esa-sceva/satcom-chunk-collection huggingface dataset:
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 1 Introduction
### Related Work and Problem Statement
In fact, in realistic scenarios, the distribution of the placement of CGC stations and the placement of SBSs within a building is independent. Furthermore, in [22], we considered sharing the satellite spectrum with small cells per building by allocating resources to small cells in the frequency-domain (FD) orthogonally in any transmission time interval (TTI) to avoid co-channel interference. However, under a certain constraint of co-channel interference, the same frequency can be reused with small cells deployed apart from one another by a minimum distance corresponding to satisfying the co-channel interference constraint resulting in a further improvement in spectral efficiency [25]. Since the spectrum is reused more than once to small cells per building, spectrum sharing within a building is no longer orthogonal.
## 2 Materials and Methods
### Problem Characterisation
#### 2.1.2 Time Window  
The delivery time window is considered flexible, as the service can start before or after the end of the time window, as long as the customer accepts it, without penalty [33; 53].  
In route planning, the time window must be respected. This means that customers with the first-time window (9 a.m.-1 p.m.) must first be inserted into the route of each team and, only after, the second-time-window customers (2 p.m.-6 p.m.) should be tended to. Therefore, even if customers are located closer to the last customer inserted in the route, the time window must be checked, and then the customer with the correspondent time window should be served (even if it means traveling a greater distance).
## 6 Numerical Examples
### An Illustrative Example
The true maximum error and the estimated error on the element are, respectively,  
\[\max_{0\leq t\leq h}\left|u(t)-u^{h}_{\text{R}}(t)\right|=0.08150929,\ \max_{0\leq t\leq h}\left|\varepsilon_{\text{R}}^{h}(t)\right|=3u^{h}_{3}/2=0. 07961783. \tag{35}\]  
Since \(\max_{0\leq t\leq h}\left|\varepsilon_{\text{R}}^{h}(t)\right|>T_{l}\), the formula in Equation (23) is used to generate a new element size  
\[h=0.5\times\left(\frac{0.8\times 0.001}{0.07961783}\right)^{\frac{2}{5}}=0.79396591. \tag{36}\]  
With this new \(h\), updated nodal values are calculated  
\[u^{h}_{3}=0.00207757,\ u^{h}_{2}=0.98747147. \tag{37}\]  
The subsequent computational steps are summarized in Table 1, from which it can be seen that although the initial element size \(h=0.5\) is very large, it converges very fast, and after 4 adaptive steps, both the estimate error and the true error satisfy the required error tolerance \(0.85T_{l}\) (Equation (22)). It can also be seen that the maximum error estimation of the reduced element is very accurate, and the nodal errors are much smaller than the element errors, to guarantee the provision of an initial value of higher order accuracy (with the error \(0.652\times 10^{-9}\)) for the next time element.
## 3 Results
### DPPH Radical Scavenging Activity  
As shown in Figure 3, among the tested compounds, ascorbic acid exhibited the highest DPPH radical scavenging activity (89.60%) (\(p\) < 0.05). SLC and ESLC also showed significantly (\(p\) < 0.05) higher DPPH radical scavenging activities (76.06% and 71.36%, respectively) than SMSP, except for ascorbic acid, which was used as a positive control.