Search Results (1,044)

Fractional memory can substantially alter the period-doubling route to chaos, but the joint effects of fractional order q and memory length M on successive bifurcations in the finite-memory Grünwald–Letnikov (GL) fractional logistic map remain unclear. We formulate the period-doubling bifurcations of its exact periodic orbits as an exact Floquet problem. An algebraically equivalent periodic–antiperiodic phase reduction lowers the nonlinear system from $mM+M+1$ to $2m+1$ unknowns, making very long memory lengths computationally accessible. The reduced formulation reproduces an independently derived fixed-point bifurcation boundary with a maximum absolute discrepancy of $2.67\times {10}^{-15}$ and is further confirmed for successive doublings of period-4, period-8, and period-16 orbits. We compute the first seven cascade levels for orbit periods $m=1,2,4,8,16,32,64$ and memory lengths up to $M=2\times {10}^6$. Increasing M shifts these bifurcation points toward smaller values of the bifurcation parameter, with markedly stronger shifts at lower q. The dependence on q is nonmonotone: decreasing q first shifts the bifurcations toward smaller values and then reverses this trend. For $m=1,8,32$, the turnover locations move from $q\approx 0.46–0.49$ at $M=100$ to $q\approx 0.24–0.25$ at $M=2\times {10}^6$. The $O\left(M^{-q}\right)$ decay of the GL memory tail explains the stronger memory-length effect at low q, while the positive near-classical sensitivities and the fixed-memory $q\to 0^{+}$ limit explain the turnover. These results provide an exact and scalable framework for quantifying how finite fractional memory reorganizes the period-doubling cascade. Full article

Low Earth orbit (LEO) satellite downlinks broadcast over wide ground footprints, exposing Earth-observation and remote-sensing sensor data to passive eavesdropping. Affine frequency division multiplexing (AFDM) is a candidate waveform for the doubly dispersive LEO channel and a natural integrated sensing and communication (ISAC) waveform whose delay–Doppler structure supports target parameter estimation; yet existing secure-AFDM schemes act only in the discrete affine Fourier transform (DAFT) parameter domain, leaving the transmitted waveform structurally recognizable. To address this gap, this paper applies time-domain waveform obfuscation to AFDM as physical layer encryption. Using a secret key, the transmitter permutes the inverse-DAFT samples and applies a phase rotation before chirp-periodic-prefix generation; the mask is unitary, so the peak-to-average power ratio is preserved exactly, and the key-holding receiver retains AFDM’s full delay–Doppler sensing capability, while a no-key receiver obtains a dense composite response that destroys target localization (sensing concentration drops from 0 dB to −16.6 dB). Secret pilot phases enable channel estimation at the legitimate receiver while blocking a naive composite-channel attack. Simulations at $N=64$ and 128 show that a wrong-key eavesdropper achieves uncoded BER within $0.01$ of $0.5$ across 0–20 dB and that blind Viterbi–Viterbi phase recovery is no more effective under QPSK (BER $0.46$–$0.48$), while the legitimate SNR penalty stays below $0.5$ dB. The mask also suppresses AFDM’s internal structure to the AWGN level under AFDM-aware processing. Time-domain obfuscation offers a complementary physical-layer security layer for confidential LEO remote-sensing data downlink and ISAC waveforms. Full article

This paper studies codimension-one transverse bifurcations of several classes of simple robust homoclinic cycles in group-symmetric systems on ${\mathbb{R}}^5$. The analysis follows a complete classification induced by the representation of the symmetric group. Sufficient conditions are established under which new homoclinic cycles or heteroclinic connections bifurcate from the original cycle. An asymptotic expansion of the Poincaré map is derived, yielding explicit criteria for the bifurcation of periodic orbits from such homoclinic cycles and for determining their stability. Full article

Simple memristive circuits are attractive chaos sources for secure communication, random-number generation, and chaotic sensing. Reliable operation, however, requires knowing where in parameter space the chaos persists and where periodic windows or coexisting attractors make it fragile. This paper constructs an operating map for an improved memristive diode-bridge band-pass-filter circuit in its two accessible tuning parameters, set by the filter capacitance and the feedback resistance ratio. Two-parameter bifurcation maps and Lyapunov exponent fields give the global layout of the plane; numerical continuation traces the stable and unstable periodic-orbit branches that organise it; and phase portraits, power spectra, component-level SPICE simulation, and prototype measurements characterise representative regimes. The plane separates into broad chaotic regions, higher-period windows, a period-adding window, and a multistability zone. A chaotic interval is reported as robust chaos only when the tracked periodic branches are all unstable, the Lyapunov exponent is positive throughout, and initial-condition scans detect no coexisting attractor; the continuation shows the same period-doubling mechanism bounding the chaotic regions in both parameters. Mapping the dimensionless results to component values yields design guidance for reaching candidate robust-chaos regions and avoiding fragile and multistable zones, including a hardware-confirmed limit on inductor loss beyond which the chaotic band collapses. Full article

