dataesr/scientific-paragraphs-categorization huggingface dataset:
B11 Clear-sky The clear-sky atmospheric column is described from ground to space by vertical profiles of temperature, pressure, water vapour mixing ratio, and a mix of other gases (CO 2 , CH4, N 2 O, CF C1, CF C2, O 2 , O 3 ). The I3RC cumulus case profiles provided with the ecRad package (the radiative transfer model developed at the ECMWF  (Hogan & Bozzo, 2018 )) are used.
B21 Gas Molecules The radiative properties of the atmospheric column are computed according to the Rapid Radiative Transfer Model for GCMs  (RRTMG, Mlawer et al. (1997) ;  Iacono et al. (2008) ). We access them via the ecRad software, that we use as a front-end for production of the RRTMG k-distributions profiles for 16 spectral intervals in the longwave (LW) region ([10-3500] cm -1 ) and 14 spectral intervals in the shortwave (SW) ([820-50000] cm -1 ). Each quadrature point, in each spectral interval, is provided with a quadrature weight that is used by our algorithms as a probability for the sampling of absorption coefficient values that are then practically used as if radiative transfer was monochromatic. The only subtility is related to the variability of the water vapor concentration in the 3D LES domain.
The water vapor mixing ratio, temperature and pressure are provided in each cell of the 3D domain. The impact of the horizontal variations of temperature and pressure on the absorption being negligible besides the impact of vapour variations (in solar computations), we only consider the vertical profiles of horizontally-averaged temperature and pressure fields to deduce the absorption coefficient profiles. For water vapor, we use the fact that the absorption coefficients of the gas mixture are roughly linear (in log/log space) with x H2O , the water vapor molar fraction, except for very small and very high values of x H2O. The ecRad software is therefore used in a preliminary step to compute and tabulate absorption and scattering coefficients for the 1D atmosphere, for each LW and SW interval and each quadrature point in each atmospheric layer, for a given discretized range of the water vapor molar fraction x H2O.
The resulting look-up table is then used within the MC algorithm to rapidly retrieve the local k-values. Details describing the model and the interpolation procedure are given in the Supporting Information, along with a plot of the relative error on LW net fluxes. The maximum relative error between two profiles computed analytically from RRTM-G vs. interpolated absorption coefficients is around 1. 2%. This is around half the maximum relative error found between profiles computed by ecRad vs. analytically, from RRTM-G data (2. 6%).
B22 Cloud Droplets The method developed by M. Mishchenko et al. (2002) , implemented in Fortran as in M. I. Mishchenko et al. (1999) , is used to solve far-field light scattering by spherical particles using the Lorenz-Mie theory. The main hypothesis are that the droplets are homogeneous and polarization is ignored. As with ecRad for gaseous absorption, this code is used externally to compute the single scattering albedo, the extinction coefficient (along with scattering and absorption coefficients), the asymmetry parameter and the phase function, all of these properties being averaged over the size distribution. We also compute the cumulative phase function and its inverse to allow efficient sampling of scattering directions. The MC algorithm accesses these data via look-up tables and performs spectral averaging over the narrow bands used in the k-distribution described above.
This way, the Mie data are uncorrelated from the gas spectral data and the same look up table can be used with various spectral models. The particular table that is used for the simulations of Section 4 is available as a NetCDF file in the starter-pack (https://www. meso-star. com/projects/high-tune/starter-pack. html). The size distribution is lognormal with an effective radius of 10 µm and a standard deviation of 1 µm. The sun azimuth angle origin is at X > 0, Y = 0 (to the East) and oriented to the North. FOV is for Field Of View. Position and target point values were rounded for readability. The data and files describing the scenes are distributed in the starter-pack, available online. On the top of these modules, an application was developed (htrdr) that makes use of the different modules to implement a Monte Carlo algorithm.
