dataesr/scientific-paragraphs-categorization huggingface dataset:
All sequences generated were successful and lead to TE1 or TE2. The sequences generated with OpenAltaRica were the same as the ones shown in Figure  3. One may see that with a relatively small example, the number of sequences generated can be relatively large. In the meantime, the computation time is very low, but it will increase with a larger and more complex system. This complexity is due to the possibility of launching an attack from any component already infected, made possible by the use of DES. Some ideas to tackle this issue are discussed in Section 6, as this is decisive in the scalability of this approach. To conclude this section, we showed that AltaRica allows us to model an autonomous vessel regarding the model of Section 5. 1. The choice of the AltaRica formal modeling language was motivated by the possibility of automatically generating the scenarios containing the names of the consecutive events.
Conclusions In this study, we conducted an experiment to support the security risk assessment of cyber-physical systems. This approach is based on discrete event systems and analyzes the effects of cyberattacks on the safety of CPS. It does so by describing systems via their hardware and software components, communication links, security measures and events (cyberattacks or system reconfigurations), which are based on the MITRE ATT&CK knowledge base. A model can be used to generate sequences of events that result in a safetycritical situation by expressing how the system would behave in the case of a cyberattack. Then, we introduce the AltaRica formal modeling language, which enables us to use analysis tools to run automatic qualitative and quantitative assessments from a model. An autonomous ship illustrative example has been modeled with DES and AltaRica to illustrate the benefits of the approach.
The MPEG Immersive Video (MIV) standard  [1]  is the outcome of a collective industry effort to support immersive media access and delivery, a critical milestone for the emerging immersive ecosystem. The first edition of the MIV standard  [12] , released at the MPEG 135 th meeting in July 2021, is based on the Visual Volumetric Video-based coding (V3C) standard  [56]  which defines the commonalities between MIV and the MPEG Video-based Point Cloud Coding (V-PCC). The MIV standard only defines the bitstream format and the decoding processes besides the supported profiles. The associated reference software, known as the Test Model for MPEG Immersive Video (TMIV)  [10] , covers, in addition, the non-normative parts which include the encoding and rendering processes. It is well understood that independent compression of multiple views and depths  [77]  results in high bitrates.
Section III-B presents a summary of the literature related to the requirements of depth estimation for immersive video. Section III-C describes the depth estimation reference software which is collaboratively developed in parallel with the TMIV in order to tackle these requirements. Section IV presents the results of performed experiments and used test conditions (Section IV-B). In Section IV-C, the efficiency of the DSDE is evaluated compared to two other MIV profiles. This section includes as well the comparison of tested MIV profiles, summarized further in the form of a discussion on the advantages and disadvantages of the DSDE. Section IV-D explains how the DSDE can take advantage of a Geometry Assistance SEI message, and corresponding results are reported. By choosing a suitable depth estimation method it is possible to maximize the quality of the rendered video presented to the viewer.
2. Both schemes have been simplified to reflect the relevant steps to run MIV in DSDE mode. The TMIV encoder selects a subset of the source views and packs their texture components into atlases  [10]. This enables one decoder instantiation to process multiple views per video sub-bitstream. The view parameters consisting of camera intrinsic and extrinsic parameters, projection type, etc. , together with views' position in the atlases (atlas data), and other crucial information for setting up the TMIV decoder such as atlas number and size, form the metadata that are encoded as a V3C  [56]  conformant bitstream. In general, the TMIV encoder is much simpler in DSDE mode, as pruning and depth processing are not performed. The TMIV decoder parses the bitstream and provides the decoded texture atlases as well as the metadata to the renderer.
Based on the requested viewport, the depth maps are estimated from the decoded views (available after reconstructing them from the decoded texture atlases). As the encoder ensures that pixel rate constraint is met in the encoded bitstream, the number of decoded views varies depending on their resolution. Nevertheless, the number of views that can be used for the estimation of depth is not restricted, therefore, all of them can be simultaneously used in this process. According to the depth maps and the view parameters, the views are projected and merged at the target view position (viewport). Holes in the synthesized view are filled through inpainting. The algorithms used by the TMIV renderer are not normative. Currently, MPEG is using the Immersive Video Depth Estimation (IVDE) software (described in Section III-C) for the development of the DSDE mode.
