Search Results (84)

Accurate estimation of young forest height is essential for assessing the carbon sequestration potential of vast Siberian boreal forests recovering from wildfires. Satellite radar interferometry, particularly PolInSAR, is a promising tool for this task. However, its application in winter conditions and over sparse young forests remains underexplored. This study proposes a novel method for estimating the height of sparse young pine (Pinus sylvestris) stands using fully polarimetric bistatic TerraSAR-X/TanDEM-X data acquired in winter. The method is based on an analysis of the multimodal distribution of the unwrapped interferometric phase of the surface scattering component, which was isolated via PolInSAR decomposition. We hypothesize that the phase centers correspond to the snow-covered ground (located between tree groups) and the rough surface formed by the upper layer of branches and needles (of the tree groups). The results demonstrate that the difference between the dominant modes of the surface scattering phase distribution correlates with the height of young trees. However, the measurable height difference is limited by the interferometric height of ambiguity. Furthermore, a temporal analysis of the phase and meteorological data revealed a strong correlation between sudden phase shifts and daytime temperature rises around 0 °C. This is interpreted as the formation of a layered snowpack structure with a dense ice crust. This study confirms the potential of X-band PolInSAR for monitoring the structure of young Siberian forests in winter but also highlights a significant limitation: the critical impact of snowpack metamorphism, particularly melt-freeze cycles, on the interferometric phase. The proposed method is only applicable to certain forest regeneration stages where tree height does not exceed the ambiguity limit and snow conditions are stable. Full article

Multiband fusion (MF) technology can generate an ultra-wideband echo (UWBE) from multiple sub-band echoes (SBEs), thereby improving radar range resolution and enhancing target recognition capabilities. However, current MF methods generally do not account for the incoherence introduced by fluctuations in the scattering characteristics of scattering centers (SCs) across different frequency bands. This oversight can lead to degraded fusion performance. To address this limitation, a novel MF method that explicitly considers the fluctuation of SC characteristics between sub-bands is proposed in this paper. Firstly, a theoretical analysis of the additional incoherent phase term introduced by these fluctuations is conducted, which demonstrates its impact on fusion accuracy. Based on this analysis, scattering centers are extracted from SBEs based on the geometrical theory of diffraction (GTD) model, and then categorized into two distinct types: intrinsic scattering centers (ISCs) and unique scattering centers (USCs). Subsequently, a new incoherent phase estimation and compensation method is proposed, leveraging this categorization to effectively mitigate the inter-sub-band incoherence. The two types of SCs are then processed through either fusion or super-resolution to generate individual UWBEs, which are finally combined to form the final UWBE. The effectiveness of the proposed method is validated using both simulated electromagnetic scattering data and static measured data. Numerical results demonstrate that the proposed method achieves significantly greater fusion accuracy compared to traditional MF approaches, confirming the practical benefits of incorporating SC fluctuation modeling into the fusion process. Full article

Eleven novel asymmetric pyrimidine derivatives were synthesized. The pyrimidine core was functionalized with a coumarin chromophore and a pro-mesogenic fragment bearing either chiral or linear alkyl chains of variable length and substitution patterns. The thermal properties were investigated using polarized optical microscopy, differential scanning calorimetry, and small-angle X-ray scattering, revealing that only selected derivatives exhibited liquid crystalline phases with ordered columnar or smectic organizations. Linear and nonlinear optical properties were characterized by UV–Vis absorption, fluorescence spectroscopy, two-photon absorption, and second-harmonic generation. Optical responses were found to be highly sensitive to the substitution pattern: derivatives functionalized at the 4 and 3,4,5 positions exhibited enhanced 2PA cross-sections and pronounced SHG signals, whereas variations in alkyl chain length exerted only a minor influence. Notably, compounds forming highly ordered non-centrosymmetric mesophases produced robust SHG-active thin films. Importantly, strong SHG responses were obtained without the need for a chiral center, as the inherent asymmetry of the linear alkyl chain derivatives was sufficient to drive self-organization into non-centrosymmetric materials. These results demonstrate that asymmetric pyrimidine-based architectures combining π-conjugation and controlled supramolecular organization are promising candidates for nonlinear optical applications such as photonic devices, multiphoton imaging, and optical data storage. Full article

