dataesr/scientific-paragraphs-categorization huggingface dataset:
The corresponding distributions illustrating the mean squared distance between the active quark and the spectator system, see Eq. (  23 ), are shown in Fig. 18. In our studies d 2 ⊥ q is finite when x → 1, which we consider to be a welcome feature imposed by our Ansatz. We Fig. 6 : Comparison between the results of this analysis, some selected GPD models and experimental data published by CLAS in Refs. [128, 129]  for d 4 σ - U U at x Bj = 0. 244, t = -0. 15 GeV 2 and Q 2 = 1. 79 GeV 2 (left) and for A - U L at x Bj = 0. 2569, t = -0. 23 GeV 2 , Q 2 = 2. 019 GeV 2 (right). The solid curves and the gray bands surrounding those curves correspond to the results of this analysis and 68% confidence levels for the uncertainties coming from DVCS data, respectively. The magnitudes of the additional uncertainties to the plotted observables and coming from unpolarized PDFs, polarized PDFs and EFFs, can be separately estimated from the bands located below the curves and labeled with "PDF unc.
", "Pol. PDF unc. " and "EFF unc. ", respectively. Each of those four uncertainties is evaluated in the analysis of the corresponding set of replicas. The inner bars on data points are for statistical uncertainties, while those outer ones are for the quadratic sum of statistical and systematic uncertainties. The dotted curve is for the GK GPD model  [73] [74] [75] , while the dashed one is for VGG  [76, [78] [79] [80]. The curves are evaluated at the kinematics of experimental data. (right) at t = -0. 12 GeV 2 and Q 2 = 2. 5 GeV 2. For further description see Fig. 6. Q 2 = 1. 8 GeV 2 Q 2 = 4 GeV 2 Q 2 = 8 GeV 2 Q 2 = 3. 2 GeV 2 Q 2 = 10 GeV 2 Fig. 9 : Comparison between the results of this analysis, selected GPD models and experimental data published by COMPASS in Ref. [57]  for the slope b at Q 2 = 1.
We performed the analysis of PDFs and obtained a set of functional parameterizations allowing the reproduction of original values and uncertainties. A small number of free parameters appearing in our approach was constrained by EFF and DVCS data. We considered all proton DVCS data, however not all of them entered the final analysis because of the used kinematic cuts and the initial data exploration. Our work was done within the PARTONS project that provides a modern platform for the study of GPDs and related topics. The quality of our fits is quite good. The fit to EFF data returns χ 2 = 129. 6 for 178 data points and 9 free parameters, while that to DVCS data returns χ 2 = 2346. 3 for 2600 data points and 13 free parameters. The good performance proves that our parameterizations, including the assumed analyticity property, are not contradicted by the used experimental data.
We consider the unsurprising discrepancy between our results and the HERA data as a possible manifestation of gluon effects, and the surprising discrepancy with unpolarized cross sections published by Hall A as a possible need for higher twist or kinematic corrections. Our analysis also favors small values of the subtraction constant. We extract the distributions of q(x, b ⊥ ) and ∆q val (x, b ⊥ ) and we study the properties of those distributions. This analysis is characterized by a careful propagation of uncertainties coming from PDFs parameterizations, EFF and DVCS data, which we achieved by using the replica method. The first successful step towards the reduction of model uncertainties has been done already by selecting PDFs parame- terizations based on the neural network technique. We plan to extend the usage of neural networks to other sectors of our future analyses for further reduction of model uncertainties.
Acknowledgements The authors would like to thank  M. Burkardt ,  M. Garçon ,  F. -X. Girod ,  B. Pasquini ,  M. V. Polyakov ,  A. V. Radyushkin ,  K. M. Semenov-Tian-Shansky  and  F. Yuan  for fruitful discussions and valuable inputs. This work was supported in part by the  Commissariat à l'Energie Atomique et aux Energies Alternatives  and by the Grant No. 2017/26/M/ST2/01074  of the  National Science Centre, Poland. The computing resources of  Świerk Computing Centre (CI Ś)  are greatly acknowledged.
