This paper addresses the adaptive finite-time fault-tolerant tracking control problem for nonlinear output feedback systems with actuator faults under multiplicative deception attacks. First, a composite observer is developed by integrating a fault compensation mechanism with the compromised output signal. Unlike conventional state observers, the proposed observer can simultaneously estimate unmeasured system states, actuator faults, and external disturbances. Second, a sig function, novel coordinate transformations, and an attack compensator are introduced to develop a nonsingular finite-time adaptive tracking controller under deception attacks. The proposed controller effectively eliminates the singularity problems inherent in traditional finite-time controllers and ensures the desired tracking performance. In addition, an improved finite-time adaptive filter is introduced to enhance control accuracy and relax constraints on design parameter selection, compared to existing filtering methods. To reduce communication burden, a dynamic event-triggered mechanism (DETM) is incorporated. The proposed control strategy guarantees practical finite-time stability of the closed-loop system while successfully avoiding the Zeno phenomenon. Simulation results further verify the effectiveness of proposed strategy.

This article proposes an adaptive fixed-time control strategy for uncertain high-order nonlinear cyber-physical system subject to deception attacks on both the sensor-to-controller (S-C) and controller-to-actuator (C-A) communication channels. First, to mitigate the impact of dual-channel attacks, mathematical tools are employed to decouple the high-order term induced by C-A channel attacks, and a robust controller is designed based on the compromised state information. Second, novel Lyapunov functions and an adaptive mechanism are constructed to transform nonlinear uncertainties into a linearly parameterized form with unknown parameters. This transformation effectively compensates for unknown control coefficients and mitigates the severe nonlinear growth caused by high-order dynamics under attacks. Third, a control strategy independent of the system's powers is developed, which relaxes the requirement for prior knowledge of system powers and avoids singularities. Finally, the effectiveness and feasibility of the proposed strategy are confirmed via simulation results.

This article addresses the security-based practical fixed-time adaptive stabilization problem for nonlinear cyber-physical systems (CPSs) with higher powers under deception attacks. First, a novel practical fixed-time stability criterion is proposed based on Nussbaum functions. The criterion introduces a relaxed convergence condition, facilitating the analysis of practical fixed-time stability for high-power nonlinear systems. Second, to compensate for the compromised states, a dual coordinate transformation structure consisting of a virtual coordinate transformation and an available coordinate transformation is proposed. This structure is applicable to both low- and high-power systems. Moreover, a security-based fixed-time control strategy is developed by combining adaptive methods with Nussbaum functions to mitigate the influence of unknown attack gains. The proposed strategy also effectively overcomes the singularity problem. Finally, the effectiveness of the designed control strategy is validated through simulations.

The 532-nm green laser light commonly used for airborne laser bathymetry (ALB) can penetrate clear shallow water but is sensitive to turbidity, which could lead to water surface uncertainty. In this study, water surface uncertainty was quantitatively assessed using a photon-counting LiDAR (PCL) with high receiver sensitivity to analyze the effect of turbidity. The qualitative results showed that the water surface heights are generally underestimated, and the surface detection accuracy in turbid water is superior to that in clear water. These findings were confirmed by statistical analysis of representative data in quantitative empirical experiments. The diffuse attenuation coefficient as a metric of water turbidity ranged from 0.14 to 4.80$\text{m}^{-1}$for clear to turbid water. The corresponding underestimated deviation ranged from 0.37 to 0.08 m, and the root-mean-square error (RMSE) was ranged from 0.39 to 0.06 m. In addition, the radiative transfer mechanism underlying the underestimation of water surface heights at different levels as water turbidity varies was determined by comparing the simulated and measured results. On the one hand, there is a high exponential relationship between the underestimation deviation and the diffuse attenuation coefficient when considering only the water optical properties. On the other hand, the presence of direct reflection component from the surface actually has an inhibiting effect on the underestimation. The present study provides reliable evidence for further understanding the interaction of green lasers with the air–water interfaces.

