Multiple Earth Research Articles

Valence electron structures and thermal and magnetic properties of RCo5 (R=light rare earth) intermetallic compounds with medium- and high-entropy design at R site

RCo5-type (R=light rare earth) medium- and high-entropy intermetallic compounds, which are designed by random occupation of equiatomic multiple rare earth elements at the rare earth site, are prepared by arc melting and vacuum heat-treatment technologies, and their crystal structures and magnetic properties are studied by X-ray diffraction and magnetic measurements. The empirical electron theory of solids and molecules is used to investigate their valence electron structures, thermal and magnetic properties. The theoretical bond lengths and magnetic moments match the experimental ones well. The study reveals that the thermal and magnetic properties are strongly related to the valence electron structures modulated by medium- and high-entropy design at rare earth. The medium and high entropized designs induce the increase of the covalence electron numbers nc. It is helpful for stabilizing the crystal structure. The number of covalence electron pair of the strongest R-CoI bond affects the melting point. The magnetic moments of RCo5-type medium- and high-entropy intermetallic compounds, which mainly originate from the 3d magnetic electrons of Co atoms, are modulated by medium and high entropized rare earth due to their exchange interactions. The magnetic moment of Co at the 2c site is larger than that of Co at the 3g site, which is due to the electron transformations from magnetic to covalence electron for bonding with the rare earth at the 3g site. Curie temperatures raise with the increasing 3d magnetic electron numbers of Co atom.

SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm

In modern life, a large amount of information is transmitted through satellites. The anti-jamming ability of satellite communication earth stations is the basis for ensuring the smooth flow of information. In the case that multiple small earth stations cannot communicate due to jamming, the minimum variance distortionless response (MVDR) algorithm based on reflector antenna array is proposed. Firstly, the gain expression of satellite communication earth station antenna is derived, and the mathematical model of the received signal of the reflector antenna array is established. Then, the MVDR beamforming algorithm based on the reflector antenna is proposed and applied to the satellite communication earth station reflector antenna array, and the anti-jamming capability of the reflector antenna array using this algorithm is analyzed through simulation. The simulation results show that the earth station reflector antenna array using MVDR algorithm can effectively achieve array gain of the direction of the satellite signal, suppress the jamming signals, increase the output signal to interference plus noise ratio (SINR) of the reflector antenna array, and increase the output signal to jamming plus noise ratio with the increase in the number of snapshots.

Strengthening of Mg Alloy with Multiple RE Elements with Ag and Zn Doping via Heat Treatment.

Strengthening Mg alloys with rare earth elements has been a research focus for several decades. To minimize the usage of rare earth elements while enhancing mechanical properties, we adopted the strategy of alloying with multiple rare earth elements, namely Gd, Y, Nd, and Sm. Additionally, to promote the precipitation of basal precipitate, Ag and Zn doping was also induced. Thus, we designed a new cast Mg-2Gd-2Y-2Nd-2Sm-1Ag-1Zn-0.5Zr (wt.%) alloy. The microstructure of the alloy and its relevance to mechanical properties in various heat treatment conditions were investigated. After undergoing a heat treatment process, the alloy demonstrated exceptional mechanical properties, with a yield strength of 228 MPa and an ultimate tensile strength of 330 MPa achieved through peak-aging for 72 h at 200 °C. The excellent tensile properties are due to the synergistic effect of basal γ″ precipitate and prismatic β' precipitate. In its as-cast state, its primary mode of fracture is inter-granular, whereas in the solid-solution and peak-aging conditions, the predominant mode of fracture is a mixture of trans-granular and inter-granular fractures.

