dataesr/scientific-paragraphs-categorization huggingface dataset:
DBM method was used for minute ahead wind speed support structures relationships between the Sparse Bayesian Learning risk assessment and 1000 datasets of hourly condition of pitch system (SBL) is used to develop a maintenance scheduling of wind power were used. No Tests from the Multi-and sensor readings. probabilistic forecasting WT gearboxes performance improvement dimensional Scaling SCADA data was used to Probability of failure and method for wind power. is observed when Fatigue prone hot-spots on WT support structures are analysed. Reliability is assessed by the S-N (MDS) method are used as train the BN accuracy of monitoring Parameters of the kernel historical data increases inputs to BN for improved The BN was tested by results are calculated functions are optimised in Hugin software was used decision-making running different failure order to improve SBL to process the data.
Weibull Fault features, e. g. RMS, compared with the binning framework was used. The fuzzy datasets (to test data in a by a n-dimensional vector. of WTs. The results are Set-membership and isolation (FDI) system both actual and ideal Sample data is described extreme loading response bounded by the Bayesian A two-part fault detection Tests were performed for to evaluate the errors used to extrapolate The errors in the model are was estimated by MCS (cross-validation) method Bayesian spline method is distribution. Failure time technique uses 10-fold Simulink model speed causes fitted to Weibull chosen. Model evaluation sample data generated by a used to model the wind failures and failure root by Rainflow counting and 330 and 800kW were were detected using 50000 distribution (MWD) was relationships between distribution was obtained Two commercial WTs of The faults of pitch system A mixture Weibull on BN taking into account updated using BNs.
Load and correlation methods Simulation results still have to be analysed statistically scale tests contain account in WT structural for to maximise a collective There are usually outliers in uncertainties response. The conventional power output. The proposed the dataset that affect accuracy Classical techniques of wind models are computationally method in this study is based of predictions. Pre-processing power forecasting do not expensive and may produce on cooperative WF control accuracy depends on the There is a need to improve reliability analysis techniques by considering anticipated degradation and accounting for estimation uncertainties There is a gap for automatic account for many variables. inaccurate structural responses Conventional techniques do quality of pre-processed data use of SCADA data in failure Thus, their precision is limited Probabilistic models focused not account for WT dynamic Conventional methods diagnosis on non-parametric methods, behaviours underestimate uncertainty in N.
A. which need more data and are wind power forecasts. They do The decisions about repair computational-intensive. not fully describe the Offshore WFs require improved maintenance planning methods due to the affect the associated costs of Previous methods cannot predicted wind power Although condition O&M. A method for O&M provide full probability BNs are very complex and distribution monitoring systems are updating is required density functions. In order to difficult to synthetically N. A. effective, false positives forecast by minutes, the explain and the number of Conventional fault diagnosis extreme conditions increase O&M cost. Thus, a Conventional reliability Gaussian distribution cannot possible structures is techniques lead to large costs It is complicated to calculate costs, failure rates, and damage models for WT gearbox method is required to improve estimation methods do not represent the error exponential due to big and noisy datasets the accuracy and robustness of account for component distributions the results interrelationships and Failure prediction is difficult Current maintenance dynamic wind speed because of inadequate strategies cause higher WT Conventional statistical models are not applicable as they only focus on parameter uncertainty but not model uncertainty Damage models are used to describe damage development.
detection thresholds, which Using existing data and Current solutions to wind accurately estimate the Other models use very large planning The binning method cannot relational database for O&M probabilistic methods a dedicated software tool and behaviours at WF sites. estimations availability were modelled by conditions. There is a need for difficult to predict wind executing Bayesian the wind power resource behaviour under stochastic LCOE Insufficient data makes it There are some challenges for Uncertainties associated with understanding of WT downtimes and increase the Compared to Maximum deflection theory, BN produced more accurate process. Both physical and even with little test data. design WTs. Uncertainties data for optimum control The downsides of having statistical uncertainty and Data from past full-scale can be incorporated to estimations.
