dataesr/scientific-paragraphs-categorization huggingface dataset:
AVAILABILITY The package of the majority voting procedure was implemented in R, and can be downloaded on LncPlankton website at https://www. lncplankton. bio. ens. psl. eu/files/tools/majorityLNC. tar. gz. The source code can be found at https://gitlab. com/a5076/majorityLNC. The database is freely available without restrictions for use by academics and non-commercial researchers. The web server is publicly available at https://www. lncplankton. bio. ens. psl. eu/. Inquiries concerning the database may be directed to debit@bio. ens. psl. eu or cruz@biologie. ens. fr.
//github. com/mhahsler/rBLAST) for online similarity search,  A RNAfold program implemented via the package LncFinder (23)  and RNAPlot implemented via the package RRNA (31)  for the calculation and the visualization of secondary structure,  ORFfinder implemented via LncFinder (23)  for the exploration of lncRNA containing sORFs, and seqinr package (https://github. com/cran/seqinr) for the translation to peptide sequences. Those functionalities were provided via an express REST API web service implemented using the R package Plumber (https://github. com/rstudio/plumber). A Shiny server function was also developed and was integrated into the logic layer. This function processes the request of the shiny prediction app from the presentation layer and uses the static part of the data layer in addition to the SQL part.
Method Our survey covers three main topics. Firstly, it introduces IoT, its applications, and its challenges. Secondly, it introduces DNS, its security drawbacks, and the relevant security extensions and protocols. Finally, it studies how DNS is used in the literature to serve IoT environments and examines how IoT might affect the DNS infrastructure. We searched for relevant articles in various databases, including IEEE Xplore, Google Scholar, ACM Digital Library, SpringerLink, and ScienceDirect, to investigate these topics. We included articles published after 2018 to ensure that our work is up-to-date. However, we also included a few articles published before 2018 if they had a significant contribution and reinforced our understanding of the topics. We included scientific conference papers, research papers, surveys, and technical reports.
DNS servers that use DNSCurve distribute their public keys by encoding them in regular nameserver (NS) records, ensuring that the public key distribution system is compatible with registries and name server software. On the other hand, clients share their public keys in the queries they send. DNSCrypt:  DNSCrypt [110]  acts between DNS clients and DNS recursive resolvers. It uses cryptographic signatures to verify that responses from the resolvers are authentic and have not been tampered with on the way. Anonymized DNSCrypt  [111]  was proposed in 2019 as an extension to further secure DNS traffic by not allowing the server to see the client's IP address. DNS-over-TLS: DNS-over-TLS (DoT)  [9, 112]  is one the few IETF-standardized DNSsecuring protocols (the other notable one being DNS-over-HTTPS)).
These technologies develop and implement proprietary solutions and use communication protocols and data representation methods that few other technologies can understand. Thus, users and devices of one IoT technology are almost always bound to communicate with users and devices of the same technology. This fact transformed IoT into distant islands or silos, each having its standards. DNS could play a role in alleviating some of IoT's interoperability issues. Its robustness and distributed nature have already encouraged some organizations to resort to it when looking for IoT solutions. The Object Name Service (ONS)  [11]  is EPC's  [67]  resolution system that is based on DNS to resolve an EPC identifier to the location of the information associated with that identifier. The work in  [135]  suggests an enhancement to the ONS framework that enables it to handle heterogeneous object code identification.
Discussion This survey aimed to investigate the potential benefits of using DNS to address the IoT environments' challenges. As illustrated in Figure  1 , IoT technologies face several challenges. The scale at which IoT is integrated into daily life and the prediction of largerscale deployments in the future demand that its challenges be scrutinized and alleviated one by one. The constrained nature of IoT devices is one of its major limitations and simultaneously helped IoT spread. The constrained IoT devices are deprived of state-of-the-art protocols. These protocols are crafted for much more powerful devices on the Internet, such as personal computers, i. e. , devices with enough processing power and memory. IoT devices, on the other hand, settle for inferior protocols, especially in terms of security, which jeopardizes data sent from and received by these devices.
However, the processing, memory, and power constraints of IoT devices, even though they denied using state-of-the-art protocols, helped to spread the adoption of IoT technology due to their affordability and accessibility. DNS and its constrained-friendly extensions designed specifically for IoT could help IoT devices take advantage of modern tools adapted to their constraints. The diverse IoT technologies that are mostly isolated from one another make it difficult to create a unified system for naming and identifying IoT devices. This is due to each manufacturer's diverse communication and data representation protocols. Many of these protocols are proprietary and are not readily available for use by other technologies. The DNS, the Internet's naming system, is the natural candidate to solve this issue.
