Residual Energy Research Articles

Reducing strong noise with the same frequency in magnetostrictive guided waves using improved orthogonal matching pursuit

ABSTRACT Noise often masks defect-related signals during the assessment of bridge cables based on guided waves (GWs) to reduce the accuracy of detection. Traditional methods of denoising struggle to provide satisfactory results when using GWs under interference by highly dense noise of the same frequency. In this context, this study optimises the traditional orthogonal matching pursuit algorithm by using a novel condition for terminating its iterations and improving its efficiency of search for atoms, and uses it to denoise GWs under interference by high-density noise with the same frequency to detect broken wires in bridge cables. The results showed that highly dense noise could be reduced to achieve accurate results when the threshold of the correlation coefficient between the best-matching atoms of the reference signal and the target signal within a range of 0.5 to 0.7 was used as the condition for terminating iterations. The proposed condition for terminating iterations is more adaptable to signals with different signal-to-noise ratios (SNRs) than conditions based on the number of iterations, residual energy ratio, and adjacent residual ratio. The proposed method also exhibited a superior capability of noise reduction in comparison with the WT method of denoising and the VMD-linked wavelet method.

A Q-Learning Based Target Coverage Algorithm for Wireless Sensor Networks

To address the problems of unclear node activation strategy and redundant feasible solutions in solving the target coverage of wireless sensor networks, a target coverage algorithm based on deep Q-learning is proposed to learn the scheduling strategy of nodes for wireless sensor networks. First, the algorithm abstracts the construction of feasible solutions into a Markov decision process, and the smart body selects the activated sensor nodes as discrete actions according to the network environment. Second, the reward function evaluates the merit of the smart body’s choice of actions in terms of the coverage capacity of the activated nodes and their residual energy. The simulation results show that the proposed algorithm intelligences are able to stabilize their gains after 2500 rounds of learning and training under the specific designed states, actions and reward mechanisms, corresponding to the convergence of the proposed algorithm. It can also be seen that the proposed algorithm is effective under different network sizes, and its network lifetime outperforms the three greedy algorithms, the maximum lifetime coverage algorithm and the self-adaptive learning automata algorithm. Moreover, this advantage becomes more and more obvious with the increase in network size, node sensing radius and carrying initial energy.

Perturbation energy extraction from a fluid via a subsurface acoustic diode with sustained downstream attenuation

Engineered subsurface structures inspired by phononic materials have shown favorable capabilities in flow control. Current efforts rely on tuning a structural resonance to the frequency of an unstable fluid mode, causing the deformations of the interfacing solid to destructively interfere with a wave-like flow instability. Although promising, the technique is most effective at targeting a single frequency mode with high-Q resonance. Additionally, several studies have shown the reduction in the perturbation energy to be spatially localized to the flow region above the subsurface strip, with a severely worsening effect downstream. Motivated by a desire to overcome these limitations, we present a different approach to methodically extract an undesirable pressure disturbance from a fluid column in a manner that capitalizes on the broad frequency range of a phononic bandgap, with the goal of permanently confining the perturbation energy within the subsurface structure. An acoustic diode (AD), i.e., a unidirectional transmitter of mechanical energy, comprised of a bi-layered phononic crystal and an auxiliary medium, interacts with a fluid cavity and provides a terminal energy sink. Two distinct ADs are presented that demonstrate active (time-variant) and passive (strain-dependent) paths to concept realization. The AD’s performance is described in terms of the time-transient energy distribution in the fluid and the interacting structure, as well as spatial wave profiles at critical time instants. The results show the system’s ability to achieve robust extraction of undesirable fluid oscillations with minimal residual energy. The concept is then tested in a fully developed plane channel flow with a superimposed perturbation, demonstrating the sustained nature of the subsurface AD’s energy trapping mechanism in addition to its ability to induce downstream attenuation.

