dataesr/scientific-paragraphs-categorization huggingface dataset:
The results show that this study successfully constructed a moderately high-performance pose estimation model. Although the accuracy results show that it has superior accuracy for predicting the position and moderately high accuracy for estimating the orientation, the errors in position and Euler angles are still prohibitively large to use in an actual docking operation. In advanced computational algorithms such as CNNs, the prediction performance depends strongly on the architecture, the dataset, and the training method. Thus, a pose estimation model constructed from a more complex pretrained model may result in higher accuracy. According to the methodology, these factors were of concern in  [27] , which constructed a model under limitations of the GPU, an RTX 2080, on a laptop. Therefore, the modified version of GoogLeNet was chosen because the model achieved successful predictions in  [17]  while having only low computational resource requirements.
Conclusions In a spacecraft docking operation, the position and attitude of the target spacecraft must be determined, and a sensor must exist that can obtain those operational parameters. Currently, visionbased algorithms have been developed concurrently with image processing using deep learning algorithms. The goal of this study was to construct a position and attitude estimation model using a deep neural network. The vision-based detection technique has the advantage that it is applicable for both cooperative space objects and non-cooperative space objects. In the implementation, the pose estimation model was constructed based on a state-of-the-art CNN model, which is a modified version of GoogLeNet that forms a general pose estimation model. Then, the model was trained on a simulated public dataset of the Soyuz spacecraft.
Conclusions In a spacecraft docking operation, the position and attitude of the target spacecraft must be determined, and a sensor must exist that can obtain those operational parameters. Currently, vision-based algorithms have been developed concurrently with image processing using deep learning algorithms. The goal of this study was to construct a position and attitude estimation model using a deep neural network. The vision-based detection technique has the advantage that it is applicable for both cooperative space objects and non-cooperative space objects. In the implementation, the pose estimation model was constructed based on a state-of-the-art CNN model, which is a modified version of GoogLeNet that forms a general pose estimation model. Then, the model was trained on a simulated public dataset of the Soyuz spacecraft.
Hypothetically, the error of the position and attitude estimation model could be corrected by the physics-based model during an actual docking operation with spacecraft in motion. In summary, to satisfy a real docking operation, many auxiliary algorithms are recommended for future research to increase the performance of the vision-based position and attitude estimation model. The particular characteristic of the vision-based CNN model is that it is very specific to the environment of the dataset. For example, the model that was trained with the simulation data will perform a satisfactory estimation of this synthetic dataset. However, to address the real docking operation, the constructed model with the knowledge of spacecraft pose estimation can be hypothetically trained further with the data from actual operation  [27].
describes the brief information about the construction of the pose estimation model through the training and testing process with the dataset. The details of implementation are described in this section, including dataset preparation, pose estimation algorithm and preprocessing, and construction of the pose estimation algorithm with different loss functions, which consists of both (1) the exponential-based model and (2) the weighted Euclidean-based model. Aerospace 2020, 7, x FOR PEER REVIEW 5 of 21.
Acknowledgments: The authors would like to thank all the lecturers, researchers, and staffs from the  International Academy of Aviation Industry, King Mongkut's Institute of Technology Ladkrabang , and the  National Astronomical Research Institute of Thailand (NARIT)  for their support during this study. Moreover, the authors appreciate the opportunity to use the Chalawan HPC cluster, which is operated and maintained by the  National Astronomical Research Institute of Thailand (NARIT)  under the  Ministry of Higher Education, Science, Research and Innovation of Royal Thai government.
