, where both inlet and outlet masses are expected to be equal.
New technologies should not only have a strong commercial potential but also a lower environmental impact and lower carbon emissions, higher resource and energy conversion efficiency, as well as higher availability for different market and operating environments than the currently existing renewable energy technologies. In this line, a number of research and innovation (R&I) projects, funded by the European Union's Horizon 2020 research and innovation programme, are developing breakthrough and game-changing solutions to tackle the above challenges and strengthen the EU's global leadership in technologies for the exploitation of renewable energy sources. A group of eleven European projects (i. e. , HIGFLY, EBIO, FLEXI-GREEN FUELS, REGEN-BY-2, TRI-HP, ComBioTES, CONDOR, TELEGRAM, PERCISTAND, GreEnergy, and X-ROTOR), funded under the H2020-LC-SC3-2018-2019-2020 call (Building a Low-Carbon, Climate Resilient Future: Secure, Clean and Efficient Energy), joined forces on a clustering workshop at the Sustainable Places 2022 (SP2022) Conference 2.
Acknowledgements The authors acknowledge and are grateful to  Tatiana Loureiro ,  Raymond Sterling ,  Luis Febres  and  Maryori Díaz  ( R2M Solution Spain S. L. , Madrid , 28032, Spain), who are part of the team leading the dissemination activities of the REGEN-BY-2 project, for their contribution to the organisation of the ' Low-TRL Renewable Energy Technologies ' workshop at the  Sustainable Places 2022 Conference  and for their assistance in the writing and editing of this article.
The work presented in this publication has been developed in the context of the following European projects: • ComBioTES, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 864496. •  CONDOR , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101006839. • EBIO, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101006612. •  FLEXI-GREEN FUELS , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101007130. • GreEnergy, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
101006963. •  HIGFLY , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101006618. •  PERCISTAND , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 850937. •  REGEN -BY-2 , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 851541. • TELEGRAM, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 101006941. • TRI-HP, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 814888. • X-ROTOR, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
Chandra X-Ray Observations All Chandra observations used in this paper are part of the same suite as those described in  Olivares et al. (2022a) , and listed in Table  C1. In the following, we briefly summarize the observations, but we refer the reader to  Olivares et al. (2022a)  for further details. The data reduction and calibration of Chandra observations were carried out using Chandra Interactive Analysis of Observations software (CIAO) 4. 12, and the Chandra Calibration Database 4. 9. 2. 1. The observations were reprocessed using the chandra_repro tool of CIAO starting from the level-one event files. Light curves were created using deflare CIAO tool in the 0. 5-8. 0 keV and 9. 0-12. 0 keV energy bands. We subtracted background flares higher than 3σ using the lightcurve filtering script lc_sigma_clip.
MUSE Observations MUSE is an optical image slicing integral field unit (IFU) with a wide field of view (FOV) of ¢1 1′, mounted on UT4 of the European Southern Observation Very Large Telescope. MUSE has a spectral resolution of R ∼3000 (FWHM = 100 km s -1 at 7000 Å), allowing us to fit individual optical emission lines. The MUSE data were reduced using the latest MUSE pipeline 2. 8. 5  (Weilbacher et al. 2014 ) and the EsoRex command-line tool. We also performed an additional sky subtraction to the one in the pipeline using Zurich Atmosphere Purge 2. 1  (Soto et al. 2016). When optical emission lines were present in the data cube, we fitted the data following the same method described by  Olivares et al. (2019). The data were also corrected for Galactic foreground extinction, estimated from the  Schlafly & Finkbeiner (2011)   Table  2  lists the archival MUSE observations of Planck clusters used in this paper, including the program ID, average DIMM seeing, exposure time, whether or not the central BCG reveals optical emission lines, and a brief description of the distribution of the optical line-emitting gas when it was detected.