Furthermore, the lack of interoperability in IoT slows down research and development efforts supporting IoT, as different technologies disperse the research efforts, making progress more individualized to each technology rather than for the whole IoT environment. As a globally distributed infrastructure, the DNS has already significantly transformed the Internet into a cohesive network. It can continue to play a role in developing the IoT environment, turning it from independent silos to a more interconnected network. Finally, regarding the security challenges of IoT, addressing these challenges becomes even more critical when we inspect the nature of the data that pass through such networks. The data that pass through IoT networks are mostly sensitive due to the diversity of IoT applications, from smart homes to medical and industrial applications.
On the one hand, the ambitious calls to connect everything promises a prosperous and more comfortable future where additional everyday objects are connected and easily managed. On the other hand, it means higher stakes, as more data shared over the network means a higher risk of security breaches. DNS can contribute immensely to improving the security of IoT communications. DNSSEC, which is being adopted progressively in today's Internet, can be used to ensure the integrity of DNS responses received by IoT devices. This helps prevent DNS cache poisoning attacks, spoofing attacks, and other DNS-related cyber threats. DNSSEC is essential for ensuring DNS response integrity, and its adoption is critical for enhancing the security of both IoT and regular Internet communications. Moreover, to ensure privacy, DNS offers several extensions, some already standardized, like DoT and DoH, but they still need to be more widely adopted.
In addition, blockchain technologies' increased security, transparency, and decentralization can benefit IoT applications. On the one hand, Distributed Sensor Networks (DSNs) relying on blockchain technologies can highly benefit from decentralized data storage, secure data transmission, transparency, and scalability offered by blockchains. On the other hand, DNS could also profit from blockchains, and this could provide a secure, decentralized name resolution and registration system  [149, 150]. Finally, IoT edge computing could benefit from DNS by leveraging DNS to discover and communicate with nearby edge computing resources.
Powered by Python Packages Snek5000 leverages a variety of Python packages, including Snakemake  (Mölder et al. , 2021) , FluidSim  (Mohanan et al. , 2019) , Pymech  (Mohanan et al. , 2022) , Matplotlib  (Hunter, 2007) , Jinja, Pytest, and Xarray  (Hoyer & Hamman, 2017) , to deliver a robust and user-friendly workflow management tool for Nek5000. These packages provide a powerful foundation for Snek5000, enabling its seamless integration with existing Python-based workflows and enhancing its overall usability.
A FluidSim extension Snek5000 is based on the CFD framework FluidSim  (Mohanan et al. , 2019) , which introduces the concept of "FluidSim solvers". A FluidSim solver consists of few files describing a set of potential and similar simulations. A concrete simulation can be created via a simple and generic Python API. For example, for the snek5000-cbox solver, During the instantiation of the Simul object, all the directories and files necessary to run the simulation have been created. We see that Snek5000 can be seen as an advanced template system. Then, one can launch the Nek5000 simulation with sim. make. exec("run_fg"). Further details of these two stages can be found in the Jinja templates and Snakemake rules, which are responsible for source-code generation and execution of Nek5000 commands respectively.
Loading simulations for data visualization, post-processing and data analysis It is very easy to "load" an existing simulation, i. e. , to recreate a Python object sim similar to the one used to create the simulation. This can be done with the function snek5000. load or with the command snek-ipy-load, which opens a IPython session with a sim variable. Snek5000 simplifies the process of reading associated parameters (in sim. params) and data, and generating visualizations, such as figures (sim. output. phys_fields. plot_hexa) and movies (sim. output. phys_fields. animate). By utilizing popular Python packages, such as Matplotlib  (Hunter, 2007)  and Xarray  (Hoyer & Hamman, 2017) , Snek5000 facilitates the creation of high-quality visualizations that can be easily customized to meet individual needs.
, 2022; Jansson et al. , 2021). The design principles of Snek5000 has inspired FluidsimFoam, currently under development, a promising FluidSim extension to bridge the gap between FluidSim and OpenFOAM  (Weller et al. , 1998). This extension allows users to create custom FluidSim solvers specifically tailored for simulations on the widely-used open-source CFD software package, OpenFOAM. By harnessing the strengths of Python and OpenFOAM, FluidsimFoam aims to provide a versatile and user-friendly environment for conducting computational fluid dynamics simulations, broadening the scope of potential applications. and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
Acknowledgements The financial support of the  SeRC ExABL  project which made this project possible is duly acknowledged. The authors also acknowledge the numerical support provided by  Olivier De-Marchi ,  Gabriel Moreau  and  Cyrille Bonamy  of the  LEGI informatics  team. This project was funded by the project  LEFE/IMAGO-2019  contract  COSTRIO. AK acknowledges the finance of his PhD thesis from the school  STEP of the University Grenoble Alpes. Part of this work was performed using resources provided under GENCI allocation number  A0120107567. A CC-BY public copyright license has been applied by the authors to the present document.
