Search Results (3,541)

Uplink/downlink (UL/DL) decoupled access has emerged as a promising paradigm for heterogeneous cellular vehicle-to-everything (C-V2X) networks in beyond 5G (B5G) and 6G systems. In multi-operator scenarios, wireless service provider (WSP) selection becomes critical for vehicles to ensure communication quality while minimizing costs. This paper investigates the performance analysis and WSP selection problem in UL/DL decoupled access C-V2X networks. We derive tractable expressions for spectral efficiency of both UL and DL using stochastic geometry, considering three decoupled access cases where UL and DL independently associate with macro base stations (MBSs) or small base stations (SBSs). We formulate a hierarchical game framework combining evolutionary game for vehicle WSP selection and non-cooperative game for WSP bandwidth allocation. An evolutionary game algorithm is proposed to reach the equilibrium, and the uniqueness of Nash equilibrium in bandwidth allocation is proved. Extensive simulations validate the analytical results and demonstrate the convergence and stability of the proposed game framework. Full article

Binding affinity prediction in computational drug discovery is confounded by trivial correlations between molecular size and measured potency. We introduce cellular sheaf Laplacians as descriptors of ligand molecular geometry that quantify geometric frustration independent of system size. Sheaves are constructed over molecular graphs by assigning three-dimensional coordinate spaces to atoms and projection operators encoding ideal bonding geometry to edges; eigendecomposition of the resulting Laplacian yields spectral features measuring inconsistencies between local geometric constraints and global topology. Applied to 14,050 protein-ligand complexes from the PDBbind v2020 refined set, MW-residualized Sheaf features capture a statistically significant geometric signal ($r_{\mathrm{partial}} = 0.171$, $p<{10}^{-70}$) that is orthogonal to the Wiener index ($r=0.013$) and persists after controlling for both molecular weight and classical graph-theoretic descriptors ($r_{\mathrm{partial}} = 0.390$, $p<{10}^{-9}$). Sheaf spectral features alone achieve predictive performance ($R^2=0.403$) approaching that of fourteen classical cheminformatics descriptors ($R^2=0.446$), and their combination yields consistent improvements across the binding affinity spectrum (RMSE $=1.43$$p{K}_d$). Permutation importance analysis confirms the Sheaf Frobenius norm as the second most influential descriptor after molecular weight. We introduce Topological Binding Efficiency as a size-normalized quality metric identifying ligands that achieve potent binding through geometric complementarity rather than molecular bulk. Gaussian mixture analysis of the maximum eigenvalue distribution among strong binders reveals two distinct spectral modes corresponding to planar aromatic and three-dimensional sp3-rich scaffolds, confirmed by significant differences in fraction of sp3 carbons and aromatic ring counts ($p<{10}^{-8}$). As an intentionally ligand-centric framework, our approach complements rather than replaces protein-aware co-modelling architectures. This work establishes cellular sheaf theory as a principled framework for encoding molecular topology with statistically significant associations with binding affinity, providing interpretable geometric insights that are inaccessible to conventional molecular descriptors. Full article

This study evaluates the potential of near-infrared spectroscopy (NIRS) combined with discriminant analysis to classify coffee quality based on sensory defects in dry parchment coffee (DPC) and green coffee. Spectral data were used to develop classification models, which were validated using both cross-validation and independent external datasets. Model performance was assessed using classification accuracy and Cohen’s kappa coefficient. The results demonstrate high classification accuracy for DPC (93.5%), with a Kappa coefficient indicating almost perfect agreement (κ = 0.90). In contrast, green coffee showed lower predictive performance (82.4%) and moderate agreement (κ = 0.55), reflecting the greater physicochemical complexity of this matrix. Importantly, the findings demonstrate that coffee quality can be reliably classified at the dry parchment stage, enabling early quality assessment without additional processing steps. This represents a significant advancement compared to previous studies, which have mainly focused on green or roasted coffee. Overall, these results highlight the potential of NIRS as a rapid, non-destructive, and objective tool for coffee quality assessment, with strong applicability in quality control and decision-making processes along the coffee production chain. Full article

