dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the relative ease of the training process and application of CNN, the approach could be transformed into an application with a user-friendly interface demanding only a minimum amount of interaction. Hence, CNNs could make eDNA metabarcoding data processing accessible even to less trained users and provide an overview of biodiversity more rapidly. A CNN trained on a complete reference database produced species composition outputs congruent with the outputs of a popular bioinformatic pipeline, but showed a tendency to predict more species than those of OBITools and historical records. Compositional differences in the outputs of pipelines have already been highlighted (e. g. 11 ) and have mainly resulted from the detection of several false positives and false negatives  52. With a binarization threshold of 0.
Hence, appropriately combining the PCR replicates could confer more robustness to the final outputs of the CNN. Refinement of the network could be added, so that the detection across multiple PCR replicates could be used to compute the final likelihood. In our study we proposed a novel application of a CNN approach to eDNA metabarcoding data, but several improvements are required before broad-scale applications to large eDNA data sets can be considered. The CNN trained in this study learns from the species class and is forced to assign the sequences to that taxonomic level. Thus, when presented with conflicting sequences, the network might assign all of them to a single species, or may split the probabilities across several species, which might then be discarded given the use of the 0. 9 binarization threshold.
In contrast, in the case of a conflict, OBITools can assign sequences to higher taxonomic levels, thus keeping information related to these species with identical sequences. In this case study, we had an ideal situation where the reference database was almost complete for the territory. The CNN could be improved to handle incomplete reference databases and to be able to assign a read to another taxonomic level or to an unknown class, rather that forcing a species-level identification and relying on the binarization threshold to reject unknown sequences. Further, we expect that it is possible to improve the CNN by implementing more stringent filters that would reduce the number of false detection and prediction errors. For instance, a filter for tag-jump handling, included in previous pipelines for eDNA metabarcoding for fish (e.
Material and methods eDNA data collection and reference database. As a test data set we used data collected in French Guiana, a c. 80,000 km 2 South American territory almost entirely covered by dense primary forest (Supplemen- tary Material Fig. 2 ). The equatorial climate, associated with abundant rainfall, has created a dense hydrographic network consisting of six major watersheds and several coastal rivers that host a highly diverse fish fauna with at least 368 strictly freshwater fish species  45. eDNA field collection was initiated in 2014 and continued until 2020. We sampled over 200 sites (see Murienne et al. 54  for details), where we filtered 30 litres of river water across a flow filtration capsule using a peristaltic pump. For the purposes of this study, we analysed only the filters collected in both the Maroni and Oyapock rivers.
We applified the 12S rRNA 'teleo' gene fragment  77  using PCR and sequenced it on an Illumina platform, generating an average of 500,000 paired-end sequence reads per sample. The DNA extraction, amplification and sequencing protocol have been described previously  19. We generated an eDNA reference database by combining fish specimens caught using various types of fishing gear. These data were complemented by fish collections carried out by environmental management agencies (DGTM Guyane, Office de l' eau Guyane, Hydreco laboratory), fish hobbyists (Guyane Wild Fish), and Museum tissue collections (MHN Geneva). Although rare for Guianese fishes, we also included existing sequence data from online databases (Genbank, Mitofish). We extracted and sequenced the 12S ribosomal gene from the collected species.
The local reference database has improved over the years  20, 22  and now covers over 368 species out of 380 estimated to occur in the region. This almost full coverage is exceptional considering the many gaps globally  51. Sample collection was authorized by both the French Ministry of Environment (DEAL) and the Guyanese National Park (PAG). The samples comply with the international rules of the Nagoya protocol for access and benefit sharing (project refs ABSCH-IRCC-FR-246820-1 and ABSCH-IRCC-FR-245902-1).
Reference data augmentation and training data set. The reference database has a full species coverage, but the number of DNA replicate sequences for each species was limited because there were only 683 sequences for 368 species. This makes training a CNN challenging for several reasons. The number of sequences per species is not balanced, there are not enough sequences to capture the entire inter-and intraspecific variation, and the noise from the sequencing process is not accounted for. To balance the data set using data augmentation procedures, we oversampled the underrepresented species before training. To increase the sequence variation, we implemented an inline sequence mutation step similar to that applied by Busia et al. 14. During each training epoch all sequences were randomly mutated.
We added between zero and two random insertions and deletions each, as well as noise in the form of a 5% mutation rate. This procedure further reduced overfitting, as no training sample was likely to be repeated twice. For the evaluations, we either added no augmentation or 2% noise and singular insertions and deletions, as we expected the PCR amplification and sequencing to be better than the 5% noise considered during the training phase. For the direct application on the raw reads, another data transformation step was required. All sequences processed in an Illumina machine retain the selected primers, and were tagged with 8-bp-long tags. During the sequencing two bases from the plate attachment sequence were often read as well. We therefore pre-and appended the forward and reverse primers, and the combined tags and attachment bps to the sequences from the reference database.