Annealing dynamics of regular rotor networks: Universality and its breakdown

The spin-vector Monte Carlo model is widely used as a benchmark for the classicality of quantum annealers but severely restricts the time evolution. The spin-vector Langevin (SVL) model has been proposed and tested as an alternative, closely reproducing the real-time dynamics of physical quantum annealers such as D-Wave machines in the dissipative regime. We investigate the SVL annealing dynamics of classical O(2) rotors on regular graphs, identifying universal features in the nonequilibrium dynamics when changing the range of interactions and the topology of the graph. Regular graphs with low connectance or edge density exhibit universal scaling dynamics consistent with the Kibble-Zurek mechanism, which leads to a power-law dependence of the density of defects and the residual energy as a function of the annealing time. As the interaction range is increased, the power-law scaling is suppressed, and an exponential scaling with the annealing time sets in. Our results establish a universal breakdown of the Kibble-Zurek mechanism in classical systems characterized by long-range interactions, in sharp contrast with previous findings in the quantum domain. Published by the American Physical Society 2025

Exploring the Effects of Chirality of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl]imidazolidine-2,4-dione and its Derivatives on the Oncological Target Tankyrase 2. Atomistic Insights.

Tankyrases (TNKS) are homomultimers existing in two forms, viz. TNKS1 and TNKS2. TNKS2 plays a pivotal role in carcinogenesis by activating the Wnt//β-catenin pathway. TNKS2 has been identified as a suitable target in oncology due to its crucial role in mediating tumour progression. The discovery of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione, a hydantoin phenylquinazolinone derivative which exists as a racemic mixture and in its pure enantiomer forms, has reportedly exhibited inhibitory potency towards TNKS2. However, the molecular events surrounding its chirality towards TNKS2 remain unresolved. Herein, we employed in silico methods such as molecular dynamics simulation coupled with binding free energy estimations to explore the mechanistic activity of the racemic inhibitor and its enantiomer forms on TNK2 at a molecular level Results: Favourable binding free energies were noted for all three ligands propelled by electrostatic and van der Waals forces. The positive enantiomer demonstrated the highest total binding free energy (-38.15 kcal/mol), exhibiting a more potent binding affinity to TNKS2. Amino acids PHE1035, ALA1038, and HIS1048; PHE1035, HIS1048 and ILE1039; and TYR1060, SER1033 and ILE1059 were identified as key drivers of TNKS2 inhibition for all three inhibitors, characterized by the contribution of highest residual energies and the formation of crucial high-affinity interactions with the bound inhibitors. Further assessment of chirality by the inhibitors revealed a stabilizing effect of the complex systems of all three inhibitors on the TNKS2 structure. Concerning flexibility and mobility, the racemic inhibitor and negative enantiomer revealed a more rigid structure when bound to TNKS2, which could potentiate biological activity interference. The positive enantiomer, however, displayed much more elasticity and flexibility when bound to TNKS2. Overall, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl) phenyl] imidazolidine-2,4-dione and its derivatives showed their inhibitory prowess when bound to the TNKS2 target via in silico assessment. Thus, results from this study offer insight into chirality and the possibility of adjustments of the enantiomer ratio to promote greater inhibitory results. These results could also offer insight into lead optimization to enhance inhibitory effects.

Analisis Komposit Epoxy HGM Carbon Fiber dan Epoxy HGM Sisal Woven sebagai Material Rompi Anti Peluru

This research examines the potential of Epoxy-HGM-Carbon Fibre and Epoxy HGM Sical Woven Fibre composites as alternative materials for bulletproof vests. These two materials were chosen because they have similar ballistic protection capabilities to kevlar but with lighter weight and more economical cost. Using the finite element method, this study simulates the resistance of each composite to the penetration and impact energy of projectiles in accordance with the NIJ 0101.06 standard to protect users from ballistic threats. The simulation results show that the Epoxy-HGM-S Sisal Woven Fibre specimen at an optimum thickness of 30 mm as well as Epoxy-HGM-Carbon Fibre at a thickness of 18 to 30 mm are able to meet the penetration, Back Face Signature (BFS), and residual kinetic energy criteria that comply with the safe limits for users. This research makes an important contribution to the identification of alternative materials that not only improve user comfort and mobility, but also maintain ballistic protection effectiveness for military and security applications, especially for personnel who need protection against ballistic projectiles.