Diode-pumped alkali vapor lasers (DPALs) offer high quantum efficiency, low thermal loading, excellent beam quality, and emission wavelengths matched to important application scenarios. Extending DPALs toward pulsed regimes is of particular interest for applications such as lidar, free-space optical communication, and precision material processing, where high peak power and flexible temporal control are required. This review surveys the key technologies underlying DPAL systems and summarizes the progress in pulsed-generation approaches. The pulsed techniques reported to date are systematically reviewed, including pump modulation, intracavity modulation, cavity dumping, and mode-locking, together with a comparison of their performance. The current status indicates that pulsed DPALs remain at an early stage, with limitations in parameter space exploration and performance scaling. Future developments are expected along several directions, including further exploration of mode-locked DPALs, burst-mode pulse generation for structured temporal output, power scaling through MOPA architectures, and spectral extension via nonlinear frequency conversion. These directions collectively define the pathway toward high-performance pulsed DPAL systems. Full article

Recent years have witnessed significant progress in deep learning-based shadow removal. However, most prior methods operate primarily in the spatial domain or rely on coarse frequency cues, while the informative role of amplitude components in the frequency domain remains largely unexplored. The amplitude spectrum encodes spectral energy that reflects global illumination and fine texture that strongly influence shadow appearance. Motivated by this observation, we propose AmpFormer, a U-shaped transformer architecture that explicitly models amplitude information for robust shadow correction. Central to AmpFormer is a lightweight SFR module inserted at each encoder–decoder stage: SFR extracts multi-scale amplitude cues from compact spectral representations, learns per-channel adaptive gains and subtle phase adjustments, and injects the recalibrated frequency features into the spatial stream. To further encourage amplitude-aware restoration, we introduce an amplitude loss that explicitly regularizes spectral energy with emphasis on global illumination consistency. Extensive experiments on standard benchmarks demonstrate that AmpFormer achieves state-of-the-art restoration quality while offering a favorable computational-efficiency-accuracy trade-off, validating the practical benefit of amplitude-aware frequency modeling for shadow removal. Full article

Accurate extraction of impervious surface areas (ISA) is essential for urban environmental monitoring, yet severe spectral confusion among complex urban land-cover types limits the performance of classifications based solely on optical imagery. To address this issue within a localized context, this study proposes a multi-source framework integrating UAV-based LiDAR (UAV-LiDAR) and high-resolution visible imagery for fine-scale ISA extraction. An improved segmentation optimization strategy, termed EGS-Optimizer, is developed to enhance boundary delineation within the object-based image analysis (OBIA) framework by coupling edge detection with global segmentation quality evaluation. A comprehensive feature set including spectral, index, texture, geometric, and terrain features is constructed, and Shapley Additive Explanations (SHAP) is applied to select the most informative variables while reducing dimensionality. The proposed framework is validated in a typical 1.45 km² built-up area in Deyang City, Sichuan Province. Experimental results demonstrate that, within this specific study area, multi-source data fusion improves classification accuracy by 3.59–5.79% compared with single-source data, while feature selection reduces the feature dimension from 45 to 21. Among the evaluated classifiers, the random forest (RF) model achieves the highest performance, with an overall accuracy of 97.24% (Kappa = 0.96). While the high accuracy highlights the efficacy of synergizing spectral and structural information for micro-landscape mapping, these findings are constrained to the demonstrated fine-scale local environment. The results provide an effective, interpretable solution for detailed neighborhood-level ISA mapping, though further validation is required before the framework can be generalized to larger or more heterogeneous urban scenarios. Full article

Accurate detection of chlorophyll-a (Chl-a) is critical for monitoring water quality in inland waters, where high concentrations of coloured dissolved organic matter (CDOM) complicate retrieval process. Reliable Chl-a estimation depends on the precise determination of the phytoplankton absorption coefficient (a_ph). This study evaluates Chl-a detection from in situ a_ph measurements and assesses the accuracy of phytoplankton absorption retrieval from Sentinel-2/MSI (S2) and Sentinel-3/OLCI (S3) using the Case-2-Regional-Coast-Colour (C2RCC) processor across diverse optical water types (OWTs) in boreal lakes. OWTs were classified based on remote sensing reflectance features, representing Clear, Moderate, Turbid, Very Turbid, and Brown conditions. CDOM absorption strongly influenced the underwater light field, particularly in Brown and Turbid waters. Linear relationships between a_ph and Chl-a were generally strong across OWTs, with improved relationships in the red spectral region (670 nm). Satellite-derived a_pig estimates showed a weak relationship with in situ data (R² = 0.26–0.45). Both sensors overestimated small a_ph values, while S3 underestimated larger ones. S2 underestimated a_ph in Clear and Brown OWTs, with median absolute percentage differences near 100% for all OWTs. These findings emphasize the challenges posed by bio-optical complexity in boreal lakes and highlight the need for OWT-specific algorithms to improve satellite-based absorption and Chl-a retrieval accuracy. Full article