Uplink Multiple Access With Semi-Grant-Free Transmission in Integrated Satellite-Aerial-Terrestrial Networks

This paper investigates a semi-grant-free (SGF) based transmission strategy to provide a flexible connectivity for various kinds of users in an integrated satellite-aerial-terrestrial network (ISATN). Herein, a high-altitude platform (HAP) termed as a grant-based user (GBU), which serves multiple mobile terminals (MTs) through space division multiple access (SDMA), wants to access a satellite network with multiple earth stations (ESs) termed as grant-free users (GFUs) simultaneously via non-orthogonal multiple access (NOMA) assisted SGF. To this end, we first propose two SGF-based uplink transmission schemes for both perfect channel state information (CSI) and imperfect CSI cases. When perfect CSI is available, a zero-forcing based beamforming (BF) scheme is used in HAP network while an adaptive transmit power allocation (ATPA) approach is adopted for SGF transmission. When only imperfect CSI is available, BF scheme employing the derived channel correlation matrix of HAP-MT link is proposed to achieve SDMA, and a novel ATPA strategy with rate probability constraint is proposed to guarantee quality-of-service of the GBU. Next, we derive the closed-form throughput expressions to evaluate the performance of the considered ISATN with the proposed two SGF-based schemes. Finally, computer simulations are conducted to validate the theoretical performance analysis and show the superiority of the proposed schemes over the related works. Moreover, our numerical results not only demonstrate a satisfactory performance of the proposed SGF-based scheme using imperfect CSI, but also reveal the impact of CSI errors on the system performance.

Clinical Presentation and Outcome of Multiple Rare Earth Magnet Ingestions in Children of Qatar. A Single-Center Experience.

Rare earth magnets are powerful magnets that can have several negative effects if ingested. The goal of our study is to describe the result of multiple rare earth magnets ingested by children in Qatar. This is observational research. We conducted a retrospective chart review and descriptive analysis of all cases of multiple rare earth magnetic ingestion that werepresented to the Emergency Department of Sidra Medicine between January 2018 and July 2022. We obtained an exemption for this study from our institutional review board (IRB). In our research, we identified 21 children having multiple rare earth magnetic ingestions. The predominant symptoms were abdominal pain and vomiting which were observed in 57% (n=12) and 48% (n=10) of the patients respectively. The most common sign was abdominal tenderness,observed in 14% (n=3) of the patients. In our sample, 38% (n=8) of the patients were managed conservatively whereas 62% (n=13) needed intervention. In our study, 48% (n=10) of the patients sustained complications. The frequent complications were intestinal perforation appreciated in 24% (n=5) and intestinal perforation with fistula formation in 19% (n=4) of the patients. The median age of these patients was two years while the median number of magnets ingested was six. The ingestions were unwitnessed, and the duration of ingestions was unknowninthemajorityofpatientswhoexperiencedcomplications (n=8/10). If numerous rare earth magnetis ingested, children are in high danger of harm. It can be difficult to pinpoint the cases in younger children due to poor communication skills, especially if the intake is unreported. Although Qatar has established restrictions banning the import of rare earth magnets, there are reported cases of children with rare earth magnets ingestions.

A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research

Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present “Awesome Spectral Indices” (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains.

Achieving superior elevated-temperature strength of Mg-12Gd-3Y alloys by Nd addition

There is a tremendous demand for heat-resistant Mg alloys in aerospace and automotive applications. In this paper, different Nd contents are added to Mg-12Gd-3Y alloy to design the joint action of multiple rare earth (RE) elements to improve the elevated-temperature (ET) strength. The Mg-12Gd-3Y-1.0Nd alloy after extrusion and aging (E-A) process showed excellent strength and toughness matching at room temperature (RT), 200 °C, and 250 °C. The E-Aed Mg-12Gd-3Y-1.0Nd alloy showed ultimate tensile strength (UTS), tensile yield strength (TYS), and elongation (EL) of 440 MPa, 312 MPa, and 6.2% at RT. The E-Aed Mg-12Gd-3Y-1.0Nd alloy showed UTS, TYS, and EL of 405 MPa, 306 MPa, and 12.6% at 200 °C. The E-Aed Mg-12Gd-3Y-1.0Nd alloy showed UTS, TYS, and EL of 382 MPa, 303 MPa, and 19.4% at 250 °C. The addition of Nd elements reduces the solid solubility of Gd, and Y in the Mg matrix, thus precipitating many Mg5RE and β′ phases in the alloy. The excellent mechanical properties of Mg-12Gd-3Y-1.0Nd alloy after E-A treatment come from the fine grain size, segregation at grain boundaries (GBs), and delicate dispersed second phases. The fine grain size is beneficial to inhibit crack expansion, allowing it to consume more energy during the expansion process and improve strength. Both grain boundary segregation and diffusely distributed second phase can effectively inhibit grain boundary sliding and enhance strength.

