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Merrillite, ideally Ca9NaMg(PO4)7, is an important accessory phosphate mineral in many different groups of meteorites, including martian meteorites, and a major carrier of rare earth elements (REE) in lunar rocks. By means of electron microprobe analysis, single-crystal X-ray diffraction, and Raman spectroscopy, we present the first structure determination of merrillite with a nearly ideal chemical composition, Ca9.00Na0.98(Mg0.95Fe0.06)Σ1.01 (P1.00O4)7, from the Suizhou meteorite, a shock-metamorphosed L6-chondrite. Suizhou merrillite is trigonal with space group R3c and unit-cell parameters a=10.3444(3), c=37.0182(11) Å, and V=3430.5(2) Å3. Its crystal structure, refined to R1=0.032, is characterized by a structural unit consisting of a [(Mg,Fe)(PO4)6]16- complex anion that forms a \"bracelet-and-pinwheel\" arrangement. Such structural units are linked by interstitial complexes with a formula of [Ca9Na(PO4)]16+, which differs from that of [Ca9(PO3[OH]]16+, [Ca9(PO3F)]16+, [Ca9(Ca0.5[]0.5) (PO4)]16+, or [(Ca9-xREE)x(Na1-x[]x) (PO4)]16+ in terrestrial whitlockite, terrestrial/extraterrestrial bobdownsite, meteoritic Ca-rich merrillite, or lunar REE-rich merrillite, respectively. The Suizhou merrillite is found to transform to tuite at high pressures, pointing to the likelihood of finding REE-bearing tuite on the Moon as a result of shock events on REE-merrillite.

In view of the small number of categories and the relatively little amount of labeled data, it is challenging to apply the fusion of deep convolution features directly to remote sensing images. To address this issue, we propose a pyramid multi-subset feature fusion method, which can effectively fuse the deep features extracted from different pre-trained convolutional neural networks and integrate the global and local information of the deep features, thereby obtaining stronger discriminative and low-dimensional features. By introducing the idea of weighting the difference between different categories, the weight discriminant correlation analysis method is designed to make it pay more attention to those categories that are not easy to distinguish. In order to mine global and local feature information, the pyramid method is employed to divide feature fusion into several layers. Each layer divides the features into several subsets and then performs feature fusion on the corresponding feature subsets, and the number of subsets from top to bottom gradually increases. Feature fusion at the top of the pyramid obtains a global representation, while feature fusion at the bottom obtains a local detail representation. Our experiment results on three public remote sensing image data sets demonstrate that the proposed multi-deep features fusion method produces improvements over other state-of-the-art deep learning methods.

Copper possesses very strong chacophile properties, but under the conditions found in meteorites, its behavior is like that of siderophile elements. The Suizhou meteorite is a highly shocked L6 chondrite. Troilite and taenite are considered the main primary carrier of copper in this meteorite, and the post-shock thermal episode is considered the main reason that elemental Cu migrates from its original host phase and forms metallic grains. The Suizhou meteorite contains a few very thin shock melt veins. The occurrence and behavior of metallic copper in this meteorite were studied by optical microscopic examination, electron microprobe analyses, and high-resolution X-ray elemental intensity mapping. Our results show that metallic copper is abundant in the Suizhou chondritic rock. Metallic copper grains adjacent to small troilite grains inside FeNi metal are the most common occurrence, and those at the FeNi metal–troilite interface are the second most common case. The metallic copper grains occurring at the interface of FeNi metal/troililte and silicate are rather rare. Metallic copper grains are not observed within the Suizhou shock veins, Instead, Cu in elemental form is transferred through shock metamorphism into FeNi metal + troilite intergrowths. Four different occurrence types of Cu in the FeNi metal + troilite intergrowths have been identified: the concentrations of Cu in the FeNi + FeS intergrowths for four occurrence types are rather close, we estimate it might be lower than 1 wt%.

