dataesr/scientific-paragraphs-categorization huggingface dataset:
5 -NB Tree -RF -Random Tree -Representative Tree model Probing, DoS, R2L, U2R -K-means -SVM -Ant Colony -SVM -PCA -Normal and Attack -AIS (NSA-GA) -SVM -NB -DT (J48) Normal and abnormal -Forked VAE -Unsupervised deep NN Probing, DoS, R2L, U2R General Purpose Networks SimpleCart) Australian Defense Force Academy --DT -SVM (least square) Probing, DoS, R2l, U2R 9 DDoS attacks University of Rio Grande do Sul Botnet dataset [35] Canadian Institute for Cybersecurity (CIC) 7 Botnet types in training set and 16 in test set Feature Selection: KDD-99 -Binary PSO -k-NN Probing, DoS, R2L, U2R -FGLCC CAIDA 2007 [40] Center for Applied Internet Data Analysis 1 hour of DDoS attack divided into 5-minute pcap files -CFA CIC DoS dataset [28] 8 DoS attack traces -Fuzzers, Analysis, Backdoors, DoS, CIC CICIDS2017 [27] 4 DoS types, Infiltration Dropbox Download and Cool disk, 14 Port Scan [122] [150] [173] Ref [123] [151] [101] [124] [125] [152] [174] [102] [126] [153] [127] [103] [104] [105] [106] [107] [108] [109] [110] [111] [112] [113] [114] [115] [116] [117] [118] [119] [120] [121] [128] [129] [154] [175] [155] [130] [156] [157] [176] [158] [159] Probing, DoS, R2L, U2R [131] KDD-99 -Fuzzy C means -Fuzzy NN / Neurofuzzy -RBF SVM Probing, DoS, R2L, U2R [132] NSL-KDD -Fuzzy Clustering NN Probing, DoS, R2L, U2R [133] KDD-99 -K-means -NN MLP Probing, DoS, R2L, U2R [134] KDD-99 -FFNN -ENN -GRNN -PNN -RBNN Probing, DoS, R2L, U2R [135] DARPA 2000 APAN using: -Markov Chain -Kmeans Clustering DDoS [136] ISCX 2012 KMC+NBC -K-Means Clustering -NB Classifier Normal and Attack [137] Bank's Credit Card Data -DT Fraud [138] KDD-99 Two variants of GMDH: -Monolithic -Ensemble-based Probing, DoS, R2L, U2R [139] Simulated dataset -Non-Parametric CUSUM Jamming [140] -KDD-99 -ELM Probing, DoS, R2L, U2R [141] -KDD-99 -NSL-KDD ANN-Bayesian Net-GR ensemble: -ANN -Bayesian Net with GR feature selection Probing, DoS, R2L, U2R [142] NSL-KDD -One-class SVM -C4.
5 DT -[143] KDD-99 -K-medoids Probing, DoS, R2L, U2R [144] KDD-99 -SVM -CSOACN Probing, DoS, R2L, U2R [145] NSL-KDD -AIS (NSA, CSA, INA) Normal and abnormal [146] KDD-99 -DT gureKddcup6percent -SVM R2L [148] Continued. -CFA (Feature Selection) Probing, DoS, R2L, U2R [147] KDD-99 -R-tree -k-NN -K-means -SVM Probing, DoS, R2L, U2R [160] Generated dataset -GPU-based ANN -BON Normal and Attack [161] NSL-KDD -DL RNN Probing, DoS, R2L, U2R 2019 -UNSW-NB15 -CICIDS2017 -Deep FFNN Exploits, Generic, Reconnaissance, types CICIDS2018 [26] -RF -Gradient Boosting Tree Shell-Code & Worms CTU-13 [37] CTU University 13 captures of botnet samples [177] DARPA [43] MIT Lincoln Laboratory 17 DoS, 12 U2R, 15 R2L, 10 Probing and 1 Data -DoS, DDoS, Web-based, Brute force, Infiltration, Heartbleed, Bot & Scan DDoSTB [32] Punjab Technical University & SBS State Technical Campus DDoS Testbed using emulated and real nodes [162] NSL-KDD -K-means -NB -Information Gain Probing, DoS, R2L, U2R [163] UNB-CIC -ANN -SVM nonTor Traffic [164] KDD-99 -Polynomial Feature Correlation -DoS 2019 -ISCX 2012 -NSL-KDD -Kyoto2006+ -IG -PCA -SVM -IBK -MLP ISCXIDS2012 [38] CIC HTTP, SMTP, SSH, IMAP, POP3, and FTP Traffic -Normal and Attack -Probing, DoS, R2l, U2R KDD-99 [42] University of California Covers 24 training attack types and 14 additional types in the test data [178] TUIDS [34] (1) TUIDS IDS dataset.