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Essential**} & \multicolumn{2}{c}{**Composition (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **Non-Essential** \\ **Amino Acid** \\ \end{tabular} } & \multicolumn{2}{c}{**Composition (\%)**} \\ \cline{2-5}  & **SLC (\({}^{\text{th}}\))** & **ESLC (\({}^{\text{th}}\))** & **Amino Acid** & **SLC** & **ESLC** \\ \hline Valine & \(5.15\pm 1.10\) (\({}^{\text{th}}\)) & \(5.28\pm 1.58\) & Cysteine & 1.49 \(\pm\) 0.23 & 2.39 \(\pm\) 0.27 \\ Methionine & \(1.45\pm 0.33\) & \(1.50\pm 0.95\) & Aspartic acid & 7.56 \(\pm\) 0.56 & 10.12 \(\pm\) 3.49 \\ Isoulecine & \(3.82\pm 0.10\) & \(4.03\pm 1.23\) & Glutamic acid & 10.13 \(\pm\) 0.70 & 12.04 \(\pm\) 4.12 \\ Leucine & \(5.41\pm 0.18\) & \(5.46\pm 1.74\) & Serine & 9.31 \(\pm\) 0.45 & 8.41 \(\pm\) 2.39 \\ Phenylalanine & \(3.67\pm 0.29\) & \(3.80\pm 1.21\) & Glycine & 12.80 \(\pm\) 0.27 & 9.38 \(\pm\) 2.37 \\ Tryptophan & \(0.75\pm 0.10\) & \(1.12\pm 0.50\) &roline & 5.34 \(\pm\) 0.32 & 5.31 \(\pm\) 1.58 \\ Lysine & \(5.03\pm 0.68\) & \(5.62\pm 1.87\) & Arginine & 13.13 \(\pm\) 0.35 & 10.79 \(\pm\) 3.03 \\ Threonine & \(3.87\pm 0.09\) & \(4.07\pm 1.18\) & Alanine & 2.64 \(\pm\) 0.13 & 2.84 \(\pm\) 0.73 \\ Histidine & \(1.14\pm 0.12\) & \(1.02\pm 0.39\) & Tyrosine & 7.31 \(\pm\) 0.28 & 6.82 \(\pm\) 2.26 \\ \hline Total & 30.29 & 31.90 & Total & 69.71 & 68.10 \\ \hline \hline \end{tabular} (\({}^{\text{th}}\)) SLC, silkworms fed the leaves of _Cudrantia tricuspidate_. (\({}^{\text{th}}\)) ESLC, enzymatic hydrolyzate from silkworms fed the leaves of _Cudrantia tricuspidate_. (\({}^{\text{th}}\)) Values are means \(\pm\) SEM.  
\end{table}
Table 1: The amino acid composition of SLC and ESLC.  
Figure 2: Total flavonoid and polyphenol contents in SLC, ESLC, and SMSP extracts. **(A)** Flavonoid content; **(B)** polyphenol content. Values are means \(\pm\) SEM. Different letters (a–c) above the bars indicate significant differences according to one-way ANOVA with [PERSON]’s multiple range test at \(p\) < 0.05. SLC, silkworms fed the leaves of _Cudrantia tricuspidate_; ESLC, enzymatic hydrolyzate from silkworms fed the leaves of _Cudrantia tricuspidate_; SMSP, steamed and lyophilized mature silkworm powder.
# Properties of Green Tea Waste as Cosmetics Ingredients and Rheology Enhancers
## 3 Result and Discussion  
### Chemical Composition  
#### 3.1.1 Overall Chemical Composition of Green Tea Residuals  
In dried green tea (_Camelia sinensis_) waste leaves from Jeju Island, the hydrophobic extract content of acetone extraction is 0.73%, and the hydrophilic extract content of hot water extraction is 11.6% (Table 2). Meanwhile, the fresh malu creeper (_Bauhinia vahili_) leaves extract yield has 2.1% by chloroform, 3.6% by acetone, 5.9% by methanol, and 6.3% by hot water [34]. The extract yields for fresh lion's ear (_Leonots leonorus_) leave (\(w/w\)) were 2.4% by acetone, 7.5% by methanol, and 8.6% by water [35]. The fresh malu creeper leaves have the highest acetone extraction content (3.6%) [34], and the green tea waste leaves have the highest hot water extraction content (11.6%). Green tea waste from Turkey has extract yields 31.38% by hot water [36].  
From the analysis of the chemical composition of green tea waste, it is found that green tea waste contains 21.4% lignin, 27.37% protein, and 38.9% polysaccharide (Table 1). Green tea residual from Wakayama Prefecture, Japan, by microwave heating at 200-230 \({}^{\circ}\)C has 36.7% neutral carbohydrate and 30.8% lignin [37]. Meanwhile, green tea residue from China contains 31% carbohydrates, 27% protein, 17% lignin, 8% polyphenols, 7% water, 6% ash, and 5% lipid [17]. Green tea GTW from Jeju Island has the highest carbohydrate content (38.9%) and protein (27.37%), while green tea residue from Japan has the most lignin content [30.8%] [37]. Tea leaf waste fiber from Malaysia has 16.2% cellulose, 68.2% hemicellulose, and 18.8% lignin [38]. Green tea from Spain contains moisture 30.46%, protein 12.27%, ash 7.35%, carbohydrate 39.13%, minerals 24.1%, formic acid 2.23%, and acetic acid 1.93% [39].