With the continuous advancement of polarization spectral sensing technology, multi-band polarization image fusion has emerged as a novel approach to image fusion. By integrating spectral and polarization information, this method overcomes the limitations of relying on a single information source and significantly improves overall image quality. To address this, this paper proposes a new polarization spectral fusion algorithm. First, feature matching is employed to achieve pixel-level spatial alignment of multi-band polarization images. Then, a fusion strategy based on multi-scale decomposition and singular value decomposition is adopted to preserve structural information and fine details. Subsequently, fractional-order processing and guided filtering are applied to enhance details and suppress noise. Finally, a progressive reconstruction from low to high scales is performed to ensure hierarchical consistency and information integrity throughout the fusion process. In addition, spectral information is utilized for color restoration, enabling the final image to achieve high spatial resolution while maintaining natural and rich color representation.Experimental results demonstrate that the proposed method effectively integrates features from different spectral bands and polarization information while preserving maximum similarity, leading to significant improvements in both image quality and detail representation. Full article

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. Full article

Due to the intrinsic Q attenuation of geological formations, seismic waves experience amplitude and frequency attenuation during propagation, which results in reduced resolution and pronounced discrepancies between shallow and deep seismic data. Specifically, deep reflections exhibit weakened amplitudes and diminished high-frequency content. To mitigate these effects, a Q-compensation method based on nonstationary inversion is proposed to enhance seismic resolution and improve vertical consistency. A nonstationary reflectivity inversion framework is first established using a nonstationary convolution model with Cauchy norm regularization. The formation quality factor (Q) is then estimated from seismic data via the spectral ratio method using selected shallow and deep time windows. By incorporating the estimated Q value and an initial seismic wavelet, the proposed inversion simultaneously compensates for Q attenuation and wavelet effects, yielding a high-resolution reflectivity series. Q-compensated seismic data are subsequently reconstructed through the convolution of this reflectivity and an appropriate seismic wavelet. Both the model test and the field data application results demonstrate that the proposed method effectively compensates for Q attenuation, significantly enhances seismic resolution, and restores amplitude and frequency consistency between shallow and deep data. Full article

Vertical-External-Cavity Surface-Emitting Lasers (VECSELs) have gained significant attention over the past two decades due to their versatility in a wide range of photonic applications. This review focuses on VECSEL configurations for dual-wavelength emission, highlighting their use in high-resolution spectroscopy, terahertz (THz) generation, and advanced optical communication. We explore recent developments in VECSEL designs, including systems utilizing birefringent crystals for polarization-based frequency separation and configurations with dual-VECSEL chips or dual-gain regions within a single cavity. These two-wavelength VECSELs enable diverse operation modes, including narrow-linewidth, pulsed, multimode, and frequency-converted emission, with high-brightness output, excellent beam quality, and tunable wavelengths. Additionally, the review discusses advancements in dual-frequency VECSELs, with applications in LIDAR systems for environmental monitoring, highly stable optical clocks, and fiber sensors. We examine improvements in cavity design, semiconductor structures, and power stabilization, which have enhanced frequency stability and spectral purity, making VECSELs suitable for precision metrology and sensing applications. Full article

In this study, a simulation-based investigation of the variations of the bit error rate (BER) and the maximum quality factor are presented for short- (S-), conventional- (C-), and long- (L-) band wavelengths in a photonic ultra-wideband (UWB) circuit using a semiconductor optical amplifier (SOA) with different bias currents and a bandpass filter (BPF). Gaussian quadruplet UWB pulses are generated at the S-, C-, and L-band wavelengths, which are commonly used in fiber transmission lines. An analysis of the temporal and spectral features of the generated pulses is carried out. The highest maximum quality factor and the lowest minimum BER are obtained in the C-band at an SOA bias current of 150 mA. This study simultaneously investigates both UWB pulse generation and transmission performance. The proposed circuit has a simple design and high applicability, as it employs a SOA, a Gaussian optical filter, a low-pass filter (LPF) and a single BPF. Full article