Effect of Data Quality Another bias in identifying cavities is the inhomogeneity of the X-ray data quality across the sample. This is evidenced by the large variation in the number of counts in Chandra observations for each source. Most cavities are detected within a radius of 40 kpc from the cluster center because they are fainter and more difficult to detect at larger distances. In addition, numerical simulations suggest that cavities fragment and disrupt after a few 100 Myr  (Brighenti et al. 2015) , which roughly corresponds to a distance of 40-50 kpc, considering an expansion velocity for the cavity of 400-500 km s -1 (Jimenez-Gallardo et al. 2022). The central number counts, which corresponds to the total number of source X-ray photons detected inside an aperture of 40 kpc, varies greatly for these clusters, ranging from a few × 10 5 to 500, and decreasing with redshift (see also the right panel of Figure  2  of  Olivares et al.
Acknowledgments The authors would like to thank the referee for providing useful and detailed comments on the manuscript. This research has made use of software provided by the Chandra X-ray Center (CXC) in the application packages CIAO. V. O. and Y. S. were supported by  NSF  grant  2107711 , Chandra X-ray  Observatory  grant  GO1-22126X , and  NASA  grant  80NSSC21K0714. M. G. acknowledges partial support by  HST GO-15890. 020/023-A  and the  BlackHoleWeather program. W. F. , C. J. , and P. N. acknowledge support from the  Smithsonian Institution  and the  Chandra High Resolution Camera  Project through  NASA  contract  NAS8-03060. W. F. also acknowledges support from  NASA  grants  80NSSC19K0116 ,  GO1-22132X , and  GO9-20109X. P. S. acknowledges support by the  Agence Nationale De La Recherche (ANR)  grant  LYRICS  ( ANR-16-CE31-0011 ).
Background As scientific endeavours become increasingly complex, research projects require larger teams which leads to an ever-increasing division of labour  1. To acknowledge different authorship roles within a project or 'contributorship', Contributor Roles Taxonomy (CRediT) has been proposed  [2] [3] [4]  , where authors' contributions are specified across 14 possible tasks ('Conceptualization', 'Data curation', 'Formal analysis', 'Funding acquisition', 'Investigation', 'Methodology', 'Project administration', 'Resources', 'Software', 'Supervision', 'Validation', 'Visualization', 'Writing-original draft', and 'Writing-review & editing'). CRediT has been adopted by many scientists and publishers  5  (at least 40 publishers currently; https://credit. niso. org/adopters/). However, CRediT lacks granularity to reveal precisely who did what, which is especially important when degrees of contributions differ.
For example, a survey of 30,770 papers using CRediT identified that 55% of authors contribute to the broad category of 'Methodology', with the average number of authors being seven per paper  6. Therefore, CRed-iT's broad categorisations limit its ability to promote transparency in contributorship. To address this limitation, we propose supplementing CRediT's methodology-related contributor roles -'Data curation', 'Formal analysis', 'Investigation', 'Methodology', 'Resources', 'Software', 'Validation', and 'Visualization'by using author initials in the Methods section to attribute these specific research steps to specific authors (e. g. , "SN obtained field data between 2003-2007" or "ML conducted all the statistical analyses, which were checked by SN"). We term this reporting system 'Method Reporting with Initials for Transparency' (MeRIT), and it extends method-reporting practices conventionally used in systematic reviews to all research outputs.
Potential obstacles and resolutions Despite all these benefits, MeRIT is likely to face some difficulties. These difficulties are often intertwined, but they can be divided into two kinds: practical and sociological. Practically, it is sometimes hard to assign a particular contribution to a single person, for example, because this person has supervised or advised the work. In such a case, you can still use MeRIT to clarify this point (e. g. , "REO conducted the statistical analysis with the support of JLP"). Also, it may not be easy to draw dividing lines on the relative contributions within a research task, for example, when coding software and analysis together. It might be possible to assign an author's contribution in percentages, expressed as the number of contributed code lines, quantifiable via platforms like GitHub.
