The future 6G network will enhance smart cities' data integration and analysis capabilities through Integrated Sensing and Communication (ISAC) technology. This paper proposes a millimeter wave (mmW) in-band full-duplex (IBFD) ISAC system architecture and explores three direction-finding techniques based on subarray splitting for its architecture. Firstly, the mutual interference problem in the IBFD ISAC system was analyzed. Using the Taylor synthesis method to suppress array sidelobes and reduce false alarm rate, combined with a two-dimensional fast Fourier transform (2D-FFT), the direction-finding accuracy is improved under an extremely low signal-to-noise ratio (SNR). Then, the performance of three direction-finding methods was compared through simulation. The simulation results showed that the sum difference beam direction-finding method has better mean square error (MSE) performance. In addition, a sum difference beam direction-finding experiment was conducted, and the direction-finding errors for moving and static targets were as low as 0.6° and 0.5°, respectively, demonstrating excellent direction-finding capability.

Recent studies reveal the vulnerability of the image segmentation foundation model SAM to adversarial examples. Its successor, SAM2, has attracted significant attention due to its strong generalization capability in video segmentation. However, its robustness remains unexplored, and it is unclear whether existing attacks on SAM can be directly transferred to SAM2. In this paper, we first analyze the performance gap of existing attacks between SAM and SAM2 and highlight two key challenges arising from their architectural differences: directional guidance from the prompt and semantic entanglement across consecutive frames. To address these issues, we propose UAP-SAM2, the first cross-prompt universal adversarial attack against SAM2 driven by dual semantic deviation. For cross-prompt transferability, we begin by designing a target-scanning strategy that divides each frame into k regions, each randomly assigned a prompt, to reduce prompt dependency during optimization. For effectiveness, we design a dual semantic deviation framework that optimizes a UAP by distorting the semantics within the current frame and disrupting the semantic consistency across consecutive frames. Extensive experiments on six datasets across two segmentation tasks demonstrate the effectiveness of the proposed method for SAM2. The comparative results show that UAP-SAM2 significantly outperforms state-of-the-art (SOTA) attacks by a large margin.

Skewed histogram shifting (SHS) is an efficient scheme in reversible data hiding (RDH) research. By employing a pair of symmetric predictors which averages part of sorted pixels around the to-be-predicted pixel, two skewed histograms are generated. With the embedding and shifting directions toward the short tail of the two histograms, SHS reduces many invalid modifications. However, the design of the symmetric predictors pair is strictly constrained, which seriously degrades the performance on both embedding capacity and distortion of this SHS scheme. In this work, we propose a generalized SHS model to remove the weight and symmetry constraints. With the help of differential evolution algorithm, the optimized parameters are obtained in a short period of time, avoiding wasting time using exhaustive search. What is more, adaptive pairwise mapping and embedding bin selection are also realized by adding parameters into the evolutionary process, which greatly improve the embedding performance without increasing too much computational complexity. Experiments demonstrate the superiority of our method by comparing it with state-of-the-art RDH schemes.

Data deduplication technology has gained popularity in modern file systems due to its ability to eliminate redundant writes and improve storage space efficiency. In recent years, the flash-friendly file system (F2FS) has been widely adopted in flash memory-based storage devices, including smartphones, fast-speed servers, and Internet of Things. In this article, we propose F2DFS (deduplication-based F2FS), which introduces three main design contributions. First, F2DFS integrates inline and offline hybrid deduplication. Inline deduplication eliminates redundant writes and enhances flash device endurance, while offline deduplication mitigates the negative I/O performance impact and saves more storage space. Second, F2DFS follows the file system coupling design principle, effectively leveraging the potentials and benefits of both deduplication and native F2FS. Also, with the aid of this principle, F2DFS achieves high-performance and space-efficient incremental deduplication. Third, F2DFS adopts virtual indexing to mitigate deduplication-induced many-to-one mapping updates during the segment cleaning. We conducted comprehensive experimental comparisons between F2DFS, native F2FS, and other state-of-the-art deduplication schemes, using both synthetic and real-world workloads. For inline deduplication, F2DFS outperforms SmartDedup, Dmdedup, and ZFS, in terms of both I/O bandwidth performance and deduplication rates. And for offline deduplication, compared to SmartDedup, XFS, and BtrFS, F2DFS shows higher execution efficiency, lower resource usage, and greater storage space savings. Moreover, F2DFS demonstrates more efficient segment cleanings than native F2FS.

In this article, we consider distributed adaptive finite-time containment control for a class of nonlinear multiagent system with external perturbation which is unknown upper bound under directed graph. Multiagent systems is nonlinear dynamics system of which inherent nonlinearities are designed as higher- and lower-order. First of all, by using information of neighbor, it is possible to construct a second-order finite-time estimator to estimate weighted average of positions and velocities of leaders. Then, a distributed adaptive finite-time containment controller is proposed to make states of followers can converge to convex hull spanned by leaders in finite time. Finally, effectiveness of proposed control algorithm is demonstrated by numerical simulation.

In recent years, the development of blockchain technology has significantly accelerated in many countries. As a fundamental characteristic of blockchain, decentralization merits considerable attention. Nonetheless, a recent trend toward centralization has been noted, in which case the collusion of a few parties could threaten the immutability of blockchains. This paper introduces DeMonitor, a new BigQuery-based monitoring tool for blockchain decentralization. DeMonitor enables multi-dimensional and multi-level measurements of the decentralization of blockchain systems. It facilitates the monitoring and comparison of the degree of decentralization of any BigQuery-supported blockchain system.

The adjacent cross correlation function (ACCF) is an effective tool to remove the range migration (RM) and reduce the order of Doppler frequency migration (DFM) in the pulsed-radar echoes of maneuvering targets, and thus has been employed in radar target detection and imaging. However, the ACCF is a nonlinear transform, which means that the signal-to-noise ratio (SNR) variation will be introduced by this operation. This letter presents a quantitative analysis of SNR for the ACCF. The frequency-domain derivation and analyses of ACCF are presented. Based on this, the SNR variation of the ACCF is analytically derived. The theoretical derivations and analyses are confirmed by numerical examples.