dataesr/scientific-paragraphs-categorization huggingface dataset:
With this, it is possible to make the network aware of the concurrent demand by multiple users for a service or content item initially provided with unicast. The detection against a pre-defined threshold can be used to dynamically trigger the establishment of an MBMS user service to offload the unicast network. When concurrent user demand for that same service/content drops below a configurable level, the delivery mode can be configured to transition back from broadcast to unicast. Expway's software is installed on a virtual machine that comprises the following components: -The Broadcast Multicast Service Center (BM-SC): the core multicast/broadcast functionalities that receive the content through the xMB interface in unicast and converts into multicast data and sends to the MBMS Gateway (MBMS-GW).
-The MBMS-GW receives the multicast data and forwards it to all relevant eNodeB (eNB) in the network. -The Broadcast Provisioning Manager (BPM): the provisioning manager that provides the web interface to schedule the eMBMS services and control the BM-SC. -The Consumption Report and Analytics receive the consumption report messages from the middleware on the phones, analyse the audience size and decide when to switch between unicast and eMBMS delivery. -The emulated Mobility Management Entity (MME) is used to handle the control plane setup of the eMBMS session. Its purpose is to bypass the MME in case the operator's MME does not support eMBMS. Expway has provided the Bittium phones with the latest Expway's middleware version that supports MooD functionality. All Expway's software in both terminal and network is compatible with the eMBMS functionalities specified in 3GPP Release-14.
Note that the complete testbed is Release-14 compliant. However, the RAN only supports the configuration of a 15 kHz sub-carrier spacing with extended CP. A diagram of the testbed architecture is shown in Fig. 1. A further aspect explored in the testbed is the provision of services like those are available today by Hybrid Broadcast Broadband TV (HbbTV)  [24]. The live adaptation of a Moving Picture Experts Group (MPEG) Transport Stream (MPEG-TS) to include HbbTV application signalling permits the transmission of regular TV services with the possibility to display data associated to the programme at the UE. This permits to use the smartphone as a TV tuner and provide access to on-demand content without the need of a specific application but a standard one that depending on the service will be able to access different content servers.
The project built an international network of RF spectrum observatories continuously collecting long-term spectrum data to study the trends in spectrum utilization and to identify frequency bands where spectrum sharing could be feasible. The TUAS 5G testbed is developed in the 5G-Xcast project to support eMBMS for demonstrating the PW system. The testbed is updated to contain the necessary network entities, e. g. , MBMS-GW and BM-SC. The architecture developed for PW demonstrations is illustrated in Fig. 3. The alert originator using the one2many Public Warning Platform (PWP) generates the PW messages, which are then transmitted to several users using PTM. The reception of the alert is triggered by a Google Firebase Cloud Messaging (GFCM) push message in the demonstrations using LTE radio.
B. Technical Description The HbbTV concept, which is currently based on Digital Video Broadcasting (DVB) along with an auxiliary broadband connection, is extended to a framework in which the broadcast signal is carried over an LTE eMBMS session and the access to unicast data is provided with LTE unicast. Target receivers under consideration are smartphones and tablets as well as TVsets. MPEG-TS is specified as a container format to encapsulate audio and video streams together with programme and system information. Three TV programmes are encapsulated with one of them linked to the HbbTV service information. The main video feed (say linear TV) can be delivered as a standard compliant MPEG-TS. HbbTV services are signalled through the linear broadcast service in the so-called application information table; the application (such as the red button  [28] ) enables auto-start and is automatically loaded when the user tunes in to the broadcast service.
C. Demonstration Setup and Results For the demonstration, a high-level architecture of the demonstrator is shown in Fig. 10. Local content is provided using VLC to playback a 30 min football sequence on a loop. This is encoded and packaged by the FFMPEG open-source multimedia suite, and made available to the framework through a webserver. The content is generated locally to avoid any issues around content rights, particularly as the original content streams are intended for the UK while the demonstrator may be shown elsewhere. The eBox components in both implementations ingest the unicast content and prepare it for delivery over multicast. It also includes an analytics server for monitoring the audience size. The provision manager provides a means for configuring and scheduling streams. In the mobile case, an LTE network is provided using Amarisoft software coupled with a software defined radio unit programmed as an eNB.
