In this letter, a robust autonomous timekeeping scheme for navigation constellations based on inter-satellite links is proposed. In this scheme, the clocks in the constellation are divided into two groups to generate two Ensemble Times (ET) respectively, then taking the two ETs as references to evaluate the frequency stability of each clock. Abnormal clocks can be identified and excluded from the clock group. After Fault Detection and Exclusion (FDE), the particle swarm optimization (PSO) algorithm is introduced to calculate the optimal weights of the two clock groups using the ensembled time of the opposite group as references respectively. Based on the optimized weights, the final Inter-satellite Link Time (ISLT) is obtained. The simulation implements ISL measurement precision and clock performance parameters of 8 BDS-3 satellites. Simulation results show that when the stability of the ensembled time is significantly higher than that of the evaluated clock, the evaluation results are very close to the true values. In the case of one abnormal clock, the long-term stability of the ISLT obtained by the proposed scheme reaches $1.6\times10^{-14}$ at $\tau=1\;\text{day}$, which is obviously better than $2.3\times10^{-14}$ at $\tau=1\;\text{day}$ using default weights. After 30 days of autonomous operation, the detrended clock difference is 7.8 ns. The research results provide a reference scheme for the autonomous timekeeping of navigation constellations.

Wideband inverse synthetic aperture radar imaging of targets with rotating components suffers from severe defocusing due to high-order nonlinear phase modulation and range cell migration (RCM). To address this problem, a wideband subband fusion single-range Doppler interferometry (WSF-SRDI) framework is proposed. The wideband echo is first partitioned into multipe narrowband subbands via a polyphase filter bank. A theoretical constraint relating the subband number to RCM confinement is derived, ensuring that the RCM remains within a single range cell per subband. Within each subband, a SRDI path-integral model is then constructed to simultaneously achieve subband imaging and rotation parameter estimation, followed by a subband fusion process to enhance the overall image quality. Finally, utilizing the estimated parameters, the rotating components are reconstructed and suppressed through least squares estimation, yielding a well-focused image of the stationary main body. Simulation results and contrast analysis demonstrate that the WSF-SRDI maintains robust performance under low signal-to-noise ratio conditions. The proposed strategy provides an efficient solution for ISAR imaging of complex targets with multiple motion components.

Addressing the accuracy and stability issues of target tracking in radar network systems, an improved Fuzzy C-Means (FCM) clustering track fusion algorithm is proposed. The method maps local tracks into a fuzzy sample space and introduces sensor measurement quality factors into the objective function. By integrating Interacting Multiple Model (IMM) filtering, information entropy is employed to quantify the uncertainty and reliability of local data, enabling adaptive non-linear weight allocation. Unlike traditional FCM, this approach effectively identifies and suppresses low-quality measurement sources without requiring precise prior noise statistics. Simulation and experimental results demonstrate that the proposed algorithm significantly enhances fusion precision and global stability, particularly during target maneuvers, providing a robust solution for multi-radar cooperative surveillance.

A compact, broadband, two-way Wilkinson power divider/combiner is presented, created in 0.18-𝜇m CMOS for use between 60 and 90 GHz. The suggested design uses stacked M5–M6 slow-wave trans- mission lines to shorten the physical quarter-wave length without need- ing extra matching capacitors, which allows for a condensed, meander- ing arrangement. Because of the strong broadside coupling between the stacked conductors, the suggested line has a phase constant about 43% greater than the standard structure at 75 GHz. This lets the necessary 90◦ electrical length be achieved with a considerably shorter physical line. Rather than using M1 as a direct shielding ground, a recurring M1 structure is put under the signal lines as an electromagnetic field-control layer, while the actual RF ground reference is made by the conductive ground under the substrate. Furthermore, an RC isolation network offers frequency-shaped odd-mode damping for wideband isolation. A back- to-back setup is used for precise on-wafer characterization. The mea- surements show an extra insertion loss of 0.5 dB at 75 GHz, a return loss exceeding 20 dB at all ports, and an isolation greater than 34 dB from 60 to 90 GHz within a small 0.086 mm2 core area. The suggested structure works well for small mmWave CMOS front-end uses.

