Search Results (84)

Satellite cloud images exhibit complex multidimensional characteristics, including spectral, textural, and spatiotemporal dynamics. The temporal evolution of cloud systems plays a crucial role in accurate classification, particularly under the coexistence of multiple weather systems. However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. To address this limitation, we propose SIG-ShapeFormer, a novel classification model specifically designed for satellite cloud images with temporal continuity. To the best of our knowledge, this work is the first to transform satellite cloud data into multivariate time series and introduce a unified framework for multi-scale and multimodal feature fusion. SIG-Shapeformer consists of three core components: (1) a Shapelet-based module that captures discriminative and interpretable local temporal patterns; (2) a multi-scale Inception module combining 1D convolutions and Transformer encoders to extract temporal features across different scales; and (3) a differentially enhanced Gramian Angular Summation Field (GASF) module that converts time series into 2D texture representations, significantly improving the recognition of cloud internal structures. Experimental results demonstrate that SIG-ShapeFormer achieves a classification accuracy of 99.36% on the LSCIDMR-S dataset, outperforming the original ShapeFormer by 2.2% and outperforming other CNN- or Transformer-based models. Moreover, the model exhibits strong generalization performance on the UCM remote sensing dataset and several benchmark tasks from the UEA time-series archive. SIG-Shapeformer is particularly suitable for remote sensing applications involving continuous temporal sequences, such as extreme weather warnings and dynamic cloud system monitoring. However, it relies on temporally coherent input data and may perform suboptimally when applied to datasets with limited or irregular temporal resolution. Full article

Previously, we reported that microplastic volatile organic compounds are present within the Chrysaora chesapeakei of Chesapeake Bay, MD. In this study, we report the presence of contaminants of emerging concern (CECs) on the hydrophobic surface of microplastic (MP) particles extracted from the C. chesapeakei, detected by Raman spectroscopy and identified by Wiley’s KnowItAll Software with IR & Raman Spectral Libraries. C. chesapeakei encounters various microplastics and emerging contaminants as it floats through the depths of the Patuxent River water column. This study identifies subsuming CECs found directly on microplastics from within C. chesapeakei in the wild using Raman spectroscopy. Among the extracted microplastics, some of the emerging contaminants found on the different microplastics were pesticides, pharmaceuticals, minerals, food derivatives, wastewater treatment chemicals, hormones, and recreational drugs. Our results represent the first of such findings in C. chesapeakei, obtained directly from the field, and indicate C. chesapeakei’s relationship with microplastics, with this species serving as a vector of emerging contaminants through the marine food web. This paper further illustrates a relationship between different types of plastics that attract dissimilar types of emerging pollutants in the same surrounding environmental conditions, underscoring the urgent need for further research to fully understand and mitigate the risks that MPs coexist with contaminants. Full article

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m²/m²), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m²/m²). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article

The coexistence of cloud and snow is very common in remote sensing images. It presents persistent challenges for automated interpretation systems, primarily due to their highly similar visible light spectral characteristic in optical remote sensing images. This intrinsic spectral ambiguity significantly impedes accurate cloud and snow segmentation tasks, particularly in delineating fine boundary features between cloud and snow regions. Much research on cloud and snow segmentation based on deep learning models has been conducted, but there are still deficiencies in the extraction of fine boundaries between cloud and snow regions. In addition, existing segmentation models often misjudge the body of clouds and snow with similar features. This work proposes a Multi-scale Feature Mixed Attention Network (MFMANet). The framework integrates three key components: (1) a Multi-scale Pooling Feature Perception Module to capture multi-level structural features, (2) a Bilateral Feature Mixed Attention Module that enhances boundary detection through spatial-channel attention, and (3) a Multi-scale Feature Convolution Fusion Module to reduce edge blurring. We opted to test the model using a high-resolution cloud and snow dataset based on WorldView2 (CSWV). This dataset contains high-resolution images of cloud and snow, which can meet the training and testing requirements of cloud and snow segmentation tasks. Based on this dataset, we compare MFMANet with other classical deep learning segmentation algorithms. The experimental results show that the MFMANet network has better segmentation accuracy and robustness. Specifically, the average MIoU of the MFMANet network is 89.17%, and the accuracy is about 0.9% higher than CSDNet and about 0.7% higher than UNet. Further verification on the HRC_WHU dataset shows that the MIoU of the proposed model can reach 91.03%, and the performance is also superior to other compared segmentation methods. Full article

Vehicular Internet of Things (V-IoT) networks, sustained by a high-density deployment of roadside units and sensor-equipped vehicles, are currently at the edge of next-generation intelligent transportation system evolution. However, offering stable, low-latency, and energy-efficient communication in such heterogeneous and delay-prone environments is challenging due to limited spectral resources and diverse quality of service (QoS) requirements. This paper presents a Priority-Aware Spectrum Management (PASM) scheme for IoT-based vehicular networks. This dynamic spectrum access scheme integrates interweave, underlay, and coexistence modes to optimize spectrum utilization, energy efficiency, and throughput while minimizing blocking and interruption probabilities. The algorithm manages resources efficiently and gives proper attention to each device based on its priority, so all IoT devices, from high to low priority, receive continuous and reliable service. A Continuous-Time Markov Chain (CTMC) model is derived to analyze the proposed algorithm for various network loads. Simulation results indicate improved spectral efficiency, throughput, delay, and overall QoS compliance over conventional access methods. These findings establish that the proposed algorithm is a scalable solution for dynamic V-IoT environments. Full article

In this study, silver nanoparticles (Ag NPs) were synthesized on the surface of rutile-phase titanium dioxide (R-TiO₂) using a plasma-assisted technique. Comprehensive analyses were conducted to investigate the structural, morphological, optical, and electrical properties of the synthesized nanocomposites. Transmission electron microscopy (TEM) images revealed the uniform decoration of Ag NPs (average size: 29.8 nm) on the R-TiO₂ surface. X-ray diffraction (XRD) confirmed the polycrystalline nature of the samples, with decreased diffraction peak intensity indicating reduced crystallinity due to Ag decoration. The Williamson–Hall analysis showed increased crystallite size and reduced tensile strain, suggesting grain growth and stress relief. Raman spectroscopy revealed quenching and broadening of R-TiO₂ vibrational modes, likely due to increased oxygen vacancies. X-ray photoelectron spectroscopy (XPS) confirmed successful plasma-assisted deposition and the coexistence of Ag⁰ and Ag⁺ states, enhancing surface reactivity. UV-Vis spectroscopy demonstrated enhanced light absorption across the spectral range, attributed to localized surface plasmon resonance (LSPR), and a reduced optical bandgap. Dielectric properties, including dielectric constants, loss factor, and AC conductivity, were evaluated across frequencies (4–8 MHz) and temperatures (20–240 °C). The AC conductivity results indicated correlated barrier hopping (CBH) and overlapping large polaron tunneling (OLPT) as the primary conduction mechanisms. Composition-dependent dielectric behavior was interpreted through the Coulomb blockade effect. These findings suggest the potential of plasma assisted Ag NP-decorated R-TiO₂ nanostructures for photocatalysis, sensor and specific electro electrochemical systems applications. Full article

The unambiguous detection of biosignatures in exoplanet atmospheres is a primary objective for astrobiologists and exoplanet astronomers. The primary methodology is the observation of combinations of gases considered unlikely to coexist in an atmosphere or individual gases considered to be highly biogenic. Earth-like examples of the former include CH₄ and O₃, and the latter includes dimethyl sulfide (DMS). To improve the plausibility of the detection of life, I argue that the isotope ratios of key atmospheric species are needed. The C isotope ratios of CO₂ and CH₄ are especially valuable. On Earth, thermogenesis and volcanism result in a substantial difference in δ¹³C between atmospheric CH₄ and CO₂ of ~−25‰. This difference could have changed significantly, perhaps as large as −95‰ after the evolution of hydrogenotrophic methanogens. In contrast, nitrogen fixation by nitrogenase results in a relatively small difference in δ¹⁵N between N₂ and NH₃. Isotopic biosignatures on ancient Earth and rocky exoplanets likely coexist with much larger photochemical signatures. Extreme δ¹⁵N enrichment in HCN may be due to photochemical self-shielding in N₂, a purely abiotic process. Spin-forbidden photolysis of CO₂ produces CO with δ¹³C < −200‰, as has been observed in the Venus mesosphere. Self-shielding in SO₂ may generate detectable ³⁴S enrichment in SO in atmospheres similar to that of WASP-39b. Sufficiently precise isotope ratio measurements of these and related gases in terrestrial-type exoplanet atmospheres will require instruments with significantly higher spectral resolutions and light-collecting areas than those currently available. Full article

The cooperation between microalgae and bacteria can enhance the carbon fixation efficiency of microalgae. In this study, a microalgae-bacteria coexistence system under high-concentration CO₂ stress was constructed, and the bacterial community structure of the entire system was analyzed using the 16S rDNA technique. Microbacterium sp., Bacillus sp., and Aeromonas sp. were screened and demonstrated to promote carbon fixation in Chlorella vulgaris HL 01 (C. vulgaris HL 01). Among them, the Aeromonas sp. + C. vulgaris HL 01 experimental group exhibited the most significant effect, with an increase of about 24% in the final biomass yield and a daily carbon fixation efficiency increase of about 245% (day 7) compared to the control group. Continuous cultivation of microalgae and bacterial symbiosis showed that bacteria could utilize the compounds secreted by microalgae for growth and could produce nutrients to maintain the vitality of microalgae. Detection of extracellular organic compounds of microorganisms in the culture broth by excitation-emission matrix spectral analysis revealed that bacteria utilized the aromatic proteinaceous compounds and others secreted by C. vulgaris HL 01 and produced new extracellular organic compounds required by C. vulgaris HL 01. The metabolic organic substances in the liquids of the experimental groups and the control group were analyzed by liquid chromatography-mass spectrometry, and it was found that 31 unique organic substances of C. vulgaris HL 01 were utilized by bacteria, and 136 new organic substances were produced. These differential compounds were mainly organic acids and their derivatives, benzene compounds, and organic heterocyclic compounds, etc. These results fully demonstrate that the carbon fixation ability and persistence of C. vulgaris HL 01 are improved through material exchange between microalgae and bacteria. This study establishes a method to screen carbon-fixing symbiotic bacteria and verifies that microalgae and bacteria can significantly improve the carbon fixation efficiency of microalgae for high-concentration CO₂ through material exchange, providing a foundation for further research of microalgae-bacterial carbon fixation. Full article

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization. Full article

Reformation of the 3.7–4.0 GHz band to expand 5G communication deployment poses a risk of 5G signals disrupting radar altimeter operation, leading to data loss or inaccuracies. Thus, this paper proposes a guard band protection method to facilitate the coexistence of 5G base stations and radar altimeters operating in the 4.2–4.4 GHz band. To enhance the adjacent channel leakage ratio (ACLR), we implemented spectral regrowth on an oversampled waveform using a high-power amplifier model, filtering out-of-band spectral emissions. The results demonstrated that a 150 MHz guard band enables coexistence, except in the case of the 16-by-16 antenna array in rural environments. Notably, for the 4-by-4 antenna array in urban environments, coexistence can be achieved using a 50 MHz guard band. The proposed mitigation techniques may also be extended to promote coexistence between non-terrestrial networks and 5G communication systems, including satellites, unmanned aerial vehicles, and hot air balloons. Full article

Unmanned aerial vehicles (UAVs) are extensively employed in pursuit, rescue missions, and agricultural applications. These operations necessitate substantial data and video transmission, requiring significant spectral resources. The unlicensed millimeter wave (mmWave) spectrum, especially in the 60 GHz frequency band, offers promising potential for UAV communications. However, WiGig users are the incumbent users of the 60 GHz unlicensed spectrum. Therefore, to ensure fair coexistence between UAV-based new radio-unlicensed (NR-U) users and WiGig users, unlicensed spectrum-sharing strategies need to be meticulously designed. Due to the beam directionality of the NR-U system, traditional listen-before-talk (LBT) spectrum sensing strategies are no longer effective in NR-U/WiGig systems. To address this, we propose a new cooperative unlicensed spectrum sensing strategy based on mmWave beamforming direction. In this strategy, UAV and WiGig users cooperatively sense the unlicensed spectrum and jointly decide on the access strategy. Our analysis shows that the proposed strategy effectively resolves the hidden and exposed node problems associated with traditional LBT strategies. Furthermore, we consider the sensitivity of mmWave to obstacles and analyze the effects of these obstacles on the spectrum-sharing sensing scheme. We examine the unlicensed spectrum access probability and network throughput under blockage scenarios. Simulation results indicate that although obstacles can attenuate the signal, they positively impact unlicensed spectrum sensing. The presence of obstacles can increase spectrum access probability by about 60% and improve system capacity by about 70%. Full article