Realizing high-quality-factor (high-Q) plasmonic resonances in the visible regime is critical for enhancing light-matter interactions and advancing biochemical sensing. However, traditional localized surface plasmon resonances (LSPRs) typically suffer from broad spectral linewidths due to severe radiative damping. In this work, we propose a simple two-dimensional symmetric gold nanohole-array metasurface that supports a symmetry-protected bound state in the continuum (SP-BIC) at normal incidence. By introducing extrinsic symmetry breaking via oblique incidence, this non-radiative dark state is successfully transformed into an observable high-Q quasi-BIC Fano resonance. Cartesian multipole decomposition reveals that this sharp mode ($\lambda \approx 688$ nm) is predominantly driven by a tightly confined Magnetic Dipole (MD) excitation, which drastically suppresses radiative leakage compared to the highly damped Electric Dipole (ED)-dominated LSPR. Consequently, the quasi-BIC mode exhibits an ultra-narrow spectral linewidth ($\mathrm{FWHM}\approx 17.4$ nm). While its bulk sensitivity ($236.9$ nm/RIU) is slightly lower than that of the LSPR mode, the exceptionally sharp resonance yields a remarkably low Limit of Detection (LOD) of $7.35\times {10}^{-3}$ RIU, achieving a nearly five-fold improvement over the traditional LSPR. Furthermore, the quasi-BIC mode maintains an outstanding Figure of Merit (FOM up to ∼19.7 ${\mathrm{RIU}}^{-1}$) across the entire sensing range. By eliminating the need for complex asymmetric nanofabrication, this robust angle-tuned design strategy provides a highly promising platform for the development of high-resolution, low-cost optical biosensors. Full article

Over the past 20 years, honey bee colony declines have been driven by multiple factors, notably diseases and parasites. The parasitic mite Varroa destructor, which weakens the bees’ immune systems, has been particularly harmful. While various synthetic acaricides are used, the chemicals may accumulate in the beeswax, endangering colony health and allowing Varroa populations to develop resistance to these acaricides. These problems have prompted interest in organic alternatives like thymol and oxalic acid. In this study, colony health was assessed through the proteins-to-phenolics spectral ratio in honey and beeswax, determined by fluorescence spectroscopy, as a ratiometric indicator of infection level in treated hives. Over two months, hives were treated with either oxalic acid, thymol, or remained untreated as controls. Neither treatment significantly affected the proteins-to-phenolics ratios in honey, ranging from 0.30 to 0.83, or in beeswax, ranging from 1.40 to 1.83, suggesting that the incorporation of these vital constituents remains stable despite acaricide application. While thymol demonstrates potential adverse effects on bee health, careful management of treatment concentrations is essential to ensure both the efficacy of Varroa control and the preservation of honey quality. These findings provide valuable insights for beekeepers regarding the safe application of organic acaricides. Full article

This study evaluated the effects of different light-emitting diode (LED) spectral qualities on the early growth of kale at the baby-leaf harvest stage in a plant factory with artificial lighting (PFAL) by integrating morphological traits, biomass accumulation, plant quality indices, vegetation indices, and chlorophyll a fluorescence. Two kale (Brassica oleracea L.) cultivars, ‘Jellujon’ and ‘Manchoo Collard’, were grown for four weeks under monochromatic red, green, and blue LEDs, a purple composite LED with far-red wavelengths, and three white LEDs with different correlated color temperatures (3000, 4100, and 6500 K). Blue LED increased shoot height by approximately 14–28%, depending on cultivar and comparison among the white LED treatments, but this elongation did not translate into superior biomass production. In contrast, white LEDs, particularly at 3000–4100 K, increased leaf area to 24.2–24.9 cm² and SPAD units to 47.3–50.2, whereas blue or green LEDs generally resulted in smaller leaves and lower SPAD units. Shoot dry weight under 3000–4100 K white LEDs reached 0.25–0.26 g in ‘Jellujon’ and 0.26–0.29 g in ‘Manchoo Collard’, approximately twofold higher than under blue or green LEDs. Compactness, Dickson quality index, root investment ratio, and leaf efficiency index were also more favorable under white LEDs, indicating improved plant sturdiness and structural stability. Green LED light was associated with lower maximum photochemical efficiency (Φ_Po) and greater energy dissipation (Φ_Do and DI_o/RC), whereas photochemical reflectance index and PI_ABS tended to be more favorable under selected white LED treatments, although these responses were partly cultivar- and treatment-dependent. Taken together, among the LED spectral quality treatments tested, 3000–4100 K white LEDs provided the most consistently favorable conditions for producing structurally robust, high-quality kale at the early growth stage in PFAL systems. The purple LED showed partial advantages in leaf development and selected physiological responses, but these effects were less consistent across cultivars and indices. Full article