esa-sceva/satcom-chunk-collection huggingface dataset:
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Analysis of Our Proposed Model
#### 5.2.2 Effect of Random and Optimal CH Selection on Our Proposed Model  
To check the suitability of optimal and random CHs of the proposed model, we perform simulations in different scenarios in which the proposed model selects the random, optimal, and random and optimal CHs. When the proposed model selects the CHs randomly, it uses the distributed algorithm to select the CHs. In this case, sometimes the numbers of CHs are near optimal. However, most of the time, the selected CHs are not optimal and consume the network resources, which rapidly leads to ending the network lifetime earlier. However, the optimal number of CHs selection is only possible with the help of the BS. Due to the BS selection, the nodes do not use their energies for computation and communication. So, the network saves a lot of energy through optimal CH selection which leads to a greater lifetime. However, the BS's CHs selection is dependent on residual energies and distance from itself. The nodes with higher energies and at the lesser distance are selected while the member nodes suffer backtracking or link breakage due to the unsuitability of received messages from CH nodes. Conversely, this problem is solved when the proposed model simultaneously uses the optimal and random CHs selection as depicted in Figure 10a. We also check the effect of optimizing the CHs selection process on ZBR and SEED. Both these protocols use distributed algorithms for CHs selection and suffer a lot due to uneven energy consumption. After optimal CHs selection, we can see the increment in the lifetime of both the models in Figure 10b.  
Figure 9: The number of clusters and CHs analysis through standard deviation, and the coefficient of variation. (**a**) The standard deviation of CHs in the network; (**b**) The coefficient of variation of CHs in the network.
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 3 Real-Time Surge Model of Turboshaft Engine
### Extended MG Model  
In the CLM, the compressor component characteristics are obtained by the compressor test-bed and expressed as three-dimensional tables of speed, pressure ratio and flow for interpolation during calculation, so that CLM can simulate the stable operation state and acceleration or deceleration dynamic process at different rotor speeds. The classic MG model describes the instability of the compressor at a certain constant rotor speed, so only one axisymmetric curve is used to describe the characteristics of the compressor. However, in fact, the compressor characteristic varies with the rotor speed, and it is generally can be expressed as a cluster of iso-speed lines. Therefore, it is necessary to expand the characteristics of the MG model to match the compressor characteristics in the CLM, but it should be noted that the parameters in the CLM are dimensional, while the parameters in the MG model are dimensionless. The specific conversion method will be introduced in the next section. According to [27], considering the influence of the rotor speed on compressor characteristics, Equation (20) is used to fit the compressor's full flow characteristics. When the rotor speed increases, the values of \(H\) and \(W\) increase, otherwise decrease, which means that the iso-speed line characteristic line is almost in parallel and the characteristic line moves higher when the rotor speed increases, and vice versa. The actual expanded compressor characteristics are shown in Figure 7 where each curve in the figure represents the functional relationship between the pressure rise coefficient and the flow coefficient at a certain rotor speed. In the calculation of the MG model, different compressor characteristic lines are called according to the rotor speed interpolation, so that the model can reflect the surge dynamic process with different characteristics.  
\[\left\{\begin{array}{l}\Psi_{C}(\Phi)=\Psi_{C0}+H\bigg{[}1+\frac{3}{2} \Big{(}\frac{\Phi}{W}-1\Big{)}-\frac{1}{2}\Big{(}\frac{\Phi}{W}-1\Big{)}^{3} \bigg{]},\Phi>0\\ \Psi_{C}(\Phi)=\Psi_{C0}+H\Big{(}\frac{\Phi}{2W}\Big{)}^{2},\Phi<0\end{array}\right. \tag{20}\]
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 3 Experimental Results and Analysis
### Ablation Experiment  
In order to verify the effectiveness of the method proposed in this paper, each component of the proposed method was experimentally verified, and the FER2013 dataset was selected to evaluate the utility of each module.  
The experimental part mainly verified the effectiveness of the proposed multi-channel feature extraction network (MFE), adaptive channel weighting module (ACW), and spatial attention module (ASA). At the same time, in order to prove the applicability of the proposed method to different networks, also considering the network lightweight requirements, this paper extracted Xception [27] to simplify the network: The components of Entry, Middle and Exit remained unchanged, but the operation of each part was not repeated. The following is also called the Xception structure. At the same time, ResNet18 was selected as the Base network in this paper, but the 7 \(\times\) 7 of the initial convolutional layer was replaced by 3 \(\times\) 3, which is still called ResNet18 below.  
