This letter presents remedial measures to compensate for the nonlinearities in the measurement chain, which comprises saturation of the Current transformer core and ferroresonance in the Voltage transformer. The approach reduces the bias in estimations to improve subsequent decision-making. The steady-state and transient performances of state-of-theart phasor measurement unit emulators are also compared in this study. The probability density function of error in phasor estimation shows the significant reduction in biases of estimates due to the reconstruction of distorted analog signals.

This work proposes an efficient two-stage jamming detection and channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicles (UAVs) communications. The proposed scheme is designed based on the unique time and frequency domain statistical characteristics of OFDM signals. In the time domain (TD), a likelihood ratio test (LRT)-based decision rule is derived as a function of the inherent correlation between the cyclic prefix (CP) samples and their counterparts in the OFDM symbol. In addition, in the frequency domain (FD), a closed-form joint jamming detection and channel estimation scheme is derived using the maximum a posteriori probability (MAP) principle as a function of the statistics of the received pilots and virtual subcarriers (VSCs) signals, which is then re-expressed using generalized MAP ratio test (GMAPRT). The system’s complexity is reduced by applying the two stages sequentially, where the possible implementation of the second stage is conditioned on the outcome of the first stage. The performance of the proposed algorithm is evaluated using Monte Carlo simulations, where the results demonstrate its effectiveness compared to the TD-only and FD-only approaches. The results confirm the superior performance of the proposed scheme compared to the cyclostationary feature (CF)-based technique under various operating scenarios.

Tracking and tracing of assets using decentralized systems demand optimized data formats to balance storage costs and retrieval performance. This paper proposes a cost-ef ective sensor data format tailored for decentralized storage of geographic point locations on XNO, a distributed ledger technology. The proposed format optimizes storage by encoding location data in a compact yet retrievable manner, leveraging spatial indexing techniques to facilitate ef icient and transparent querying. This solution presents a promising approach for scalable and af ordable asset tracking and tracing using the highly available and transparent decentralized environments, with potential applications across various industries.

Many computing services need to be accessed through the Internet, which makes them inherently exposed to a large number of cybersecurity threats. Several attacks are thwarted by intrusion prevention mechanisms, but with the size and complexity of current systems, an attacker can break in sooner or later. Attackers can exploit a system vulnerability or steal user access credentials. Once the intrusion occurs, it is very likely that the state of the system is corrupted by the attacker. The classical solution against these corruptions is to keep regular backups to allow the reversal of the effects of intrusions. However, changes to the state made since the last backup are permanently lost. This situation has led to the development of work on intrusion recovery systems that revert the effect of attacks on the state of the system without losing legitimate changes. We present a survey of the literature in the area, explaining the different approaches and suggesting open directions for future research.

Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and continuous dynamic data augmented with a self-supervised learning technique (VICReg). An online Fitts' Law test with 20 participants evaluated the usability and effectiveness of each classifier. Results demonstrate that temporal models, particularly LSTMs trained with continuous dynamic data, significantly outperformed traditional approaches. Furthermore, VICReg pretraining led to additional improvements in online performance and user experience. Qualitative feedback highlighted the importance of smooth, jitter-free control and consistent performance across movement classes. These findings underscore the potential of continuous dynamic data and self-supervised learning for advancing sEMG-PR-based myoelectric control, paving the way for more intuitive and user-friendly prosthetic devices.