Reflection-driven turbulence in the super-Alfvénic solar wind

In magnetized, stratified environments such as the Sun's corona and solar wind, Alfvénic fluctuations ‘reflect’ from background gradients, enabling nonlinear interactions that allow their energy to dissipate into heat. This process, termed ‘reflection-driven turbulence’, likely plays a key role in coronal heating and solar-wind acceleration, explaining a range of detailed observational correlations and constraints. Building on previous works focused on the inner heliosphere, here we study the basic physics of reflection-driven turbulence using reduced magnetohydrodynamics in an expanding box – the simplest model that can capture local turbulent plasma dynamics in the super-Alfvénic solar wind. Although idealized, our high-resolution simulations and simple theory reveal a rich phenomenology that is consistent with a diverse range of observations. Outwards-propagating fluctuations, which initially have high imbalance (high cross-helicity), decay nonlinearly to heat the plasma, becoming more balanced and magnetically dominated. Despite the high imbalance, the turbulence is strong because Elsässer collisions are suppressed by reflection, leading to ‘anomalous coherence’ between the two Elsässer fields. This coherence, together with linear effects, causes the growth of ‘anastrophy’ (squared magnetic potential) as the turbulence decays, forcing the energy to rush to larger scales and forming a ‘ $1/f$ -range’ energy spectrum in the process. Eventually, expansion overcomes the nonlinear and Alfvénic physics, forming isolated, magnetically dominated ‘Alfvén vortices’ with minimal nonlinear dissipation. These results can plausibly explain the observed radial and wind-speed dependence of turbulence imbalance (cross-helicity), residual energy, fluctuation amplitudes, plasma heating and fluctuation spectra, as well as making a variety of testable predictions for future observations.

Van der Pol Oscillator, its Control Theoretical Analysis Using Dissipative Canonical Equations and its Lyapunov Function

In this research paper, beginning with the Lagrangian and generalized velocity proportional (Rayleigh) dissipation function of a physical/engineering system, the Lagrange-dissipative model ( {L,D}-model briefly) of the system is initially developed. Upon satisfying the prerequisite condition for a Legendre transform, the Hamiltonian function can be obtained. With the Hamiltonian function and the generalized velocity proportional (Rayleigh) dissipation function, dissipative canonical equations can be derived. Using these dissipative canonical equations as the state-space equations of the system allows for the investigation of observability, controllability, and stability properties. In addition to the equilibrium (or critical or fixed) points of the system, stability properties can also be verified through a Lyapunov function as a residual energy function (REF). Since the proposed method is valid for both linear and nonlinear systems, it has been applied to the Van der Pol oscillator/equation.

Residual Energy and Broken Symmetry in Reduced Magnetohydrodynamics

Alfvénic interactions that transfer energy from large to small spatial scales lie at the heart of magnetohydrodynamic turbulence. An important feature of the turbulence is the generation of negative residual energy—excess energy in magnetic fluctuations compared to velocity fluctuations. By contrast, an MHD Alfvén wave has equal amounts of energy in fluctuations of each type. Alfvénic quasi-modes that do not satisfy the Alfvén wave dispersion relation and exist only in the presence of a nonlinear term can contain either positive or negative residual energy, but until now, an intuitive physical explanation for why negative residual energy is preferred has remained elusive. This paper shows that the equations of reduced MHD are symmetric in that they have no intrinsic preference for one sign of the residual energy over the other. An initial state that is not an exact solution to the equations can break this symmetry in a way that leads to net-negative residual energy generation. Such a state leads to a solution with three distinct parts: nonresonant Alfvénic quasi-modes, normal modes produced to satisfy initial conditions, and resonant normal modes that grow in time. The latter two parts strongly depend on initial conditions; the resulting symmetry breaking leads to net-negative residual energy both in Alfvénic quasi-modes and ω = k ∥ V A = 0 modes. These modes have net-positive residual energy in the equivalent boundary value problem, suggesting that the initial value setup is a better match for solar wind turbulence.

Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles

The Internet of Vehicles (IoV) transforms the automobile industry through connected vehicles with communication infrastructure that improves traffic control, safety and information, and entertainment services. However, some issues remain, like data protection, privacy, compatibility with other protocols and systems, and the availability of stable and continuous connections. Specific problems are related to energy consumption for transmitting information, distributing energy loads across the vehicle’s sensors and communication units, and designing energy-efficient approaches to processing received data and making decisions in the context of the IoV environment. In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. Our model leverages a reward matrix and the Bellman equation to determine the optimal path from source to destination, effectively managing communication overhead. The model considers critical parameters such as Remaining Energy Level (REL), Bandwidth and Interference Level (BIL), Mobility Pattern (MP), Traffic Condition (TC), and Network Topological Arrangement (NTA), ensuring a comprehensive approach to route optimization. Extensive simulations were conducted using NS2 and Python, demonstrating that OptiE2ERL significantly outperforms existing models like LEACH, PEGASIS, and EER-RL across various performance metrics. Specifically, our model extends the network lifetime, delays the occurrence of the first dead node, and maintains a higher residual energy rate. Furthermore, OptiE2ERL enhances network scalability and robustness, making it a superior choice for IoV applications. The simulation results highlight the effectiveness of our model in achieving energy-efficient routing while maintaining network performance under different scenarios. By incorporating a diverse set of parameters and utilizing RL techniques, OptiE2ERL provides a robust solution for the challenges faced in IoV networks. This research contributes to the field by presenting a model that optimizes energy consumption and ensures reliable and efficient communication in dynamic vehicular environments.

Evolution of Magnetohydrodynamic Turbulence in the Expanding Solar Wind: Residual Energy and Intermittency

Evolution of Magnetohydrodynamic Turbulence in the Expanding Solar Wind: Residual Energy and Intermittency

Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.

A Hybrid Data-Driven Machine Learning Framework for Predicting the Impact Resistance of Composite Armor

Composite armor plays a crucial role as the primary defense against high-velocity impacts from fragments and projectiles. However, balancing the need for lightweight structures with the requirement for robust protection remains a significant engineering challenge. Traditional approaches for predicting the protective performance of armor typically involve a combination of experimental testing and numerical simulations, both of which can be resource-intensive and costly. In contrast, data-driven methods combined with machine learning have demonstrated the potential to significantly reduce both time and economic costs, highlighting their substantial advantages in various engineering domains. Unfortunately, a mature machine learning framework for predicting the performance of multilayer composite armor against high-velocity impacts from large fragments has yet to be established. In this paper, a novel data-driven framework for predicting the ballistic performance of composite armor using a hybrid model of Support Vector Machine and Deep Neural Network was established. This framework employed hyperparameter optimization to enhance predictive performance, yielding a model with excellent accuracy. The proposed model was adaptable to multilayered armor with varying layer thicknesses, enabling rapid predictions of armor penetration, residual projectile kinetic energy, and armor deformation.

An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance. Through mimicking the natural scavenging behavior of African vultures, AVOACS continuously balances energy consumption on nodes resulting in an increase in network stability and lifetime. For CH selection, we use AVOACS, which considers the following parameters: communication mode decider, the distance between sink and node, residual energy, and intra-cluster distance. In comparison to the OE2-LB protocol, simulation findings demonstrate that AVOACS enhances stability, network lifetime, and throughput by 21.5%, 31.4%, and 16.9%, respectively. The results show that AVOACS is an effective clustering algorithm for energy-efficient operation in heterogeneous WSN environments as it contributes to a large increase of network lifetime and significant enhancement of performance.

Hybrid Mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi‑hop clustering routing protocol for cognitive sensor networks

