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Higher spatial resolution and larger imaging scene are always the goals pursued by advanced spaceborne synthetic aperture radar (SAR) system. High-resolution and wide-swath SAR imaging can provide more information about the illuminated scene of interest on the one hand but also come with some new challenges on the other. The induced new challenging problems include curved orbit, Earth’s rotation, and spherical ground surface. Most existing image formation algorithms suffer from performance deficiency in these challenging cases, either in focus accuracy or computational efficiency. In this article, an accurate Fourier transform relationship between the phase history domain data and the scene reflectivity function is derived under an arbitrary radar trajectory by exploiting the spherical geometry property of the spaceborne SAR data collection. Using the derived new data model, an image reconstruction algorithm based on Fourier inversion is proposed. The new algorithm has the inherent capability of correcting for the curved orbit and spherical ground surface effect. Meanwhile, the out-of-plane motion effect induced by the Earth’s rotation can also be compensated by a two-step phase correction and data projection procedure embedded in the Fourier inversion reconstruction. The new algorithm inherits the merits of both time domain and frequency domain algorithms and has excellent performance in both focus accuracy and computational efficiency. Both simulation and real data processing results validate the effectiveness of the proposed imaging algorithm.

Road network maintenance scheduling mainly considers budget limits in the previous studies and largely ignores the capital constraints. This study proposes a bi-objective mixed integer programming model, in which the net present value (NPV) is maximized and the increased total system travel time (ITSTT) due to maintenance activities under the capital constraints is minimized. Since the road network flows cannot reach an equilibrium state overnight due to the variation of network capacity, a link-based day-to-day dynamics model is developed to simulate the transient fluctuation in the traffic flows and calculate the total system travel time of each day. The bi-objective model is solved by the nondominated sorting genetic algorithm-II (NSGA-II) that generates a set of optimal Pareto solutions. The TOPSIS method is then adopted to determine the best compromise solution. Finally, a case study is conducted to demonstrate the effects of key parameters on the values of the two objectives.

The polar format algorithm (PFA) is a popular choice for general bistatic synthetic aperture radar (BiSAR) imaging due to its computational efficiency and adaptability to situations with complicated geometries or arbitrary flight trajectories. However, efficient and accurate compensation of 2-D residual phase errors induced by the wavefront curvature remains challenging when obtaining high-quality BiSAR PFA images. In this article, an analytical expression for the phase errors in the wavenumber domain is derived. With this, the inherent structural characteristics of the phase errors are formulated, where the 2-D phase errors can be reduced to 1-D phase errors for an optimal coordinate system. Moreover, the mapping relationship between distorted point targets in the optimal imaging coordinates and their actual point targets in the original imaging coordinates is investigated. By exploiting the structural characteristics and the mapping relationship, a highly efficient BiSAR PFA wavefront curvature compensation method is proposed. This allows a reduced-dimensional space-variant filter to be constructed to mitigate the 2-D defocusing effect without compromising the compensation accuracy, with the distortion correction being performed using interpolation. The proposed method significantly reduces the complexity required for residual 2-D phase error compensation while simplifying the distortion correction, resulting in notable robustness. The effectiveness of the method is demonstrated using point target and scene target simulations.

New forms of transport, such as autonomous delivery robots (ADRs), have attracted considerable attention in recent years for their potential use as green last-mile delivery alternatives because of their flexibility in some areas unreachable by van. However, their low efficiency, delivering a limited number of orders each trip, limits their application in the last mile. Also, the cost and emission impact of new forms of transport on the last-mile delivery network is still not clear. To address these issues, we developed a new two-echelon delivery system that combines traditional vans and ADRs, making use of their individual advantages to overcome their drawbacks and enhance efficiency in the deliveries. The objective of this study was to develop a new approach based on a metaheuristics methodology to minimize transport and emission costs through modeling and to solve the extension of two-echelon load-dependent vehicle routing problems with mixed vehicles (2E-LDVRP-MV). The complicated 2E-LDVRP-MV problem was formulated as a mixed-integer programming (MIP) model and solved efficiently with a cluster-based artificial immune algorithm (C-AIA) in which clustering was employed to allocate customers. We performed a set of numerical experiments with a solver and the proposed C-AIA heuristics. C-AIS highlights the operational implications of four parameters: ratios between load and the empty vehicle weight; vehicle types; emission levels; and customer densities. Our results provide a unique perspective on tactical planning for a sustainable urban logistics system, and this new two-echelon system could be applied in e-grocery last-mile delivery networks where vans and ADRs are combined.

• The in-situ oxidization and precipitation behaviors in laser powder bed fusion (LPBF) processed Co-Cr-W alloy were investigated. • The corresponding strengthening behaviors of observed nano-oxide inclusions and nano-precipitates were studied. • A nano-oxide with high Si content, together with a bcc type Co5Cr3Si2 nano-precipitates were reported for the first time in LPBF Co-Cr-W alloys. • The strengthening mechanism of widely dispersed nano-oxides was disclosed, whose strengthening contribution was calculated to be ∼158 MPa. Additive manufacturing exhibits great potentials for the fabrication of novel materials due to its unique non-equilibrium solidification and heating process. In this work, a novel nano-oxides dispersion strengthened Co28Cr9W1.5Si (wt.%) alloy, fabricated by laser powder bed fusion (LPBF), was comprehensively investigated. During the layer-by-layer featured process, in-situ formation of Si rich, amorphous, nano-oxide inclusions was observed, whose formation is ascribed to the high affinity of Si to oxygen. Meanwhile, distinctive body-centered cubic (BCC) Co5Cr3Si2 nano-precipitates with an 8-fold symmetry were also confirmed to appear. The precipitates, rarely reported in previous studied Co-Cr alloys, were found to tightly bond with the in-situ oxidization. Furthermore, the morphologies, the size distributions as well as the microstructure of the interface between the matrix and the inclusions were investigated in detail and their influence on the tensile deformation was also clarified. The existence of transition boundaries between these inclusions and the matrix strongly blocked the movement of dislocations, thereby increasing the strength of the alloy. It was understood that when the plastic deformation proceeds, the fracture occurs in the vicinity of the oxide inclusions where dislocations accumulate. A quantitative analysis of the strengthening mechanism was also established, in which an additional important contribution to strength (∼ 169 MPa) caused by the effects of in-situ formed oxide inclusions was calculated. [ABSTRACT FROM AUTHOR]

ABSTRACTPolar format algorithm (PFA) is an efficient image formation algorithm for spotlight-mode synthetic aperture radar (SAR). However, the derivation of PFA relies upon the approximation of planar wavefront, and the resulting wavefront curvature error (WCE) causes severe SAR image degradation, e.g., distortion and defocus. The distortion stems from the first-order WCE and the defocus mainly comes from the quadratic WCE. To eliminate the defocusing effects, the existing space-variant post-filtering algorithms (SVPFAs) mainly compensate the quadratic term of WCE and ignore the residual high-order terms. Actually, the residual odd-order WCE will also broaden the main-lobe of the target and the residual even-order WCE will lead to the problem of sidelobe asymmetry. To tackle these issues, this letter proposes a modified SVPFA to correct both the quadratic and the residual high-order WCE. First, based on the derivation of PFA, the accurate expression of WCE is obtained. Second, the quadratic and residual high-order WCE are separated without complicated high-order two-dimensional Taylor expansion. Finally, the WCE is corrected by phase compensation and geometric distortion correction, respectively. The effectiveness of the proposed algorithm is verified by simulation experiments.