Typical functions associated to the different modules are cited as illustrations in Table  C2. The sources can be downloaded online (https://www. meso-star. com/projects/high-tune/high-tune. html) and user-guides are provided on the website. A starter-pack is also provided with the data and scripts necessary to reproduce the examples of Section 4. The set-up of the scenes are summarized in Table  C1. However, the most useful user-guide for the interested reader is the commented code that implements the renderer, using the various functions of Table  C2. Indeed, this code was in part developed to illustrate the use of the different libraries and modules, to serve as a basis for futher developments, or as an example to implement new algorithms. To test these tools in the context of multiple scattering, we implemented several benchmark experiments and compared our calculations against published results, e.
Acknowledgments Our many thanks go to  F. Brient  for providing us with the FIRE stratocumulus LES field,  C. Strauss ,  D. Ricard  and  C. Lac  for providing us with the 5m resolution congestus LES field, and  C. Coustet  for useful discussions. We acknowledge support from the  Agence Nationale de la Recherche  ( ANR , grants  HIGH-TUNE ANR-16-CE01-0010 , http://www. umrcnrm. fr/high-tuneand  MCG-RAD   ANR-18-CE46-0012 ), from the  french Programme National de Tldtection Spatiale  ( PNTS-2016-05 ), from  Region Occitanie  (Projet CLE-2016 EDStar) and from the  French Minister of Higher Education, Research and Innovation  for the PhD scholarship of the first author. The data and sources described in this paper are available at https://www. meso-star. com/projects/high-tune/high-tune. html.
Flow/Interface Association Once a terminal is equipped with several interfaces, it becomes possible to use two or more interfaces simultaneously. The terminal in a zone covered by HWNs can discover different RATs with different characteristics (e. g. , UMTS, LTE, WiFi, WiMAX, etc. ). The characteristics of RATs such as the WiMAX and UMTS are dynamic, which will affect their ability to support a service optimally. The wireless network technologies are developed to fulfill the specific requirements of applications, for example LTE-A is developed to fulfill the multimedia services such as VoIP, HD video streaming, interactive video gaming, etc. ). In case the multi-interface terminal runs several applications at the same time, heterogeneous data flows with different requirements in terms of QoS need to be fulfilled.
In this case, it would be advantageous to provide each data flow with an appropriate network interface that meets its requirements while maximizing the global terminal utility. Example: A simple scenario is presented in Figure  1 , where three applications (Telepresence, E-mail, and video Streaming) are run in a multi-homed terminal which is equipped with three network interfaces (LTE, 802. 11ac, and 802. 11b). In real-time critical application, jitter, packet loss, and low latency are mandatory. The Telepresence flow will be associated with LTE as it has low values of these parameters (for simplicity, we did not consider the cost criterion in this example). For a background application even a network with low performance will suffice, then the E-mail flow will be associated with 802. 11b.
A more accurate method is to use a dedicated software such as Spirent Test Center, JDSU QT600, Netcps, IxChariot, Iperf3, Ttcp, netperf, NetPIPE, Flowgrind or bwping for measuring different parameters. We have chosen Iperf3  [25] , a widely used open-source and cross-platform tool for network performance measurement and tuning. It can produce standardized performance measurements for any network. Iperf3 has client and server functionality, and can create data streams to measure different parameters between the two ends in one or both directions. The data streams can be either TCP, UDP or SCTP. Throughput is measured with TCP while UDP is used for Jitter and Packet loss. Iperf3 does not measure Latency, therefore another tool is required for that. Latency can be either One-Way Delay (OWD), i.
The module objective is to send the data read from a file through the given network interface using TCP socket, if the network is disconnected or down then the second interface is used. If all interfaces are down, the thread status is updated to "disconnected" and communicated to the main program to force a new network probing, followed by a NIS. The selection results are communicated back to the thread, and transmission is resumed. The module will run continuously until all data are sent or discarded by the user (e. g. file deleted). The end of data flow is determined by setting a timer on the given file, if the timer runs out and no write event is reported by the inotify API then the flow is considered close, and the thread will exit.
