Satellite networks are vital in facilitating communication services for various critical infrastructures. These networks can seamlessly integrate with a diverse array of systems. However, some of these systems are vulnerable due to the absence of effective intrusion detection systems, which can be attributed to limited research and the high costs associated with deploying, fine-tuning, monitoring, and responding to security breaches. To address these challenges, we propose a pre-trained Large Language Model for Cyber Security, for short PLLM-CS, which is a variant of pre-trained Transformers, which includes a specialized module for transforming network data into contextually suitable inputs. This transformation enables the proposed LLM to encode contextual information within the cyber data. To validate the efficacy of the proposed method, we conducted empirical experiments using two publicly available network datasets, UNSW_NB 15 and TON_IoT, both providing Internet of Things (IoT)-based traffic data. Our experiments demonstrate that proposed LLM method outperforms state-of-the-art techniques such as BiLSTM, GRU, and CNN. Notably, the PLLM-CS method achieves an outstanding accuracy level of 100% on the UNSW_NB 15 dataset, setting a new standard for benchmark performance in this domain.

The main advantage of composite materials is the ability to tailor the properties of stacking sequences. Unsymmetrical laminate involves an arbitrary even or odd number of fiber layers oriented at different angles wherein identical fiber orientation above and below mid-surface does not exist. In the case of these laminates, the coupling deformation is undesired during mechanical loads and symmetrical laminates may not be the best solution for all cases. In this work, an effort has been made to present the methodology to minimize the end effects of bending-extension coupling in the case of unsymmetrical laminates to make it widely applicable. The infinite plate containing the circular and elliptical hole is studied with various in-plane loading conditions. The end effect of bending-extension coupling is presented by the multi-objective problem of minimization of the normalized tangential force and moment. The non-dominated sorting heat transfer search algorithm is employed to solve this optimization problem wherein TOPSIS decision-making technique is used to obtain the best values from the Pareto solution. The different cluster-based methods are proposed to utilize the unsymmetrical laminates most effectively. The effects of these clusters on the bending-extensional coupling are investigated and results reveal that cluster design presenting each fiber angle value distinctly is the best design among all recommended in the present investigation. More than 70% of the bending-extensional coupling is reduced in most cases. A unique correlation is found between the cluster design and fiber angles of the optimum stacking sequences. The proposed approach and designs will support the potential use of unsymmetrical laminates by improving properties through tailoring stacking sequences.

The potential flow around the square cylinder with rounded corners is investigated by using complex variable approach. The area outside the square geometry with finite corner radius is mapped to the outer region circular geometry using hypotrochoidal transformation. The superposition of complex potential functions for uniform flow, circulatory flow, and vortex flow is employed along with mapping function to investigate various flow parameters around the square cylinder for different corner radius, angle of incidence, and vortex locations around the cylinder. Some of the results attained using present method are compared with the results extracted from ANSYS FLUENT and existing literature for the same geometrical and input flow parameters.

Empathy is a complex psychological concept consisting of affective and cognitive aspects. It plays a crucial role in human communication. Dialogue systems are designed to interact with people in natural language. Integrating empathy into dialogue systems is valuable to enhance user experience, although it is a challenging task. Existing studies in Empathetic Conversational Response Generation (ECRG) have made notable advancements. However, their focus has mainly been on specific facets of empathy, such as mimicking the user’s emotions and integrating commonsense knowledge to enhance cognitive empathy. This leaves opportunities for further exploration in areas such as enhancing self-other awareness. This paper will conduct a comprehensive review of empathy theories to bridge this gap, offering a theoretical understanding of empathy in various dimensions. Additionally, it critically reviews existing studies on ECRG, including algorithms, methods to address affective empathy and cognitive empathy, datasets, and evaluation methods. By highlighting the technical challenges and opportunities, this review provides guidance for researchers on AI research for modeling empathy in dialogue systems by investigating empathetic response generation for conversational AI modeling.

Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any convolution layer within a CNN. However, its maximum effectiveness is shown when placed at the output end of final convolution layer. We test RiT in the application of scale-invariance where we attempt to classify scaled images from benchmark datasets. Our results show promising improvements in the networks ability to be scale invariant whilst keeping the model computation cost low.

The cracks present in a mechanical/structural member alter flexibility and thereby change modal parameters. The one-dimensional open cracks in the multi-span Euler–Bernoulli beam are modeled as rotational spring. The supports are modeled as a combination of rotational and linear springs. A computer code is developed to obtain numerical results from the formulation. The effect of crack locations, crack depths, several supports, and cracks on various modal parameters are presented for isotropic multi-span cracked beams.

The analytical solution for uniform potential flow around two similar or dissimilar polygonal cylinders is obtained by using complex potential function for a doublet trapped in an annulus. The annulus of two circles is mapped conformally to the pair of polygonal cylinders by unique combination of bilinear and hypotrochoidal transformation and mathematical formulas to get velocity, pressure and hydrodynamic force distribution around two polygonal cylinders are presented. The effects of center distance between the cylinders, shape, orientation and corner radii of cylinders, and flow angle on the hydrodynamic interaction between the cylinders are also investigated and presented. The comparison of some of the numerical results obtained using present method is done with the results obtained from commercial CFD software package and results from literature.

Modelled closely on the feedforward conical structure of the primate vision system - Convolutional Neural Networks (CNNs) learn by adopting a local to global feature extraction strategy. This makes them view-specific models and results in poor invariance encoding within its learnt weights to adequately identify objects whose appearance is altered by various transformations such as rotations, translations, and scale. Recent physiological studies reveal the visual system first views the scene globally for subsequent processing in its ventral stream leading to a global-first response strategy in its recognition function. Conventional CNNs generally use small filters, thus losing the global view of the image. A trainable module proposed by Kumar & Sharma [24] called Stacked Filters Convolution (SFC) models this approach by using a pyramid of large multi-scale filters to extract features from wider areas of the image, which is then trained by a normal CNN. The end-to-end model is referred to as Stacked Filter CNN (SFCNN). In addition to improved test results, SFCNN showed promising results on scale invariance classification. The experiments, however, were performed on small resolution datasets and small CNN as backbone. In this paper, we extend this work and test SFC integrated with the VGG16 network on larger resolution datasets for scale invariance classification. Our results confirm the integration of SFC, and standard CNN also shows promising results on scale invariance on large resolution datasets.

The Micro-electromechanical devices operate under the stable operating range. The demarcating Voltage and the displacement separating the stable and unstable operating range are called Pull-in parameters. The accurate determination of these parameters is one of the essential step in the MEMS design. In this paper the Static Analysis of electrostatically actuated micro-cantilever beam is carried out to obtain Pull-in Voltage and displacement considering fringing field effects using Galerkin Method. The fringing field effects are incorporated by considering three different fringing field Models and the results are compared.

The main objective of an expected maximization (EM) based envelop semi-supervised neural net learning approach in this paper is to develop a model for forecasting peak CPU usages under unpredictable web traffic (load conditions) in a large enterprise applications environment with several hundred live applications. This proposed approach forecasts the likelihood of extreme peak response time because of the stressed CPU due to a burst in incoming web traffic from the live IT applications and then predicts the CPU utilization under extreme load (peak) conditions. The enterprise complex IT infrastructure consists of many applications running simultaneously in real-time. Features are extracted after analyzing the CPU-load patterns of demand which are mainly hidden in the data related to key transactions of the IT applications. This method generates synthetic CPU load profiles by simulating virtual users and execute the key transactions in the test environment. This model is used to predict the excessive peak utilization under peaked CPU conditions. We have used envelope expectation maximization classifier method with forced learning, attempting to extract and analyze the parameters that maximize the likelihood of the model after marginalizing out the unknown labels. This has resulted in mitigating the risks of system failures and enabled us to manage the IT capacity within 3 days from 7 days. This model has helped in IT capacity planning and optimal usages of existing IT infrastructure with minimal risk.

