Miracle-dz/UniFAR-FTUs huggingface dataset:
Security-Enhanced Operational Architecture for Decentralized Industrial Internet of Things: A Blockchain-Based Approach

The remarkable development of the Industrial Internet of Things (IIoT) has undoubtedly elevated industrial operations to a more intelligence and efficiency level, yet it has also introduced a range of security challenges. The widespread of intelligent IoT devices has greatly expanded the attack surface for cyber-attacks. Additionally, the cloud-based centralized management architecture of traditional IIoT is susceptible to single-point-of-failure, which exacerbates the security risks. Nowadays, the secure and decentralized nature of blockchain has been considered a promising solution to address the security and privacy challenges in IIoT. This article proposes a blockchain-based operational architecture for IIoT (SecureArchi- IIoT) to enhance security and privacy in IIoT operations. Under this architecture, a set of smart contracts are designed to provide operational functionalities that are suitable for actual industrial demands. An operational control policy is designed to realize precise and effective management of the operation permissions with distinct granularity. Furthermore, a reputation-based behavioral punishment mechanism is developed to enhance the security performance of the proposed architecture. The prototype of the proposed architecture is implemented in a private IIoT environment to demonstrate its feasibility and effectiveness. Experimental results confirm that the proposed architecture outperforms the traditional architecture in aspects of security and privacy and maintains acceptable real-time performance.
The bounce of a blunt body from a water surface at high horizontal speed

We consider the two-dimensional blunt body impact into water with large horizontal speed, where the vertical motion of the body is free. For large horizontal speed of the body, the hydrodynamic loads can be high enough for the body to bounce out of the water without deep penetration of the body through the water surface. This phenomenon was utilised in Wallis’s bouncing bomb (see Johnson, 1998). On the other hand, it can be a hazard for planing high-speed boats (see Faltinsen, 2005) and for the safe landing of aircraft on the water surface. Difficulties arise in the modelling and the computation of such impact processes, not only with identifying the location of the spray root zone but also the position of the water separation from the body surface. To understand the behaviour of the separation point during the oblique impact, simplified models of the phenomenon are required. Although we are aware of the roles of viscosity and surface tension in separation processes, we aim to model separation as inviscid (Sun and Faltinsen, 2007) which is suitable for bodies of large dimensions such as an aircraft fuselage. For oblique impact of a two-dimensional blunt body on the water surface, jets initially occur at the rear and front of the wetted area (see Fig. 1(a)); later the rear jet disappears and the fluid separates from the rear of the body surface (see Fig. 1(b)). We show that before separation starts the hydrodynamic pressure under the body falls below atmospheric pressure. In the absence of any well-established criterion, we consider three possible criteria of inviscid separation and show that the choice changes the body motion significantly.
Development of intelligent tutor for enhancing Think-Pair-Share in learning of Electrical Machines in Colleges of Education (Technical) in North East, Nigeria

This study developed an Intelligent Tutor for enhancing Think-Pair-Share in learning of Electrical Machines in Colleges of Education (Technical) in North East, Nigeria that will improve access to students and give them flexibility in learning. The study adopted a multi-method research design involving Research and Development (R & D) and Descriptive Survey research designs. The population of the study was 25 persons consisting of 10 lecturers of Electrical/Electronic Programme in Federal College of Education (Technical) Potiskum and Federal College of Education (Technical) Gombe, 15 industrial personnel, 5 each from Ashaka Cement, Bajoga, Savannah Sugar Plantation, Numan and Adama Beverages, Yola. Three research questions and three null hypotheses guided the study. The instruments were subjected to face and content validation by experts. The instrument, Intelligent Tutor Questionnaire for Electrical Machines (ITQEM) was then incorporated in the Intelligent Tutor (IT) for enhancing Think-Pair-Share in learning of Electrical Machines. Data collected from industrial personnel and lecturers’ validation were analyzed using mean and standard deviation, two-tail t-test was used for test of significance between the responses of industrial personnel and Electrical/Electronic lecturers at 0.05 level of significance. The findings of the study showed that three topics and 60 items were found to be appropriate for inclusion in the Intelligent Tutor. It was recommended among others that, since Research grants such as Tetfund NRF, Bench-work and PTDF should give priorities to scholars working on Intelligent Tutors and Artificial Intelligences in general to allow equal access for learners from all angles of the country which would in turn harnesses the untapped resourcefulness of the lecturers in Electrical Machines Principles of Operation.
Determination of acidity in ambient air

