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It is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality. [ABSTRACT FROM AUTHOR]

Traditional jamming technologies have become less effective with the development of anti-jamming technologies, especially with the appearance of intelligent transmitters, which can adaptively adjust their transmission strategies. To deal with intelligent transmitters, in this paper, a game-based intelligent jamming scheme is proposed. Considering that the intelligent transmitter has multiple transmission strategy sets whose prior probabilities are unknown to the jammer, we first model the interaction between the transmitter and the jammer as a dynamic game with incomplete information. Then the perfect Bayesian equilibrium is derived based on assumptions of some prior information. For more practical applications when no prior information about the transmitter is available at the jammer, a Q-learning-based method is proposed to find an intelligent jamming strategy by exploiting the sensing results of the wireless communications. The design of the jammer's reward function is guided by the game utility and the reward is calculated based on the Acknowledgement/Negative Acknowledgement feedback of the receiver. Simulation results show that the proposed scheme has only 0.5 % loss in jamming utility compared to that of the perfect Bayesian equilibrium strategy. Compared to existing jamming schemes, a higher packet error rate can be achieved by the proposed scheme by consuming less jamming power. [ABSTRACT FROM AUTHOR]

This research aims to experimentally characterize cooling, clogging and jamming of a dry granular flow in a chute partially obstructed by a stopping wall with two slits adjacent to the side walls. We ensemble‐average velocities, determined with Particle Tracking Velocimetry, and its fluctuations, to compute mean flow and granular temperature fields. Full chute‐wide jamming is triggered by the formation of stable arch‐like clogging structures in front of the slits. The statistical distribution of the clogging instant is not heavy‐tailed, which indicates that clogging occurs only when the flow through the slits is liquid‐like. An upstream‐progressing jamming wave eventually forms, similar to that observed in fully obstructed chutes. There is no triple point anywhere, since the flow cools down to a granular liquid before jamming. We identified three main stages of jamming wave propagation. The initial buildup is characterized by high values of the upstream Froude number, slow progression, and transformation of kinetic into potential energy. This occurs with significant granular head losses, as particles attempt to flow over the jam. In the second stage, accretion becomes dominant, characterized by smaller head losses and, consequently, higher values of the jamming wave celerity. In the third stage, the jamming wave propagates against a cooler but faster flow that pushes against the jam, slightly increasing the wave strength. Accretion is the main mechanism of jammed mass increase, justifying a further increase of wave acceleration. The macroscopic aspects of the jamming wave dynamics can be described, as a first approximation, by shallow‐water theory. Key Points: The flow through the slits enters the clogging regime as it cools down. Chute‐wide generalized jamming results from cloggingThere are no triple points in the system. Jamming only incorporates particles that have cooled to liquid‐like behaviorThe dynamics of the jamming wave exhibit three stages, with different roles of accretion and upward dissipative modes of jamming [ABSTRACT FROM AUTHOR]

Accurate jamming detection is a prerequisite and important support for efficient anti‐jamming communication in integrated sensing and communications. In recent years, the reactive follower jamming, characterized by the high jamming efficiency and the difficulty of detection, is seen as the main opponent for frequency hopping spread spectrum communication, which poses a threat to the reliability of wireless communication. Based on the background, a multiple‐nodes based cooperative jamming detection scheme is proposed for accurately detecting the reactive follower jamming. Specifically, the secondary nodes send the average energy of the suspected attacked symbols to the primary user as the uplink data in each detection period, and then the detection result is provided by the detection threshold. The optimal detection threshold is derived for the primary user according to the uplink data and channel conditions. In addition, the theoretical probability of detection under the optimal threshold is derived, and the complexity of the scheme is analysed. Simulation results show that the proposed multiple‐nodes based cooperative jamming detection scheme for reactive follower jamming can effectively improve the probability of detection. [ABSTRACT FROM AUTHOR]