IRS-Aided Uplink Transmission Scheme in Integrated Satellite-Terrestrial Networks

This paper proposes an intelligent reflecting surface (IRS)-aided uplink transmission scheme to expand the wireless coverage and improve spectral efficiency for an integrated satellite-terrestrial network (ISTN). Here, multiple earth stations communicate with the satellite via non-orthogonal multiple access (NOMA) technology, while multiple direct users and blockage users access the cellular network through space division multiple access and IRS-aided NOMA technology, respectively. In particular, we first aim at maximizing the ergodic sum rate of the ISTN subject to the quality-of-service requirements and transmit power budgets of all users, yielding a constrained optimization problem. Then, based on the assumption that only the statistical channel state information (CSI) is available, we propose a two-layer alternating algorithm to solve the non-convex original problem. The algorithm combines successive convex approximation with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> -procedure approach to calculate the phase shifts of IRS and transmit powers of all users in the first layer, and uses Charnes-Cooper method with semi-definite programming to calculate beamforming vectors at the BS in the second layer. Furthermore, with the help of the zero-forcing beamforming principle, we propose a low-complexity algorithm to solve the problem that can reduce the computational load effectively. Finally, simulation results demonstrate that our proposed algorithms can obtain a higher ergodic sum rate than the benchmarks.

Multi-Classifier Pipeline for Olive Groves Detection

Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.

Deforestation intensifies daily temperature variability in the northern extratropics

While the biogeophysical effects of deforestation on average and extreme temperatures are broadly documented, how deforestation influences temperature variability remains largely unknown. To fill this knowledge gap, we investigate the biogeophysical effects of idealized deforestation on daily temperature variability at the global scale based on multiple earth system models and in situ observations. Here, we show that deforestation can intensify daily temperature variability (by up to 20%) in the northern extratropics, particularly in winter, leading to more frequent rapid extreme warming and cooling events. The higher temperature variability can be attributed to the enhanced near-surface horizontal temperature advection and simultaneously is partly offset by the lower variability in surface sensible heat flux. We also show responses of daily temperature variability to historical deforestation and future potential afforestation. This study reveals the overlooked effects of deforestation or afforestation on temperature variability and has implications for large-scale afforestation in northern extratropic countries.

Deep reinforcement learning-based autonomous mission planning method for high and low orbit multiple agile Earth observing satellites

Concerning the onboard autonomous mission planning problem of high and low orbiting agile Earth observation satellites (AEOSs), which requires high algorithm timeliness, a deep reinforcement learning (DRL) based algorithm, namely, the multi-satellite mission planning (MSMP) algorithm, is proposed. The algorithm uses neural networks with an ‘encoder-decoder’ structure and designs a mechanism that enables each satellite to select requests in turn. These two designs allow the algorithm to achieve simultaneous scheduling of multiple satellites. After that, the MSMP uses a REINFORCE with Critic Baseline algorithm to optimize its selection strategy. Computational experiments show that the proposed algorithm can reduce the reasoning time by a factor of tens compared to the adaptive task assignment large neighborhood search (A-ALNS) algorithm, while being able to keep the revenue rate difference to A-ALNS below 5%.

