dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Impact of the splitting dataset strategy The performance of the models on the high-bitrate datasets is reported in Table  III. We do not test the OC-SVM on this data splitting strategy due to the large training time of this model. The same findings, as those in the previous section, can be made. However, we note the better performance of iForest regarding the contamination ratio and the better performance of AE and LSTM-VAE when δ = 0. Those results can be explained by the significant difference between the training set and the test set. The downlink bitrates of the training set are about 120-200 Mbps while those of the test sets are lower (40-80Mbps). This difference seems to favor iForest allowing it to easily isolate the anomalous samples. For the AE and LSTM-VAE, the performance reported here can be misleading about the efficiency of those models in dealing with the datasets, since as mentioned previously, the used threshold is sub-optimal and does not allow to catch all the anomalies.
VI. CONCLUSION This paper presented a comparative evaluation of unsupervised machine learning models for anomaly detection in cloud gaming sessions. It presents our analysis of the performance of different ML algorithms and our assessment of the robustness of these models in the face of contaminated datasets. Our results show that the OC-SVM is the most robust to the contamination but requires longer time for training and inference, while the iForest model requires the presence of contamination to be efficient. Our comparative study also shows that reconstruction-based approaches do not perform well with an empirical rule of thresholding as they fail to catch all the anomalies in the data. They require a good threshold value to be assessed fairly in a comparative study. For the detection of cloud gaming quality, we found that a thorough attention must be given to the data splitting and also to the definition of an anomalous situation.
86±0. 02 60. 51±0. 04 95. 52±0. 23 88. 65±0. 25 73. 6±0. 07 71. 93±0. 07 50 60. 98±0. 05 68. 65±0. 54 64. 59±0. 24 100 60. 28±0. 05 54. 47±0. 31 57. 23±0. 19 0 99. 02±0. 05 79. 65±0. 06 88. 28±0. 03 5 95. 55±0. 43 7. 86±0. 07 14. 52±0. 12 AE 10 20 94. 09±0. 79 91. 45±1. 28 5. 02±0. 08 3. 00±0. 07 9. 53±0. 14 5. 80±0. 14 50 80. 61±1. 86 1. 44±0. 04 2. 83±0. 07 100 77. 68±1. 12 1. 28±0. 03 2. 52±0. 06 0 98. 44±0. 76 80. 27±1. 3 88. 42±0. 83 5 98. 58±0. 7 2. 25±1. 02 4. 38±1. 95 LSTM-VAE 10 20 98. 43±4. 01 83. 95±25. 31 1. 15±0. 8 0. 83±0. 43 2. 25±1. 56 1. 65±0. 83 50 88. 83±14. 22 0. 72±0. 37 1. 43±0. 74 100 95. 37±2. 21 0. 75±0. 26 1. 48±0. 51 are different from normal samples in the training dataset. It uses this information to isolate the anomalies. The OC-SVM, similar to iForest, assumes a certain proportion of anomalies in the training set and performs better with no contamination (F1= 74%).
Deleterious mutation prediction in RNAs is a sub-problem of the RNA folding prediction problem, which is fundamental in RNA bioinformatics. Thus, all tools for deleterious mutations analysis utilize methods developed for the RNA folding problem. The most common methods for RNA folding prediction in general are energy minimization methods that use dynamic programming, for example the mfold server  [14] , RNAstructure  [7]  and the ViennaRNA package and server  [4, 5, 6]. For the sub-problem considered in this work, the first publicly available methods for the analysis of deleterious mutations in RNAs were the RNAmute java tool  [1]  and a web server RDMAS  [8]. Both of these methods utilize the Vienna RNA package for RNA folding prediction and are able to analyze only single point mutations in RNA sequences.
To deal with multiple point deleterious mutations, the MultiRNAmute program  [2]  was developed, which uses an efficient method to find multiple point mutations using suboptimal folding solutions of an RNA sequence. The approach used in MultiRNAmute is based on examining a limited number of mutations, which stabilize some distant suboptimal secondary structure or/and destabilize the optimal secondary structure of the RNA sequence under consideration. Other approaches, among which the most well-known is RNAmutants  [11] , were also developed. A major limitation of the above described methods is that the methods are able to predict only substitution mutations, but not insertions or deletions. The suggested approach to extend the MultiRNAmute to predict deletions and in-sertions was briefly introduced in  [3].