A new method for estimating the aspect angle of ships at sea from an ISAR is developed. The ISAR AutoTrack (IAT) algorithm uses the information from the adaptive motion compensation velocity to improve the tracker estimation of the ship aspect angle and thus to improve the estimation of the ship length. The IAT is based on classical methods of autofocus for synthetic aperture radar. The mean of this velocity yields the error in the in-range component of the ship velocity; the linear time trend of the velocity determines the cross-range component of the ship velocity. Since the IAT creates its estimates during the ISAR time window it is unaffected by ship maneuvers. The IAT has two methods for implementing the algorithm. The Search Method tests a range of errors in course and speed to find the values that best model the data. The Analytical Method uses explicit formulas for the in-range and cross-range components of the ship velocity vector. Both methods benefit from an intelligent smoothing process that removes system errors, random noise, and ocean waves. The goal of the IAT is to measure ship length to within 10 percent over all azimuth angles and ranges relative to the aircraft and for (unsigned) aspect angles from 5 to 85 degrees. To develop a method that satisfies these requirements the IAT is evaluated for three types of tracking errors-errors in the ship speed, course, and Lat/Lon position. But errors in range are assumed to be corrected by routine algorithms in the ISAR processor. Thus, the position error reduces to error in the initial bearing to the ship. The IAT is validated by the analysis of data from an airborne test radar for 22 ship cases. This analysis showed an improvement in the RMS aspect angle from 26 degrees for the tracker to 8 degrees after the IAT. When the tracker position estimate is replaced with the AIS ship position data the error is reduced to 5 degrees; and the ship length error is nearly the same as if the full AIS results for course, speed and position were used. The data analysis also revealed some interesting insights into the effects of shadowing and multipath scattering on the length estimates. Algorithm testing with simulated data shows that the IAT can readily correct for errors in ship speed and course when the initial bearing is known well. However, at higher aspect angles errors in bearing lead to large aspect and length errors. Thus, for the desired range of operating conditions the IAT may need to be supplemented by hardware or software improvements in the bearing estimate. In addition, at azimuth angles ahead or behind the radar flight direction the IAT has a partial blind spot where the cross-range velocity cannot easily be estimated. To remedy this problem the time derivative of the bearing also needs to be estimated, giving a more direct estimate of the cross-range velocity. Recommendations for further development and testing of the IAT are given in the summary.

Graph structure learning (GSL) aims to assist graph neural networks to yield effective node embeddings for downstream tasks, especially in scenarios with the absence of structures or the existence of unreliable edges. Most GSL models are built on the i.i.d. assumption across training and testing data. However, this assumption can be violated where test data contains out-of-distribution samples. Consequently, those models are limited in generalization, which will lead to a poor structure. On the other hand, while they have made great progress, additional optimized parameters are required due to their implementation with parametric models. To tackle the above problems, we propose a novel Generalizable and Non-parametric Structure learning framework named GNS, which can be easily and effectively applied to various tasks. GNS neither relies on i.i.d. assumption nor even involves any parameters being optimized, instead to find an appropriate similarity between nodes and an associated threshold to establish desirable structures. Specifically, we first incorporate the candidate neighbor distributions for nodes to refine the similarity. Then, we introduce an adaptive threshold discovery method inspired by Fisher's criterion to determine final structures. Extensive experiments demonstrate that GNS excels not only in OOD scenarios but also in the general classification and regression prediction tasks.

This article aims to address a crucial aspect raised by Berkan Dulek in his comments [1] on the original paper, "An Efficient Algorithm for Maneuvering Target Tracking"[2]

Ransomware is one of the most critical cybersecurity threats, targeting systems globally and causing substantial financial and data losses. Developing an effective and efficient detection mechanism within operating systems is essential for mitigating these risks, which is where the novel integration of LSTM-based machine learning within the Linux kernel offers a significant advancement. The proposed approach involves the deployment of an LSTM model capable of analyzing system call sequences in realtime, identifying anomalous behavior indicative of ransomware activities with high accuracy. The research outlines the model's design, including data collection, preprocessing, and the intricate integration process into the Linux kernel to facilitate seamless communication and classification. Extensive experimental evaluation demonstrated that the LSTM model achieved superior detection performance when compared to traditional methods, maintaining a high level of accuracy and low false positive rates. The findings indicate that this method not only provides a proactive defense mechanism against emerging ransomware threats but also enhances the overall security framework of Linux systems through its adaptive learning capabilities and efficient resource utilization.

Coherent signals, such as a time varying voltage appearing on a radio antenna, are fundamentally different than non-coherent signals, such as the power magnitude of an individual pixel in a CCD camera. This discussion explores the details of the differences between coherent and non-coherent signals and demonstrates a generalized detector for coherent signals based on Hermetian quadratic forms. Hermetian quadratic forms appear frequently in coherent signal detection and classification processes. This includes power detection and many adaptive processing applications. We show how to generalize this concept to Neural Nets and other Machine Learning systems, and provide examples of the benefits of doing so.