(2) TUIDS Scan dataset. (3) TUIDS DDoS dataset Tezpur University (22 DDoS attack types) -Probing, DoS, R2l, U2R NSL-KDD [41] CIC Improvement of KDD'99 dataset [165] KDD-99 -PCA -k-NN -Softmax Regression Probing, DoS, R2L, U2R [166] KDD-99 Optimized Backpropagation by Conju-gate Gradient algorithm -Fletcher Reeves -Polak Ribiere Probing, DoS, R2L, U2R [167] 2019 -KDD-99 -NSL-KDD -UNSW-NB15 -Kyoto2006+ -WSN-DS -CICIDS2017 -4 DoS attacks (Blackhole, Grayhole, STA2018 [36] University of St Andrews Transformation of UNB ISCX (contains 550 features) -Deep NN -Logistic Regression -NB -k-NN -SVM Flooding & Scheduling) Unified Network Dataset Los Alamos National Laboratory 90 days of Network and Host logs [31] -Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Waikato [39] RIPE Network Coordination Center -[179] Special Purpose Networks -DT -AB Shell-Code & Worms 4SICS ICS [50] Netresec --RF -DoS, DDoS, Web-based, Brute force, Infiltration, Heartbleed, Bot & Scan 15 different real situations covering cyber-attacks (DoS & Spoofing), Anomalies Water System French Naval Academy breakdown (Sensor Failure & Wrong connection), sabotage (Blocked [45], [46] Measures & Floating Objects) -Powell Beale KDD-99 -Kernel Clustering Probing, DoS, R2L, U2R [168] Simulated Dataset -MLP -SVM -J48 -NB -Logistic -RF Features Selection: -BFS-CFS -GS-CFS Individual and Combination Routing At-tacks: -Hello Flood -Sinkhole -Wormhole 2019 -NSL-KDD -Genetic Algorithms Bot-IoT [44] Attacks include DoS/DDoS, OS and Service Scan, Keylogging and Data The center of UNSW Canberra Cyber Exfiltration -Kernel ELM -DT Probing, Dos, R2L, U2R [180] IoT devices captures [47] Aalto University represents that data of 31 smart home IoT devices of 27 different types -k-NN -MLP -SVM 7 traffic categories (Browsing, Email, Chat, Audio/Video-Streaming, FTP, Tor-nonTor dataset [48] CIC VoIP, P2P) [169] KDD-99 -FLN -PSO Probing, DoS, R2L, U2R 2020 Generated dataset -AdaBoost -Decision Stump -J48 14 traffic categories (VPN-VOIP, VPN-P2P, etc.