Bi-phasic (with a local minimum) response of fluorescence to scattering when probed by a single fiber (SF) was first observed in 2003. Subsequent experiments and Monte Carlo studies have shown the bi-phasic turning of SF fluorescence to occur at a dimensionless reduced scattering of ~1 and vary with absorption. The bi-phase of SF fluorescence received semi-empirical explanations; however, better understandings of the bi-phase and its dependence on absorption are necessary. This work demonstrates a quasi-analytical projection of a bi-phasic pattern comparable to that of SF fluorescence via photon-transport analyses of fluorescence in a center-illuminated-area-detection (CIAD) geometry. This model-approach is principled upon scaling of the diffuse fluorescence between CIAD and a SF of the same size of collection, which expands the scaling of diffuse reflectance between CIAD and a SF discovered for steady-state and time-domain cases. Analytical fluorescence for CIAD is then developed via radial-integration of radially resolved fluorescence. The radiance of excitation is decomposed to surface, collimated, and diffusive portions to account for the surface, near the point-of-entry, and diffuse portion of fluorescence associated with a centered illumination. Radiative or diffuse transport methods are then used to quasi-analytically deduce fluorescence excited by the three portions of radiance. The resulting model of fluorescence for CIAD, while limiting to iso-transport properties at the excitation and emission wavelengths, is compared against the semi-empirical model for SF, revealing bi-phasic turning [0.5~2.6] at various geometric sizes [0.2, 0.4, 0.6, 0.8, 1.0 mm] and a change of three orders of magnitude in the absorption of the background medium. This model projects a strong reduction in fluorescence versus strong absorption at high scattering, which differs from the semi-empirical SF model’s projection of a saturating pattern unresponsive to further increases in the absorption. This framework of modeling fluorescence may be useful to project frequency-domain and lifetime pattens of fluorescence in an SF and CIAD. Full article

In radar target recognition, the canonical scatterer model (CSM) serves as an effective alternative to the scatterer center model (SCM) for efficiently characterizing electromagnetic (EM) scattering properties of complex targets. Based on physical optics (PO) and the stationary phase method (SPM), this paper analytically derives the novel extension of the CSM for six canonical scatterers: plate, dihedral, trihedral, cylinder, cone, and sphere. The proposed polarization-dependent framework isolates the polarimetric response from CSMs’ intrinsic geometries, reducing the full-polarimetric matrix to an explicit function exclusively governed by bistatic radar spatial configurations. Experimental validation demonstrates mean relative percentage errors (MRPEs) in radar cross section (RCS) of 0.3%, 2%, 2.6%, 3%, 6%, and 7%. This model constitutes a foundational prototype for scattering dictionaries addressing both forward and inverse EM scattering problems, possessing significant practical utility in radar target recognition and image interpretation. Full article

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. Full article

The degree of anisotropy and heterogeneity in coarse-grained materials significantly affects ultrasonic propagation behavior and scattering. This paper proposes a vector coherent factor threshold total focusing imaging method (VCF-T-TFM) for austenitic stainless steel, based on material properties, through a combination of simulation and experimentation. Three types of austenitic stainless steel weld test blocks with varying degrees of heterogeneity were selected containing multiple side-drilled hole defects, each with a diameter of 2 mm. Full-matrix data were collected using a 32-element phased array probe with a center frequency of 5 MHz. The grain size and orientation of the material were quantitatively observed via electron backscatter diffraction (EBSD). By combining the instantaneous phase distribution of the TFM image, the coarse-grained material coherence compensation value (C_A) and probability threshold (P_T) were optimized for different heterogeneous regions, and the vector coherence imaging threshold (γ) was adjusted. The defect imaging results of homogeneous material (carbon steel) and three austenitic stainless steels with different levels of heterogeneity were compared, and the influence of coarse-grained, anisotropic heterogeneous structures on the imaging signal-to-noise ratio was analyzed. The results show that the VCF-T-TFM, which considers the influence of material properties on phase coherence, can suppress structural noise. Compared to compensation results that did not account for material properties, the signal-to-noise ratio was improved by 97.3%. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

In realistic hyperspectral image (HSI) cross-scene classification tasks, it is ideal to obtain target domain samples during the training phase. Therefore, a model needs to be trained on one or more source domains (SD) and achieve robust domain generalization (DG) performance on an unknown target domain (TD). Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. However, recent studies have shown that the activation functions of neurons in a network exhibit asymmetry for different categories, which results in the learning of task-irrelevant features while attempting to learn task-related features (called “feature contamination”). For example, even if some intrinsic features of HSIs (lighting conditions, atmospheric environment, etc.) are irrelevant to the label, the neural network still tends to learn them, resulting in features that make the classification related to these spurious components. To alleviate this problem, this study replaces the common nonlinear generative network with a specific linear projection transformation, to reduce the number of neurons activated nonlinearly during training and alleviate the learning of contaminated features. Specifically, this study proposes a dimensionally decoupled spatial spectral linear extrapolation (SSLE) strategy to achieve sample augmentation. Inspired by the weakening effect of water vapor absorption and Rayleigh scattering on band reflectivity, we simulate a common spectral drift based on Markov random fields to achieve linear spectral augmentation. Further considering the common co-occurrence phenomenon of patch images in space, we design spatial weights combined with label determinism of the center pixel to construct linear spatial enhancement. Finally, to ensure the cognitive unity of the high-level features of the discriminator in the sample space, we use inter-class contrastive learning to align the back-end feature representation. Extensive experiments were conducted on four datasets, an ablation study showed the effectiveness of the proposed modules, and a comparative analysis with advanced DG algorithms showed the superiority of our model in the face of various spectral and category shifts. In particular, on the Houston18/Shanghai datasets, its overall accuracy was 0.51%/0.83% higher than the best results of the other methods, and its Kappa coefficient was 0.78%/2.07% higher, respectively. Full article

Cubic-phase SnSe possesses exceptional crystal structure symmetry while maintaining non-harmonic bond characteristics and ultra-low lattice thermal conductivity, exhibiting superior thermoelectric (TE) application potential compared to its orthorhombic counterpart. Despite recent advancements, systematic investigations on the combined effects of composite engineering strategies in optimizing TE properties of cubic-phase SnSe-based materials remain scarce. In this study, multi-walled carbon nanotubes (MWCNTs) are incorporated into the cubic-phase Sn_0.50Ag_0.25Bi_0.25Se_0.50Te_0.50 to regulate its TE performance through a combination of ultrasonic dispersion and rapid hot-pressing sintering. The introduced MWCNTs promote the formation of “high-speed channel” for carrier transport and serve as additional phonon-scattering centers, resulting in a synergistic optimization of electrical and thermal transport properties. A maximum ZT value of 0.85 is achieved in the prepared 1.50 wt.% MWCNTs/Sn_0.50Ag_0.25Bi_0.25Se_0.50Te_0.50 sample at 750 K, representing a 21% improvement compared to the pristine Sn_0.50Ag_0.25Bi_0.25Se_0.50Te_0.50 sample. This finding establishes a scalable nano-composite engineering paradigm for enhancing TE performance of cubic-phase SnSe-based materials. Full article

This paper presents an open-source time-dependent three-dimensional scalar photorefractive beam propagation model (PRProp3D) based on the well-known split-step method. The angular spectrum method is used for the diffractive steps, and the nonlinearities accumulated at the end of each diffractive step are applied using spatially varying phase screens. Comparisons with previously published experimental results are given for image amplification, photorefractive amplified scattering (fanning) and photorefractive screening solitons. Artifacts can be mitigated by use of step sizes less than 5~10 micrometers and by careful choice of the transverse computation grid size to ensure adequate sampling. Wraparound effects associated with the use of discrete Fourier transforms are mitigated by apodization and beam centering. Full article

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network. Full article

Phase contrast imaging is well-suited for studying weakly scattering samples. Its strength lies in its ability to measure how the phase of the electron beam is affected by the sample, even when other imaging techniques yield low contrast. In this study, we explore via simulations two phase contrast techniques: integrated center of mass (iCOM) and ptychography, specifically using the extended ptychographical iterative engine (ePIE). We simulate the four-dimensional scanning transmission electron microscopy (4D-STEM) datasets for specific parameters corresponding to a scanning electron microscope (SEM) with an immersive objective and a given pixelated detector. The performance of these phase contrast techniques is analyzed using a contrast transfer function. Simulated datasets from a sample consisting of graphene sheets and carbon nanotubes are used for iCOM and ePIE reconstructions for two aperture sizes and two electron doses. We highlight the influence of aperture size, showing that for a smaller aperture, the radiation dose is spent mostly on larger sample features, which may aid in imaging sensitive samples while minimizing radiation damage. Full article

Inverse synthetic aperture radar (ISAR) imaging techniques are frequently used in target classification and recognition applications, due to its capability to produce high-resolution images for moving targets. In order to meet the demand of ISAR imaging for electromagnetic calculation with high efficiency and accuracy, a novel accelerated shooting and bouncing ray (SBR) method is presented by combining a Graphics Processing Unit (GPU) and Bounding Volume Hierarchies (BVH) tree structure. To overcome the problem of unfocused images by a Fourier-based ISAR procedure under wide-angle and wide-bandwidth conditions, an efficient parallel back projection (BP) imaging algorithm is developed by utilizing the GPU acceleration technique. The presented GPU-accelerated SBR is validated by comparison with the RL-GO method in commercial software FEKO v2020. For ISAR images, it is clearly indicated that strong scattering centers as well as target profiles can be observed under large observation azimuth angles, $\Delta \phi =90°$, and wide bandwidths, 3 GHz. It is also indicated that ISAR imaging is heavily sensitive to observation angles. In addition, obvious sidelobes can be observed, due to the phase history of the electromagnetic wave being distorted resulting from multipole scattering. Simulation results confirm the feasibility and efficiency of our scheme by combining GPU-accelerated SBR with the BP algorithm for fast ISAR imaging simulation under wide-angle and wide-bandwidth conditions. Full article

Aluminum garnets display exceptional adaptability in incorporating mismatching elements, thereby facilitating the synthesis of novel materials with tailored properties. This study explored Ce³⁺-doped Tb₃Al_5−xSc_xO₁₂ crystals (where x ranges from 0.5 to 3.0), revealing a novel approach to control luminescence and photoconversion through atomic size mismatch engineering. Raman spectroscopy confirmed the coexistence of garnet and perovskite phases, with Sc substitution significantly influencing the garnet lattice and induced A_1g mode softening up to Sc concentration x = 2.0. The Sc atoms controlled sub-eutectic inclusion formation, creating efficient light scattering centers and unveiling a compositional threshold for octahedral site saturation. This modulation enabled the control of energy transfer dynamics between Ce³⁺ and Tb³⁺ ions, enhancing luminescence and mitigating quenching. The Sc admixing process regulated luminous efficacy (LE), color rendering index (CRI), and correlated color temperature (CCT), with adjustments in CRI from 68 to 84 and CCT from 3545 K to 12,958 K. The Ce³⁺-doped Tb₃Al_5−xSc_xO₁₂ crystal (where x = 2.0) achieved the highest LE of 114.6 lm/W and emitted light at a CCT of 4942 K, similar to daylight white. This approach enables the design and development of functional materials with tailored optical properties applicable to lighting technology, persistent phosphors, scintillators, and storage phosphors. Full article