Versatile synthesis of dendritic mesoporous rare earth–based nanoparticles

Rare earth–based nanomaterials that have abundant optical, magnetic, and catalytic characteristics have many applications. The controllable introduction of mesoporous channels can further enhance its performance, such as exposing more active sites of rare earth and improving the loading capacity, yet remains a challenge. Here, we report a universal viscosity-mediated assembly strategy and successfully endowed rare earth–based nanoparticles with central divergent dendritic mesopores. More than 40 kinds of dendritic mesoporous rare earth–based (DM-REX) nanoparticles with desired composition (single or multiple rare earth elements, high-entropy compounds, etc.), particle diameter (80 to 500 nanometers), pore size (3 to 20 nanometers), phase (amorphous hydroxides, crystalline oxides, and fluorides), and architecture were synthesized. Theoretically, a DM-REX nanoparticle library with 393,213 kinds of possible combinations can be constructed on the basis of this versatile method, which provides a very broad platform for the application of rare earth–based nanomaterials with rational designed functions and structures.

Rare Earth Market, Electric Vehicles and Future Mobility Index: A Time-Frequency Analysis with Portfolio Implications

This study investigates the co-movements between the Solactive Electric Vehicle and Future Mobility Index (EVFMI) and multiple rare earth elements (REEs). We applied a TVP-VAR model and bivariate wavelet coherence approach to capture co-movements both in the time and frequency domain considering short-, medium- and long-term investment horizons. Using daily returns from 1 June 2012 to 4 June 2021, the results of the TVP-VAR model show that individual REEs and the EVFMI have strong return connectedness and are heterogenous over time. The bivariate wavelet coherence approach reveals that Dysprosium, Neodymium, Praseodymium and Terbium returns have positive co-movement (in-phase) with the EVFMI in the medium-term and long-term. In contrast, Cerium, Europium, Lanthanum and Yttrium returns have negative co-movements (out-phase) with the EVFMI in the medium-term and long-term. We find strong positive co-movements between the MVIS Global Rare Earth/Strategic Metals Index (MVREMX) and EVFMI at multiple wavelet scales. Following the lead/lag relationship, Cerium, Europium and Lanthanum, Yttrium returns are leading the EVFMI, and Neodymium, Dysprosium, Praseodymium, Terbium and MVREMX returns are lagging to the EVFMI. This study, therefore, suggests heterogenous hedging and diversification properties of REEs over time and investment horizons. Specifically, Cerium, Europium, Lanthanum and Yttrium act as strong hedges in long-term investment horizons and Neodymium, Dysprosium, Praseodymium and Terbium are weak hedges or diversifiers in short-term investment horizons. These results may be of particular interest to investors and relevant to policymakers considering multiple investment horizons.

GANSim‐3D for Conditional Geomodeling: Theory and Field Application

AbstractWe present a Generative Adversarial Network (GAN)‐based 3D reservoir simulation framework, GANSim‐3D, where the generator is progressively trained to capture geological patterns and relationships between various input conditioning data and output earth models, and is thus able to directly produce multiple 3D realistic and conditional earth models from given conditioning data. Conditioning data can include 3D sparse well facies data, probability maps, and global features, such as facies proportion. The generator only includes 3D convolutional layers, and once trained on a data set consisting of small‐size data cubes, it can be used for geomodeling of 3D reservoirs of large arbitrary sizes by simply extending the inputs. To illustrate how GANSim‐3D is practically used and to verify GANSim‐3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are taken as conditioning data. First, we create training, validation, and test datasets consisting of 64 × 64 × 64‐size 3D cave facies models integrating field geological patterns, 3D well facies data, and 3D probability maps. Then, the 3D generator is trained and evaluated with various metrics. Next, we apply the pretrained generator for conditional geomodeling of two field cave reservoirs of size 64 × 64 × 64 and 336 × 256 × 96, respectively. The produced reservoir realizations prove to be diverse, consistent with field geological patterns and field conditioning data, and robust to noise in the 3D probability maps. Each realization with 336 × 256 × 96 cells only takes 0.988 s using 1 GPU.