) covering Browsing, VPN-nonVPN dataset [49] Email, Chat, Streaming, File Transfer, VoIP, TraP2P -RF -SVM -MLP -DDoS Mobile Applications [181] Android Adware and [170] NSL-KDD -UNSW-NB15 -Deep Auto-Encoder Probing, DoS, R2L, U2R [171] -NB -BayesNet 2020 NSL-KDD -Deep NN Probing, DoS, R2l, U2R General Malware Dataset 1900 application (Adware, General Malware and Benign) CIC [52] [182] 1929 Botnet samples covering 14 families (AnserverBot, Bmaster, -ANN -KDD-99 -NSL-KDD -DL -NDAE -Stacked NDAEs Probing, DoS, R2L, U2R [80] KDD-99 -KFRFS -NB -IBK -AdaBoost -MFNN Probing, DoS, R2L, U2R [172] 2020 -KDD-99 -Ontology -NB -DT Probing, Dos, R2L, U2R Android Botnet dataset [53] DroidDream, Geinimi, MisoSMS, NickySpy, Not Compatible, PJapps, [183] Pletor, RootSmart, Sandroid, TigerBot, Wroba, Zitmo -RF 2020 Generated dataset -Local Outlier Factor -Isolation Forest Port Scanning, HTTP & SSH Brute Force & SYN Flood Android Malware Genome [54] North Carolina State University More than 1,200 malware samples [184] Where: AndroMalShare [55] Botnet Research Team and Xi'an Jiaotong More than 85,000 Android malware samples University -SMO -RF Kharon Malware Dataset [51] 7 malware deeply examined, 10 malware (one sample each) and 2 partially Kharon project examined KDD-99 -K-means -k-NN Probing, DoS, R2L, U2R Continued.
The tag sequence were created with the oligotag command within the open-source OBITools software suite  (Boyer et al. , 2016) , so that all pairwise tag combinations were differentiated by at least five different base pairs  (Taberlet et al. , 2018). DNA amplification was performed in a 20-μL total volume containing 10 μL of AmpliTaq Gold 360 Master Mix (Applied Biosystems, Foster City, CA, USA), 0. 16 μL of 20 mg ml-1 bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland), 0. 2 μM of each primer, and 2 μL of diluted DNA extract. DNA extracts from soil and filters were diluted eight times while DNA extracts from leaves were diluted four times. Amplifications were performed using the following thermocycling program: an initial activation of DNA polymerase for 10 min at 95°C; x cycles of 30 s denaturation at 95°C, 30 s annealing at 53°C and 56°C for Bacteria and Fungi, respectively, 90 s elongation at 72°C; and a final extension at 72°C for 7 min.
Bio-informatic analyses of raw reads The Illumina raw sequence reads were processed separately for each library using the OBITools software suite  (Boyer et al. , 2016) , specifically dedicated to metabarcoding data processing. First, the raw paired-ends were assembled using the illuminapairedend program, and the sequences with a low alignment score (fastq average quality score < 40) were discarded. The aligned sequences were then assigned to the corresponding PCR replicates with the program ngsfilter, by allowing zero and two mismatches on tags and primers, respectively. Strictly identical sequences were dereplicated using the program obuniq, and a basic filtration step was performed with the obigrep program to select sequences within the expected range length (i. e. , longer than 65 or 39 bp for Fungi and Bacteria, respectively, excluding tags and primers), without ambiguous nucleotides, and observed at least 10 times in at least one PCR replicate.
The remaining unique sequences were grouped and assigned to Molecular Taxonomic Units (MOTUs) with a 97% sequence identity using the Sumatra and Sumaclust programs  (Mercier et al. , 2013). The Sumatra algorithm computes pairwise similarities among sequences based on the length of the Longest Common Subsequence and the Sumaclust program uses these similarities to cluster the sequences  (Mercier et al. , 2013). Abundance of sequences belonging to the same cluster were summed up and the cluster center was defined as the MOTU representative of the cluster  (Mercier et al. , 2013). The taxonomic classification of each MOTU was performed using the ecotag program  (Boyer et al. , 2016) , which uses full-length metabarcodes as references. The ecoPCR program  (Ficetola et al. , 2010)  was used to build the metabarcode reference database for each marker.
Briefly, ecoPCR performs an in silico amplification within the https://doi. org/10. 5194/acp-2019-1147 Preprint. Discussion started: 9 January 2020 c Author(s) 2020. CC BY 4. 0 License. EMBL public database (release 133) using the Fung02 and Bact02 primer pairs and allowing a maximum of three mismatches per primer. The resultant reference database was further refined by keeping only sequence records assigned at the species, genus and family levels. After taxonomic assignment datasets were acquired, further processing with the open source R software (R studio interface, version 3. 4. 1) was performed to filter out chimeras, potential contaminants, chimeras and failed PCR replicates. More specifically, MOTUs that were highly dissimilar to any reference sequence (sequence identity < 0. 95) were considered as chimeras and discarded.
