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Ion escape to space through the interaction of solar wind and Mars is an important factor influencing the evolution of the Martian atmosphere. The plasma clouds (explosive bulk plasma escape), considered an important ion escaping channel, have been recently identified by spacecraft observations. However, our knowledge about Martian plasma clouds is lacking. Based on the observations of the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft, we study a sequence of periodic plasma clouds that occurred at low altitudes (∼600 km) on Mars. We find that the heavy ions in these clouds are energy-dispersed and have the same velocity, regardless of species. By tracing such energy-dispersed ions, we find the source of these clouds is located in a low-altitude ionosphere (∼120 km). The average tailward moving flux of ionospheric plasma carried by clouds is on the order of 107 cm−2 s−1, which is one order higher than the average escaping flux for the magnetotail, suggesting explosive ion escape via clouds. Based on the characteristics of clouds, we suggest, similar to the outflow of Earth’s cusp, these clouds might be the product of heating due to solar wind precipitation along the open field lines, which were generated by magnetic reconnection between the interplanetary magnetic field and crustal fields that occurred above the source.

Lithium aluminum hydride (LiAlH 4 ) is an interesting high capacity hydrogen storage material with fast hydrogen release kinetics when mechanically activated with additives. Herein, we report on a novel approach to produce nanoscale LiAlH 4 via a bottom-up synthesis. Upon further coating of these nanoparticles with Ti, the composite nanomaterial was found to decompose at 120 • C in one single and extremely sharp exothermic event with instant hydrogen release. This finding implies a significant thermodynamic alteration of the hydrogen properties of LiAlH 4 induced by the synergetic effects of the Ti catalytic coating and nanosizing effects. Ultimately, the decomposition path of LiAlH 4 was changed to LiAlH 4 → Al + LiH + 3/2H 2 .

Principal skewness analysis (PSA) has been introduced for feature extraction in hyperspectral imagery. As a thirdorder generalization of principal component analysis (PCA), its solution of searching for the locally maximum skewness direction is transformed into the problem of calculating the eigenpairs (the eigenvalues and the corresponding eigenvectors) of a coskewness tensor. By combining a fixed-point method with an orthogonal constraint, it can prevent the new eigenpairs from converging to the same maxima that has been determined before. However, the eigenvectors of the supersymmetric tensor are not inherently orthogonal in general, which implies that the results obtained by the search strategy used in PSA may unavoidably deviate from the actual eigenpairs. In this paper, we propose a new nonorthogonal search strategy to solve this problem and the new algorithm is named nonorthogonal principal skewness analysis (NPSA). The contribution of NPSA lies in the finding that the search space of the eigenvector to be determined can be enlarged by using the orthogonal complement of the Kronecker product of the previous one, instead of its orthogonal complement space. We give a detailed theoretical proof to illustrate why the new strategy can result in the more accurate eigenpairs. In addition, after some algebraic derivations, the complexity of the presented algorithm is also greatly reduced. Experiments with both simulated data and real multi/hyperspectral imagery demonstrate its validity in feature extraction.