Threats to physical layer security from jamming attacks make wireless cognitive communication systems vulnerable. Global Positioning System signal is vulnerable to these attacks. Over the last decade, several types of jamming detection techniques have been proposed, antijamming‐based classical and machine learning (ML) techniques. Most of these techniques are inefficient in detecting jammers. Thus, there is a great need for efficient and quickest jamming detection technique‐based classifier using receiver operating characteristic (ROC) curve for different threshold values with high accuracy. In this work, we compare the efficiency of the proposed orthogonal distance (OD) and score distance (SD) method‐based robust principal component analysis (PCA) in ML classification in detecting jamming signals. Two hypotheses are proposed to distinguish between the presence and absence attack problem. Using this compressed data matrix obtained from modulated wideband converter (MWC) structure via centralized cooperation directly as input of the proposed classifier combined‐based ROC curve for real‐time detection scenarios. The performance of this proposed algorithm‐based robust PCA was evaluated and compared using the detection anomaly rate (DAR%), and false alarm rate (FAR%), area under curve (AUC), and accuracy. The performance of obtained results is good. [ABSTRACT FROM AUTHOR]

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations—the machine-learning-based, localization-based, and behavior-based methods. [ABSTRACT FROM AUTHOR]

This review article explores jamming attacks, a critical security threat disrupting communication in wireless sensor networks (WSNs). We begin by introducing WSNs and highlighting the detrimental effects of jamming. The article then delves into various research directions for mitigating jamming in WSNs. This includes exploring different jamming techniques, their impact, and potential countermeasures. We categorize jamming techniques and analyze existing detection and localization mechanisms. Furthermore, we examine current security approaches for combating jamming attacks in WSNs. We identify unresolved research challenges in this area and compare our survey with previous work on jamming in WSNs. This review provides a comprehensive overview of jamming threats in WSNs, outlining existing solutions and future research directions for robust network security. [ABSTRACT FROM AUTHOR]

Abstract It is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality.

Abstract Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data.

Abstract The authors investigate the performance of an original multifunction system with two in‐band full‐duplex capable joint radar, communications and security (JRCS) transceivers in the presence of an eavesdropper. The system concept can be generalised to a network of more than two JRCS transceivers and multiple eavesdroppers, despite the present study focusing on the three‐node scenario. By combining a bandlimited pseudonoise waveform with a data‐containing orthogonal frequency‐division multiplexing (OFDM) waveform, the authors are able to ensure a certain jamming‐to‐signal power ratio (JSR) at the eavesdropper, whilst ideal synchronisation ensures that jamming causes no deterioration to their own data transfer or radar sensing performance as it is possible to remove just the known pseudonoise waveform. To validate the system, the authors investigate through simulations the OFDM symbol error rates of all the receivers, radar target signal‐to‐interference‐plus‐noise power ratios as well as receiver operating characteristic curves, and eavesdropper's detection signal‐to‐noise power ratios through two‐branch receiver cross‐correlation. The results show that already a very low JSR of −20 dB can improve physical‐layer security without a significant increase in friendly symbol error rate or deterioration in radar performance. Additionally, other full‐duplex transceivers potentially occupying the same radio resources improve the secrecy even further.

Abstract Reactive jammers select jamming strategies according to the users’ responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes.

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi‐realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre‐Decluttering Dual‐Stage UNet (D2UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D2UNet, the authors also design a multi‐stage loss function to achieve progressive training. Extensive experimental results demonstrate that D2UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal‐to‐jamming ratio, 0.9952 structure similarity index measure) on the semi‐realistic dataset. Its practical effect is further verified on measured data. [ABSTRACT FROM AUTHOR]

Will proliferated satellites enable a transparent battlefield in a way that might transform continental warfare? Many now think so. We argue, however, that anti-satellite (ASAT) capabilities will limit satellites’ potential contribution, but only if land forces adapt by deploying ground-based jamming and dazzling technologies. In a sustained measure-countermeasure competition, these non-kinetic approaches enjoy advantages that can deny an opponent effective use of space for battlefield surveillance. If ground forces contest the use of space in this way, the future battlefield will be much less transparent than many now expect. [ABSTRACT FROM AUTHOR]

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research. [ABSTRACT FROM AUTHOR]