Data analysis Unless specified otherwise, all exploratory statistical analyses were achieved with R. Rarefaction and extrapolation curves were obtained with the iNext 2. 0-12 package  (Hsieh et al. , 2016) , to investigate the gain in species richness as we increased the sequencing depth for each sample. Alpha diversity estimators including Shannon and Chao1 were calculated with the phyloseq 1. 22-3 package  (McMurdie and Holmes, 2013) , on data rarefied to the same sequencing depth per sample type (see Table  S2  for details on the rarefaction depths). Non-metric multidimensional scaling (NMDS) ordination analysis was performed to decipher the temporal patterns in airborne microbial community structures (phylum or class taxonomic group) in air samples. These analyses were achieved with the metaMDS function within the vegan package  (Oksanen et al.
, 2010b; Pietrogrande et al. , 2014). Therefore, all other SC species measured in our series can be considered to be most likely the result of the mechanical resuspension of crop residues (e. g. , leaf debris) and microorganisms attached to them. Other confirmations of this interpretation stem from the excellent daily covariations observed in the PM10 between SC species levels and ambient cellulose, widely considered as a reliable indicator of the plant debris source in PM studies  (Bozzetti et al. , 2016; Hiranuma et al. , 2019). Microbial abundance and community structure in samples from the surrounding environment can provide further useful information on sources apportionment and importance. Our data indicates that the airborne microbial genera most positively correlated to SC species are also distributed in surrounding environmental samples from both surface soils and leaves, suggesting a dominant influence of the local environments for microbial taxa associated with SC species, as opposed to long-range transport.
Monitoring sea-level changes The monitoring of sea-level changes in Venice relies on both in-situ data acquired by tide gauges (sect 2. 1) and remote sensing observations provided by satellite radar altimetry (sect. 2. 2). Tide gauges record sea-level heights with reference to a permanent benchmark on land. As a consequence, they provide measurements of RSL embedding the effects of vertical land motion (Sect. 3). Tide gauge data sets consist of local, long-term measurements acquired at high frequency and accuracy  (Zerbini et al. , 2017). Satellite radar altimetry measures geocentric sea-level changes  (Fu and Cazenave, 2001; Stammer and Cazenave, 2017)  that are therefore independent of variations of the local land level changes  (Gregory et al. , 2019) , hence missing a potentially key component of RSL  (Wöppelmann and Marcos, 2016).
In order to improve the monitoring, it is possible to take advantage of the data collected by various radar altimeters flying at the same time. However, this requires a characterization of the inter-mission biases and the development of suitable interpolation schemes of the independent ground tracks. Multimission datasets are typically distributed over regular grids. An additional potential limiting factor for the Venice area is the degradation of the technique performance towards the coast resulting from the contaminating presence of land in the satellite footprint and from the enhanced inhomogeneity of the local ocean surface. Limitations and possible perspectives of coastal altimetry in the Adriatic Sea have been discussed in several studies since the late 1990s  (Cipollini et al. , 2008; Fenoglio-Marc et al.
, 2012; Vignudelli, 1997; Vignudelli et al. , 2011 Vignudelli et al. , , 2019a)). This has motivated further investigations  (Cipollini et al. , 2013; Passaro et al. , 2014)  based on the latest coastal altimetry datasets (e. g. , CTOH, see  Birol et al. , 2017)  and/or reprocessing initiatives (e. g. , ALES, see  Passaro et al. , 2014). In the Northern Adriatic, these studies analyzed data around Venice and Trieste, including their validation against tide-gauge measurements. The results show that a reasonable increase in quantity and quality of data can be achieved compared to standard products up to a few kilometers from the coastline. The comparative assessments with tide gauges confirm that correlation of coastal altimetry products is always higher than standard products and that the difference in sea-level estimates provided by the two techniques is typically below 10 cm in proximity of the point of closest approach to the tide gauge.