Software-defined satellite networks (SDSNs) enhance resource utilization and flexibility in space-based networks by leveraging a global view and programmability. A highly reliable control plane is essential to sustain network operations. However, the highly dynamic topology and physical failures in Low Earth Orbit (LEO) environments can cause satellite node outages or inter-satellite link disruptions, leading to control plane interruptions and local load imbalances. To address this, we propose a switch migration mechanism for failure recovery and establish a multi-objective migration model that jointly optimizes control link delay, controller load variance, and normalized migration ratio. To accommodate distinct dynamic characteristics such as frequent topology changes, failure-intensive periods, and stable periods, we design two algorithms: a robust migration algorithm, DNSGA-II, which features population diversity maintenance and environmental awareness, and an efficient migration algorithm, IHAOAVOA, which integrates strong global exploration with powerful local exploitation. Simulation results show that IHAOAVOA rapidly converges under large-scale failures, achieving millisecond-level delay recovery and low normalized migration ratio overhead during failure-intensive periods, while DNSGA-II focuses on long-term load balancing and system stability during stable periods, effectively suppressing localized controller overload. By adopting IHAOAVOA during topology fluctuations or high-failure phases to reduce delay, and switching to DNSGA-II during stable phases to optimize load distribution, the overall network robustness can be improved under the evaluated failure scenarios. This work provides effective support for achieving highly reliable control in SDSNs under failure scenarios. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Satellite battery subsystems are fundamental for reliable operation during periods when solar power is unavailable. Their performance is strongly influenced by the harsh and variable thermal conditions of the space environment. Pre-launch thermal simulations are routinely employed to evaluate subsystem behaviour; however, their ability to fully represent in-orbit dynamics remains limited. Meanwhile, machine learning (ML) approaches have emerged for satellite health monitoring, yet their practical role alongside conventional thermal analysis is not clearly established. This study investigates satellite battery temperature prediction by integrating pre-launch thermal simulation, real in-orbit telemetry, and data-driven ML forecasting within a unified framework. A dataset of 27,552 temperature measurements from an operational low-Earth-orbit satellite was analysed. A sliding-window regression approach was used to predict one-hour-ahead minimum and maximum battery temperatures, consistent with operational thermal margins. Three models—linear regression, Random Forest, and Extreme Gradient Boosting—were trained using historical temperature data and evaluated against telemetry and simulation outputs using MAE, RMSE, and R² metrics. Results indicate that linear regression achieved the highest accuracy (R² up to 0.98, MAE ≈ 0.20 °C), outperforming more complex models. ML-based predictions captured thermal behaviour more effectively than static simulation outputs under nominal conditions, while all predicted values remained within the acceptable operational range (10–30 °C). Rather than replacing physics-based methods, this work demonstrates that interpretable ML models can serve as an effective real-time complement to thermal simulations, offering practical insights into model selection and enhancing satellite battery thermal monitoring. Full article

This research examines the behavioral shifts within a discrete-time predator–prey framework, constructed by applying the forward Euler discretization to a continuous model. The system incorporates Smith’s growth dynamics for the prey population alongside a Holling type III functional response to characterize predator behavior. Through bifurcation analysis, it is demonstrated that the interior fixed point undergoes stability loss via Neimark–Sacker and period-doubling transitions, leading to the emergence of quasiperiodic oscillations and chaos. Furthermore, the application of normal-form theory verifies the nondegeneracy of these bifurcations and establishes the direction of the resulting orbits. We use phase portraits, Lyapunov exponents, and bifurcation diagrams to confirm the model’s rich dynamics. These numerical tools demonstrate how the system moves from stable equilibria to more intricate behaviors. The application of partial rank correlation coefficients reveals the most influential parameters governing the system’s asymptotic population levels, providing a global perspective on parameter sensitivity. The Ott–Grebogi–Yorke (OGY) chaos control strategy is employed to suppress unwanted bifurcations and stabilize chaotic oscillations within the system. These results underscore the role of nonlinear interactions and discrete-time frameworks in precipitating unpredictable population fluctuations while simultaneously offering a suite of mechanisms for enhancing the stability of ecological networks. Full article

Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) are emerging as a promising connectivity solution for Internet of Things (IoT) sensing applications deployed in remote, isolated, or infrastructure-limited environments. However, sparse LEO constellations inherently lead to intermittent connectivity, long service gaps, and frequent disruptions, challenging conventional 5G architectures that assume continuous end-to-end availability. This paper presents and experimentally validates a distributed 5G Core architecture enhanced with Store-and-Forward (S&F) capabilities to enable reliable delivery of IoT sensing data over intermittently connected LEO-NTN scenarios. The proposed architecture distributes selected 5G Core functions between ground and satellite nodes and introduces an S&F module capable of locally buffering uplink IoT data during periods without feeder-link connectivity and forwarding them once the ground connection is restored. A functional prototype is implemented using Open5GS, UERANSIM, and an emulated satellite node, and experimentally evaluated under representative intermittent-connectivity conditions. The results demonstrate that the proposed architecture successfully preserves and delivers IoT sensing data across temporary link disruptions. The experimental findings confirm the feasibility of integrating S&F mechanisms into distributed 5G Core architectures and provide practical insights into the design of resilient IoT sensing services over sparse LEO constellations. Full article

To address the pressure of space target census posed by low-orbit mega-constellations such as Starlink, this paper proposes an LEO target census constellation design method based on a layered NSGA-II algorithm. Six decision variables—orbital altitude, inclination, number of orbital planes, number of satellites per plane, sensor field of view, and focal length—are considered. Coverage rate, revisit period, and constellation cost are taken as optimization objectives to establish a multi-objective optimization model. To overcome the issues of slow convergence and susceptibility to local optima in high-dimensional decision spaces faced by classical multi-objective optimization algorithms, the decision variables are divided into the orbit layer, configuration layer, and sensor layer. NSGA-II evolution is performed layer by layer, with elite retention and global archive passing of high-quality solutions. Using high-precision 24-h ephemeris of 496 Starlink satellites generated by STK as the simulation object, the proposed layered NSGA-II is compared with classical NSGA-II, MOPSO, MOEA/D, and SPEA2. Results show that the layered NSGA-II achieves a hypervolume (HV) of 13.16, outperforming the other algorithms. Under the condition of maintaining 99.6% coverage, the recommended constellation solution achieves a revisit period as low as 7.6 h and a constellation cost of 0.429, demonstrating significantly better comprehensive performance than other algorithms. Convergence speed is improved by approximately 36% compared to classical NSGA-II. This method provides an efficient and engineering-applicable optimization approach for LEO target census constellation design. Full article

Faced with the complex space electromagnetic environment, the long solar illumination periods of medium-Earth-orbit (MEO) satellites, and the unique operating limitation under which the batteries operate under shallow charge–discharge cycles, this paper proposes a dedicated life evaluation method suitable for MEO satellite batteries. The proposed method quantifies battery performance degradation by analyzing the statistical correlation between the discharge capacity and discharge cut-off voltage during eclipse phases. Using seven years of on-orbit-measured data covering 14 eclipse seasons, we build prediction models for the cut-off voltage via Random Forest and XGBoost, with the eclipse season index and discharge capacity set as model input features. The mean squared error (MSE) of both models is lower than 0.003, demonstrating the high reliability of the modeling framework. Under the condition of a fixed discharge capacity, no obvious battery degradation exceeding the measurement uncertainty is detected within the observation period. The magnitude of potential voltage degradation is comparable to or even smaller than the 2 mV acquisition uncertainty, such that no measurable degradation phenomenon can be identified in the available dataset. At present, long-term on-orbit-measured data of MEO satellite batteries are scarce in engineering research. This study provides valuable full-measurement data support for the optimized design, autonomous on-orbit management, and life evaluation of NCA lithium-ion batteries and fills the gap in long-term on-orbit datasets for MEO satellite power systems. Full article