Low Earth orbit (LEO) satellite-assisted mobile edge computing (MEC) is a promising solution for supporting computation-intensive and delay-sensitive services in satellite-terrestrial integrated networks (STINs). In this letter, we study a joint task offloading and computing resource allocation problem in LEO satellite MEC systems, aiming to minimize a weighted sum of system energy consumption and execution delay under resource constraints. The formulated problem jointly optimizes user-satellite association, task offloading ratio, and satellite CPU allocation, and is a mixed-integer nonconvex optimization. To efficiently solve it, we develop a low-complexity alternating optimization framework based on block coordinate descent, where each subproblem admits an efficient solution. Simulation results demonstrate that the proposed scheme consistently outperforms baseline schemes in terms of both energy efficiency and latency.

This manuscript presents a 12-bit 100-MS/s nonbinary successive approximation register (SAR)-assisted analog-to-digital converter (ADC) implemented in a 65-nm CMOS process. To achieve an optimal trade-off among conversion speed, power consumption, and chip area, this work proposes key design techniques, including a nonbinary capacitive digital-to-analog converter (CDAC) with multi-reference and Vcm-based switching, a coarse-fine comparator integrated with on-chip offset calibration, and optimized dynamic asynchronous loop (DAL) and dynamic SAR logic (DSL). Measurement results demonstrate that at 100 MS/s, the ADC achieves a peak effective number of bits (ENOB) of 10.11 bits, a peak signal-to-noise and distortion ratio (SNDR) of 62.64 dB, and a Nyquist-Walden figure of merit (FoM) of 21.87 fJ/conversion-step. The core area is merely 0.034 mm², with a total power consumption of 2.4 mW under a 1.0-V supply.

A two-stage learning framework based on a long-range structure-enhanced masked autoencoder is developed for few-shot radar specific emitter identification (SEI). To improve the modeling of long-range fingerprint structures and the discriminative stability of embeddings under limited labeled samples, a self-supervised masked reconstruction task is first constructed for one-dimensional axially integrated bispectrum (AIB) sequences to learn structural priors from unlabeled data. A ResLKA-TS encoder, consisting of a residual backbone, large-kernel attention, and soft-threshold shrinkage, is then employed to enhance the representation of distributed fingerprint structures. In the few-shot fine-tuning stage, center loss is further introduced to improve intra-class compactness and inter-class separability in the embedding space. Experiments on a measured dataset collected from eight ADALM-PLUTO devices of the same model show that, at an SNR of 20 dB, identification accuracies of 88.00\% and 91.87\% are achieved under 10-shot and 15-shot settings, respectively, exceeding those of asymmetric masked autoencoder (AMAE) by 5.12 and 4.69 percentage points. The method also maintains advantages under low-shot and different SNR conditions.

Vision-Language-Action (VLA) models condition robot actions on natural language, yet sensitivity to instruction wording has not been characterised. This letter evaluates OpenVLA-7B on three manipulation tasks, comparing action differences from synonymous rephrasing (e.g., ”put” vs. ”place” vs. ”set”) against differences from specificity variation (brief vs. step-by-step). Across 5 scenes per task with balanced comparisons (n = 50 pairs each), phrasing sensitivity is task-dependent: one task shows significantly larger phrasing than specificity differences (1.6x, p = 0.018), one shows no difference (p = 0.957), and one trends in the opposite direction (p = 0.092). In aggregate, phrasing and specificity produce comparable action differences (p = 0.395). Both exceed the stochastic noise floor by 2-4x. The results indicate that VLA instruction sensitivity is real but task-specific, and that deployment robustness cannot be assumed from single-task evaluation.

