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The rich feature information contained in the diverse remote sensing data has also exhibited growing potential in the field of image classification. However, the processing of multi-feature data still grapples with the challenges posed by the “curse of dimensionality” and the high computational costs. This paper proposes a remote sensing feature parameter selection method. This method is based on the Gini index of a Random Forest (RF) classifier and employs a 10% threshold decision to identify the optimal combination of remote sensing feature parameters. First, spectral features, texture features, temperature features, elevation features, and principal component features are selected to create a stack of remote sensing images. Second, multiple sets of decision trees are established to cross-validate the contributions of various feature parameters. The feature rankings are determined based on the normalized mean of feature importance. Subsequently, a threshold isset to filter out remote sensing feature parameters that meet the criteria. Finally, an iterative parameter optimization process is carried out to obtain the optimal combination of remote sensing feature parameters. The Landsat 8 image covering Hangzhou Bay in eastern China and the Sentinel-2 remote sensing image of Yancheng Nature Reserve in Jiangsu, China were selected for experiments. The results show that the feature parameters of the remote sensing image screened by the method in this paper are representative, and the proposed method demonstrates strong adaptability and generalization performance in complex environments. The study has important guiding significance for regional spatial planning and sustainable development.

In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto–Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto–Istanbul and Toronto–London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.

In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms.

The time-correlated single-photon-counting (TCSPC) three-dimensional (3D) imaging lidar system has broad application prospects in the field of low-light 3D imaging because of its single-photon detection sensitivity and picoseconds temporal resolution. However, conventional TCSPC systems always limit the echo photon flux to an ultra-low level to obtain high-accuracy depth images, thus needing to spend amounts of acquisition time to accumulate sufficient photon detection events to form a reliable histogram. When the echo photon flux is increased to medium or even high, the data acquisition time can be shortened, but the photon pile-up effect can seriously distort the photon histogram and cause depth errors. To realize high accuracy TCSPC depth imaging with a shorter acquisition time, we propose a high-flux fast photon-counting 3D imaging method based on empirical depth error correction. First, we derive the photon flux estimation formula and calculate the depth error of our photon-counting lidar under different photon fluxes with experimental data. Then, a function correction model between the depth errors and the number of echo photons is established by numerical fitting. Finally, the function correction model is used to correct depth images at high photon flux with different acquisition times. Experimental results show that the empirical error correction method can shorten the image acquisition time by about one order of magnitude while ensuring a moderate accuracy of the depth image.

Driven by an eccentric mass through a two-layered cantilevered plectrum, the electromagnetic energy harvester (EEH) can convert low-frequency mechanical vibrations into continuous uni-directional rotation. To optimize the performance of the EEH, electromagnetic analysis of the EEH was conducted. Three-phase winding permanent magnet (PM) topology was employed, and combinations of different coils and magnet pole numbers were designed. Then, the finite element method (FEM) was applied to analyze the influence of the combinations of the coils and pole numbers as well as the PM dimensions on the three-phase induced voltage. Prototypes with different configurations were fabricated and the analysis effectiveness was confirmed. Furthermore, different types of stator yokes were designed to enhance the magnetic field. Compared to the original prototype, the output voltage of the optimal prototype increased by 0.5 V with the same rotation speed, and the harmonic components were sufficiently low. Then, experiments with excitation by linear reciprocating motions and swing motions were conducted. Under different exciting conditions, the optimal prototype can also induce the highest voltage amplitude. With an increase in the weight of the eccentric mass, a long duration can be reached that lasts up to 12 s. In summary, the proposed optimization can achieve a high-efficiency and high-power density EEH.

Breeze energy is a widely distributed renewable energy source in the natural world, but its efficient exploitation is very difficult. The conventional harvester with fixed arm length (HFA) has a relatively high start-up wind speed owing to its high and constant rotational inertia. Therefore, this paper proposes a harvester with a helix s-type vertical axis (HSVA) for achieving random energy capture in the natural breeze environment. The HSVA is constructed with two semi-circular buckets driven by the difference of the drag exerted, and the wind energy is transferred into mechanical energy. Firstly, as the wind speed changes, the HSVA harvester can match the random breeze to obtain highly efficient power. Compared with the HFA harvester, the power coefficient is significantly improved from 0.15 to 0.2 without additional equipment. Furthermore, it has more time for energy attenuation as the wind speeds dropped from strong to moderate. Moreover, the starting torque is also better than that of HFA harvester. Experiments showed that the HSVA harvester can improve power performance on the grounds of the wind speed ranging in 0.8–10.1 m/s, and that the star-up wind speed is 0.8 m/s and output peak power can reach 17.1 mW. In comparison with the HFA harvester, the HSVA harvester can obtain higher efficient power, requires lower startup speed and keeps energy longer under the same time. Additionally, as a distributed energy source, the HSVA harvester can provide a self-generating power supply to electronic sensors for monitoring the surrounding environment.