C. Demonstration Setup and Results The demonstrator setup is shown in Fig. 14 , where the hardware components are depicted. The multi-link server is able to reroute the data packets through the different available links, and in this case, Wi-Fi as the unicast connection and a multicast session over eMBMS. At the user end, the multi-link client performs an adequate data merger operation between the traffic delivered via multicast and unicast. In our case, the client is able to analyse the multicast traffic from the eMBMS connection and evaluate packet losses. In the case that a packet drops and errors occur in the eMBMS multicast stream, a repair mechanism starts and the missing packets/chunks are requested to the server and sent via the unicast link. An example result shown in Fig. 15  indicates improved reliability by multi-link bonding at the user device.
The UE receives the multimedia components primarily using the eMBMS broadcast. Further, the receivers that are not broadcast capable fetch the content via the bonded unicast capacity. As detailed in  [20] [34], the system has been successfully demonstrated, simulating an accident taking place in a port. Alert content describing the incident and providing instructions for the alert recipients has been generated and transmitted using the developed system. The delivery using eMBMS appeared to be robust. Dynamic spectrum allocation has been demonstrated successfully. In the demonstration both LiveU multi-link Databridge and eMBMS devices connected rather quickly to the dynamically added network as soon as it became available. Bonding connections on the LiveU Databridge has been demonstrated with multiple LTE networks being used concurrently to deliver the data to several UEs, where the LiveU Databridge acted as a Wi-Fi access point via which the alerts were transmitted to the UEs.
C. Demonstration Setup and Results Based on the provided description and architecture, ATSC 3. 0 service layer software for both server and client are interfaced with the 5G system-level simulator provided by 1 M, N and P refer to the number of vertical, horizontal and polarization arrangement of antenna elements, respectively. 2 The 8 vertical antenna elements for each polarization are hard-wired and are connected to a TXRU. 3 Multicast MCS index is selected to provide as low error rate as possible while maintaining sufficient transmission rate for the service delivery. Nomor Research. The multimedia content given by BT is prepared and stored on the server. Multi-Link logic described previously is provided by BLB and integrated into the simulator. MPEG-DASH compatible ExoPlayer is used for content playback, which is also interfaced with the Smartlib QoE Library and QoE Analytics solutions from Broadpeak for QoE monitoring and reporting.
In this article, we present a new strategy that compares all the reads that map at several positions, and their annotations when available. In many cases, all the hits co-localize with the same feature annotation (a duplicated miRNA or a duplicated gene, for instance). When different annotations exist for a given read, we propose to merge existing features and provide the counts for the merged features. This new strategy has been implemented in a tool, mmannot, freely available at https://github. com/ mzytnicki/mmannot.
A final strategy, implemented in ShortStack  [8] , adapts the previous idea to sRNA-Seq. Here, the estimation of the profile expression models the highly irregular expression profile, which is used to assign the multi-mapping reads. Some papers, such as  [9] , use a radically different pipe-line. They first collect different nucleotidic databases (usually, one for each class), then map the reads to the databases. A read mapping to the database of a given class suggests that the read belongs to this class. However, this pipe-line does not solve per se the problem of the multi-mapping reads, since one read can map to several databases. The second source of ambiguity, where several annotations overlap, may have two causes. First, there may be an inconsistency in the annotation. For instance, miRNAs are not known to co-localize with snoRNAs.
Third, quantification tools usually expect the library to be stranded, or unstranded. Therefore, it is not (directly) possible to quantify reads which are antisense with respect to a gene, and the reads that can adopt any direction (for instance, in flanking regions), in a single run. Last, multi-mapping reads are usually discarded (this is the default behavior), or counted once per gene. To solve these ambiguities, we propose a new strategy which is implemented into a sRNA class quantification tool, called mmannot. mmannot is freely available at https://github. com/ mzytnicki/mmannot. In this work, we compared mmannot with the other five methods described here.