Accurate weather and climate prediction is essential for early warning systems that improve response strategies to climate-related events. This study explores the use of association rule mining (ARM) techniques to analyze large-scale meteorological datasets. We focus on the weather patterns of Tallinn and Tartu, investigating variables such as wind speed, temperature, precipitation, and humidity, and their influence on weather intensity. A distributed ARM approach is employed using the Apollo framework, which utilizes serverless functions to enhance scalability and performance. Results show Apollo outperforms traditional systems like Apache Spark by approximately 15% in terms of processing speed, while extracting a greater number of meaningful rules. Time-series analysis was also applied to investigate temporal weather trends. Our findings highlight the potential of this approach for enhancing weather prediction systems and offer a foundation for future research in this area.

This paper explores the synergistic use of offshore wind, wave energy, and photovoltaics for maritime hydrogen refueling stations, focusing on their impact on storage requirements. Two Mediterranean locations, eastern Crete and the Galite islands, were selected as candidate locations for installing maritime hydrogen refueling stations due to their high potential in wind, wave, and solar energy. The study employs a Storage-to-Production Ratio (SPR) index, which quantifies the storage needed to maintain a consistent and uninterrupted hydrogen supply to ships year-round, relative to the total annual hydrogen production. Our findings reveal that integrating either wind or wave energy with solar power significantly decreases the SPR, attributed to the low correlation between solar and the other two energy sources. This reduction is substantial, lowering the SPR by up to 20%. Practically, assuming a refueling station with a consistent annual hydrogen supply of approximately 20.000 tons, the synergy could lead to a reduction of the hydrogen storage by 4.000 tons, i.e. 0.2×20.000 tons. On the other hand, combining wind and wave energy in Mediterranean Sea does not contribute to the SPR reduction due to substantial temporal correlation between wind and wave sources.

Stream processing applications have been widely adopted due to real-time data analytics demands, e.g., fraud detection, video analytics, IoT applications. Unfortunately, prototyping and testing these applications is still a cumbersome process for developers that usually requires an expensive testbed and deep multidisciplinary expertise, including in areas such as networking, distributed systems, and data engineering. As a result, it takes a long time to deploy stream processing applications into production and yet users face several correctness and performance issues. In this paper, we present stream2gym, a tool for the fast prototyping of large-scale distributed stream processing applications. stream2gym builds on Mininet, a widely adopted network emulation platform, and provides a high-level interface to enable developers to easily test their applications under various operating conditions. We demonstrate the benefits of stream2gym by prototyping and testing several applications as well as reproducing key findings from prior research work in video analytics and network traffic monitoring. Moreover, we show stream2gym presents accurate results compared to a hardware testbed while consuming a small amount of resources (enough to be supported in a single commodity laptop even when emulating a dozen of processing nodes).

Complex Query Answering (CQA) on knowledge graphs is a fundamental yet challenging task, which can be formalized as answering a subset of first-order logic queries containing logical conjunction, disjunction, negation, and existential quantifiers. Recent research reveals that Link Predictors (LPs) trained on 1-hop queries can generalize to various types of complex queries. However, existing methods neglect crucial characteristics of LPs' outputs, including the effects of highly relevant entities and uncertainty. What's worse, as they model logical operations by fuzzy set operations, these methods suffer from problems like inflexibility, sensitivity to noise, and inconsistency with priority in human cognition, which limits their performance, especially on queries with negation. To address these challenges, we propose an efficient fuzzy system for CQA that requires no extra training overheads and is plug-and-play with existing LP-based methods. Firstly, we expand the output of LPs by two complementary membership functions of weak and strong relevance, which help to distinguish the target entities from highly relevant and irrelevant entities. Subsequently, we model logical operations through fuzzy rule bases and infer the final predictions via defuzzification, providing a flexible and tractable scheme for modeling logical operations. Finally, the effectiveness of the proposed fuzzy system is validated by its outstanding performance on benchmark datasets when compared to state-of-theart methods. The source code of our proposed method is available at https://anonymous.4open.science/r/FuzzSys-CQA-E285.