Nucleation and epitaxy growth of high-entropy REBa2Cu3O7–δ (RE= Y, Dy, Gd, Sm, Eu) thin films by metal organic deposition

High-entropy REBa2Cu3O7–δ (RE = Y0.7 + Dy0.2 + Gd0.2 + Sm0.2 + Eu0.2) (HE-REBCO) superconducting films doped with multiple rare earth elements were successfully fabricated with thickness up to 800 nm by a trifluoroacetate-metal organic deposition (TFA-MOD). The enhanced entropy change ΔS of the HE-REBCO system promotes the c-axis growth of REBCO thin film in the competition with a/b-axis growth. The microstructure and element distribution were investigated by the transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS). The results show that HE-REBCO films have a great amount of stacking defects, lattice distortions and various rare earth oxides. Furthermore, the microstrain of HE-REBCO films increases significantly compared to the undoped YBCO and (Y, Dy)BCO films. Finally, the superconducting critical temperature (Tconset) of HE-REBCO films is about 93 K, and the in-field properties are significantly improved, especially at the temperature of about 77 K. The maximum pinning force density (Fp,max) of the HE-REBCO samples at 77 K is 1.7 times higher than that of the undoped YBCO films.

Subseasonal Earth System Prediction with CESM2

Abstract Prediction systems to enable Earth system predictability research on the subseasonal time scale have been developed with the Community Earth System Model, version 2 (CESM2) using two configurations that differ in their atmospheric components. One system uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ∼140 km, and it includes fully interactive tropospheric and stratospheric chemistry [CESM2(WACCM6)]. Both systems are utilized to carry out subseasonal reforecasts for the 1999–2020 period following the Subseasonal Experiment’s (SubX) protocol. Subseasonal prediction skill from both systems is compared to those of the National Oceanic and Atmospheric Administration CFSv2 and European Centre for Medium-Range Weather Forecasts (ECMWF) operational models. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-m temperature, precipitation, the Madden–Julian oscillation, and North Atlantic Oscillation to its previous version and to the NOAA CFSv2 model. Overall, skill of CESM2(CAM6) and CESM2(WACCM6) is a little lower than that of the ECMWF system. In addition to typical output provided by subseasonal prediction systems, CESM2 reforecasts provide comprehensive datasets for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region from CESM2(WACCM6). It is shown that sudden stratosphere warming events, and the associated variability in the MLT, can be predicted ∼10 days in advance. Weekly real-time forecasts and reforecasts with CESM2(CAM6) and CESM2(WACCM6) are freely available. Significance Statement We describe here the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): CESM2 with the Community Atmosphere Model, version 6 [CESM2(CAM6)] and CESM 2 with Whole Atmosphere Community Climate Model, version 6 [CESM2(WACCM6)] as its atmospheric component. These two systems provide a foundation for community-model based subseasonal prediction research. The CESM2(WACCM6) system provides a novel capability to explore the predictability of the stratosphere, mesosphere, and lower thermosphere. Both CESM2(CAM6) and CESM2(WACCM6) demonstrate subseasonal surface prediction skill comparable to that of the NOAA CFSv2 model, and a little lower than that of the ECMWF forecasting system. CESM2 reforecasts provide a comprehensive dataset for predictability research of multiple aspects of the Earth system, including the whole atmosphere up to 140 km, land, and sea ice. Weekly real-time forecasts, reforecasts, and models are publicly available.