###### Abstract  
Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naive Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT-IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naive Bayes, with an accuracy of 98.8%.  
intrusion detection; anomaly detection; IoT; machine learning; cyber-attacks; network security; BoT-IoT +
Footnote †: journal: Applied sciences  
Article
# Study of High-Frequency Pulsating Flow Control Valves
## 2 Working Principle  
The schematic diagram of the high-frequency sinusoidal flow control valve proposed in this paper is shown in Figure 1. The HPFCV is composed of a constant differential pressure-reducing valve and a sinusoidal flow valve, in which the role of the constant differential pressure-reducing valve is to control the inlet and outlet pressure difference of the sinusoidal flow valve to a predetermined pressure difference \(\Delta p\). The sinusoidal flow valve is driven by a servo motor to rotate the eccentric wheel shaft, which in turn drives the valve spool to reciprocate according to the motion law of Equation (1a). According to the valve port flow equation, the valve outlet flow can be described as sinusoidal (1b).  
\[x_{v2}=x_{0}sin\omega t+x_{0} \tag{1a}\]\[q_{3}=C_{d}W_{2}x_{\rm v2}\sqrt{\frac{2}{\rho}\Delta p} \tag{1b}\]  
where \(x_{\rm v2}\) is the sinusoidal flow valve spool displacement; \(x_{0}\) is the eccentric wheel axis of the eccentric distance; \(W_{2}\) is the sinusoidal flow valve spool area gradient; \(q_{3}\) is the sinusoidal flow valve inlet flow; \(\Delta p\) is the constant differential pressure reducer valve spool pressure difference between the two ends; and \(\rho\) is the density of the fluid.  
Based on this, the HPFCV can control the frequency of the output sinusoidal flow signal by changing the rotational speed of the motor. It can also adjust the difference between the inlet and outlet pressures by altering the pre-compression of the spring of the constant difference pressure-reducing valve to change the amplitude of the sinusoidal flow signal. Thus, it can realize the generation of high-frequency unidirectional sinusoidal flow signals within an adjustable range.
## 4 Analysis and Modeling of Aircraft Parameters
### Modeling of Aircraft Parameters  
Through the analysis of a single factor, the influence of different aircraft parameters on the optimal cruise point is clarified. However, due to the complex interaction among these parameters, the influence of a single factor has limited ability to guide aircraft design, and it is necessary to consider simultaneous changes of different aircraft parameters. Therefore, it is of significance to establish a mathematical model that can comprehensively describe the relationship between the aircraft parameters and optimal cruise point.  
A neural network (NN) is a complex network system that simulates the human brain. It consists of a large number of simple neurons connected with each other [36; 37; 38]. A feed forward neural network (FFNN) is a kind of neural network. In recent years, it has been widely used in data predicting and so on [39]. In this paper, due to there being six parameters, it is difficult to model the influence law by polynomial fit explicitly. Therefore, FFNN is employed to establish a mathematical model of (\(Ma_{opt}\), \(h_{opt}\)) and (\(X_{L}\), \(X_{D}\), \(X_{A}\), \(X_{I}\), \(X_{m}\), \(X_{S}\)); the model has six inputs and two outputs.  
Firstly, 500 sample points are generated for the six inputs from the Optimal Latin Hypercube Distribution [40] in a range of 0.8 to 1.5, and the values of (\(Ma_{opt}\), \(h_{opt}\)) for the 500 samples are obtained by the PSO for the optimal cruise point in Figure 5. Furthermore, 400 of the samples are used as the training set, 75 as the validation set, and the other 25 as the test set. The logsig function is employed in the hidden layer, and the output layer transfer function is Purelin Linear. The L-M method is employed as the learning method. The structure of the FFNN is displayed in Figure 12.  
