Aerospace dielectric components of orbital spacecrafts are frequently exposed to harsh space environment, where beam irradiation leads to persistent charging and discharging of these dielectric materials. Here the dynamic charge transport behaviors of advanced aerospace dielectrics under electron beam irradiation are investigated by combining the drift-diffusion electron-hole transport model and synthetic surface charging diagnostics. Based on the measured trap state distribution and surface conductivity, numerical simulation of typical aerospace dielectrics with a focus on the polyimide is performed. The simulation reveals the spatial-temporal evolution of microscopic quantities consisting of the current density, charge density, electric field distribution, in addition to the time evolution of macroscopic quantities namely the sample current, secondary emission yield (SEY) and surface potential. Factors affecting the dielectric charging process, including beam energy, beam current, material type, and sample thickness, are analyzed. Dedicated relation between the beam energy and final surface potential is determined, while the beam current is found to only affect the charging speed. The effects of material type on the charging process are due to a range of combined channels via different primary range, permittivity, surface resistivity, trap state distribution, electron affinity, etc. The observed trends of surface potential, sample current, and SEY in experiment are generally consistent with the model prediction, and possible explanations for the discrepancies are provided. In the end, implications of the obtained conclusions for the electrostatic discharges on aerospace dielectrics are discussed.

Ransomware continues to pose a significant threat to cybersecurity, particularly affecting critical systems running on Linux. The novel application of the random forest algorithm for detecting ransomware on Linux systems offers a significant advancement, leveraging machine learning to enhance detection accuracy and adaptability. The methodology involved collecting a diverse dataset of ransomware samples and benign files, followed by meticulous feature extraction and the application of a robust random forest model. Performance evaluation demonstrated high precision, recall, and overall accuracy, surpassing existing detection methods such as support vector machines and neural networks. Comparative analysis highlighted the model's superior ability to handle high-dimensional data and manage complex feature interactions, resulting in more reliable and accurate detection. Despite the computational complexity and extensive preprocessing requirements, the findings underscore the model's potential to significantly improve cybersecurity measures against ransomware. The comprehensive evaluation provides valuable insights into the development of more effective detection mechanisms, affirming the random forest algorithm's pivotal role in mitigating ransomware threats on Linux systems.

In this paper, we present an advanced conceptual framework for integrating Artificial General Intelligence (AGI) into engineering education (Artificial General Intelligence Integration into Engineering Education (AGI2E2)). Our aim is to harmonize technical skills with intrinsic human qualities, such as creativity and ethical reasoning, essential for the Industry 5.0 landscape. This framework is based on comprehensive literature reviews and empirical data from student and faculty surveys, analyzed to assess AGI's potential to foster these human-centered qualities. Our proposal includes dynamic, interdisciplinary learning environments, personalized educational trajectories, continuous adaptation, and lifelong learning, addressing the ethical issues of AGI application. This framework not only advances engineering education to meet Industry 5.0 demands but also fosters holistic, human-centered learning experiences, preparing technically competent engineers equipped with vital interpersonal skills. This work builds upon previous research presented at a conference on integrating AI into engineering education.

A document by Christopher Thompson. Click on the document to view its contents.

With the advent of zero trust (ZT) security architectures, vendors can bolster their services' security by continuously verifying every end-to-end traffic flow through the policy enforcement point (PEP). However, the demand for dedicated hardware substantially hinders the extensive deployment of PEP across networks. Consequently, the PEP-based verification can incur overwhelming cost when accommodating network requests involving a sequence of end-to-end traffic flows such as service function chains (SFCs). In this work, we introduce the concept of ZT as a function (ZTaaF) to minimize the verification cost in PEP-based SFC deployment. The ZTaaF enhances the verification flexibility and saves the verification cost by virtualizing the hardware-based PEP into a software module and enabling the inter-trust among the servers from the same vendor. Based on the ZTaaF paradigm, we define a novel problem called SFC deployment with ZTaaF (SFCZT). After analyzing the challenges in SFCZT, we propose an efficient algorithm based on the layered graph technique named ZTaaF-aware SFC embedding (ZAN). Through thorough mathematical analysis, we demonstrate ZAN's optimality when the network provides sufficient resources for the incoming SFC request. Extensive simulation results validate ZAN's optimality under the above assumption and show that ZAN significantly outperforms the benchmarks.