In clustered cognitive radio sensor networks (CRSNs), availability of free channels, spectrum sensing and energy utilization during clustering and cluster head (CH) selection is essential for fairness of time and event-driven data traffic. The existing multi-hop routing protocols in CRSNs generally adopt a perfect spectrum sensing which is not same in the practical spectrum sensing of nodes in real networks. High imbalance in residual energy between the selected CHs negatively impacts the delivery of data packets. Hence, hybrid mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi-hop clustering routing protocol (HMABFOA) is proposed as an imperfect spectrum sensing approach for achieving better utilization of downlink energy harvesting and sustain maximized degree of energy between the nodes in the network. This HMABFOA scheme reduces the negative impact of imperfect spectrum sensing for extended network lifetime which sustains the capabilities of the network surveillance. It helped in constructing a distributed cluster with multi-hop routing selection between clusters depending on a energy level function that explores and exploits the factors associated with CHs selection. The merits of Mexican axolotl optimization algorithm (MAOA) is used for better CH selection and cluster formation with energy stability is sustained in the network. Further bitterling fish optimization (BFOA) algorithm is used for optimized multi-hop route between the clusters with minimal energy consumption and maximized spectrum sensing that proves better channels availability. The simulation results guaranteed maximized network lifetime of 24.38%, spectrum utilization rate of 24.58%, and minimized energy utilization of 25.62%, better than the baseline approaches.

A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Unmanned aerial vehicles (UAVs) play a crucial role in various applications, including environmental monitoring, disaster management, and surveillance, where timely data collection is vital. However, their effectiveness is often hindered by the limitations of wireless sensor networks (WSNs), which can restrict communications due to bandwidth constraints and limited energy resources. Thus, the operational context of the UAV is intertwined with the constraints on WSNs, influencing how they are deployed and the strategies used to optimize their performance in these environments. Considering the issues, this paper addresses the challenge of efficient UAV navigation in constrained environments while reliably collecting data from WSN nodes, recharging the sensor nodes’ power supplies, and ensuring the UAV detours around obstacles in the flight path. First, an integer linear programming (ILP) optimization problem named deadline and obstacle-constrained energy minimization (DOCEM) is defined and formulated to minimize the total energy consumption of the UAV. Then, a deep reinforcement learning-based algorithm, named the DQN-based UAV detouring algorithm, is proposed to enable the UAV to make intelligent detour decisions in the constrained environment. The UAV must finish its tour (data collection and recharging sensors) without exceeding its battery capacity, ensuring each sensor has the minimum residual energy and consuming energy for transmitting and generating data, after being recharged by the UAV at the end of the tour. Finally, simulation results demonstrate the effectiveness of the proposed DQN-based UAV detouring algorithm in data collection and recharging the sensors while minimizing the total energy consumption of the UAV. Compared to other baseline algorithm variants, the proposed algorithm outperforms all of them.

Secure Cluster Based Routing Scheme for Neuro‐Fuzzy Assisted MANET in 5G

ABSTRACTThe Fifth Generation (5G) of wireless networks has increasingly focused significantly on energy‐saving methods for mobile communication systems. The objective of combining MANET with 5G communication is to achieve a high data throughout while simultaneously reducing latency and cost. The main issues with MANET are elevated energy use, security, and reduced longevity. The self‐configuring characteristics and dynamic architecture of MANET may lead to frequent changes in topology and resource allocation. This mobility results in frequent connection failures, which increase routing overheads and decrease data transmission dependability. A secured clustering technique is essential to enhance the routing performance and establish reliable routing. In MANET, the cluster mechanism is the prominent way to improve the network lifetime and routing mechanism. The proposed secure cluster routing‐based neuro‐fuzzy logic (SCR‐NFL) establishes a secure cluster mechanism via the use of the Trust value method. It guarantees cluster stability by taking into account node mobility and energy while selecting cluster heads. The important components in the proposed system are Mobility (M), Residual Energy Level (REL), and Trust Value (TV). The neural fuzzy logic system is implemented to handle the data uncertainty and make effective decisions during routing path selections. The proposed SCR‐NFL performance is measured in an NS‐2 simulation environment. The result is compared with the existing Improved Particle Swarm Optimization Algorithm, Trust‐Based Secure Neuro Fuzzy Clustering mechanism and Mobility Aware Energy Efficient Clustering system using Particle Swarm Optimization mechanism. The performance parameters for comparison are packet delivery ratio, average number of clusters, end‐to‐end delay, average number of cluster instances, mobility speed and mobility distance.