Interhemispheric Asymmetries in Ionospheric Electron Density Responses During Geomagnetic Storms: A Study Using Space‐Based and Ground‐Based GNSS and AMPERE Observations

AbstractWe utilize Total Electron Content (TEC) measurements and electron density (Ne) retrieval profiles from Global Navigation Satellite System (GNSS) receivers onboard multiple Low Earth Orbit (LEO) satellites to characterize large‐scale ionosphere‐thermosphere system responses during geomagnetic storms. We also analyze TEC measurements from GNSS receivers in a worldwide ground‐based network. Measurements from four storms during June and July 2012 (boreal summer months), December 2015 (austral summer month), and March 2015 (equinoctial month) are analyzed to study global ionospheric responses and the interhemispheric asymmetry of these responses. We find that the space‐based and ground‐based TECs and their responses are consistent in all four geomagnetic storms. The global 3D view from GNSS‐Radio Occultation (RO) Ne observations captures enhancements and the uplifting of Ne structures at high latitudes during the initial and main phases. Subsequently, Ne depletion occurs at high latitudes and starts progressing into midlatitude and low latitude as the storm reaches its recovery phase. A clear time lag is evident in the storm‐induced Ne perturbations at high latitudes between the summer and winter hemispheres. The interhemispheric asymmetry in TEC and Ne appears to be consistent with the magnitudes of the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) high latitude integrated field‐aligned currents (FACs), which are 3–4 MA higher in the summer hemisphere than in the winter hemisphere during these storms. The ionospheric TEC and Ne responses combined with the AMPERE‐observed FACs indicate that summer preconditioning in the ionosphere‐thermosphere system plays a key role in the interhemispheric asymmetric storm responses.

A low accretion efficiency of planetesimals formed at planetary gap edges

Observations and models of giant planets indicate that such objects are enriched in heavy elements compared to solar abundances. The prevailing view is that giant planets accreted multiple Earth masses of heavy elements after the end of core formation. Such late solid enrichment is commonly explained by the accretion of planetesimals. Planetesimals are expected to form at the edges of planetary gaps, and here we address the question of whether these planetesimals can be accreted in large enough amounts to explain the inferred high heavy element contents of giant planets. We performed a series of N-body simulations of the dynamics of planetesimals and planets during the planetary growth phase, taking gas drag into account as well as the enhanced collision cross section caused by the extended envelopes. We considered the growth of Jupiter and Saturn via gas accretion after reaching the pebble isolation mass and we included their migration in an evolving disk. We find that the accretion efficiency of planetesimals formed at planetary gap edges is very low: less than 10% of the formed planetesimals are accreted even in the most favorable cases, which in our model corresponds to a few Earth masses. When planetesimals are assumed to form beyond the feeding zone of the planets, extending to a few Hill radii from a planet, accretion becomes negligible. Furthermore, we find that the accretion efficiency increases when the planetary migration distance is increased and that the efficiency does not increase when the planetesimal radii are decreased. Based on these results, we conclude that it is difficult to explain the large heavy element content of giant planets with planetesimal accretion during the gas accretion phase. Alternative processes most likely are required, such as accretion of vapor deposited by drifting pebbles.

A Novel Machine Learning–Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data by Combining Downscaling and Regression

Abstract Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks

Asteroid exploration has been attracting more attention in recent years. Nevertheless, we have just visited tens of asteroids, whereas we have discovered more than 1 million bodies. As our current observation and knowledge should be biased, it is essential to explore multiple asteroids directly to better understand the remains of planetary building materials. One of the mission design solutions is utilizing asteroid flyby cycler trajectories with multiple Earth gravity assists. An asteroid flyby cycler trajectory design problem is a subclass of global trajectory optimization problems with multiple flybys, involving a trajectory optimization problem for a given flyby sequence and a combinatorial optimization problem to decide the sequence of the flybys. As the number of flyby bodies grows, the computation time of this optimization problem expands maliciously. This paper presents a new method to design asteroid flyby cycler trajectories utilizing a surrogate model constructed by deep neural networks approximating trajectory optimization results. Because one of the bottlenecks of machine learning approaches is the heavy computation time to generate massive trajectory databases, we propose an efficient database generation strategy by introducing pseudo-asteroids satisfying the Karush–Kuhn–Tucker conditions. The numerical result applied to Japan Aerospace Exploration Agency’s mission shows that the proposed method is practically applicable to space mission design and can significantly reduce the computational time for searching asteroid flyby sequences.