In addition, although the algorithm used in MultiRNAmute program is considerably more efficient than any brute-force algorithm, it still may become exponential for sizable inputs such as sequences longer than 100 nts and large multiple point mutations sets. Herein out motivation is to develop a method that predicts some predefined number (user's input) of deleterious mutations of different types and stops, without searching all "good" mutations as in MultiRNAmute. The paper is organized as follow. We first prove the NP-hardness of predicting the most deleterious set of mutations in structural RNAs. We show that, even for a very simple energy model, the associated optimization problem is NPcomplete. We then describe a fast algorithm, based on the approach used in MultiRNAmute, for the prediction of a predefined number of deleterious multiple point insertion, deletion and substitution mutations.
Our new method is named IndelsRNAmute, and is freely available at: https://www. cs. bgu. ac. il/ ~dbarash/Churkin/SCE/IndelsRNAmute/ 2 Problem definition and NP-hardness Non-canonical base pairs do not contribute to the energy in the model. For a given RNA w, a mutation is a pair µ = (i, b), expressing the choice of a new, mutated, nucleotide b ∈ Σ -{w i } for the position i. An edit script M = {µ 1 ,. , µ m } consists in a set of mutations, each acting on a different position. Denote by (w M (resp. S M ) the application of an edit script M onto a sequence w (resp. structure S). Note that, when edit operations are limited to single-points mutations, one has S M = S. An RNA w is a nucleotide sequence of length n over an alphabet Σ = {A, C, G, U}. A secondary structure is a set of base-pairs S = {(a i , b i )} i ⊂ [1, n] 2 such that a i < b i ,.
Methods Similar to the MultiRNAmute method, the IndelRNAmute method uses suboptimal secondary structures as a starting point. The motivation behind this decision is to start with some distant (from optimal structure) suboptimal structures and to convert such suboptimal structures to an optimal one by introducing wise mutations, which stabilize the stems of the suboptimal structure and destabilize stems of the optimal one. The mutation analysis algorithm consists of several steps. First, given an input sequence with several input parameters, the Minimum Free-Energy (MFE) and a set of suboptimal secondary structures are calculated using the RNAfold and RNAsubopt programs from the Vienna RNA package  [4, 5] , followed by a filtering step to reduce the number of suboptimal structures. Next, for each optimal and suboptimal structure, stems are identified and used for selecting good single-point mutations of several types: insertions, deletions and substitutions, depending on the user's choice.
The mean streamwise velocity of the laboratory flume was 0. 538 m/s, resulting in a standard deviation of 0. 0116 m/s. At each location, 2000 (U y ) to 10,000 (U x ) measurements were recorded over a maximum time interval of 200 s. The LDA data was post-processed and stored with the commercial software BSA Flow using a Dantec Flow Explorer DPSS 300 2D, which allowed for the acquisition of raw data at a single measurement location. The same equations applied by the commercial software were used to compare the results from each of the numerical simulation setups. The software calculates the signal quality for each measurement based on the signal to noise ratio (SNR), which was used to qualitatively assess all LDA measurements before comparison with the CFD models. The lowest SNR value of 70.
Acknowledgements The research work presented in this paper has received funding from  European Union   Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie Actions , Grant Agreement No. 860800. Laboratory measurements were conducated as part of the  RETERO  project, sponsored by the  German Federal Ministry of Education and Research  Grant no. 161L0152A. Jeffrey A. Tuhtan's contribution was funded by the  Estonian Research Council  Grant  PRG1243  and  Maarja Kruusmaa  was funded by  EXCITE  supported by the  Estonian Centre of Excellence in IT , funded by the  European Regional Development Fund  Project nr  2014-2020. 4. 01. 15-0018. The authors are grateful to  M. Yanneck Kiiski  for the design and 3D printing of the fish geometry and the CAD model of the laboratory flume.
It is designed to reproduce the historical+ssp245 forced response. It is initialized as any historical member from a parent piControl state, with its own internal variability. The HEAT, STRESS  Configuring these experiments is undertaken in three main steps (Fig. 1 ): Step 1. Setting up the CTL experiment from the piControl fluxes. This required listing all the components needed to force the ocean from the coupled model, in order to reproduce as closely as possible the piControl variability for 251 years, and adapting the code as to enable the flux forcing. This step is described in section 3. Step 2. Setting up the ALL experiment by adding the perturbation components on all surface fluxes, and ensuring the response is consistent with the historical+ssp245 large ensemble. This is described in section 4.
