dataesr/scientific-paragraphs-categorization huggingface dataset:
| INTRODUCTION Software-defined networking (SDN) has become one of the most important network architectures to simplify network management in a logically centralised way. SDN decouples and abstracts the Control plane and Data plane: The Control plane is logically centralised, and the Data plane follows the Control plane's decisions. Also, new control functions can be added to SDN by creating software-based logic in the Application plane. In such a network, the administrator can configure network policies from a software-based controller without changing any device configuration as in traditional network systems. Because of those benefits, SDN is widely applied in practice, most typically in the IoT network. Now, the administrator only needs one computer to be able to configure all IoT devices in the network without complicated configuration as before.
| Overview of Software-Defined Networking The SDN architecture enables network programing and management to make the network more flexible and easier to be managed. The Open Networking Foundation  [2]  defines the SDN architecture with three main planes as follows (Figure  1 ): � Application plane: This layer is considered as an open platform to develop applications for network control and security (e. g. routing, intrusion detection system, firewalls etc. ). The Application plane communicates with the Control plane through Northbound APIs that, unlike the Southbound API, do not have any commonly accepted standard. Popular SDN controllers such as ONOS  [3] , Opendaylight  [4] , and Faucet  [5]  build their RESTful API and GUI (Graphical User Interface) for the interaction between applications and their controller core.
So, any transactions containing invalid data can be easily detected and/ or removed. Blockchain ensures the reliability of data transmission in a decentralised way without any third-party involvement. The blocks are arranged in a chronological order. The first block in the chain is called the 'Genesis Block', and it does not have any parent block. This architecture ensures that the data in the system is immutable, so it is often applied for security purposes as in Refs. [13, 14]. Blockchain is designed according to peerto-peer architecture, so all data in the system will be stored in a distributed manner without going through a centralised database. As we know, SDN is a system where a lot of data is exchanged, and it is unreasonable to store all that data in a centralised server. So it makes sense to use Blockchain to improve the SDN network.
In addition, smart contract is also one of the outstanding technologies of the Blockchain platform. With smart contracts, system administrators can customise their transactions in a variety of ways to ensure tighter transactions or ensure everyone is more compliant with policies. The use of smart contract to customise the data storage function of the SDN network is also a way to ensure system reliability. For example, an administrator can add a staking mechanism to hold users accountable for their actions or add a mechanism for authentication, authorisation etc. However, among all the advanced technologies of the Blockchain platform, the consensus mechanism is the most prominent. A consensus algorithm is a decisive factor for the success of Blockchain technology  [15, 16]. As we all know, SDN is a system where large amounts of data are constantly being exchanged, so ensuring the reliability of all that data in real time is difficult and expensive.
The mentioned research studies showed that the traditional architecture of Cloud storage services cannot handle all the data transmitted from IoT devices. These traditional architectures have many drawbacks such as security, data recovery, latency, and scalability aspects. Using the combination of Blockchain and SDN instead of traditional Cloud architecture not only solves all of the above problems but also helps the system administrator to significantly improve performance and cost. Similar to the survey  [28] , Pohrmen et al. [29]  also discuss the combination of Blockchain and SDN to secure IoT networks. Besides, they also analyse challenges, solutions as well as opportunities of this topic. Unfortunately, the above three surveys have two important shortcomings: (1) They only focussed on the security issues.
Similar to Ref. [30] , the system is divided into three main models: Trust Feature Model, Computing Model and Networking Model. The goal is to facilitate synchronisation and simplify network management. In Ref. [32] , the authors also propose a solution for energy trading of an SDVN that uses the combination of SDN and Blockchain technology. The solution aims to establish a distributed network of energy providers based on the SDN architecture. This architecture is deployed simultaneously with the SDVN architecture. The Blockchain acts as a data storage platform rather than a physical database, supporting the communication between SDVN and energy providers' network. In this way, the drivers always know the energy trading points near them, and the transactions are performed quickly and correctly.