The experimental design is shown in Table 1. Experiment 1 is the experimental result in the baseline network ResNet18. Experiment 2 is the result obtained in the Xception structure. Experiment 3 is to verify the effectiveness of the attention module ASA proposed in this paper in the Xception structure. Experiment 4 is the effectiveness experiment of adding auxiliary branches (MFE) to the Xception structure. Experiment 5 is to verify the effectiveness of the adaptive channel weighting module (ACW) in the Xception structure. Experiment 6 is to verify the effectiveness of the ACW method in the Base network. Compared with the original baseline network, the accuracy is improved by 0.46%. Experiment 7 is the experimental result after constructing a multi-channel feature extraction network (MFE) on the basis of the baseline network, and the accuracy reaches 73.50%. Experiment 8 is to verify the effectiveness of the attention module (ASA) in the baseline network. Compared with the Base network, the accuracy is improved by 0.65%. Experiment 9 is the experimental result of the final network structure MAWNet in this paper. It can be seen from the experimental data that the accuracy of MAWNet proposed in this paper is improved by about 1% compared with the baseline network, reaching 73.81%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Base(ResNet)** & **Xception** & **MFE** & **ACW** & **ASA** & **Acc** \\ \hline \�
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
## 3. Results and Discussion
### HPLC and ESI-TOF-MS Analyses  
Applying HPLC, the concentrations of UA in extract samples were determined. The standard curve for measuring UA content used was: \(y\) = 7224.4\(x\) + 23.182 (\(R^{2}\) = 0.9999). In the range of 10-600 ug/mL, the HPLC method revealed a good linear calibration fit. Quantification was achieved by comparing the chromatographic peak areas for the extracted samples with those of reference standards. As showed in Figure 5A, the analysis yielded profiles with a retention time of 24.677 \(\pm\) 0.04 min for UA. After separation and concentration, the crude extracts were preliminarily purified by using alkalization and acidification, and the result showed that the purity of UA can reach up to 55.56%.  
Figure 5: The high performance liquid chromatography (HPLC) chromatograms of the _Hedytis diffusa_ extract obtained using subcritical water extraction (**A**) and mass spectra of unsolic acid (**B**).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Solvent Type**} & **Organic Solvent Consumption** & \multirow{2}{*}{**Extraction Time**} & **UA Yield** \\  & & **(L/kg Material)** & & **(mg/g Material )** \\ \hline SWE & Ultrapure water & 0 & 20 min & 5.94 \(\pm\) 0.03a \\ ME & Ethanol & 10 & 12\(\sim\)24 h & 3.49 \(\pm\) 0.2d \\ HRE & Ethanol & 20 & 1\(\sim\)2 h & 4.66 \(\pm\) 0.1c \\ UE & Ethanol & 15 & \(>\)20 min & 5.37 \(\pm\) 0.26b \\ MAE & Ethanol & 20 & \(>\)15 min & 4.45 \(\pm\) 0.25c \\ \hline \hline \end{tabular} Notes: The same letters are not significantly different at the 0.05 level, and different letters mean significant difference at the 0.05 level.  
\end{table}
Table 3: Comparison of extraction methods for their solvent type, solvent consumption, time-consumption and extraction efficiency.  
In order to further identify the UA, the purified extract was analyzed using ESI-TOF-MS analysis. Since unsolic acid contains a carboxyl group, the possibility of using positive or negative ionization mode was evaluated [40]. Under selected ESI-TOF-MS conditions, the acquired m/z value was 455.3532 (Figure 5B), which is consistent with the calculated mass for UA [M\(-\)H]\({}^{-}\) C\({}_{30}\)H\({}_{48}\)O\({}_{3}\) of 455.3631.
