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The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems. [ABSTRACT FROM AUTHOR]

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model's effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques. [ABSTRACT FROM AUTHOR]

The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data. [ABSTRACT FROM AUTHOR]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics. [ABSTRACT FROM AUTHOR]

A new evaporative crystallization method was used to plate copper on water-soluble NaCl particles. Cu(NO3)2·3H2O was crystallized on NaCl particles by the heated ethanol volatilization, and then thermally decomposed to obtain CuO coating which was finally reduced to Cu coating. The affecting factors of coating effect and the influence of copper-plated NaCl particles on the pores structure of infiltrated AlSi12 foam were studied. The thermal oxidation pretreatment of NaCl particles increased the number of oxygen-containing functional groups and the uniform distribution of thermal etch pits, which improved the bonding strength between NaCl particles and Cu(NO3)2·3H2O coating. The addition of Tween-60 and PEG-6000 inhibited the micro-dissolution of NaCl particles in ethanol and promoted the formation of complete CuO coating. Infiltrated AlSi12 foam prepared from copper-plated NaCl particles had a more homogeneous pore structure and smaller pore size deviations than infiltrated AlSi12 foam obtained with uncoated NaCl particles, indicating that copper plating significantly improved the wettability of NaCl particles and AlSi12 alloy melt. This paper presents a new and low-cost surface treatment method, which provides a useful reference for the surface modification and application of water-soluble particles. [ABSTRACT FROM AUTHOR]

Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability. [ABSTRACT FROM AUTHOR]

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data. [ABSTRACT FROM AUTHOR]

Shadow indexes can effectively and easily detect shadows in remote sensing images. Although various shadow indexes are used, there is no consensus regarding the optimal one. The evaluation criteria used to determine the performance of shadow indexes are relatively homogeneous. A multi-criteria evaluation of widely used shadow indexes is necessary to analyse the application performance of each index in different scenarios. The eight most common shadow indexes were selected for this study. In addition to the standard visual inspection and accuracy assessment criteria, the cosine of solar incidence (cosi) was introduced to evaluate the sensitivity of the indexes to illumination intensity. Furthermore, in order to evaluate the separability of shadows and confusable objects, the interquartile range (IQR) of shadows and confusable objects was used to calculate the 1.5IQR separation ratio (1.5IQRSR). The multi-scale evaluation included mountain and urban scenarios, with the sensitivity to illumination intensity used to assess mountain scenarios and the capacity to separate confusable objects used to assess urban scenarios. The results obtained for each evaluation criterion were aggregated to form the multi-criteria evaluation score of the shadow indexes. The results show that the combinational shadow index (CSI), shadow detector index (SDI), and shadow index (SI) perform relatively well for each evaluation criterion. SI and CSI had the highest shadow detection accuracy in mountain and urban areas, respectively. The shadow enhancement index (SEI) and CSI had relatively high sensitivity to illumination intensity, especially CSI. In addition, CSI has the highest water separability but low separability for blue and dark impervious surfaces. SDI and SI have high separability for confusable objects, but they are ineffective in separating water bodies and less sensitive to illumination intensity. In summary, CSI, SDI, and SI excel in many application contexts, whereas the performances of the remaining five indexes were similar in all respects. [ABSTRACT FROM AUTHOR]

