Search Results (447)

Surface roughness is a key tribological property commonly characterized by the power spectral density (PSD) of surface topography. However, the recent Surface Topography Challenge demonstrated that measurements of identical surfaces may yield PSD curves differing by several orders of magnitude depending on the laboratory and measurement method. Such discrepancies can arise from measurement artifacts, including spike-like outliers and macroscopic surface curvature. In this work, we analyze these effects and propose a correction procedure for recovering the intrinsic roughness spectrum. The method combines nonlinear median filtering for artifact detection with robust PSD reconstruction based on multiple one-dimensional surface sections. Outliers are removed in real space, the macroscopic shape is eliminated by detrending, and the PSD is obtained as the median of spectra from individual line scans. Tests on synthetic surfaces with known roughness spectra contaminated by curvature and artificial spikes demonstrate that the method reliably recovers the original spectrum even when artifacts dominate the raw data. Application to experimentally measured surfaces further indicates that some apparent roughness features may originate from measurement noise and stitching artifacts rather than the true surface structure. Full article

We present a reproducible pipeline that converts color images into quantitative fluorescence maps by combining spectral measurement with a linear mixture model. The method is designed specifically for quantitative comparisons of Glo Germ™ on images of hands taken under different experimental conditions with controlled illumination. The emission spectrum of Glo Germ is measured using a spectral photometer and normalized to obtain its spectral power density function. This spectrum is projected into CIE XYZ coordinates and incorporated into a linear mixture model in which each pixel contains contributions from white light, UV-illuminated skin reflectance, and fluorophore emission. Component magnitudes are estimated with non-negative least squares, yielding a grayscale image whose intensity is a monotonic proxy for local fluorophore density. Spatial integration provides an image-level summary proportional to total detected material. Compared with single-channel proxies, the observer suppresses background structure, improves contrast, and remains radiometrically interpretable. Because the method depends only on measurable spectra and linear transforms, it can be reproduced across cameras and extended to other fluorophores. Full article

Waste electronic components are valuable secondary resources containing various metals. Analyzing their elemental distribution is crucial for developing recycling methods. Micro- X-ray fluorescence (μ-XRF) is commonly used for this purpose, but traditional polychromatic X-ray excitation creates high background scattering. This masks trace element signals, impairing detection limits and accurate identification of minor valuable or hazardous elements. To address this, this study developed a monochromatic μ-XRF spectrometer using a low-power molybdenum-target X-ray tube. The system integrates polycapillary lenses for X-ray regulation and a flat crystal for monochromatization, producing a micron-sized monochromatic X-ray spot with high power density. This design eliminates scattered background from the primary continuous spectrum and enhances excitation efficiency by concentrating photon flux, enabling high-brightness monochromatic beams even at low tube power. The spectrometer was validated by analyzing a waste printed circuit board. High-resolution elemental mapping successfully revealed clear distribution patterns of major elements like copper, nickel, and iron, consistent with their physical structures. These images allowed intuitive differentiation of compositional differences across functional regions. This technique effectively overcomes the background interference caused by polychromatic excitation and is expected to further enhance the quality and reliability of elemental distribution imaging. It provides a powerful tool for formulating precise, scientific recycling strategies for waste electronics. Full article

Polyhalite, K₂SO₄•MgSO₄•2CaSO₄•2H₂O, a ternary evaporite mineral, is commonly found in evaporitic rock salt strata, where it acts as an indicator mineral for potash evaporite deposits. As a directly exploitable mineral potash fertilizer, polyhalite serves as an important substitute for potassium resources. The thermodynamic properties of polyhalite remain poorly characterized experimentally; consequently, current estimates predominantly rely on predictive modeling and indirect experimental approaches. The Raman spectra of free SO₄²⁻ vibrational modes in various sulfate minerals are sensitive to the local symmetry and hydrogen-bonding environment within crystal hydrates, and are directly influenced by the surrounding crystal field. This sensitivity makes Raman spectroscopy a powerful tool for investigating and identifying the crystal structures of sulfate minerals. In this work, the thermodynamic and Raman vibrational properties of polyhalite were investigated using density functional theory (DFT). Phonon calculations at the optimized geometry were employed to compute polyhalite’s key thermodynamic properties—specific heat, entropy, enthalpy, Gibbs free energy, and Debye temperature—over a temperature range of 0–1000 K. The results showed that: (1) the computed volume exhibited minimal error, approximately 0.87%, compared to experimental data; (2) the calculated values for the isobaric heat capacity and entropy were 420.72 and 531.39 J·mol⁻¹·K⁻¹ at 298.15 K, respectively; and (3) the calculated value for the free energy of formation at 298.15 K was −5670 kJ·mol⁻¹. The computed Raman spectrum results showed that the typical spectral features of polyhalite are: (1) ν1 for 1024 cm⁻¹, symmetric stretching mode; (2) ν2 for 464 cm⁻¹, symmetry bending mode; and (3) ν4 for 627 cm⁻¹, anti-symmetry bending mode. Full article