All the simulations are done on the Jean-Zay supercomputer held at IDRIS center in France (http://www. idris. fr/eng/ jean-zay/jean-zay-presentation-eng. html). The main cpu consumption of this protocol is the coupled piControl experiment (430 000 cpu hours), which is needed to output the surface fluxes at high-enough frequency. Running the ocean-only simulations are much cheaper (220 000 cpu hours for the 6 ocean-only experiments). Because this was a novel framework and we had to run many sensitivity tests to the different parameters necessary to force the ocean model, the total cpu time consumed for this work is evaluated at 1 million cpu hours, accounting for 3-4 tCO 2 e. Code and data availability. The code modified for the development of the numerical experiments with NEMO3. 6 is available here: https: //github.
Radiosonde data from the Russian station Shojna As the selected AR takes its origin in north-west of Russia, radiosonde data at different Russian research stations from the Integrated Global Radiosonde Archive (IGRA,  Durre et al. (2006) ) network have been explored. Vertical profiles during the AR development were captured by radiosonde measurements at the Shojna research station (official WMO station name: Shoyna), located in the Russian Arctic, at the western Kanin Peninsula, between the White and Barents Seas (67. 88 • N, 44. 17 • E; see Fig. 1 ). Accordingly, its radiosonde data for specific humidity, air temperature and wind speed on 5 th and 6 th June (at 12:00 UTC for both day) are included in our analysis.
PAMTRA forward simulator Comparing atmospheric models to observations can be done in observational or modelling space. For studies in the modelling space, retrieval algorithms are required to derive modelled quantities from the observations (observation-to-model). To compare measurements and models in the observational space (model-to-observation), appropriate forward operators for the observation wavelengths are needed. Here we make use of the Passive and Active Microwave TRAnsfer tool (PAMTRA;  Mech et al. , 2020) , a software package suited for the simulation of passive and active observations in microwave frequency region based on atmospheric model output. PAMTRA has been applied to the ICON-LAM simulation output to derive the T B for the AR event for the MHS frequencies with model output on 3 km and 6 km horizontal resolutions and for ERA5 on a 0.
29 km (respectively R3B08 and R3B09 following ICON's grid terminology). For both grid resolutions, 70 vertical model levels are used, extending from the surface (with the lowermost vertical layer of 20 m) to the top height of 23 km, with a gradually coarser vertical resolution with increasing height. The LAM simulations are initialized from global ICON analysis at 13. 15 km horizontal grid spacing (R03B07, here after ICON-GLOBAL, Deutscher Wetterdienst (2020)) and 90 vertical model levels up to 75 km. The LAM simulations are forced at the lateral boundaries by three hourly ICON global forecast data (i. e. output data from operational DWD forecasts), and also include sea ice and SST updates (from the global forecasts runs). The model is run over the selected AR period (from 5 th to 7 th June 2017) in the so-called forecast mode to force the model to stay close to the observed weather situation, i.
All ICON simulations show a comparable IWV evolution to the 6-hourly radiosonde data and to ERA5, although with a consistent slightly higher IWV magnitude (by ca. 2 kg m -2 ). If the relatively good agreement of ERA5 IWV  The change in the vertical profiles of specific humidity and wind speed shows a similar AR signature (Fig. 9 ) as detected at Ny-Ålesund. With the AR arrival (Fig. 9b ), the low-level specific humidity increases by a factor of 4, compared to the pre-AR conditions (Fig. 9a ). This means that on 6 th June 2017, a large increase of humidity occurs between 1 km and 2 km of altitude and reaches values of almost 7 g kg -1. However, compared to Ny-Ålesund (Figs. 6a  and 6b ), the maximum humidity at Shojna (Fig. 9a  and 9b) appears at a slightly lower altitude (at ca. 1 km), indicating that the AR moves to slightly higher (by ca.