We must be careful with such quantification, however, as quantity does not equate to quality  14. It is probably best to be handled in the text; for example, "AW led the software development with the assistance of EG & DPW, and additional support from SMW to improve scalability". Notably, explicitly acknowledging error checking and supporting roles in data collection and analysis, like the examples above, may increase error-prevention activities, reducing mistakes that lead to paper corrections and retractions. Sociological issues are potentially more challenging to address, although this situation is not unique to MeRIT (i. e. , CRediT and any other systems of author contributions would also suffer from them). Power dynamics can undoubtedly influence the implementation of MeRIT. Supervisors can pressure student-lead authors to attribute more credit to supervisors and/or other senior academics or underplay the contributions of students or research staff.
The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a bioinformatics pipeline called Palidis that recognizes insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying Palidis to 264 human metagenomes identifies 879 unique insertion sequences, with 519 being novel and not previously characterized. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events across bacterial classes.
Unit transposons are a similar type of transposable element to insertion sequences containing a pair of ITRs but can also carry ARGs as well as transposases  [3]. For simplicity, the abbreviation 'IS' will be used hereafter to mean insertion sequence or unit transposon. ARGs can also be carried by composite transposons that are bounded by two copies of two different insertion sequences, which can move together in a single unit  [6]. A composite transposon can contain one or more passenger genes, such as ARGs, flanked by two insertion sequences and with two TSDs at both ends  [3]. Microbial genomes can be annotated for ISs by querying reference databases of known transposable elements, such as ISfinder  [7] , but these databases are small and do not represent many transposable elements in nature.
Impact Statement Insertion sequences are a class of transposable element that play an important role in the dissemination of antimicrobial resistance genes. However, it is challenging to completely characterize the transmission dynamics of insertion sequences and their precise contribution to the spread of antimicrobial resistance. The main reasons for this are that it is impossible to identify all insertion sequences based on limited reference databases and that de novo computational methods are ill-equipped to make fast or accurate predictions based on incomplete genomic assemblies. Palidis generates a larger, more comprehensive catalogue of insertion sequences based on a fast algorithm harnessing genomic diversity in mixed microbial communities. This catalogue will enable genomic epidemiologists and researchers to annotate genomes for insertion sequences more extensively and advance knowledge of how insertion sequences contribute to bacterial evolution in general and antimicrobial resistance spread across microbial lineages in particular.
THEORY AND IMPLEMENTATION Palidis is implemented as a Nextflow pipeline with all dependency software packaged in one container image. The input file of Palidis is a tab-delimited manifest text file that contains information on the read file IDs, the file paths to the read fastq. gz files, sample ID and file paths to the assemblies. The output files are a FASTA file of ISs and accompanying tab-delimited file of information. The following steps are also illustrated in Fig. 1. Step 1: reads from mixed microbial communities are pre-processed and run through pal-MEM to identify reads containing repeat sequences Firstly, the FASTQ files are converted to FASTA files with headers prepended with their sequence order (e. g. Seq1, Seq2 etc. ). A software tool, called pal-MEM (https://github. com/blue-moon22/pal-mem), was developed and applied an efficient maximal exact matching algorithm  [13]  to identify repeat sequences that may represent ITRs.
Step 3: candidate ITRs are clustered using CD-HIT-EST. ISs are identified by ITRs that are of the same cluster and are reverse complements of each other The candidate ITRs are clustered using CD-HIT-EST  [15]  where nucleotide sequences that meet a (1) sequence identity threshold c, (2) a global G 1 or local alignment G 0, (3) alignment coverage for the longer sequence aL, (4) alignment coverage  for the shorter sequence aS and (5) minimal alignment coverage control for the both sequences A (that can be specified by the user). The ISs are generated in a FASTA format with an accompanying tab-delimited file containing the sample ID, assembly name, start and end positions of the ITRs and their cluster using a Python script. The ISs must contain ITRs that (1) belong to the same cluster, (2) are within the minimum and maximum specified ITR length, (3) are within the minimum and maximum IS length and (  4 ) are reverse complements of each other where the two sequences aligned using blastn  [16]  (with parameters -task blastn -word_size 4) have 'Strand=Plus/Minus' and 'Identities' greater than or equal to the specified minimum ITR length.