Large language models have dramatically transformed various fields by enhancing the capabilities of artificial intelligence in understanding and generating human language. The novel concept of predicting critical mixture ratios for continual pre-training introduces a significant advancement in optimizing the performance and efficiency of such models. Through leveraging advanced machine learning techniques, the study develops a predictive framework that systematically varies mixture ratios and evaluates their impact on model performance. The results highlight substantial improvements in accuracy, precision, recall, and F1 score, demonstrating the efficacy of optimized mixture ratios. Furthermore, the study shows that models trained with these optimized ratios exhibit enhanced generalization capabilities, maintaining high performance across unseen datasets. The research also addresses the reduction in training time, translating to operational efficiency gains and cost savings. Despite the promising findings, the study acknowledges several limitations, including the need for more diverse datasets and a deeper exploration of hyperparameter interactions. Future research directions include expanding the scope of data, integrating reinforcement learning techniques, and developing sophisticated validation methods. The study's insights and methodologies provide a valuable foundation for future research and practical implementations, aiming to create more effective and adaptable language models for various applications.

This paper presents an Adaptive Spatial-Temporal Convolutional Neural Network (ASTCN) model for traffic state prediction, which learns the complex spatial-temporal information of traffic data through trainable temporal embedding and graph adjacency matrix. The gate control mechanism is used to control the proportion of different temporal embedding to improve prediction accuracy. In the visualization of the temporal embedding, it is found that the influence of the weekly temporal embedding on prediction accuracy is the greatest, followed by the daily temporal embedding, and the monthly temporal embedding is the smallest. Meanwhile, a new method for constructing graph adjacency matrices is proposed, and the experimental results show that the two types of graph adjacency matrices have different effects on improving the prediction accuracy of traffic speed and traffic flow. Therefore, this paper fuses the two graph adjacency matrices to make the ASTCN model achieve better prediction accuracy on both traffic speed and traffic flow datasets than when the two are present separately. The final prediction results of the ASTCN model in the comparative experiment show excellent performance.

Large-scale biomedical entity recognition and relation extraction are essential foundational tasks for downstream text mining tasks and applications, such as knowledge graph construction. Because many relations span sentence boundaries, document-level entity recognition and relation extraction is closely aligned with real-world demands. However, identifying complex and diverse entities and relations within a limited timeframe is challenging. Therefore, we propose an end-to-end approach called BioECR for biomedical document-level named entity recognition, coreference resolution, and relation extraction. This approach utilizes a bi-encoder structure combined with biomedical entity types and descriptions to solve nested biomedical entities in linear time, thereby enhancing the ability to recognise complex entities and relations. Then a composition graph convolutional neural network was proposed to address the noise in conventional graph convolutional networks, thereby reducing time overhead and selectively fusing multiple entities or contextual information. Finally, by combining entity type clustering methods, the problem of coreference errors among multiple types of entities is solved easily and quickly. Experimental results demonstrate that our approach achieves stateof-the-art performance on all subtasks across three biomedical document-level datasets called CDR, GDA, and BioRED, and our approach reduces the inference time by approximately 60%.

Vehicular Ad-Hoc Networks (VANETs) has drawn great attention due to their potential role in enabling Intelligent Transportation System (ITS). VANETs using a connected-car technology forms an important part of ITS as it provides communication between different neighboring vehicles known as Vehicle-to-Vehicle Communication (V2V). In this paper, we have proposed an integrated simulation tool for modeling and testing traffic scenarios and V2V based applications. This integrated simulated environment is designed by combining two software packages, i.e., Simulation of Urban Mobility (SUMO) as traffic simulator and Network Simulator (NS3) for building dynamic network of nodes. It also enables us to implement and verify if a traffic management algorithm generates the desired effect. For evaluating our simulator, a vehicle crash prediction algorithm has been designed and tested which predicts the risk of a crash and alerts the vehicles to prevent collisions.