Indoor positioning technology is one of the important supporting technologies for location services. Since WiFibased indoor positioning technology is unsuitable for scenarios where WiFi APs are sparse or missing (such as underground parking lots), magnetic fields are the only available signal source. However, magnetic positioning methods still face the problem of the high cost of magnetic map construction. This paper proposes a method for constructing a magnetic grid map of a multi-story parking lot that integrates crowdsourced vehicle and pedestrian data. This method uses the three-dimensional sparse magnetic field map obtained from vehicle data as the skeleton, performs preliminary floor allocation on pedestrian data, and combines key frame association information, floor information, and neighborhood loop information to iteratively update the graph optimization problem to achieve three-dimensional trajectory reconstruction of pedestrians and vehicles. On this basis, this method defines crowdsourced trajectory estimation and magnetic grid map generation as an optimization problem, establishes a connection between the two through multiple constraints such as trajectory relative pose constraints, magnetic field vector space consistency, and magnetic field spatial distribution continuity, and obtains the optimal estimate of magnetic grid maps and trajectories on each floor through a multi-step iterative optimization method. The effectiveness of the proposed scheme is verified by testing a simulated data set in a typical twostory underground parking lot scenario. The average positioning error of the jointly generated trajectories of pedestrians and vehicles is 1.75m, and the average positioning error based on the crowdsourced magnetic field map is 2.87m.

A time harmonic electromagnetic (EM) source inside a 2D metallic cavity is considered. Scatterers of known geometry and dielectric constants are present inside the cavity. These scatterers alter the fields in the cavity. The coordinates of the source are unknown, while those of the scatterers are given. The magnitude of the resulting electric field is measured at certain locations in the cavity. The goal is to locate the source from these magnitude-only measurements. As there is only one source, its localization is formulated as a sparse source reconstruction problem. The 2D metallic cavity is discretized using a Finite Difference scheme. Determination of the source location is posed as a constrained minimization problem with the cost being the norm of the error between the measured and the simulated sumulated (obtained using the discretized Maxwell's equation). An additional L1 regularization is added to the cost to impose sparsity. Linear constraints based on the principle of Electromagnetic Time Reversal (EMTR) are derived and enforced. Numerical results are presented for random choices of measurement locations. Numerical experiments suggest that the source can be localised with measurements taken at 5% of the total points on the search grid.

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as graph neural networks (GNNs), recurrent neural networks (RNNs), and Transformers, have enhanced predictions by modeling regional interactions, managing autoregressive time series, and identifying long-term dependencies. However, prior works often feature shallow integration of these models, leading to simplistic graph embeddings and inadequate analysis across different graph types. Additionally, excessive reliance on historical COVID-19 data limits the potential of utilizing future data, such as lagged policy information. To address these challenges, we introduce ReGraFT, a novel sequence-to-sequence (Seq2Seq) model designed for robust long-term forecasting of COVID-19. ReGraFT integrates multigraph-gated recurrent units (MG-GRU) with adaptive graphs, leveraging data from individual states, including infection rates, policy changes, and interstate travel. First, ReGraFT employs adaptive MGGRU cells within an RNN framework to capture interregional dependencies, dynamically modeling complex transmission dynamics. Second, the model features a self-normalizing priming (SNP) layer using Scaled Exponential Linear Units (SeLU) to enhance stability and accuracy across short, medium, and long-term forecasts. Third, ReGraFT systematically compares and integrates various graph types, such as fully connected layers, pooling mechanisms, and attention-based structures, to provide a nuanced representation of interregional relationships. By incorporating lagged COVID-19 policy data, ReGraFT refines forecasts, demonstrating a 2.79% reduction in the root mean square error (RMSE) compared to state-of-the-art models. This work provides accurate long-term predictions, aiding in better public health decisions. Our code is available at https://github.com/mfriendly/ReGraFT.

The P versus NP problem is a cornerstone of theoretical computer science, asking whether problems that are easy to check are also easy to solve. "Easy" here means solvable in polynomial time, where the computation time grows proportionally to the input size. While this problem's origins can be traced to John Nash's 1955 letter, its formalization is credited to Stephen Cook and Leonid Levin. Despite decades of research, a definitive answer remains elusive. Central to this question is the concept of NP-completeness. If even one NP-complete problem could be solved efficiently, it would imply that all problems in NP could be solved efficiently, proving P equals NP. This research proposes that EXACT CLOSURE, a notoriously difficult NP-complete problem, can be solved efficiently, thereby potentially establishing the equivalence of P and NP. This work is an expansion and refinement of the article "Note for the P versus NP problem", published in IPI Letters.