Decoupling uplink and downlink access (DUDA) has latterly proven to effectively enhance transmission efficiency in wireless communication systems, with particular effectiveness observed in both terrestrial and unmanned aerial vehicle (UAV) systems. In this paper, we propose an innovative DUDA approach specifically designed for non-geostationary orbit (NGEO) multi-layer satellite systems (MSS), integrating strategies to mitigate in-line interference to ensure spectral coexistence between geostationary Earth orbit (GEO) and NGEO satellites. Notably, the interference from the main lobe of directional antennas on NGEO satellites is meticulously characterized using a spherical surface model based on the geocentric angle. Within the framework of proposed DUDA method, a user terminal (UT) can establish communication with the satellite which provides the highest average power of received signal in compliance with the unique exclusion angle constraints of NGEO satellites. The association probability of DUDA is analyzed based on stochastic geometry. The performance evaluation, conducted in terms of transmission rate, reveals that the proposed DUDA methodology yields significant improvements when compared to conventional access schemes. Full article

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture. Full article

Accurate mapping of vegetation in the coexisting area of paddy fields and wetlands plays a key role in the sustainable development of agriculture and ecology, which is critical for national food security and ecosystem balance. The phenology-based rice mapping algorithm uses unique flooding stages of paddy rice, and it has been widely used for rice mapping. However, wetlands with similar flooding signatures make rice extraction in rice–wetland coexistence challenging. In this study, we analyzed phenology differences between rice and wetlands based on the Sentinel-1/2 data and used the random forest algorithm to map vegetation in the Poyang Lake Basin, which is a typical rice–wetland coexistence zone in the south of China. The rice maps were validated with reference data, and the highest overall accuracy and Kappa coefficient was 0.94 and 0.93, respectively. First, monthly median composited and J-M distance methods were used to analyze radar and spectral data in key phenological periods, and it was found that the combination of the two approaches can effectively improve the confused signal between paddy rice and wetlands. Second, the VV and VH polarization characteristics of Sentinel-1 data enable better identification of wetlands and rice. Third, from 2018 to 2022, paddy rice in the Poyang Lake Basin showed the characteristics of planting structure around the Poyang Lake and its tributaries. The mudflats were mostly found in the middle and northeast of Poyang Lake, and the wetland vegetation was found surrounding the mudflats, forming a nibbling shape from the lake’s periphery to its center. Our study demonstrates the potential of mapping paddy rice in the rice–wetland coexistence zone using the combination of Sentinel-1 and Sentinel-2 imagery, which would be beneficial for balancing the changes between paddy rice and wetlands and improving the vulnerability of the local ecological environment. Full article

Corals are habitat-forming organisms on tropical and sub-tropical reefs, often displaying diverse phenotypic behaviors that challenge field-based monitoring and assessment efforts. Symbiont chlorophyll (Chl) is a long-recognized indicator of intra- and inter-specific variation in coral’s response to environmental variability and stress, but the quantitative Chl assessment of corals at the reef scale continues to prove challenging. We integrated field, airborne, and laboratory techniques to test and apply the use of reflectance spectroscopy for in situ and reef-scale estimation of Chl a and Chl c2 concentrations in a shallow reef environment of Kāne‘ohe Bay, O‘ahu. High-fidelity spectral signatures (420–660 nm) derived from field and airborne spectroscopy quantified Chl a and Chl c2 concentrations with demonstrable precision and accuracy. Airborne imaging spectroscopy revealed a 10-fold range of Chl concentrations across the reef ecosystem. We discovered a differential pattern of Chl a and Chl c2 use in symbiont algae in coexisting corals indicative of a physiological response to decreasing light levels with increasing water depth. The depth-dependent ratio of Chl c2:a indicated the presence of two distinct light-driven habitats spanning just 5 m of water depth range. Our findings provide a pathway for further study of coral pigment responses to environmental conditions using field and high-resolution airborne imaging spectroscopy. Full article