The authors investigate the performance of an original multifunction system with two in‐band full‐duplex capable joint radar, communications and security (JRCS) transceivers in the presence of an eavesdropper. The system concept can be generalised to a network of more than two JRCS transceivers and multiple eavesdroppers, despite the present study focusing on the three‐node scenario. By combining a bandlimited pseudonoise waveform with a data‐containing orthogonal frequency‐division multiplexing (OFDM) waveform, the authors are able to ensure a certain jamming‐to‐signal power ratio (JSR) at the eavesdropper, whilst ideal synchronisation ensures that jamming causes no deterioration to their own data transfer or radar sensing performance as it is possible to remove just the known pseudonoise waveform. To validate the system, the authors investigate through simulations the OFDM symbol error rates of all the receivers, radar target signal‐to‐interference‐plus‐noise power ratios as well as receiver operating characteristic curves, and eavesdropper's detection signal‐to‐noise power ratios through two‐branch receiver cross‐correlation. The results show that already a very low JSR of −20 dB can improve physical‐layer security without a significant increase in friendly symbol error rate or deterioration in radar performance. Additionally, other full‐duplex transceivers potentially occupying the same radio resources improve the secrecy even further. [ABSTRACT FROM AUTHOR]

This paper investigates dual-hop Reconfigurable Intelligent Surface (RIS) wireless communication systems with malicious jamming, where the destination node faces jamming from a malicious jammer with a RIS-Equipped Unmanned Aerial Vehicle (UAV) relay. We model the channel gains for Tx-RIS and Jammer-RIS links with a Rician distribution, while the RIS-Rx link follows a Nakagami-m distribution, and the jamming status is modeled as a Bernoulli-distributed random variable. We derived and provided closed-form expressions for the probability density functions (PDFs) of the legitimate channel and jamming channel in RIS-Equipped UAV wireless communication systems. Additionally, a new closed-form expression for the PDF of the received signal-to-jamming ratio (SJR) is derived. Using the Gauss–Laguerre Approximation method, we calculate the Average Bit Error Rate (ABER) under Binary Phase Shift Keying (BPSK) and Quadrature Amplitude Modulation (QAM) schemes. We analyze the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on ABER performance through theoretical analyses and simulation results. Our theoretical analyses and simulation results indicate that an increase in the probability of malicious jamming significantly raises the ABER. For example, under favorable channel conditions, the ABER for BPSK modulation was observed to be as low as 10 − 5 , whereas under poor channel conditions, the ABER increased to 10 − 2 . Additionally, by reducing the distance between the transmitter and the RIS, the ABER can be improved. The legitimate RIS performs better when closer to the transmitter. These findings highlight the critical impact of channel conditions and the deployment of the RIS on the overall system's performance. Our results offer valuable insights into designing and evaluating the performance of RIS-Equipped UAV wireless communication systems in the presence of malicious jamming, aiding in the development of countermeasures to enhance system resilience and security. The derived expressions are validated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Reactive jammers select jamming strategies according to the users' responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others' strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes. [ABSTRACT FROM AUTHOR]

Bottlebrush polymer surfactants (BPSs), formed by the interfacial interactions between bottlebrush polymers (BPs) with poly(acrylic acid) side chains dissolved in an aqueous phase and amine-functionalized ligands dissolved in the oil phase, assemble and bind strongly to the fluid-fluid interface. The ratio between NBB (backbone degree of polymerization) and NSC (side chain degree of polymerization) defines the initial assembly kinetics, interface packing efficiency, and stress relaxation. The equilibrium interfacial tension (γ) increases when NBB < NSC, but decreases when NBB ≫ NSC, correlating to a pronounced change in the effective shape of the BPs from being spherical to worm-like structures. The apparent surface coverage (ASC), i.e., the interfacial packing efficiency, decreases as NBB increases. The dripping-to-jetting transition of an injected polymer solution, as well as fluorescence recovery after photobleaching experiments, revealed faster initial assembly kinetics for BPs with higher NBB. Euler buckling of BPS assemblies with different NBB values was used to characterize the stress relaxation behavior and bending modulus. The stress relaxation behavior was directly related to the ASC, reflecting the strong influence of macromolecular shape on packing efficiency. The bending modulus of BPSs decreases for NBB < NSC, but increased when NBB ≫ NSC, showing the effect of molecular architecture and multisite anchoring. All-liquid printed constructs with lower NBB BPs yielded more stable structured liquids, underscoring the importance of macromolecular packing efficiency at fluid interfaces. Overall, this work elucidates fundamental relationships between nanoscopic structures and macroscopic properties associated with various bottlebrush polymer architectures, which translate to the stabilization of all-fluidic printed constructs.