However, the simulated modal conversion rates are in good agreement with the analytical conversion models. It is plausible that these differences fall within the range of uncertainties associated with the analysis techniques and the validation data sets. • The HYCOM and altimetry sea-surface height time series have durations of one year and about 20 years, respectively, which prevents a direct comparison of stationary tide signals. To permit an apples to apples comparison, we apply a correction to account for the decrease in the stationary signal as a function of the duration of a time series that includes both tidal and mesoscale variability ( Appendix). This correction improves the correlation, regression, and variance ratios between the altimetry and corrected HYCOM data sets.
falciparum  [40]. This corresponds to densities (G4FS/kb) of 0. 076 for G4H1. 2 and 0. 019 for G4H1. 5 (Table  1 ), which are lower (by 1. 6-fold) than in Dictyostelium discoideum, an organism whose genome harbours a similar high AT content (78%)  [47, 48]. G4FS were found on all chromosomes, with some chromosomic regions displaying higher G4 density, for instance chromosome ends and central regions (Fig  1A ). Indeed, chromosome ends in Plasmodium are capped by telomeric repeats prone to G4 formation  [51, 52]. In agreement with previous studies  [29, 40] , G4FS with score of 1. 2 or higher co-localize with the multigene var family that are found in subtelomeric regions and in internal gene clusters on chromosomes 4, 7, We also compared our G4 dataset with the recently published list of G4 structures identified in vitro, in the P.
G4FS are associated with recombination events in subtelomeric regions The presence of G-quadruplexes has been reported to induce genome instability i. e. higher than normal rates of mutation, in yeast  [56]  and human cells  [16]. While diseases such as cancer can be a consequence of genome instability, for pathogens like Plasmodium this could lead to antigen diversification and would thus be advantageous for host immune evasion. We therefore assessed the distance from each recombination hotspot  [40]  defined with a width of less than 10 nt (n = 168) to its nearest G4FS. For comparison, a null dataset of breakpoint positions randomly shuffled 10 times across the genome was generated and the distances to their nearest G4FS were determined for three different G4Hunter thresholds. The mean distances from each G4FS to a recombination breakpoint were 2.
, respectively. In all conditions, the difference between the G4 dataset and the null dataset was found to be highly significant (Wilcoxon test ��� p-value <0. 001). These results show a much stronger link between G4FS and recombination hotspots than what was previously reported by Stanton et al. [40] , where the mean distance to the nearest G4 was far larger (133 kb). These findings point out to a putative role of the G4FS clustering pattern, observed within the var gene regions that encode the, surface exposed, submodules of PfEMP1.
G4FS frequency is plotted on y-axis. The most significant conserved motifs identified with MEME software  [55]  are illustrated on the upper right panel. No conserved motif was found for P. blacklocki. https://doi. org/10. 1371/journal. pgen. 1008917. g004 https://doi. org/10. 1371/journal. pgen. 1008917. g005 levels of expression. Extracted from the native loci, the presence of the G4 structure on the non-coding strand of the ectopic reporter gene was correlated with decreased transcription, while the G4 on the coding strand did not affect gene expression. Moreover, the effect on gene regulation required the G4 stabilization by the G4 ligand PDS. Although these conditions did not reflect the genomic context, as the G4s were inserted into an exogenous promoter, these results demonstrate that PDS may target G4s in vivo, in Plasmodium.
For this reason we turned to the G4Hunter algorithm, a tool that has been extensively used for the prediction of G4s in different types of genomes, including GC poor ones  [41, 47] , to perform the most comprehensive analysis to date of G4FS in malaria parasites. To ensure that G4Hunter was indeed suitable to study the P. falciparum genome we undertook different comparison tests between our dataset and the one generated using the G4-seq method  [31] , and showed that former outperformed the latter in various instances. As biophysical validation experiments have demonstrated G4Hunter's accuracy at thresholds, typically between 1. 2 and 2. 0, we looked at G4s predicted with a high threshold (1. 75), that were not found by G4-seq (108/145), and stable G4 formation was highly likely for most of them.
Previous studies performed on human cells that were exposed to TMPyP4 or the bisquinolinium compound PhenDC3 have highlighted both direct and indirect effects of G4 stabilization on genome-wide expression changes  [70, 71]. Indirect effects could be the result of the downstream effect of G4-mediated gene regulation on other genes or the non-specific binding of the G4-ligand. Amongst the PDS-mediated de-regulated genes we found genes involved in ribosome biogenesis and several metabolic pathways related to DNA biology, consistent with PDS directly binding nucleic acid G4 targets. Furthermore, our DE dataset included half of the members of the ApiAP2 family of transcription factors and helicases such as RecQ  [42]  (log2FC = -1,3; p-value = 7. 7E -22 ) whose downregulation has previously been shown to lead to the de-regulation of several hundred genes.
