dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We would like to thank  Ana Bastos  for input on an earlier version of the manuscript. Financial support. This research has been supported by the  European Space Agency  (grant no. ESRIN/4000123002/18/I-NB ), the  H2020 European Institute of Innovation and Technology  (grant nos. CHE (776186 ), VERIFY (776810) and BACI ( 640176)),  E-SHAPE  ( GA 820852 ),  SEDAL  (grant agreement  647423 ), the  National Science Foundation  under cooperative agreement no. 1852977  and the  U. S. Department of Energy  (no. DE-SC0016323 ). The article processing charges for this open-access publication were covered by the  Max Planck Society. Review statement. This paper was edited by  Paul Stoy  and reviewed by two anonymous referees.
The paper describes the structure and content of the Information System database containing information on earthquake events, which is developed and supported within the framework of computer support for the EMERCOM of the Russian Federation. The database is assigned to provide analytical support for decision making in case of an emergency situation, including tools for mathematical simulation of hazardous excitation, the response of elements at risk to excitation and loss generation. The calibration procedure of the earthquake vulnerability functions for buildings and structures using the database with descriptions of events is presented. The calibrated functions of earthquake vulnerability for buildings of different types are applied to provide an acceptable accuracy of situational assessments for the case of a strong earthquake.
The number of casualties can be considerably reduced by timely and appropriate measures by rescuers  (Aleksandrov et al. 2019). Fast and reliable information on a situation is necessary for making proper decisions about search-and-rescue operations and measures to be taken to provide humanitarian aid during the first hours following an event. In Russia, as in many other countries worldwide, a near real-time reliable forecasting of losses is based on the use of information systems (IS) such as the Automated Information Management System of the Russian Unified Emergency Prevention and Response System, the Russian abbreviation to be used in what follows being AIUS RSChS  (Izmalkov 2017а, b; Kachanov et al. 2011; 2014). The efficiency of an IS is supported by high reliability in the fast prediction of the current situation based on data contained in the AIUS RSChS database of events.
These data are used to calibrate the software that is employed in the assessments of situations, including models for the behavior of various buildings under seismic excitation. It is supposed that timely and reliable estimates of possible losses accelerate the response of decision making and reduce the time people stay in the hit zone, which allows to reduce the losses. We provide a brief description of the structure and contents of database on events as recorded by the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM) using the AIUS RSChS. The procedure for applying the database to calibration of an earthquake vulnerability model and examples to illustrate the enhancement of earthquake-related loss assessment based on calibrated models are given.
A BRIEF DESCRIPTION OF THE DATABASE ON EVENTS RECORDED BY AIUS RSCHS The database that stores descriptions of events is part of the AIUS RSChS data storage that is developed and supported within the framework of computer support for the EMERCOM of the Russian Federation. For instance, for earthquakes, the information includes magnitude, epicenter coordinates, source depth, seismic intensity, macroseismic data, as well as resources and forces used for the response. In addition to the data storage, the AIUS RSChS also includes software that consists of numerous units and interfaces. The development of this system relied on using Web-technology from Microsoft, in particular «. NETCORE and C #» (https://docs. microsoft. com/ru-ru/dotnet/core) (Izmalkov 2017a, b). The database application fields are the following: • storage and structuring of the documented data on disaster sources and elements at risk, for instance, information about residential buildings inventory; • data support for forecasting the level of hazard and risk, as well as for preparing warnings, including those disseminated by mass media; • storage of data acquired from recording the events that have been classified as emergency situations; • data provision for the analytical support of decision making in case of an emergency situation, including tools of mathematical simulation of hazardous excitation, the response of elements at risk to excitation and loss generation; • data provision for risk assessment and identification of risk zones; • data provision for preparing reports on the situation, weather conditions, forces and equipment involved, as well as losses.
The generalized AIUS RSChS block diagram (Fig. 1 ) includes principle functional units, the data storage, and a user interface, as described in detail by  Kachanov et al. (2011; 2014). The principle units are «Inventory», «Analytics» and «Operational management». The names of the units display their functions; description of units is supplemented by input and output information. The «Analytics» unit contains mathematical models for the main types of technological accidents and natural hazards, including earthquakes and secondary processes. In the case of earthquakes, the software of the unit allows to simulate shaking intensity distribution, behavior of buildings and population, resources and forces needed for rescue and other urgent operations. The computations may be made just after the event, as well as for expected future «scenarios».