CNN training and evaluation using split sampling. We investigated the performance of a CNN approach trained on the reference database at the species level. To encode DNA sequence information, each canonical base (A, C, T, G) and each IUPAC ambiguity code was translated to an appropriate four-dimensional probability distribution over the four canonical bases (A, T, C, G), including uncertain base reads (e. g. W and S). For example ' A' became [1,0,0,0] and 'W' became [0. 5, 0, 0, 0. 5]. The neural network was designed and optimized through a series of tests that allow the optimal set of correct DNA features to be selected. In particular, we explored an exhaustive number of model sizes, including one to three layers of 2D (depth-wise separable) convolutions with 4-16 filters each, one to three fully-connected layers with varying numbers of neurons each, and a softmax activated output layer which produces a probability distribution over all possible taxonomic labels.
We applied dropout regularization and used leaky rectified-linear activation for all but the last layers. We used TensorFlow 1 to train the CNNs with all the aforementioned data augmentations. Due to the sparse data set, we characterized and evaluated the performance of the neural networks using three different methods. First, we applied random split-sampling from the reference database. This established a proper separation between the training and validation data, but less than half the species in the reference data set had two or more sequences, resulting in a reduced range of species that could be included. Specifically, only 156 out of 368 species possessed two or more unique sequences and were considered for the split data set. Next, we trained several networks on the full reference data set with all 368 species and validated them using the original non-augmented reference data.
CNN application on the raw illumina sequences. We applied the best CNN directly to the raw outputs from the Illumina sequencing, where we omitted all the preprocessing steps from OBITools. The CNN was expected to learn how to ignore the primer, as it was constant for all presented sequences. Furthermore, the output sequences from the Illumina sequencer were fixed in length (150 bp), so we fixed the input width of the CNN to this size. We systematically zero-padded or truncated the input sequences to this length during training, evaluation, and application. After the training phase with the reference database and the application on fastq, we developed a custom code for the fast demultiplexing of the reads. By focusing on the tag information in the first few positions of the sequence and not considering read errors in tags, we reduced the demultiplexing to a few simple look-ups in a hash table (currently 5), therefore reducing computation time with limited information loss.
Each lander should contain a sensitive broadband seismometer, laser retroreflectors, heat flow probes, and electromagnetic sounders. The landers should be long-lived (>6 years, with a goal of 10 years) to maximize science and allow other nodes to be added by international and commercial partners during the lifetime of the mission, thus increasing the fidelity and value of the data obtained. The baseline LGN mission architecture requires an orbiter to serve as the primary communication relay for the farside lander. This concept requires mission operations to manage command and control of the orbiter and four landers. The mission operations team should primarily utilize direct to Earth communication links to talk to all landers on the nearside of the Moon. Operations will have the option to communicate with the nearside landers via the orbiter relay communication links for redundancy.
, 2000). Shaded surface relief is derived from LOLA topography  (Smith et al. , 2017). Additionally, the identification of different lunar surface terranes from new global surface compositional data have produced a shift in our understanding of the global evolution model of the Moon  (Jolliff et al. , 2000; Laneuville et al. , 2018). These data demonstrate that there is a fundamental limitation to the ALSEP geophysical datasets, as all were collected in or very near one anomalous region, the Procellarum KREEP (Potassium, Rare Earth Elements, Phosphorus) Terrane (PKT) (Figure  1 ). Subsequent orbital missions have expanded the global geophysical picture of the interior (e. g. , Lunar Prospector (LP), Gravity Recovery And Interior Laboratory (GRAIL), Kaguya, Lunar Reconnaissance Orbiter (LRO), etc.
), but only a landed long-lived geophysical network can address the significant questions that remain unanswered by Apollo: • How does the overall composition and structure of the Moon inform us about initial differentiation of terrestrial planets? • What is the state, structure, and composition of the mantle and is it consistent with the lunar magma ocean hypothesis (or are there resolvable discontinuities)? • What range of bulk compositional models for the crust, mantle, and core are compatible with combined LGN and previous datasets? • What is the present heat budget and how could the Moon experience magmatism for >3 b. y. ? • What is the crust and mantle heterogeneity within and between different terranes? • Based on a constrained size, state and composition, how did the lunar core form and could it have supported a global magnetosphere (as indicated by sample analyses -e.
g. ,  Weiss and Tikoo, 2014; Mighani et al. , 2020) ? New geophysical data are needed to address these questions and add greater fidelity to datasets already obtained. The Apollo seismic network possessed a narrow aperture and was emplaced on the edge of and within a special crustal terrane, but has necessarily been used to extrapolate global interior properties. For example, the GRAIL gravity data that inform crustal thickness are constrained by Apollo seismic data  (Khan and Mosegaard, 2002; Lognonne et al. , 2003; Chenet et al. , 2006) , but the fidelity across the lunar surface is poor due to the narrow aperture of the Apollo passive seismic network and localization in the thinned and likely anomalous nearside crust of the PKT (e. g. ,  Hood, 1986). Another example is the large discrepancies among the size (and nature) of the lunar core defined by seismic, lunar laser retroreflector (LLR), and GRAIL data, which reflect the lack of fidelity in Apollo seismic and ongoing LLR data (e.