Tested methods are GANet  [14]  and GWCNet  [6]. The GANet is a method that improves the accuracy of the depth estimation in some particularly challenging areas, such as occlusions, reflective regions, and thin structures. The GWCNet provides efficient representations to compute feature similarities and decreases the inference computational cost. While deep learning methods for depth estimation constitute a major direction for research, the use of conventional methods remains very convenient in the development stage of the standardization process, as domain shift problems that could occur for deep learning-based methods are eliminated, decreasing the overall complexity of conducted experiments, as retraining is not required. The most widely known methods are based on optimization using Markov Random Fields (MRF)  [17]  and often provide very high-quality estimated depth maps, which is shown in the rankings of the Middlebury database and benchmark  [68].
B. Common Test Conditions In order to conduct fair comparisons between different experimental schemes, common test conditions are essential. The MPEG Video Coding group defines such common test conditions  [11]  for assessing different competing proposals. We strictly follow these test conditions in all our experiments. The TMIV 9 reference software  [10]  processes input views and depths (when present) to produce atlases and corresponding metadata. It compresses and decompresses these atlases using VVenC and VVdeC  [50] , a fast implementation of VVC. It renders final output views in the same position as the input views. In DSDE mode, depth estimation is initiated prior to view synthesis. The following "pixel-rate" constraints are imposed on all configurations of TMIV: • The combined luma sample rate across all decoders does not exceed 1069547520 samples per second (as in HEVC Main10 profile level 5.
The sequences were acquired with planar or convergent rigs equipped with various number of cameras. A brief summary of test sequences is available in Table  II. In all experiments 17 frames are used for the evaluation. Besides in listed sources, a subset of test sequences is available publicly on the MPEG MIV website  [86]. The CTC defines the three anchors of the TMIV. The MIV Atlas and the MIV View anchors belong to the MIV ESDE, while the MIV Decoder-Side Depth Estimation anchor belongs to the MIV DSDE. Each anchor describes a different strategy of utilizing the TMIV. In the MIV Atlas anchor, the TMIV encoder first labels each view as either "basic" or "additional". Then, the encoding preserves the basic views and subdivides the additional views into patches that only contain non-redundant data deviating from the basic views' field of view  [1].
Therefore, DERS can be considered as a proper reference for the presented framework. GANet and GWCNet are state-of-the-art end-to-end stereo matching networks, based on cost volume matching with 3D convolution. These methods were chosen as one of the highly recognized methods in the literature. The experiment evaluates whether these methods can be efficiently utilized in the MIV context without performing any retraining. For both methods, we used the default models pre-trained on the KITTI dataset  [25] ,  [26]. Only perspective and rectified content is included in this analysis to ensure fair testing conditions for deep learning approaches, trained by default on such type of test sequences. The disparity maps produced by GANet and GWCNet are subsequently converted into depth maps  [27]. 2) Results: The quality of the synthesis for five different rates for all tested depth estimation methods is presented in Table X.
Target selection and crRNA and RPA primer design Target genes were selected as either unique or based on their conservation between Trypanosoma spp. using literature and publicly available data from TriTrypDB (https://tritrypdb. org/tritrypdb/app; RRID: SCR_ 007043). Candidate genes were aligned using BLAST with the Trypanosomatidae (taxid: 5654) nucleotide collection database from the National Center for Biotechnology Information (NCBI; RRID: SCR_ 006472). Alignments to ensure conservation of targets across the Trypanozoon subgenus or exclusivity between T. brucei subspecies was performed using Clustal Omega. Data available in TriTrypDB was used to identify SNPs found in different field isolates in the target genes 7SLRNA, SODB1 and TgSGP). 18  To identify the SNPs in the SRA gene, the sequence variants AF097331, AJ345057, AJ345057  19, 20  were aligned using Clustal Omega and visualized in Jalview 2.
