esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### FLC-PID Validation  
The validation of the operation of the PID + FLC controller algorithm was done by simulation. MatLab-Simulink software [32] and a HP Zbook 15 G4 workstation with a 3.0 GHz processor and 32 GB RAM (64 bit) [33] were used for simulations.  
Step forms with different amplitudes were used, which represent the magnitude of the change in wind speed, either increasing or decreasing. This is intended to demonstrate the performance of the algorithm in terms of rotor safety; i.e., when an abrupt change or wind gust occurs, the rotor increases its speed beyond the nominal speed, which could generate damage to the system mechanics or cause the magnetic saturation of the generator by suspending electrical generation. Figure 16 shows the wind speed model used for validation.  
The results of the rotation speed of the generator shaft are shown in Figure 17. It is observed that with the PID-FLC controller, rotation speed peaks are generated when abrupt changes in wind speed occur. This is good and indicates that when the increase in wind speed occurs, the generator shaft accelerates, but quickly returns to the nominal  
Figure 15: Membership functions for PID gains. (**a**) proportional constant \(K_{p}\), (**b**) integral time \(K_{i}\), and (**c**) derivative time \(K_{d}\).  
A mathematical analysis was performed using an integration method to quantify the rotation speed in the generator shaft. According to the wind speed pattern established for the simulation, with the PID-FLC controller, the speed was maintained at nominal values 57% of the time, while with the classical PID controller, it was only for 20% of the time. This result assures us to reduce the dynamic loads to the rotor by 285%. Additionally, with the PID-FLC controller, the generator shaft speed value is higher than the nominal speed 10% of the time, while with the classical PID, the speed value was higher than the nominal speed 37% of the time, which increased the risk of magnetic saturation of the generator by 370%.  
Figure 18 documents the pitch angle movement. When the PID-FLC controller detects a change in wind speed, with the anemometer at 450 m distance, the pitch angle movement starts early, while the PID controller starts moving seconds later, until the wind speeds up the turbine. It is also observed that with the proposed PID-FLC, the pitch angle displacements are of greater magnitude, so the speed of the generator shaft remains at nominal speed values, while with the classic PID controller, the pitch angle displacement increases, only as the error in generator shaft speed increases.  
Finally, Figure 19 shows the temporal transition of the gains of the proposed FLC-PID controller along with the gains in the ideally tuned PID controller.
# A Deep Learning-Based Mobile Application for Monkeypox Detection
## 2 Methods
### Proposed Model
#### 2.3.1 Base Model Architecture  
* After \(3\times 3\) Convolution, Batch Normalization and ReLU activation follow. The first layer of MobileNet has a kernel dimension of \(3\times 3\times 3\times 32\). The input dimension is \(224\times 224\times 3\) and the output dimension is \(112\times 112\times 32\).
* After \(3\times 3\) depthwise convolution, batch normalization and ReLU activation are performed. A \(1\times 1\) Convolution follows this sub-unit, followed by Batch Normalization and ReLU activation. As a result, the sequence of these two subunits creates our second unit.
* The MobileNet-Tiny network is designed to process RGB images with dimensions of \(224\times 224\times 3\) as input. These images are then passed through a series of convolution layers and Bottleneck Residual Blocks (BRBs). The purpose of these layers and blocks is to extract and learn relevant features from the input image. After completing this process, the network produces a feature map with dimensions of \(7\times 7\times 320\).
* This feature map represents a condensed representation of the original image, where each element on the map corresponds to a specific feature that the network has learned to recognize.
* Afterwards, the feature map, along with the other feature maps from BRB4, BRB5, and BRB6, is fed into the SSDLite predictor layers to generate detections. These pass through a Non-Maximum Suppression layer, which then filters them and generates the final detections and bounding boxes.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 2 Related Work
### IRS Only or Integrated to BS  
Some researchers investigated principles and recent trends in the RIS [18; 19; 20]. [PERSON] et al. [18] described why the IRS is considered to play an important role in the next generation networks and introduced how people develop the IRS from electronics, mechanics, and materials approaches. [PERSON] et al. [19] described the basic principles of RISs both from physics and communications perspectives, based on which they presented the performance evaluation of multiantenna-assisted RIS systems. This study suggested that there are a lot of applications for the IRS as well. Although researchers normally assume that the IRS has prior knowledge of the propagation environment, [PERSON] et al. [20] proposed IRSs with integrated sensing capabilities and designed IRSs electromagnetically. In ref. [21], the simulation results illustrate the importance of BS-RIS-user association optimization in RIS-assisted cellular networks and verify the effectiveness of the proposed joint association and beamforming design algorithm. Commercialization is also in progress. TMYTEK [22] developed an IRS product that has fixed angles of incidence and reflection of radio waves, which contributes to expanding indoor coverage by reflecting radio waves entering through windows. NTT DOCOMO created a prototype of a metasurface that can arbitrarily control the ratio of reflection and transparency of radio waves [16]. This design helps to seamlessly integrate with metropolitan building environments through the use of such transparent metasurfaces.