The existing neural network model in urban land-subsidence prediction is over-reliant on historical subsidence data. It cannot accurately capture or predict the fluctuation in the sequence deformation, while the improper selection of training samples directly affects its final prediction accuracy for large-scale urban land subsidence. In response to the shortcomings of previous urban land-subsidence predictions, a subsidence prediction method based on a neural network algorithm was constructed in this study, from a multi-factorial perspective. Furthermore, the scientific selection of a large range of training samples was controlled using a K-shape clustering algorithm in order to produce this high-precision urban land subsidence prediction method. Specifically, the main urban area of Kunming city was taken as the research object, LiCSBAS technology was adopted to obtain the information on the land-subsidence deformation in the main urban area of Kunming city from 2018–2021, and the relationship between the land subsidence and its influencing factors was revealed through a grey correlation analysis. Hydrogeology, geological structure, fault, groundwater, high-speed railways, and high-rise buildings were selected as the influencing factors. Reliable subsidence training samples were obtained by using the time-series clustering K-shape algorithm. Particle swarm optimization–back propagation (PSO-BP) was constructed from a multi-factorial perspective. Additionally, after the neural network algorithm was employed to predict the urban land subsidence, the fluctuation in the urban land-subsidence sequence deformation was predicted with the LSTM neural network from a multi-factorial perspective. Finally, the large-scale urban land-subsidence prediction was performed. The results demonstrate that the maximum subsidence rate in the main urban area of Kunming reached −30.591 mm ⋅ a − 1 between 2018 and 2021. Moreover, there were four main significant subsidence areas in the whole region, with uneven distribution characteristics along Dianchi: within the range of 200–600 m from large commercial areas and high-rise buildings, within the range of 400–1200 m from the under-construction subway, and within the annual average. The land subsidence tended to occur within the range of 109–117 mm of annual average rainfall. Furthermore, the development of faults destroys the stability of the soil structure and further aggravates the land subsidence. Hydrogeology, geological structure, and groundwater also influence the land subsidence in the main urban area of Kunming. The reliability of the training sample selection can be improved by clustering the subsidence data with the K-shape algorithm, and the constructed multi-factorial PSO-BP method can effectively predict the subsidence rate with a mean squared error (MSE) of 4.820 mm. The prediction accuracy was slightly improved compared to the non-clustered prediction. We used the constructed multi-factorial long short-term memory (LSTM) model to predict the next ten periods of any time-series subsidence data in the three types of cluster data (Cluster 1, Cluster 2, and Cluster 3). The root mean square errors (RMSE) were 0.445, 1.475, and 1.468 mm; the absolute error ranges were 0.007–1.030, 0–3.001, and 0.401–3.679 mm; the errors (mean absolute error, MAE) were 0.319, 1.214, and 1.167 mm, respectively. Their prediction accuracy was significantly improved, and the predictions met the measurement specifications. Overall, the prediction method proposed from the multi-factorial perspective improves large-scale, high-accuracy urban land-subsidence prediction. [ABSTRACT FROM AUTHOR]

In order to prevent landslide and collapse in the mining area, it is essential not only to gain a full understanding of the surface deformation information about the open-pit mining area and surrounding dumps, but also to closely monitor the development of surface deformation. Small baseline set (SBAS) technology can be applied to extract the high-resolution spatial-temporal deformation information from the mining areas. Due to decoherence, however, it is difficult to obtain the effective information on deformation from some areas where the results are monitored. This paper proposes a kriging interpolation method that performs better in filling the missing values in the InSAR monitored results by integrating GPS measured data with InSAR monitored data. In the experimental process, 69 Sentinel-1 image data were collected from July 2018 to November 2020. The SBAS-InSAR technology was applied to monitor the surface deformation caused to the Kunyang phosphate mining area and surrounding dumping sites. It was found out that all the mining areas across the Kunyang Phosphorus Mine and the surrounding dumps suffered severe surface deformation, with the maximum line of sight (LOS) deformation rate of −132 mm/yr located in the dump in the north of the second mining area. The verification result shows that InSAR measurement results are consistent with the deformation estimated from GPS measurements. The root mean square error of most points is maintained within ±10 mm. The Kriging interpolation results of fusion GPS data are in good agreement with the GPS results. The root mean square errors at the 4 verification points are ±6.2 mm, ±4.6 mm, ±6.4 mm, ±7.8 mm. The method proposed in this paper is effective in filling in the missing values in the incoherent area, and can be applied to mine surface deformation monitoring with high reliability. The results of this paper serve as a scientific basis for the prevention and control of landslides, collapses and other hidden risks of geological disasters posed by the mining operations conducted in the Kunyang Phosphorus Mine. [ABSTRACT FROM AUTHOR]