Acknowledging the need for bolstering disaster resilience, this paper contributes a vision of leveraging the latest advances in wireless sensor network (WSN) technology, unmanned aerial vehicles (UAVs) and mobile crowdsensing to enhance the ability of network-assisted disaster prediction, assessment and response. Around 47% of the overall losses and 45% of the insured losses derived from inland flooding that occurred in Europe, Canada, Asia and Australia. Altogether, at around US$ 45bn, losses from natural catastrophes were below the average amount for the past ten years (US$ 85bn). Insured losses totaled approximately US$ 13bn. Thus, in this paper, we focus our attention on inland flooding events. Some recent works tackle the problem of a system design dedicated to use in natural disasters.
Interconnection of System Components Figure  4  shows the interconnection of the three system components: sensors, UAVs, smartphones through the UAV server and the web server. The UAVs are gathered by fleets, where each fleet of UAVs sends telemetry data, while each UAV sends its video. All the data sent by the UAVs are received by the UAV server, which is connected to the UAVs through a local wireless network. In return, the UAVs receive Mavlink commands from the UAV server. The UAV server centralizes all the data sent by the UAVs. Additionally, it also exposes each video stream for the web clients and also receives the command messages sent by the web server. The Web server is the central element of the architecture: it stores telemetry and stream processing data into a database, and also provides an API to the web clients.
The core of the web server is a program written in Golang which is responsible for launching the different modules in several threads. The web server comprises the following modules: • API: The API is responsible for exposing a RESTful API to the clients and enables two-way communication between the clients and the UAVs. On the one hand, the web application can query the database through the API module to retrieve data such as the last telemetry of a UAV or retrieve a snapshot of the video stream. On the other hand, clients, if authorized, can also send commands to the UAV fleet.
• Processing modules: These modules are responsible for processing data coming from the UAV fleet. Each type of data is assigned to a specific sub-module: -Streaming: Manages the websocket stream video servers and synchronizes the different video inputs (UAV streams) with the outputs (video players which are requesting a given stream). -Screenshot: Manages the reception of the snapshot resulting from the video stream and its analysis by OpenCV (by using face-detection algorithms) as well as allows the recording of streaming information into the database (addresses where UAVs publish their streams, and the addresses where the web client can retrieve them). -Telemetry: Processes telemetry data received and stores them in a database.
Sensor nodes that are used in the project are Digi XBee enabled ready-to-use sensors, which come in plastic casings, are battery powered and integrate acceleration, temperature, luminosity and humidity sensors. The communication modules integrated in these sensors is the Digi 868 RF protocol that operates on 868 MHz and that is characterized by 1-4 km of communication range with up to 80 kbps data rate. Data emitted by the sensors are collected at the sensor gateway (Digi ConnectPort X2) and transmitted, in normal operating mode, towards the web server through a 5G dedicated link. The second part of the sensing plane comprises the smartphones used by the people that happen to be around the disaster area. By using the pre-installed mobile emergency application, they have the possibility to participate in disaster alarm and sensing by sharing their view on the disaster.
The setup of the gateway is done in a Python script, with the goal to assemble all the sensor readings, format them and transfer them towards the web server through the Ethernet port. The main goal of the implemented WSN visualization application is to show the measurements provided by the WSN. The Digi XBee LHT sensors are equipped with a temperature, humidity and ambient light sensor modules, which are configured to measure these values periodically and send them towards the collection point (Digi XBee sensor gateway). When the application is launched, it retrieves all the sensors readings from the web server. The map integration allows the user to see the geographical distribution of the measurements. Three buttons, T, H, and L, represent the particular physical value that is being displayed (temperature, humidity, and ambient light; Figure  6 ).
Web Server The web server contains a database with all the information about users, their activity logs, the information about available and replacement drones, as well as the information provided by the WSN. Furthermore, the server keeps all the information about the availability of UAV servers and active UAVs (available UAVs on each server). The server script allows the transmission of control messages from the responders and transmission of video stream from the active drone back towards the responder. The exchange of the web server with the different components of the proposed architecture is illustrated in Figure  14.