Future work on this topic would be needed to fully estimate and understand the contribution of AR events on the surface energy budget in the polar regions. For the first time this study analyses, with high resolutions simulations, a moisture intrusion event which was identified as an AR during the ACLOUD campaign during the 5 th to 7 th June 2017 over the Nordic Seas. The AR reached Ny-Ålesund and the Norwegian Sea by elongating from the North-western coast of Siberia and over the Kara Sea and the Arctic Ocean. With the help of observational datasets (HATPRO, GNSS, radiosondes and satellite-borne measurements) and a reanalysis product (ERA5), the spatio-temporal structure of the AR was assessed and its representation in the ICON-LAM at 6 km and 3 km horizontal resolution was evaluated. A large moisture anomaly, as large as 15 kg m -2 compared to the climatological mean, was observed at the location of the AR, with actual IWV values greater than 25 kg m -2.
e. pre-AR state of low IWV) has not been reached yet. The GNSS dataset shows a relatively similar evolution, with however lower IWV values during the three-day period, as well as a smaller maximum, around 19 kg m -2 , during the passage of the AR on 6 th June 2017. Although only 6-hourly, the radiosonde results also show a similar IWV evolution with a large increase of IWV values during the AR passage. A slight one-to-two hour advance of the maximum IWV appears in the simulations compared to the observations, i. e. the maximum IWV occurs at 12:00 UTC in ERA5 and at 13:00 UTC in the ICON simulations, while it was observed at about 14:00 UTC. Nonetheless, a relatively realistic representation of this temporal evolution of IWV is seen in the ICON-LAM simulations as well as in ICON-GLOBAL and ERA5.
https://doi. org/10. 5194/acp-2021-501 Preprint. Discussion started: 12 July 2021 c Author(s) 2021. CC BY 4. 0 License. time series to the radiosonde values might come from the assimilation of observations,Crewell et al. (2021b)  showed that Russian radiosonde stations may often be too dry compared to satellite data (ex. IASI, MIRS). Hence, the higher IWV values found with the ICON simulations may represent more accurately the evolution of the humidity at Shojna station. Nonetheless, the relatively good agreement between the radiosonde data and the reanalysis and model datasets is also seen in Table2, which summarises the statistics (minimum, maximum, mean, standard deviation, based on all values available within the 24 hours of the specific day) of IWV at Shojna, with ICON-LAM6, ICON-LAM3, ICON-GLOBAL and ERA5, for the 5 th , 6 th and 7 th June 2017.
datasets/ecmwf-reanalysis-v5. The observational data from Ny-Ålesund are available at the PANGAEA data repository, for radiosondes (https://doi. org/10. 1594/PANGAEA. 879822), HATPRO (https://doi. org/10. 1594/PANGAEA. 902142) and surface radiation measurements (https://doi. org/10. 1594/PANGAEA. 887502). The radiosonde data for the Shojna station from the IGRA network (https://www. ncdc. noaa. gov/data-access/weather-balloon/integrated-global-radiosonde-archive) are available at: https://www1. ncdc. noaa. gov/pub/data/igra/. The MHS satellite data can be found at: https://www. avl. class. noaa. gov/saa/ products/welcome.
Acknowledgements. We gratefully acknowledge the funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -project  268020496 TRR 172 , within the  Transregional Collaborative Research Center "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes , and  Feedback Mechanisms  (AC)3, and the first author of this study acknowledges the funding by the  Helmholtz Climate Initiative REKLIM. CV was supported by  Fundação para a Ciência e Tecnologia (FCT)  (Portugal), by the PhD Grant reference  SFRH/BD/129154/2017. IG and CV thank  FCT/MCTES  for the financial support to CESAM ( UIDP/50017/2020+UIDB/50017/2020 ), through national funds. The authors thank the  Alfred Wegener Institute (AWI)  and the  German Climate Computing Centre (DKRZ)  for the use of the computer time and storage.
We gratefully thank the  Deutscher Wetterdienst (DWD)  for their help with the ICON model setup and for the ICON-GLOBAL dataset. The authors would like to greatly thank  Christoph Ritter  (from  AWI ),  Tiina Nygård  (from the  Finnish Meteorological Institute)  and  Melanie Lauer  (from the  Institute of Geophysics and Meteorology of the University of Cologne ) for their help with the  Ny-Ålesund , the IGRA and MHS datasets respectively.
Airport Service Rate The main input of the model is demand rate, service rate, and the route of flights. Parameters such as demand rate and flight route can be obtained directly through flight schedules and historical flight information. Service rates of airports and main route points need to be processed through historical data. First, the entire day from 0:00 to 24:00 will be divided into 280 equally time windows, t i. Let i be the ith five minutes window of the day. The service rates µ and arrival rates λ are assumed to be constant within each time window. To compute the service rate of an airport, we should consider two factors: the number of incoming flights and the impact of weather. Flight data operated during extreme weather was cleaned out. We select the date when the number of departing flights reaches at least 70% of the maximum departures.