2019). The calibration of this kind satisfactory mimics learning processes for artificial intelligence systems. After calibration, any mathematical model that is implemented as a software module first determines the area of its calibration and retrieves the values of area-specific calibration parameters. The next step is the assessment itself. All area-specific calibration parameters specific to areas and software modules are stored in the AIUS RSChS storage. Figure  2  shows the structure of the storage and the locations of the tables that contain calibration data in this structure. The thicker line in Fig. 2  indicates the «Database with descriptions of events» and databases with «Zones with stable calibration parameters of mathematical models». These zones are formed during the calibration of the mathematical models.
CALIBRATION PROCEDURE OF THE EARTHQUAKE VULNERABILITY FUNCTIONS FOR RESIDENTIAL BUILDINGS In this section, we discuss the calibration procedure for the earthquake vulnerability functions of buildings and structures using the database with descriptions of events. This procedure requires specifying the form of mathematical vulnerability model (function) used in a computer simulation. The last decades saw great effort devoted to the development of earthquake vulnerability models. One of the key papers reviewing the methodologies over the last 30 years was written by  Calvi et al. (2006). The number of types and forms for the representation of these functions as reported in the literature is steadily growing. The earliest study that proposed vulnerability matrices was (Martel 1964). These were later modified by  Whitman (1973).
The mean and variance are estimated using maximum likelihood method in relation to computed values and the data contained in the AIUS RSChS data base. The calibration of a mathematical vulnerability model is made taking into account the relationship between the parameters in the Gauss distribution and the geographic area, as well as the boundaries to the zones where the same calibration parameters are used. The calibration procedure is as follows. At the first step, the boundary of the area is specified depending on the properties of the used construction materials and the designs of structures, which are determined by the geographic location of the area, the level of earthquake hazard, and the level of its economic development. Next, the data related to the engineering impacts of earthquakes is collected.
The number of records on same-type buildings belonging to a certain vulnerability class affects the accuracy of the parameter estimates in a family of earthquake vulnerability functions. Nevertheless, the analysis of data on the impacts of strong earthquakes in Russia and worldwide has shown that, in spite of considerable economic and social losses, for some regions there is no exhaustive information on high states of damage d. When such cases are encountered, it becomes necessary to search for analogous regions where the impact of disastrous earthquakes has been recorded and stored with a high level of detail. The AIUS RSChS includes the possibility of record the events and produce the damage tables for any location worldwide. In the next section, we are going to show how a database containing calibration parameters can be used in the calculating module to simulate the behavior of buildings in order to provide acceptable accuracy in situational assessments in case of a strong earthquake.
The maximum magnitude М max for this zone is estimated at 8. 0, with hypocentral depth h = 20 km. The impact of the scenario earthquake was calculated using the AIUS RSChS software (Extremum System)  (Larionov et al. 2000; Larionov et 2003b; Sushchev et al. 2010; Larionov et al. 2017). State of damage d calculations were based on regional calibration parameters that determine earthquake vulnerability functions in accordance with the regional seismic intensity scale (Berzhinski 2001) (Table  2 ) and generalized vulnerability function parameters used in the AIUS RSChS for «default» calculations in any region worldwide in case no regional functions are available (Table  3 ). The generalized parameters were derived by processing an empirical data set on earthquake impact observations for earthquakes of intensity I greater than VI MMSK-86 grades that have occurred since the 1960s in Russia, Moldavia, Georgia, Armenia, Turkmenia, Uzbekistan, and other countries.
The rms deviation of the intensity σI = 0. 5 for all types of buildings. Building  Fig. 8. The theoretical impact of the scenario earthquake obtained by using parameters of the generalized vulnerability functions (shown in Table  3 ) Average state of damage d aver : 1-1; 2-2; 3-3; 4-4; 5-5; Number of inhabitants: 6 -500,000 and more; 7 -100,000 up to 500,000; 8 -10,000 up to 100,000; 9 -5,000 up to 10,000; 10 -500 up to 5,000; 11 -less than 500. Fig. 9. Distribution of settlements with different damage grades; a -when using parameters of the generalized vulnerability functions; b -when using parameters of the regional vulnerability functions Table  4. The estimated proportion of buildings that have received a certain state of damage d due to the scenario earthquake obtained using parameters in the regional vulnerability functions presented in Table  2  DISCUSSION OF THE RESULTS The present paper focuses on the AIUS RSChS database, which contains descriptions of the earthquake events; the database allows for subsequent improvements to the entire Extremum System by incorporation of the collected and systematized data from assessments of the situation in a disaster zone.