Traditional Low Earth Orbit (LEO) satellite constellation design methods, primarily driven by geometric coverage, fail to satisfy the non-uniform and dynamic tracking requirements of moving targets. To address this, a multi-objective optimization framework for large-scale satellite constellations is proposed. This framework is task-driven, constraint-guided, and integrates space and ground segments. A quantitative model is established to characterize the multi-target tracking capability of space-based sensing systems. The model explicitly links the constellation revisit period, payload detection and positioning performance, target maneuverability, and the maximum trackable target density. These relationships are then formulated as optimization objectives and constraints. To capture temporal consistency in observation performance, the coefficient of variation of revisit time is introduced as an independent optimization objective. This yields a three-objective optimization problem that addresses tracking performance, coverage uniformity, and system cost, enabling Pareto-optimal constellation design solutions. Simulation results demonstrate that the proposed method improves track association performance in representative maritime target tracking scenarios when compared with conventional coverage-driven constellation designs. The proposed framework provides a systematic and implementable approach for constellation design by integrating capability modeling with multi-objective optimization at the system level. Full article

Numerous systems in electrical engineering, biology, and mechanical structures can be modeled using dynamical systems theory. This paper examines the behavior of a 3D dynamical system with six parameters, specifically its damped or periodic oscillations and asymptotic properties as functions of six physical parameters. The system is integrated explicitly through a smooth solution of a third order nonlinear differential equation, yielding exact parametric expressions that describe a heteroclinic orbit. To analyze parameter influence, we apply the Multistage Optimal Homotopy Perturbation Method (MOHPM). Its main advantage is the small number of iterations required, due to the effective choice of auxiliary convergence control functions. The MOHPM solutions agree closely with numerical results, demonstrated qualitatively through figures and quantitatively through tables. Accuracy is further assessed by comparison with the Optimal Homotopy Perturbation Method (OHPM). A qualitative analysis of errors is also provided. Full article

The Collatz map is a simple deterministic transformation whose orbit structure remains highly nontrivial. A recent direction-phase decomposition partitions each orbit into upward and downward steps, and numerical observations indicate that the number of upward phases, $N_{\uparrow }$, follows an approximate Gamma distribution. In this work, we provide a mechanistic explanation for this statistical regularity by modeling the occurrence of upward phases in the odd-compressed, or Syracuse, version of the Collatz map as a homogeneous Poisson process. From the mean-field logarithmic balance and the geometric distribution of 2-adic valuations, we derive closed-form expressions for the Gamma parameters: the scale parameter $\theta =2/{(2-{\log }_{2}3)}^2\approx 11.61$ is constant, whereas the shape parameter K grows logarithmically with the maximal initial value $X_0=2L+1$. We also analyze the closure conditions for periodic orbits, showing that nontrivial cycles are severely constrained, which supports the plausibility of the statistical framework. Numerical validation for L ranging from ${10}^5$ to ${10}^{15}$ confirms the theory with relative errors below $3\%$, and a bias-corrected mean estimate reduces the error to ${10}^{-3}$–${10}^{-2}\%$. These results establish a quantitative link between the arithmetic properties of the Collatz map and Gamma-type statistics, and suggest possible extensions to generalized Collatz-type problems. Full article

Monitoring ground surface movements is crucial for slope failure hazard assessment. A total of 168 Sentinel-1A SAR ascending orbit datasets were analyzed (January 2018 to May 2024) to identify slope failure-susceptible locations in the Saguenay–Lac-Saint-Jean (SLSJ) region in Quebec, Canada, using persistent scatterer interferometric synthetic aperture radar (PSInSAR). Validation against four regional Global Navigation Satellite System (GNSS) stations shows strong agreement, with correlation coefficients (r > 0.9) across stations and root-mean-square error (RMSE) values of 10.8 and 12.7 mm between the PSInSAR and GNSS cumulative displacement time series over the six-year period. Annual Line-of-Sight (LOS) surface displacement rates generally ranged from 0 to 20 mm/yr, and the median absolute deviation (MAD) method was subsequently applied to the PSInSAR time series to identify anomalous persistent scatterer (PS) points within PS-detectable areas as candidate displacement signals. Integrating them with historical slope failure records (HSFRs) highlights partial spatial correspondence with the official landslide susceptibility map (LSM). Results show that the official LSM covers about 45% of HSFRs and 50% of anomalous PS points, with the remainder located outside its mapped boundaries. Conversely, our approach achieved over 80% overlap between anomalous PS points and HSFRs within areas where PS observations are available, identifying candidate slope failure-susceptible areas beyond those currently mapped by the official LSM, thereby warranting further investigations. This is exemplified by a 2022 landslide detected beyond the LSM boundaries. The proposed approach could serve as a complementary framework for mapping slope failure-susceptible locations within the study region. Full article