Chosen Technologies It is worth noting that the technologies chosen to implement the proof of concept system have been selected after an accurate study of the existing technologies to use those that would allow a smooth integration in a larger 5G IoT ecosystem. The technologies chosen for the proof of concept system are the following: ZeroMQ is a high-performance asynchronous messaging library, aimed at use in distributed or concurrent applications. It provides a message queue, but, unlike message-oriented middleware, a ZeroMQ system can run without a dedicated message broker. FlatBuffers is an efficient cross platform serialization library and it was originally created by Google for performing critical applications. What makes FlatBuffers special is that it represents hierarchical data in a flat binary buffer, in such a way that it can still be accessed directly without parsing and unpacking, while supporting data structure evolution.
It supports the most obscure ancient formats up to the cutting edge. It is also highly portable-FFmpeg compiles and runs under a wide variety of build environments, machine architectures, and configurations. JSMpeg is a video player written in JavaScript that consists of MPEG-TS demuxer, MPEG1 video and MP2 audio decoders, WebGL and Canvas2D renderers and WebAudio sound output. JSMpeg can load static videos via Ajax and allows low latency streaming via web sockets. It can work in any modern browser (Chrome, Firefox, Safari, and Edge). JSMpeg can connect to a web socket server that sends out binary MPEG-TS data. When streaming, JSMpeg tries keeping latency as low as possible-it immediately decodes everything it has, ignoring video and audio timestamps altogether. OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-time computer vision for all operations related to image processing.
The library is composed of around 3000 algorithms, including sets of both classic and cutting edge computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, and extract 3D models of objects. The architecture proposed in this paper relies on this technology due to its richness in the algorithms it offers. As described in Ref. [16] , there are three ways of communicating between software blocks, using a database, services, or messages. In our architecture, we use the following three methods: • Communication through a service is used between the API module and the clients.
However, for many video players, the latency is much more noticeable in Configuration 2. We are convinced this is due to the JavaScript nature of the video player and the web page obviously has more work in its background process. Note that these tests were made with a video resolution of 640 × 480 px but some tests with video resolution of 1280 × 960 px showed similar results. Furthermore, a disadvantage of JSMpeg comes from its use of canvas element. While the web page is not displayed by the browser, the page content is not rendered (even though background tasks such as video retrieving continue to progress). It is only when we come back to the web page that the content of the canvas is changed, which produces an extra latency by displaying in canvas all previous stored data.
Data Architecture Performance As we are bound by resource constraints and our intent is to smoothly integrate our architecture in a 5G IoT context, which will make massive use of virtualization techniques, we started virtualizing the software architecture. Indeed, we used a virtual test environment to assert the proper functioning of the architecture. The first thing we had to do is to virtualize each part of the architecture into a virtual machine. Instead of using virtual machines, we used containers with Docker (https://www. docker. com). In place of virtualizing the entire OS, the kernel is shared across containers, thus reducing the load on the host machine. This enables us to start a high number of containers with a reduced resource consumption compared to an equivalent number of virtual machines.
The following step is to setup a virtual network for the containers. We create and manage the network between containers also with Docker. Moreover, Docker also associates a container name to a DNS name into the virtual network, thus allowing for an easy network communication between containers. As we wanted to test the architecture in an unstable networking context, we needed a tool to simulate errors on the virtual network. We chose Pumba (https://github. com/alexei-led/pumba), a network emulation tool for Docker. This open-source tool helps us simulate an unreliable network by letting us introduce lag, packet loss, or rate limitation into a virtual network between containers. Usage and setup of this tool are further described in Section 6. 3. 1. Through the mentioned setup, we measured the performance of the following output parameters: network latency, bandwidth, CPU and RAM usage.