5. Main: This module implements the main execution routine and brings all other modules together. It monitors a given folder for file creation events using Linux inotify API. In case of an event, a Transmission thread is created and assigned to the newly created file, then network's parameters and flow's information are fetched through the Network probing and Policies modules, respectively. The NIS is executed once the previous information is available, and the results are fed to the Transmission thread to start sending the data. It also routinely monitors network events such as newly discovered networks or network disconnection, and reacts by forcing the network probing execution followed by NIS, the new results are propagated to the active Transmission threads.
Server The server uses Linux Ubuntu 16. 04. 4. The implementation is fairly simple, it accepts connection from clients and stores the received files locally. If a file already exists the server responds with the end-of-file position to the client. This is useful in case of disconnection so the client can resume sending from the last successfully sent packet. Another objective is to provide iperf3 server for network testing, it does so by running iperf3 server on multiple ports if possible for concurrent testing. The number of ports is limited by the server's connection uplink and/or downlink capacity, depending on the test direction. The connection's throughput is required to be at least equal to the clients' connections throughput combined for the measurement to be accurate. In this case study, the server is running on Amazon EC2 with 70 Mbits/sec downlink and 65 Mbits/sec uplink Internet connection, which is more than enough to provide concurrent testing for one client with three Internet connections rating at 20 Mbits/sec max.
With the rapid increase in the popularity of social networks, the propagation of rumors is also increasing. Rumors can spread among thousands of users immediately without verification and can cause serious damages. Recently, several research studies have been investigated to control online rumors automatically by mining rich text available on the open network with deep learning techniques. In this paper, we conducted a systematic literature review for rumor detection using deep neural network approaches. A total of 108 studies were retrieved using manual research from five databases (IEEE Explore, Springer Link, Science Direct, ACM Digital Library, and Google Scholar). The considered studies are then examined in our systematic review to answer the seven research questions that we have formulated to deeply understand the overall trends in the use of deep learning methods for rumor detection.
Apart from this, our systematic review also presents the challenges and issues that are faced by the researchers in this area and suggests promising future research directions. Our review will be beneficial for researchers in this domain as it will facilitate researchers' comparison with the existing works due to the availability of a complete description of the used performance matrices, dataset characteristics, and the deep learning model used per each work. Our review will also assist researchers in finding the available annotated datasets that can be used as benchmarks for comparing their new proposed approaches with the existing state-of-the-art works.
B. RUMOR DETECTION PARADIGMS Many rumor detection methods have been proposed in recent years, and most of them are based on machine learning (ML) techniques. The common challenge in most of these works is related to feature extraction from a considered dataset. The task of manual extraction of features requires a lot of time and effort, with a limited efficiency in detecting rumors for most of these works. Recently, deep neural networks have been proposed to simplify the extraction of features and to provide a strong ability for abstract representation learning Li et al. [36]. According to Cao et al. [8] , most of the automatic rumor detection systems consider the problem of rumor detection as a binary classification task and they may be classified into one of the following three paradigms: • Machine learning (ML) (in particular, handcraftedfeatures-based ML) paradigm: They apply hand-crafted features to describe the distribution of rumors in high dimensional space.
C. CONTRIBUTIONS Our contributions with this paper are summarized as follows: -This survey was conducted in a systematic manner guided by the instructions mentioned in Keele  [29]. The Systematic Literature Reviews (SLR) methodology aims to provide an assessment of a research subject as fair as possible by being auditable and repeatable. According to Keele  [29] , the overall purpose of the SLR is to provide a complete list of all studies related to a particular subject while relying on three phases: planning, conducting, and reporting the review. Accordingly, we explain the strategy used for conducting this survey in detail. -Available datasets used by researchers to validate their approaches in social networks are examined. -Different architectures of DL for rumor detection are investigated.