Each column provides the percentage of reads, classified by annotation. The ambiguous category includes hits that overlap several annotations. sRNAs with low counts were grouped into the category "other". The dots in the bars provide the percentages of the reads that where annotated. Likewise, the proportion of miRNAs are over-estimated in the human and the zebrafish data sets. As a result, using uniquely mapping reads only provides a highly distorted image of the complete sRNA class repertoire associated to a data set.
Multi-mapping aware strategies increase the number of annotated reads For all data sets, using multi-mapping strategies increases considerably the percentage of annotated reads, showing that the repertoire of expressed regions is largely associated to repeated regions in all genomes. For the A. thaliana data set, the number of annotated reads is 0. 8-1. 8M using the "unique" strategy, whereas it is 1. 4M-3. 0M for featureCounts, 2. 1M-4. 4M for MMR, 2. 1M-4. 3M for ShortStack, 2. 2M-4. 5M for the "random" and "ratio" strategies, and 2. 5M-5. 2M using mmannot. The "sequence" strategy gives similar results as mmannot. All the other real-life benchmarks rank the tools similarly. The "unique" and "feature-Counts" use the least number of reads (since they behave similarly). "mmannot" and "sequence" use the greatest number of reads.
Some multi-mapping reads may have hits that do not co-localize with any annotation. These reads may be unannotated by the "random" strategy, and the associated weight in the "ratio" strategy is lost. These reads can also be misplaced by MMR and ShortStack. As a consequence, some of these reads are not quantified, resulting in less annotated reads. In the "mmannot" strategy, all the hits of a read are compared with an annotation, and used to annotate the read. unable to map to the genome. The H. sapiens dataset provides the best annotation rate (almost up to 100%), because the quality of the reads is extremely good (we cannot exclude that low quality reads have been removed by the sequencing facility), and because the annotation is also exhaustive. Fig 7 confirms that mmannot gives the best annotation rates, together with the "sequence" method, while the "unique" and "featureCounts" methods provide worst results.
Finally, we wanted to confirm that mmannot uses significantly more reads than other methods. We performed a one-sided Wilcoxon signed-rank test between the number of reads used by mmannot on all datasets, and the number of reads used by the other methods. Results, shown in Table  1 , clearly show that mmannot significantly uses more reads. Note, however, that the "MMR" and "sequence" methods seem to give slightly better results than other methods. This is however, only caused by the fact that these methods could not be used for the H. sapiens dataset. The test is thus less powerful in these cases. Thus, mmannot improves considerably our knowledge of the structural and functional annotation of the gene repertoire associated to small RNA-seq data sets.
Both mapping tools yield similar results Results on almost all datasets show that BWA maps slightly more reads. Moreover, BWA also provides much more hits per read than bowtie. This is a probably an advantage for BWA. However, the number of misplaced reads is close to zero in both cases (see  Fig 8). Our results show that both tools give a similar picture of the sRNA repertoire. The only exception is D. rerio, where BWA maps substantially more reads, and most of these reads are ambiguous. After inspection of these reads, we discovered that these reads match highly repeated regions: they match piRNA annotations, as well as gene flanking regions and introns. This confirms that BWA can find more hits per read than bowtie. Note that choosing other parameters for the mapping tools could lead to dramatic changes in the results.
Changing the order of the priorities does not change the quantification profile given by mmannot The data sets show the quantification of the sRNA classes for two types of configuration files. The "mmannot BWA" uses a somewhat expected configuration file, where one read mapping both a miRNA and a piRNA would count for a miRNA. We reversed the ordering of the priorities in the "rev" columns. Strikingly, the results do not significantly differ. The only exception is the human data set. Using the expected configuration file, the number of miRNAs is strictly greater than the number of piRNA. However, using the reverse configuration file, the number of piRNAs is much greater. To understand why, we compared the miRNA and the piRNA annotations. It turns out that 1178 out of the 3934 annotated miRNAs overlap a piRNA annotation.