A new phyllosilicate mineral, lipuite (IMA2014-085), has been discovered from the N'Chwaning III mine, Kalahari Manganese Fields, Northern Cape Province, Republic of South Africa. It occurs as platy, tabular, or granular crystals and veined agglomerate in association with Mn-bearing sugilite, taniajacoite, pectolite, richterite, norrishite and namansilite. Lipuite is dark red-brown with vitreous lustre, red streak, an estimated Mohs hardness of 5 and the measured density is 2.83(3) g/cm3. It is biaxial (+) and characterised by bright red to dark red colour in thin section with measured refractive indices in white light: α=1.635(1), β=1.653(1), γ=1.670(1) and 2V=86(2)°. The Raman spectra of lipuite are composed of over 21 bands at 109, 146, 162, 183, 206, 244, 288, 342, 362, 455, 496, 520, 552, 613, 669, 886, 930, 971, 1097, 3487 and 3540 cm-1. The empirical formula from microprobe analyses is (based on total number of cations=27.5 and structural refinement): K1.12Na8.16(Mn4.77Fe0.07)Σ4.84 Mg0.44[Si11.97O30(OH)4](PO4)0.94O2(OH)2·4H2O. The idealised formula is: KNa8Mn3+5Mg0.5[Si12O30(OH)4](PO4)O2(OH)2·4H2O. Lipuite is orthorhombic, space group Pnnm, a=9.080(3), b=12.222(3), c=17.093(5) Å, V=1897.0(9) Å3 and Z=2. The strongest powder X-ray diffraction peaks [d, Å (I)(hkl)] are: 9.965(40)(011), 2.938(33)(310), 2.895(100)(311), 2.777(38)(224), 2.713(53)(320), 2.483(32)(126), 2.086(35)(046) and 1.534(40)(446). The crystal structure of lipuite is characterised by sheets of SiO4 tetrahedra that are linked together along [010] by K+, Na+, Mn3+, Mg2+ and P5+ cations, as well as hydrogen bonds. These tetrahedral sheets consist of 14-membered rings of SiO4 tetrahedra that zigzag along [100]. The two independent Mn3+ cations are both octahedrally coordinated. They form five-membered, edge-shared octahedral clusters between the SiO4 tetrahedral sheets. Lipuite represents a rather unique structure type and its silicate tetrahedral sheets can be considered a derivative of the silicate sheets in mica.

The Suizhou meteorite is a heavily shocked and melted vein-containing L6 chondrite. It contains a minor amount of diopside with a (Ca 0.419 Mg 0.466 Fe 0.088 )SiO 3 composition, and a shock-metamorphosed diopside grain associated with ringwoodite and lingunite was found in a melt vein of this meteorite. Our electron microprobe, transmission electron microscopic and Raman spectroscopic analyses revealed four silicate phases with different compositions and structures inside this shock-metamorphosed diopside grain, termed phase A, B, C and D in this paper. Phase A is identified as orthorhombic (Ca 0.663 Mg 0.314 )SiO 3 -perovskite which is closely associated with phase B, the vitrified (Mg 0.642 Ca 0.290 Fe 0.098 )SiO 3 perovskite. Phase D is assigned to be (Mg 0.578 Ca 0.414 )SiO 3 majorite which is associated with phase C, the vetrified Ca-rich Mg-perovskite with a (Mg 0.853 Ca 0.167 )SiO 3 composition. Based on high-pressure and high-temperature experiments, the diopside grain in the melt vein of the Suizhou meteorite would have experienced a P–T regime of 20–24 GPa and 1800 – > 2000 °C. Such P–T conditions are high enough for the decomposition of the diopside and the formation of four different silicate phases. The orthorhombic (Ca 0.663 Mg 0.314 )SiO 3 perovskite found in the Suizhou L6 chondrite might be considered as the third lower-mantle silicate mineral after bridgmanite and davemaoite after the detailed analyses of its crystal structure and physical properties being completed.

One of the major concerns in 5G IoT networks is that most of the sensor nodes are powered through limited lifetime, which seriously affects the performance of the networks. In this article, Compressive sensing (CS) technique is used to decrease transmission cost in 5G IoT networks. Sparse basis is one of the important steps in the CS. However, most of the existing sparse basis-based method such as DCT (Discrete cosine transform) and DFT (Discrete Fourier Transform) basis do not capture data structure characteristics in the networks. They also do not take into consideration multi-resolution representations. In addition, some of sparse basis-driven methods exploit either spatial or temporal features, resulting in performance degradation of CS-based strategies. To address these challenging problems, we propose a novel spatial–temporal correlation basis algorithm (SCBA). Subsequently, an optimal basis algorithm (OBA) is provided considering greedy scoring criteria. To evaluate the efficiency of OBA, orthogonal wavelet basis algorithm (OWBA) by employing NS (Numerical Sparsity) and GI (Gini Index) sparse metrics is also presented. In addition, we discuss the complexity of the above three algorithms, and prove that OBA has low numerical rank. After experimental evaluation, we found that OBA is capable of the sparsest representing original signal compared to spatial, DCT, haar-1, haar-2, and rbio5.5. Furthermore, OBA has the low recovery error and the highest efficiency.

