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An extension of the linear H ∞ filter, presented here as the extended H ∞ particle filter (E H ∞ PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the H ∞ filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended H ∞ particle filter (E H ∞ PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The E H ∞ PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the E H ∞ PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles. [ABSTRACT FROM AUTHOR]

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the "spatial-attribute" unified distance metric is useful, and that the SANNWR model showed the best performance. [ABSTRACT FROM AUTHOR]

We investigate the order of exponential sums involving the coefficients of general L -functions satisfying a suitable functional equation and give some new estimates, including refining certain results in preceding works [X. Ren and Y. Ye, Resonance and rapid decay of exponential sums of Fourier coefficients of a Maass form for G L m (ℤ) , Sci. China Math.58(10) (2015) 2105–2124; Y. Jiang and G. Lü, Oscillations of Fourier coefficients of Hecke–Maass forms and nonlinear exponential functions at primes, Funct. Approx. Comment. Math.57 (2017) 185–204]. [ABSTRACT FROM AUTHOR]

• The mechanism of triggering and driving drought propagation differs. • Triggering drought propagation highly relies on meteorological drought patterns. • Driving drought propagation is primarily depends on underlying surface. • Meteorological and hydrological drought condition will intensify in future scenarios. Exploring the propagation from meteorological drought to hydrological drought and its potential influence factors is crucial for early warning of hydrological drought. As a case study in the Xijiang River Basin (XRB), South China, the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) series from 1961 to 2013 were used to represent the meteorological drought and hydrological drought with daily runoff generated by VIC model. An identification process for the threshold and the mechanism of drought propagation was conducted based on rank order analysis and Copula theory. The drought propagation regulations of drought variables, geographical location, and lag-time were established by linear or non-linear functions. Eventually, the propagation regulations were applied to the hydrological drought prediction from 2021 to 2050. Results indicated that: (1) The spatial-temporal patterns of meteorological drought are the critical factors in triggering drought propagation, and the effects of meteorological drought area, drought severity, drought duration, drought spatial concentration, and drought persistent duration increase successively. The critical threshold for triggering drought propagation is the combined frequency of the above five drought variables greater than 0.14. (2) The geographic location, center number, duration, severity, and area of two drought types follow the linear, two-piecewise, power, linear and logarithmic functions, respectively. Drought mainly propagation from west to east in three-dimensional space, and the lag-time was less than three months, with the maximum being 78 days. Unlike triggering drought propagation, driving drought propagation is synthetically associated with meteorological, soil water, groundwater, and topography conditions. (3) Under the MRI-ESM2-0 model, the severity and area of hydrological drought under different scenarios in the 2021–2050 will increase compared with the reference period 1961–2013, and the extreme hydrological drought exceeding history may occur under scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. The findings can help policy-makers manage the early stages of hydrological drought based on meteorological drought conditions and thus reduce the negative impact of drought hazards. [ABSTRACT FROM AUTHOR]

• Phenotypic traits derived from the SFM-MVS method can estimate sugar beet economic benefit using the PLSR model constructed with multi-year data. • We designed a non-linear function to determine sugar beet purchase price suitable for sugar beet production in Inner Mongolia, China. • We developed an automatic multi-view image acquisition device of beet taproot. • We proposed an automatic pipeline based on SFM-MVS method to reconstruct 3D point clouds and to obtain 10 phenotypic traits of beet taproot. Selecting and breeding crop varieties with high economic benefits is of great significance for social stability and development. The economic benefit of crops is usually reflected by the purchase price. Traditional estimation of economic benefits using purchase price formula based on manual measured traits is time-consuming. Structure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits and has the potential for the efficient and timely estimation of economic benefits for sugar beet. In this study, a framework was developed to obtain phenotypic traits in order to estimate the economic benefits of sugar beet with 207 genotypes based on the calculation of a non-linear formula and the partial least square regression (PLSR) model. The first part of the framework was the designing of a low-cost portable equipment that can be used to obtain multi-view images of taproot in order to facilitate its three-dimensional (3D) reconstruction based on SFM-MVS method. The following part was the development of an automated pipeline for estimating ten traits from the reconstructed 3D taproot. Good agreement was found between measured and estimated traits with R2 >0.97. The PLSR model constructed using the data in 2018 was used to predict the data in 2019 with moderate performance (R2 = 0.5). A new PLSR model built using 70 % of the data collected in 2018 and 2019 could predict the remaining 30 % of the data with a higher R2 of 0.61. The model built with multi-years data had a higher accuracy in estimating phenotypic traits, which suggests that PLSR model can estimate beet economic benefit by using the SFM-MVS method with multi-year data. The current method is more efficient than the manual measurement and may provide a basis for selecting and cultivating sugar beet with high economic benefit. [ABSTRACT FROM AUTHOR]