Data analysis We used the fluorescence given by the negative control, where water is used as input material, as the background fluorescence. For the analysis of the clinical samples the following ratios were calculated for every target assessed: • Negative template controls Ratio (Rntc) = Foldchange over the initial baseline fluorescence: Rntc = FCntc t = 3h FCntc t = 0 Mean Rntc = mean Rntc of 3 replicates Where FC, fluorescence readout • Positive template controls Ratio (Rpc) and sample Ratio (Rsample) = Fold-change over the background fluorescence at time 3h. Background fluorescence is given by the negative template control reaction: Rpc = FCpc t = 3h FCntc mean t = 3h Mean Rpc = mean Rpc of 3 replicates Rsample = FCsample t = 3h FCntc mean t = 3h Mean Rsample = mean Rsample of 3 replicates Where FC, fluorescence readout Fluorescence ratios from positive and negative samples were computed by receiver operating characteristic (ROC) curve analysis for determining test positivity and validation thresholds (Figure  S2 ).
Ethics statement Blood from healthy human donors was provided by ICAReB platform (Clinical Investigation & Access to Research Bioresources) in the Centre for Translational Science, at the Institut Pasteur (Paris). All participants gave written informed consent in the frame of the healthy volunteers Diagmicoll cohort (Clinical trials NCT03912246) after approval of the CPP Ile-de-France I Ethics Committee (2009, April 30th). For the WHO HAT Specimen biobank samples, approval was given by the WHO Ethical Review Committee, each national ethical committee where samples were taken and the national Ministries of Health.
2  and Table  S4 ). A single isolate, AnTat 22. 1, classified as T. b. gambiense Group 1 was positive for both TgSGP SHERLOCK and SRA SHERLOCK (Table  S4 ). Sequence analysis showed that the SRA SHERLOCK target amplicon shared 83. 3% identity with a VSG (Tbb1125VSG-4336, accession number: KX700900) that was expressed in this sample (Figure  S10a ). There are 7 nucleotide mismatches between the SRA guide and the homologous region in the VSG sequence (Figure  S10b ), and this is the most likely source of the cross-reactivity. In spite of the 1. 8% cross-reactivity observed with the SRA SHERLOCK within the group of samples analyzed, 100% of the strains were detected with the corresponding test, confirming the specificity of TgSGP SHERLOCK and SRA SHERLOCK for diagnosis across endemic regions.
Acknowledgments We would like to thank the following people for kindly providing parasite strains and/or parasite NAs used in this study:  Annette MacLeod  ( University of Glasgow, Glasgow, UK ),  Gerald Spaeth  and  Artur Scherf  ( Institut Pasteur, Paris, France ),  Philippe Büscher  and  Nick Van Reet  ( Institute of Tropical Medicine, Antwerp, Belgium ). We would also like to thank  Emeline Perthame  ( Bioinfomatics and Biostasistics HUB, Institut Pasteur, Paris, France ). This project has received funding from the  Institut Pasteur  to BR and LG ( PTR-175 SHERLOCK4HAT ), from the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence   Integrative Biology of Emerging Infectious Diseases (LabEx IBEID ), and from the  Agence Nationale de la Recherche  ( ANR-PRC 2021   SherPa ).
This study was funded by the Institut Pasteur (PTR-175 SHERLOCK4HAT), the French Government's Investissement d'Avenir program Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases (LabEx IBEID), the Agence Nationale pour la Recherche Scientifique (ANR-PRC 2021 SherPa) and designed by study investigators. The funder had no role in this study.
Performance. Our method is implemented in C++ (singlethread) and uses the Eigen library [ GJ*10 ] for its ease of use when dealing with homogeneous representations as well as solving small linear algebra systems appearing in the computation of the Riemannian gradient. All timings are recorded on a Lenovo 20EQS27P00 laptop with an Intel i7-6820HQ CPU model with 32GB of memory. Table  8  shows the processing times of the different steps of our method on input data of various sizes. The creation and simplification of 2D partitions are fast and scalable, typically a few seconds for large urban scenes. By implementing the main geometric operations in the horizontal 2D plane, we avoid the time-and memoryconsuming issues arising from the creation and manipulation of 3D polyhedral arrangements. Adding this simplification method greatly improves the performance of the lift optimization step by reducing the number of cells in the partition.
We expect more observations in the future, and then we could further improve the model (e. g. , a 3D model with some more physical factors, such as hydraulic stress and temperature distribution, given by petrini et al. , 2020). grateful to the Asian School of the Environment (ASE) for allowing him to complete this work by extending his contract at NTU for one year and a half. This work is Earth Observatory of Singapore contribution (No. M4430217. B50. 706022) and the Ministry of Science and Technology (Grant Nos. MOST 109-2116-M-008-029-MY3, MOST 110-2124-M-002-008, and MOST 110-2634 -F-008-008). We note that there are no data sharing issues in the paper since all of the numerical information has been provided in the table and figures. Jiao Liqing carried out the simulations and prepared the manuscript.