The regression results after training are illustrated in Figure 13. It can be seen that the target value and output results are basically on the same line, and the value of the regression coefficient is close to 1, which shows a good training effect.  
The model estimated values and accurate values of the 25 points in the test set are compared in Figure 14. It can be seen that the estimated values are within the deviation range of 3% of the accurate value, indicating that the trained neural network model can accurately describe the relationship between (\(Ma_{opt}\), \(h_{opt}\)) and (\(X_{L}\), \(X_{D}\), \(X_{A}\), \(X_{I}\), \(X_{m}\), \(X_{S}\)).  
Figure 11: Influence of different influence factors on the fuel consumption.  
Based on this model, given the value of the six factors, the values of (\(Ma_{opt}\), \(h_{opt}\)) can be quickly obtained.  
Figure 14: Comparison between estimated value and accurate value: (**a**) Estimated value and accurate value of \(Ma_{opt}\); (**b**) Estimated value and accurate value of \(h_{opt}\).  
Figure 12: The structure of FFNN.  
Figure 13: Regression results of FFNN after training.
## 2 Vibration Signal Analysis
### Dataset  
The proposed condition monitoring methods described in this research were tested and validated using the award winning FEMTO-ST Institute 2012 PHM Challenge dataset [62], which has found widespread use in this field [63; 64; 65]. This complete dataset comprises of vibration signals from the accelerated degradation of 17 bearings, performed at three different operating speed and load conditions: (1800 rpm and 4000 N), (1650 rpm and 4200 N) and (1500 rpm and 5000 N) [62]. This dataset provides realistic fault modes achieved under accelerated ageing conditions as opposed to data generated from artificial fault modes, e.g., drilled holes, machined narrow cuts or indentation lines to emulate the occurrence of hairline cracks.  
Accordingly, for each test case, the details on the specific type of failure mode or element (e.g., ball, inner race or outer race or cage) is not known or provided. This work is more concerned with monitoring degradation rather than diagnosis of the specific failure mode. The test setup was composed of three main parts: a rotating part, a degradation generation (loading) part and a sensing measurement part. The bearing's vibration amplitudes were recorded by two miniature accelerometer sensors, Dytran, Model 3035B, placed orthogonally to one another on the vertical and horizontal axis of the bearing under test. This sensor pair was placed radially on the external race of the bearing. The acceleration measures were sampled by the analogue to digital converter (ADC) at 25.6 kHz [62].  
Each recording consists of bursts of 2560 samples (0.1 s duration), which were obtained every 10 s throughout the test bearings' lifetimes up until the point of failure. Failure for these test bearings was defined to be the point where the amplitude of the vibration signals surpassed the reference acceleration threshold value of 20 g. This threshold was carefully chosen as it also avoided any considerable propagation that could severely damage the test-bed mounts and fixtures [62].
## 2 Materials and Methods
### Modeling Method
#### 2.2.1 Overall Model Structure  
The overall architecture of the model is depicted in Figure 1. Training data were first partitioned with a batch size of 320, and piecewise pooling averaging (PPA) was employed to compress the 4200-dimensional spectra to 128 dimensions. After preprocessing, the input tensor had a shape [320,1,128] corresponding to [batch_size,channel,seq_length].  
An initial convolutional module was placed at the network front end to extract low-level features; this consists of a convolutional layer (kernel size = 3, padding = 1) followed by a ReLU activation function, thereby introducing nonlinearity. The resulting features are  
Figure 1: Overall architecture of ReSE-AP Net.  
forwarded to a residual network augmented with a squeeze-and-excitation (SE) channel-attention mechanism. This residual network contains two residual blocks, each comprising two convolutional layers--the first expanding the channel dimension and the second maintaining it--together with batch normalization and ReLU activation. Within the main branch of each block, the features produced by the two convolutions are re-weighted by the SE attention to dynamically enhance informative spectral bands and suppress noise. The attention-refined output is then added to the shortcut pathway, forming the residual connection. The shortcut both mitigates gradient vanishing and network degradation and enables lower-level information to flow directly to deeper layers, fostering feature reuse and preventing information loss.  