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m2. Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m2. Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m2. In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively. [ABSTRACT FROM AUTHOR]

The effects of semi-solid extrusion temperature change, extrusion pressure, SiC content and T5 heat treatment on the microstructure, mechanical properties, and wear resistance of SiC particle strengthened high aluminum zinc-base alloy ( SiC P / ZA 2 7) composites were studied. The results show that semi-solid extrusion broke the dendrites of SiC P / ZA 2 7 composites, refined their grain structure, and improved particle aggregation. The density, hardness, yield strength, tensile strength and elongation of SiC P / ZA 2 7 composites first increased and then decreased when the extrusion temperature and SiC content increased, and also increased when the extrusion pressure rose. The optimal extrusion temperature, pressure and SiC content are 475 ∘ , 15 MPa and 10 wt.%, respectively. T5 heat treatment further refined the crystalline grains and promoted Mg 3 2 Al 4 7 Cu 5 and CuZn 5 to precipitate as strengthening phases, which improve the mechanical properties and wear resistance of SiC P / ZA 2 7 composites. Consequently, the hardness, yield strength, tensile strength and elongation of the heat-treated composites improved by 18.99%, 9.66%, 4.93% and 9.76%, respectively. The wear loss of the heat-treated composites reduced by 31.65% under a load of 1600 N and a rotational speed of 200 r/min compared with the as-cast composites. [ABSTRACT FROM AUTHOR]

At present, as a research hotspot for time series data (TSD), the deep clustering analysis of TSD has huge research value and practical significance. However, there still exist the following three problems: (1) For deep clustering based on joint optimization, the inevitably mutual interference existing between deep feature representation learning progress and clustering progress leads to difficult model training especially in the initial stage, the possible feature space distortion, inaccurate and weak feature representation; (2) Existing deep clustering methods are difficult to intuitively define the similarity of time series and rely heavily on complex feature extraction networks and clustering algorithms. (3) Multidimensional time series have the characteristics of high dimensions, complex relationships between dimensions, and variable data forms, thus generating a huge feature space. It is difficult for existing methods to select discriminative features, resulting in generally low accuracy of methods. Accordingly, to address the above three problems, we proposed a novel general two-stage multi-dimensional spatial features based multi-view deep clustering method 1DCAE-TSSAMC (One-dimensional deep convolutional auto-encoder based two-stage stepwise amplification multi-clustering). We conducted verification and analysis based on real-world important multi-scenario, and compared with many other benchmarks ranging from the most classic approaches such as K-means and Hierarchical to the state-of-the-art approaches based on deep learning such as Deep Temporal Clustering (DTC) and Temporal Clustering Network (TCN). Experimental results show that the new method outperforms the other benchmarks, and provides more accurate, richer, and more reliable analysis results, more importantly, with significant improvement in accuracy and spatial linear separability. [ABSTRACT FROM AUTHOR]

Design efficient electrocatalysts become significant for the hydrogen evolution reaction (HER) in alkaline solution. Here, the HER performance on Pt-Co(OH) 2 has been investigated by using DFT calculations. Compared with pure Pt, the Pt-Co(OH) 2 shows excellent hydrogen binding energy (−0.29 eV) and hydrogen adsorption free energy (0.02 eV). More strikingly, the water dissociation process on Pt-Co(OH) 2 is an exothermic reaction while that of Pt is an endothermic reaction, demonstrating that the Pt-Co(OH) 2 is an excellent catalyst for HER in alkaline medium. Then the two-step electrodepositions was used to fabricate Pt-Co(OH) 2 on the carbon paper (CP). The Pt-Co(OH) 2 /CP shows the lowest overpotential of only 37.6 mV at a current density of 10 mA cm−2 in 0.1M KOH, the smallest Tafel slope of 58 mV dec−1 and the good stability for 20000s without obvious degradation. Image 1 • The first time to study the HER performance on Pt-Co(OH) 2 by DFT calculations techniques. • The computational results show that the Pt-Co(OH) 2 is an excellent catalyst for HER in alkaline medium. • By two-step electrodepositions to prepare Pt-Co(OH) 2 , the experiment confirm the results of computational. • The Pt-Co(OH) 2 /CP shows the lowest overpotential and the smallest Tafel slope compared with those of the other samples. [ABSTRACT FROM AUTHOR]

