(1) The Environmental Trace Gases Monitoring Instrument-2(EMI-2) is a high-quality spaceborne imaging spectrometer that launched in September 2021. To evaluate its radiometric calibration performance ...in-flight, the UV2 and VIS1 bands of EMI-2 were cross-calibrated by the corresponding bands (band3 and band4) of TROPOMI over the pseudo-invariant calibration site Dome C. (2) After angle limitation and cloud filtering of the Earth radiance data measured by EMI-2 and TROPOMI over Dome C, the top of atmosphere (TOA) reflectance time series were calculated. The spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize the uncertainties caused by the different spectral response functions (SRF) of sensors. In addition, a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The radiation performance of EMI-2 is evaluated using the TOA reflectance ratio of EMI-2 and TROPOMI, combining the SAF correction and RTM-based correction methods. (3) It was shown that the time series trending of the TOA reflectance ratio between EMI-2 measurements and TROPOMI demonstrate flat characteristics and strong correlation. The mean reflectance ratios range from 0.998 to 1.09. The standard deviation of the reflection ratio is less than 3%. For 328 nm, 335 nm, 340 nm, 460 nm, and 490 nm, the mean values are close to one, and the relative radiometric bias estimated through EMI-2 and TROPOMI intercalibration is less than 3%, and for other wavelengths, the biases are less than 6%, except for 416 nm, which behaves higher than 7%. The cross-calibration results show that the radiometric calibration of EMI-2 is within the relative accuracy requirement.
A hybrid chaotic mapping algorithm(HCM) based on 1‐DCM (one‐dimensional chaotic map) and Lorenz is proposed to address the security issues of low‐dimensional chaotic systems and the complexity of ...high‐dimensional chaotic systems. Firstly, the color images are divided into three high 4‐bit planes (BP) and low 4‐bit planes. By optimizing the parameters and structure of trigonometric function, the new arcsine‐sine (AS) function is designedto generate the 1‐DCM and permutation matrix, which is constructed to change the plane position and pixel position of the high 12BP to obtain the scrambling plane, which is encrypted by Lorenz chaotic system to produce the selfencryption matrix, with which the low 12BP is encrypted to generate the low bit encryption planes. Then, the ciphertext image is obtained by combining the high bit scrambling plane and low bit encryption plane. The SHA function is also used as the chaotic system's parameter generator, which improves the correlation between plaintext images and the key and resists selective plaintext attacks. The simulation results show that by modularizing the image's high and low bit planes, the encryption process is simplified and improved. The security and reliability of color image transmission are also improved.
With the continuous development of chaotic systems, they have increasingly become the core of the field of image encryption, and the good performance of chaotic systems is crucial for image ...encryption. Some two-dimensional chaotic maps still have drawbacks such as uneven distribution and small key space, which are prone to destruction. To this end, a new two-dimensional logic infinite folding iterative mapping is proposed, and an encryption algorithm is designed based on this. Experimental analysis shows that the chaotic map has good chaotic characteristics. Secondly, a binary bidirectional zigzag transform image scrambling algorithm is proposed. Compared with traditional zigzag transform, binary bidirectional zigzag transform has more sufficient dislocation effects and greatly reduces the correlation between adjacent pixels in the image. Finally, a bidirectional diffusion algorithm was used to destroy the image completely, making it difficult to be deciphered. Besides, the combination of the SHA-256 algorithm with the plaintext image provided better resistance to plaintext attacks. Experimental simulations illustrate that the encryption algorithm can effectively resist various attacks with high security and is not easy to crack.
The baseline drift artifact (BDA), namely, the baseline bias between the Hadamard transform (HT) spectrum and the signal averaging (SA) spectrum was unfortunately found in the HT pulsed separation ...techniques, which makes the HT analytical chemistry face the challenge of non-stability and unreliability. In order to find out the origin and eliminate the BDA phenomenon, the multiplexing mechanism of Hadamard transform ion mobility spectrometry (HT-IMS) has been analyzed in this research. Through simulation and experimental comparisons, the non-ideal overlap defect in Hadamard multiplexing process was suggested as the cause for the BDA phenomenon. Eventually, with a correction method, the BDA phenomenon could be well addressed, which guarantees the reliability and stability of HT techniques without increasing the hardware complexity.