## 3 Results and Discussion
### Measurement Properties and Experimental Results
The vertical dashed line in the figure indicates the limit when material transport has taken place. The horizontal dashed line marks the intersection of the fitted curve and the vertical line. This shows the point above which change in impedance of material transport can be assumed. The measurement results show that in most cases,] the material transport could be correctly detected by the change in impedance. However, the deviations show that the reproducibility of the measurement should be addressed. This can be improved by a more robust sensor design, as the high forces, which are partly unevenly distributed due to the nature of the material, can cause slight deformations of the sensor. These measurement results verify the correlation between the impedance of the flat coil and the relative change in the water content of the soil mixture, which was previously shown in the simulation results. They also prove that it is possible to detect an erosion process in the material with the method of impedance spectroscopy.
## 3 Proposed Methodology
### Feature Extraction
#### 3.2.1 Features for RFI Classification:  
To address RFI classification phase, by inspiring from [21] the six extracted features from each received signals are mean, standard deviation, skewness, Real-Signal Kurtosis (RSK), average power, and average power of the wavelet coefficients (4 th approximation and details from 1 st to 4 th Order) as follows by assuming each received signal \(x\) with size 1 by \(n\):  
* Mean: \[\mu(x)=\frac{1}{n}\sum_{i=0}^{n-1}x(i)\] (1)
* Standard deviation (\(\sigma\)): \[\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}|x(i)-\mu(x(i))|}\] (2)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Total number of samples & 4800 \\ \hline Length of generated signals & 32,448 (8 ms) \\ \hline Sampling frequency & 40 \(\times\) 10\({}^{6}\) Hz \\ \hline Modulation types & QPSK, 8 APSK, 16 APSK, and 32 APSK \\ \hline AWGN power & 140 dBm (\(SNR\cong\) 9 dB) \\ \hline No. of each signal type per modulation type & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Real-time dataset characterization.  
* Skewness (I/Q): As in [36], the skewness of a signal is computed as: \[Skewness=\frac{E(x-\mu(x))^{3}}{\sigma^{3}}\] (3)
* The Real-Signal Kurtosis (RSK): This feature has been proposed by the author of [37] in which the kurtosis of In phase and Quadratic (I/Q) components of the signal is computed, and finally the RSK is obtained by averaging the computed kurtosis (I/Q). \[kurtosis=\frac{E((x-\mu(x))^{4}}{\sigma^{4}}\] (4) \[RSK=\frac{|Kurtosis_{I}|+|Kurtosis_{Q}|}{2}\] (5)
* The average power: according to [38], the average power of each signal over its length is calculated as: \[AveragePower=\frac{1}{n}\sum_{i=1}^{n}|x(i)|^{2}\] (6)
* Average power of the wavelet coefficients: First, each observation (\(x\)) is decomposed up to 4 levels (in this case, the higher levels than 4 are less informative) using 10 th Daubechies wavelet (db 10). Notably, different types of the wavelet have been tested and the best classification result obtained using db 10. Secondly the average power of the 4 th approximation and details of 1 st to 4 th level is computed by applying Equation (6).  
Therefore, the size of each feature vector for the RFI classification phase is 1 by 10.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 1 Introduction
It compared pixel- and object-based methods using solely Planet data, as well as a combination of Planet, Sentinel-1, and Sentinel-2 data. The findings revealed that utilizing this combined dataset enhanced classification accuracy [17]. Another study combined Planet imagery, Landsat-8, and Sentinel-2 to classify land use land cover maps of Central Italy using GEE [18]. In this study, the comparison was made between pixel- and object-based approaches using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The results revealed that the RF algorithm achieved greater accuracy than SVM when classifying land use and land cover. Additionally, it was noted that the object-based method can be resource-intensive, particularly when dealing with higher-resolution data, given the computational constraints of Google Earth Engine (GEE). The objective of this study is to produce a very high-spatial-resolution UGS map for Jakarta city with over 10 million urban inhabitants [19]. Two machine learning algorithm classifiers, namely Random Forest (RF) and Classification and Regression Tree (CART), will undergo evaluation. This study aims to assess the pixel-based classification results by utilizing solely Planet data and a combination of Planet, Sentinel-1, and Sentinel-2 data through a supervised approach.
# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
## 5 Experimental Verification
### Experimental Data Recording and Analysis  
The control system uses the conventional PID control scheme and the Smith fuzzy PID control strategy optimized by the genetic algorithm to control the density of the slurry mixing process, and the trajectory module of the controller obtains an experimental output curve for each experiment, which represents the control signal of the controller to the valve. The experimental output curves of the conventional PID control and the [PERSON] fuzzy PID control optimized by the genetic algorithm were compared with their respective theoretical simulation curves, and the results obtained are shown in Figure 16.  
During the experiments, the current valve openness percentage could be read using the valve openness indicator dial, and the valve openness was recorded to achieve control of the valve by the conventional PID controller and the [PERSON] fuzzy PID controller optimized by the genetic algorithm, as shown in Figure 17.  
By analyzing the experimental output results of the slurry mixing control system, it can be seen that the experimental curve is not identical to the simulation curve; however, the difference was within the tolerance of error, which verifies the effectiveness of the designed controller.  
It can be seen that the [PERSON] fuzzy PID controller optimized by the genetic algorithm has a smaller overshoot, a faster response speed, and shorter regulation time than the  
Figure 16: Control algorithm output curves. (**a**) PID control output curve. (**b**) GA Smith fuzzy PID control output curve.  
conventional PID control in the face of the time-delay system of the slurry density control when comparing the results of the two experiments. Compared with the conventional PID control, the [PERSON] fuzzy PID controller optimized by the genetic algorithm has obvious advantages and sufficient control performance. According to the experimental curve of valve openness, we were able to verify that the [PERSON] fuzzy PID control optimized by the genetic algorithm will not produce large fluctuations and oscillations in the valve openness control process. Additionally, it has high stability and control accuracy and a short regulation time, which is more suitable for application in the control of slurry density time-delay systems.  
As we simplified some of the procedures in the experiments and there were interference and instability factors noted during the experimental process, there may be some deviation between the experimental results and the simulation results; however, the results still verify the fact that the [PERSON] fuzzy PID control optimized by the genetic algorithm has high stability and control accuracy and can achieve better control effects.
## 4 Results and Discussions
### Analysis  
The performance of the proposed method is checked against state-of-the-art (e.g., QAM, BPSK) and modulation-free schemes. The parameters considered in the analysis are L.S., MMSE, BER, and PSNR. The details of the comparison are presented in the following sections. Figures 14 and 15 and Tables 12-15 shows the applicability of channel estimators in OFDM. Looking at OFDM, the BER behavior of LSDFT is quite different, and its value tends to zero as SNR increases. There are two reasons for this. (i) The LS-DFT is used as a Wiener filter when the residual disturbance remains unchanged. (ii) With selectivity, the number of holes in the frequency response increases. The equalizer coefficient Z.F. Fm = 1/H\({}^{*}\)m is used to estimate the error since the high-frequency equalization stage causes more symbol detection errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Efficiency of DFT-LS WIENER**} \\ \hline
**Channel Taps** & **LS (\%)** & **LS-DFT (\%)** & **MMSE (\%)** & **MMSE-DFT (\%)** \\ \hline
2 & 95.41 & 95.5 & 92.3 & 91.9 \\ \hline
6 & 85.9 & 79.1 & 58.7 & 56.3 \\ \hline
10 & 83.36 & 82.3 & 73.01 & 70.2 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Efficiency of DFT-LS WIENER over L.S., LS-DFT, MMSE, MMSE-DFT when Number of channel taps varied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Efficiency of DFT-LS WIENER**} \\ \hline
**Cyclic Prefix Length** & **LS. (\%)** & **LS-DFT (\%)** & **MMSE (\%)** & **MMSE-DFT (\%)** \\ \hline
1/8 th of K & 95.4 & 94.1 & 88.9 & 87.7 \\ \hline
1/4 th of K & 94.91 & 93.7 & 84.7 & 83.1 \\ \hline
1/2 th of K & 92.3 & 90.2 & 54 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Efficiency of DFT-LS WIENER over L.S., LS-DFT, MMSE, MMSE-DFT when pilot frequency varied.  
Figure 15: Comparison of S.E.R. for proposed with existing technique.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Efficiency of DFT-LS WIENER**} \\ \hline
**Pilot Frequency** & **L.S. (\%)** & **LS-DFT (\%)** & **MMSE (\%)** & **MMSE-DFT (\%)** \\ \hline
16 & 85 & 79.5 & 84.9 & 84.1 \\ \hline
32 & 85.6 & 81.1 & 65.9 & 50.2 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Efficiency of DFT-LS WIENER over L.S., LS-DFT, MMSE, MMSE-DFT when pilot frequency varied.  
