Search Results (5,314)

The deterioration of rocks’ mechanical properties during the late stage of water injection development significantly reduces the rock-breaking efficiency of PDC bits. In this study, X-ray diffraction mineral composition analysis and triaxial compression mechanics tests were used to systematically characterize the weakening mechanism of water injection on reservoir rocks. Based on an analysis of mechanical experimental characteristics, this study proposes a multi-scale collaborative optimization method: establish a single tooth–rock interaction model at the micro-scale through finite element simulation to optimize geometric cutting parameters; at the macro scale, adopt a differential bit design scheme. By comparing and analyzing the rock-breaking energy consumption characteristics of four-blade and five-blade bits, the most efficient rock-breaking configuration can be optimized. Based on Fluent simulation on the flow field scale, the nozzle configuration can be optimized to improve the bottom hole flow field. The research results provide important theoretical guidance and technical support for the personalized design of drill bits in the later stage of water injection development. Full article

Scalar multiplication is the basis of the widespread elliptic curve public key cryptography. Standard scalar multiplication is vulnerable to side-channel attacks that are able to infer the secret bit values by observing the power or delay traces. This work utilizes the arithmetic properties of scalar multiplication to propose two scalar multiplication algorithms to insulate ECC implementations from side-channel attacks. The two proposed designs rely on randomly permuting the ordering and storage locations of the different scalar multiplication values $2^{i}G$ as well as the corresponding secret key bits $k_i$. Statistical analysis and Python 3.9.13implementations confirm the validity of the two algorithms. Numerical results confirm that both designs produce the same results as the standard right-to-left scalar multiplication algorithm. Welch’s t-test as well as numerical simulations confirm the immunity of our proposed protocols to side-channel attacks. Full article

This work extends our previous research on spectral clustering for mitigating nonlinear phase noise in optical communication systems by presenting the first complete FPGA implementation of the algorithm, including on-chip eigenvector computation with parallelization strategies. The implementation addresses the computational complexity challenges of spectral clustering through a heterogeneous CPU/FPGA co-design approach that partitions algorithmic stages between ARM processors and the FPGA fabric. While the achieved processing speeds of approximately 36 symbols per second do not yet meet the requirements for commercial optical transceivers, our hardware prototype demonstrates the feasibility and practical challenges of deploying advanced clustering algorithms on real-time hardware architectures. We detail the parallel Jacobi method for eigenvector computation, the Greedy K-means++ initialization strategy, and the comprehensive hardware mapping of all clustering stages. The system processes streaming m-QAM data through a windowed architecture and integrates a demapper to ensure label consistency, demonstrating improved bit error rate performance compared to K-means under severe phase noise conditions of −90 dBc/Hz at a 1 MHz offset. This implementation offers valuable insights into memory bandwidth limitations and resource utilization trade-offs, underscoring the crucial role of FPGAs as a bridge between algorithm development and high-speed optical system deployment. Full article

Underwater wireless optical communication (UWOC) is a key technology in ocean resource development, and its link stability is often limited by the difficulty of optical alignment in complex underwater environments. In response to this difficulty, this study has focused on improving the Real-Time Detection Transformer v2 (RT-DETRv2) model. We have improved the underwater light source detection model by collaboratively designing a lightweight backbone network and deformable convolution, constructing a cross-stage local attention mechanism to reduce the number of network parameters, and introducing geometrically adaptive convolution kernels that dynamically adjust the distribution of sampling points, enhance the representation of spot-deformation features, and improve positioning accuracy under optical interference. To verify the effectiveness of the model, we have constructed an underwater light-emitting diode (LED) light-spot detection dataset containing 11,390 images was constructed, covering a transmission distance of 15–40 m, a ±45° deflection angle, and three different light-intensity conditions (noon, evening, and late night). Experiments show that the improved model achieves an average precision at an intersection-over-union threshold of 0.50 (AP50) value of 97.4% on the test set, which is 12.7% higher than the benchmark model. The UWOC system built based on the improved model achieves zero-bit-error-rate communication within a distance of 30 m after assisted alignment (an initial lateral offset angle of 0°–60°), and the bit-error rate remains stable in the 10⁻⁷–10⁻⁶ range at a distance of 40 m, which is three orders of magnitude lower than the traditional Remotely Operated Vehicle (ROV) underwater optical communication system (a bit-error rate of 10⁻⁶–10⁻³), verifying the strong adaptability of the improved model to complex underwater environments. Full article

We present a digital-to-analog converter (DAC) with full-swing DAC output and a proposed half-power supply calibration technique. To generate a full-swing DAC output, symmetric thermometer decoders and an output selector are implemented to select the appropriate current cell according to the output voltage range. Furthermore, to improve the linearity, we propose a half-power supply calibration circuit consisting of comparators and calibration counters to control the current of the current cells at the half-power supply voltage point, where the voltage mismatch typically occurs. The DAC was fabricated in a 28 nm CMOS process, with a full chip area of 0.95 mm × 0.93 mm. The measurement results demonstrate a maximum voltage mismatch improvement of 95% when using the proposed half-power supply calibration technique, with DNL and INL values of 0.39 and 1.15 LSB. The total power consumption was 73.8 mW at 100 MSPS, with analog and digital supply voltages of 1.8 and 1.0 V, respectively. Full article

Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blocking. The blocking predictors enable proactive defragmentation and resource reallocation within network controllers. In this paper, we propose two novel deep learning models (based on CNN–BiLSTM and CNN–LSTM) to predict blocking in EONs by combining spatial feature extraction from spectrum snapshots using 2D convolutional layers with temporal sequence modeling. This hybrid spatio-temporal design learns how local fragmentation patterns evolve over time, allowing it to detect impending blocking scenarios more accurately than conventional methods. We evaluate our model on the simulated NSFNET topology and compare it against multiple baselines, namely 1D CNN, 2D CNN, k-nearest neighbors (KNN), and support vector machines (SVMs). The results show that the proposed CNN–BiLSTM/LSTM models consistently achieve higher performance. The CNN–BiLSTM model achieved the highest accuracy in blocking prediction, while the CNN–LSTM model shows slightly lower accuracy; however, it has much lower complexity and a faster learning time. Full article

The paper presents experimental results for a modified pulsed plasma thruster (PPT) with solid propellant, using a coaxial anode–cathode design. Graphite from pencil leads served as propellant, and a tungsten trigger electrode was tested to reduce carbonization effects. Experiments were performed in a vacuum chamber at 0.001 Pa, employing diagnostics such as discharge current/voltage recording, power measurement, ballistic pendulum, time-of-flight (TOF) method, and a Faraday cup. Current and voltage waveforms matched an oscillatory RLC circuit with variable plasma channel resistance. Key discharge parameters were measured, including current pulse duration/amplitude and plasma channel formation/decay dynamics. Impulse bit values, obtained with a ballistic pendulum, reached up to 8.5 μN·s. Increasing trigger capacitor capacitance reduced thrust due to unstable “pre-plasma” formation and partial pre-discharge energy loss. Using TOF and Faraday cup diagnostics, plasma front velocity, ion current amplitude, current density, and ion concentration were determined. Tungsten electrodes produced lower charged particle concentrations than graphite but offered better adhesion resistance, minimal carbonization, and stable long-term performance. The findings support optimizing trigger electrode materials and PPT operating modes to extend lifetime and stabilize thrust output. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

This paper presents an approach to error correction in wireless communication systems, with a focus on the Bluetooth Low Energy standard. Our method uses the redundancy provided by the cyclic redundancy check and leaves the transmitter unchanged. The approach has two components: an error-detection algorithm that validates data packets and a neural network with reject option that classifies signals received from the channel and identifies bit errors for later correction. This design localizes and corrects errors and reduces transmission failures. Extensive simulations were conducted, and the results demonstrated promising performance. The method achieved correction rates of 94–98% for single-bit errors and 54–68% for double-bit errors, which reduced the need for packet retransmissions and lowered the risk of data loss. When applied to images, the approach enhanced visual quality compared with baseline methods. In particular, we observed improvements in visual quality for signal-to-noise ratios between 9 and 11 dB. In many cases, these enhancements were sufficient to restore the integrity of corrupted images. Full article

The interlaminar drilling of CFRPs is a new machining method different from traditional drilling, in which the feed direction of the drill bit is parallel to the interlayer interface. To reasonably select tools for CFRP interlaminar drilling, four different types of tool structures, including twist drills, dagger drills, candlestick drills, and step drills, are employed to conduct interlaminar drilling. The axial force and the morphologies of material damage are extracted, the comprehensive damage factors are calculated, and the relation among tool structures, machining parameters, and outlet damage is analyzed. Results show that the peak axial force induced by the four types of tool structures reduces sequentially. The dagger drill and the candlestick drill tend to cause burrs and large-area surface tears, respectively, while the twist drill and the step drill will lead to more significant 3D tears. Among the four tools, the average comprehensive damage factor produced by twist drills is the smallest, making it more suitable for CFRP interlaminar drilling. In addition, this study establishes a mathematical prediction model for the peak axial force and the comprehensive damage factor and optimizes the process parameter combination of twist drills, with the spindle speed set to 4732.87 r/min and the feed speed to 0.137 mm/r. Full article

The completeness and integrity of multimodal medical data are critical determinants of surgical success and postoperative recovery. However, because of issues such as poor sensor contact, small vibrations, and device discrepancies during signal acquisition, there are frequent missing values in patients’ medical data. This issue is especially prominent in rare or complex cases, where the inherent complexity and sparsity of multimodal data limit dataset diversity and degrade predictive model performance. As a result, clinicians’ understanding of patient conditions is restricted, and the development of robust algorithms to predict preoperative, intraoperative, and postoperative disease progression is hindered. To address these challenges, we propose Med-Diffusion, a diffusion-based generative framework designed to enhance sensor data by imputing missing multimodal clinical data, including both categorical and numerical variables. The framework integrates one-hot encoding, simulated bit encoding, and feature tokenization to improve adaptability to heterogeneous data types, utilizing conditional diffusion modeling for accurate data completion. Med-Diffusion effectively learns the underlying distributions of multimodal datasets, synthesizing plausible data for incomplete records, and it mitigates the data sparsity caused by poor sensor contact, vibrations, and device discrepancies. Extensive experiments demonstrate that Med-Diffusion accurately reconstructs missing multimodal clinical information and significantly enhances the performance of downstream predictive models. Full article

