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With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency.

The existing literature finds that finance has a significant impact on carbon emissions, but there is a lack of theoretical explanation on whether and how digital finance, an important new financial form, affects carbon emissions. This paper uses balanced panel data at the provincial level in China from 2011 to 2018 as a sample to empirically test the relationship between digital finance and carbon emissions and introduces three exogenous events to test the impact of policy shocks. The results show that digital finance has a significant inhibitory effect on carbon emissions; the implementation of the policies of ‘G20 High-Level Principles for Digital Financial Inclusion’, ‘Environmental Protection Tax Law of the People’s Republic of China’, and ‘Interim measures for the management of greenhouse gas voluntary emission reduction’ strengthens the suppression of carbon emissions by digital finance, and the robustness test also supports the protection of digital finance. The research conclusions of this article provide theoretical evidence for understanding the relationship between digital finance and other new financial formats and carbon emissions and provide an empirical basis for policy-makers to promote the development of digital finance to reduce carbon emissions.

Remote sensing provides crucial support for building damage assessment in the wake of hurricanes. This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source. Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage. We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss function. We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering. The proposed model leads to an 86.3% overall accuracy of damage classification for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery. Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.

Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

Lieb lattice has been predicted to host various exotic electronic properties due to its unusual Dirac-flat band structure. However, the realization of a Lieb lattice in a real material is still unachievable. Based on tight-binding modeling, we find that the lattice distortion can significantly determine the electronic and topological properties of a Lieb lattice. Importantly, based on first-principles calculations, we predict that the two existing covalent organic frameworks (COFs), i.e., sp 2 C-COF and sp 2 N-COF, are actually the first two material realizations of organic-ligand-based Lieb lattice. Interestingly, the sp 2 C-COF can experience the phase transitions from a paramagnetic state to a ferromagnetic one and then to a Néel antiferromagnetic one, as the carrier doping concentration increases. Our findings not only confirm the first material realization of Lieb lattice in COFs, but also offer a possible way to achieve tunable topology and magnetism in organic lattices. Although artificial Lieb lattices have been recently synthesized, the realization of a Lieb lattice in a real material is still challenging. Here the authors use tight-binding and first principle calculations to predict tunable topology and magnetism in recently discovered two-dimensional covalent-organic frameworks.

Life on Earth depends on photosynthesis for its conversion of solar energy to chemical energy. Photosynthetic organisms have developed a variety of light-harvesting systems to capture sunlight. The largest light-harvesting complex is the phycobilisome (PBS), the main light-harvesting antenna in cyanobacteria and red algae. It is composed of phycobiliproteins and linker proteins but the assembly mechanisms and energy transfer pathways of the PBS are not well understood. Here we report the structure of a 16.8-megadalton PBS from a red alga at 3.5 Å resolution obtained by single-particle cryo-electron microscopy. We modelled 862 protein subunits, including 4 linkers in the core, 16 rod–core linkers and 52 rod linkers, and located a total of 2,048 chromophores. This structure reveals the mechanisms underlying specific interactions between linkers and phycobiliproteins, and the formation of linker skeletons. These results provide a firm structural basis for our understanding of complex assembly and the mechanisms of energy transfer within the PBS. Single-particle cryo-electron microscopy is used to resolve the structure of the phycobilisome, a 16.8-megadalton light-harvesting megacomplex, from the red alga Griffithsia pacifica at a resolution of 3.5 Å. Illuminating the phycobilisome The largest light-harvesting complex is a 16.8-megadalton megacomplex called the phycobilisome. Sen-Fang Sui and colleagues have used single-particle cryo-electron microscopy to solve the structure of this hemispherical complex from the red alga Griffithsia pacifica , visualizing 860 protein components and 2,048 chromophores—the parts of a molecule that cause it to be coloured. This structural achievement provides a mechanistic understanding of how the complex can accommodate changing light conditions and how energy transfer occurs.