Figure 15 compares the performance of the DWT-LS-WIENER algorithm with other traditional techniques such as the QAM and BPSK schemes. For these methods, the BER ranges from 103 to 104 with SNR gains of 1 to 11 dB. In the proposed method, BER exists between 101 and 102 with SNR of 10 and 11 dB with significant performance. Hence, the introduced DFT-LS-WIENER approach outperforms compared to conventional approaches.
## 2 Methodology  
The proposed deep hybrid LSTM-CNN model is used to dynamically adjust to changes in occupancy, load demand, weather, and energy prices [4, 5, 9, 10, 11]. The hybrid architecture combines LSTM temporal forecasting with CNN spatial feature extraction to predict load consumption, operational costs, and carbon emissions. The adaptive system leverages IoT data to refine predictions under changing conditions, ensuring responsive and accurate optimization. The experimental setup and algorithms are similar to those in [4, 5].  
Real data from a variety of sources is employed. This includes occupancy and load demand data from [7, 8], IoT sensor data from [12], weather data from [3, 10], and energy prices from [3]. Data from IoT sensors (e.g., motion detectors and door sensors) and smart devices (e.g., thermostats and lighting) are integrated with utility data to infer occupancy patterns. The AMPds dataset [7, 8] provides high-resolution time-series data on electricity, water, and natural gas consumption. This is employed for load demand modeling under seasonal and weather variations. IoT sensors support real-time adjustments by detecting load usage changes [12]. Energy-efficient technology such as heat pumps is also used [13]. This ensures efficient and sustainable Smart Green Buildings (SGBs) [14].
## 3 Results and Discussions
### Ammonia
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multicolumn{2}{c}{**NH\({}_{3}\) Emission, mg m\({}^{-2}\) Day\({}^{-1}\)**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{3-3} \cline{5-6}  & & \multicolumn{2}{c}{**Treatment**} & & \\ \cline{3-3} \cline{5-6}  & & **5\% Biochar** & **10\% Biochar** & & **5\% Biochar** & **10\% Biochar** \\ \hline
1 st week (\(n\) = 18) & 279.5 & 193.3 & 152.0 & 18.3 & 0.008 & 0.001 \\
2 nd week (\(n\) = 15) & 111.1 & 92.2 & 68.0 & 8.6 & 0.151 & 0.006 \\
3 rd week (\(n\) = 6) & 42.5 & 40.0 & 35.3 & 2.7 & 0.649 & 0.079 \\ Overall (\(n\) = 39) & 178.3 & 130.8 & 101.7 & 12.1 & 0.018 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Weekly variations in average ammonia emissions. (Mean \(\pm\) S.D. )reduction performance can likely be attributed to the moisture content of the feedstock and differences in biochar properties. One significant factor is the initial moisture content; in this study, it was approximately 75%, compared to 60% in [PERSON] et al. (2018) and 50-55% in [PERSON] et al. (2018). High moisture levels in feedstocks can present a significant challenge to NH\({}_{3}\) adsorption efficacy; water vapor may compete with gaseous compounds for adsorption sites on porous materials, thereby limiting the availability of active sites for odorant capture ([PERSON] et al., 2018). [PERSON] et al. (2018) demonstrated that water vapor significantly inhibited CO\({}_{2}\) adsorption in biochar-packed beds by impeding diffusion into micropores and reducing breakthrough time. Although the exact moisture threshold remains undefined, prior studies suggest that excessive moisture can reduce the accessibility of internal pores and impair gas transport. Additionally, the superior performance reported by [PERSON] et al. may also reflect differences in biochar physicochemical properties. Their modified biochar exhibited a much larger specific surface area (399-485 m\({}^{2}\) g\({}^{-1}\)) and pore volume (0.15-0.27 cm\({}^{3}\) g\({}^{-1}\)) compared to unmodified rice husk biochar. These enhancements likely increased the number of accessible sorption sites and facilitated mass transfer of NH\({}_{3}\) species.