In Refs. [35, 36] , they both move towards distributed architectures for smart buildings/cities. IoT networks are large systems, with many diverse network devices and data, so designing a distributed architecture for this system is not easy. These two studies propose a common solution to use SDN networks to forward data between IoT devices. They use Blockchain to design the system in a distributed architecture (where each SDN controller acts as a node in the Blockchain system) to optimise the performance and the latency. The Blockchain consensus algorithms will be responsible for validating the information before adding it to the block. However, these solutions also open up many challenges. First, the -239 heterogeneous IoT network is often based on many different platforms, such as 2G, 3G, 4G, Wifi, Bluetooth, LAN Network etc.
Hence, management and synchronisation networking has encountered many difficulties. Second, it is also worth mentioning that in a network where data is exchanged a lot, the adoption of Blockchain, despite the performance improvement, consumes a lot of energy and cost. In Ref. [37] , the authors proposed a hybrid architecture between Blockchain and SDN networks for spectrum management in mobile networks. Current SDN implementations are not tailored to support business arrangements that promote interoperability between mobile network operators (MNOs), and the architecture proposed in this study is the solution to that problem. In this model, an SDN controller will be deployed to manage all MNOs, and a smart contract deployed on the Blockchain platform will be responsible for converting information of MNOs according to standards, helping MNOs to interact with each other smoothly.
Blockchain, SDVN  [33]  Improvement of IoT network performance This model uses the SDN architecture to deploy a network of secure fog nodes (these fog nodes act as nodes in the Blockchain system). It also brings computing resources to the edge of the IoT network so that the traffic in the network can be streamlined and have small latency. It is worth mentioning that this model uses a new consensus algorithm, called Proof of Service, a combination of PoW and PoS. Blockchain, SDN, IoT  [35, 36]  Distributed architectures for smart building/city These architectures use SDN networks to forward data between IoT devices. They use Blockchain to design the system in a distributed architecture where each SDN network represents a Blockchain system node. The goal is to optimise the system performance and minimise latency.
Blockchain, SDN, IoT  [37]  Spectrum management in mobile networks The architecture proposed in this study helps to promote interoperability between different MNOs. In this model, an SDN controller will be implemented to manage all MNOs, and a smart contract deployed on the Blockchain platform will be responsible for converting information of MNOs according to standards, help MNOs interact with each other smoothly. Blockchain, SDN  [38]  Multipath data transmission in Blockchains The authors introduced a solution as follows: First, they use OFDP and LLDP to explore the network topology. Second, they introduced an algorithm that uses DNS packet lengths to compute the degree of linkage between sub-streams. Finally, they rely on computed values and use the spanning-tree architecture to route data transmission in the network.
blockchain, SDN NAM NGUYEN ET AL. -241 SDN network security both in intra-domain and interdomain, while Brainchain only does it in intra-domain. These two architectures include four main components: FS (Flows Statics collection) uses sFlow (sampled Flow) to collect data on the OpenFlow protocol; ES (Entropy calculation Scheme) measures data's disorder/randomness using entropy calculation; BF (Bayes network-based Filtering scheme) uses machine learning based on the Bayes algorithm for real-time attack prevention; DM (DNS Mitigation scheme) minimises illegal DNS flows. Cochain used Blockchain to save a blacklist of nodes with suspicious action to prevent attacks in inter-domain, then blocking the flow of information from/to those nodes. Another security solution is proposed in ChainSecure  [45].
The latter has the function to control and detect any illegal flow rules introduced into the system. The DDoS attack is a dangerous type of attack that is easy to execute but difficult to prevent. Also, it may have serious consequences for the system. Hence, some studies have focussed on preventing this type of attack in the SDN network. In Ref. [47] , the authors introduce a solution to establish a set of rules for the flows and a blacklist of untrusted nodes, then apply them in OpenFlow. However, this article's solution stores the blacklist in the Blockchain, thus preventing the bad guy in the internal system from being able to modify them. In Ref. [48] , the authors introduce a model that consists of two main components: SecPoliMod (Security Policy Module) and LogMod (Log Module). The former is the component that monitors network devices based on their security priorities, and the devices that have higher priority are monitored more closely.