This research aims to investigate the impact of religious values on the adoption of Government Resource Planning (GRP) systems by public sector employees in Saudi Arabia. The study also explores the impact of demographic characteristics as moderating factors between the religious factors and perceptions of GRP systems in Saudi government agencies. Not many studies have been conducted on religious values concerning to what extent technological innovation is embraced. Most research on the effects of cultural norms has been conducted in Western countries and little has been published on Arab nations. This research fills that knowledge gap by investigating the effect of religious values on public sector employees’ adoption of technological innovation in Saudi Arabia. Theoretically, this study related to religious factors will facilitate an understanding of the issues affecting individual employees’ adoption of new technologies in the workplace context where conservative social and religious values hold sway. The study developed a conceptual model based on two theories: Unified Theory of Acceptance and Use of Technology and Theory of Reasoned Action. The sample data comprised 340 responses to an online survey questionnaire sent to employees at the Ministry of Foreign Affairs, Saudi Arabia. Data were analyzed using multivariate statistical analysis. Results show that 37% of the variance ( ${\mathrm {R}}^{2}=.372$ ) of employees’ attitudes to GRP application can be explained by the effect of religious variables. Findings show that the religious factors - perfection (Itqan) (t (340) =7.678, $\text{p} < 0.000$ ), cooperation (Ta’awun) (t (340) =4.007, $\text{p} < 0.000$ ), and transparency (Shaffaf) (t (340) =4.700, $\text{p} < 0.000$ ) exerted a significant effect on users’ attitude to the system’s usage. However, responsibility (Mas’uliyyah (t (340) =1.284, $\text{p} < 0.200$ ) did not reveal any level of significance. This research will help managers identify and benchmark strategies to motivate technology adoption in their workplaces and customize them to best fit their users’ unique characteristics in a traditional and conservative society such as Saudi Arabia. Contributions, implications, limitations of this study and what future research could pursue are highlighted in the paper.

In this paper, some investigations on two-dimensional steady potential flow over polygonal shaped cylinders are presented. The polygonal shapes with finite corner radius are obtained using Hypotrochoidal mapping function. The complex potential function derived using Milne-Thompson circle theorem, along with Hypotrochoidal mapping is employed to get various flow parameters over different shaped cylinders. The uniform and nonuniform flow conditions are arrived at, by taking infinite and finite distance of vortex from the cylinder, respectively. The velocity and pressure distribution over various shaped cylinders are presented and some of them are compared with the results from commercial software package (ANSYS) and available literature. Formulas are developed to obtain velocity and pressure distribution around various shaped geometries. Also, the effect of various geometrical parameters on velocity and pressure distribution is studied.

In today’s era, the agriculture industry plays an important role for the Indian economy. In India, more than 80% of households depend on the agriculture industry. These agriculture industries are majorly affected by unusual rainfall. A lot of crops are going into loss due to unwanted rainfall. Thus, there is a need to forecast the time-based rainfall data for different geographical areas. In this paper, we are using the rainfall-based time series data of Madhya Pradesh and Rajasthan (from 1901 to 2015) for forecasting rainfall data. We had implemented a rolling forecasting based supervised machine learning model with the assumption that rainfall data broadly affected by its geographical attributes like distance from sea, height of location from sea, etc. From the result, we found that the 12% amount of rainfall data is affected by its geographical location. We achieved an RMSE of 134.65 mm for Madhya Pradesh and 173.43 mm for Rajasthan state.

The dynamics of the micro-cantilever with linearly varying width under electrostatic actuation is presented. The restraining displacement and voltage dissociating the unstable and stable operating region are determined by Bubnov-Galerkin approach. The influence of the variation in geometry of micro-beam and change in material parameters on the pull-in conditions is investigated. The increase of 24.27% is obtained in pull-in displacement when the tip width is reduced to 0.