The results of field surveys covering both the observed impact and a description of the location, time, seismic intensity, and type of the damaging excitation at various sites are recorded in the database. During calibration, the expected states of damage obtained by modeling are compared with the results of field surveys in the disaster zone. The goal of this comparison is to minimize the discrepancies, which is necessary for calibration of the mathematical models for vulnerability of buildings. A result that differs from the observed value by no more than 30% is generally considered to be acceptable. When dealing with seismic events that involve damage and destruction of buildings, the database enables successive calibration of the vulnerability models. The first step includes classification of the buildings, defining the boundary of the region where the construction code, structural solutions, and construction materials are similar.
The example (Table  2 , Table  3 , Figure  7 , and Figure  8 ) compares the results of modeling the impact of a seismic event with all equal parameters, except for the parameters of the vulnerability functions. The example shows that the use of generalized parameters considerably increases the errors in assessing the consequences of an earthquake compared with the case when the regional parameters were used. From this example it can be seen that the use of generalized data on vulnerability for assessing earthquake impact was a matter of necessity for users of the Extremum System. It can also be concluded from the example that all seismic events should be recorded with subsequent incorporation of the survey results in the database, thus enabling refinement of the regional scales and calibration of the system in order to enhance the accuracy of near real-time damage and loss estimates.
• A special AIUS RSChS database receives detailed information on the level of damage to buildings due to each seismic event. The structure of a record has special sections for storing formalized data on the location of the study object (coordinates and address), a description of its structural design and construction materials, an assessment of earthquake resistance carried out when the object was subjected to inventory, damage suffered due to the seismic excitation of a certain intensity. • interconnection is established between the records with descriptions of events and their impacts. • the database used for calibration contains information on the boundary of the region where the same calibration parameters are used. For all seismic events that fall in a selected region, the same set of vulnerability functions will be used; the functions are characterized by the mean and rms deviation of the intensity; • there is an indication of the seismic intensity scale, which is used to classify the buildings within the region.
3. The goal of the calibration process is to minimize the discrepancies between the degrees of damage that were obtained by modeling and those reported after the field survey. The calibration is performed by adjusting the mean and rms deviation that characterize each vulnerability function. This can only be done with a fixed seismic intensity scale that provides a detailed description of the vulnerability classes. 4. The structure of the AIUS RSChS data depository has been adjusted in the optimal way to provide the storage of the calibration data. The data are collected by the survey specialists from the sites and are revised in subsequent reports of engineering seismologists and structural designers, as well as in scientific publications that contain analyses of the observations. 5. After revision of the records or input of additional data, a second calibration is carried out to improve (by selflearning processes) those blocks in the System which are responsible for the simulation of possible earthquake scenarios.
Support vector machine, Bayesian probabilistic classifier  [15]  , and support vector machine classifiers are largely used in epilepsy detection  [16]. Apart from wavelet transform and EMD, empirical wavelet transform also has found its good application in seizure detection. It has been applied for extraction of efficient features for detecting seizure events from surface EEG signal  [17]. Orthonormal triadic wavelet based wavelets along with statistical features and KNN classifier achieved good accuracy  [18]. But there is a risk of over-training and also these classifiers require a large number of database for training. Hidden Markov model (HMM) for seizure detection is preferred in this work as it requires less sample for training. Also, we have observed very little work in seizure detection using HMM based classifier.
In our previous work  [19]  , HMM based classifier approach with entropy features and Hjorth parameters was proposed. In the present work, we have used TQWT based features and feature fusion technique to evaluate seizure detection accuracy for eight different class combinations. The present approach aims at designing a system capable of achieving good accuracy with low training samples. The proposed approach focuses on designing automatic HMM based seizure detection algorithm for intracranial EEG signals. By using a small number of dataset (20%), we have achieved the accuracy up to 100%. Simple and efficient features are extracted and feature fusion approach is used to generate single fused feature set. We have presented detailed analysis of contribution of each selected feature for classification.
Dataset 2 Surface EEG seizure dataset  [21]  is used for testing the proposed approach. Online EEG database is used for experimentation which was collected at the Children's Hospital, Boston. The subjects had drug resistant seizure. A total of 23 subjects' EEG signals were collected to verify surgery necessity after discontinuing drug intake inpatients. EEG sampling frequency was 256 Hz with 16-bit resolution. For most of the subjects, there were 23 or more EEG files. International 10-20 EEG system was followed for recording EEG signals. In this work the entire dataset is used for evaluation accuracy of the proposed model. Uniformity is maintained by selecting 18 channels common to all subjects.
Entropy features In this work entropy features  [24]  parameters are used for differentiating seizure, interseizure and healthy EEG pattern. EEG signals are segmented into two parts. Shannon entropy and collision entropy are extracted from each EEG segment in frequency and time domain. Based on conditional probability, transfer entropy is evaluated. The data is divided into two parts and transfer entropy is evaluated between them. Entropy features are preferred as it represents the randomness in the signal. The spectral entropy indicates the frequency variation of signal. Transfer entropy indicates the dynamics of the system.
Clustering Clustering involves assigning data points to different groups based on similarity. Similarity of points is measured by the minimum distance, connectivity or intensity. Clustering of data points is achieved by an iterative process. In this work agglomerative hierarchical clustering  [29] [30]  is used to divide data into different groups. Agglomerative clustering is performed by finding the similarity between different objects. In this work, Chebyshev distance is used as measure of similarity of data. Based on similarity total dataset is divided into binary hierarchical cluster tree. Then based on maximum cluster number the tree is divided. In this work, entire dataset is divided into three clusters. Each cluster number act as symbol for feature vector. Cluster number is selected based on total class to be classified.
True positives (TP) and true negatives (TN) per class combination are calculated for analyzing classifier efficiency. Mathematically accuracy, specificity and sensitivity are defined as follows. Accuracy = T P + T N T P + T N + FP + FN Speci f icity = T N T N + FP , Sensitivity = T P T P + FN (7) Where FP=false positive, FN=false negative Proposed approach is also verified on surface EEG database. A total of 10 non-seizure and seizure EEG segments are used for training of the HMM model. 2  and 3 fused feature for D-E is more efficient in time domain as compared to frequency domain features. Classification accuracy achieved for AB-E and AC-E are comparable in both time and frequency domain features. Seizure-interseizure (CD-E) class achieved good accuracy in time domain features. Fused feature for class ABC-E is more efficient in frequency domain.
Model evaluation on surface EEG data Complete surface EEG database was used for evaluating the seizure detection algorithm. EEG signal is decomposed using TQWT up to level 10 with Q factor and r factor equal to 3. The time domain features are extracted from 5-second window at a time from continuous EEG seizure and nonseizure EEG signals. Each EEG channel is processed separately and features from all channels are combined to classify seizure EEG signals. Maximum of all entropy, Hjorth parameter, and conditional probability features extracted from every 1 minute of data are used for classification.
Comparison with other state-of-the-art methods Table  6  gives a comparison between state-of-the-art methods and proposed approach for seizure detection using Bonn database. Our proposed HMM based approach is giving good accuracy as compared to SVM and bagging based approach in state-of-the-art methods. We have also compared our HMM based approach with that in our previous work  [19]  and observed higher accuracy. The accuracy has increased due to the application of TQWT features and feature fusion approach. The proposed approach is tested on MATLAB platform with core i5 system. The system took overall 0. 28 seconds for processing and classifying the data. With increase in processing speed, the present system can be used for real time seizure detection. Simple features are used making the system simple and efficient.
is divided into 5 datasets consisting of seizure, healthy and interseizure EEG signals. Sampling frequency is 173. 61 Hz with spectral bandwidth of the acquisition system between 0. 5 Hz to 85 Hz. The total duration of signal is 23. 6 seconds and has a total number of 4 097 samples. Two set A and B are surface EEG signals recorded from healthy person in eye closed and eye open condition. Set C and D are intracranial EEG signals and set E consists of seizure EEG segments. Each set consists of 100 EEG segments. Autoclassification accuracy for eight different cases is presented in this work such as healthy-seizure classification (A-E, AB-E), seizure-interseizure classification (C-E, D-E, CD-E) and seizurenonseizure classification (AC-E, ABC-E, ABCD-E).
Introduction Understanding and predicting how genetic and environmental perturbations alter the behavior and subsequently the phenotype of an organism is a major goal of systems biology. Given the flood of biological data (genomic, proteomic, and transcriptomic) and interaction knowledge presented in pathway databases, significant efforts have been made in reconstructing different genome-scale networks for a wide variety of organisms ranging from bacteria to humans  [1] [2] [3] [4]. Due to their central role in the functioning of an organism, metabolic and gene regulatory networks have been extensively studied recently. Metabolic networks describe all the biochemical reactions that operate in a living organism; they are typically reconstructed from annotated genome sequences with the available biochemical knowledge.
The second category includes methods where transcriptomic data can be used to improve the prediction of the metabolic response to environmental and genetic perturbations. For these methods, transcript levels are used to determine the activity of genes in a given condition or between different conditions. Such information is then used to set bounds on reaction fluxes catalyzed by associated enzymes and to return a metabolic flux distribution that is consistent with the gene expression data. As presented in Table  1 , there exist some methods that belong to both categories. Table  1. Overview of the main approaches used to integrate the genome-scale metabolic and gene regulatory networks: Classification of methods according to their intended functionality regarding metabolic flux distribution prediction, context-specific models building, or both.