Focused on the Beidou satellite system (BDS), dual satellite system (GPS/GLONASS) and three satellite system (BDS/GPS/GLONASS), the parameters influencing the quality of static and real-time dynamic positioning date of the GNSS systems, such as the multipath effect, ionospheric delay, data utilization rate and signal-to-noise ratio, number of satellites and accuracy factor PDOP value, have been comparatively analyzed in this paper. The results of tests have shown that compared to the single satellite system BDS the average multipath errors (mp1 and mp2) of multi satellite system fusion decreased by 18 and 8% respectively, due to the increase in the number of visible satellites in multi-satellite fusion, it is possible to compensate each other to a certain extent, thus weakening the influence of the multipath effect on the positioning results. When the cut-off angle is greater than 40°, the single system cannot meet the need of navigation and positioning, however having more visible satellites than the single system it is possible for multi satellite system fusion to maintain lower and more stable PDOP value for high precision positioning services. [ABSTRACT FROM AUTHOR]

Abstract By using the observation data and products of precise obit and clock offset from Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) and GNSS Research Centre, Curtin University in this paper, the positioning performance of BDS/QZSS satellite navigation system has been analyzed and evaluated in aspects of the quantity of visible satellites, DOP value, multipath effect, signal-to-noise ratio, static PPP and kinematic PPP. The analysis results show that compared to BDS single system when the cutoff angle are 30°and 40°, the DOP value of BDS/QZSS combined system has decreased above 20%, and the quantity of visible satellites increased about 16–30% respectively, because of the improved spatial geometric configuration. The magnitude of satellite multipath effect of BDS system shows the trend of MEO > IGSO > GEO, which is consistent with that of QZSS satellite system, as the constellation structure of the two systems is similar. The variation tendencies of signal-to-noise ratio with respect to elevation angle of the two systems are almost the same at all frequencies, showing that at the same elevation angle the signal-to-noise ratio of MEO satellites is higher than that of IGSO satellites, as the higher obit is the lower transmitting power is obtained. For having a specially designed obit, the variation of signal-to-noise ratio of BDS system is more stable. However, the magnitude of signal-to-noise ratio of QZSS system appears the trend of frequency 3 > frequency 2 > frequency 1. The static PPP performance of the BDS/QZSS combination system has been improved more significantly than the BDS single system in E, N and U directions. When the cutoff angle are at 7°, 15° and 30°, the PPP accuracy is increased about 25–34% in U direction, 10–13% and 23–34% in E and N directions respectively. When the elevation angle is large (40°), compared to BDS single system at lower elevation angles (7° and 15°) the PPP accuracy of the BDS/QZSS combination system is improved above 30% in U direction. In kinematic PPP performance, compared to BDS single system, the accuracy, availability and reliability of the BDS/QZSS combination system has been improved too, especially at large elevation angles (30° and 40°), the kinematic PPP accuracy in E and U directions has been improved about 10–50%, and above 50% in U direction. It can be concluded that the combination with QZSS system can improve the positioning accuracy, reliability and stability of BDS system. In the future, with the improvement of the satellite construction of Japan's QZSS system and the global networking of China's BDS satellites, the QZSS satellites will contribute greatly to improve the positioning accuracy, reliability, availability and stability of GNSS systems in areas such as cities, mountains, densely-packed buildings and severely covered areas in Asian-Pacific region. [ABSTRACT FROM AUTHOR]

Cr 2 N is the most promising blowing agent for the preparation of steel foam using melt foaming method. In this work, to obtain a blowing agent with suitable density and gas decomposition characteristics for steel melt foaming, Ni-P/Cr 2 N composite powder was prepared by electroless plating. The surface morphology, phase, coating thickness, density and decomposition characteristics of Ni-P/Cr 2 N composite powder were analyzed. The results indicate that the surface of Ni-P/Cr 2 N composite coating powder is covered by the high nickel and low phosphorus layer which has a dense and uniform cell structure. The decomposition rate of the Ni-P/Cr 2 N composite powder is 7.46 mW/mg slower than that of the uncoated Cr 2 N powder at 1107.4 °C. When the plating time is 30 min, the thickness of Ni-P layer reaches 2.86 μm, the density of the Ni-P/Cr 2 N composite powder is 7.45 g/cm 3 , and maximal decomposition rate temperature reaches 1500 °C. These findings suggested that Ni-P/Cr 2 N composite powder meets the requirements of decomposition temperature and density of the blowing agent used to produce steel foam with a uniform pore structure by the melt foaming method. [ABSTRACT FROM AUTHOR]

Ground-based radar interferometry, an innovative technology for surface deformation monitoring has the advantages of high-security, high-resolution, adapting to all kinds of weather conditions and so on. Ground-based radar interferometry using microwave signal as transmission signal, is susceptible to atmospheric disturbance. How to eliminate the atmospheric disturbance influence is the key to achieve high-precision monitoring. Accordingly, this article aims at developing an effective atmospheric disturbance correction model. Firstly, IBIS- L system and corner reflectors as the stable points were placed. Secondly, microwave refractive index was calculated through the atmospheric parameters. Furthermore, according to the additional displacement acquired by IBIS-L system, the radial distance, azimuth distance of corner reflectors, and microwave refractive index, the atmospheric disturbance correction model in using IBIS-L system for slope monitoring was established. Finally, the additional displacement calculated from the model is in comparison with additional displacement by IBIS-L system monitoring. Experiments show that, the model can effectively eliminate the influence of atmospheric disturbance, and ensure monitoring accuracy. [ABSTRACT FROM AUTHOR]

TiH 2 particles have been treated by oxidation and coating treatment, formed a SiO 2 /TiO x composite layer on the surface TiH 2 particles, which could effectively delays the gas evolution of TiH 2 particles. The morphology of oxidized TiH 2 particles is similar to as-received TiH 2 particles, when further coated TiO x /TiH 2 particles with SiO 2 , the surface becomes smoother. At 650 °C, the low-level decomposition platform time of gas release is retarded over 180s. When progressive coating with SiO 2 , the low level decomposition platform time is prolonged, however, the total amount of gas release is decreased. A balance between the delay in gas evolution and the total gas release is best achieved by oxidation with 400 °C/6h + 500 °C/1 h and coating with 50 mL Na 2 SiO 3 (0.5 mol/L). [ABSTRACT FROM AUTHOR]

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth's surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds. [ABSTRACT FROM AUTHOR]