Because they have different activation energies, the relative contributions of iron and water can be separated along a geotherm. While simultaneous inversion of temperature and composition from electrical conductivity is possible, a more robust result is obtained by combining these data with heat flow, which provides an independent estimate of the geotherm. The joint heat flow and electrical-conductivity constraints on temperature and iron/water content can be combined with seismological estimates of temperature and bulk mineralogy for further refinement of the state of the Moon's interior. The LGN mission will allow more intricate questions to be addressed that have resulted from previous work, such as: • Do shallow moonquakes represent movement along thrust faults (e. g. ,  Watters et al.
Geodetic Earth-based Lunar Laser Ranging Observatories (LLRO) were first developed in the 1960s to send short laser pulses toward the Apollo retroreflector arrays on the lunar surface where the Cube Corner Retroreflectors (CCRs) reflected the laser pulses directly back to the LLRO. By highly accurate timing of light travel time, the distance to the Apollo retroreflector array was determined to centimeter scale, that is, to better than one part in 10 12. LLR has continued over the past 50 years and the analyses of these data have illuminated key details within lunar geophysics, including detecting: the existence and shape of the liquid core, an oblate spheroid with the short axis parallel to the rotation axis of the Moon  (Williams et al. , 2009) ; the k2 Love number and tidal Qs  (Williams and Boggs, 2015) ; Core Mantle Boundary (CMB) dissipation  (Williams et al.
LGN can, however, exploit an alternative method that will yield more robust and better-resolved results, without requiring a reference orbiter. In the magnetotelluric method (MT), electricalconductivity structure is determined by simultaneous measurement of the electric and magnetic fields at a single site (e. g. ,  Simpson and Bahr, 2005; Chave and Jones, 2012). By eliminating the orbital measurement, data can be interpreted consistently across all three environments described above. Furthermore, MT is largely insensitive to finite wavelengths in the plasma that introduce distortions in the TF at frequencies of 10s of mHz and higher  (Grimm and Delory, 2012). A larger bandwidth enables resolution of shallower structure than previous, particularly if the traditional fluxgate magnetometer is complemented with a search coil magnetometer.
5 mm. Precision at this level has already been obtained at the Apache Point Observatory Lunar Laser-ranging Operation (APOLLO) LLRO. The second row addresses the improvement in the factors when a fourth NGLR is deployed at the south pole (i. e. , the Artemis III site). A second set of simulations  (Williams et al 2020)  is shown in the third and fourth rows for 3 and 4 NGLRs with range accuracies of 0. 15 mm, addressing the limiting accuracy that the NGLR will support in the current design of the LGN deployment. The basic NGLR package can support very high accuracy ranging  (Currie et al. 2013). The accuracy improvement for a given science result is estimated for a single range for the basic NGLR package  (Currie et al. 2013) , with the normal point being obtained by multiple ranges. These results require an upgrade in LLRO hardware.
The simulations assumed that the NGLR are deployed at Mare Crisium (Longitude: 59, Latitude: 17), a Western Site region (Longitude: -50, Latitude: 20), a south western site (Longitude: -55, Latitude: -45), which are the sites expected to the explored by missions of the NASA CLPS program with NGLR funded by the NASA Lunar Surface Instrument and Technology Payloads (LSITP) program, and the south pole Artemis III site. We expect that the LLR science improvements due to any of the nearside LGN sites to be comparable. If both LGN and CLPS deployments are successful, along with the currently existing retroreflector arrays the science results will be further improved, particularly the Love Numbers and the selenodetic coordinate system. The latter will improve the accuracy of maps of various properties of the lunar surface, particularly catalogs like that from LRO.
Acknowledgements D. C. acknowledges his research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ) and at the  University of Maryland, College Park  under a contract with  Notre Dame University  ( 203769UMD ). We acknowledge the use of imagery from  Lunar QuickMap  (https://quickmap. lroc. asu. edu), a collaboration between  NASA ,  Arizona State University & Applied Coherent Technology Corp. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
V. SIMULATION VERIFICATION A detailed system is structured in Matlab environment to explore the proposed control strategy by using two-parallel inverters. The system parameters and control gains are identical to those used for the experimental investigations as listed in Table  2. Fig. 2  depicts schematic diagram of the proposed observer-based control system. Fig. 3. a illustrates response of w cd and w cq for the ac output filter with a step of the RM S output voltage from 80 V to 120 V at t = 20ms. The components w c0 and V C0 equal zero. The measured components perfectly follow their respective references. Figs. 3b ,c demonstrate response of the output three-phase voltages and their components V cd and V cq with the control strategy which are pure sine waves for P L = 3 kW. The proposed FBC adjusts the output ac bus voltage to its reference set value under the normal conditions.