Abstract Accurate jamming detection is a prerequisite and important support for efficient anti‐jamming communication in integrated sensing and communications. In recent years, the reactive follower jamming, characterized by the high jamming efficiency and the difficulty of detection, is seen as the main opponent for frequency hopping spread spectrum communication, which poses a threat to the reliability of wireless communication. Based on the background, a multiple‐nodes based cooperative jamming detection scheme is proposed for accurately detecting the reactive follower jamming. Specifically, the secondary nodes send the average energy of the suspected attacked symbols to the primary user as the uplink data in each detection period, and then the detection result is provided by the detection threshold. The optimal detection threshold is derived for the primary user according to the uplink data and channel conditions. In addition, the theoretical probability of detection under the optimal threshold is derived, and the complexity of the scheme is analysed. Simulation results show that the proposed multiple‐nodes based cooperative jamming detection scheme for reactive follower jamming can effectively improve the probability of detection.

Experimental setups using steel and transparent glass pipes, along with equally spaced steel stamped and transparent plastic tees, were designed for testing. The experimental results provided insights into the influence of flow directions, pig length, material properties, and air consumption on the dynamics and strength of the pigs within the tees. Furthermore, the study identified the causes of temporary halting, jamming, and the mechanisms of damage to cylindrical pigs made of hyperelastic materials within pipeline tees.

Abstract Space‐time adaptive processor (STAP) has been widely used for global navigation satellite system (GNSS) anti‐jamming receiver due to its good anti‐jamming performance. When direction of satellite is unknown, STAP can be implemented based on power inversion (PI) criterion. However, existing space‐time PI algorithm will introduce tens to hundreds of degrees biases into carrier phase, and sometimes will even cause cycle slips, which will reduce the success rate of ambiguity resolution, ultimately deteriorating positioning accuracy. A distortion‐less carrier phase tracking space‐time PI algorithm is proposed. The main novelty is that the proposed algorithm keeps the coefficients of the temporal taps as real values by imposing constraints on the weights of the antenna array. Several experiments are implemented to verify the effectiveness of the proposed algorithm. For comparison, the results of PI algorithm and minimum variance distortion‐less response (MVDR) algorithm are shown. Results show that when the number, style, and direction of interferences and the direction of GNSS signal vary, different degrees of biases are introduced into carrier phases for the PI and the MVDR algorithm. However, no bias is introduced into the proposed algorithm. As a result, the effectiveness of the proposed algorithm is verified.

Abstract With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed.

A broad class of blocked or jammed configurations of particles on the one-dimensional lattice can be characterized in terms of local rules involving only the lengths of clusters of particles (occupied sites) and of holes (empty sites). Examples of physical relevance include the metastable states reached by the zero-temperature dynamics of kinetically constrained spin chains, the attractors of totally irreversible processes such as random sequential adsorption, and arrays of Rydberg atoms in the blockade regime. The configurational entropy of ensembles of such blocked configurations has been investigated recently by means of an approach inspired from the theory of stochastic renewal processes. This approach provides a valuable alternative to the more traditional transfer-matrix formalism. We show that the renewal approach is also an efficient tool to investigate a range of observables in uniform ensembles of blocked configurations, besides their configurational entropy. The main emphasis is on their structure factor and correlation function. [ABSTRACT FROM AUTHOR]

The jammed liquid‐liquid interface offers an elegant approach to achieve non‐equilibrium shapes of liquids. The interfacial jamming subsequently brings about formation of 2D "solid‐like" assembly. In this work, biphasic reaction between a perylenediimide based luminogen benzoperylenediimide monoanhydride (Bp(Im)2MA) and diamines are demonstrated that rendered amide bond formation and in turn amide nanoconjugates across the oil‐water interface. Amide nanoconjugates formed with different diamines showed diamine alkyl chain length dependent varied interfacial properties and sizes. Jamming of the interface is judiciously utilized to induce aggregation of amide nanoconjugates which successfully established macroscopic Aggregation Induced Emission‐switching (AIE‐switching) behavior. Further, the versatility of this approach is extended to analyze the reactivity of chiral amino acids. The reaction between the Bp(Im)2MA luminogen and chiral amino acids exhibits macroscopic enantioselectivity at jammed interface via diverse surface coverage of the amino acids‐based amide nanoconjugates. The formation of discrete nanoscopic amide interfacial assembly for each of the L‐ and D‐ amino acids isomers is corroborated with Atomic Force Microscopy (AFM) images. This study extends a fascinating pathway to aid in understanding and fabrication of structurally diverse jammed interfaces and aggregation of organic molecules across an oil‐water interface. [ABSTRACT FROM AUTHOR]