1,5-Anhydroglucitol (1,5-AG) is a sensitive biomarker for real-time detection of diabetes mellitus. In this study, an electrochemical biosensor to specifically detect 1,5-AG levels based on persimmon-tannin-reduced graphene oxide-PtPd nanocomposites (PT-rGO-PtPd NCs), which were modified onto the surface of a screen-printed carbon electrode (SPCE), was designed. The PT-rGO-PtPd NCs were prepared by using PT as the film-forming material and ascorbic acid as the reducing agent. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultraviolet–visible spectroscopy (UV–vis), and X-ray diffraction (XRD) spectroscopy analysis were used to characterise the newly synthesised materials. PT-rGO-PtPd NCs present a synergistic effect not only to increase the active surface area to bio-capture more targets, but also to exhibit electrocatalytic efficiency to catalyze the decomposition of hydrogen peroxide (H2O2). A sensitive layer is formed by pyranose oxidase (PROD) attached to the surface of PT-rGO-PtPd NC/SPCE. In the presence of 1,5-AG, PROD catalyzes the oxidization of 1,5-AG to generate 1,5-anhydrofuctose (1,5-AF) and H2O2 which can be decomposed into H2O under the synergistic catalysis of PT-rGO-PtPd NCs. The redox reaction between PT and its oxidative product (quinones, PTox) can be enhanced simultaneously by PT-rGO-PtPd NCs, and the current signal was recorded by the differential pulse voltammetry (DPV) method. Under optimal conditions, our biosensor shows a wide range (0.1–2.0 mg/mL) for 1,5-AG detection with a detection limit of 30 μg/mL (S/N = 3). Moreover, our electrochemical biosensor exhibits acceptable applicability with recoveries from 99.80 to 106.80%. In summary, our study provides an electrochemical method for the determination of 1,5-AG with simple procedures, lower costs, good reproducibility, and acceptable stability.

Glypican-3 (GPC3), as an emerging biomarker, has been shown to be beneficial for the early diagnosis and treatment of hepatocellular carcinoma (HCC). In this study, an ultrasensitive electrochemical biosensor for GPC3 detection has been constructed based on the hemin-reduced graphene oxide-palladium nanoparticles (H-rGO-Pd NPs) nanozyme-enhanced silver deposition signal amplification strategy. When GPC3 specifically interacted with GPC3 antibody (GPC3Ab) and GPC3 aptamer (GPC3Apt), an “H-rGO-Pd NPs-GPC3Apt/GPC3/GPC3Ab” sandwich complex was formed with peroxidase-like properties which enhanced H2O2 to reduce the silver (Ag) ions in solution to metallic Ag, resulting in the deposition of silver nanoparticles (Ag NPs) on the surface of the biosensor. The amount of deposited Ag, which was derived from the amount of GPC3, was quantified by the differential pulse voltammetry (DPV) method. Under ideal circumstances, the response value was linearly correlated with GPC3 concentration at 10.0–100.0 μg/mL with R2 of 0.9715. When the GPC3 concentration was in the range from 0.01 to 10.0 μg/mL, the response value was logarithmically linear with the GPC3 concentration with R2 of 0.9941. The limit of detection was 3.30 ng/mL at a signal-to-noise ratio of three and the sensitivity was 1.535 μAμM−1cm−2. Furthermore, the electrochemical biosensor detected the GPC3 level in actual serum samples with good recoveries (103.78–106.52%) and satisfactory relative standard deviations (RSDs) (1.89–8.81%), which confirmed the applicability of the sensor in practical applications. This study provides a new analytical method for measuring the level of GPC3 in the early diagnosis of HCC.