A laboratory and field study was performed to construct and evaluate an automated, semi-continuous monitor for ambient air concentrations of nitric acid (HN03) and ammonia (NH ) utilizing tungstic acid-coated denuder tubes. In addition, atmospheric 3 concentrations of hydrochloric acid (HCI) were determined. Manual techniques for atmospheric NH measurement were also compared. Interference in HN03 3 measurements was rletermined with nitrogen dioxide, nitrous acid, and particulate ammonium nitrate. Atmospheric sampling for HN03 was done in Riverside, CA during September 1984 with the automated tungstic acid technique (TAT), and by the denuder difference method (DDM), considered the reference procedure for HN03• Ammonia and HCl were measured with impregnated filter methods. In addition, NH3 was measured by a denuder tube collection procedure. Nitrous acid was the only material studied which exhibited significant interference in nitric acid response with the automated TAT. Atmospheric nitric acid levels with the automated TAT showed 1 hour average values up to 45 μ g/m3 (17 .5 ppb). These values yielded moderate correlation with o (r = 0.78) and high correlation (r = 0.94) 3 with HN03 by the ODM. However, the HN03 values averaged about 50% higher than those by the reference procedure. Ammonia measurements with impregnated filters showed high correlation (r = 0.98) with oxalic acid-coated denuder tube collected samples, but the former averaged 12% higher. Ammonia measurements with the automated TAT were not quantitative because of difficulties in calibration. Hydrochloric acid values ranged up to 3 μg/m 3 with a pronounced diurnal variation, maxima occurring during the period 0800-1200 or 1200-1600 hr. Particulate chloride levels were similar in magnitude to those for HCl and showed no correlation with HCl.
GPS as a geodetic tool for geodynamics in northern Victoria Land, Antarctica

The VLNDEF (Victoria Land Network for DEFormation control) project started in 1999 with the aim of detecting crustal deformation in Northern Victoria Land (Antarctica) over an area that had never been surveyed by a dense GPS network before. After a brief summary of the Italian geodetic activities carried out since 1991, the paper presents the results obtained from the processing of data collected from 1999 to 2003. In particular, processing strategies were dealt with, in order to produce horizontal and vertical displacement maps through GPS observations. Absolute motions in a global reference frame have been investigated using a double approach, which allowed us to make considerable progress in detecting movements and standardizing the data analysis. The analyses provide absolute horizontal velocities ranging between 17 mm yr−1 and 8 mm yr−1, with greater motions in the northernmost area. The subtraction of the rigid plate motion provides relative displacements, which may contribute to the understanding of neotectonics and geology, whereas the pattern of the vertical crustal motions detected, with average values of +1.3 mm yr−1, is essential to detect the effect of Glacial Isostatic Adjustment (GIA) and other geophysical signals, and to redefine theory and numerical models used without any direct measurements.
Auroral activities observed by SNPP VIIRS day/night band during a long period geomagnetic storm event on April 29-30, 2014

The Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi-NPP represents a major advancement in night time imaging capabilities. The DNB senses radiance that can span 7 orders of magnitude in one panchromatic (0.5-0.9 μm) reflective solar band and provides imagery of clouds and other Earth features over illumination levels ranging from full sunlight to quarter moon. When the satellite passes through the day-night terminator, the DNB sensor is affected by stray light due to solar illumination on the instrument. With the implementation of stray light correction, stray light-corrected DNB images enable the observation of aurora occurred in the high latitude regions during geomagnetic storms. In this paper, DNB observations of auroral activities are analyzed during a long period (> 20 hours) of geomagnetic storm event occurred on Apr. 29-30, 2014. The storm event has the Bz component of interplanetary magnetic field (IMF) pointing southward for more than 20 hours. During this event, the geomagnetic storm index Dst reached -67 nT and the geomagnetic auroral electrojet (AE) index increased and reached as high as 1200 nT with large amplitude fluctuations. The event occurred during new moon period and DNB observation has minimum moon light contamination. During this event, auroras are observed by DNB for each orbital pass on the night side (~local time 1:30am) in the southern hemisphere. DNB radiance data are processed to identify regions of aurora during each orbital pass. The evolution of aurora is characterized with time series of the poleward and equatorward boundary of aurora, area, peak radiance and total light emission of the aurora in DNB observation. These characteristic parameters are correlated with solar wind and geomagnetic index parameters. It is found that the evolution of total area-integrated radiance of auroral region over the southern hemisphere correlated well with the ground geomagnetic AE index with correlation coefficient = 0.71. DNB observations of aurora help understand the relations among solar wind variation, auroral activities and geomagnetic responses.
Physics-Informed Deep Learning-Based Approach for Probabilistic Modeling of Degradation

