Hyperspectral infrared atmospheric sounder II (HIRAS II) onboard in Fengyun-3E (FY-3E) early-morning-orbit meteorological satellite is the second-generation hyperspectral infrared (IR) sounder of Chinese Meteorological Administration (CMA). HIRAS II has three consecutive spectral bands named long-wave (LW), medium-wave (MW), and short-wave (SW) band with 1671 more channels than its predecessor HIRAS I. Channel selection is implemented to get the optimal subset for application in numerical weather prediction (NWP) and to explore the contribution of each spectral band. The iterative channel selection method using information content (IC) is applied in temperature and water vapor channel selection based on temperature and humidity sampled profiles respectively. In total, 150 channels were chosen, consisting of 35 LW temperature channels, 65 MW temperature channels, and 50 MW water vapor channels. The temperature channel selection shows that the selected channels in LW band present most information in upper troposphere, while the selected channels in MW band present most information in the mid and lower troposphere. It is found that the humidity of atmosphere profile has large impact on the water vapor channel selection. As the increase of profile humidity, the maximum contribution layer of channel IC shift upward and the degrees of freedom for signal (DFS) profile become sharper and narrower, which results in the variation of channel selection.

Ocean surface currents play a crucial role in understanding air-sea interactions, multiscale dynamics, and biogeochemical processes. Satellite remote sensing has become a powerful tool for global surface current mapping. In the past, the Maximum Cross Correlation (MCC) method has been commonly used to estimate high-resolution surface currents over large areas. However, it has limitations in resolving finer, rotational and deformational motions, producing erroneous and incoherent vectors. This study evaluates the reliability and robustness of a state-of-the-art window deformation iterative multigrid (WIDIM) approach in deriving high-resolution surface currents from realistic numerical model outputs. Comparisons with the ‘perfect’ model currents show that the WIDIM method significantly outperforms the MCC method, especially in resolving strong rotational and deformation eddy velocity fields. The WIDIM method produces more accurate and coherent surface current estimates, with higher spatial resolution and fewer biases and errors. The application of the WIDIM method to satellite imagery also indicates that the WIDIM method provides remarkable improvement over the MCC method. Our findings highlight the potential of the WIDIM approach for advancing oceanographic research and practical applications.

The South China Sea Warm Current (SCSWC) represents a significant circulation pattern in the Northern South China Sea (NSCS), yet its existence and characteristics remain controversial due to limited direct measurements. This study employs observations from the Himawari-8 satellite to map surface currents over the NSCS shelf. Rare continuous 4-day satellite observations offer a comprehensive view of the spatial pattern and synoptic variability of SCSWC, aligning well with concurrent satellite-observed sea surface temperature (SST) and salinity. Our findings indicate that the episodic SCSWC bursts appear highly sensitive to the rapidly weakening of northeasterly winds, evidenced by the corresponding surface winds. The composite analysis of five northeastward flow events further elucidates the relationship between wind forcing and surface currents. The comparison of the observed surface flows with the model currents confirms the transient response of the SCSWC to northeasterly wind relaxations. The self-organizing map (SOM) analysis identifies synoptic variability in northeastward flow events (~33.33%), occurring during weak northeasterly winds. This multidisciplinary approach enhances our insights into the SCSWC dynamics and holds promise for advancing oceanographic modeling and forecasting efforts in the region.

The design of the light-weight infill structure is a hot research topic in additive manufacturing. In recent years, various infill structures have been proposed to reduce the amount of printing material. However, 3D models filled with them may have very different structural performances under different loading conditions. In addition, most of them are not self-supporting. To mitigate these issues, a novel light-weight infill structure based on the layer construction is proposed in this article. The layers of the proposed infill structure continuously and periodically transform between triangles and hexagons. The geometries of two adjacent layers are controlled to be self-supporting for different 3D printing technologies. The machine code (Gcode) of the filled 3D model is generated in the construction of the infill structure for 3D printers. That means 3D models filled with the proposed infill structure do not need an extra slicing process before printing, which is time consuming in some cases. Structural simulations and physical experiments demonstrate that our infill structure has comparable structural performance under different loading conditions. Furthermore, the relationship between the structural stiffness and the parameters of the infill structure is investigated, which will be helpful for non-professional users.

Driven by the global need for decarbonization, low carbon power generators are currently being deployed at a rapid rate. Subsequently, the power grid is posed with high operational risks due to intermittent power generation and uncertain energy demand. Along with demand-side management, energy storage plays a critical role in balancing energy supply with demand. The techno-economic analysis is conducted to compare different technological options to meet an energy problem (e.g., grid support and off-grid energy supply). Several techno-economic analyses have been conducted for low carbon energy technologies; however, different approaches were used and it is difficult to compare the present works of literature. This paper describes the on-going work of IEEE P2814, a current standards project developing recommended practices on techno-economic metrics for hybrid energy and storage systems. This standards project defines techno-economic terminologies used in the development, construction, and operation of renewable energy and electrical energy storage systems. Here, a preliminary techno-economic framework is presented and discussed. Some key aspects that need to be considered in the techno-economic analysis include the timescale of analysis and quantifying carbon emission contribution of the technologies. Future work for the standards project will be described.

We propose a new algorithm to estimate the phase of speech signal in the mixture of audio sources under the assumption that the magnitude spectrum of each source is given. The pre- vious method, multiple input spectrogram inversion algorithm (MISI), often performs poorly when the magnitude spectro- grams estimated are not accurate. This may be because it im- poses a strict constraint that the summation of source wave- forms should be exactly the same as the mixture waveform. Our proposing algorithm employs a new objective function in which this constraint is relaxed. In this objective function, the difference between the summation of source waveforms and the mixture waveform is the target to be minimized. The perfor- mance of our method, modified MISI is evaluated on two dif- ferent experimental settings. In both settings it improves the audio source separation performance compared to MISI.

For microgrids (MGs) optimal operation, one heated topic is the uncertainty management associated with renewable variations and electricity load forecasting errors. On the other hand, the networking of MGs is receiving an increasing attention in recent years. In this paper, an interactive energy management strategy is developed for high renewable-penetrated MGs. The control method includes two steps. In the first step, a local optimization is proposed for each microgrid to minimize the operation cost during the whole scheduling periods. In the second step, a global optimization is conducted for networked microgrids. CVaR based risk averse measure is introduced here to provide a risk-hedging strategy for microgrids energy management. Formulated models are solved by the easily implemented and computationally inexpensive mix integer linear programming (MILP) solver. Case studies demonstrate the feasibility of the proposed method by identifying optimal scheduling results.

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users' thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability.