Search Results (248)

The temperature of the middle atmosphere is of great significance in the coupled study of the upper and lower layers. A pure rotational Raman–Rayleigh scattering LiDAR system was developed for profiling the middle atmospheric temperature at daytime and nighttime continuously by employing an ultra-narrow band interferometer. The comparisons between LiDAR detections and radiosonde data show that the LiDAR system has temperature detection capabilities of 80 km and 60 km at night and during the day, respectively. The results demonstrate that our method can reliably detect the atmospheric temperature in the middle atmosphere. The significant non-uniformity in the horizontal distribution of temperature in the middle atmosphere and the vertical gradient of atmospheric temperature could be observed by using the developed LiDAR. 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

Lipid-shelled microbubbles (MBs) and echogenic liposomes (ELIPs) have been proposed as acoustofluidic theranostic agents after having been proven to be efficient in diagnostics as ultrasonic contrast agents. Their mechanical properties—such as shell stiffness, friction, and resonance frequency—are critical to their performance, stability, oscillatory dynamics, and response to sonication. A precise characterization of these properties is essential for optimizing their biomedical applications, however the current methods vary significantly in their sensitivity and accuracy. This review examines the experimental and theoretical methodologies used to quantify the mechanical properties of MBs and ELIPs, discusses how each approach estimates shell stiffness and friction, and outlines the strengths and limitations inherent to each technique. Additionally, the effects of parameters such as temperature and lipid composition on MB and ELIP mechanical behavior are examined. Four characterization methods are analyzed, including frequency-dependent attenuation, optical observation, atomic force microscopy (AFM), and laser scattering, their advantages and limitations are critically assessed. Additionally, the factors that influence the mechanical properties of the MBs and ELIPs, such as temperature and lipid composition, are examined. Frequency-dependent attenuation was shown to provide reliable shell elasticity estimates but is influenced by nonlinear oscillations, AFM confirms that microbubble stiffness is size-dependent with smaller bubbles exhibiting higher shell stiffness, and theoretical models such as modified Rayleigh–Plesset equations increasingly incorporate viscoelastic shell properties to improve prediction accuracy. However, many of these models still assume radial symmetry and neglect inter-bubble interactions, which can lead to inaccurate elasticity values when applied to dense suspensions. In such cases, using modified frameworks like the Sarkar model, which incorporates damping and surface tension explicitly, may provide more reliable estimates under nonlinear conditions. Additionally, lipid composition and temperature significantly affect shell mechanics, with higher temperatures generally reducing stiffness. On the other hand, inconsistencies in experimental protocols hinder direct comparison across studies, highlighting the need for standardized characterization methods and improved computational modeling. Full article

Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel multi-modal Raman light sheet microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532 nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics. Full article

Quantitative precipitation estimation using polarimetric radar in attenuation-prone frequency (X-band) in tropical regions characterized by convective rain systems with high intensities is a major challenge due to strong attenuations that can lead to total signal extinction over short distances. However, some authors have addressed this issue in Benin since 2006 in the framework of the African Monsoon Multidisciplinary Analysis program. Thus, with an experimental setup consisting of an X-band polarimetric weather radar (Xport) and a network of rain gauges, investigations have started on the subject with the aim of improving rainfall estimates. Based on simulated polarimetric variables and using a Multilayer Perceptron artificial neural network, several bi-variable and tri-variable algorithms were assessed in this study. The data used in this study are of two categories: (i) simulated polarimetric variables (Rayleigh reflectivity $Z$, horizontal attenuation $A_h$, horizontal reflectivity $Z_h$, differential reflectivity $Z_{dr}$, and specific differential phase $K_{dp}$) and rainfall intensity (R) obtained from Rain Drop Size Distribution (DSD) measurements used for algorithm evaluation (training and testing); (ii) polarimetric variables measured by the Xport radar and rainfall intensity measured by rain gauges used for algorithm validation. The simulations are performed using the T-matrix code, which leverages the scattering properties of spheroidal particles. The DSD measurements taken in northwest Benin were used as input for this code. For each spectrum, the T-matrix code simulates multiple variables. The simulated data (first category) were divided into two parts: one for training and one for testing. Subsequently, the best algorithms were validated with the second category of data. The performance of the algorithms during training, testing, and validation was evaluated using metrics. The best selected algorithms are $A1:R(Z,K_{dp})$ and $A12:R(Z_{dr},K_{dp})$ (among the bi-variable); $B2:R(Z_h,Z_{dr},K_{dp})$ and $B3:R(A_h,Z_{dr},K_{dp})$ (among the tri-variable). Tri-variable algorithms outperform bi-variable algorithms. Validation with observation data (Xport measurements and rain gauge network) showed that the algorithm $B3:R(A_h,Z_{dr},K_{dp})$ performs better than $B2:R(Z_h,Z_{dr},K_{dp})$. Full article

The New Zealand Kaikoura Earthquake (M_w 7.8, 14 November 2016) produced co-seismic flashes of earthquake light near the ground at midnight, 230 km north of the epicentre. Mostly, there was a white hemisphere in the atmosphere just above the ground, up to 250 m radius, the colour becoming radially increasingly dark blue. Fifteen videos were available for analysis which led to the following new or reaffirmed conclusions: (i) the blue colour is due to Rayleigh Scattering (new explanation); (ii) the light also sometimes occurs within low clouds but not as lightning—this is a new classification of earthquake light; (iii) the lithology may be greywacke, broadening previous literature emphasis on igneous sources; (iv) the light is most probably explained in our study area by seismically pressured microscopic quartz producing electric fields emerging into the atmosphere and reacting with it—mechanisms relying on particle-grinding or creation of cracks in rock are unlikely in the study area; (v) within the Wellington study area, the light is mostly independent of faults or their movement and is caused by seismic impulses which have travelled hundreds of kilometres from the epicentre—this possible independence from faults has not been clearly emphasised previously; and (vi) electrical grid problems are not the explanation. Full article

Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain largely unexplored. Our work addresses this gap by deriving mathematical expressions for the upper and lower bounds of cross-range resolution in ISAR imaging based on the computational resolution limit (CRL) theory for line spectrum reconstruction. Leveraging these explicit expressions, we first explore influencing factors of these bounds, including the traditional Rayleigh limit, number of scatterers, and peak signal-to-noise ratio (PSNR) of the scatterers. Then, we elucidate the minimum resource requirements in ISAR imaging imposed by CRL theory to meet the desired cross-range resolution, without which studying super-resolution algorithms becomes unnecessary in practice. Furthermore, we analyze the tradeoffs between the cumulative rotation angle, radar transmit energy, and other factors that contribute to optimizing the resolution. Simulations are conducted to demonstrate these tradeoffs across various ISAR imaging scenarios, revealing their high dependence on specific imaging targets. Full article

Due to its miniature size and single-pass nature, Unmanned Aerial Vehicle (UAV)-borne array synthetic aperture radar (SAR) is capable of obtaining three-dimensional (3D) electromagnetic scattering information with a low cost and high efficiency, making it widely applicable in various fields. However, the limited payload capacity of the UAV platform results in a limited number of array antennas and affects 3D resolution. This paper proposes a 3D imaging method for UAV-borne SAR based on nested difference co-arrays and azimuth multi-snapshots. We first designed an antenna arrangement based on nested arrays, generating a virtual antenna twice as long as the original one. Then, we used a difference co-array method for 3D imaging. The required multi-snapshot data were obtained through azimuth down-sampling, rather than traditional spatial averaging methods. Due to the slow flight of the UAV, this method could generate multiple SAR images without affecting the two-dimensional resolution. Based on simulations and real data verification, the proposed algorithm overcomes the problem of two-dimensional resolution decline caused by traditional spatial averaging methods and improves three-dimensional resolution ability, theoretically achieving half the Rayleigh resolution. Full article

To suppress the influence of aerosols scattering on the double-edge detection technique and achieve high-accuracy measurement of the wind field throughout the troposphere to the lower stratosphere, an ultraviolet 355 nm Rayleigh–Brillouin Doppler lidar technology based on a dual-pass dual Fabry–Perot interferometer (FPI) is proposed. The wind speed detection principle of this technology is analyzed, and the formulas for radial wind speed measurement error caused by random noise and wind speed measurement bias caused by Mie scattering signal contamination are derived. Based on the detection principle, the structure of the lidar system is designed. Combining the wind speed measurement error and measurement bias on both sides, the parameters of the dual-pass dual-FPI are optimized. The free spectral range (FSR) of the dual-pass dual-FPI is selected as 12 GHz, the bandwidth as 1.8 GHz, and the peak-to-peak spacing as 6 GHz. Further, the detection performance of this new type of Rayleigh–Brillouin Doppler lidar with the designed system parameters is simulated and analyzed. The simulation results show that at an altitude of 0–20 km, within the radial wind speed dynamic range of ±50 m/s, the radial wind speed measurement bias caused by aerosol scattering signal is less than 0.17 m/s in the cloudless region; within the radial wind speed dynamic range of ±30 m/s, the bias is less than 0.44 m/s and 0.91 m/s in the simulated cumulus cloud at 4 km where aerosol backscatter ratio R_β = 3.8 and cirrus cloud at 9 km where R_β = 2.9, respectively; using a laser with a pulse energy of 350 mJ and a repetition frequency of 50 Hz, a 450 mm aperture telescope, setting the detection zenith angle of 30°, vertical resolution of 26 m@0–10 km, 78 m@10–20 km, and 260 m@20–30 km, and a time resolution of 1 min, with the daytime sky background brightness taking 0.3 WSr⁻¹m⁻²nm⁻¹@355 nm, the radial wind speed measurement errors of the system during the day and night are below 2.9 m/s and 1.6 m/s, respectively, up to 30 km altitude, below 0.28 m/s at 10 km altitude, and below 0.91 m/s at 20 km altitude all day. Full article

The excavation of the Lieshan Kiln site represents a significant advance in the field of ceramic archaeology. Previous scholars fixated on the white porcelain unearthed from this kiln, yet this study zeroed in on celadon from the Northern Song and Jin Dynasties. Celadon samples were analyzed using colorimetry, energy-dispersive X-ray fluorescence spectroscopy (ED-XRF), scanning electron microscopy (SEM), polarizing microscopy, X-ray photoelectron spectroscopy (XPS), and thermal expansion analysis. Results revealed that material and technological advancements in the production of the Lieshan Kiln and reveal the special phase-separated structure in the glaze of the transparent celadon, with a weakly reduced firing atmosphere. Celadon bodies from both periods were crafted from local sedimentary clays in a single-ingredient formula, with the Jin Dynasty refining the preparation, leading to enhanced density and higher firing temperatures compared to the Northern Song Dynasty. The celadon glaze, a high-calcium type made up of glaze ash and specific clays, differed from the body materials. The high SiO₂/Al₂O₃ molar ratio, along with Fe₂O₃ and trace P₂O₅, promoted phase separation. Glaze coloration was modulated by the interaction of Fe³⁺ and Fe²⁺ ions, and chemical coloration by Fe ions prevailed when phase-separated particles were minute enough to avoid Rayleigh or Mie scattering. In conclusion, the study deepens the understanding of ancient ceramic production by exploring the phase separation structure and coloring mechanism of the celadon. Full article

Polarimetric radar systems play a crucial role in enhancing microwave remote sensing and target identification by providing a refined understanding of electromagnetic scattering mechanisms. This study introduces the concept of polarization scattering regions as a novel tool for describing the polarization characteristics across three spectral regions: the polarization Rayleigh region, the polarization resonance region, and the polarization optical region. By using ellipsoidal models, we simulate and analyze scattering across varying electrical sizes, demonstrating how these sizes influence polarization characteristics. The research leverages Cameron decomposition to reveal the distinctive scattering behaviors within each region, illustrating that at higher-frequency bands, scattering approximates spherical symmetry, with minimal impact from the target shape. This classification provides a comprehensive view of polarization-based radar cross-section regions, expanding upon traditional single-polarization radar cross-section regions. The results show that polarization scattering regions are practical tools for interpreting polarimetric radar data across diverse frequency bands. The applications of this research in radar target recognition, weather radar calibration, and radar polarimetry are discussed, highlighting the importance of frequency selection for accurately capturing polarization scattering features. These findings have significant implications for advancing weather radar technology and target recognition techniques, particularly as radar systems move towards higher frequency bands. Full article

This study analyses the wide-band algorithm, Cloud-J v.8.0, from the point of view of the validity of the choice of wide spectral intervals to accelerate the calculations of photolysis rates in the lower and middle atmosphere, considering the features of solar radiation propagation, and to assess the influence of the processes of reflection and scattering on molecules, aerosols, and clouds. The results show that the calculations performed using Cloud-J v.8.0 are in agreement with the data obtained using the high-resolution LibRadtran model. The study also considers the factors influencing the propagation of the solar flux through the atmosphere in Cloud-J v.8.0, which occurs following theoretical concepts. It is shown that the presence of cloud layers can increase photolysis rates by up to 40% in the above-cloud layer and decrease them by up to 20% below the cloud layer. The presence of volcanic aerosol can increase the photolysis rates in the upper part of the layer and above it by up to 75% and decrease them by up to 75% in the underlying atmosphere. Rayleigh scattering can both enhance photolysis rates in the troposphere and reduce them at large zenith angles. Thus, Cloud-J offers a robust method for modelling atmospheric photodissociation processes with high computational efficiency. Full article

With the demand for energy gravimetric and volumetric density in electrical vehicles, lithium-ion batteries are undergoing a trend toward larger formats, along with maximized cell-to-pack efficiency. Current battery thermal management systems and battery modeling, relying on point measurement (thermocouples/thermistors), face challenges in providing comprehensive characterization for larger batteries and extensive monitoring across the pack. Here, we proposed a novel Rayleigh-scattering-based distributed optical fiber sensor to deliver thermal images of a large prismatic cell. Using an optical fiber of 1 mm diameter wrapped around the cell, the optical sensor delivered over 400 unique measurement locations at 3 mm spatial resolution. During a 1.0 C charge, the optical-measured maximum temperature difference was 8.2 °C, while point-like thermocouples, located at the cell front surface and rear surface center, only had a 0.8 °C maximum temperature difference. Moreover, the all-surface-covered optical sensor identified hotspot generation around the vicinity of the tabs, highlighting the essential role of tabs. The maximum temperature on the negative current tab reached 113.9 °C during a 1.5 C discharge, while the hottest spot on the cell surface was only 52.1 °C. This was further validated by the operando thermal image in both the time domain and the spatial domain, facilitating a detailed analysis of the thermal-behavior-like heat generation on the current tabs, transmission through the surface, and dissipation to the cell bottom. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite was launched on 18 November 2017. The on-orbit calibration of the NOAA-20 VIIRS visible and near-infrared (VisNIR) bands has been very stable over time. However, NOAA-20 operational M1 (a dual gain band with a center wavelength of 0.41 µm) sensor data records (SDR) have exhibited persistent scene-dependent striping over clear-sky ocean (high gain, low radiance) since the beginning of the mission, different from other VisNIR bands. This paper studies the root causes of the striping in the operational NOAA-20 M1 SDRs. Two potential factors were analyzed: (1) polarization effect-induced striping over clear-sky ocean and (2) imperfect on-orbit radiometric calibration-induced striping. NOAA-20 M1 is more sensitive to the polarized lights compared to other NOAA-20 short-wavelength bands and the similar bands on the Suomi NPP and NOAA-21 VIIRS, with detector and scan angle-dependent polarization sensitivity up to ~6.4%. The VIIRS M1 top of atmosphere radiance is dominated by Rayleigh scattering over clear-sky ocean and can be up to ~70% polarized. In this study, the impact of the polarization effect on M1 striping was investigated using radiative transfer simulation and a polarization correction method similar to that developed by the NOAA ocean color team. Our results indicate that the prelaunch-measured polarization sensitivity and the polarization correction method work well and can effectively reduce striping over clear-sky ocean scenes by up to ~2% at near nadir zones. Moreover, no significant change in NOAA-20 M1 polarization sensitivity was observed based on the data analyzed in this study. After the correction of the polarization effect, residual M1 striping over clear-sky ocean suggests that there exists half-angle mirror (HAM)-side and detector-dependent striping, which may be caused by on-orbit radiometric calibration errors. HAM-side and detector-dependent striping correction factors were analyzed using deep convective cloud (DCC) observations (low gain, high radiances) and verified over the homogeneous Libya-4 desert site (low gain, mid-level radiance); neither are significantly affected by the polarization effect. The imperfect on-orbit radiometric calibration-induced striping in the NOAA operational M1 SDR has been relatively stable over time. After the correction of the polarization effect, the DCC-based striping correction factors can further reduce striping over clear-sky ocean scenes by ~0.5%. The polarization correction method used in this study is only effective over clear-sky ocean scenes that are dominated by the Rayleigh scattering radiance. The DCC-based striping correction factors work well at all radiance levels; therefore, they can be deployed operationally to improve the quality of NOAA-20 M1 SDRs. Full article

Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery. Full article