They summarise many common types of attacks and provide solutions to prevent them. Their solution focusses on two main parts: the Control Plane's security and the Data plane in an SDN network. They mentioned many solutions for different types of attacks, however, in general, their mechanism relies on using smart contracts to build an authentication method. There are also many studies for solutions of using Blockchain-based SDN to secure Cloud Computing systems as follows. � The article  [52]  proposes how to apply Blockchain and SDN's combination to secure the data-sharing service between machines in the Cloud Computing system. In this model, each network device in SDN will correspond to a node in the Blockchain system, responsible for validating shared data on the Cloud platform. In this way, system administrators can easily manage the operations of nodes using the SDN network while ensuring the Cloud Computing system's security with Blockchain technology.
The IoT network devices will be deployed as a Fog node in the Data plane of SDN. With this, administrators can easily programme and manage the operation of IoT devices through SDN. As in most studies, Blockchain will act as a platform for storing the state and information of the system to help ensure reliability. � In Ref. [55] , the authors introduced an optimised Blockchain-SDN Framework for resource management in IoT. The proposed architecture consists of three main layers: Perception Layer, Edge Layer and Cloud Layer. IoT environment consisting of devices and sensors will be deployed on the Perception Layer. A distributed architecture of multiple SDNs will be deployed on the Edge Layer to manage IoT devices. The Cloud Layer will implement Blockchain architecture as a data storage platform.
Blockchain, SDN, OpenFlow  [52]  Ensuring the safety of the sharing data service Each SDN device corresponding to a Blockchain node is responsible for validating shared data on the Cloud platform. Blockchain, SDN, Cloud Computing costs. PoA and SST save costs and have fast block generation times, thus ensuring performance and latency when applied to SDN networks. Another solution is to optimise the amount of data transferred in the network. In SDN networks, topology discovery protocols such as OFDP or LLDP use many data streams due to the broadcasting mechanism applied to all ports of network devices. The improvement can be performed by sending message packets to a certain port of the network devices. Additionally, the cache can store long-time information (e. g. DNS information) to reduce the number of packets in the network.
| Synchronisation and confidentiality Another challenge in SDN is the issue of synchronisation. There is currently no standard method to support synchronisation in SDN networks. In Ref. [30] , the authors have proposed a solution that uses Blockchain to support synchronisation in the SDN network. However, that solution has the disadvantages of high latency and high cost. Another solution for the synchronisation issue in SDN networks that can be considered is using a shared database. Moreover, REST API can be used to establish communication between nodes in the network. Another challenge is ensuring data confidentiality. Every transaction in the Blockchain is publicly visible to all users across the network, however, there is also sensitive information that needs to be kept confidential such as financial or medical information.
qRT-PCR Experiments 2D data correspond to PCT cells differentiated for more than 1 week in differentiation media. Primer sequences, designed with: https://www. ncbi. nlm. nih. gov/tools/primer-blast, were as follow. Primer efficiency was measured on 4 serial dilutions from 1× to 1,000× cDNA. The efficiency E was calculated according to E = 10 1/slope. Efficiencies calculated were between 93 and 111%. mATP1A1 primers were found on Origene (atp1a1-mouse-qpcrprimer-pair-nm_144900). See SI for the sequences of primers. Total RNA was extracted from differentiated cells using NucleoSpin RNA (Macherey-Nagel). Reverse transcription reactions were performed on 2 µg of total RNA with the high-capacity cDNA reverse transcription kit (Thermo Fisher Scientific) with random hexamers, and mixed with the Applied Biosystems TM PowerUp TM SYBR TM Green Master Mix (Thermo Fisher Scientific) and 5 pmol of both forward and reverse primers (see below).