The acquisition of dynamic information on the coastline is of great significance for the Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous coastline, numerous tidal flats, and so on have brought many challenges to research involving coastline extraction and the analysis of the spatial–temporal dynamics. This study has developed a method for extracting coastline information using multi-temporal remote sensing images based on tasseled cap transformation and mathematical morphology, allowing the temporal and spatial evolution of the coastline to be analyzed. The results showed that the proposed method can effectively attenuate the influence of suspended sediments, winding coastline, and shoals on the extraction of coastline information, and allow researchers to extract the spatial position of the coastline more accurately. The total coastline length of the Zhoushan archipelago increased by 327.36 km during the study period from 2000 to 2018, the average growth in length was 18.19 km, and the average growth rate was 0.72% over the 19-year period. The total area enclosed by the coastline of the Zhoushan archipelago increased by 112.26 km2 during the study period from 2000 to 2018, the average growth in area was 6.24 km2, and the average growth rate was 0.49% over the 19-year period. Construction, reclamation, and ocean engineering are the main causes of changes to the coastline of the Zhoushan archipelago. This study is useful for accurate coastline information extraction using remote sensing images in a complex marine environment and for protecting coastal resources in the archipelago.

The main challenge in extracting coastal aquaculture ponds is how to weaken the influence of the “same-spectrum foreign objects” effect and how to improve the definition of the boundary and accuracy of the extraction results of coastal aquaculture ponds. In this study, a recognition model based on the U2-Net deep learning model using remote sensing images for extracting coastal aquaculture ponds has been constructed. Firstly, image preprocessing is performed to amplify the spectral features. Second, samples are produced by visual interpretation. Third, the U2-Net deep learning model is used to train and extract aquaculture ponds along the coastal region. Finally, post-processing is performed to optimize the extraction results of the model. This method was validated in experiments in the Zhoushan Archipelago, China. The experimental results show that the average F-measure of the method in the study for the four study cases reaches 0.93, and the average precision and average recall rate are 92.21% and 93.79%, which is suitable for extraction applications in aquaculture ponds along the coastal region. This study can quickly and accurately carry out the mapping of coastal aquaculture ponds and can provide technical support for marine resource management and sustainable development.

As a popularly used technique for feature learning in graphs, network embedding aims to represent each node as a low-dimensional vector to support efficient graph analytic tasks, such as node classification, link prediction, and visualization. The key to this representation method is that the embedding vector of a node should preserve its properties in the graph as much as possible. Most traditional network embedding algorithms only consider the local neighborhood as the context to build the node representation and fail to capture the important hierarchical clustering property ubiquitous in real-world graphs. To solve this problem, we propose ACE, a novel network embedding method, to preserve the features of hierarchical clustering structures. ACE works by using an ant colony-based graph coarsening algorithm to group the nodes according to their relationship to achieve a multi-level clustering pyramid of the input graph. Then, we generate the embedding vectors from multiple layers of the graph pyramid and blend these multi-level vectors into the final representation of nodes based on the PCA dimension reduction algorithm. We demonstrate the effectiveness of ACE over state-of-the-art network embedding algorithms on the node classification tasks in several real-world graph datasets. The experiments show that ACE is easy to be integrated with other network embedding algorithms, such as DeepWalk, Line, node2vec, and SDNE, to significantly improve their performance by up to 22% on Macro F1. The source code and the datasets used in this paper are available on Github (https://github.com/so-link/ACE_Embedding).

Water, as an important part of ecosystems, is also an important topic in the field of remote sensing. Shadows and dense vegetation negatively affect most traditional methods used to extract water body information from remotely sensed images. As a result, extracting water body information with high precision from a wide range of remote sensing images which contain complex ground-based objects has proved difficult. In the present study, a method used for extracting water body information from remote sensing imagery considers the greenness and wetness of ground-based objects. Ground objects with varied water content and vegetation coverage have different characteristics in their greenness and wetness components obtained by the Tasseled Cap transformation (TCT). Multispectral information can be output as brightness, greenness, and wetness by Tasseled Cap transformation, which is widely used in satellite remote sensing images. Hence, a model used to extract water body information was constructed to weaken the influence of shadows and dense vegetation. Jiangsu and Anhui provinces are located along the Yangtze River, China, and were selected as the research area. The experiment used the wide-field-of-view (WFV) sensor onboard the Gaofen-1 satellite to acquire remotely sensed photos. The results showed that the contours and spatial extent of the water bodies extracted by the proposed method are highly consistent, and the influence of shadow and buildings is minimized; the method has a high Kappa coefficient (0.89), overall accuracy (92.72%), and user accuracy (88.04%). Thus, the method is useful in updating a geographical database of water bodies and in water resource management.