Electrostatic micro actuators are commonly deployed micro electro mechanical system (MEMS) devices due to their unpretentious construction and well-matched micro fabrication processes. The phenomenan of pull-in instability puts substantial restrictions on the execution of electrostatically driven MEMS beam type actuators by restraining the range of travel. A larger working range is desirable for a wide variety of tuning applications. In this paper, mechanism of pull-in instability and means to extend the useful working range of the microactuator by changing the design is presented. It shows drastic improvement in the results. Important conclusions are drawn from the results.

In today era, huge amount of data generated by social and commercial organization like amazon, Facebook, twitter and what Sapp.These data may contain knowledge about users.The hidden knowledge in data set, lead researcher to study about that.The opinion mining is an interesting and challenging area for research community.The mining of opinions are difficult task for company and users.The meaning of opinion is decided by a context at run time.All weighing scheme generally use a static weigh for opinion representation.The weight of term exist in opinion should change by context.In this paper we present a model to evaluate the similarity between opinions by using context at run time.We test our model with review of cellular phone users.From the result we prove that in opinion similarity measurement weight of term cannot be consider as static it vary from one context to another context

A semi-supervised classifier is used in this paper is to investigate a model for forecasting unpredictable load on the IT systems and to predict extreme CPU utilization in a complex enterprise environment with large number of applications running concurrently. This proposed model forecasts the likelihood of a scenario where extreme load of web traffic impacts the IT systems and this model predicts the CPU utilization under extreme stress conditions. The enterprise IT environment consists of a large number of applications running in a real time system. Load features are extracted while analysing an envelope of the patterns of work-load traffic which are hidden in the transactional data of these applications. This method simulates and generates synthetic workload demand patterns, run use-case high priority scenarios in a test environment and use our model to predict the excessive CPU utilization under peak load conditions for validation. Expectation Maximization classifier with forced-learning, attempts to extract and analyse the parameters that can maximize the chances of the model after subsiding the unknown labels. As a result of this model, likelihood of an excessive CPU utilization can be predicted in short duration as compared to few days in a complex enterprise environment. Workload demand prediction and profiling has enormous potential in optimizing usages of IT resources with minimal risk.

This study investigates the impact of users' socio-cultural orientation and religious values on government resource planning systems in the Kingdom of Saudi Arabia. Although many studies have been conducted on the adoption of technology in developed nations, only a few analyses have focused on the Middle Eastern region. This research fills that gap. The study developed an integrated conceptual research model based on existing technology acceptance theories, namely theory of reasoned action, technology acceptance model, unified theory of acceptance and use of technology and Hofstede's cultural dimensions theory. An online survey questionnaire was sent to 1677 employees at the Ministry of Foreign Affairs in Saudi Arabia and a total of 377 completed questionnaires were received of which 340 were considered usable, making a response rate of 22.48%. Data was analyzed using multivariate statistical analysis. The analysis finds there is a relationship between the socio-cultural and religious constructs with reference to attitude about using government resource planning systems. Results reveal that 55% of the variance (R 2 = 0.557) of employees' attitude to the GRP application can be explained by the effect of socio-cultural and religious variables. Findings show that cultural values (t (340) = 3.862, p<; 0.000), social network (t (340) = 4.095, p<; 0.000), peers' influence (t (340) = 4.515, p<; 0.000) and religious values (t (340) = 5.062, p<; 0.000) are significant predictors of GRP acceptance. Furthermore, the analysis shows demographic factors moderate between the determinants and users' perceptions. These findings have important implications for the acceptance and implementation of GRP systems in Saudi Arabia.

This article presents the analytical investigation of stress concentration factor around the circular and square hole in an infinite carbon/epoxy plate at four different temperatures of 23, 60, 90, and 120 ℃. The micromechanics model is executed in conjunction with a complex variable approach to calculate the stress distribution around these cut-outs considering biaxial and uniaxial loading. The carbon fibers are assumed to be thermal insensitive materials and elastic properties of epoxy are defined at various temperatures. The influences of temperatures, fiber angles and stacking sequence on maximum stress concentration factor are stated. The results show that the values of the stress concentration factor are highly affected by these parameters. The present article will serve as a tool for designers who wish to study the behavior of composites at various temperatures.