Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval

<italic>Agnostic matrix phase retrieval</italic> (AMPR) is a general low-rank matrix recovery problem given a set of noisy high-dimensional data samples. To be specific, AMPR is targeting at recovering an <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula>-rank matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {M}^*\in \mathbb {R}^{d_1\times d_2}$</tex-math></inline-formula> as the parametric component from <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> instantiations/samples of a semi-parametric model <inline-formula><tex-math notation="LaTeX">$y=f(\langle \mathbf {M}^*, \mathbf {X}\rangle, \epsilon)$</tex-math></inline-formula>, where the predictor matrix is denoted as <inline-formula><tex-math notation="LaTeX">$\mathbf {X}\in \mathbb {R}^{d_1\times d_2}$</tex-math></inline-formula>, link function <inline-formula><tex-math notation="LaTeX">$f(\cdot, \epsilon)$</tex-math></inline-formula> is agnostic under some mild distribution assumptions on <inline-formula><tex-math notation="LaTeX">$\mathbf {X}$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> represents the noise. In this paper, we formulate AMPR as a rank-restricted largest eigenvalue problem by applying the second-order Stein's identity and propose a new spectrum truncation power iteration (STPower) method to obtain the desired matrix efficiently. Also, we show a favorable rank recovery result by adopting the STPower method, <italic>i.e.</italic>, a near-optimal statistical convergence rate under some relatively general model assumption from a wide range of applications. Extensive simulations verify our theoretical analysis and showcase the strength of STPower compared with the other existing counterparts.
Enhancing Microgrid Small-Signal Stability and Reactive Power Sharing Using ANFIS-Tuned Virtual Inductances

Microgrid as the main building block for future smart grids is prone to instability originated from converter-based distributed generations (DG). Herein, we first analyze the small-signal stability of an inverter-interfaced microgrid. Afterwards, a novel adaptive network fuzzy inference system (ANFIS)-based optimization method is introduced which aims at online tuning of virtual inductances (VI) in the islanded microgrids. The data for ANFIS training is drawn by particle swarm optimization (PSO) algorithm and the proposed objective function. A total of 140 load scenarios are considered to provide optimal VI in each load condition and generate optimal data for ANFIS training. This process minimizes reactive power mismatches and improves microgrid stability in different load levels. The simultaneous application of PSO algorithm and ANFIS training facilitates the objectives pursued by current study. Finally, the trained ANFIS networks are installed in the converter control. The adaptive performance of ANFIS controllers makes the converters responses independent from load change location and value. The effectiveness of the proposed control methodology is verified using simulations studies.
Woody Above-Ground Biomass Estimation on Abandoned Agriculture Land Using Sentinel-1 and Sentinel-2 Data

Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R2 = 0.84 and RMSE = 41.2 t·ha−1 (35.1%), parametrized for an AGB range of 4 to 350 t·ha−1. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub–tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition.
Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops.
Analysis of Crustal Movement and Deformation in Mainland China Based on CMONOC Baseline Time Series

In this paper, we propose a method for the analysis of tectonic movement and crustal deformation by using GNSS baseline length change rates or baseline linear strain rates. The method is applied to daily coordinate solutions of continuous GNSS stations of the Crustal Movement Observation Network of China (CMONOC). The results show that: (a) The baseline linear strain rates are uneven in space, which is prominent in the Tianshan, Sichuan-Yunnan, Qinghai-Tibet Plateau, and Yanjing areas, with a maximum value of 1 × 10−7 a−1, and about two orders smaller in the South China block, the Northeast block, and the inner area of the Tarim basin, where the average baseline linear strain rates are 1.471 × 10−9 a−1, 2.242 × 10−9 a−1, and 3.056 × 10−9 a−1, respectively; (b) Active crustal deformation and strong earthquakes in the Xinjiang area are mainly located in the north and south sides of the Tianshan block; the compression deformations both inside the Tarim block and in the southern Tianshan fault zone are all increasing from east to west, and the Tarim block is not a completely “rigid block”, with the shrinkage rate in the west part at about 1~2 mm/a; (c) The principal directions of crustal deformation in the Xinjiang, Tibet, and Sichuan-Yunnan regions are generally in the north—south compression and east—west extension, indicating that the collision and wedging between the Indian and Eurasian plates are still the main source of tectonic movements in mainland China.
Semi-Supervised Deep Learning for Lunar Crater Detection Using CE-2 DOM

Lunar craters are very important for estimating the geological age of the Moon, studying the evolution of the Moon, and for landing site selection. Due to a lack of labeled samples, processing times due to high-resolution imagery, the small number of suitable detection models, and the influence of solar illumination, Crater Detection Algorithms (CDAs) based on Digital Orthophoto Maps (DOMs) have not yet been well-developed. In this paper, a large number of training data are labeled manually in the Highland and Maria regions, using the Chang’E-2 (CE-2) DOM; however, the labeled data cannot cover all kinds of crater types. To solve the problem of small crater detection, a new crater detection model (Crater R-CNN) is proposed, which can effectively extract the spatial and semantic information of craters from DOM data. As incomplete labeled samples are not conducive for model training, the Two-Teachers Self-training with Noise (TTSN) method is used to train the Crater R-CNN model, thus constructing a new model—called Crater R-CNN with TTSN—which can achieve state-of-the-art performance. To evaluate the accuracy of the model, three other detection models (Mask R-CNN, no-Mask R-CNN, and Crater R-CNN) based on semi-supervised deep learning were used to detect craters in the Highland and Maria regions. The results indicate that Crater R-CNN with TTSN achieved the highest precision (of 91.4% and 88.5%, respectively) in the Highland and Maria regions, even obtaining the highest recall and F1 score. Compared with Mask R-CNN, no-Mask R-CNN, and Crater R-CNN, Crater R-CNN with TTSN had strong robustness and better generalization ability for crater detection within 1 km in different terrains, making it possible to detect small craters with high accuracy when using DOM data.
Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data