Image Representation and Analysis Images acquired were analyzed on ImageJ software (NIH). For visual representation in figures, color balance was individually adjusted for each image. For some images in Figure  3 , a denoising was performed with Safir ImageJ plugin  (Kervrann and Boulanger, 2006). For analysis of 3D confocal stacks at high resolution, Pkd1 +/- and Pkd1 -/-tube diameters in a horizontal section were manually measured at the center part of the field. Quantification of cell density was performed by manually counting nuclei on 100 × 50 µm 2 area, randomly chosen for each image at the middle of the inferior half of the tube. Four images (over 28) with Pkd1 +/-tubes of aberrant sizes (superior to 125 µm), very likely corresponding to initial aberrant tubes, were removed from analysis.
As a control, we verified that even in this close proximity Pkd1 +/-cells still did not deform collagen tubes (not shown). Strikingly, with this reduced spacing between tubes, some Pkd1 -/-tubes could be in close proximity after dilation, with the creation of a plane interface between tubes (Figure  6A ). We observed that both the rate of deformation and the maximal deformation induced by Pkd1 -/-cells were increased in the 100 µm spaced tubes compared to the 200 µm spaced condition (Figures  6B, C ). The maximum over time of the normalized diameters in 100 µm spacing chips was significantly larger than in the corresponding laminin-coated 200 µm spacing chips (respectively, 1. 6 ± 0. 07, n = 26, vs. 1. 33 ± 0. 06 for laminin-coated conditions, n = 15, p = 0. 001, Figure  6C ). These data strongly suggest that tube proximity modulates the geometry and the rate between neighbor tube deformation in an ADPKD model.
ACKNOWLEDGMENTS We acknowledge all the members of the  PBME group (Pascal Silberzan ,  Axel Buguin ), in particular  Pierre Ucla , and of the MMBM group (SDs),  Physico-Chimie Curie , for their precious support in the process development and in microfabrication. We are very grateful to BMBC platform for their technical assistance in cell culture. We thank  Ludovic Leconte ,  Olivier Leroy , the  IPGG platform  ( ANR-10-EQPX-34 ) and the  Curie imaging platform (PICT-IBiSA ) for discussions and support in image processing. We are grateful to  Marco Pontoglio ,  Fabiola Terzi ,  Frank Bienaimé ,  Sophie Saunier ,  Stefan Somlo ,  Eric Honoreì , and  Amanda Patel  for insightful discussions about renal diseases, and  Philippe Marcq ,  François Amblard ,  Jean-Louis Viovy ,  Jean-François Joanny , and  Jacques Prost  for important discussions about the physical aspects.
FUNDING This work was received the support of " Institut Pierre-Gilles de Gennes" laboratoire d'excellence, "Investissements d'avenir" program   ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-31. SM was the recipient of an  IPGG grant. This work received support from the Grants  ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02  [ LabEx CelTisPhyBio-Cell (n)Scale]. This work was funded by  CNRS  ( Centre National de la Recherche Scientifique ),  Institut Curie , and  Sorbonne Université.
We present a direct comparison of the Pan-Andromeda Archaeological Survey (PAndAS) observations of the stellar halo of M31 with the stellar halos of 6 galaxies from the Auriga simulations. We process the simulated halos through the A 2PA AS pipeline and create PAndAS-like mocks that fold in all observational limitations of the survey data (foreground contamination from the Milky Way stars, incompleteness of the stellar catalogues, photometric uncertainties, etc). This allows us to study the survey data and the mocks in the same way and generate directly comparable density maps and radial density profiles. We show that the simulations are overall compatible with the observations. Nevertheless, some systematic differences exist, such as a preponderance for metal-rich stars in the mocks. While these differences could suggest that M31 had a different accretion history or has a different mass compared to the simulated systems, it is more likely a consequence of an under-quenching of the star formation history of galaxies, related to the resolution of the A simulations.