Acknowledgments We thank  Professor Sylvain Barbot  for the extremely helpful comments and suggestions. Paul Tapponnier is Fig. 16. Crack swarms at (a) the beginning and (b) the end of one year in the simulation period. The crack swarms are compared to the seismic observations in (Wang et al. , 2018). The locations and the dip angles of the slab and the splay fault are consistent with observations.
It has been a common understanding that DTMs derived from a particular imaging dataset can only achieve a lower, or at the best, similar effective spatial resolution compared to the input images, due to the various approximations and/or filtering processes introduced by the photogrammetric or photoclinometric pipelines. With recent successes in deep learning techniques, it has now become more practical and effective to improve the effective resolution of an image using super-resolution restoration (SRR) networks  [30] , retrieving pixel-scale topography using single-image DTM estimation (SDE) networks  [27, 29] , and eventually, combining the two techniques to potentially produce subpixel-scale topography from only a single-view input image. SRR refers to the process of improving the spatial resolution and retrieving highfrequency details from a given lower-resolution image by combining subpixel or multiangle  [31] [32] [33]  information contained in multiple lower-resolution inputs, or through inference of the best possible higher-resolution solution using deep learning techniques  [30, [34] [35] [36].
In particular, the deep learning-based SRR methods, either using residual networks  [37] [38] [39] , recursive networks  [40, 41] , attention-based networks  [42, 43] , and/or using generative adversarial networks (GANs)  [44] [45] [46] , have become more and more popular over the last decade, not only in the field of picture/photo enhancement, but also in the field of Earth observation for improving the quality and resolution of satellite imagery  [34] [35] [36]. In parallel, SDE refers to the process of deriving heights or depths from a given singleview image, using traditional "shape from shading" (photoclinometry) techniques  [22] [23] [24] 47, 48] , or learning the "height/depth cues" from a training dataset using deep learning techniques. Such deep learning-based SDE methods, using residual networks  [49] [50] [51] , conditional random fields  [52] [53] [54] [55] , attention-based networks  [56, 57] , GANs  [58, 59] , and U-nets  [60, 61] , have been fairly successful in recent years, not only in the field of indoor/outdoor scene reconstruction but also in the field of remote sensing for topographic retrieval using planetary orbital images  [26] [27] [28] [29].
In this paper, we propose combining the use of SRR and SDE to boost the effective resolution of optical single-image-based DTMs to subpixel-scale. The in-house implementations of the MARSGAN (multi-scale adaptive-weighted residual super-resolution generative adversarial network) SRR system  [30, 35]  and the MADNet (multi-scale generative adversarial U-net based single-image DTM estimation) SDE system  [27, 29]  are employed for this study. Our study site is within the 3-sigma ellipses of the Rosalind Franklin ExoMars rover's planned landing site (centred near 18. 275 • N, 335. 368 • E) at Oxia Planum  [28, 62, 63]. We use the 4 m/pixel TGO CaSSIS "PAN" band images  [15]  and the 25 cm/pixel MRO HiRISE "RED" band images  [17]  as the input test datasets. We apply MARSGAN SRR to the original CaSSIS and HiRISE images, and subsequently, we apply MADNet SDE to the resultant 1 m/pixel CaSSIS SRR images and the 6.
In-house photometric correction and mosaicing software  [21, 66]  is used to produce an image mosaic from the resultant single-strip CaSSIS SRR images. In addition, we also show examples of full-strip 6. 25 cm/pixel HiRISE SRR ORIs and 12. 5 cm/pixel HiRISE SRR MADNet DTMs produced from the 25 cm/pixel HiRISE "RED" band PDS ORIs. The resultant CaSSIS SRR and SRR-DTM products have been delivered to the ExoMars PanCam team for multi-resolution and ultra-high-resolution 3D visualisation using PRo3D  [67, 68]. The resultant CaSSIS, HiRISE SRR and DTM products are submitted as supporting material and are being published through the ESA GSF archive  [69, 70]  (see https://www. cosmos. esa. int/web/psa/ucl-mssl_meta-gsf (accessed on 22 December 2021)). The layout of the rest of the paper is as follows.