The pansharpening method based on spatial injection is prone to spectral distortion due to the spatial detail injection amount mismatch. For this reason, this paper proposes a method that combines adaptive intensity component extraction and spatial detail injection optimisation. Firstly, a new method for extracting the I component of multispectral (MS) image is proposed, which decomposes each band of MS image and panchromatic (PAN) image according to the structure-energy mechanism through the joint bilateral filter and then determines the weight coefficient of each band of MS image to extract the I component by solving the minimum optimisation problem of the structure layer of PAN image and the energy layer of MS image. Secondly, a spatial detail extraction method combining local spatial frequency and local energy is proposed, and the method can fully extract the fine detail features of PAN images and MS images. In this paper, many experiments are conducted on QuickBird, GeoEye-1 and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods, and qualitative and quantitative analyses of the experimental results show that the proposed method has certain superiority. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is capable of detecting large areas of potentially unstable slopes. However, traditional time-series InSAR methods yield fewer valid measurement points (MPs) in alpine canyon regions. Distributed Scatterer (DS) Interferometry (DSI) technology serves as a potent tool for monitoring surface deformation in complex land cover areas; nonetheless, it grapples with high computational demands and low efficiency when interpreting deformation across extended time series. This study proposes an image compression–based DSI (ICDSI) method, which, building upon the DSI method, utilizes principal component analysis (PCA) to compress multi-temporal SAR images in the time dimension. It develops a module for compressing long-time sequence SAR images, acquires the compressed image (referred to as a virtual image), and integrates the developed image compression module into the DSI data processing flow to facilitate the inversion of long-time sequence InSAR land surface deformation information. To validate and assess the credibility of the ICDSI method, we processed a total of 78 ascending and 81 descending scenes of Sentinel-1A images spanning the period 2019–2021 using Small Baseline Subset (SBAS), DSI, and the ICDSI method proposed in this paper. Subsequently, these methods were applied to detect landscape displacements on both coasts of the Jinsha River Basin. The investigation reveals that the ICDSI method outperforms SBAS and DSI significantly in monitoring landslide displacements, enabling the detection of more measurement points (MPs) while utilizing less raw data. The accomplishments of this research program carry crucial theoretical implications and practical application value for the detection of surface deformation using long-time series InSAR. [ABSTRACT FROM AUTHOR]

• An aluminum foam with a double-layer structure was prepared and investigated. • The sound absorption valley value of the structure is higher than homogeneous structure. • A Lu acoustic-modified model for the aluminum foam with a double-layer structure is established. • Acoustic impedance with gradient and series–parallel configurations enhances sound absorption. Open-cell aluminum foam exhibits sound absorption valleys in the 2500-4000 Hz range, reducing its sound absorption performance in the 800–6300 Hz range. This paper prepared a novel aluminum foam with a double-layer structure using the infiltration casting method, and its pore structure, sound absorption performance, acoustic model, and sound absorption mechanism are investigated. The double-layer structure consists of a conventional pore (CP) layer with a single pore size of 250 μm or 600 μm and a porous cell wall (PCW) layer with double pore diameters of 250 μm and 600 μm. As the thickness ratio between the CP and PCW layers decreases, the acoustic absorption valley value of the double-layer structure increases when combined with a small-pore CP layer. In contrast, it decreases slightly when combined with a large-pores CP layer. The double-layer structure reaches a sound absorption valley value and average sound absorption coefficient of 0.912 and 0.902, respectively. The double-layer structure's sound absorption valley value is better than that of a homogeneous structure, improving sound absorption performance. A Lu modified model is established to characterize the sound absorption of the double-layer structure, which closely matches the experimental results. The modified model further analyzed the vital geometrical parameters affecting the absorption valleys. Acoustic impedance analysis shows the enhanced sound absorption valleys are due to: 1) Gradient acoustic impedance reduces sound reflections at the air-structure interfaces and increases secondary reflection at the interfaces in the structure; 2) The series–parallel configuration increased the resonance peak bandwidth, while the dual-sized resonance cavities regulated the resonance peak frequency. [ABSTRACT FROM AUTHOR]

Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results. [ABSTRACT FROM AUTHOR]

Al-Ti-C-(Ce) grain refiners were prepared by combining in-situ reaction, hot extrusion, and adding CeO2. The effects of second phase TiC particle size and distribution, extrusion ratio, and Ce addition on the grain-refining performance of grain refiners were investigated. The results show that about 10 nm TiC particles are dispersed on the surface and inside of 100–200 nm Ti particles by in-situ reaction. The Al-Ti-C grain refiners, which are made, by hot extrusion, of a mixture of in-situ reaction Ti/TiC composite powder and Al powder, increase the effective nucleation phase of α-Al and hinder grain growth due to the fine and dispersed TiC; this results in the average size of pure aluminum grains to decrease from 1912.4 μm to 504.8 μm (adding 1 wt.% Al-Ti-C grain refiner). Additionally, with the increase of the extrusion ratio from 13 to 30, the average size of pure aluminum grains decreases further to 470.8 μm. This is because the micropores in the matrix of grain refiners are reduced, and the nano-TiC aggregates are dispersed with the fragmentation of Ti particles, resulting in a sufficient Al-Ti reaction and an enhanced nucleation effect of nano-TiC. Furthermore, Al-Ti-C-Ce grain refiners were prepared by adding CeO2. Under the conditions of holding for 3–5 min and adding a 5.5 wt.% Al-Ti-C-Ce grain refiner, the average size of pure aluminum grains is reduced to 48.4–48.8 μm. The reason for the excellent grain-refining and good anti-fading performance of the Al-Ti-C-Ce grain refiner is presumedly related to the Ti2Al20Ce rare earth phases and [Ce] atoms, which hinder agglomeration, precipitation, and dissolution of the TiC and TiAl3 particles. [ABSTRACT FROM AUTHOR]