Natural land movements The Venice area is naturally subsiding. This process is characterized by a long-term component controlled by tectonics/geodynamics and sedimentation, active on time spans of about 10 6 -10 4 yr, and a short-term component controlled by glaciation cycles and due to GIA processes acting on periods of 10 3 -10 4 yr  (Antonioli et al. , 2017; Cuffaro et al. , 2010; Stocchi et al. , 2005). Depending on the time interval considered, different datasets are available for investigating the rate of vertical land movement. Subsidence rates up to 2 Myr ago can be inferred from the thickness of the different layers of Quaternary sediments. Over this time frame, sedimentation rates are equivalent to subsidence rates, since the entire sedimentary sequence was deposited in shallow marine to continental environments  (Massari et al.
Late Pleistocene and Holocene After the Last Glacial Maximum several lagoons developed along the Adriatic Sea, formed by the rivers flowing into the sea. Only two of them, the Grado and Venice lagoons, still exist today, while the rest have been infilled by sedimentation  (Tambroni and Seminara, 2006). Recent stratigraphic information about Holocene sea levels (2-6 kyr Cal BP) were obtained from lagoonal deposits found in boreholes between the Tagliamento River and the city of Venice. Other data were derived from archaeological markers reported in the abundant literature available for the Venice Lagoon and its mainland  (Antonioli et al. , 2009; Fontana et al. , 2017; Lambeck et al. , 2011). The shell base of the lagoon indicates a subsidence rate over the last 7. 3 kyr of 1. 6±0. 3 mm/year  (Antonioli et al.
org/10. 5194/nhess-2020-351 Preprint. Discussion started: 12 November 2020 c Author(s) 2020. CC BY 4. 0 License. of RSL rise between ~5 and ~2 m in the last 5,000 years. This shows that GIA models are constantly being updated due to improvements in the constraining RSL datasets and of modeling techniques. Based on the work quoted above, the rate of long-term RSL change in Venice due to the melting of the late-Pleistocene ice sheets does not appear to be tightly constrained  (Tosi et al. , 2013). Further uncertainties arise from the effects of the melting of the Würm Alpine ice sheet, whose chronology remains uncertain regarding several aspects  (Spada et al. , 2009). Nonetheless, the long-term rate of Venetian RSL change due to GIA can be assessed in the range between -0. 2 and +0. 5 mm/year based on the published works cited above.
4 and 2. 5 mm/year. Vecchio et al. (2019)  also modelled the time series by means of the superposition of a straight line and three Empirical Mode Decomposition components, suggesting a slightly larger trend of 2. 78 mm/year. Zerbini et al. (2017)  isolated the effect of subsidence on the Venetian RSL time series by deriving an empirical curve from levelling data of benchmarks close to the tide gauge, GPS and InSAR heights (Fig. 5c ). After removing the estimated subsidence from the tidegauge data, the secular trend of the corrected time series was 1. 23±0. 15 mm/year for the period 1872-2012 (see Table  5 ). It should be stressed that the trend analysis has little meaning without the correction of subsidence effects because the linear model is otherwise inadequate to represent the Venice time series.
The ALES retracker, with a proper threshold on error, recovers significantly more data in the 10 km near the coast  (Passaro et al. , 2014). A number of satellite radar altimetry operational products (along-track and gridded) dedicated to the monitoring of open-ocean sea level exists, whose quality is constantly improved. Various experimental coastal altimetry products are also now available and validated in some regions, thus being used for sea-level research in the coastal zone  (Gómez-Enri et al. , 2019). An updated table is accessible at www. coastalt. eu/datasets. Importantly, merging altimeter data from different missions requires homogenous re-processing and minimization of drifts and systematic biases between missions. For the Northern Adriatic, an opportunity for progress is provided by the ESA SLCCI extension (CCI+), which will process along-track data from additional satellite missions using re-tracked data, dedicated coastal geophysical corrections and improved editing that will then be combined in a global grid with higher resolution near the coast (Anny Cazenave, personal communication).