Noisy function outliers within the datasets expert judgement can be tests may be used for prior Methodology for DBN can reduce result bias. Faster values can also be used. are reduced. The method is incorporated into the design data used in the analysis be used for different processing time with also more robust because of results Bayesian updating improves O&M decision-making based on system reliability BNs can make inference forecasting problems. The improved accuracy Bayesian methods can help high-resolution data about the probabilities of all average error is more With SBL, there is no need capture the dynamic Reliable for informing connected events. The acceptable than with other to estimate some parameters degradation over time. regarding real-time risk This method produces better sensor readings are collected techniques and thus there is less System state can be management fault detection results online and used to update the Pre-posterior decision computation time.
It uncertainty and availability conditions more adaptive and accurate method can be used to make allows for control of the environmental factor impact The failure dependency The degradation model can fault diagnosis method decisions under uncertain network complexity database design is useful for be updated with inspection conditions The uncertainty can be better accounted for by using an averaged model. BMA produced more robust long-term wind speed distributions for all sites Incorporating inspection flexible to solve minimum cut sets Incorporating CM and using Bayesian methods supervised controls more analysis as they do not need estimations update reliability model most critical events classification, making fault trees for reliability the accuracy of reliability techniques can be used to FEA of the structure utility (cost) associated for account the likelihood of the BNs are more suitable than inspection results improves Information from NDI newer information from analysis and include the The BN method takes into to the observations can be made with BN update the model based on BNs can be used for HAZID sampling probability density function estimates diagnosis under uncertainty Bayesian methods can improved detection time information through Gibbs convergence of the modelled damage models and failure State identification and fault flexible in data handling reduced.
A. Data was obtained prior test data of convergence, from simulations and Daily variable data are slowing down the not real measurements not considered iteration process BN can become too N. A. Accuracy of BN updating complex. The The method performs N. A. method depends on accuracy of the BN poorly with large the posterior N. A. model depends on the datasets. The accuracy function quantity of input data Approximation of the model is not the used for the estimation techniques still need to best for very short-N. A N. A. be used in Bayesian term or very long-term More variables need to analysis wind power forecasts be included in the N. A. probabilistic The accuracy of N. A. estimation to ensure diagnosis depends on robustness quality of input data. It N. A. is only designed for More failure data is The method has not binary classification required to improve taken into account problems (i.
Recorder systems When a HCW provides nursing care to a patient, he/she must track the care in the nursing care record. The validity of this information must be certified with the time and date of the nursing care and with the signature of the HCW. We compared different traceability systems that were used in patient medical records. The oldest traceability system was a dedicated paper form. This sheet of paper was placed into the patient's paper-based medical record. The second traceability system, the software Pharma® (version 5. 8. 70602. 1 300), was introduced in our institute in 2013 and allows the traceability of medical prescriptions and administered drugs but also provides some of the patient's parameters, such as urine strip, temperature and weight. The third system, introduced in our institute at the beginning of 2016, was an EHR (Axigate® version: 5.
6. 1P9) embedded in the Hospital Information System (HIS). This EHR allows the registration of some other parameters, such as inserting and monitoring a peripheral venous catheter, inserting and monitoring a urinary catheter, providing an inpatient booklet, and monitoring isolation. Finally, the last system, introduced in June 2016, was the Patient Smart Reader (PSR) and was installed in 8 patient rooms (Cohort B). The PSR system is a personal digital assistant with a barcode scanner that allows for bedside recording of nursing care (https://vimeo. com/205512348). With the PSR, the HCW can record, at the patient's bedside and in real time, nursing care such as the insertion and monitoring of urinary or peripheral venous catheters by using barcoded implements, or the HCW can directly record various parameters such as body temperature, blood pressure or pain directly onto the PSR.