All strategies leave a high number of reads unannotated Whatever the multi-mapping strategy used, or the organism under study, all data sets show a high percentage of reads that are located in intergenic regions, located away from upstream and downstream regions, and for which no annotation is available (up more than 20% for A. thaliana). This lack of annotation is partially resolved with ambiguous annotations for D. rerio data sets (where about 25% of the reads are annotated as ambiguous).
Analysis of the ambiguous reads of A. thaliana data set We focused on the ambiguous reads of the A. thaliana data set in order to analyze their origin in that organism. Most of them come from downstream-upstream regions, which are probably intergenic duplicated regions. Then, the most frequent ambiguous annotations involve a transcribed region within the downstream, upstream, or intron regions. These elements might be produced by the unannotated regions to target genes, or belong to a non-functional genomic duplication. Interestingly, the most frequent ambiguous annotation involving two transcribed regions is miRNA-gene (-). In plants, a miRNA and its target may be 100% identical, and this poses a real problem to the annotation of the reads. We decided to study the pairs of miRNAsgenes that were involved in this class, with at least 100 reads supporting this association.
We saw, on the human data set, that the piRNA annotation might be flawed, and this dramatically alters the read annotation produced by our method. However, a simple, common-sense, ordering of the annotation types (favor "miRNA" when a read overlaps a miRNA annotation and a piRNA annotation) produces the expected results. Alternatively, discarding unexpected annotations (e. g. piRNAs in brain) might also be a sensible method. In the future, we would like to find a way to optimally sort the annotation types. Most annotation methods, including mmannot, use simple rules for annotating the reads, mostly based on priorities given to annotation types. For instance, a read co-localizing with a miRNA and an intron is annotated as "miRNA". Whereas simple rules exist ("miRNA" > "introns"), other rules are arbitrary ("miRNA" is not comparable with "snoRNA").
Data used We used published data sets, covering several eukaryotic organisms, where the related articles provided a quantification of the expression of the sRNA classes. Table  2  summarizes the data used. The H. sapiens data set originally contained 16 samples. We display here only the five first samples, for the sake of brevety. The other sample produce similar results. Note that at most 10% of the reads are unmapped, and the average percentage of unmapped is around 3%. Although not analyzing unmapped read may bias the results of the study, we believe that the impact is greatly limited. In this benchmark, we recorded the time spent by mmannot with the output statistics files option enabled. These files take substantial time to be computed, and mmannot is much faster with default options.
We also compared our tool with the widely used featureCounts  [10] , with the caveats we mentioned in the Introduction. First, we extracted the small RNA classes from the annotation file, added missing features (introns and flanking regions), and generated another annotation file in SAF format (read by featureCounts). An example script (used for the A. thaliana data set) is given as Supporting Information. Second, we used featureCounts as usual. Of note, fea-tureCounts either discards multi-mapping reads, or counts one hit per mapping gene. We chose the first option.
Organism We also implemented a last strategy. We extracted the sequences from the previously generated annotation file, and the genome sequence. We thus built a nucleotidic database of the sequences of the regions of interest. We then mapped the reads to this database. However, we noticed an unwanted behavior: some reads, which mapped with no error on the genome (this information was given by the standard genome mapping strategy we used for mmannot), mapped with one or more errors on the database. As a result, this strategy classifies them as belonging to one of the known class, whereas they most likely come from some unknown locus. In order to avoid this bias, we also included the whole genome in the database. If a read maps to the genome and a known sRNA class (with the same number of errors), the reads will be attributed to the sRNA.
If a read maps to the genome with n errors, and a known sRNA class with m > n errors, the read will be declared unannotated. However, this strategy does not solve the multi-mapping problem, and a read can map to several sRNA classes. In this case, we attributed the multi-mapping read to the first class given by the mapping tool (here, BWA). The code used to generate the database, and map the reads is given in Supporting Information. Of note, this strategy requires substantially much more memory, since each read may map to the genome, and its sRNA sequence, on both strands. As a result, we could not use it on our computer on the human data set, because it required more that 32GB or RAM. mmannot robustness to the ordering of the features. The configuration file sorts all the features that will be annotated and quantified (e.