Step 5: final output A Python script generates a FASTA file of ISs and a tab-delimited file of information including (1) their name [containing information on the length of the IS, the InterPro or PANTHER accession(s) identified in step 4 and their position(s)], (2) sample ID, (3) contig, (4) start and end positions of the two ITRs on the corresponding contig and (5) description of the protein family represented by the accession(s). Using Palidis to create Insertion Sequence Catalogue v1. 0. 0 A catalogue of insertion sequences was generated using Palidis applied to 264 human oral and gut metagenomic reads from the Human Microbiome Project (Data S1)  [18]. The reads were quality controlled, filtered and assembled as previously described  [19]. A total of 2517 ISs were identified from 1837 contigs across 218 (out of 264) samples with Palidis v3.
1. 0 using default parameters (--min_itr_length 25 --max_itr_length 50 --kmer_length 15 min_is_len 500 --max_is_len 3000 cd_hit_G 0 cd_hit_c 0. 9 -cd_hit_G 0 cd_hit_aL 0. 0 cd_hit_aS 0. 9) (Data 2). The ISs were then clustered using CD-HIT-EST v4. 8. 1 (with a sequence identity threshold -c 0. 95 and default parameters) to create the insertion sequence catalogue (ISC). ISC contains a FASTA file of 879 unique ISs between 524 and 2999 bp in length (Fig. 2a ) and containing 87 unique transposases (Fig. 2b ) (https://github. com/blue-moon22/ISC). In order to identify ISs from ISC that have previously been discovered, ISC was queried against ISfinder using their online blast search tool (https://www-is. biotoul. fr/search. php) (with e-value 0. 01 and using default parameters on 7 October 2022).
Altogether, 360(41. 0 %) ISs have hits in ISfinder, while the remaining 519 are novel. Overall, 60 have a strict homology with ISs from ISfinder (e-value <1e-50), while the other 300 have a loose homology (0. 01>e-value ≥ 1e-50). The origins of the ISs were determined by querying the ISC against a COBS index of 661 405 bacterial genomes (referred to as the 661 k database) from European Nucleotide Archive (ENA) (http://ftp. ebi. ac. uk/pub/databases/ENA2018-bacteria-661k/661k. cobs_compact)  [12]  using cobs query v0. 1. 2 (with -t 0. 9 and using default parameters). In total, 155 ISs were located in 791 samples (NCBI BioSample IDs) from the 661 k database. Metadata was available for 785 for these samples from the 661 k database study  [12]. Altogether, 684 samples came from isolates that had an associated taxonomic identity, whereas the other 85 samples were sequenced from microbial communities or were unclassified (known as 'bacterium').
These 684 samples originate from 63 known genera (Fig. 3a ) with 138 known species (not labelled sp. ) (Fig. 3b ). Many ISs were shared across bacterial genomes of different species, genera and classes. Overall, 52 and 70 ISs originate from more than one known genus (Fig. 4a ) and known species (not labelled sp. ), respectively. The IS that is shared across most genera (21 genera and 46 species) and was also found in ISfinder as IS1249, is IS_length_1391-IPR001207_495_804 (Fig. 4b , blue square), containing a Transposase, mutator type. The IS that is shared across the most genera but was not found in ISfinder, is IS_length_2555-IPR036397_1291_1768-PTHR35004_877_2065-IPR012337_1315_1720, containing a RV3428C-related Transposase (Fig. 3b , red square). Many ISs found in multiple Bacteroides, Corynebacterium, Curibacterium and Prevotella species are solely represented by those not found in ISfinder, suggesting that in its current release (7 October 2022), ISfinder database is underrepresenting ISs in these genera (Fig.