dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-sequencing analysis We removed adapter sequences and performed quality trimming using TRIMMOMATIC  (Bolger et al. , 2014). Reads were mapped to the reference genome using STAR, v. 2. 7. 0e  (Dobin et al. , 2013)  with the parameter outFilterMismatchNoverLmax 0. 05. We input the bam files to R v. 3. 5. 1 using the package R/SAMTOOLS. We obtained the genome annotation from Araport11 and gene models and read counts per gene were obtained with the packages GENOMIC FEATURES and GENOMIC ALIGNMENTS, respectively. We removed low-expressed genes and analyzed 28 843 for differential expression using DESEQ2 after performing regularized log transformation  (Love et al. , 2014). We compared all the replicates from β-estradiol-induced XVE-jsi1-mCherry lines with the replicates from control XVE-mCherry lines with and without βestradiol induction and kept genes with log FC > 1.
5 and adjusted P < 0. 05. We performed Gene Ontology (GO)-term analysis for biological processes using the THALEMINE tool  (Krishnakumar et al. , 2014). The data sets were deposited in National Center for Biotechnology Information's (NCBI's) Gene Expression Omnibus and are accessible through GEO Series accession no. GSE142128 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE142128). To assess the significance of enrichment for TF binding sites, we first determined the direct target genes of ERF3, 4, 7, 8, 10, 11 and ZAT10 using available DNA affinity purification sequencing (DAP-seq) data  (O'Malley et al. , 2016). We overlapped each list of putative direct target genes with genes upregulated upon jsi1 induction (FC > 1. 5, P < 0. 05). We determined the significance of the overlapping genes with Fisher's exact test Ó 2020 The Authors New Phytologist Ó 2020 New Phytologist Foundation  New Phytologist (2021) 229: 3393-3407  www.
Reverse transcription PCR for RNA-sequencing validation Total RNA was extracted from three independent replicates from each A. thaliana line (XVE-jsi1-mCherry-1 and 2 and XVE-mCherry) using the same protocol for RNA-sequencing (RNAseq) samples. Complemetary DNA (cDNA) was generated from total RNA using the iScript cDNA synthesis kit (Bio-Rad). We performed quantitative reverse transcription (qRT)-PCR using FastStart Universal SYBR Green Master mix (Roche) according to the manufacturer's instructions. The relative amount of amplicons in the samples were calculated with the 2 ÀΔΔC t method  (Livak & Schmittgen, 2001)  with actin2 (AT3G18780) as the reference gene  (Czechowski et al. , 2005). We calculated FC in the expression level of each gene in the XVE-jsi1-mCherry lines compared with the XVE-mCherry line, and data are represented for each jsi1-mCherry line as the mean of three replicates.
We calculated statistically significant differences in gene expression between each jsi1-mCherry line and mCherry line using ANOVA followed by Dunnett's multiple comparison test with P < 0. 05. For evaluation of the induction of the ERF branch in maize, 10 EGB seedlings infected with SG200 were collected at 4 and 6 dpi. Mock seedlings were infected with water, and tissue was collected for each time point. Three independent replicates were performed for infected and mock tissue at each time point. RNA extraction, cDNA synthesis, and qRT-PCR and data analysis were performed as previously described but using a cyclin-dependent kinase (CDK; GRMZM2G149286) as the reference gene  (Lin et al. , 2014). Primers used for RT-PCR are described in Table  S2.
, 2014) , and ZmACS6, ZmPR5 and ZmOSM34. Gene induction was evaluated during U. maydis infection at 4 and 6 dpi where Jsi1 expression is relatively high (Figs 4a, S1b). Most genes tested were found to be significantly induced at 4 and 6 dpi, with the exceptions of ZmERF2 and ZmOSM34 (which were not found to be induced) and ZmERF1 (which was significantly induced only at 4 dpi) (Fig. 4a ). These data indicate that U. maydis infection induces genes associated with the ERF branch in maize. To test whether Jsi1 is able to induce these genes in maize, we used detached maize leaves to express Jsi1 and Jsi1m under the 35S promoter via biolistic bombardment and evaluated gene expression 10 h after bombardment. ZmERF1, ZmERF1a and ZmPR5 were induced by expression of Jsi1-mCherry compared with Jsi1m-mCherry in three replicates, whereas ZmERF105 was induced in two replicates, and ZmERF12 and ZmACS6 were only induced in a single replicate (Fig.
Acknowledgements We would like to acknowledge  Fernando Navarrete  for useful comments during the development of this project;  Dr Ari Pekka Mähönen  and  Mikko Herpola  for providing us the XVE inducible cassette;  Rothamsted Research Limited  and  Dr Kostya Kanyuka  for kindly providing us the vector PV101 for virus-based protein expression in maize;  Dr J. Matthew Watson  for editing; GMI/ IMBA/IMP service facilities, especially  Dr Robert Heinen  and  Ms Zuzana Dzupinkova  from the  Molecular Biology services ,  Mr Pawel Pasierbek  from the  BioOptics facility , and  Mr Mathias Madalinski  from the  Protein Chemistry Core facility  for their excellent technical support, and the  Plant Sciences Facility  at the  Vienna BioCenter Core Facilities GmbH ; we thank also  Dr Diaa El-Din Daghma  and  Dr Jochen Kumlehn  for technical support.
This work was supported by the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/ 2007-2013) / ERC  grant agreement no. GA335691  (' Effectomics '), the  Austrian Science Fund  (FWF; I  3033-B22 ,  P27818-B22 ) and the  Austrian Academy of Sciences. IF and KZ were supported by the  Deutsche Forschungsgemeinschaft  ( INST 186/822-1 ).
The success of an IDS is challenged by the network's implementation flaws and the complexity of the attacks, where it also has to deal with the availability and heterogeneity of the traffic data sources to find malicious behaviors. IDSs' performance is another challenge, as real-time network monitoring requires very fast processing and inspection of network traffic. The amount of data generated in the network represents thus a major problem. When the traffic is huge, the IDS will be unable to inspect every arriving packet. As defined by the Institute of Electrical and Electronics Engineers (IEEE), the Internet of Things (IoT) is a collection of sensors that form a network connected to the Internet. IDSs designed for IoT environments should be implemented on programmable devices, such as FPGAs, to facilitate adaptation to IoT environments.
They also lack scalability and are limited to low-dimensional problems. Therefore, using traditional ML approaches is not suitable in such environ-ments with limited resources. As for intelligent IoT devices, the detection of anomalies and intrusions requires real-time data processing, however, traditional ML approaches are not designed to handle real-time data streams. In addition, ML algorithms assume that the entire data is available for processing during the learning phase, which is not true for IoT data. This poses many challenges when traditional ML approaches have to process a huge amount of data, especially, when the data dimensionality is high  [9]. This discussion is applicable for security-related functions in IoT where realtime data is processed to identify anomalies, intrusions, etc.
Content-based sampling strategies are also called filtering algorithms and are outside the scope of this work. The aim of this study is to check whether summarized (sampled) are sufficient to detect attacks in high-speed networks. We aim at quantifying the robustness of the traffic characteristics under different sampling strategies. The sampling process selects some traffic items from the whole received traffic based on the used sampling policy and chosen sample size. Thus, sampling is considered to be an approximate method of measurement. Since in the sampled dataset many packets are not sampled, the traffic distribution of sampled data may deviate from the distribution and statistical characteristics of the original traffic. Taking these sampled data as the input of any attack detection algorithm will inevitably affect the accuracy of the detection algorithm.
The intersection of the chosen characteristics sets in each database separately formed a robust final feature set. Various sampling techniques are applied to assess the robust feature set. Results showed that sampling techniques have a slight impact on reducing the degradation behavior of the anomaly detection process and that the characteristics of many packets are most affected by distortion. Data sampling depends on the method of measuring the data. Therefore, sampling is subject to a certain variance in the total traffic distribution that affects the accuracy of the anomaly detection results. To deal with this problem, Pan et al. [21]  suggested a method of measuring packet sampling based on the IP flow. The sampling rate is variable and depends on the arrival process sequence of the IP flow at both packet and flow levels and the flow size.
Subsequently, the sampling probability is adjusted based on the number of unlike samples in the stream. Two evaluation metrics are used, RMSE to measure volume anomalies in the size of the IP stream and the hit detection rate to measure the sequence of variance in the stream. The results showed that for a sample rate of 1%, the proposed solution detected 27 out of 30 of worm and DDoS attacks, while traditional random sampling only detected three. Singh et al. [22]  studied the statistical impact of data sampling on traffic analysis by calculating the statistical parameters for unsampled data set and then for sampled data. Subsequently, a comparative analysis of the unsampled and sampled data sets was performed. The following sampling algorithms were used to sample the data: Simple Random Sampling (SRS), Systematic sampling, and under-over sampling.
The following attributes from the NSL KDD dataset were considered: duration, src_bytes, dst_bytes, wrong_fragment, num_compromised, num_file_ creations, and srv_count. The statistical parameters used to compare the network characteristics before and after sampling were: the mean, the range including the minimum and maximum values for each attribute, and the standard deviation showing the distribution of the network. Silva et al. [18]  developed a data sampling framework based on a multi-layered design. The framework selects the characteristics and sampling technique according to the measurement task. The implementation of the framework is based on a sampling taxonomy that determines the granularity, the selection scheme, and the selection trigger. A comparison between the above papers and our work is summarized in Table  1.
III. METHODOLOGY This paper focuses on studying existing static sampling techniques. The efficiency of a sampling policy depends on its capability to balance its precision with the computational resources required. This work investigates the statistical effect of sampling the traffic stream and the execution time needed to sample the traffic. Our methodology consists of using the sampling algorithms presented in in this paper to summarize a real traffic dataset. This allows us to understand the behavior of these algorithms. In order to quantify the distortion introduced by the sampling procedures, we consider comparing different statistical metrics. The overall quantification of statistical changes between sampled and unsampled traffic is defined by the Overall Statistic (OS) calculated as follows  [37] ,  [38] : OS = |(µ 0 -µ)| µ 0 + |(med 0 -med)| med 0 + |(std 0 -std)| std 0 (3) where µ 0 is the real average value estimated before sampling the traffic, µ is the estimated average value of a traffic calculated after sampling, med 0 and med are the median values of a traffic parameter being estimated for the before and after sampling respectively, and std 0 and std are the standard deviation values of the traffic estimated before and after sampling respectively.
Our experiment in Section IV can be thus summarized as follows: • Scenario 1 -Without sampling: In this scenario, the mean, standard deviation, and median of the unsampled dataset are calculated. • Scenario 2 -With sampling: In this scenario, the mean, standard deviation, median, and OS are calculated based on the sampled data. Different sampling strategies with different parameters will be considered. The computational resources and execution time needed for sampling are also calculated. In the end, a comparative analysis of both scenarios is carried out. The dataset used in this work is the NSL-KDD  [39]  which has been developed to enhance the KDD CUP 99 dataset. The main concern with the KDD CUP 99 is the huge number of duplicate records in the training and testing subsets, which leads to inaccurate intrusion detection results  [39].
A. COMPUTATIONAL RESOURCES The traffic scenario used to evaluate the sampling policies is the training set of the NSL-KDD dataset which consists of 125. 974 records with a size of 18. 662 MBytes. The computational resources of packets sampling techniques are analyzed according to the execution time which depicts the time spent in summarizing the traffic. A higher sampling ratio leads to more packets selected. Thus, intuitively, the computational resources of sampling algorithms should be proportional to the sampling rates. The execution times of non-stream and stream policies presented in this paper are evaluated in Figures  3  and 4  respectively, based on the sampling ratio and window size (for stream sampling algorithms).
V. CONCLUSION AND OPEN RESEARCH CHALLENGES In this paper, we investigated the statistical impact of network traffic sampling to quantify the amount of deterioration that the sampling process introduces with respect to non-sampled traffic. By performing an off-line analysis of the NSL-KDD dataset, we carried out an experimental comparison of existing sampling techniques and studied their impact on several well-known statistical measures to assess the level of degradation introduced by sampling. Different sampling policies were evaluated, and different features and attacks were considered. Our study suffers from the following limitations: • Without loss of generality, in our work, we evaluated the performance of sampling algorithms on intrusion detection using the NSl-KDD database. However, there are other data sets, such as CAIDA  [41] , CIDDS  [42] , etc.
• The NSL-KDD database contains about 150K records divided into training and testing subsets. It consists of 41 attributes and includes 22 attacks types. These attacks are of four categories: DoS, probe, R2L, and U2R. In our future work, we aim to test the impact of sampling on the detection of many other types of network attacks. Knowing the traffic parameters, a convenient sampling algorithm with calculated compromise (accuracy vs. computational cost) can be configured and fine-tuned accordingly. Several open issues that would benefit from further studies can be identified. • Static vs. dynamic packets sampling. The sampling ratio impacts directly the accuracy of the built sample  [43] ,  [44]. Static packets' sampling algorithms have been used for many years. With these algorithms, all items are somehow selected randomly, except for the deterministic sampling, with a predefined sampling ratio.
Introduction Extensive ice-penetrating radar data have been collected over the years in the Dome C region of the East Antarctic Plateau. These data were initially collected to characterize the Dome C ice core site. Subsequently, the Dome C region was revealed as one of the promising regions to have retained million-year-old ice, based on modeling thermodynamic results  (Van Liefferinge and Pattyn, 2013; Fischer et al. , 2013; Van Liefferinge et al. , 2018). The rare combination of thick ice, relatively low snow accumulation rate, low geothermal heat flux, slow ice flow and clear undisturbed englacial stratigraphy made this site one of the primary target drilling sites for the Beyond EPICA -Oldest Ice European project (BE-OI), with the added advantage of its proximity to the Concordia Station for logistics (Fig.
g. ,  Morse et al. , 1998; Eisen et al. , 2008; Koutnik et al. , 2016; Parrenin et al. , 2017; Muldoon, 2018)  or providing largescale constraints to 1D, 2D and 3D ice flow models (e. g. ,  Leysinger Vieli et al. , 2007; Leysinger Vieli et al. , 2011; Passalacqua et al. , 2018; Muldoon, 2018; Sutter et al. , 2021). Here we provide a spatially extensive IRH data set (data set release: Cavitte et al. , 2020, https://doi. org/10. 15784/601411), centered on the Dome C region of the central East Antarctic Plateau. The ages of the IRHs are established using the AICC2012 ice core chronology  (Bazin et al. , 2013; Veres et al. , 2013). The radar transects were collected over a period of 10 years, by several institutes and under numerous projects. We archive this data set based on the standards established by previous IRH data contributions  (e.
Ice-penetrating radar systems The IRHs were traced across multiple ice-penetrating radar surveys that deployed several generations of modern icepenetrating radar sounders over a decade, between 2008 and 2018, over the Dome C region (Fig. 1 ). The primary set was collected by the University of Texas at Austin Institute for Geophysics (UTIG) and the Australian Antarctic Division (AAD) as part of the ICECAP project (International Collaborative Exploration of the Cryosphere through Airborne Profiling,  Cavitte et al. , 2016)  between 2008 and 2015. This included the Oldest Ice candidate A (OIA) survey flown by ICECAP in January 2016  (Young et al. , 2017). Data were collected with the High Capability Airborne Radar Sounder (HiCARS) 1 and 2 and its Multifrequency Airborne Radarsounder for Full-phase Assessment (MARFA) descendant; in this paper we refer to these data as HiCARS.
The skiequipped DC-3T Basler was able to operate from Concordia Station, allowing for significant local coverage. The Vostok-Dome C airborne radar transect was flown by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas using the Multi-Channel Coherent Radar Depth Sounder (MCoRDS, version 2) in a single flight line in 2013. A P-3 Orion operating from McMurdo Station collected these data as part of NASA Operation Ice Bridge. We also use a subset of the LDC ground-based radar survey, towed behind a PistenBully PB300 tractor, collected by the Beyond EPICA -Oldest Ice (BE-OI) European Consortium using the British Antarctic Survey's (BAS) Deep Looking Radio Echo Sounder (DELORES) radar system  (King et al. , 2009). The main characteristics of each radar system are listed in Tables  1  and 2.