The device-to-device (D2D) communication that underlays cellular networks is a key enabler in the process of improving the utilization spectrum and energy efficiency (EE) of 5G systems. Most EE optimization studies have focused solely on a single-band configuration or single cell, while practical deployments inherently involve multi-cell and multi-band interference coupling that significantly affects the power allocation and system-level EE performance. In this study, we investigated EE maximization for multi-band, multi-cell D2D underlaying networks and propose two hybrid metaheuristic optimization algorithms: the evolutionary algorithm enhanced-particle grey wolf optimizer (EA-PGWO) and the memetic particle-guided grey wolf optimizer with derivative local learning (MPGWO-DLL). For fairness and a more comprehensive evaluation, three baseline algorithms—the derivative algorithm (DA), particle swarm optimization (PSO), and the genetic algorithm (GA)—were benchmarked and compared against our proposed algorithms. The proposed hybrid algorithms use population-based global exploration with local refinement to increase and stabilize the optimization under non-convex and interference-limited conditions. From the obtained simulation results, we obtained a clear outperformance from both the EA-PGWO and MPGWO-DLL in terms of EE against all three baseline algorithms and across varying D2D and cellular user densities. Among all the evaluated methods, MPGWO-DLL achieved the highest EE gains due to its memetic local learning stage combined with its derivative-guided refinement. Full article

Response spectra (RS) provide an efficient link between earthquake ground motions and structural demand. Still, rare event screening for long-period, resonance-sensitive systems is often approximated by applying uniform multipliers to a design-basis earthquake (DBE) spectrum to represent beyond-design-basis earthquake (BDBE) levels. This paper develops critical excitation (CE) based response spectra (CE-RS) as a spectrum-format, low-overhead screening tool that makes period-local resonance sensitivity explicit while remaining anchored to code-defined hazard levels. This paper develops CE-RS as a response-spectrum-based screening tool for identifying period-local resonance sensitivity at code-defined hazard levels by using the CE framework to search, within an admissible set defined by bounded power spectral density (PSD) content and intensity constraints, for the input that maximizes structural response. Code-based target spectra are adopted as hazard anchors, consistent with the intent of probabilistic seismic hazard analysis (PSHA), at representative sites in Australia (Canberra; AS 1170.4:2024, Site Class Be) and the United States (San Francisco; ASCE/SEI 7-22, Site Class BC). For each site, a spectrum-compatible seed accelerogram is generated to reproduce the 5% damped target spectrum and to calibrate admissible-set bounds using peak ground acceleration (PGA), peak ground velocity (PGV), and Arias intensity. CE is then performed period-by-period over the long-period range to obtain CE-RS ordinates, which are compared with the DBE target and conventional BDBE-type references formed by uniform spectrum scaling. The resulting framework provides a code-comparable, site-anchored interpretation of long-period demand influenced by resonance effects, supporting rapid prioritization in preliminary design and in the screening of existing long-period-sensitive infrastructure for strengthening/rehabilitation. Full article

Electric vehicles (EVs) represent a key pathway toward reducing greenhouse gas emissions and fossil fuel dependence. Although significant advances have been achieved in energy storage technologies for EVs, a structured comparative assessment that jointly evaluates batteries, supercapacitors, and their hybridisation remains lacking. This review addresses that gap by systematically comparing lithium-ion, lead-acid, and nickel-based batteries with electrochemical double-layer capacitors (EDLCs), pseudocapacitors, and hybrid capacitors across ten performance and sustainability criteria. A literature-informed scoring framework, supplemented by sensitivity analysis under alternative weighting scenarios, is employed to rank the technologies. Particular attention is given to Hybrid Energy Storage Systems (HESS), which combine the high energy density of lithium-ion batteries with the high power density and long cycle life of supercapacitors. The review synthesises evidence that HESS can improve overall energy efficiency by up to 20% and extend battery lifetime by 30–50%, thereby reducing raw-material extraction, electronic waste, and lifecycle cost. Second-life pathways and circular-economy implications are discussed in depth. The findings demonstrate that neither batteries nor supercapacitors alone can satisfy the full spectrum of EV energy demands; instead, their integration within HESS offers the most balanced, sustainable, and economically viable solution. This work provides actionable insights for engineers, policymakers, and stakeholders engaged in next-generation sustainable mobility. Full article