Kumar and Shah  [32]  presented a comprehensive survey highlighting how an actor is involved in spreading false information as well as how algorithms are developed to detect false information. Zhou and Zafarani (2018) highlighted some potential characteristics of fake news while also employing DL methods for detection. In summary, most of the published surveys focus generally on rumor detection without focusing on how DL techniques may be employed for rumor detection. In the current paper, we will present a systematic literature review exploring the most important works related to rumor detection on microblogging such as Twitter and Weibo using only the DL techniques. The proposed survey will also focus on public datasets used in the studied works. Table  1  depicts existing surveys on rumor detection over social media.
C. SOURCE OF INFORMATION To conduct our systematic literature review, the following digital libraries and datasets have been selected: -IEEE Explore (www. ieeexplore. ieee. org) -Springer Link (www. springerlink. com) -Science Direct (www. sciencedirect. com) -ACM Digital Library (https://dl. acm. org) -Google Scholar (www. scholar. google. com) The search keywords used to find relevant studies were ''Rumor detection'' OR ''Fake information'' AND ''Deep Learning'' OR ''Deep Neural Networks'' OR  [name_of_DL_method]. Resulting papers after the search step involve both rumor detection and DL. A preliminary step is needed to remove redundant papers. Details about the number of resulting papers are reported in Table  3. In addition, 11 works are identified through the references' list and ''cited by'' searching.
V. DEEP LEARNING IN RUMOR DETECTION The study of the considered studies shows that different DL models, frameworks and approaches are proposed for rumor detection. Each study has tested its proposed approach on different datasets to ensure the reliability and credibility of their study. In the following sections, we start by reviewing the most commonly used real-world datasets for the task of rumor detection (a complete list is shown in Table  7 ). Thereafter, we focus specifically on the datasets used in the considered studies.
1) PHEME DATASET The PHEME is a public dataset, which is made by  Zubiaga et   The dataset contains, for all five events, 5,802 annotated tweets of which 1,972 were rumors and 3,830 non-rumors. For each event, the authors grouped the related tweets in one directory with two subfolders: a subfolder containing rumors' tweets and the other containing non-rumor s' tweets. Both the folders have subfolders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet.
2) KAGGLE DATASET The dataset is available in comma-separated values (CSV) format and consists of three files containing a collection of webpages cited separately in Snopes. com,  6 Emergent. info,  7 nd Politifact. com. 8 Researchers can conduct multi-labels classification task easily due to the well-organized structure of the dataset, which is presented as follows: -Snopes dataset is 16. 9k x 12; where the class labels of the rumor assigned by Snopes. com editors are true, false, mfalse (mostly false), mtrue (mostly true), mixture, or unverified. -Emergent dataset is 2145 x 15; where the class labels of rumors assigned by emergent. inf editors are true, false, or unverified. -Politifact dataset is 2932 x 12; where the class labels of the rumor assigned by politifact. com editors are true, mostly-true, half-true, barely_true, mostly-false, false, or pants-fire (''pants on fire'').
3) NEWLY EMERGED RUMORS ON TWITTER The newly emerged rumors dataset is a collection of rumors that rise and fall in a short time. The collection consists of 12 different datasets. The datasets allow researchers to conduct the rumor classification task as well as the rumor tracking task. The class labels of rumor assigned by editors are true, anti-rumor, a question about the rumor, and post not related to the rumor (even though it contains the queries related to the rumor, but does not refer to the rumor).
5) CREDIBILITY CORPUS The corpus consists of datasets containing information that occurred on social media written in French and English. These datasets are made by crawling the microblogging platform Twitter as well as from the web documents. The corpus consists of: (i) one corpus describing texts from the web database about rumors and disinformation; (ii) four corpora from Twitter about specific rumors (two each in English and French); (iii) four corpora from Twitter (two each in English and French); and (iv) four corpora from Twitter about specific rumors (two each in English and French). The size of the corpus and the amount of collected rumors differ from one corpus to another; however, they share a common column ''rumor indicator'' with value 1 (is a rumor) or -1 (not a rumor).