BVLOS infrastructure inspection In order to illustrate the development process, we use, in the next sections, a BVLOS Infrastructure Inspection example. This use case describes the inspection of a building with a UAV. The inspection is executed by turning all around the building in a BVLOS scenario (UAV Beyond the Visual Line of Sight of the safety telepilot). Even if the flight plan is executed automatically, the aerial regulation requires that the safety telepilot can take back manual control at any moment, which assumes permanent video feedback. This feature is provided by a stream skill which adjusts the video compression rate to guarantee a minimum frame rate and a minimum image quality criteria. During the inspection, we assume that the UAV travels through different communication  The UAV skillset has three pieces of data that it makes available to its users: its current position (position), its home position (homepoint), and its battery level (battery).
Skills modeling The modeling relies on a language that we want to be unambiguous and that allows describing the capabilities of different robots. It is called robot-language. This language supports the modeling, the various analyses, and the generation of the code that is then embedded and deployed on different robots. It is important to notice that the runtime semantics of a skillset relies on three distinct elements: its model defined using robotlanguage; its functional definition (the skillset implementation) implemented by C++ code placed in specific locations (called hook), and its use by the decision layer which produces (so-called external) requests on the skillset.
Skillset skill The robot-language allows skills to be defined using the following elements: inputs are used in the functional algorithm of the skill. Each input is defined as a data and thus has a name and a type. start is used to define some effects on the resources of the skillset before starting. -invariant is used to specify mandatory properties to the execution of the skill. If at least one invariant is violated, then the skill is stopped. -progress is used to monitor the execution of the skill. It produces monitoring data periodically during the execution of the skill. -interrupt is used to specify the effects applied after the skill is interrupted. Any skill can be interrupted. -success is used to specify the different execution success states of the skill and their corresponding effects on the resources after its completion.
Interfaces The communication between the decision layer and the skillset managers is implemented through ROS2 topics, so the ''interfaces'' package contains only messages. Using bare ROS2 topics (instead of the service or action layers) allows us to better configure the quality of service regarding the desired execution semantics. The messages in the package interfaces include both messages corresponding to the data used by the skillset and the skills (data, input, output, progress,. ), but also all messages corresponding to the requests and responses of the data, the events and the skills of the skillset. In Fig. 16 , this package corresponds to the uav_skillset_interfaces package.
Skillset package The skillset package (uav_skillset in Fig. 16 ) contains an abstract ROS2 node provided as a C++ library. This node contains the various data specified in the robot-language skillset model, as well as the resources. It declares all topics allowing the dialogue with the skillset manager (from the decision layer) but also its introspection (internal of resources, data and skills). The generated code of the node implements the logic of the different elements present in the model (resource logic, skills state machine, effects on resources, guard management,. ) that have been described in Section 4. This node provides an ''empty'' and abstract (i. e. , virtual in C++) implementation of the different hooks of the model. These hooks will have to be completed later by the developer to add the functional part useful for the execution of the final skillset.
finally, we have proposed a process based on fault trees, to assess whether fault tolerance at the skills level is correctly implemented in relation with the functional layer. We have illustrated this process on an application involving an autonomous UAV performing an inspection mission in BVLOS conditions. The language and the associated tools for modelchecking and code generation are available at: https://onera-ro bot-skills. gitlab. io Future works are two-fold. First, in order to improve the proposed process and the assessment of the dependability of the architecture, we plan to integrate a testing step in the process. To do so, we are currently investigating how we can take benefit of the skillset model and the Fault-Tree Analysis of each skill to generate test cases that are relevant to cover the fault tree.
In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript web application to render spatial networks based on optimized geometrical criteria that reshape the link profiles.
While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the possibility to interactively customize the visualization via several controlling parameters, including network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped with additional options allowing to improve the final aesthetics, such as the color/size of both nodes and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source software which can be freely downloaded and updated via a github repository. Here, we provide a detailed description of its main features and algorithms together with a guide on how to use it. Finally, we validate its potential on several synthetic and real spatial networks from infrastructural to biological systems.
