dataesr/scientific-paragraphs-categorization huggingface dataset:
II. LITERATURE REVIEW SYNTHESIS building structures after earth quake  [22]. For the successful recognition of disaster, it is mandatory that data of the event must be transmitted timely with higher reliability therefore two network QoS metrics were adopted. For simulation a set of five different cases of traffic flow on nodes based on Backoff algorithm was applied to reduce the collisions. Battery backup was also the major problem but data discontinuation cannot be neglected. Recorded data was classified into three types a) In first type there would be no data will be recorded and observed as the building didn't suffer any kind of damage. b) Secondly the standing structure with the recorded data as it suffered damage. c) Destroyed and completely devastated structures that shows and represent that building has been collapsed.
The Ptolemy II/Visual sense framework was designed for the simulation  [23]. Initially Global Positioning System (GPS) modules were deployed at the east coast of Peninsular Malaysia to find out the intense weather hazards like heavy flooding. The high frequency of rainfall may decrease the soil tendency resulting in flash floods. GPS were located in the west coast of Peninsular Malaysia. Rainfall data has been selected from the department of irrigation and drainage (DID). GPS and meteorological data have been processed to find out the PWV (Precipitable water vapor) by using Trowav 2. 0 designed in MATLAB. The weighted mean temperature was utilized to accurately change zenith wet delay (ZWI). Statistical reading of minimum and maximum values of GPS was taken as an average. The average value is higher during pre-phase for overall the cases.
Information and data have been gathered from the Malaysian Meteorological Department situated in Petaling Jaya. Previous data from the period of (2007 o 2010) that was analyzed by rain gauges was taken. After the assessment of data, it was decided that to train the network data of this period (1 Jan 2010 to 31 st Jan 2010) will be used. Data that was taken from the department contained error, noise and missed information having N/A and -33. 3 values. Data cleaning task can be utilized to recover the missed data values or deviations. N/A and -33. 3 were replaced by 0 and 0. 1. It's necessary to improve the deficiencies of data for the smooth and proper validation and testing. The model having the minimum error was chosen to estimate in contrast with the actual results. Mean Square error was calculated to observe the error, MSE value was 0.
2. A system was developed using SCADA (supervisory control and data acquisition) technology to avert flash floods, in first and second model (A and B) forward back propagation (type of neural network) were used. The third layer used the multi-layer perceptron (MLP) having structure of five nodes in first layer and one node in second layer having output layer (5-5-5-1)  [31]. ANN is most and widely used tool for the identification and locating the different type of the fields. After the demonstration of the ANN training by Rosenlatt in 1958 ANN is recognized as a discovery model. Flood water level has been forecasted by using neural network autoregressive model with exogenous input (NNARX) technique. This model was developed using MATLAB neural network toolbox. Rainfall intensity is based on various factors (variables) like pressure, temperature, wind, speed and direction, therefore the flood forecast warning system must be very accurate and intelligent to provide an initial warning  [32].
The procedure was an extension of ARX model; five inputs were feed to the NNARX model to guess the hazard (flood water level). The forecasting time can be modified at any normal water level so that, the rapid enhancement in level may be identified. ST1, ST2, ST3 and ST4 that demonstrated four higher stream and dy/dk defined the changing of water level at the flash flood location. The data which was used for testing, validation and simulation was taken from 1 st nov. 2014 to 1 st dec. 2014 from department of irrigation and drainage Malaysia. Patterns of data were classified by training, validation and testing the proposed algorithm. Basically, NNARX used numerical method to examine in contrast with simulated and actual data. Initially it was observed that water levels are higher than the predicted modeling but after sometime it was near the actual water level that proved that this prediction model was reliable  [33].