Satellite sensors like Landsat 8 OLI (L8) and Sentinel-2 MSI (S2) provide valuable multispectral Earth observations that differ in spatial resolution and spectral bands, limiting synergistic use. L8 has a 30 m resolution and a lower revisit frequency, while S2 offers up to a 10 m resolution and more spectral bands, such as red edge bands. Translating observations from L8 to S2 can increase data availability by combining their images to leverage the unique strengths of each product. In this study, a conditional generative adversarial network (CGAN) is developed to perform sensor-specific domain translation focused on green, near-infrared (NIR), and red edge bands. The models were trained on the pairs of co-located L8-S2 imagery from multiple locations. The CGAN aims to downscale 30 m L8 bands to 10 m S2-like green and 20 m S2-like NIR and red edge bands. Two translation methodologies are employed—direct single-step translation from L8 to S2 and indirect multistep translation. The direct approach involves predicting the S2-like bands in a single step from L8 bands. The multistep approach uses two steps—the initial model predicts the corresponding S2-like band that is available in L8, and then the final model predicts the unavailable S2-like red edge bands from the S2-like band predicted in the first step. Quantitative evaluation reveals that both approaches result in lower spectral distortion and higher spatial correlation compared to native L8 bands. Qualitative analysis supports the superior fidelity and robustness achieved through multistep translation. By translating L8 bands to higher spatial and spectral S2-like imagery, this work increases data availability for improved earth monitoring. The results validate CGANs for cross-sensor domain adaptation and provide a reusable computational framework for satellite image translation.

Aiming at the requirement of space situational awareness on the full-domain and all-time coverage capability of low Earth orbit space targets and focusing on the design of a space-based space target awareness system, a space target awareness constellation design method based on simulation comparison is put forward. On the basis of systematically analyzing the distribution of space targets’ orbits, the low Earth orbit space target surveillance mission mode, key indicators, and constraints of space target surveillance constellations are designed, and three space target surveillance constellation basic configurations are constructed. This paper randomly samples surveillance objects based on the stratified space target orbit distribution and selects a heterogeneous space target surveillance constellation in sun-synchronous morning–twilight orbit that meets the requirements of the surveillance mission model and capability by using simulations and comparisons. The experiments show that the constellation can provide a satisfactory observation arc segment for cataloging and orbiting more than 80% of low Earth orbit targets.

This study proposes a task planning approach for a distributed constellation dedicated to space target monitoring, grounded in an adaptive genetic algorithm. The approach is designed to address challenges such as the growing number of space targets and the complex constraints inherent in space target monitoring activities. After reviewing the research progress of distributed satellite task planning and adaptive genetic algorithms, a distributed task model featuring master-slave satellites was developed. This model integrates multi-constraint modeling and aims to optimize key performance indicators: task yield rate, task completion rate, resource utilization rate, and load balancing. To enhance the approach, the contract net algorithm is fused with the adaptive genetic algorithm: Firstly, in the tendering phase, centralized tendering is adopted to reduce communication overhead; Secondly, in the bidding phase, improved genetic mechanisms (e.g., dynamic reverse adjustment of crossover and mutation probabilities) and a dynamic population strategy are employed to generate task allocation schemes; Thirdly, in the bid evaluation and winning phase, differentiated strategies are applied to non-repetitive and repetitive tasks. Simulation validation shows that this approach can complete 80% of space target monitoring tasks, balance satellite loads effectively, and manage space target catalogs efficiently.

Photosynthetic organisms use huge arrays of pigments to draw light energy into the core of photosystem II. The arrangement of these pigments influences how much energy reaches the reaction center. Pi et al. determined the structure of photosystem II from a diatom in complex with an antenna of fucoxanthin–chlorophyll a/c binding proteins (FCPs) (see the Perspective by Büchel). The specialized pigments in this complex allow microalgae to harvest light within a wide range of the visible spectrum. The FCPs are arranged in a pattern analogous to light-harvesting complexes in plants. Science , this issue p. eaax4406 ; see also p. 447 The cryo-EM structure of a diatom photosystem II complex suggests energy transfer and dissipation pathways. Diatoms play important roles in global primary productivity and biogeochemical cycling of carbon, in part owing to the ability of their photosynthetic apparatus to adapt to rapidly changing light intensity. We report a cryo–electron microscopy structure of the photosystem II (PSII)–fucoxanthin (Fx) chlorophyll (Chl) a/c binding protein (FCPII) supercomplex from the centric diatom Chaetoceros gracilis . The supercomplex comprises two protomers, each with two tetrameric and three monomeric FCPIIs around a PSII core that contains five extrinsic oxygen-evolving proteins at the lumenal surface. The structure reveals the arrangement of a huge pigment network that contributes to efficient light energy harvesting, transfer, and dissipation processes in the diatoms.