Deep learning (DL) models have gained significant popularity for the prognostics of systems experiencing degradation. However, there are two major concerns with such models. Firstly, they require a substantial amount of training data due to their large number of parameters. Secondly, they disregard the underlying physics and solely fit the available data, leading to potentially weak generalization capabilities when faced with unseen out-of-distribution data in the field. This study aims to tackle these challenges by incorporating the underlying physics of degradation into DL models. The objective is to develop a novel DL-based approach in conjunction with Bayesian filtering, enabling physics-informed probabilistic life prediction for systems subject to environmentally induced degradation. The proposed framework consists of two main components: physics discovery and degradation prediction. The former involves identifying the dominant stress agents and formulating the underlying physics of degradation. The latter predicts the degradation of the system by incorporating the discovered physics into a DL model. It is expected the results indicate that by combining data-driven DL with physics-based insights, more robust and reliable life predictions can be achieved, addressing the limitations of DL approaches. This framework holds promise for enhancing decision-making processes related to maintenance strategies in various industries.
Diversity of Sundanese Forms of Address Usage in Family

This study aims to describe the diversity of Sundanese forms of address in families. The use of Sundanese forms of address in Sundanese ethnic families is currently mixed with Indonesian addresses, especially in urban families. The problems studied are related to the types of forms of address and the influence of social variables on the choice of forms of address. The method used is descriptive. Data collection is actualized by using a survey distribution method through an open-ended questionnaire. The selected respondents are the students of Sundanese Literature of the Faculty of Cultural Sciences of Universitas Padjadjaran, who come from and live on the border of Bandung City. The theoretical basis used in this study is a sociolinguistic approach. The results of the analysis show that the types of Sundanese forms of address used in the family include proper names, kinship terms, terms of endearment, personal pronouns, and combinations of forms of address. Social variable factors in the form of situations that influence a change of a selection of types forms of address are happy, sad, and angry situations. This study contributes to the viewpoint that language attitudes are also determining factors in the use of the address.
Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.
Conservation tillage and fertiliser management strategies impact on basmati rice (Oryza sativa L): crop performance, crop water productivity, nutrient uptake and fertility status of the soil under rice-wheat cropping system

Background The sustainability of paddy production systems in South Asia has recently been affected by a decline in soil health and excessive water usage. As a response to the global energy crisis, escalating costs of synthetic fertilisers, and growing environmental concerns, the utilization of organic plant-nutrient sources has gained considerable attention. Emerging adaptation technologies, including conservation tillage and innovative approaches to fertilizer management, present practical choices that can significantly contribute to the long-term preservation of soil fertility. Methods The two year-long field experiment was completed in sandy loam soil during rainy (Kharif) seasons in 2019 and 2020 at the crop research centre farm of Sardar Vallabhbhai Patel University of Agricultural & Technology, Meerut, Uttar Pradesh to analyze the impacts of different tillage establishment of the crop and its methodologies as well as integrated nutritional management approaches on rice growth, yield, productivity of water, nutrient uptake, and fertility status of soil under a rice-wheat rotation system. The experiment was set up in a factorial randomized block design and replicated three times in a semi-arid subtropical environment. Results The conventionally transplanted rice puddled (CT-TPR) grew substantially better taller plants, and higher dry matter buildup leads to increased yields than transplanted rice under raised wide bed (WBed-TPR). WBed-TPR plots had more tillers, LAI, CGR, RGR, and yield characteristics of the rice in two year study. CT-TPR increased grain yield by 4.39 and 4.03% over WBed-TPR in 2019 and 2020, while WBed-TPR produced the highest water productivity (0.44 kg m−3) than CT-TPR, respectively. The 100% RDF+ ZnSO4 25 kg ha−1 + FYM (5 t ha−1) + PSB (5 kg ha−1) + Azotobacter 20 kg ha−1 (N6) treatment outperformed the other fertiliser management practices in terms of crop growth parameters, yields of grain (4,903 and 5,018 kg ha−1), nutrient uptake and NPK availability, organic soil carbon. Among the fertilizer management practices, with the direct applications of the recommended dose of fertilizer (RDF), farm yard manure (FYM), phosphate solubilizing bacteria (PSB), Azatobactor and zinc worked synergistically and increased grain yields by 53.4, 51.3, 47.9 and 46.2% over their respective control treatment. Conclusions To enhance rice productivity and promote soil health, the study suggests that adopting conservation tillage-based establishment practices and implementing effective fertilizer management techniques could serve as practical alternatives. It is concluded that the rice yield was improved by the inclusive use of inorganic fertiliser and organic manure (FYM). Additionally, the study observed that the combination of conventional puddled transplanted rice (CT-TPR) and N6 nitrogen application resulted in enhanced rice crop productivity and improved soil health.
Javanese script as cultural artifact: Historical background