In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m−3 and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m−3 SSC_2. The selected habitat suitability index model displayed a high correlation (R2 = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO.
Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.
Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.
A design method for collaborative systems of systems applied to Metropolitan Multi-Mode Transport System

Systems of systems arise when independently owned, operated and developed systems can achieve mutual benefits by working together. In collaborative systems of systems, there is no directing entity that instructs others how and when to collaborate. Instead, the collaboration relies on independent decisions by the constituent systems to form collaborating constellations, and the benefits are emergent properties of this. In this paper, we describe a design method for engineering collaborative systems of systems. We apply the method to the design of a collaborative system of systems for mobility in a rural setting close to an urban area and to truck platooning.
Single Image Dehazing Using Wavelet-Based Haze-Lines and Denoising

Haze reduces the contrast of an image and causes the loss in colors, which has a negative effect on the subsequent object detection; therefore, single image dehazing is a challenging visual task. In addition, defects exist in previous existing dehazing approaches: Pixel-based dehazing approaches are likely to result in insufficient information to estimate the transmission, whereas patch-based ones are prone to generate shadows. They both also tend to induce color deviations. Therefore, this study proposes a novel method based on multi-scale wavelet and non-local dehazing. A hazy image is first decomposed into a low-frequency and three high-frequency sub-images by wavelet transform. Non-local dehazing and wavelet denoising are then employed on the low-frequency and high-frequency sub-images to remove the haze and noise, respectively. Finally, a haze-free image is obtained from the reconstruction of sub-images. The proposed method focuses on the dehazing and denoising on the low-frequency and high-frequency images respectively, through which the details on the image can be well preserved. Experimental results indicate that the proposed method is superior to the state-of-the-art approaches in both quantitative and qualitative evaluation on the synthetic and real-world image datasets.
A Robust H∞ Fault Tolerant Controller for Uncertain Systems Described by Linear Fractional Transformation Model

In this article, a robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> fault tolerant control law is addressed for a class of the uncertain dynamical systems represented via linear fractional transformation. To this objective, a state-feedback controller law is utilized for achieving the control objective. Thus, a linear matrix inequality based performance condition would be derived to guarantee that the disturbance suppression is accomplished in the uncertain system. Hence, the gains of the robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller would be suitably determined by checking the feasibility of such a linear matrix inequality problem. The proposed control technique is numerically simulated in two dynamical uncertain systems (i.e., a typical control system and a mechanical robotic arm). Considering the disturbance rejections and transient responses, the results illustrate the efficiency of the recommended robust technique compared with the existing control methods.
Soils on Different Physiographic Units in Lower Brahmaputra Valley Zone of Assam: III. Humic Substances

Distribution of humic substances was studied in the soils developed on different physiographic units in lower Brahmaputra valley zone of Assam. Major portion of the soil organic carbon remained unhumified in the surface horizon. Fulvic acid constituted the major part (47.6–90.0%) of the humus carbon. Humic acid decreased with soil depth whereas fulvic acid was found to increase in the illuvial horizons of Ruptic-Ultic Dystrochrept on piedmont plain and Typic Hapludult on monadnock. The formation of metal-humus complexes was observed with humic and fulvic acid fractions. But humic acid fraction was found to form more complexes with both Fe and Al.
Additional Reference Height Error Analysis for Baseline Calibration Based on a Distributed Target DEM in TwinSAR-L

In this paper, additional reference height errors, caused by the penetration depth and Signal to Noise Ratio (SNR) decorrelation in desert regions in L-band spaceborne bistatic interferemetric SAR, will introduce significant errors in nowadays baseline calibration method based on distributed target and consequent DEM products. To quantify these two errrors, this paper takes the TwinSAR-L mission as an example, gives an introduction of TwinSAR-L, outlines the theoretical baseline accuracy requirements that need to be satisfied in the TwinSAR-L mission and addresses the additional reference height errors caused by the penetration depth and SNR decorrelation in desert regions in general by taking the TwinSAR-L mission as an example. Based on ALOS-2 data from a dry desert region in the east of Xing Jiang, this paper quantitatively analyzes these additional reference height errors. The results show that the additional reference height errors resulted from the penetration depth and the SNR decorrelation are 1.295 m and 1.39 m, respectively, which would even cause 6.4 mm and 8.6 mm baseline calibration errors. These errors would seriously degrade the baseline calibration accuracy and the consequent DEM product quality. Therefore, our analysis is of great significance not only for baseline calibration, but also for high-quality DEM’s generation, accuracy assessment and geophysical parameters’ quantitative inversion and application.
Single Phase Grid Connected Inverter Controls using Three-Pole Three-Zero Compensator

In this paper, a single-phase dual stage grid-connected solar inverter is presented. The dual-stage converter comprises of a DC-DC boost converter and a DC-AC inverter connected to the grid through an LCL filter. A complex pole-zero pair based three-pole three zero (3P3Z) compensator and a PI controller is proposed to control the grid current and DC bus voltage, respectively. The 3P3Z compensator is a good candidate to track the current reference and control the resonance peak introduced by the LCL filter without the need for active or passive damping. Moreover, this compensator efficiently supports the grid under various disturbance scenarios. A detailed analysis of the proposed control design is presented and validated for various grid disturbance conditions through simulation studies in PSIM.
Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.