With the evolution of smart grids, power communication networks are increasingly required to support high-bandwidth and diversified services such as high-definition video, real-time control, and positioning—services that impose dual challenges of communication capacity and spectrum constraints—under severe resource limitations. Conventional orthogonal modulation schemes exhibit significant limitations in spectral efficiency and concurrent access capabilities, particularly in supporting high-density user environments. To address this, we propose a communication system based on non-orthogonal overlapped chirp modulation, in which the intrinsic symmetry properties of chirp waveforms are utilized to enhance system design and performance. We first construct the system architecture with a multi-symbol concurrent transmission scheme and introduce continuous orthogonal phase modulation to improve symbol distinguishability and mitigate inter-symbol interference—an approach that effectively harnesses signal symmetry for interference suppression. At the receiver, a low-complexity demodulation algorithm based on correlation matrix computation is developed, further improved through oversampling techniques that exploit temporal and spectral symmetry in signal design. Monte Carlo simulations confirm that the proposed system outperforms traditional orthogonal chirp and orthogonal frequency division multiplexing systems in bit error rate performance and spectral efficiency across varying signal-to-noise ratios and modulation schemes. The proposed NOOC system achieves spectral efficiency scaling linearly with concurrency level K, reaching up to 16 bits/s/Hz for K = 16 with BPSK, compared to 1 bit/s/Hz in orthogonal systems. The study provides both a theoretical foundation and practical insights for developing symmetry-aware, efficient, and reliable air interface technologies suitable for future power-private networks. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path. The mechanism uses a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal to ensure self-leveling and alignment in gravity on uneven terrain. Unlike approaches that rely on broadband digitization and require active control and a stable power supply, this housing–gimbal mechanism performs mechanical filtering for sub-Hz signal amplification and higher frequency attenuation without power. Response spectrum analysis shows that the transmissibility can be tuned to achieve peak sensitivities in the 0.5–0.8 Hz range. When tuned to 50–55 mm pendulum length and under assumed undamping, the pendulum-mounted mechanism improves detectability at best by 10–100× relative to a bare sensor for moderate magnitude ($M_s$ = 3–6) in a 12 h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10⁻⁸ m/s²/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow $N_m^{min}\propto {\left(\mathrm{S}\mathrm{N}\mathrm{R}\right)}^{1.2}{\left(\mathrm{A}\mathrm{S}\mathrm{D}\right)}^{1.2}{f}_s^{0.6}$, where $f_s$ is the quake wave frequency. The damping ratio, considering both structural damping and viscous drag, is estimated to be in the order of 10⁻³ to 10⁻². A probabilistic sensitivity analysis is performed to account for the inherent uncertainty in the spectral mismatch between the narrowband sub-Hz resonance of the designed mechanical filter and the peak frequencies of seismic events; the derived probability model suggests strategies for improving the detection probability in the 0.01–1 Hz range. Full article

Continuous-Variable Quantum Key Distribution (CVQKD), based on quantum mechanical principles, offers theoretically unconditional security and represents a crucial direction for future secure communications. However, its application in marine environments faces challenges such as high attenuation, scattering, and turbulence in seawater, severely impacting quantum signal transmission and secure key generation efficiency. Orbital angular momentum (OAM) multiplexing technology leverages the orthogonality of photon OAM modes to transmit multiple independent quantum signals in parallel within a single spatial channel. In this scheme, each OAM mode serves as an independent sub-channel, enabling simultaneous key distribution across multiple modes, thereby significantly enhancing the system’s secure key rate and spectral efficiency. This paper proposes an OAM-multiplexed CVQKD scheme tailored for marine channels. Based on Yi’s power spectrum model for marine turbulence refractive index fluctuations, we derive expressions for OAM mode probability density and detection probability. Through system modeling and performance analysis, we investigate the impact of marine turbulence on OAM modes, as well as on the secure key rate and transmission distance of CVQKD systems. Results indicate that higher-order OAM modes exhibit more pronounced turbulence effects, leading to reduced key rates and limited transmission distances. The OAM multiplexing approach significantly enhances system key rates, providing theoretical and technical references for constructing high-rate seawater quantum communication networks. Full article