The work considers the N-server distributed computing scenario with K users requesting functions that are linearly-decomposable over an arbitrary basis of L real (potentially non-linear) subfunctions. In our problem, the aim is for each user to receive their function outputs, allowing for reduced reconstruction error (distortion) ϵ, reduced computing cost (γ; the fraction of subfunctions each server must compute), and reduced communication cost (δ; the fraction of users each server must connect to). For any given set of K requested functions-which is here represented by a coefficient matrix F ∈ R K×L-our problem is made equivalent to the open problem of sparse matrix factorization that seeks-for a given parameter T , representing the number of shots for each server-to minimize the reconstruction distortion 1 KL ∥F − DE∥ 2 F over all δ-sparse and γ-sparse matrices D ∈ R K×N T and E ∈ R N T ×L. With these matrices respectively defining which servers compute each subfunction, and which users connect to each server, we here design our D, E by designing tessellated-based and SVD-based fixed support matrix factorization methods that first split F into properly sized and carefully positioned submatrices, which we then approximate and then decompose into properly designed submatrices of D and E. For the zero-error case and under basic dimensionality assumptions, the work reveals achievable computationvs-communication corner points (γ, δ) which, for various cases, are proven optimal over a large class of D, E by means of a novel tessellations-based converse. Subsequently, for large N , and under basic statistical assumptions on F, the average achievable error ϵ is concisely expressed using the incomplete first moment of the standard Marchenko-Pastur distribution, where this performance is shown to be optimal over a large class of D and E. In the end, the work also reveals that the overall achieved gains over baseline methods are unbounded.

The paucity of orthogonal resource blocks has led to focused research on non-orthogonal multiple access (NOMA) signaling. This paper considers a multiuser downlink hybrid non-orthogonal multiple access (NOMA) framework with direct links to near-users (NUs) as well as far-users (FUs). It is shown that intelligent power allocation, together with mode switching and user selection, is the key to high spectral efficiency (SE) and energy efficiency (EE). To enhance the system throughput, intelligent NU and FU selection, and switching among NOMA Mode (NM), cooperative NOMA mode (CNM), and OMA mode (OM) have been considered. Switching to OM when both NM and CNM fail ensures performance equivalent to OM. The closedform expressions for the outage probability, mode probability, throughput, and EE are derived with mode switching and user selection. It is demonstrated that choosing the optimal target rate and NOMA power allocation coefficient is essential to maximize the EE. Only statistical channel knowledge is used for this purpose. Furthermore, we demonstrate that the proposed framework outperforms known schemes in terms of throughput and EE. In particular, the energy savings with the proposed scheme can be very significant. Monte Carlo simulations validate the derived expressions.

Mobile services increasingly depend on precise and reliable positioning solutions. This article provides an overview of the 5G services in need of positioning and discusses how 5G positioning can be tailored to meet specific requirements and device capabilities. It presents the 5G positioning architecture, standardized techniques and requirements outlined in various 3rd Generation Partnership Project (3GPP) 5G releases. It also surveys the literature for the latest advancements in 5G and beyond positioning techniques such as sidelink positioning, carrier phase, Reconfigurable Intelligent Surface (RIS)-aided, massive MIMO, beamforming and hybrid techniques, by reviewing the most recent literature on the subject. Lastly, the article addresses the practical challenges associated with implementing positioning solution in 5G networks.

A document by Minjie Chen . Click on the document to view its contents.

APPLICATION OF ONION EXTRACT AS A CORROSION INHIBITOR FOR MILD STEEL IN A SODIUM CHLORIDE MEDIUM.Nnadikwe Johnson1.,Iheme Chigozie2.,Chinemerem Joy Johnson3.Imo State University.,Petroleum And Gas Engineering Department

This paper introduces a cyber-physical test bench for metropolitan area digital substations, which serves as a platform for studying advanced cyber-attacks and evaluating intrusion detection systems. The test bench provides a hardwarein-the-loop environment with a high Technology Readiness Level (TRL). Additionally, the paper demonstrates the testbed's capabilities by testing and validating an AI-based intrusion detection system developed by an industrial company. The demonstration includes testing the cybersecurity tool against a wide range of cyber-attacks, such as false data injection, packet replay, and time desynchronization, on relevant substation automation protocols commonly used in EU power grid infrastructures like IEC-60870-5-104 and IEC61850. The results of the demonstration indicate that the test bench effectively simulates realistic impacts on substation operation when subjected to attacks, thus providing the opportunity to validate the anomaly detection solution.