Adaptive Clustering and Trust Aware Routing (ACTAR) for Wireless Sensor Networks in IoT Environments

ABSTRACTThe Internet of Things (IoT) has emerged as a revolutionary technology, connecting a vast number of people to the internet through various devices such as smartphones, laptops, sensors, and more. An essential component of IoT, the wireless sensor network (WSN), enables automation in processes like sensing, node monitoring, and data transmission. However, the full potential of IoT is hindered by cyber threats and unreliable communication. Although several security algorithms exist, they are not suitable for energy‐constrained sensor nodes. To address this issue, this paper proposes an energy‐efficient security mechanism called adaptive clustering and trust aware routing (ACTAR) for IoT‐WSNs. ACTAR operates in three phases: adaptive and hybrid clustering (AHC), multiobjective function‐based cluster head selection (MOCH), and trust aware routing (TAR). First, AHC utilizes a nonuniform clustering mechanism to categorize the network into nearby and distant clusters. Next, the selection of cluster heads is based on four metrics: coverage, communication cost, residual energy, and node proximity. Finally, TAR calculates the trust degree of sensor nodes by evaluating their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is selected as the next‐hop forwarding node, followed by the route with the highest trust degree. Extensive simulations of ACTAR demonstrate its performance in terms of malicious detection rate, false‐positive rate, residual energy, and packet delivery ratio. Comparative analysis shows that ACTAR outperforms existing methods, proving its superiority.

On Alfvénic turbulence of solar wind streams observed by Solar Orbiter during March 2022 perihelion and their source regions

It has been recently accepted that the standard classification of the solar wind solely according to flow speed is outdated, and particular interest has been devoted to the study of the origin and evolution of so-called Alfvénic slow solar wind streams and to what extent such streams resemble or differ from fast wind. In March 2022, Solar Orbiter completed its first nominal phase perihelion passage. During this interval, it observed several Alfv\'enic streams, allowing for characterization of fluctuations in three slow wind intervals (AS1-AS3) and comparison with a fast wind stream (F) at almost the same heliocentric distance. This work makes use of Solar Orbiter plasma parameters from the Solar Wind Analyzer (SWA) and magnetic field measurements from the magnetometer (MAG). The magnetic connectivity to the solar sources of selected solar wind intervals was reconstructed using a ballistic extrapolation based on measured solar wind speed down to the (spherical) source surface at 2.5 R$_s$ below which a potential field extrapolation was used to map back to the Sun. The source regions were identified using SDO/AIA observations. A spectral analysis of in situ measured magnetic field and velocity fluctuations was performed to characterize correlations, Alfv\'enicity, normalized cross-helicity, and residual energy in the frequency domain as well as intermittency of the fluctuations and spectral energy transfer rate estimated via mixed third-order moments. A machine learning technique was used to separate proton core, proton beam, and alpha particles and to study v--b correlations for the different ion populations in order to evaluate the role played by each population in determining the Alfvénic content of solar wind fluctuations. The comparison between fast wind and Alfvénic slow wind intervals highlights the differences between the two solar wind regimes: The fast wind is characterized by larger amplitude fluctuations, and magnetic and velocity fluctuations are closer to equipartition of energy. In fact the Alfv\'enic slow wind streams appear to be on a spectrum of wind types, with AS1, originating from open field lines neighboring active regions and displaying similarities with the fast wind in terms of fluctuation amplitude and turbulence characteristics, but not with respect to the alpha particles and proton beams. The other two slow streams differed both in their sources as well as plasma characteristics, with AS2 coming from the expansion of a narrow coronal hole corridor and AS3 from a region straddling a pseudostreamer. The latter displayed the coldest and highest density but the slowest stream with the smallest fluctuation amplitude and greatest magnetic energy excess. It also showed the largest scatter in proton beam speeds and the greatest difference in speed between proton beam and alpha particles. This study shows how the old fast--slow solar wind dichotomy, already called into question by the observations of slower Alfv\'enic solar wind streams, should further be refined, as the Alfv\'enic slow wind, originating in different solar wind regions, show significant differences in density, temperature, and proton and alpha-particle properties in the inner heliosphere. The observations presented here provide the starting point for a better understanding of the origin and evolution of different solar wind streams as well as the evolving turbulence contained within.

Extending WSN Lifetime with Enhanced LEACH Protocol in Autonomous Vehicle Using Improved K-Means and Advanced Cluster Configuration Algorithms

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection.