The Effect of Spatial Resolution and Temporal Sampling Schemes on the Measurement Error for a Moon-Based Earth Radiation Observatory

The Moon-based Earth radiation observatory (MERO) is a new platform, which is expected to advance current Earth radiation budget (ERB) research with better observations. For the instrument design of a MERO system, ascertaining the spatial resolution and sampling scheme is important. However, current knowledge about this is still limited. Here we proposed a simulation method for the MERO-measured Earth top of atmosphere (TOA) outgoing shortwave radiation (OSR) and outgoing longwave radiation (OLR) fluxes and constructed the “true” Earth TOA OSR and OLR fluxes based on the Clouds and Earth’s Radiant Energy System (CERES) data. Then we used them to reveal the effects of spatial resolution and temporal scheme (sampling interval and the temporal sampling sequence) on the measurement error of a MERO. Our results indicate that the spatial sampling error in the unit of percentage reduces linearly as the spatial resolution varies from 1000 km to 100 km; the rate is 2.5%/100 km for the Earth TOA OSR flux, which is higher than that (1%/100 km) of the TOA OLR flux. Besides, this rate becomes larger when the spatial resolution is finer than 40 km. It is also demonstrated that a sampling temporal sequence of starting time of 64 min with a sampling interval of 90 min is the optimal sampling scheme that results in the least temporal sampling error for the MERO system with a 40 km spatial resolution, note that this conclusion depends on the temporal resolution and quality of the data used to construct the “true” Earth TOA OSR and OLR fluxes. The proposed method and derived results in this study could facilitate the ascertainment of the optimal spatial resolution and sampling scheme of a MERO system under certain manufacturing budget and measurement error limit.

Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.

Doppler distortion removal based on Dopplerlet transform and re-sampling for wayside fault diagnosis of train bearings

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to remove the Doppler distortion, and applied in the fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functions-Dopplerlet atoms. According to the Morse acoustic theory and Doppler effect, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established as the re-sampling vector in time domain. Through the resample, the Doppler distortion effect can be removed. Finally, simulations and experiments with practical acoustic signals of train bearings with a defect on the outer race and the inner race are carried out, and the results verified the effectiveness of this method. Comparing with the other methods of Doppler distortion removal, this method works without measuring the motion parameters in advance, and is quite robust to noise. Meanwhile, this method has the potential to eliminate the Doppler distortion of original signal with multiple sources.

Influence of Storage Conditions on the Quality of Cotton Seeds in Benin Republic

Background The availability of good quality seeds is synonymous with improved farming, especially cash crops such as cotton. However, serious problems with seed germination have been reported recently by cotton farmers in Benin Republic. The assumptions formulated at the base with regard to this situation remain to be verified technically. Thus, this study aims to evaluate the influence of storage conditions on the quality of cotton seeds in Northern Benin. Temperature and relative humidity were assessed followed by a seed sampling sequence in seven (07) cotton seeds stores according to three main periods, ranging from the establishment in conservation to the following seasonal production. Germination tests were then carried out on each sample followed by data analysis using R and Minitab17 software.Results There is a large variation in the germination rate of cotton seed during their storage period. The probabilities values ​​(Pvalue1 = 0.023, Pvalue2 = 0.001 and Pvalue3 = 0.038) respectively associated with the three samples and the various coefficient of variation (CV) between stores (CV1 = 2.42%, CV2 = 7.1% and CV3 = 8.88%) explain a significant difference not only between the stores but also from one sample to another with regard to sampling periods. There is a strong progressive decrease in seed germination (Germination rate 1 > Germination rate2 > Germination rate3), which is responsible for the failure observed by the growers during sowing. Thus, seeds lose an average of 15% of their initial germination capacity already at one month of storage. This is generally negative due to all the storage conditions and system in the stores.Conclusions The excessive increase in temperature and the considerable decrease in relative humidity in stores are the main factors of significant loss of germination capacity of cotton seeds. In view of this situation, it is desirable that technical measures be taken in this direction in order to better preserve the quality of the seeds made available to producers for an optimization of the cotton sector in Benin.

Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

<div><div>We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.</div></div><div><br></div>

Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

<div><div>We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.</div></div><div><br></div>