Data analysis We used descriptive statistics, and we described data compliance in terms of frequencies and percentages. The compliance to traceability (as defined by variable recorded/variable to be recorded) of nursing care using different tools (paper sheet versus paper sheet and Pharma software or any kind of recording systems versus PSR) was analyzed. The obtained differences in compliance were defined as significant when p<0. 05 (chi-square test). Similarly, the relative risk and a 95% confidence interval were reported.
Second, recording at the point of care avoids the loss of information between the patient's bedroom and the nearest computer. Moreover, the data collected by the PSR can be directly transferred to the care plan in the patient's electronic files of the hospital information system. We evaluated ten variables because of their mandatory character, but the PSR contains up to 80 variables and allows for decision-making. For instance, once a nurse has registered the introduction of a peripheral venous catheter, the system will ask her if the catheter should be removed during each shift (8 hours).
Observing changes in Earth surface topography is crucial for many Earth science disciplines. Documenting these changes over several decades at regional to global scale remains a challenge due to the limited availability of suitable satellite data before the year 2000. Declassified analog satellite images from the American reconnaissance program Hexagon (KH-9), which surveyed nearly all land surfaces from 1972 to 1986 at meter to sub-meter resolutions, provide a unique opportunity to fill the gap in observations. However, large-scale processing of analog imagery remains challenging. We developed an automated workflow to generate Digital Elevation Models and orthophotos from scanned KH-9 mapping camera stereo images. The workflow includes a preprocessing step to correct for film and scanning distortions and crop the scanned images, and a stereo reconstruction step using the open-source NASA Ames Stereo Pipeline.
, 2019). However, prior to the Shuttle Radar Topography Mission (SRTM) in 2000, no elevation data are available at a global scale with the decameter resolution required to study Earth surface deformation. Documenting surface changes prior to year 2000 requires the exploitation of older remote sensing archives, typically from aerial platforms  (Kjeldsen et al. , 2015; Girod et al. , 2018). A potential source of data to fill in the temporal gap is declassified American intelligence satellite imagery, such as from the Corona and Hexagon programs  (Galiatsatos et al. , 2007; Burnett, 2012; Fowler, 2013). Here we will focus on the Hexagon program.
Relevance for the Study of Changes in Earth Surface With their high resolution, high quality and open access, the scanned KH-9 MC images comprise one of the best global satellite data sets for the 1970s. Accordingly, the MC archive has attracted a lot of attention in the scientific community. The first scientific references to the data consisted of a qualitative image interpretation to identify archaeological features  (Fowler, 2004; Scardozzi, 2008) , glacier features  (Grant et al. , 2009; Narama et al. , 2010a; Narama et al. , 2010b) , landslides  (Larsen and Montgomery, 2012)  and land-cover change  (Kivinen and Kumpula, 2014). The first quantitative processing of the stereo images was performed by  Surazakov and Aizen (2010) , who revealed the potential of the data to generate historical topography and to study Earth's surface deformation.
Stereo Processing The stereo processing and DEM generation is performed using NASA's open-source Ames Stereo Pipeline (ASP). The software abilities and workflow for processing Earth observation satellite data have been described extensively in  Shean et al. (2016)  and  Beyer et al. (2018). Here we discuss the details of processing historical (frame camera) imagery with ASP. ASPy iterative workflow consists of the following steps: camera initialization from crude image footprints, generation of inital DEMs, co-registration of the DEMs to a reference and update of the camera positions/orientations, refinement of the camera intrinsic parameters for each KH-9 MC, final DEM generation and DEM composite.
, 2015; Shean et al. , 2020)  because most glaciers' dimensions exceed the spatial correlation range typically estimated in these studies ( < 500 m). Additionally, this equation can be extended to any number of "nested" spherical models with varying ranges and sills, by summing the variances, to account for several ranges of spatial correlation (Appendix in  Rolstad et al. , 2009). To constrain a nested spherical variogram model, we calculated elevation difference maps between 48 KH and -9 MC DEMs and the ArcticDEM. For each difference map, 2000 points were randomly extracted over stable terrain and used to calculate a variogram with lag distances ranging from 0 to 120 km (the swath width of a KH-9 MC image) using Python's scikit-gstat library  (Mälicke and Schneider, 2019). The variance was divided by the total variance of each difference map (i.