The A 2PA AS pipeline From these raw mocks that include all "stars" down to an absolute magnitude of   = 2 (∼ 6 magnitude fainter than the tip of the RGB at the distance of M31), we degrade the In the rest of the paper, the mock stars generated from a simulation's star particles will be referred as "stars" to differentiate them from the real stars of the PAndAS observations. Here, we use the pre-2014 CFHT/MegaCam photometric system, before the current set of filters was built. It is worth noting that the fields obtained before 2007 have been observed with a slightly different -band filter (see Figure  3  of  McConnachie et al. 2018 ) but this change is subtle enough that it should not affect our results. data so as to be as close as possible to the observed PAndAS data. The different steps we perform are: 1.
Apparent magnitude For all unmasked "stars", their apparent magnitudes in the  and  bands are determined from the absolute magnitudes, given in the initial mock stellar catalogue, and from their individual heliocentric distances, computed in the previous step. To account for the foreground extinction produced by the ISM of the MW, the absolute magnitudes are reddened using the  ( -) extinction map of  Schlegel et al. (1998). We further assume a 10% uncertainty on these values to mimic our likely imperfect extinction correction for the PAndAS data and to avoid reddening the data by the exact same amount we will later de-redden them by when studying the mocks like we study the PAndAS data. With the knowledge of  ( -) at a given location in the survey, we redden the data, using the coefficients from  Martin et al.
Completeness With the next step, we aim to take the photometric completeness of the survey into account. To estimate the completeness of PAndAS in each band, we compare the number of PAndAS point source objects as a function of magnitude with the number of point sources observed in the deeper Hubble Space Telescope (HST) fields obtained by  Mackey et al. (2007)  and  Mackey et al. (2013)  around globular clusters of M31. These deep HST fields have been observed in the 606 and 814-bands by the Advanced Camera for Surveys (ACS), and are ∼2. 5-3 magnitudes deeper than the PAndAS survey. The data reduction of these fields and the star/galaxy separation will be described in a future contribution  (Mackey et al. in prep. ). Over the 46 fields observed by HST, we selected the 14 fields located around globular clusters B517, H1, PA 02, The Hubble  606 and  814 filters correspond roughly to  and  in the Johnson system, so we will abusively refer to them as such in the rest of the paper.
The A 2PA AS pipeline presented here (https://github. com/GFTh and one realisation of each mock are publicly available online (https://wwwmpa. mpa-garching. mpg. de/auriga/gaiamock. html). The different columns of the catalogues are described in Appendix A. We encourage the application of this pipeline to other state-of-the-art cosmological simulations such as EA-GLE (The EAGLE team 2017), Illustris TNG  (Pillepich et al. 2018a) , Latte/FIRE  (Wetzel et al. 2016; Hopkins et al. 2018) , or ARTEMIS  (Font et al. 2020) , so as to compare the results obtained from these simulations with the observations and between each other. Only then will be able to efficiently constrain the different parameters of the simulations, especially those relating to the complicated baryonic physics.
GT acknowledge support from the  Agencia Estatal de Investigación (AEI)  of the  Ministerio de Ciencia e Innovación (MCINN)  under grant with reference ( FJC2018-037323-I ). FAG acknowledges financial support from  CONICYT  through the project  FONDECYT Regular Nr. 1181264 , and funding from the  Max Planck Society  through a Partner Group grant. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
AbstractThe results of a SPARQL query are generally presented as a table with one row per result, and one column per projected variable. This is an immediate consequence of the formal definition of SPARQL results as a sequence of mappings from variables to RDF terms. However, because of the flat structure of tables, some of the RDF graph structure is lost. This often leads to duplicates in the contents of the table, and difficulties to read and interpret results. We propose to use nested tables to improve the presentation of SPARQL results. A nested table is a table where cells may contain embedded tables instead of RDF terms, and so recursively. We introduce an automated procedure that lifts flat tables into nested tables, based on an analysis of the query. We have implemented the procedure on top of Sparklis, a guided query builder in natural language, in order to further improve the readability of its UI.