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input–multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel. [ABSTRACT FROM AUTHOR]

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison. [ABSTRACT FROM AUTHOR]

The flow of dense suspension of non-Brownian particles has been considered by various studies affected by their significance in a variety of industries and natural phenomena. In this study, we investigate the effect of polyhedron morphology on shear thickening, shear jamming, and hysteresis characteristics of non-Brownian suspension of acrylate particles. Particles with the same chemical nature and three different shapes of spherical (aspect ratio Γ = 1), elliptical paraboloid (Γ ≈ 1), and boat-shaped (Γ ≈ 3) are fabricated via photopolymerization-based methods. Studied suspensions show the shear-thinning behavior at low shear stresses and shear thickening behavior at the higher range of shear stress. Also, the strength of observed shear thickening is enhanced for the suspensions of polyhedron particles, which can be attributed to the heightened degree of interparticle frictional contacts. Furthermore, it is found that angularity not only shifts the predicted frictionless and frictional jamming packing fractions to lower values but also expands the shear jamming packing fraction range. Finally, a history-dependent hysteresis is observed in all samples due to the different particle spatial structures forming in ascending and descending flow modes. The observed hysteresis loops strongly depend on the volume fraction and diminish near the jamming packing fraction due to the restricted mobility space of particles. In addition, the tumbling of elongated particles also can decrease the hysteresis loop by enhancing viscosity in the ascending flow mode, where the structures are not fully developed. [ABSTRACT FROM AUTHOR]

Space‐time adaptive processor (STAP) has been widely used for global navigation satellite system (GNSS) anti‐jamming receiver due to its good anti‐jamming performance. When direction of satellite is unknown, STAP can be implemented based on power inversion (PI) criterion. However, existing space‐time PI algorithm will introduce tens to hundreds of degrees biases into carrier phase, and sometimes will even cause cycle slips, which will reduce the success rate of ambiguity resolution, ultimately deteriorating positioning accuracy. A distortion‐less carrier phase tracking space‐time PI algorithm is proposed. The main novelty is that the proposed algorithm keeps the coefficients of the temporal taps as real values by imposing constraints on the weights of the antenna array. Several experiments are implemented to verify the effectiveness of the proposed algorithm. For comparison, the results of PI algorithm and minimum variance distortion‐less response (MVDR) algorithm are shown. Results show that when the number, style, and direction of interferences and the direction of GNSS signal vary, different degrees of biases are introduced into carrier phases for the PI and the MVDR algorithm. However, no bias is introduced into the proposed algorithm. As a result, the effectiveness of the proposed algorithm is verified. [ABSTRACT FROM AUTHOR]

The spreading behaviour of cohesive sand powder is modelled by Discrete Element Method, and the spreadability and the mechanical jamming are focused. The empty patches and total particle volume of the spread layer are examined, followed by the analysis of the geometry force and jamming structure. The results show that several empty patches with different size and shapes could be observed within the spread layer along the spreading direction even when the gap height increases to 3.0D90. Large particles are more difficult to be spread onto the base due to jamming, although their size is smaller than the gap height. Size segregation of particles occurs before particles entering the gap between the blade and base. There are almost no particles on the smooth base when the gap height is small, due to the full-slip flow of particles. The difference of the spread layer and spreadability between the cases with rough and smooth base is reduced by the increase of the gap height. An interesting correlation between jamming effect and local defects (empty spaces) in the powder layer is identified. The resistance to particle rolling is important for the mechanical jamming reported in this work. The jammed particles with a larger size ratio tend to be more stable. [ABSTRACT FROM AUTHOR]

The adversarial machine learning approaches are modelled to provide a defence mechanism during the prediction of cloning and jamming attacks launched over the wireless communication process. The transmitter is supplied with a pre-trained classifier to analyze the status of the channel based on the sensing nature and determine the other transmission process. The learning method gathers all acknowledgements and fusion made between nodes and the channel's current state to build a learning model that can accurately identify the succeeding transmission constraint caused by network jamming. In this instance, compared to random jamming procedures, an inventive anticlone detection strategy aims to minimize the number of clones and jamming found throughout the network model. The transmitter analyzes the power restrictions over the sensor networks using the learning-based fisher score (FS). Here, an adversarial network model (ANM-FS) is fused to diminish the computational time to collect the training dataset by examining the incoming samples. With this defence mechanism, the transmitter intends to predict the false prediction rate (FPR) and design a better model for providing a reliable classifier. Systematically, the transmitter identifies the floating of attacks over the network model and adopts the defending mechanism to mislead the injected clone, enhancing the throughput and reducing the prediction error. [ABSTRACT FROM AUTHOR]