A question arises as to whether the camera intrinsic parameters can be assumed constant for the duration of a mission. Changes in instrument temperature during an orbit or spacecraft vibrations could cause slight changes in these parameters. To test this we estimated intrinsic parameters for individual stereo pairs or consecutive tracks following the same approach used for the full mission. The estimated camera parameters showed some variability but the differences were equally large for images acquired at a few minutes or several weeks interval. Because of this, we attributed the variability in image-pair parameters to the uncertainty of the retrieval method and differences in image quality or scene characteristics, and not to real changes in camera parameters. Pre-flight and on-orbit KH-9 MC camera calibration was performed with 2 μm required accuracy  (Burnett, 2012)  but the detailed calibration data are not presently available, either still classified or not archived.
ACKNOWLEDGMENTS We thank  Etienne Berthier  for sharing the historical Alaska DEM,  Mike Willis  for his help with the ancillary DEMs and  Antoine Rabatel  for sharing (unused) data set over the French Alps. We would like to thank  Tobias Bolch  for valuable feedback on a first round of reviews. We thank the JPL super-computing facilities and in particular  Edward Villanueva  for his technical support. This work would not have been possible without the open access to the KH-9 MC images and the outstanding work of the  USGS  to make this data accessible to everyone. We are particularly grateful to  Tim Smith  and  Ryan Longhenry  for the exchanges regarding the scanning issues. We acknowledge open access data provided by the  Polar Geospatial Center (ArcticDEM) , the  DLR  (TanDEM-X 90 m DEM) and  NASA (SRTM).
For these reasons, there is a practical need for efficient methods that can profile microbial communities at high genomic resolution. In contrast to 16S rRNA gene sequencing, shotgun metagenomics has the potential to resolve microbial communities to the strain level. This is because it generates reads from throughout the genomes of all the community members. It also has the additional advantages of reduced levels of bias and the capability to reconstruct genomes. There are many methods for reference-based strain resolution from metagenome data  [1, 35, 42] , but they are, and will continue to be, limited by the challenge of comprehensively isolating and sequencing the genomes of diverse microbial strains. Comprehensive reference genome databases may be possible for a few slowly evolving species or particularly well studied pathogens but for the entirety of a complex community it is unlikely to ever be tractable.
For example, in a recent de novo large-scale binning study of the relatively well-studied human gut microbiome, it was found that 77% of the species recovered did not have a reference genome in public databases  [31]. This suggests that even less of the strain-level diversity in those samples would be represented in a genome database. These observations motivate the need for de novo methods of metagenomic strain resolution. In the metagenomics context, we adopt the definition of a 'metagenome strain' as a clonal subpopulation with sufficiently low levels of recombination with other strains, that it can be distinguished genetically from them. This does not require that recombination between strains does not occur, rather that either because of physical separation or selection, it has not been sufficiently strong relative to the rate of mutation  [40] , to generate a continuum of diversity throughout the genome.
If multiple haplotypes matched to the same reference they were denoted as 'Repeated'. If a reference had no predicted haplotypes matched to it, it was denoted as 'Not found'. The bars give the total numbers in each category summed over MAGs for the two methods (DESMAN and STRONG) and the panels results for the four different data sets with increasing number of samples (Synth S03, Synth S05, Synth S10 and Synth S15). Synth_S03 Synth_S05 Synth_S10 Synth_S15 D E S M A N S T R O N G D E S M A N S T R O N G D E S M A N S T R O N G D E S M A N S T R O N G abundance gave an adjusted R 2 of 0. 84 averaged across sample numbers for STRONG vs. 0. 80 for DESMAN. When this was restricted to MAGs where the number of strains was correctly predicted, then both algorithms did better but STRONG still out performed DESMAN, with a mean R 2 of 0.
98 compared to 0. 93. Although the quantity varied across the four data sets, roughly 1/3 of the SCGs were filtered during the BayesPaths as outliers (see -Table  1 ). The STRONG pipeline outperforms DESMAN, but it still misses strains that are present. In total across all MAGs and data sets, 63/276 i. e. 22. 8%, of strains were missed by STRONG.
Strain error rate Method DESMAN STRONG Figure 4. Error rates in strains found against coverage depth for STRONG and DESMAN algorithms in the synthetic community data sets. For the 'Found' strains we computed per base error rate to the matched reference, this is shown on the y-axis, against strain total coverage depth summed across samples on the x-axis, both axes are log transformed. The results are separated across methods (DESMAN and STRONG) and sample number in the synthetic community. Some of these, 7 out of 63, were below the minimum coverage of detected strains (5. 68), but most were not, suggesting that either they were not sufficiently divergent in terms of nucleotides or coverage profiles to be detected. Examination of phylogenetic trees for the haplotypes and reference genomes constructed using the SCGs revealed that in many cases 'Not found' strains had identical SCG haplotypes to those that were resolved.
In Figure  6 , we use the Anvi'o program  [16]  to summarise information on phylogeny, taxonomy, normalised coverages in the ten samples, and whether the MAGs changed significantly in total abundance, together with the number of strains resolved by STRONG and if those strain relative proportions changed significantly with time. This was restricted to just those 114 MAGs with an aggregate coverage greater than twenty to simplify the diagram. The Nanopore sequencing provides us with a means to directly test the validity of the STRONG haplotype reconstructions, at least for the most abundant MAGs. The most 11/33 abundant MAG, Bin 72, had an aggregate short read coverage depth of 2364. 25, across all the samples. This MAG was assigned to the phylum Cloacimonadota using the GTDB taxonomy  [11].
If strains are functionally redundant to one another we would expect significant neutral fluctuations over time. Therefore this could be evidence for intra-species niche partitioning. In general, we found a good correspondence between haplotypes inferred from Nanopore reads and the STRONG predictions in the AD data set. For the most abundant MAG, Bin 72, they matched very closely. In addition, the relative abundances of strains were consistent across the two sequencing technologies, despite the use of different DNA extraction protocols, and the different biases inherent in library preparation and sequencing platforms. These technical elements in the data generation process are known to introduce bias at the species level  [12] , 14/33 but our findings suggest that intraspecies abundance may generally be robust against such biases, which makes sense in that all the strains of a species will have similar physical properties and genomic traits.
Conclusion We have introduced a complete bioinformatics pipeline, STrain Resolution ON assembly Graphs (STRONG), that is capable of extracting single-copy core gene variant graphs from short read metagenome coassemblies for individual metagenome assembled genomes (MAGs). We demonstrated how these graphs and associated per-sample unitig coverages can be used in a 15/33 novel Bayesian algorithm, BayesPaths, to find MAG strain number, haplotypes and abundances. This approach achieves superior accuracy to variant based methods on synthetic communities and predictions on real data that match those from long Nanopore long reads. STRONG is freely available from https://github. com/chrisquince/STRONG.
AD sample collection We obtained ten samples from a farm anaerobic digestion bioreactor across a period of approximately one year. The sampling times, metadata and accession numbers are given in Table  S4. The reactor was fed on a mixture of slurry, whey and crop residues, and operated between 35-40 • C, with mechanical stirring. Biomass samples were taken directly from the AD reactor by the facility operators and shipped in ice-cooled containers to the University of Warwick. Upon receipt, they were stored at 4 • C and then sampled into several 1-5mL aliquots within a few days. DNA was usually extracted from these aliquots immediately but some were first stored in a -80 • C freezer until subsequent thawing and extraction.