Conclusions The City of Venice and the surrounding lagoon ecosystem are critically affected by variations in RSL height driven by a host of diverse processes. These encompass oceanic processes driving RSL variations from diurnal astronomical oscillations to climatic interannual-to-multi-centennial fluctuations, and vertical land movements causing RSL variations on time scales ranging from a few decades due to, e. g. , anthropic activities, to multi-millennial trends due to tectonic activity. This review has summarized and reassessed recent progress in the estimation, understanding and prediction of the individual contributions to RSL by exploiting new observational datasets, improved statistical methods and more realistic numerical simulations of ocean and Earth system components achieved in the past decade.
We confirm the existence of a strong link between interannual and interdecadal variability observed in Venetian sea levels and in the large-scale atmospheric circulation over the North Atlantic during the winter semester, particularly with the North Atlantic Oscillation (about 46% of shared variability for January-March average time series in the period 1872-2019). The review has highlighted a number of major gaps of knowledge as well. Among these: altimetry data are recorded rather far from Venice and may not represent the lagoon RSL variability; uncertainties in geologic trends remain difficult to assess; a reliable framework is lacking to combine uncertain future estimates of RSL change due to individual contributions, which provides for a major opportunity for progress to better constrained ranges of future projections; historical evidence demonstrates that subsidence can be temporarily dominated by the anthropic component.
https://doi. org/10. 5194/nhess-2020-351 Preprint. Discussion started: 12 November 2020 c Author(s) 2020. CC BY 4. 0 License. Several altimetric datasets have been used to estimate sea-level trends in the Venice area. Fenoglio-Marc et al. (2012a) estimated a trend of 5. 9±1. 4 mm/year over the period 1993-2008 for an along-track point about 80 km away from Venice (see their Table2). Rocco (2015)  obtained trends of 4. 18±0. 92 mm/year (period 1993-2014) and 3. 40±0. 99 mm/year(period 1993-  2013)  for the closest grid point to the Venice tide gauge in the AVISO and SLCCI V1 products, respectively, with both estimates consistent with each other within errors. A reprocessing of the SLCCI V2 data set over the period 1993-2015 yielded a trend of 4. 25±1. 25 mm/year(Vignudelli et al. , 2019b), further reduced to 4.
Acknowledgments The authors want to thank the  European Space Agency (ESA)  that funded the  Climate Change Initiative  to produce a climate quality record of sea level from satellite altimetry. This work is partially funded by  ESA  under Phase 2 Bridging Phase to CCI (cont. 4000109872/13/I-NB -Contract Change Notice 6 ). The  Centro Previsioni e Segnalazioni Maree of the Venice Municipality  is acknowledged for providing the tide gauge data. Thanks are due to  Francesco De Biasio  ( CNR-ISP ) for the joint work during the  CCI  project. Wavelet coherence spectra are calculated using the cross wavelet and wavelet coherence package by  Aslak Grinsted ,  John Moore  and  Svetlana Jevrejeva. The code to compute regional MSL projections was formerly developed by  Gonéri Le Cozannet. Scientific activity by DZ, AR and SR performed in the  Research Programme Venezia2021 , with the contribution of the Provveditorato for the Public Works of Veneto,  Trentino Alto Adige  and  Friuli Venezia Giulia , provided through the concessionary of  State Consorzio Venezia Nuova  and coordinated by CORILA.
Study area and data collection Relative likelihoods of North Atlantic right whale and recreational vessel cooccurrence within the SEUS were estimated. The study area comprised 13,702 km² and ranged from approximately 31°34'N to 29°46'N in northing and 81°18'W to 80°29'W in easting (Fig. 1 ). Surveys were only flown in favorable weather conditions, targeting a visibility of at least 3. 7 km and a Beaufort sea state of 3 or less. The surveys were designed such that observers (one on each side of the plane) recorded the locations of North Atlantic right whale (s) with greater precision than that of recreational vessels. When a North Atlantic right whale was sighted, the plane veered from its track line to fly directly over the animal(s) and record the coordinates. In contrast, observers only recorded the number of recreational vessels within a distance of 2.