We report the discovery of TiO 2 -II in the unmelted rock of the shocked Suizhou L6 chondrite. Natural TiO 2 -II was previously found in ultrahigh-pressure metamorphic and mantle-derived rocks, terrestrial impact structures, and tektite. Our microscopic, Raman spectroscopic, electron microprobe and transmission electron microscopic investigations have revealed: (1) All observed TiO 2 -II grains are related with ilmenite and pyrophanite; (2) TiO 2 -II occurs as needle- and leaf-shaped inclusions in ilmenite and patch-, tape-shaped body in pyrophanite; (3) The composition of TiO 2 -II is identical with that of its precursor rutile; (4) The Raman spectrum of TiO 2 -II is in good agreement with that of natural and synthesized α-PbO 2 -type TiO 2 ; (5) TiO 2 -II occurs mainly in the form of well-ordered nano-domains and small mis-orientation among the domains can be observed. (6) All electron diffraction reflections from TiO 2 -II can be indexed to α-PbO 2 structure in space group Pbcn with lattice parameters of a  = 4.481 Å, b  = 5.578 Å and c  = 4.921 Å; (7) The exsolution inclusions of rutile from host ilmenite are mostly connected with an alternation process along the lamellar twinning plane of ilmenite induced by shock-induced high pressure and high temperature; (8) The P–T regime of 20–25 GPa and 1000 °C estimated for the Suizhou unmelted rock is suitable for phase transition of rutile into TiO 2 -II phase.

The ancylite supergroup has been approved by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association, with the general crystal chemical formula ( M 3+ x M 2+ 2– x )(CO 3 ) 2 [(OH) x ⋅(2– x )H 2 O] (1 ≤ x ≤ 2, Z = 2). The ancylite supergroup can be divided into two groups defined by different proportions of the M cation and hydroxyl anion and/or water molecule: the ancylite group is defined for 1 ≤ x ≤ 1.5; the kozoite group is defined for 1.5 < x ≤ 2. The ancylite supergroup minerals are orthorhombic with space group Pmcn , or monoclinic with space group Pm 11, and have a crystal structure with species-defining trivalent and divalent M cations ( M = La 3+ , Ce 3+ , Nd 3+ , Ca 2+ , Sr 2+ and Pb 2+ ) which centre ten-vertex polyhedra formed by oxygen atoms at three independent O sites. Two vertices of the triangular (CO 3 ) 2– anion are oxygen atoms, whereas the third one, O(3), is statistically filled with (OH) – groups and H 2 O molecules. The triangular faces of three oxygen atoms of M O 10 coordination polyhedra join the chains of this ten-vertex polyhedron, which is extended along the c axis. The (CO 3 ) triangles connect chains in three dimensions. To date, eight valid mineral species with M 2+ = Sr 2+ , Ca 2+ and Pb 2+ belong to the ancylite group [ancylite-(La), ancylite-(Ce), calcioancylite-(La), calcioancylite-(Ce), calcioancylite-(Nd), gysinite-(La), gysinite-(Ce) and gysinite-(Nd)]. Two hydroxyl carbonates with only rare earth elements as species-defining cations, kozoite-(La) and kozoite-(Nd) are members of the kozoite group.

Kenoargentotetrahedrite-(Fe) is observed as greenish-grey anhedral grains, 50–150 μm in size, in association with galena, sphalerite and chalcopyrite in the Bajiazi Pb-Zn deposit of magmatic-hydrothermal type, Liaoning, China. The empirical formula from electron microprobe analyses is Ag5.50Cu4.17Fe1.75Zn0.31Sb3.96As0.04S12.08, corresponding to the ideal formula Ag6Cu4Fe2Sb4S12. The crystal structure of kenoargentotetrahedrite-(Fe) has been determined and refined by single-crystal X-ray diffraction with R1 = 0.0192 for 1866 (404 unique) reflections. It is cubic, space group I4¯3m with unit cell parameters a = 10.4928(2) Å, V = 1155.26(7) Å3 and Z = 2. The structure of kenoargentotetrahedrite-(Fe) is characterized by a poor occupancy of 0.05 of the octahedral S(2) site with the S(2)-M(2) bonding length of 1.9994(8) Å. The six Ag atoms at M(2) around S(2) form an octahedron cluster (Ag6)4+ with the valence state of +4 and Ag-Ag distance of 2.8276(1) Å. The structure is identical to that by Rozhdestvenskaya et al., being composed of a collapsed sodalite-like framework of corner-connected M(1)S4 tetrahedron forming cages containing M(2)6-octahedron cluster, encircled by four SbS3 trigonal pyramids. It is related to the tetrahedrite group minerals with the existence of the (Ag6)4+ cluster replacing the S(2)-centered Ag6 octahedron according to the substitution mechanism 6M(2)Ag+ + S(2)S2−=M(2)(Ag6)4+ + S(2) S.

Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings’ spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node’s residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets’ sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.