The Correlation between Airport Service Rates in the MAS The airport capacity envelope, as a representation of airport capacity, is widely used to study the relationship between airport departure rates and airport arrival rates. It has been extended to analyze the relationship between arrivals and departures between different airports in a MAS. To capture the relations between departures and arrivals, quantile regression is used to estimate the linear relationship between the two variables. Let τ be the probability of a data point in the dataset that is less than or equal to a certain value y, i. e. , τ quantile of y, for which the general expression is τ = P(Y ≤ y(τ)). When using quantile regression to analyze the correlation of traffic flows at airports i and j, it is necessary to set the number of quantile points to minimize the residuals.
Results We apply the queuing network model to study the MAS in Guangdong-Hong Kong-Macao Greater Bay Area. There are five airports in the MAS which are Guangzhou Baiyun International Airport (ICAO code: ZGGG), Shenzhen Bao'an International Airport (ICAO code: ZGSZ), Zhuhai Jinwan Airport (ICAO code: ZGSD), Huizhou Pingtan Airport (ICAO code: ZGHZ), and Macau Airport (ICAO code: VMMC). Hong Kong airport is not considered because its traffic flow has been separated from these five airports. The two main airspace route points YIN and LMN are identified by the air traffic control authorities that have a huge impact on the whole MAS operation. We obtained the 2019 flight data to calculate the parameters of the model. Figure  6  shows the geographical positions of the airports and route points. The gray line is the boundary of the terminal airspace of the MAS.
In contrast to other TRIZ tools, while most interviewees are aware of the Su-Field model and inventive standards, few actively use them, that is, they only use them occasionally when they think it is essential. This situation is caused by a number of reasons; the high level of abstraction and imprecise description of the inventive standards is one of the most important ones. In a previous work, work  [8, 9] , the development of an intelligent knowledge management system-IngeniousTRIZ-facilitated the process of Su-Field analysis on the basis of inventive principles ontology, the inventive standards ontology and its corresponding SWRL (Semantic Web Rule Language) reasoning. The IngeniousTRIZ system was used to solve industrial engineering problems in an autonomous way. However, these ontologies and rules cannot describe and catch any imprecision or uncertainty that may exist, for instance.
For the first type of methods, Bobillo and Straccia  [21]  proposed a methodology in which OWL 2 annotation properties were used to extend traditional ontologies into fuzzy ones. In order to support this method, a Protégé plug-in was explored to edit fuzzy ontologies and some parsers were developed to translate the described fuzzy ontologies into the languages supported by some fuzzy DL reasoners. For the second type of methods, Wlodarczyk et al. [22]  proposed SWRL-F, which is a fuzzy logic extension of SWRL. SWRL-F extends the functionalities of Protégé and the modified SWRLJessTab (https://protege. stanford. edu/conference/2009/slides/SWRL2009ProtegeConference. pdf accessed on 5 September 2021) with fuzzy logic capabilities. In  [23] , a fuzzy extention of fuzzy rules (named f-SWRL) was proposed.
Fuzzy Analysis Ontology Fuzzy analysis ontology is constructed to represent the fuzzy knowledge necessary in Su-Field analysis, where four fundamental fuzzy concepts are defined, namely FuzzyVariable, FuzzyValue, FuzzySet and FuzzyTerm (http://rorchard. github. io/FuzzyJ/fuzzyJDocs/ index. html accessed on 5 September 2021). There is a fuzzy concept defined by Fuzzy-Variable that provides a name (for instance, possibility), units of the variable if needed, a range of discourse for the variable (for instance, a range from 0 to 100), as well as a set of major fuzzy terms (such as possible, likely, impossible), which will be used to describe the specific fuzzy concept related to FuzzyVariable. FuzzyValue depicts a concrete example of the fuzzy concept of FuzzyVariable, for instance, when indicating the condition "If it is difficult to introduce ferromagnetic or to perform magnetization", defines the "difficult" FuzzyVariable term "difficulty" and then creates a FuzzyValue by specifying the FuzzyVariable "difficulty" and a language expression (in the case of "not easy").