This paper introduces the advantages of LIDAR technology as an active remote sensing method, and the successful applications of flood dynamic monitoring and evaluation, soil erosion and river mapping. In this paper I introduce the airborne LIDAR technology into the monitoring and management area of water blooms by using the concentration inversion of chlorophyll as point of entry. This paper shows the application prospect of airborne LIDAR technology in monitoring and managing the data of inland water bodies by combining the production of international research, and it shows what should be taken to make the airborne LIDAR monitoring of water into practical in our country.
This paper presents an 8-element 140 GHz wafer-scale phased-array transmitter in CMOS. It is based on intermediate-frequency (IF) beamforming transmit channels with 5-bit phase and 4-bit gain control at the IF band (9–14 GHz) with LC-based Wilkinson combiners, a shared local-oscillator (LO) multiplier chain and distribution network. On-chip image rejection filters (IRF) and LO leakage cancellation circuits are designed in each channel to improve the array in-band linearity and suppress the LO leakage. Differential on-chip antenna feeds (on top metal) are electromagnetically (EM) coupled to high-efficiency patch antennas on a 100-µm-thick quartz superstrate. The antennas are placed λ/2 (~140 GHz) apart between adjacent channels in the horizontal and vertical directions with a chip size of 5.4 mm × 5.1 mm. The 4 × 2-element array can scan to ±30° in the elevation plane (E-plane). The phased-array transmitter results in a measured EIRP of 27–32 dBm at 134–146 GHz. It supports up to 16 Gb/s data rate per carrier for both 16 and 64QAM waveforms at an EIRP of 22 and 20 dBm, repectively, with −25 dBc EVM (for 64QAM). To the best of our knowledge, this paper presents the first CMOS wafer-scale phased-array transmitter at 140 GHz, resulting in high radiated power and high speed communications.
Real-time simulation technique of power systems is becoming realizable due to the growing significant computational power of computing platform. This paper builds a real-time prototypical platform based on PXI and LabVIEW as its main hardware and software architecture. Taking advantage of the integration characteristics of NI products, the platform embodies high expansibility and good compatibility and fits the needs of studying in university labs. Based on the platform, a real-time algorithm considering the multiple switching events is developed with a tradeoff between the computing power and the simulation accuracy. To validate the algorithm on the real-time prototypical platform, three-phase voltage source converter circuit and buck chopper circuit are studied with a 10μs time-step and 2kHz PWM frequency. The captured real-time results demonstrate high goodness of fit in comparison with the offline simulation of the systems in PSCAD/EMTDC.
This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1 The prediction results in this data archive are generated by various models: 1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. 2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range. 3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. Other datasets: 1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary). 2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM2.5 dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency. Reference: Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1] Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).
This paper analyzes a feasible spacecraft flight plan that uses gravitation assistance to transport the spacecraft from Earth to the circular orbit around Saturn (the spacecraft is in a circular orbit around the Earth, with an orbital period of 90 minutes and a total mass of 5000 kg, including fuel) by establishing a low thrust transfer orbit model and calculates the minimum amount of fuel required, which is 1878.73kg. There is also an attempt to evaluate different options for controlling the ion thrusters during the journey, and one of the schemes inspired by the Cassini Huygens spacecraft is proposed and considered optimal. Adopting this plan, the total journey time is calculated to be 14.2 years.
Based on the local energy Internet system project of State Grid Customer Service Center Northern Park, this paper introduces the general idea and system integration of local energy Internet design. In view of the system integration technology, this paper explores and studies the local energy Internet from the three aspects, namely planning and terminal access integration, equipment and system integration, system and system integration. It is found out that the integration technology can effectively gather energy sources and data to realize efficient integration between the energy flow and the data flow.
This article presents fully integrated power amplifiers (PAs) with eight-way low-loss power combining for $D$ -band applications in the GlobalFoundries CMOS 45RFSOI process. The eight-way power combining (four-way differential) common source (C.S.) and cascode amplifiers are implemented using four-stage differential PA unit cells as building blocks. The eight-way power combining network is composed of a four-way balun-short-transmission-line (balun-STL) combiner and a conventional quarter wavelength transmission line (QWL TL) combiner. The simulated two-stage eight-way combiner in situ (loaded) ohmic loss is only 1.1–1.4 dB at 130–150 GHz. The eight-way power-combining C.S. amplifier has a small-signal gain of 24 dB at 140 GHz with a 1.2-V supply and a 3-dB bandwidth of 131–150 GHz. The saturated output power (Psat) and output 1-dB compression point (OP 1 dB ) are 16.8–17.5 and 13–14.2 dBm at 130–150 GHz, respectively. The corresponding peak power-added efficiency (PAE) is 11.7%–14.2%. The eight-way power combining cascode amplifier achieves a small-signal gain of 24.8 dB at 135 GHz with a 3-dB bandwidth of 133–148 GHz. The corresponding Psat is 16.3–19 dBm at 125–150 GHz with a peak PAE of 6.5%–12.1%. To the best of our knowledge, these PAs achieve the highest Psat and OP 1 dB at the $D$ -band in CMOS.
This paper reports a multi-order system dynamic model of the novel planar gyro - Center Support Quadruple Mass Gyro (CSQMG) with an application of dynamic analysis. Compared with the dual-mass resonance system, a resonator with quadruple masses possessing 12 degree of freedom (DOF) is more complicated, and so is the error analysis. As a consequence, the lumped parameter model in a generalized coordinate is realized by regarding the N-shaped beam as two springs of the in-plane quadrature directions and simplifying the Y-shaped beam as the combination of a lever and a torsional spring. With the help of this new model, all of the eight planar natural vibration modes in the CSQMG can be calculated precisely. Comparing to the Finite Element Method (FEM) simulation and experimental measurement, the analytical natural frequencies basing on the simplified model show a maximum error of less than 1.09%, which means the multi-order system model can be applied to the system parameter identification in the future work.
In this paper, we study the dynamic pricing of electricity service provider in the day-ahead spot market. Since the wholesale electricity price that the electricity service provider obtains from the utility grid is constantly changing, and the user's response behavior is unknown, setting a suitable retail price is a big challenge for the electricity service provider. In response to this problem, we propose a method based on elasticity transfer and reinforcement learning, which transfers the elasticity of the implemented demand response region to the region where the user elasticity is unknown, as the initial reference for dynamic pricing, and then uses the SARAS learning algorithm for practical exploration and learning. The simulation results show that the elasticity transfer to the new region as the initial reference can significantly improve the learning rate compared to the system without the initial reference. Therefore, the proposed method can maximize the price of the electricity service provider and the user by setting the optimal price at a faster rate without prior knowledge of the user.
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