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Atmospheric-correction-free red tide quantification algorithm for GOCI based on machine learning combined with a radiative transfer simulation
No full textSatellite ocean color observation has been an effective way to detect red tide in a wide coastal area of many countries. However, red tide water, which has high red and infra-red reflectance, often causes failure in atmospheric correction, making quantification algorithms based on remote sensing reflectance produce large errors. This study proposes a new framework that tackles the difficulties in red tide quantification stemming from atmospheric variability, limited in-situ training data, and image artifacts, through the combined use of radiative simulation, machine learning, and in-situ measurements. The framework was applied to the geostationary ocean color imager (GOCI) to monitor Margalefidinium blooms that are frequent in Korean coasts. The estimation results were validated first quantitatively with independent ship survey data, and then qualitatively with other 3 red tide algorithms. Finally, the operational robustness of the proposed framework was analyzed based on the data acquired in the entire outbreak period in 2018. The results showed that the proposed algorithm produced a high correlation with field data (R-2 similar to 0.89) and high detection rate and low false alarms compared to the other red tide algorithms. The monitoring result for 2018 also demonstrated that the initiation, expansion, peak, and termination of red tide were successfully identified by the satellite data, which coincides with the field survey results provided by the national fishery agency
Semiconducting Electronic Structure of the Ferromagnetic Spinel HgCr2Se4 Revealed by Soft-X-Ray Angle-Resolved Photoemission Spectroscopy
No full textWe study the electronic structure of the ferromagnetic spinel HgCr2Se4 by soft-x-ray angle-resolved photoemission spectroscopy (SX-ARPES) and first-principles calculations. While a theoretical study has predicted that this material is a magnetic Weyl semimetal, SX-ARPES measurements give direct evidence for a semiconducting state in the ferromagnetic phase. Band calculations based on the density functional theory with hybrid functionals reproduce the experimentally determined band gap value, and the calculated band dispersion matches well with ARPES experiments. We conclude that the theoretical prediction of a Weyl semimetal state in HgCr2Se4 underestimates the band gap, and this material is a ferromagnetic semiconductor
Nucleation-controlled growth of Cu thin films electrodeposited directly on ALD Ru diffusion barrier in additive-free electrolyte for Cu interconnect
No full textDirect Cu electrodeposition on a 3-nm-thick atomic-layer-deposited (ALD) Ru diffusion barrier layer was investigated for Cu interconnect in Si-based microelectronic devices. A Cu-ammonia-citrate (Cu-NH3-Cit) electrolyte was employed with no additives. The nucleation behavior of Cu on Ru was dependent on the cathodic potential applied to ALD Ru/SiO2/Si wafer specimen whether it is instantaneous or progressive. Preferable instantaneous nucleation with a high area density of Cu nuclei on Ru contributed to the formation of uniform Cu thin films. Lastly, direct Cu electrodeposition onto Ru at a constant cathodic potential of -1.0 V filled 30-nmwide and 120-nm-deep trenches with void-free Cu
Tunable growth of one-dimensional graphitic materials: graphene nanoribbons, carbon nanotubes, and nanoribbon/nanotube junctions
No full textGraphene nanoribbons (GNRs) and carbon nanotubes (CNTs), two representative one-dimensional (1D) graphitic materials, have attracted tremendous research interests due to their promising applications for future high-performance nanoelectronics. Although various methods have been developed for fabrication of GNRs or CNTs, a unified method allowing controllable synthesis of both of them, as well as their heterojunctions, which could largely benefit their nano-electronic applications, is still lacking. Here, we report on a generic growth of 1D carbon using nanoparticles catalyzed chemical vapor deposition (CVD) on atomically flat hexagonal boron nitride (h-BN) substrates. Relative ratio of the yielded GNRs and CNTs is able to be arbitrarily tuned by varying the growth temperature or feeding gas pressures. The tunability of the generic growth is quantitatively explained by a competing nucleation theory: nucleation into either GNRs or CNTs by the catalysts is determined by the free energy of their formation, which is controlled by the growth conditions. Under the guidance of the theory, we further realized growth of GNR/CNT intramolecular junctions through changing H-2 partial pressure during a single growth process. Our study provides not only a universal and controllable method for growing 1D carbon nanostructures, but also a deep understanding of their growth mechanism, which would largely benefit future carbon-based electronics and optoelectronics
Optoelectronic Manifestation of Orbital Angular Momentum Driven by Chiral Hopping in Helical Se Chains
No full textChiral materials have garnered significant attention in the field of condensed matter physics. Nevertheless, the magnetic moment induced by the chiral spatial motion of electrons in helical materials, such as elemental Te and Se, remains inadequately understood. In this work, we investigate the development of quantum angular momentum enforced by chirality by using static and time-dependent density functional theory calculations for an elemental Se chain. Our findings reveal the emergence of an unconventional orbital texture driven by the chiral geometry, giving rise to a nonvanishing current-induced orbital moment. By incorporating spin???orbit coupling, we demonstrate that current-induced spin accumulation arises in the chiral chain, which fundamentally differs from the conventional Edelstein effect. Furthermore, we demonstrate optoelectronic detection of the orbital angular momentum in the chiral Se chain, providing an alternative to the interband Berry curvature, which is ill-defined in low dimensions
Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests
No full textIdentifying important factors (e.g., features and prediction models) for forest aboveground biomass (AGB) estimation can provide a vital reference for accurate AGB estimation. This study proposed a novel feature of the canopy height distribution (CHD), a function of canopy height, that is useful for describing canopy structure for AGB estimation of natural secondary forests (NSFs) by fitting a bimodal Gaussian function. Three machine learning models (Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (Xgboost)) and three deep learning models (One-dimensional Convolutional Neural Network (1D-CNN4), 1D Visual Geometry Group Network (1D-VGG16), and 1D Residual Network (1D-Resnet34)) were applied. A completely randomized design was utilized to investigate the effects of four feature sets (original CHD features, original LiDAR features, the proposed CHD features fitted by the bimodal Gaussian function, and the LiDAR features selected by the recursive feature elimination algorithm) and models on estimating the AGB of NSFs. Results revealed that the models were the most important factor for AGB estimation, followed by the features. The fitted CHD features significantly outperformed the other three feature sets in most cases. When employing the fitted CHD features, the 1D-Renset34 model demonstrates optimal performance (R2 = 0.80, RMSE = 9.58 Mg/ha, rRMSE = 0.09), surpassing not only other deep learning models (e.g.,1D-VGG16: R2 = 0.65, RMSE = 18.55 Mg/ha, rRMSE = 0.17) but also the best machine learning model (RF: R2 = 0.50, RMSE = 19.42 Mg/ha, rRMSE = 0.16). This study highlights the significant role of the new CHD features fitting a bimodal Gaussian function and the effects between the models and the CHD features, which provide the sound foundations for effective estimation of AGB in NSFs
A solution-based synthetic method using high-entropy layered double hydroxides as precursors for compositionally complex metal oxide structure
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GaMiCO: Game-slicing based Multi-interface Computation Offloading in 5G Vehicular Networks
No full textSmart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game's real-time convergence to an NE, reveal the NE's near-optimal performance, and present the efficacy of the proposed game slicing
Unleashing the Potential of Sodium-Ion Batteries: Current State and Future Directions for Sustainable Energy Storage
No full textRechargeable sodium-ion batteries (SIBs) are emerging as a viable alternative to lithium-ion battery (LIB) technology, as their raw materials are economical, geographically abundant (unlike lithium), and less toxic. The matured LIB technology contributes significantly to digital civilization, from mobile electronic devices to zero electric-vehicle emissions. However, with the increasing reliance on renewable energy sources and the anticipated integration of high-energy-density batteries into the grid, concerns have arisen regarding the sustainability of lithium due to its limited availability and consequent price escalations. In this context, SIBs have gained attention as a potential energy storage alternative, benefiting from the abundance of sodium and sharing electrochemical characteristics similar to LIBs. Furthermore, high-entropy chemistry has emerged as a new paradigm, promising to enhance energy density and accelerate advancements in battery technology to meet the growing energy demands. This review uncovers the fundamentals, current progress, and the views on the future of SIB technologies, with a discussion focused on the design of novel materials. The crucial factors, such as morphology, crystal defects, and doping, that can tune electrochemistry, which should inspire young researchers in battery technology to identify and work on challenging research problems, are also reviewed