Those methods can be found in many existing software such as Gephi, Cytoscape or Pajek just to cite a few 1. The main working strength of all these methods is the possibility to freely rearrange the node positions so as to optimize some quality function. However, many real networks are spatially embedded and their fixed node location actually conveys important constraints to the resulting topological structure  [8]. This is intuitively related to the cost needed to establish long distant connections in the system. For example, it has demonstrated how brain systems optimize the trade-off between the network efficiency and the metabolic energy needed to build long axonal connections  [9]. Hence, in spatial networks the position of the nodes is a feature that we might not want to visually alter.
Graphical user interface and main features Vizaj is intended to be lightweight tool that can be run without any installation. To this end, we coded Vizaj in javascript using the Three. js framework (https://three. js). Three. js is a GPU-accelerated 3D processing javascript language compiled with WebGL, a low-level graphics API designed specifically for the web. Notably, Vizaj is hosted November 3, 2022 3/13 online, which makes it usable on any machine through internet, and it runs standalone without relying on proprietary browser plugins. Vizaj is licensed under the terms of the BSD-3-Clause license. A dedicated graphical user interface (GUI) facilitates the interactive manipulation and creation of the visualization by the end user (Fig. 2 ). The whole process consists of three steps: i) the data import, ii) the network visualization, and iii) the export of the final outcome.
Graphical export Once correctly tuned, the final network visualization can be saved either as a 3D object or as a picture. The 3D object is exported with the standard file format for threedimensional scenes and models, ie GL transmission format (. gltf). This file can then be used in 3D modeling tools such as Blender, Unity, or other Three. js based scripts 2. The picture format is. tiff with a 96 DPI resolution and horizontal and vertical dimension of 6000 and 3500 pixels, respectively. Note that node labels are not saved in the picture, but only visualized in the website when hit by the cursor.
This is particularly problematic for the smooth usage of the GUI, as many manipulations and customization require the redrawing of all the links. While some improvements can be done in the future by optimizing our code, we notice that there are intrinsic limitations in the three. js code that one cannot directly modify. As our ultimate goal was to generate high-quality static visualizations, Vizaj can still be considered a useful tool even in the case of very large spatial networks. November 3, 2022 5/13.
Conclusions Vizaj was designed to fill in the gap of spatial network visualization and exploration in 3D. By preserving the actual location of the nodes in the network, while changing the way links can be drawn, the user can achieve a more intuitive interpretation of the organizational features of the system under investigation. As such, we hope that Vizaj can be used extensively by a broad community of scientists and practitioners, so as to facilitate the communication of their results as well as provide high-quality artistic representations. Note that Vizaj can also be used to visualize non-spatial networks provided that a node position is given as input, eg precomuted from standard layout algorithms. Currently, Vizaj is not optimized to automatically process and visualize huge dataset samples.
Vizaj, explain its controlling parameters, and validate its potential on synthetic and real networks from social to biological systems. Vizaj can be directly used at https://bci-net. github. io/vizaj/. Being an open source project, Vizaj 's code and sample data can be freely accessed at the github page https://github. com/BCI-NET/vizaj. following, we show the working principles of 1 https://gephi. org, https://cytoscape. org, https://mrvar. fdv. uni-lj. si/pajek/ November 3, 2022 2/13.
More recently, Pandey and colleagues captured an intermediate (presumably E:I) at a single time point (66 ms) after mixing BlaC crystals with SUB  34. Although subunits B/D displayed already a covalently bound adduct, an intact, non-covalently bound SUB was observed in subunits A/C (Fig. 2a ). Subunits A/C are rather inactive, since they did not participate at all in the reaction with CEF in earlier experiments  1, 33. Given the inactivity of subunits A/C, it was not clear whether the reaction with SUB takes more time to complete or proceed at all. Therefore, a time-series of MISC datasets is necessary, to further investigate both the binding of SUB inhibitor to and its subsequent reaction with the BlaC. As in any time-resolved experiment, except in those performed on ultrafast time scales  [44] [45] [46] [47]  , multiple states can mix into any single time point observed during the reaction 48.
The rSVs contain information on the population dynamics of the species involved in the mechanism  14, 72. The earlier program SVD4TX  14  and a newer version  73  could not be applied to X-ray data when large unit cell changes occur during the reaction since these implementations relied on a region of interest that is spatially xed. This is not given when the unit cell changes. In order to accommodate changing unit cells, a new approach was coded by a combination of custom bash scripts and python programs described below.