Drawbacks and Directions for Improvement The main drawback of our software architecture lies into the server and its database, as there is only one server for n drones. A way to improve this is to transform the server and the database into a scalable Docker service, enabling multiple instances of the server to run. However, such scaling also gives rise to other issues, such as replication and inconsistency. These issues are out of scope of this paper, but are tackled in the literature  [16]. The other main drawback is represented by the quality of the network. Indeed, the latency mainly depends on the quality of the network rather than the number of data transmitted. Thus, to ensure the practical usage of the architecture, we first have to ensure a good quality of the network.
, 2014; Hipp, 2015; Oh & Manos, 2008; Piredda, Simeone, Attimonelli, Bellarosa, & Schirone, 2011; Simeone et al. , 2013; though cf. Chen, Zeng, Liao, Yan, & Zhang, 2017 and Yang et al. , 2017  for recent successes in East Asian oaks). In this study, we present an 80-SNP DNA barcode for species identification and hybrid studies of a suite of common eastern North American white oaks. Our addresses solves the problem of identifying barcode-informative loci through careful selection of a core set of informative and easily amplified SNPs from a broadly sampled RAD-seq dataset  (McVay, Hipp, & Manos, 2017). We then develop PCR primers for multiplex amplification and SNP genotyping using the MassARRAY platform and apply this DNA barcode to a pilot study of hybridization among white oaks in the living collections of The Morton Arboretum.
| RAD-seq genotyping and clustering RAD-seq DNA extraction, library preparation, and sequencing were conducted as presented previously in oaks  (Cavender-Bares et al. , 2015; Hipp et al. , 2014 Hipp et al. , , 2018;; McVay, Hauser et al. , 2017 , McVay, Hipp et al. , 2017). Briefly, DNA for all RAD-seq samples was extracted from fresh or frozen material using the DNeasy plant extraction protocol (DNeasy, Qiagen, Valencia, CA, USA). DNA extractions were gel-quantified in agarose by visual comparison with a 1 kb+ Ladder (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). Quality, read lengths, and base composition of FASTQ data were assessed in R v. 3. 3. 1 ("Bug in your hair"; R Core Team 2016) using the ShortRead package  (Morgan et al. , 2009). FASTQ files are publically available under NCBI BioProject PRJNA376740 (https://www.
ncbi. nlm. nih. gov/bioproject/376740). RAD-seq fastq data were analyzed using PyRAD version 1. 64  (Eaton, 2014) (www. dereneaton. com/software). In this pipeline, sequences are clustered first by individual using USEARCH  (Edgar, 2010) , which allows sequences within clusters to vary in indels, nucleotide polymorphisms, and sequencing strand (direction). After clustering, heterozygosity and sequencing errors are jointly estimated from the base counts observed across all sequences, sites, and clusters using the likelihood equation of  Lynch (2008). Heterozygotes are inferred by a binomial probability based on these parameters. Bases that could not be assigned with ≥95% probability were treated as unknown (N). Any locus possessing more than two haplotypes within individuals after correcting for sequencing errors was discarded, under the assumption that it included one or more paralogous sequences.
For each individual, each locus was summarized as a consensus sequence, and consensus sequences were clustered among individuals to generate a data matrix for each locus. Due to variation in sequencing coverage and mutation F I G U R E 1 Flow diagram outlining analysis used in this study and future directions. RAD-seq (orange arrows) data from previous project were analyzed using PyRAD/RADami and Stacks. A stringent approach (dotted purple arrows) and relaxed approach (solid blue arrows) were used to develop two lists of potential SNP (pSNPs). These were subjected to the primer design software Assay Design Suite and data analysis using TYPER 4. 0 software, screened, and checked for SNP validation. A list of verified SNPs was re-entered to the primer design software to select for 80 suitable SNP multiplexes, which comprise the barcode (turquoise arrows).