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. [ABSTRACT FROM AUTHOR]

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images. [ABSTRACT FROM AUTHOR]

Noise pollution has become one of the environmental hazards in parallel with air pollution, water pollution, and solid waste pollution worldwide. Cellulose aerogel is a new porous sound-absorbing material that is highly efficient, green, recyclable, and degradable, showing bright prospect for the application in noise suppression. In this study, we fabricated multifunctional acoustic absorptive cellulose nanocrystal (CNC) aerogels by employing calcium chloride as the green crosslinker of CNC, followed by freeze drying. The as-prepared CNC aerogels exhibit excellent broadband acoustic absorption performance with a high maximum absorption coefficient of 0.99 (at 2960 Hz), an average absorption coefficient of 0.85 (in the range of 600–6400 Hz), and a wide absorption bandwidth of 4673 Hz with the absorption coefficient greater than 0.8. The dissipation mechanism of sound energy in the fabricated CNC aerogels is predicted by a designed porous media model. Moreover, good thermal stability, ultralight property (density of 0.036 g/cm3), and high diffuse reflection characteristics (average diffuse reflectance of 98.17% for light) of these CNC aerogels were demonstrated as well. Such versatile aerogel materials not only pave the way for the development of sustainable and efficient acoustic absorption materials, but also have great potential applications in broad fields including construction, transportation, and environment acoustics. [ABSTRACT FROM AUTHOR]

Constructing molecular-scale precise host–guest networks by sequential co-deposition strategy. [Display omitted] • Molecular beam epitaxy and thermal annealing for constructing chiral Kagome-like templates. • Kagome-like template precisely regulates the adsorption of the guest molecule C 60. • Density functional theory simulations reveal the adsorption behavior of host and guest molecules on Cu(1 1 1). Scanning tunneling microscopy for high-resolution characterization of molecules. • A controllable and efficient method to design atomically precise ordered host–guest assemblies advancing the approach is yet another step toward the fabrication of molecular devices. Supramolecular self-assembly offers a feasible route for nanopatterning and functionality. The construction and precise control of selective adsorption at the molecular scale play essential roles in fundamental sciences and practical applications. Molecular tiling such as trihexagonal tiling Kagome networks has potential promising chemical and physical properties. Distorted Kagome-like networks controllable fabrication is possible by coupling molecular functional groups and substrate atoms. A chiral Kagome-like network was constructed using IFDO ([1,2-b]fluorene-6,12-dione) as precursor molecules complemented with molecular beam epitaxy and annealing strategies. Revealing arrangements of the assembled network of O-Cu metal coordination linked Kagome-like networks on the Cu(1 1 1) surface and their assembly mechanisms and cavity characteristics were explored in detail through scanning tunneling microscopy combined with density functional theory. Based on the electronic state difference between the hole center of the network and the molecular backbone, the host–guest network was successfully prepared by co-depositing C 60 molecules. Kagome-like networks could as an efficient template for regulating the adsorption configuration of C 60. Our results demonstrate a controllable and efficient method to design atomically precise ordered host–guest assemblies on solid surfaces, advancing the approach is yet another step toward the fabrication of molecular devices. [ABSTRACT FROM AUTHOR]