Airborne bathymetry LiDAR, such as Mapper5000, experienced varied water-bottom-return intensities. Under the high bottom-return signal condition, owing to the clear water body, high bottom reflectance, low depth, or high-gain setting of the detector, the peak power of the received signal from the bottom often exceeds the linear dynamic range of the 532-nm detector assembly. The resulting signal saturation distorted the recorded bottom-return waveforms and resulted in undesired depth bias exceeding 2.0 m in certain extreme cases. In this study, we developed a low-complexity peak position restoration method for this “saturation in-water range bias” correction based on an intersection point of double linear approximations of the leading and trailing edges of the saturated bottom waveform double line intersection (DLI). The validation of corrected results with consistent sonar depth measurements indicated that the DLI method demonstrated considerable robustness to the effect of pulse stretching under saturation conditions and reduced the mean absolute depth bias from 1.46 to 0.47 m. Compared with Gaussian and exponentially modified Gaussian (EMG) fitting algorithms, the DLI was faster and the most effective at reducing depth bias under strong saturation conditions. The success of DLI benefited from the small width of the transmitted laser pulse and the sharp slope of the leading edge of the saturated bottom waveform. However, the interference of volume backscattering signals at shallow depths could affect the performance of DLI, which should be carefully removed.

In recent years, various coral reef retrieval methods have experienced considerable progress due to a variety of observational instruments and innovative parameter calculation techniques. However, these labor-intensive methods are deficient in handling high-precision remote sensing mapping of coral reef benthic environments, facing challenges, including enhancing the robustness of scaled coral reef retrieval against varying water depths and complex water column conditions. To overcome these limitations, we propose a novel method for coral reef benthic mapping. Our method primarily consists of two central components: water depth extraction and coral reef information acquisition. Accurate bathymetry is critical for coral reef remote sensing inversion. To obtain more precise water depth, we propose the Bathymetry Transformer model. Our Bathymetry Transformer model aggregates vast amounts of active and passive remote sensing data, generates accurate bathymetry (with a 0.375-m RMSE, ranging from 0- to 12-m water depth) that eliminates the need for in situ examinations, and maintains a harmonious balance between greater bathymetry precision and finer spatial resolution. Based on this, water column corrections are then applied, and the results derived from various active and passive remote sensing processes, along with their respective band calculation outcomes, are fed into the proposed coral reef Transformer (CR Transformer) model to generate high-accuracy coral reef benthic mapping results. Copious experimental outcomes affirm that our CR Transformer surpasses current state-of-the-art (SOTA) methods in computational efficiency and results accuracy. Impressively, the CR Transformer achieves a notable mean intersection over union (mIoU) of 91.25% and an accuracy of 95.71% on the validation dataset.

Unmanned aerial vehicle (UAV)-borne laser scanning systems using photon-counting technology are applied to high-resolution water surface mapping with high efficiency. Affected by vast noise photons in raw data, the detection of surface photons from a weak reflective target like water still faces challenges in low signal-to-noise ratio (SNR) application scenarios. Noise filtering of raw data and surface detection from possible signals are two essential steps for water surface detection. In this letter, a water surface height retrieval algorithm is investigated for characterizing terrain and surface height. The proposed algorithm implements multilevel filtering to minimize noise photons and subsequently extracts the topmost boundary points as water surface photons using a modified alpha-shape to derive the water level elevation. Noise filtering results show that the multilevel filtering approach is effective in preserving signal photons integrity at low SNR. Moreover, the accuracy assessment further substantiates the robustness of the methodology in calm waters, and the root mean square error (RMSE) for the estimated water surface height was 0.02 m compared with percentile heights. Our algorithm provides an efficient solution for high-resolution water surface mapping in UAV-borne photon-counting LiDAR (PCL).

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.

Significant range differences were identified between shallow and deep channels of the Mapper5000 bathymetry light detection and ranging (LiDAR) system with segmented field-of-view (FOV) receivers. Range difference varied with depth and water optical properties. The main feature was the maximum value in range difference curves, which ranged from 0.3 to 0.6 m and usually exceeded the International Hydrographic Organization (IHO) accuracy standards. Sensitivity analyses based on a semianalytical Monte Carlo simulation model revealed that the scattering phase function and laser beam divergence angle played more important roles in causing pulse dispersion and determining the amplitude and position of maximum range difference than absorption and scattering coefficients. A range difference correction method by fitting existing shallow and deep channel data in the overlapping range with a cubic polynomial was proposed to correct the deep channel data in the entire depth range that LiDAR can detect. Depth discontinuity at the junction of the shallow channel and deep channel measurements was successfully removed, and the mean and standard deviation of corrected range differences were within 0.01 and 0.1 m, respectively. A combination of range difference correction and mean bias corrector can be an alternative method for depth bias correction of segmented-FOV LiDAR when referenced sonar data is not available.

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997–2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun-Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh-ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters.

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m-1), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m-1which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.