Mathematical optimization, in both continuous and discrete forms, is well established and widely applied. This work addresses a gap in the literature by focusing on large-number optimization, where integers or fractions with hundreds of digits occur in decision variables, objective functions, or constraints. Such problems challenge standard optimization tools, particularly when exact solutions are required. The suitability of computer algebra systems and high-precision arithmetic software for large-number optimization problems is discussed. Our first contribution is the development of Python implementations of an exact Simplex algorithm and a Branch-and-Bound algorithm for integer linear programming, capable of handling arbitrarily large integers. To test these implementations for correctness, analytic optimal solutions for nine specifically constructed linear, integer linear, and quadratic mixed-integer programming problems are derived. These examples are used to test and verify the developed software and can also serve as benchmarks for future research in large-number optimization. The second contribution concerns constructing partially increasing subsequences of the Collatz sequence. Motivated by this example, we quickly encountered the limits of commercial mixed-integer solvers and instead solved Diophantine equations or applied modular arithmetic techniques to obtain partial Collatz sequences. For any given number J, we obtain a sequence that begins at $2^J-1$ and repeats J times the pattern ud: multiply by $3x_j+1$ and then divide by 2. Further partially decreasing sequences are designed, which follow the pattern of multiplying by $3x_j+1$ and then dividing by $2^m$. The most general J-times increasing patterns (ududd, udududd, …, ududududddd) are constructed using analytic and semi-analytic methods that exploit modular arithmetic in combination with optimization techniques. Full article

Fractional-order chaotic systems have emerged as powerful tools in secure communications and multimedia protection owing to their memory-dependent dynamics, large key spaces, and high sensitivity to initial conditions. However, most existing fractional-order image encryption schemes rely on fixed-order chaos and conventional solvers, which limit their complexity and reduce unpredictability, while also neglecting the potential of variable fractional-order (VFO) dynamics. Although similar phenomena have been reported in some fractional-order systems, the coexistence of hidden attractors and stable equilibria has not been extensively investigated within VFO frameworks. To address these gaps, this paper introduces a novel discrete variable fractional-order dark matter–dark energy (VFODM-DE) chaotic system. The system is discretized using the piecewise constant argument discretization (PWCAD) method, enabling chaos to emerge at significantly lower fractional orders than previously reported. A comprehensive dynamic analysis is performed, revealing rich behaviors such as multistability, symmetry properties, and hidden attractors coexisting with stable equilibria. Leveraging these enhanced chaotic features, a pseudorandom number generator (PRNG) is constructed from the VFODM-DE system and applied to grayscale image encryption through permutation–diffusion operations. Security evaluations demonstrate that the proposed scheme offers a substantially large key space (approximately $2^{249}$) and exceptional key sensitivity. The scheme generates ciphertexts with nearly uniform histograms, extremely low pixel correlation coefficients (less than 0.04), and high information entropy values (close to 8 bits). Moreover, it demonstrates strong resilience against differential attacks, achieving average NPCR and UACI values of about 99.6% and 33.46%, respectively, while maintaining robustness under data loss conditions. In addition, the proposed framework achieves a high encryption throughput, reaching an average speed of 647.56 Mbps. These results confirm that combining VFO dynamics with PWCAD enriches the chaotic complexity and provides a powerful framework for developing efficient and robust chaos-based image encryption algorithms. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

Building change detection involves identifying areas where buildings have changed by comparing multi-temporal remote sensing imagery of the same geographical region. Recent advances in Transformer-based methods have significantly improved remote sensing change detection. However, current Transformer models still exhibit persistent limitations in effectively extracting multi-scale semantic features within complex scenarios. To more effectively extract multi-scale semantic features in complex scenes, we propose a novel model, which is the Transformer-based Multi-Scale Semantic Extraction Change Detection Network (MSSE-CDNet). The model employs a Siamese network architecture to enable precise change recognition. MSSE-CDNet comprises four parts, which together contain five modules: (1) a CNN feature extraction module, (2) a multi-scale semantic extraction module, (3) a Transformer encoder and decoder module, and (4) a prediction module. Comprehensive experiments on the standard LEVIR-CD benchmark for building change detection demonstrate our approach’s superiority over state-of-the-art methods. Compared to existing models such as FC-Siam-Di, FC-Siam-Conc, DTCTSCN, BIT, and SNUNet, MSSE-CDNet achieves significant and consistent gains in performance metrics, with F1 scores improved by 4.22%, 6.84%, 2.86%, 1.22%, and 2.37%, respectively, and Intersection over Union (IoU) improved by 6.78%, 10.74%, 4.65%, 2.02%, and 3.87%, respectively. These results robustly substantiate the effectiveness of our framework on an established benchmark dataset. Full article