Bioinformatics pipelines were conducted with R (v3. 5. 2). The two R scripts containing G4Hunter functions and bioinformatics procedures are provided as supplementary files (S1 Method and S2 Method, respectively). Putative G4 forming sequences (G4FS) were retrieved and sorted according to their G4 folding propensity, where high scores represent high likelihood of folding. We then applied different thresholds to select G4FS with scores above 1. 2, 1. 5 and 1. 75, resulting in three G4 lists (G4H1. 2, G4H1. 5 and G4H1. 75). Note that G4FS shorter than the starting window (25 nt) may be obtained as a result of the G4Hunter optimization procedure. For example, with a threshold of 1. 2, a run of 8 consecutive G is sufficient to generate a score of 1. 28 (assuming no other C and G are present) in a window of 25 nt.
Bioinformatics analysis Gene annotation files matching the genome files were downloaded from PlasmoDB  [46]. All bioinformatics procedures were conducted with R (v3. 5. 2) using the following packages: Gen-omicFeatures (v1. 34. 7)  [72] , Biostrings (v2. 50. 2) and rtracklayer (v1. 42. 2)  [73]. For the fold enrichment analysis, a first feature list was created with five categories: promoter, transcript, exon, intron and intergenic regions. In annotation files, transcript genomic coordinates correspond to the coding sequence with intron(s). For the promoter region, we arbitrary defined a window of 2 kb upstream of the translation start site ATG. Although most TSS are located less than 1 kb of the start codon  [50, 74] , we increased the window to 2 kb, to include var promoter regions in their entirety  [39] , in which canonical G4FS were previously predicted  [40].
G4FS intervals that mapped to a promoter region but overlapped with the CDS of the nearest gene were excluded from the analysis. We calculated the density of G4FS in genomic features by dividing the number of G4FS by the feature length and multiplying by 1,000. The three G4 lists were randomly shuffled 1,000 times across the genome and the mean density of randomized G4 lists in genomic features was determined. For each feature category, the ratio of G4FS density over the mean density of random G4FS was calculated and plotted as fold-enrichment. For promoter regions, we computed the distance from a given G4 to the nearest (downstream and upstream) genes and then to retrieve gene information such as geneID, gene product and gene strand using a python script. The list of genes containing at least one G4FS in the promoter region (i.
RNA-Seq data analysis Trimmed reads from each FASTQ sample file were mapped to the P. falciparum 3D7 genome (version 28;  [46] ) using the BWA-MEM software  [83]  at default settings. In all samples, we obtained 95% of mapped reads, on average with 24× coverage. To count reads, we used bedtools multicov (v2. 26. 0). Differential expression analysis was done using DESeq2 (v1. 20. 0) [84]  with the threshold set to adjusted p-value <0. 01 and >2-fold change of expression. Gene ontology (GO) enrichment analysis of differentially expressed genes was performed using topGO (R package version 2. 32. 0).