Applications of the barcode include genotyping of North American white oaks and hybridization studies (yellow arrows) at the restriction site defining RAD loci, the resulting data matrix was not complete for all loci in all individuals. The following parameters were specified for PyRAD: minimum depth of reads per withinsample cluster: 10; maximum number of sites in a read that can have a quality score of less than twenty: 4; clustering threshold (percent similarity): 0. 85; minimum number of samples in each across-sample cluster: 20; maximum number of individuals with a shared heterozygous site in an across-sample cluster: 3. All other settings used default values. For comparison, RAD-seq data were also clustered using Stacks  (Catchen, Amores, Hohenlohe, Cresko, & Postlethwait, 2011; Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013).
Barcodes were first removed using gawk prior to filtering out low-quality reads with the "process_radtags" module of Stacks  (Catchen et al. , 2011 (Catchen et al. , , 2013)). The wrapper program "denovo_map. pl" provided by Stacks was used to perform de novo assembly of RAD-seq. Ten reads were set as the minimum to be considered as a stack (m); distance between two stacks to be assembled to loci was set to two (M); and the distance between loci to be merged was set to four (n). Filtering criteria were set to 75% of individuals in a population required to be present in a loci (r) and 10 as the minimum number of populations in which a locus must be present to be considered (p).
| SNP identification in RAD-seq dataset SNPs were identified using two methods. In the first, pairwise F ST was calculated between all pairs of oak species using the RADami (version ≥1. 1-2)  (Hipp et al. , 2014)  and hierfstat (version 0. 04-10)  (Goudet, 2013)  packages of R. The resulting F ST matrix identified loci with fixed or nearly fixed differences (pairwise F ST ≥ 0. 95) between any pair of white oak species, identified the location of SNPs within the locus, and identified how many pairwise comparisons were at or above the threshold F ST level (of 0. 95). This approach generated a list of more than 1,090 loci containing 4,020 potential SNPs of F ST ≥ 0. 95, all of which were exported as FASTA files. Loci were then filtered under both stringent criteria and relaxed criteria. The stringent approach excluded SNPs with ambiguity codes of any kind and resulted in 1,068 potential SNPs (Supplementary File 2).
These were further subjected to manual inspection, and SNPs were retained if they were present in at least 50% of individuals from one species to be considered for SNP screening; this threshold was chosen to maximize the number of SNPs we could consider in design without wasting undue time on SNPs that might provide little power to distinguish among species. FASTA files were formatted with custom bash scripts that retrieved one species-specific SNP per locus sequence and masked other SNPs using IUPAC ambiguity codes (Supplementary File 2). The relaxed approach treated heterozygotic and ambiguous positions as potentially informative, as long as at least one individual per species was homozygous at the position distinguishing species; this expanded the list of potential SNPs to 1698. For both the relaxed and stringent approaches, loci were considered suitable for SNP identification only if they had at least 20 bp available for primer design at both the 5′ and 3′ ends.
For Stacks data, the program "populations" was used to compute pairwise F ST between species. Loci were derived from the vcf output file. Output files (vcf and fasta) were obtained and further processed with custom bash scripts and manually selected in Excel (Microsoft, Washington State, USA) to create a list of potential SNPs. The corresponding position of a SNP within a RAD-seq stack was identified by a custom script from the output files (sumstats and vcf; Supplementary File 3). Stand-alone NCBI-BLASTN version 2. 2. 30  (Altschul, Gish, Miller, Myers, & Lipman, 1990 ) was utilized to determine overlap of loci between PyRAD and Stacks. The command "makeblastdb" was used to convert each loci dataset into a database. BLASTN (version 2. 2. 30+) was used to compare PyRAD loci to a Stacks loci database followed by reciprocal BLASTN of Stacks loci against a PyRAD loci database.
Maximum-likelihood (ML) phylogenetic analysis was then conducted for each matrix to evaluate whether each SNP set could identify species known from prior work  (Hipp et al. , 2018; McVay, Hipp et al. , 2017)  to be monophyletic. Phylogenetic analysis was conducted using PhyML  (Guindon et al. , 2010)  with mode = "A la carte" and number of bootstraps = 100, as implemented in phylogeny. fr (Dereeper et al. , 2008). FigTree version 1. 4. 2 (http://tree. bio. ed. ac. uk/software/figtree/) was used to visualize results.