VI. EXPERIMENTAL VALIDATION For further validation of the simulation tests, experimental validations are executed for 5 kW test system comprising two parallel inverters as depicted in Fig. 5. The proposed FBC method is realized by MATLAB/Simulink-RTW software. The control method is implemented owing to the dSPACE-1005 real-time control card. All the measurements are attained by digital oscilloscope: Tektronix TDS5104B and Yokogawa WT1800E. Fig. 6  shows the performance of the tested system by using the proposed FBC while initial operating point is set to V dc = 500 V, V rms = 110 V, P L = 3. 2 kW. Fig. 6. a illustrates the three-phase ac voltages and load currents for P L = 3. 2 kW and the output voltage of the ac bus V abc is fixed at 110 V. The reported voltage THD is equal to 1. 8%. Figs.
Several studies have been conducted using this database. For the Walloon region in Belgium,  Leteinturier et al. (2006)  developed a set of agro-environmental indicators to compare crop rotations. More recently,  Levavasseur et al. (2016)  designed a software, called RPG Explorer, which allows the extraction of rotations for a given territory. Before 2015, LPIS data were only available at the block level (i. e. a group of close fields that are cultivated by the same farmer, but that can be cultivated with different crops the same year), so decision rules had to be implemented to estimate the distribution of crops within the blocks. As these rules can be cumbersome to define and depend on local conditions, these studies have generally been conducted at the scale of small territories, for example at the watershed scale  (Rizzo et al.
, 2008; Detlefsen and Jensen, 2007). More recently, this work has been extended to improve the identification of crops from satellite images, taking into account the main crop rotations  (Osman et al. , 2015). This investigation builds on these previous examples and proposes an application of network analysis to identify crop rotations for a given territory. Crop rotation networks were built at the regional scale using data from the French LPIS. These rotations were then compared using different indicators from network analysis. In particular, the return time of soft winter wheat, which is the most cultivated crop in the country, was estimated for each region from simulations of crop rotations.
Creation of transition matrices LPIS from 2017 to 2020 were used to determine crop rotations for the 22 regions of mainland France for three crop successions (from 2017 to 2018, from 2018 to 2019 and from 2019 to 2020). Concerning the regions, it was chosen to use the boundaries of the former administrative regions of France, because many statistical data are still made available for these regions  (Agreste, 2020). The French LIPS contain an attribute "ID_PARCEL" which allows to identify each field, and thus to follow its evolution from one year to another. This association could only be carried out for part of the fields because the borders of some of them were modified from one year to the next. The percentage of fields with information about the previous crop was stable for three periods studied, with two thirds of the fields being kept in each period (i.
We present LatNet Builder, a software tool to find good parameters for lattice rules, polynomial lattice rules, and digital nets in base 2, for quasi-Monte Carlo (QMC) and randomized quasi-Monte Carlo (RQMC) sampling over the sdimensional unit hypercube. The selection criteria are figures of merit that give different weights to different subsets of coordinates. They are upper bounds on the worst-case error (for QMC) or variance (for RQMC) for integrands rescaled to have a norm of at most one in certain Hilbert spaces of functions. We summarize what are the various Hilbert spaces, discrepancies, types of weights, figures of merit, types of constructions, and search methods supported by LatNet Builder. We briefly discuss its organization and we provide simple illustrations of what it can do.
, U n-1 } ⊂ (0, 1) s for which (i) each U i individually has the uniform distribution over [0, 1) s , and (ii) the points keep their highly-uniform distribution collectively. Randomizations that provably preserve the low discrepancy generally depend on the type of QMC construction: some are used for lattice points and others for digital nets. In some cases, the randomization may even improve the convergence rate of the mean square discrepancy. The RQMC estimator μn,rqmc = 1 n n-1 ∑ i=0 f (U i ), (4) which is now random, is unbiased for µ and one wishes to minimize its variance. For more details on RQMC, see for example  [23, 27, 30, 31, 38, 45, 46, 47]. The aim of this paper is to introduce LatNet Builder, a software tool designed to construct good lattice and digital point sets for QMC and RQMC, in any number of dimensions, for an arbitrary number of points, arbitrary weights on the subsets of coordinates, arbitrary smoothness of the integrands, a variety of construction and randomization methods, and several choices of discrepancies.