Acknowledgments We are grateful to  A. Gilabert  for her valuable support on G4 sorting analysis, and we thank  T. D. Otto  ( University of Glasgow, UK ) for giving access to Laverania genomes prior to public release. We thank  S. H. Adjalley  ( Wellcome Sanger Institute, UK ) for sharing her data on TSS in P. falciparum and  E. Gomez-Dı ´az  ( IPBLN, Spain ) for giving us access to her ATAC-Seq data. We also thank  A. Scherf  ( Pasteur Institute, France ) for the gift of pHLIRH plasmids. Sequencing was performed by the  MGX-Montpellier GenomiX facility , which acknowledge financial support from  France Genomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
This work was supported by the  Fondation pour la Recherche Medicale  [ ARF20150934098  to EG,  DEQ2018033199  to  JJLR] and the program  " ANR JCJC " [project no. 194659  to ARG] of the  French National Research Agency ; the program " Investissements d'avenir " of the  French National Research Agency  [ ANR-11-LABX-0024-01 ParaFrap  to JJLR, ANR-10-LABX-The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Mid-Infrared Instrument (MIRI) on the James Webb Space Telescope (JWST ), has imaging, four coronagraphs and both low and medium resolution spectroscopic modes. Being able to simulate MIRI observations will help commissioning of the instrument, as well as get users familiar with representative data. We designed the MIRI instrument simulator (MIRISim) to mimic the on-orbit performance of the MIRI imager and spectrometers using the Calibration Data Products (CDPs) developed by the MIRI instrument team. The software encorporates accurate representations of the detectors, slicers, distortions, and noise sources along the light path including the telescope's radiative background and cosmic rays. The software also includes a module which enables users to create astronomical scenes to simulate.
The flexibility of the system that combines all of the above allows MIRI to make significant contributions to a large range of science topics. MIRISim is a Python package developed by the MIRI team to simulate the expected performance of all of MIRI's modes except coronagraphy. As such, it is able to create realistic simulations of the kinds of science targets described above. Not only can this simulator be used by the commissioning team to verify new calibrations as the on-orbit performance of the instrument is verified, but it can be used by observers wishing to understand whether their proposed observations will allow them to obtain their science goals, or for theorists to show what their predictions would look like if observed with MIRI. MIRISim is not alone in being able to be used by the community to better understand how an instrument or telescope works, or to demonstrate the relationship between what can be observed and theorists predictions.
Noteable examples of other simulators which allow the user to simulate their own specific observations include the interferometric simulator for observatories such as ALMA and the JVLA built into CASA 1  , the ImSim 2  and PhoSim 3  packages for the Rubin Observatory, and the MIRAGE 4  package for NIRCam and NIRISS instruments. In this paper, we describe the architecture of MIRISim (Section 3), and how it simulates MIRI observations. We then give a brief description of how to install and use MIRISim in Section 4. We present the outputs of MIRISim in Section 5, and conclude in Section 6.
MIRISim COMPONENTS MIRISim is a Python package consisting of a number of distinct components, some of which simulate the various light paths through the instrument (ImSim, MIRI-MAISIE and LRSSim), while others orchestrate (OBSSim), create astronomical scenes (SkySim), and process detector effects (SCASim) for the simulation. A modular approach to the design of MIRISim was chosen in order to build flexibility to incorporate increasing detail of the instrument characteristics as hardware was tested. The design of the simulator reflects the modular approach to the hardware and the use of common detectors between the imager and spectrometers. It also facilitates reuse of software modules to build simulators for future instruments. The user interface to MIRISim is described in Section 4, but here we describe the individual components that make up the simulator.
For reference, the path through the MIRISim components for MRS and imager/LRS simulations are given (respectively) in Figures  4  and 5. In these figures, the flow of data through MIRISim starts with users inputting a reference astronomical scene to be simulated, and the observation parameters for the simulation which follow the nomenclature used in the JWST Astronomer's Proposal Tool (APT) 7. The orchestration module (OBSSim) then parses the scene and simulation parameters and sends the commands to the relevant modules which create the sampled scene (or sky cube) and then create a model of how the detector would be illuminated (creating an illumination model) this output and the simulation parameters are then passed to the Sensor Chip Assembly simulator (SCASim) where the exposures are calculated, and the final detector images are created.
SkySim The SkySim module creates the astronomical 'scene' used by MIRISim to simulate the observation with the use requested instrument setup. A 'scene' here is defined as a collections of objects placed within the instrument field of view with spatial and spectral properties which can be mapped into illumination models for the detectors. We use the same terminology here as in the JWST Exposure Time Calculator (ETC), but allow for more flexibility in the definition of objects within a scene. The user can build up a set of objects to represent what the sky is expected to look like with MIRI. These 'scenes' can take the form of a series of point and extended 'sources' (emitters in the field of view) specified by the user with positions, extents and shapes (for extended objects), and spectral energy distributions (SEDs) of various types.