The output of the residual network is subsequently fed into an atrous spatial pyramid pooling (ASPP) module. ASPP comprises three parallel atrous-convolution branches with dilation rates of 1, 2, and 4, respectively, to capture multi-scale features with varying receptive fields. Concurrently, a global-average-pooling branch compresses the sequence dimension to obtain global statistics, which are then restored to the original sequence length via nearest-neighbor upsampling to align with the atrous branches. Features from the atrous convolutions and the global branch are merged in a fusion layer, yielding a composite representation that integrates local, intermediate, large-scale, and global information.  
The fused features are further downsampled by a max-pooling layer (kernel size = 2, stride = 2), flattened, and passed through a fully connected layer to produce the final output, enabling end-to-end mapping from spectra to soil-element predictions.
# Development of Lift Control System Algorithm and P-M-E Analysis in the Workplace
## 1 Introduction
Some recent studies have demonstrated a lift system that employs fuzzy control systems and PLC. PLC is shown to be fast and adaptable to multiple inputs and outputs to meet traffic demands, which makes it suitable for this present study [31; 32]. Other benefits of using PLC in this type of design is that it can be incorporated in more complex applications and can be easily adapted to other systems, such as the control of automated machinery systems, including cranes and robotic arm manipulator [33; 34]. P-M-E refers to the optimal relationship between personnel (people in the work place and responsible for the operation of machine), machine (the computer and other control systems) and the environment (the prescribed work conditions for personnel-machine interaction). Even though fuzzy logic systems are very intelligent, they still lack the full mastery of the entire system, especially relating to installation, troubleshooting and maintenance. As a result, lifts can be considered to be a purely personnel-machine-environment system. Therefore, they require operators to oversee the optimal functioning. Sometimes, depending on the number of operators required, the conditions of the workplace change. Hence, the need for this analysis is to achieve a safe, highly efficient and cost effective system and work environment for the operators [35; 36]. Lifts are considered to be vital assets in corporate buildings and as a result, its maintenance is paramount. In this paper, the development of the lift control network is briefly introduced in Section 2, with the development of the control algorithm explained; the fuzzy control system described based on its input variables, fuzzification, fuzzy inference and defuzzification processes; and the PLC tasks illustrated. The ladder logic diagram is introduced and compared for different scenarios in Section 2.2. Finally, the analysis of the P-M-E system in the workplace is discussed in Section 3. The present study presents a simplified lift control system using a PLC algorithm based on fuzzy logic scheme that can be easily implemented and adapted to other control system designs.
## 2 Basics of the CNN
### Feature Extraction  
In the training process, the CNN is used to extract the features from the data based on the convolution operation. The convolution operation includes a convolution layer followed immediately by a pooling layer. The feature extraction process is shown in Figure 1. From bottom to top, \(a\) represents the input data, \(b\) represents the feature data which is obtained by the convolution operation and the ReLu (Rectified Linear Units) activation function, and \(c\) represents the feature data which is obtained after the pooling process. Red and green patches represent different salient features, blue patches represent the label of the data(or interesting target), the purple patch in \(a\) represents the convolution kernel, and the purple patch in \(b\) represents the feature which is obtained from the purple patch in \(a\) by the convolution operation and the ReLu activation function. More specifically, the CNN works as follows.  
First, the salient features of \(a\) are preserved, and then passed to a ReLu function for post processing. Second, the non-salient features of \(a\) are filtered out by ReLu via setting the minus in the feature map to 0. The derived features are \(b\). Finally, more abstract features \(c\) of the input data will be obtained based on the pooling layer. If features \(c\) are not abstract enough, a second convolution operation is needed. The process is repeated until the most representative features are obtained. This results in deepening the layers of the CNN. In the whole process, the data-label of \(a\) may be filtered out, but its main neighborhood features can be preserved for judging \(a\)-label. In addition, it is worth noting that there is no clear conclusion how many layers of convolution operations are appropriate. Thus, the visual convolution operation is needed.  
Figure 1: The processing of feature extraction in deep convolution neural network (CNN).