In Section 2. 1, we introduce the main datasets from CaSSIS and HiRISE. In Section 2. 2, we revisit the MARSGAN SRR and MADNet SDE methods, and introduce the training details, followed by an explanation of the processing and DTM evaluation methods in Section 2. 3. In Section 3. 1, we give an overview of the CaSSIS SRR and SDE results, followed by qualitative assessments of the CaSSIS SRR and SDE results in Section 3. 2, and qualitative assessments of the HiRISE SRR and SDE results in Section 3. 3. In Section 3. 4, we show quantitative evaluation of the CaSSIS and HiRISE DTM products. In Section 4, we discuss issues and uncertainties before drawing conclusions in Section 5.
Datasets TGO CaSSIS is a moderately high-resolution, multispectral (from ~500 nm to ~950 nm for visible and near-infrared (NIR)), push-frame (an intermediate between a line scan camera and a framing camera) stereo imager, with the goal of extending the coverage of the MRO HiRISE camera and to produce moderately high-resolution DTMs of the Martian surface  [15]. CaSSIS provides colour images consisting of the "BLU" band (centred at 499. 9 nm for blue-green), the "PAN" band (centred at 675. 0 nm for orange-red), the "RED" band (centred at 836. 2 nm for NIR), and the "NIR" band (centred at 936. 7 nm for NIR)  [16]. Due to the limitation on the data transfer time, CaSSIS generally collects colour images from three out of the four bands at lower spacecraft altitudes (where ground velocity is higher), and all four bands only at higher altitudes (both with width reduction for some of the colour bands).
CaSSIS images are typically sampled at 4 m/pixel nominal spatial resolution (~4. 6 m/pixel effective spatial resolution) with a swath width of about 9. 5 km and a swath length of about 30-40 km. Due to the non-sun-synchronous orbit and its 74 • inclination angle, CaSSIS is able to image sites at different local times of different seasons, making CaSSIS images an ideal dataset for studying the Martian surface changes. At the time of the writing of this paper, CaSSIS images covered 4. 3% of the Martian surface (by 28 August 2021). In contrast, MRO HiRISE is designed to image very fine-scale surface features of Mars at 25 cm/pixel nominal spatial resolution (~30 cm/pixel effective spatial resolution), with a smaller swath width of about 6 km (for panchromatic) and 1. 2 km (for colour), but a larger swath length of up to 60 km  [17].
The total loss function of MARSGAN ensures the network is trained towards a "balanced" SRR solution combining perceptual-driven enhancement and pixelwise difference-based enhancement. In  [30] , the MARSGAN model was trained with a collection of down-sampled (1 m/pixel) HiRISE PDS ORIs (lowerresolution training samples) and full-resolution (25 cm/pixel) HiRISE PDS ORIs (higherresolution training samples). In this work, we retrain the MARSGAN model based on the initial study of the "real-world" image degradation that is described in  [77] , and our previous study of SRR of Earth observation imagery  [35] , wherein we demonstrated the positive impact of using a post-processed training dataset to improve the SRR quality and to minimise synthetic artefacts. The "post-processing" of the training dataset that is employed in this work contains three aspects.
Firstly, the original HiRISE training samples (lower-resolution and higherresolution; from  [30] ) are manually screened and lower-quality samples are removed from the training dataset. Secondly, we down-sample the higher-resolution HiRISE training samples from 25 cm/pixel into 50 cm/pixel to reduce the native noise (e. g. , stripes or fixed pattern noise) that is commonly found when zooming in to 100% display of the 25 cm/pixel HiRISE samples, and subsequently, the lower-resolution HiRISE training samples are downsampled from 1 m/pixel into 2 m/pixel to form the lower-resolution counterpart of the training dataset. Thirdly, a sequence of weighted median, bilateral filtering, and Gaussian blurring is applied to the lower-resolution HiRISE training samples to simulate the blurring effect of lower-resolution images, instead of using only the bicubic down-sampling process on the higher-resolution counterpart, as this was demonstrated as being insufficient and inappropriate to model the degradations of orbital and real-world images  [35, 77].