Reads were also mapped to the sequence of sRNAs using BWA (the "sequence" strategy). The simulated data set was generated by the script sim_sRNA_library. py [8] , which was trained on our first A. thaliana sRNA-Seq data set and miRBase v22. 1  [31]. We added coding genes, pseudogenes, and transposable element annotations (given by TAIR10) to the MIRNA, TAS and HET annotation (generated by the script). All the experiments were performed on a machine with 6 Intel Xeon CPU E5-1650 v4 cores at 3. 60GHz and 32GB RAM running Ubuntu 19. 04. All the commands used in this analysis are given as Supporting Information.
g. satellite-based). Filtering performance is evaluated in terms of non-ground removal and ground preservation compared to ground-truth elevation data, as tested on six different topographic datasets of challenging vegetated terrace landscapes. Section 2 of this article elaborates on the technical details of the TERRA algorithm (2. 1), the background of the several test sites (2. 2), the diverse origin of topographic data (2. 3), the parameterisation of the algorithm (2. 4), and the experimental design for performance assessment of TERRA (2. 5). Filtering results by TERRA and its performance are then presented in Section 3, while Section 4 furthermore touches on its limitations and further potential.
According to the Nyquist-Shannon theorem of sampling, the η parameter should be chosen in order to preserve terrain contour curvature at least two times coarser than c (η >= 2c). The M parameter should be initiated with the value of b, given that the iterative filtering will "scrap" a given non-terrain object cell by cell, with a maximum number of b (this iterative filtering is illustrated in Figure  2 ). The latter λ parameter is less sensitive and controls the desired level of smoothing on terrain data related to the used kernel averaging function. The TERRA algorithm is freely available as R script under GNU GPL licence at: https://www. umr-lisah. fr/?q=fr/scriptsr/terra-script-r (see Supplementary Material A).
Topographic data sources The various sites offer an interesting set of test cases, given their diversity in topographic data source. In Treviso (TRE) and Verona sites (VER-O and VER-D), very high-resolution elevation data (0. 1 m) was obtained using Unmanned Aerial Vehicles (UAVs) and photogrammetric processing. An independent source of ground elevation data was provided through accurate field-measurements of DGPS reference points. In Roujan, high-resolution surface topography data (1 m) was obtained from Pleiades optical satellite imagery (ROU-P) and LiDAR laser altimetry (ROU-L), with the latter also providing a reference terrain model without vegetation. The Kamech dataset (KAM), high-resolution elevation data (0. 3 m) was obtained by photogrammetric processing of digital aerial imagery acquired by the Tunisian office of topography and cadastre.
Pleiades satellite In Roujan, Pleiades satellite imagery was recorded during the leaves-off period of January 2013 (ROU-P). A stereo pair of images was taken with a high incident angle of 30° (base-to-height ratio of about 1/1. 6). A 1-m DSM was constructed using MICMAC software  (Pierrot-Deseilligny et al. , 2011; Pierrot-Deseilligny and Paparoditis, 2006). For more technical details on the photogrammetric analysis of this particular dataset, the authors refer to  Sofia et al. (2016). Validation with ground-measured DGPS points showed a vertical error standard deviation of 0. 51 m.
Airborne SfM photogrammetry For Kamech (KAM), aerial imagery was available from airborne survey performed by the Tunisian office of topography and cadastre during June 2010. The whole set of images was acquired with a Vexcel UltraCamXp at an average altitude of 5200 m. From this set, four images covering the area of interest were extracted. Each image has 11310*17310 pixels with an average ground sampling distance of 0. 3 m. Four GCPs picked on Google Earth imagery were used to obtain an absolute geographic reference (estimated vertical accuracy <10 m). SfM photogrammetric processing was carried out using Agisoft Photoscan software and a DEM at 0. 3 m resolution was exported. For this test site, no validation data is available.