By doing so, IRHs with slopes up to 10 • can be detected at depth. This processing comes at the expense of increased noise and requires more accurate platform position information (which is sometimes unavailable) and computer time. However, we have found foc2 to be vital for tracking deep IRHs (Fig. 2 ). Focused SAR is therefore most useful when reflector geometries are complex (e. g. , steep slopes), while pik1 is better suited to relatively smooth internal stratigraphy as it has the highest SNR of all processing products. The resulting post-processed radar data characteristics are listed in Table  2. The IRH data set presented here has been traced using the pik1 unfocused SAR and foc2 2D focused SAR products. These two processing types do not differ much in the Dome C region, due to the exceptionally continuous and relatively flat internal stratigraphy.
DELORES After GPS-based precise positioning of the raw DELORES radar data, transects are processed using ReflexW (Sandmeier Geophysics) software. Several filters are applied, in the time and depth domains, to increase the SNR of the IRHs, including suppression of the direct air wave, bandpass filtering, a gain function to correct for spherical spreading loss and migration to collapse diffractions to their source points. The original sampling period of 4 ns is averaged to 12 ns in the post-processed data. Because the collection of groundbased radar data is dependent on the speed of the vehicle, which varies due to the terrain roughness during collection, the radar data are interpolated onto a 5 m regular horizontal spacing during post-processing. The resulting post-processed radar data characteristics are listed in Table  2.
Internal reflecting horizons (IRHs) IRHs are interpreted using Landmark's Decision Space Desktop 5000. 8. 3. 0 software. All radar transects are loaded into the software prior to interpretation, and the built-in basemap is used to track the same IRH continuously from one transect to the next through the use of crossover points (Fig. 3 ). Prior to loading, the radar transects are flattened to the surface, which is itself set at zero two-way travel time (TWTT) so that surface echoes and internal stratigraphy match between systems and IRHs can be reliably traced across cross-cutting transects. At each crossover point, the software indicates the TWTT of the IRH traced while the 2D viewer allows the visualization of the intersecting radar transects side-by-side to ensure the traced IRH is continuous across the intersection.
This geometry creates steeply dipping IRHs which are difficult to recover in the post-processed data. For HiCARS transects, the type of processing applied (pik1 or foc2) affects the geometry of the IRH. If both types of processing are available for the same transect (this is especially the case for the OIA survey), the IRH is traced separately both in pik1 and in foc2, and each version is then considered as a separate data set. In using several radar systems, there is the added difficulty of tracing IRHs reliably from one radar system to the next at crossover points  (Cavitte et al. , 2016; Winter et al. , 2019). Cavitte et al. (2016)  have shown that transferring IRHs from the HiCARS system to the MCoRDS system is achievable, despite the differences in their range resolutions. Because MCoRDS has a finer range resolution (4.
30 m for MCoRDS vs. 8. 42 m for HiCARS), some of the IRHs selected in the Hi-CARS radargrams correspond to two IRHs on the MCoRDS radar transect (Fig. 4 , panel a). However,  Cavitte et al. (2016)  show that the IRHs can be traced over hundreds of kilometers and still match between the two radar systems within their age uncertainty ranges. Similarly,  Winter et al. (2019)  show that their IRHs, traced using the Alfred Wegener Institute (AWI) radar system with a 50 ns pulse source waveform and a range resolution of 5 m, can be matched to the HiCARS IRHs presented in this data release. Similarly, tracing the IRHs across from the airborne-collected HiCARS radar data to the ground-based DELORES radar data is also tractable, with a difference in range resolution of 2. 85 m (8. 42 m vs. 11. 09 m).
Code and data availability The IRHs presented in this study can be found publicly on the US Antarctic Program Data Center (USAP-DC): https://doi. org/10. 15784/601411 (Cavitte et al. , 2020). The code for the 1D steady-state ice flow model is available publicly at https://github. com/parrenin/IsoInv (last access: 1 September 2017,  Parrenin, 2017). The CReSIS radargrams can be accessed at https: //data. cresis. ku. edu/data/rds/2013_Antarctica_P3/CSARP_ standard/20131127_01 (last access: 1 September 2020, CReSIS, 2020), the HiCARS radargrams can be accessed at https://doi. org/10. 26179/5wkf-7361 (Young et al. , 2021) , https://doi. org/10. 5067/W2KXX0MYNJ9G (Blankenship et al. , 2017) , and the DELORES profiles discussed in this paper will be available from https://data. bas. ac. uk (last access: 14 October 2021, BAS, 2020).