Android part would be observing the water level constantly. Image of photograph was captured and evaluated by the android. If the water level exceeded the threshold limit and after the identification of the event android began to communicate with the server using 3G. It took input from the thermal transducer and echolocation sensor (echoes are sent and waited to be reflected back) also utilized the GIS (Geographic information system) data and images. HC-SR04 echolocation sensor has been used to observe the water level. Sensors give the echo time of sound and this PWM is changed into the length data. The sensor comprised of two parts, transmitter and receiver. Field testing was performed in Nang Rong waterfall that was heavily influenced by thunderstorm in 2013. It was observed and recorded that high rate of inaccurate estimation was achieved as high level of noise due to the temperature change, continuously precipitations and heavy winds  [35].
Particle Swarm Optimization (PSO) can be used in most complicated medical areas like breast cancer and heart diseases. PSO's simplicity can be regarded as most important merit. Particles have been trained and learnt from its own knowledge and neighbor particle knowledge. Ackley's function can investigate and estimate the expression vigorously. Average data that has been noted down hourly from five stations were selected for the input. Output was declared as water level. Data during 2012 and 2014 floods were selected for the experiment. Multiple Perceptron (MLP) is type of ANN. It comprises of three parts named as input layer, hidden layer and output layer. There are joined and interconnecting weights among them. There are two factors which can be estimated a. ) Number of iteration b. ) Optimum number of particles.
The back of antenna was filled with the lossy di-electric medium with similar di-electric properties having the same di-electric properties like normal healthy breast issues. The simulation was performed by utilizing ANN on the MATLAB. The false positive rate (FP) was acknowledged as a positive alarm rate. The negative false rate (FN) is the data that was categorized falsely negative. Fake alerts were minimized from 22% to the 2% with the variation of the signal to noise ratio from 1db to 40 db  [38]. The electro cardiogram (ECG) signal was used widely to analyze the heart health. Arrhythmias can be regarded as physical variations. Investigation of irregular pattern is mandatory for prevention of heart. HRV (Heart rate variability) analysis is very fruitful in finding heart health  [39]. Many approaches has been carried out like ANN with fuzzy, support vector machine and generalized discriminant analysis  [40] ,  [41].
C. RADAR AND SATELLITE BASED OBSERVATIONS The phased array radar at National Weather Radar Test bed (NWRT) possessed the diameter of 12ft (3. 7m), maximum transmitting power of 750 kw and operating frequency of 3. 2 GHz. Pulse compression and most of the new waveform technologies (a signal processing approach generally used by radar, sonar and echography to enhance the resolution range as well as the SNR by modulating low level signal to a higher signal) has been used in armed forces but rarely used for the investigation of weather because of the scattered behavior of the water precipitation vapor (defined by IEEE 686-2008, Standard Radar Definitions). Usually the main data contained from Polari metric radar (transverse waves) spectral motion of reflection, mean, average radial velocity, spectrum width, cross correlation coefficient, phase difference and linear depolarization ratio.
The numerical method was developed for the calculation of temporal distances, more than three hours elapsed time was required for the data processing to train the data values in a model. A nowcasting system comprised of two main categories. The First one calculated the recent direction and second one predicted the rainfall position and volume. Motion calculation based upon three sequential radar-based observations that were named as Ae1, Ae2 and Ae3. Ifi represented the predicted image and Afi represented the image received by radar at the same date. These two images were examined in contrast to each other. Generally, X band images were imported from ground radars after every five minutes having angular resolution of one degree and radial resolution of 150 meters. The bounced readings have been remodeled into humidity rates by using Z-R formula and second transformed from polar grid to the Cartesian  [46].
5 km and the velocity of 2. 13 cm/s. DBF radar transmitted 512 pulses with repeated time of 0. 64 msec. Dragon was installed at two sites (Site A: Omaezaki and Site B: Hamaoka) 8 km away from the Enshu Seashore. Accuracy and precision have been determined by comparing the wave current acceleration with the current measuring equipment (meter). Impact of current by the heavy wind or air can be easily minimized by the current meter data. DBF radar based data and current meter data were correlated to estimate the tsunami  [51]. Serious weather conditions and heavy rainfall, torrents, cyclone and rainstorms lead towards the casualties and infrastructure loss. Lightening is one of the most dangerous hazards. It is also china's threat that causes the damage of transmission lines in china. CG flashes are commonly identified by lightening detection network (LDN).