OBSSim OBSSim is the MIRISim module that orchestrates the rest of the components. It reads in the inputs (via a configuration parser), and ultimately creates the FITS file outputs of each stage of the simulation. It sets up dither positions, coordinates the number of exposures, and keeps track of the timing of the simulated observation to ensure that the header metadata is realistically tracking the time it takes for an observation. For the MRS light path, OBSSim has the added task of creating a skycube by griding the components of the scene created in SkySim onto a cube object which can then be read by MIRI-MAISIE. OBSSim is also responsible for processing the illumination model outputs of the three light path simulator components (ImSim, LRSSim or MIRI-MAISIE) and passing them to the sensor chip assembly simulator (SCASim) along with the observing parameters (number of groups, integration and exposures, whether to simulate SLOW or FAST mode, etc) input by the user to create the detector image which is the end product of MIRISim.
LRSSim The LRS has two modes: slitted and slit-less. LRSSim simulates both of these modes, dispersing the light from the required part of the simulated 'scene'. Slitless mode is to be used for point sources, and when used in this mode, only the SLITLESSPRISM sub-array (located towards the top left of the detector. In slit mode, the full imager field of view is simulated. The positions of the targets on the detector are shown in Figure  2  of  Kendrew et al. (2015). Similarly to ImSim, LRSSim applies transmission, distortion, PCE (or optional spectral response) and PSF CDP (or, optionally the webbPSF model) to the dispersed data to produce an illumination model, which OBSSim then delivers to SCASim to create the final detector images. To support commissioning but also normal observations, LRSSim can optionally simulate the complete detector array to identify possibly disturbing (extended) nearby sources outside the slit or slitless area.
SCASim The electronic (pixel gain, dark current and pixel response) and detector (reset anomaly, last frame effect, droop and drift) effects of the Sensor Chip Assembly (SCA) itself are described in detail in  Ressler et al. (2015). The SCAs for the imager/LRS and MRS detectors are identical  (Ressler et al. 2015 , as described in greater detail in ), and as such, the outputs from MAISIE, LRSSim and ImSim can all be processed by the same simulator module. The Sensor Chip Assembly simulator (SCASim;  Beard et al. 2012)  is the final component of MIRISim. It simulates the most important MIRI detector effects of the instrument. These include known cosmetic effects (e. g. bad pixels), detector dark current, electronic non-linearity effects, the pixel flat-field, noise sources (e. g. read noise, shot noise) and a plug-in cosmic ray event library (based on Robberto, M.
Installation MIRISim has been developed and tested on Apple (macOS) and Linux systems, and the minimum system requirements for running MIRISim are comparable to those for the JWST calibration pipeline. The code itself is Python 3 based, and MIRISim has been bundled into an Anaconda 8  environment to ease installation. Detailed installation instructions for MIRISim can be found on the MIRICLE website 9. From this website, users can download an installation script. This self-updating bash script will always download the most recent version of MIRISim and install it in a new anaconda environment. The primary environment variables to be set are the location of the Calibration Data Products (CDPs), the PySynPhot libraries, and where MIRISim is being installed.
User Input Files Whether working at the command line, or from within Python, MIRISim takes a number of input files which define parameters that define how the astronomical scene is to be setup, what kind of simulation to run, and a number of advanced features (primarily for use in JWST commissioning). These files, and their formatting are described in more detail in the MIRISim Users Guide 10. The input files can be used at both the command line, or as inputs into Python runs of the simulator. Through the rest of this text, we refer to the scene, simulation and simulator files with these template names. The scene file (e. g. scene. ini) houses the specification of the astronomical scene to be simulated (e. g. a point source 0. 5 from the field center with a blackbody SED), and simulation. ini houses the properties of the simulation itself (e.