1.0. INTRODUCTION.The automotive industry is constantly seeking innovative solutions to enhance engine performance, reduce emissions, and improve fuel efficiency. One approach is the use of fuel additives, which can play a crucial role in optimizing engine performance and mitigating the negative impacts of particulate matter (P.M.S) and asphaltene globules (A.G.O) on engine components. This study aims to explore the potential of fuel additives in boosting automotive engine performance, with a focus on their effects on P.M.S and A.G.O.,Particulate matter (P.M.S) and asphaltene globules (A.G.O) are common issues in automotive engines, resulting from the combustion of fossil fuels. P.M.S refers to the tiny particles that can clog engine oil passages, causing wear and tear on moving parts, while A.G.O are sticky, tar-like substances that can accumulate on engine surfaces, leading to increased wear and tear, corrosion, and decreased fuel efficiency. These issues can lead to reduced engine performance, increased maintenance costs, and negative environmental impacts.,Fuel additives have been proposed as a potential solution to mitigate these issues, by altering the chemical properties of the fuel to reduce the formation of P.M.S and A.G.O. However, the effectiveness of fuel additives in real-world scenarios is not well understood, and further research is needed to explore their potential benefits and optimal application.This study seeks to address this knowledge gap by investigating the effects of fuel additives on P.M.S and A.G.O, with the goal of identifying optimal additive formulations and application strategies to boost automotive engine performance, reduce emissions, and improve fuel efficiency. The automotive industry is incessantly seeking innovative solutions to enhance engine performance, reduce emissions, and improve fuel efficiency. One promising approach is the use of fuel additives, which have been shown to mitigate the negative impacts of particulate matter (P.M.S) and asphaltene globules (A.G.O) on engine components [1, 3, 5, 7, 9, 11, 13]. Fuel additives can alter the chemical properties of the fuel, reducing the formation of P.M.S and A.G.O, and subsequently improving engine performance and reducing emissions [2, 4, 6, 8, 10, 12].Previous studies have investigated the effects of fuel additives on engine performance and emissions, with promising results. For instance, Agrawal (2012) found that fuel additives can improve engine performance and reduce emissions by up to 15% [1]. Similarly, Chauhan and Kumar (2015) discovered that fuel additives can reduce particulate matter emissions by up to 20% [4]. Moreover, Dhar and Agarwal (2016) reported that fuel additives can improve fuel economy by up to 10% [5].Despite these promising findings, the effects of fuel additives on P.M.S and A.G.O are not well understood, and further research is needed to explore their potential benefits and optimal application. This study aims to address this knowledge gap by investigating the effects of fuel additives on P.M.S and A.G.O, with the goal of identifying optimal additive formulations and application strategies to boost automotive engine performance, reduce emissions, and improve fuel efficiency.

Video Foundation Models (ViFMs) aim to develop general-purpose representations for various video understanding tasks by leveraging large-scale datasets and powerful models to capture robust and generic features from video data. This survey analyzes over 200 methods, offering a comprehensive overview of benchmarks and evaluation metrics across 15 distinct video tasks, categorized into three main groups. Additionally, we provide an in-depth performance analysis of these models for the six most common video tasks. We identify three main approaches to constructing ViFMs: 1) Image-based ViFMs, which adapt image foundation models for video tasks; 2) Video-based ViFMs, which utilize video-specific encoding methods; and 3) Universal Foundation Models (UFMs), which integrate multiple modalities (image, video, audio, text, etc.) within a single framework. Each approach is further subdivided based on either practical implementation perspectives or pretraining objective types. By comparing the performance of various ViFMs on common video tasks, we offer valuable insights into their strengths and weaknesses, guiding future advancements in video understanding. Our analysis reveals that image-based ViFMs consistently outperform video-based ViFMs on most video understanding tasks. Additionally, UFMs, which leverage diverse modalities, demonstrate superior performance across all video tasks. We provide the comprehensive list of ViFMs studied in this work at: https://github.com/NeeluMadan/ViFM Survey.git.

This paper highlights the use of clustering algorithms to group power system nodes based on the dynamic responses to large disturbance events. An overall framework of analysis is presented. Starting from the choice of the events to analyze, the features used as inputs to the clustering procedure are defined, and the appropriate clustering algorithm is chosen. The features include parameters taken from the dynamic response and distance matrices calculated from the electrical distance, the Euclidean distance, and the Hausdorff and Wasserstein global distances. A statistically significant analysis is presented to select the number of clusters and compare the results obtained from clustering algorithms that depend on random choices in their solution process. The calculations are carried out on the dynamic model of the Great Britain 36-bus electricity transmission network. The most significant results of this study have been obtained by executing the spectral clustering algorithm with inputs from the Wasserstein distance matrix.