The results proved that false alarm rate reduced drastically  [54] ,  [55]. Automatic Target Recognition Algorithms produce more false alarms as they are dependent on the environmental conditions where they are installed. ATR generates more fake alerts due to the Mine Like Textures (MLTs) and regions with high clutter density. ATR can identify in flat surfaces very accurately. False alarm depends on the correlation with the complexity and anisotropy of the sea-floor Nelson and Kingsbury  [56]. These complexities can be utilized to feed and educate the neural network. Dual Tree Complex Wavelet Transform (DTCWT) is used to measure the textural characteristics like areas of the sea having same design and patterns. Clutter characteristics are investigated by Markov Random Field (MRF). Haar-cascade ATR is trained to identify the truncated cones, wedges and cylinders in the data sets B-D.
Minor researches have been carried out to resolve the problem of precipitation measurement in complicated landscape and topography without errors  [62] ,  [63]. Analysis was performed at mountains Trentino-Alto Adige region, situated in the eastern Italian Alps for error observation. The area was around 13607 km 2 of complicated landscape region along with the altitude 65 meter to almost 4000 m. Parameters were calibrated and measured again by using CMORPH  (2000) (2001) (2002) (2003) (2004) (2005)  data, PERSIAN data and 3B4RT data for the detailed error investigation  [64] -  [66].
Risk based early warning system case study was carried out at the German city of Achen. Georeferenced files consisted the following data for GIS design, precipitation velocity, timings, precipitation intensity, affected area, video and images of flood with time and actual depth of the flow rate  [70]. Flash flood event was estimated which was caused by the severe rainfall eighty millimeter per day in northern Morogoro and Tanzania. Flash flood event was caused due to the overflow of Ngerengere river. Automatic weather observations may boost the accuracy in identifying the flood events accurately  [71]. There are so many deciding parameters which have been used for the robust estimation of floods like meteorological properties, time duration, precipitation intensity, wind velocity, time variation of rainfall, watershed occupied area such as size, slope, length, soil humidity and vegetation cover  [72] ,  [73].
Another fresh case study was conducted in Tanzania which elaborated that mainly flash floods were caused by the severe precipitation patterns  [74]. Recent researchers also proved that severe precipitation also depends upon the sea surface temperatures (SSTs)  [75]. River density, forest density, soil, geomorphology, landslide density, mean slope of tributes, topographic wetness index and mean annual precipitation index was estimated for the identification of flash floods on early basis. Grading of estimation was divided into five categories. Real time data for the precipitation from iMETOS was also collected. Nine types of maps were utilized to evaluate the floods accurately  [76]. Thresholds of flash floods risk assessment vary widely due to the slope and rainfall intensity. Flash floods levels were categorized into four levels in Vietnam.
A flood prediction system was developed using satellite altimeter. The root mean square for the prediction of the flash floods in Bangladesh was found to be 0. 75m to 1. 5m  [84]. Flash floods were also identified using satellite synthetic aperture radar(SAR). Data was collected of pre and post floods from Japan Aerospace Exploration Agency's Advanced Land Observing Satellite-2. Greater spatial resolution was achieved from Satellite-2. The differences were correlated between the pre and post flood  [85]. Digital Basin Technology was adopted to reduce the flash floods disaster in Yellow river floods  [86]. Flood scene ontology [FSO] was designed to determine the topological and direction information to mitigate the flood disasters  [87]. Mathematical equations depending on multiple factors were designed for the modeling of the system  [88].
The European NAFLD Registry is an international, prospectively recruited observational cohort study that aims to establish a large, highly-phenotyped patient cohort and linked bioresource. Here we describe the infrastructure, data management and monitoring plans, and the standard operating procedures implemented to ensure the timely and systematic collection of high-quality data and samples. Already recruiting subjects at secondary/tertiary care centres across Europe, the Registry is supporting the European Union IMI2-funded LITMUS 'Liver Investigation: Testing Marker Utility in Steatohepatitis' consortium, which is a major international effort to robustly validate biomarkers that diagnose, risk stratify and/or monitor NAFLD progression and liver fibrosis stage. The European NAFLD Registry has the demonstrable capacity to support research and biomarker development at scale and pace.
This is because histopathological examination of liver tissue is required to differentiate NAFL from NASH, grade steatohepatitis activity and accurately stage fibrosis. [5]  However, liver biopsy is not widely utilised outside specialist centres as it is resource-intensive and carries a small but appreciable risk of complications. Therefore, a broad collaborative effort is required to pool sufficient data from comprehensively phenotyped patients at specialist centres to assemble an adequately sized cohort that may be leveraged to support research. The European NAFLD Registry (ENR) is an innovative, international patient registry with an associated bioresource that is assembling a large, well-characterised patient cohort specifically to support translational and discovery science.
Overview The European NAFLD Registry is a prospectively recruited, international observational study to assemble a cohort of highly-phenotyped patients across the full spectrum of NAFLD that will facilitate crosssectional and longitudinal analyses (clinicaltrials. gov registration NCT04442334)  [13]. The comprehensive dataset includes clinical information, liver histopathology and imaging data derived from healthcare records during routine medical care and is supplemented by lifestyle and quality of life data, as well as biological sample collection. The European NAFLD Registry infrastructure hosts a uniform dataset of patient level data that is split into two distinct sub-sections: i. The 'Metacohort': comprises prospectively recruited NAFLD cases that consented to participate during recruitment for European studies into NAFLD pathogenesis conducted from 2010 to December 2017 (i.
e. the FP7-FLIP, H2020-EPoS and HEPAmet cohorts)  [14] [15] [16] , plus cases with compatible consent and data collection processes derived from previous investigator-led single-centre cohorts. Data from these cohorts have been integrated into a single dataset whilst maintaining data quality and retaining knowledge of the source. ii. The 'LITMUS Study Cohort': comprises prospectively recruited NAFLD cases acquired since 1st January 2018 as part of the IMI2funded LITMUS project  [17]. Recruitment is ongoing across Europe according to a common Master Study Protocol with a comprehensive data monitoring process and standard operating procedures for sample handling implemented. Recruitment is currently active at > 25 centres in 13 countries (Fig. 1  and Table  1. ) Patients with that are enrolled into the European NAFLD Registry will be followed-up for up to 10-years to allow assessment of long-term outcomes.
Objectives The primary objective of the European NAFLD Registry observational study is to assemble a 'real-world' cohort of well-characterised patients across the full spectrum of NAFLD and to collect associated clinical information, biological samples and imaging data for crosssectional and longitudinal analyses to support research into disease natural history. Key secondary objectives are to support research addressing the pathophysiology of NAFLD using a range of state-of-the-art scientific techniques with integrated bioinformatics. Additional secondary measures include the study of dietary habits, lifestyle/activity factors and symptom burden. To characterise and integrate key intrinsic factors multiple 'omics' approaches (genetic, epigenetic, transcriptomic, metabolomic, proteomic and metagenomic) will be applied to understand inter-individual variation in severity of hepatic injury, serum and hepatic 'omic' profiles and their interaction with environmental (behavioural/ dietary/lifestyle) factors that determine how a patient feels, how the disease progresses over time and long term outcomes.
2. Overview of the European NAFLD registry governance structure. regression from NAFL through NASH to fibrosis and cirrhosis. Through the Registry, research is powered to provide the required clarity on biomarker analytical and clinical validity at scale and pace, generating the requisite level of high-quality data to support biomarker validation and evidence needed for regulatory qualification under the joint EMA Committee for Medicinal Products for Human Use (CHMP) and the US Food and Drug Administration (FDA) processes.
Organisation & oversight The European NAFLD Registry operates across multiple territories and so a comprehensive organisational and oversight structure has been established (Fig. 2 ) that defines lines of responsibility as well as project management, data management, data monitoring and sample handling processes. Oversight and recruitment into the Registry are structured according to three tiers of geographical hierarchy: central leadership and coordination, national oversight and site level delivery. Coordination of the study, including establishing the European NAFLD Registry Master Protocol, defining standard operating procedures, management of the web-based eCRF database, controlling data access and biological sample use are managed centrally by the Registry Management Team. This group comprises the Chief Investigator, Study Managers, Data Managers, Ethics Lead and representatives from the Registry Central Biobank.
However, the conduct of the study according to these centrally defined processes, including responsibility for study sponsorship, regulatory compliance, gaining the necessary national and/or local ethical approvals for recruitment and site data-monitoring as centrally defined is devolved to a budget-holding National Lead Investigator in each country (Table  1 ). These senior investigators are experienced clinical trialists with extensive knowledge of the national research frameworks in which they operate. Thus, the Registry is able to tailor its activities to varying national research ecosystems whist ensuring robust data collection in compliance with the International Conference on Harmonisation -Good Clinical Practice (ICH-GCP) requirements  [18].
Database Personal data including clinical data are collected, and such data are protected in accordance with the European General Data Protection Regulation (GDPR). Data is held within the Registry in a pseudoanonymised (linked-anonymised) form to avoid personally identifiable data transfer or processing outside a subject's usual clinical care team at the recruiting site. A purpose-built MySQL relational database has been developed to facilitate data capture during recruitment into the Registry. The webaccessible secure front-end database comprises 14 related electronic clinical record form (eCRF) data-tables and contains around 1200 data fields, equating to approximately 1-2 megabytes of data per subject and is designed to fulfil the regulatory requirements for record keeping  [19]. Data are held in a Clinical Data Interchange Standards Consortium (CDISC, www.
cdisc. org) compliant format to aid information system interoperability for future collaborative projects. The database holding the project user records and collected records from sites is hosted on a multi-machine replication cluster with multiple physical servers. In the event of machine failure or catastrophic data loss, one of the remaining database servers would be promoted to become the master server for the European NAFLD Registry application. Database records are archived to a separate physical server three times daily, once per day for the previous 31 days and once per month for the previous 12 months. In addition, an archive copy of the data is retained on Newcastle University's Research Data Warehouse according to local information security policy. This ensures a copy of the data is available for up to tenyears after the last date of access.
Network access to the database is controlled by specific role at the individual level. methotrexate, tamoxifen, tetracycline, high dose oestrogens, valproic acid) 6. Any contraindication to liver biopsy 7. Recent (within 3 months) change in dose/regimen or introduction of Vitamin E (at a dose of ≥400 IU/day), betaine, s-adenosyl methionine, ursodeoxycholic acid, silymarin or pentoxifylline 8. Non-local language speaker/unable to access interpreter 9. Patients not meeting the inclusion criteria or judged by the investigator to be unsuitable for inclusion into the study.
Data collection A comprehensive dataset is collected from the medical notes at the 'baseline' enrolment clinical attendance (usually coincident with a liver biopsy). This detailed dataset includes demographics, anthropometrics, a range of clinical (co-morbidities, current medication), laboratory (clinical haematology, biochemistry, immunology), histopathological and radiological data (Table  3 ). Diagnostic imaging data are collected for all patients undergoing clinically indicted investigations. These include routine diagnostic ultrasound and Fibroscan™ (vibration-controlled transient elastography  [VCTE] ) as well as any other modalities that may be used as part of local practice at recruiting sites. In addition, selected patients are invited to opt-in to participate in a nested imaging study in which MRI-PDFF, MR LiverMultiScan™, deMILI, and MR Apparent Diffusion Coefficient, as well as elastography (acoustic radiation force impulse [ARFI]; and magnetic-resonance elastography [MRE]) are captured outwith standard care.