The rapid quantification of nitrate nitrogen concentration plays a pivotal role in monitoring soil nutrient content. Nevertheless, the low detection efficiency limits the application of traditional ...methods in rapid testing. For this investigation, we utilized a digital microfluidic platform and 3D-printed microfluidics to accomplish automated detection of soil nitrate nitrogen with high sensitivity across numerous samples. The system combines digital microfluidics (DMF), 3D-printed microfluidics, a peristaltic pump, and a spectrometer. The soil solution, obtained after extraction, was dispensed onto the digital microfluidic platform using a micropipette. The digital microfluidic platform regulated the movement of droplets until they reached the injection area, where they were then aspirated into the 3D-printed microfluidic device for absorbance detection. Implementing this approach allows for the convenient sequential testing of multi-samples, thereby enhancing the efficiency of nitrate nitrogen detection. The results demonstrate that the device exhibits rapid detection (200 s for three samples), low reagent consumption (40 µL per sample), and low detection limit (95 µg/L). In addition, the relative error between the detected concentration and the concentration measured by ultraviolet spectrophotometry is kept within 20%, and the relative standard deviation (RSD) of the measured soil samples is between 0.9% and 4.7%. In the foreseeable future, this device will play a significant role in improving the efficiency of soil nutrient detection and guiding fertilization practices.
Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize ...variational mode decomposition (PSVMD–CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load forecasting. To avoid manual selection of VMD parameters, the Sparrow algorithm is adopted to optimize VMD by decomposing short-term power load data into multiple subsequences, thus significantly reducing the volatility of load data. Subsequently, the CNN (Convolution Neural Network) is introduced to address the fact that the GRU (Gated Recurrent Unit) is difficult to use to extract high-dimensional power load features. Finally, the attention mechanism is selected to address the fact that when the data sequence is too long, important information cannot be weighted highly. On the basis of the original GRU model, the PSVMD–CGA model suggested in this paper has been considerably enhanced. MAE has dropped by 288.8%, MAPE has dropped by 3.46%, RMSE has dropped by 326.1 MW, and R2 has risen to 0.99. At the same time, various evaluation indicators show that the PSVMD–CGA model outperforms the SSA–VMD–CGA and GA–VMD–CGA models.
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), ...isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R2 by 0.04.
Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically ...seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.
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•3D microelectrode configuration was introduced into C4D microfluidics.•The proposed C4D microfluidic chip effectively improves detection sensitivity.•The novel C4D microfluidic can ...quantify soil N, P and K nutrients on site.
Soil macronutrient nutrients (N, P and K) are critical for crop growth and agricultural production. The rapid and quantitative determination of soil nutrient content is of great importance to guide precise fertilization. To address the difficulty of synchronizing the detection of soil N, P and K with high sensitivity, this paper proposes a novel C4D microfluidic device with integrated 3D microelectrodes, which achieves lower detection limits for nutrients detection. The chip consisted of a crossover-type electrophoretic microchannels system and a 3D microelectrodes C4D system. 3D microelectrodes were constructed through clever design with only a single photolithography process required. The proposed microfluidic chips were used to detect potassium, ammonium, nitrate, and phosphate, with experimental results of standard solutions showing low detection limits of 5.24 × 10−5 g/L, 2.81 × 10−5 g/L, 2.35 × 10−5 g/L and 2.38 × 10−5 g/L. The microfluidic chips showed good reproducibility with relative standard deviations of less than 5 %. Separation experiments were also performed on soil samples with a resolution of K+ and NH4+ greater than 1.1. Accuracy was evaluated by adding 10 mg/L and 50 mg/L standards to the soil samples and results showed that recoveries were maintained at 80–120 %. The microfluidic chip with integrated 3D microelectrodes proposed in this work would effectively address the need for on-site, rapid detection of soil nutrients.
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