This paper presents a fully-integrated frequency-modulated continuous-wave (FMCW) transceiver chip operating from 55 to 66 GHz, fabricated in a 65 nm silicon-on-insulator (SOI) CMOS process. The chip incorporates a wide-tuning-range voltage-controlled oscillator (VCO) with four digitally selectable states, a driver amplifier, a power amplifier, a low-noise amplifier, and passive mixers, providing a compact solution for high-resolution millimeter-wave radar and short-range communication systems. An innovative hybrid digital-analog VCO tuning architecture, combined with co-optimized transmit/receive front-end design, enables robust noise and linearity performance within a small core area. Measurement results demonstrate a transmitter saturated output power of 12 dBm and an output return loss better than 8 dB across the band. The receiver achieves 10 dB of gain, a 9.8 dB noise figure, and an input 1‑dB compression point of ‑18 dBm. The integrated VCO exhibits a phase noise better than –87.71 dBc/Hz at 1 MHz offset.

Maglev train has no contact with rail in high-speed operation, which is significantly different from traditional wheel-rail train. A crucial issue of the lightning overvoltage on maglev train is grounding characteristics, however, which has not been fully and thoroughly investigated. In this letter, the multi-path grounding of maglev train is analyzed. A new and advantageous circuit model for researching the grounding characteristics and impacts is demonstrated. And, an experiment of lightning injection has been designed to validate the proposed model.

This paper presents a K-band high-linearity power amplifier (PA) implemented in a 0.7-μm InP DHBT process. Operating from 17.7 to 21.2 GHz, the PA employs a cascode topology with an adaptive mixed-bias network (current mirror and resistive divider) to stabilize the operating point and enhance linearity. The unit-cell PA achieves 19-20 dBm output power, 54% PAE, 22.5-26.1 dB gain, and VSWR <1.3. At 3-dB back-off, it exhibits 39% efficiency and IMD3 < -37 dBc. A four-cell combined PA delivers 23-23.5 dBm output power, 44-47% PAE, 20.1-21.5 dB gain, and VSWR <1.5, with IMD3 < -37 dBc at 3-dB back-off. The design offers an excellent efficiency-linearity trade-off, making it suitable for 5G/6G and satellite communication systems.

reprint, amsmath,amssymb, aps, ]revtex4-2 We propose two new families of multidimensional periodic arrays for digital video watermarking. Starting from a Sidelnikov sequence x of length pl-1 having n-1 coprime factors mi we construct a base array by folding x into an (n-1)-dimensional periodic array. The two families are then obtained by composing the base array with a shift sequences defined by applying logarithmic quadratic and logarithmic fractional functions to a generation of the direct products of the additive groups Zmi. We show that both families exhibit peak auto-correlation on the order of p2l, while non-peak auto-correlation and cross-correlation are on the order of pl for l ≥ n. Consequently, the proposed constructions achieve superior correlation ratios compared to existing methods and attain optimal low cross-correlation values with respect to the Welch bound.

[1]¿p#1 Performance degradation attacks selectively and stealthily reduce service quality, seeking to avoid abrupt interruptions that facilitate detection. In Software-Defined Networking (SDN), the centralization of the control plane and the programmability of the data plane increase the attack surface, especially via controller management interfaces (e.g., REST APIs). This letter investigates an application-oriented attack based on intermittent injection of drop rules via the ONOS controller’s REST API. Unlike denial of service by saturation, this approach preserves global connectivity and keeps conventional metrics (such as ICMP RTT) apparently stable. Experiments with Mininet and ONOS show that the perceived availability of an HTTP service can be significantly degraded even without obvious signs of infrastructure failure. The results highlight the stealthiness of the attack and the limitations of connectivity-based monitoring alone.

In this letter, we consider any complex network that has the following properties: 1) packet arrivals are Poisson; 2) packet sizes are hyper-Erlang distributed; 3) packets are routed based on a fixed routing strategy; 4) packet sizes remain unchanged when they traverse from node to node; 5) there are a large number of packet flows, which ensure sufficient traffic mixing. We then develop a simple approximation model for the complementary cumulative density function (CCDF) of the end-to-end delay. By using simulation, the approximated CCDF from our model is shown to be in good agreement with the sample CCDF from simulation results and always gives a less-than-0.01 Kolmogorov-Smirnov distance.

Fully polarimetric range-Doppler (RD) radar images, while rich in polarimetric scattering information, often yield suboptimal feature representations when directly used as input to detection networks, due to inherent coherent speckle noise and inter-channel information redundancy. To address this limitation, this paper proposes a data fusion method based on Pauli decomposition to integrate polarimetric features. This approach establishes a mapping mechanism from physical scattering attributes to visual semantic features. Specifically, the multi-polarimetric RD data are decomposed via Pauli decomposition into surface, volume, and dihedral scattering components, which are then mapped into a high-contrast three-channel RGB image. Simulation results indicate that, compared with conventional fully polarimetric imagery, the three-channel RGB image achieves a background noise suppression of 23.2 dB, improves the signal-to-noise ratio (SNR) of corner reflectors by 7.9 dB, and enhances the target-to-background contrast by approximately 3.5 times. Further experiments based on the YOLOv8 model demonstrate that the proposed method effectively mitigates feature masking in high clutter-to-signal ratio environments. Both ship targets and corner reflectors exhibit stable improvements of 2–3\% in detection precision (P) and mean average precision at IoU=0.5 (mAP50), confirming the effectiveness and superiority of fusing physical polarimetric features with deep learning-based visual representations.

Travel-time imaging problems seek to reconstruct an image of reflectivity of a scene by measuring travel time (and amplitude, phase) of electromagnetic or acoustic signals. Multistatic, in this context, means that the transmitters and receivers need not be co-located. The reflectivity is anisotropic if it depends on direction, and in the multistatic case this means incoming and outgoing direction. Travel-time problems can be formulated as generalized Radon transforms of integrals over isochrones, in the planar case ellipses with transmitter and receivers at foci. In a simplified case where transmitters and receivers are distant from the scene, isochrones can be approximated by straight lines. We relate this to tensor ray transforms, specifically the longitudinal ray transform of Sharafutdinov, and discuss the implication of its known null-space. In the volumetric case isochrones are spheroids and we relate the problem to the normal Radon transform of tensor fields.

This paper proposes a Zener-based segmented curvature compensation voltage reference. The first-order compensation is implemented by drawing out PTAT current from the specific node of trimming resistor. Then, a segmented curvature compensation based on sequential voltage-comparison temperature segmentation using a single comparator is employed. The segmented curvature compensation current Ic generated by IDAC, which is complementary to the first-order compensated Vref . Temperature segmentation with a single comparator avoid the mismatch among multiple comparators, thereby improving the curvature compensation and temperature coefficient(TC). The proposed segmented curvature compensation scheme is applicable to multi-level voltage references. The circuit is designed with a 0.18 µm BCD process. Post-simulation results confirm the validity of these designs of Vref =1.2 V@VDD=1.8 V and Vref =3.3 V@VDD=5 V, achieving TC of 0.73-1.26 ppm/°C and 0.66-1.21 ppm/°C over the range of -40 °C to 125 °C respectively.

Recent deep learning-based RIS phase control models achieve competitive performance, but their scalability is constrained by rapidly increasing computational complexity as the number of reflective elements grows. We propose RISnet-LiteMix, a lightweight MLP-Mixer-based RIS phase control model that captures inter-user interactions with significantly lower computational complexity. In particular, the LiteMixer block in RISnet-LiteMix applies dimensional compression or expansion across different perspectives, thereby reducing computational burden while enabling effective learning of channel representations. Consequently, RISnet-LiteMix reduces the number of parameters by 47% and computational complexity by 70% relative to existing MLP-Mixer-based models, without compromising transmission performance.