The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in the long term, future wireless systems must fundamentally prioritize energy-efficient and autonomous operation. Integrated sensing and communication (ISAC) is emerging as a key enabler for next-generation systems by jointly supporting sensing and communication through shared spectrum, hardware, and signal processing resources. In IoT systems, sensing of target parameters, e.g., range, angle, velocity and identity, etc., form the basis of autonomous and environment-aware applications. However, this integration increases overall power consumption due to the added coordination overhead and the workload placed on shared hardware components. To this end, backscatter communication provides a low-power alternative that enables passive data transmission through energy harvesting and sharply reduces the need for active radio circuits. However, the coexistence of sensing and backscatter functions introduces mutual interference, which often requires large multiple-input multiple-output (MIMO) arrays for effective mitigation. Furthermore, sensing performance depends heavily on line-of-sight conditions, while backscatter links operate only over short ranges. Although increasing array size or transmit power can extend coverage, it imposes substantial energy and hardware costs and undermines sustainability goals. To address these limitations, cell-free MIMO is emerging as a promising candidate technology for next-generation systems. Cell-free MIMO relies on a dense deployment of distributed access points that cooperate to serve devices across a wide area. This cooperation enables effective beamforming and interference management, providing spatial diversity comparable to large, centralized antenna arrays without incurring their associated hardware or power costs. They also enable aggregation of weak double-hop reflections, reduced effective-illumination distances, multi-view sensing, and robustness to blockage, which is invaluable to backscatter communication. This perspective article introduces the foundations, challenges, and architectural considerations of cell-free backscatter-aided integrated sensing and communication (CF-BISAC) systems. By leveraging the advantages of battery-less backscatter IoT devices and the distributed nature of cell-free MIMO, CF-ISABC aims to maximize sensing and communication performance under strict energy constraints, contributing toward energy-aware ISAC systems capable of supporting high-density, low-power wireless applications. Full article

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

With the rapid development of mega-cities, clarifying the wind field characteristics of high-density urban areas is crucial for the accurate assessment of wind loads on newly built or temporary structures. Taking the high-density urban area of Shanghai as a case study, this research utilizes long-term wind field monitoring data obtained from a super high-rise building under construction. Statistical methods are employed to analyze the mean wind and fluctuating wind characteristics of such sites. The results indicate the following: the mean wind direction distribution is generally consistent with code statistics, with dominant wind directions varying significantly by season; the mean wind profile exponent at the site is 0.39, which is slightly higher than the reference value for Terrain Category D specified in codes; turbulence intensity tends to stabilize as wind speed increases, and the ratio of along-wind to cross-wind turbulence intensity is 1:0.59, which is slightly lower than the code-suggested value and shows a significant positive correlation with the gust factor. The mean peak factor is 2.52, while the mean longitudinal and lateral turbulence integral length scales are 118 m and 45 m, respectively. For strong wind samples, the longitudinal wind spectrum agrees well with the Davenport spectrum, whereas the lateral power spectrum correlates well with the Von Karman spectrum. This study provides a scientific basis and data support for wind load calculation and structural safety assessment in Shanghai and other high-density cities. Full article

In phenomenological studies of multiparticle production, transverse-momentum spectra measured in experiments frequently display an approximately power-law falloff, for which the Tsallis-type functional form is commonly employed as an effective parametrization. Within this framework, the emergence of such spectra is interpreted as a manifestation of nonextensive statistical behavior. An analogous power-law structure, however, can be reproduced without explicitly postulating Tsallis statistics by assuming the presence of intrinsic fluctuations of the local temperature (T) in the hadronizing medium; in that case, the observed deviations from a purely exponential spectrum are encapsulated by the nonextensivity index (q). We show that temperature fluctuation mechanisms capable of generating Tsallis-like power-law distributions in multiparticle production necessarily induce nontrivial inter-particle correlations among the emitted hadrons. Building on this observation, we outline a strategy to discriminate fluctuations realized on an event-by-event basis from those arising predominantly through event-to-event variability. Such a separation may be particularly pertinent for the characterization of high-multiplicity (high-density) final states produced at the Large Hadron Collider. Full article