1) LIAR DATASET The LIAR dataset is a well-balanced benchmark dataset, which includes 12. 8K human-labelled short statements from POLITIFACT. COM's API Wang  [59]. The used class label for the truthfulness ratings in the dataset is fine-grained and each statement is categorized into six groups: pants-fire, false, barelytrue, half-true, mostly-true, and true. Figure  12  presents an excerpt from the LIAR datasets listed in Wang  [59]. In K6 and K12, an annotated dataset comprising about 12. 8K annotated short statements with six fine-grained classes and the information about the authorization source is used. Statements are reported from 2007 to 2016. To enhance the quality of the dataset, Roy et al. [57]  suggested adding more labelled data as well as the actual statements of the speaker to train the model more accurately.
2) PHEME DATASET Although the PHEME dataset is a benchmark dataset for stance detection, authors of K1, K2 and K11 used it to experiment on the rumor detection task. In K11, approximately 5800 tweets centered on five rumors stories collected and annotated within the journalism use-case of the project are used. The dataset contains also twitter conversations, which are initiated by fake news. The conversations include tweets and responses of the public on the false news. The dataset contains 330 conversational threads out of which 297 are in English and 33 are German. In K1, Alkhodair et al. [1]  used the PHEME dataset to train the baseline classifiers as well as to check the performance of the proposed LSTM model. They also found that the different feature engineering yields different results per each event of PHEME dataset.
3) TWITTER AND WEIBO DATASET Instead of using available datasets, some researchers prefer to use and collect their own datasets. Social networking like Twitter and Sina Weibo make sample data available to researchers through their APIs Lomborg and Bechmann  [39]. Authors of papers K2, K4, K8, K13, K18, K23, and K24 tested their approaches on available Twitter dataset or Sina Weibo dataset as is without any preparation, whilst others such in K5, K9, K10 and K25 made some filtration or enrich the dataset to conform an acceptable class balancing. The Twitter dataset contains 498 rumors, which are collected using different keywords extracted from the real-time rumordebunking website namely Snopes. com. This dataset also contains 494 non-rumors events, while, Weibo dataset contains 2,313 rumors and 2,351 non-rumors events-for more details, interested readers are referred to Ma et al.
[44]. In K10, Wang et al. [58] used the dataset collected by Boididou et al. for the Verifying Multimedia Use task that takes place as part of the 2015 MediaEval Benchmark Boididou et al. [5]. The data is coming from Twitter as well as from Weibo. The dataset, which is collected from Twitter, is used for detecting fake content. For this purpose, the authors kept textual content as well as the image/video attached with the tweets. Tweets without any text or image are removed. The Weibo dataset is collected from authoritative news sources of China from May 2012 to January 2016 Jin et al. [26]. To enhance the quality of the dataset, the authors followed the preprocessing steps described in (Ma et al. [43] ; Wu et al. [63] , which also involved removing duplicated and low-quality images. In K3, K7 and K12, the authors preferred to collect and construct their own corpus.
Although this approach is acceptable in the research community, there is no guarantee that the quality of constructed corpus is high and later this will complicate the comparison made by other researchers. In K7, Li et al. [36]  made their dataset by obtaining a set of known rumors from Weibo through Weibo APIs. The dataset has the same number of related microblogs to ensure the balance of the dataset. Accordingly, the dataset contains the same number of rumors and non-rumors events. In K12, the dataset comprises about 40K microblogs that are approximately balanced: 9600 true information, 8000 rumors, 8000 biases, 8000 fake news, and 8000 spams. Another interesting dataset obtained by Weibo APIs is found in K19. Jin et al. [26]  built their corpus by attached to the original tweet text the images and available surrounding social contexts from the rumor and non-rumor sources.
At the end, the corpus contains about 40k tweets with images. not 'newsworthy' or that did not have an explicit confirmation of veracity are eliminated. The final dataset consists of 94 true and 446 false stories. In K17, Chen et al. [12]  used two public datasets Kwon et al. [33] ; Castillo et al. [9] , and their own dataset containing 498 rumors collected by using keywords extracted from verified fake news published on Snopes. com.