In this paper, we present a study investigating the impact of jamming in a Dual-Hop free-space optical (FSO) communication system assisted by reconfigurable intelligent surfaces (RIS) in the presence of a malicious jammer. We analyze the combined effects of atmospheric turbulence (AT), pointing error (PE), and angle of arrival (AoA) fluctuation of unmanned aerial vehicles (UAVs). Closed-form expressions for the overall average bit error rate (ABER) are derived while considering these impairments. To mitigate the jamming effect, we explore a Single-Input Multiple-Output (SIMO) FSO system and derive the end-to-end Average Bit Error Rate (ABER) under various jamming scenarios. Additionally, we conduct a comprehensive study by examining different placements of the malicious UAV jammer and RIS, drawing insightful conclusions on system performance. The analytically derived expressions are validated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imaging is critical in military and national defense applications. Spaceborne SAR systems operate in fixed orbits, making them vulnerable to the jamming effects of ground radar signals. This study quantitatively analyzed jamming effects based on actual interference cases observed in the currently operational satellite SAR. Subsequently, various jamming scenarios were designed for hostile satellite SAR systems. SAR jamming simulations were conducted based on the ground radar specifications derived from real jamming cases by considering the jamming-to-clutter ratio (JCR) as a key metric for evaluating the jamming performance. The SAR image corruption was quantitatively analyzed for various ground jammers, with the results verified by comparison with real SAR jamming cases. This study attempted to understand the principles of satellite SAR jamming and contribute to the development of defense strategies against adversarial space surveillance systems in the near future. [ABSTRACT FROM AUTHOR]

Pipe flows are commonly found in nature and industry as an effective mean of transporting fluids. They are primarily characterized by their resistance law, which relates the mean flow rate to the driving pressure gradient. Since Poiseuille and Hagen, various flow regimes and fluid rheologies have been investigated, but the behavior of shear-thickening suspensions, which jam above a critical shear stress, remains poorly understood despite important applications (e.g., concrete or food processing). In this study, we build on recent advances in the physics of shear-thickening suspensions to address their flow through pipes and establish their resistance law. We find that for discontinuously shear-thickening suspensions (large particule volume fractions), the flow rate saturates at high driving stress. Local pressure and velocity measurements reveal that this saturation stems from the emergence of a frictional soliton: a unique, localized, superdissipative, and backpropagating flow structure coexisting with the laminar frictionless flow phase observed at low driving stress. We characterize the remarkably steep effective rheology of the frictional soliton and show that it sets the resistance law at the whole pipe scale. These findings offer an unusual perspective on low-Reynolds suspension flows through pipes, intriguingly reminiscent of the transition to turbulence for simple fluids. They also provide a predictive law for the transport of such suspensions in pipe systems, with implications for a wide range of applications. [ABSTRACT FROM AUTHOR]

The flowing, jamming, and avalanche behavior of granular materials is satisfyingly universal and vexingly hard to tune: A granular flow is typically intermittent and will irremediably jam if too confined. Here, we show that granular metamaterials made from particles with a negative Poisson's ratio yield more easily and flow more smoothly than ordinary granular materials. We first create a collection of auxetic grains based on a re-entrant mechanism and show that each grain exhibits a negative Poisson's ratio regardless of the direction of compression. Interestingly, we find that the elastic and yielding properties are governed by the high compressibility of granular metamaterials: At a given confinement, they exhibit lower shear modulus, lower yield stress, and more frequent, smaller avalanches than materials made from ordinary grains. We further demonstrate that granular metamaterials promote flow in more complex confined geometries, such as intruder and hopper geometries, even when the packing contains only a fraction of auxetic grains. Moreover, auxetic granular metamaterials exhibit enhanced impact absorption. Our findings blur the boundary between complex fluids and metamaterials and could help in scenarios that involve process, transport, and reconfiguration of granular materials. [ABSTRACT FROM AUTHOR]