The paper shows how the distinctive writing system of Javanese (aksara Jawa) changed over the centuries, following the course of the cultural history of Java, from the Hindu period, through the Islamic period, into the colonial period. This development enabled the emergence of an important literature, and is linked with shifts in the materials used and the change from manuscript to printing.
METHOD OF DELAMINATED DECOMPACTION CORRECTION

The uniform porositydepth function drawing up on observed data does not express the compaction history of beds because different clastic beds are different in lithology and compaction process in a well or an identical region. The original porosity lied on clastic size and lithofacies so that the original porosity of a sort clastic bed can be considered as the same, but the compaction process could be different from each other. For the sake of reliability of decompaction correction, this paper provides a method of delaminated decompaction correction. First, beds in a well or an identical region are delaminated into some "compaction unit beds" based on their lithology, age, compaction process and so on. Then, the respective porositydepth function which expresses each delaminated "compaction unit bed" needs to be found. The original porosity in the porositydepth function could be determined by modeling sediments. The "compaction model" of delaminated "compaction unit bed" in a well or an identical region could be different or same but it should be reasonable to express their compaction process. And last, using the backstrip method computes the paleaothickness and original thickness based on "conversation law of solid thickness of beds".
Seismic characterization of fractured reservoirs: a resolution matrix approach

Summary Natural fractures in reservoirs play an important role in determining fluid flow during production, and knowledge of the orientation and density of fractures is required to optimize production. Variations in reflection amplitude with azimuth and incidence angle are sensitive to the presence of fractures. The variation in reflection coefficient of seismic P-waves as a function of azimuth and offset for an arbitrary number of differently oriented vertical fractures is analyzed to identify which parameter combinations are well-resolved for various experimental geometries. The results show how to optimize seismic acquisition to help choose the location of infill wells, the orientation of deviated wells, and the relative orientation of neighboring infill wells to ensure adequate drainage.
A Comparison of Sampling Methods for Dealing with Imbalanced Wearable Sensor Data in Human Activity Recognition using Deep Learning

—Human Activity Recognition (HAR) holds significant implications across diverse domains, including healthcare, sports analytics, and human-computer interaction. Deep learning models demonstrate great potential in HAR, but performance is often hindered by imbalanced datasets. This study investigates the impact of class imbalance on deep learning models in HAR and conducts a comprehensive comparative analysis of various sampling techniques to mitigate this issue. The experimentation involves the PAMAP2 dataset, encompassing data collected from wearable sensors. The research includes four primary experiments. Initially, a performance baseline is established by training four deep-learning models on the imbalanced dataset. Subsequently, Synthetic Minority Over-sampling Technique (SMOTE), random under-sampling, and a hybrid sampling approach are employed to rebalance the dataset. In each experiment, Bayesian optimization is employed for hyperparameter tuning, optimizing model performance. The findings underscore the paramount importance of dataset balance, resulting in substantial improvements across critical performance metrics such as accuracy, F1 score, precision, and recall. Notably, the hybrid sampling technique, combining SMOTE and Random Undersampling, emerges as the most effective method, surpassing other approaches. This research contributes significantly to advancing the field of HAR, highlighting the necessity of addressing class imbalance in deep learning models. Furthermore, the results offer practical insights for the development of HAR systems, enhancing accuracy and reliability in real-world applications. Future works will explore alternative public datasets, more complex deep learning models, and diverse sampling techniques to further elevate the capabilities of HAR systems.