## 3 Bending Test Results and Discussion
### Energy Absorption Capacity  
The energy absorption was calculated from the area of the stress and deflection curves obtained from the bending tests. Figure 5 shows the relationship between the deflection and energy absorption of the beams. In the plain concrete beam, the residual strength was very small after cracking, and the increase in the energy absorption capacity was minimal with increases in deflection. However, the energy absorption capacity of the SFRC beams increased almost linearly with increasing fiber content.  
Figure 6 shows the energy absorption capacities of \(T_{600}\) and \(T_{150}\) with increasing fiber volume fractions. \(T_{600}\) and \(T_{150}\) are the energy absorption capacities at deflections of \(L\)/600 (=0.75 mm) and \(L\)/150 (=0.0 mm), respectively. The linear lines obtained from the regression analysis with respect to the fiber volume percentages from zero to 1.00% show that \(T_{600}\) and \(T_{150}\) increase proportionally to the increase in the steel fiber content. The slope of the regression line of \(T_{150}\) is greater than that of \(T_{600}\), which means that the steel fibers effectively maintained greater residual strength than the cracking  
Figure 4: Residual strengths \(f_{600}^{eq}\) and \(f_{150}^{eq}\) with increasing fiber volume percentages from 0.33 to 1.00%.  
Figure 5: Energy absorption and deflection curves of the plain concrete and steel fiber-reinforced concrete (SFRC) beams obtained from the bending tests.  
strength until the deflection of \(L/600\). For the concrete beam with a fiber volume percentage of 1.00%, \(T_{600}\) and \(T_{150}\) were 31.02 and 143.72 J, which correspond to 3.47 and 11.25 times the values of the non-reinforced plain beams, respectively.
## 4 Distributed BS Assignment Algorithm
### Overview of the BS Assignment Process  
The overall BS assignment process is divided into two phases, as illustrated in Figure 2: (i) Phase 1 aims to find a feasible BS assignment solution following the proposed pricing scheme; and (ii) given the BS assignment solution from Phase 1, the aim of the Phase 2 is to exploit the remaining capacity at BSs to improve (if possible) the quality of the assignments in terms of network utility. A detailed description of the steps performed of the algorithm running at the BS and mobile device sides is given in Sections 4.2 and 4.3, respectively.  
Figure 2: Sequence diagram of the BS assignment process performed based on the information exchange between each mobile device and its candidate BSs.
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 2 Materials and Methods
### Micro Gas Turbine (MGT) Engine Fabrication
#### 2.2.1 Turbocharger Selection  
The pressure ratio and airflow rate were used to estimate the compressor speed (rpm) and required engine efficiency. This should be within an acceptable operation range (generally between the surges and choke lines), as displayed in Figure 3a. A turbocharger was selected based on a maximum mechanical efficiency of 78%, a maximum pressure ratio of 2.6, and a compression flow rate of 58 ips/m (26.37 kg/s). Based on the selected turbocharger parameters, an equivalent combustion chamber was salvaged from an existing jet engine to adapt to the fabricated testbed engine, as displayed in Figure 3b. The fuel combustion takes place in a chamber fitted with four injectors to introduce and atomize the charged lean fuel mixture into the combustion chamber. The combustion chamber is linked directly with the turbine wheel to prevent energy loss. The turbine wheel is radial in shape with an inlet port for the combustible gases and an exit port for the GHG exhaust emissions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S/n** & **Component** & **Specifications** \\ \hline
1 & Power & 50 kW (67 hp) \\ \hline
2 & Mechanical efficiency & 78\% \\ \hline
3 & Compression flow rate & 58 ips/m (26.37 kg/s) \\ \hline
4 & Maximum pressure ratio & 2.6 \\ \hline
5 & Plasma torch temperature & 5000–8000 \({}^{\circ}\)C \\ \hline
6 & Fabricated fuel tank storage capacity & 20 L in volume \\ \hline
7 & Fuel pump pressure & 8-bar \\ \hline
8 & Fabricated and assembled starter system & 300 L cylindrical air tank \\ \hline
9 & Fabricated ignition system & 240 V to 40 kV transformer and a spark plug \\ \hline \hline \end{tabular}
\end{table}
Table 1: Micro gas turbine nomenclature.  
Figure 3: Fabricated GTE (**a**) compressor map and (**b**) adapted combustion chamber.
# Numerical Analysis of Friction Reduction of Grouting in Long-Distance Pipe Jacking
## 3 Case Study
### Improved Theoretical Method for Calculating Frictional Resistance  
In long-distance pipe jacking projects, the proportion of front resistance is very small, so it is suitable only to improve theoretical methods for calculating the frictional resistance by the simulation results.  
First, the total normal stress can be calculated based on the data in Table 4. The formula is expressed as  
\[\sigma_{z}=\sum_{i=1}^{n}\gamma\times h \tag{5}\]  
In this formula,  
\(\sigma_{z}\) is the total normal stress in the vertical direction;  
\(n\) is the number of soil strata;  
\(\gamma\) is the saturated unit weight of each stratum;  
\(h\) is the d = thickness of each stratum.  
The effective normal stress at both the bottom and the top of the pipe can be calculated. In this case, the effective normal stress is 474 kPa at the bottom and 403 kPa at the top.  
Second, the total normal stress can be estimated based on the \(\sigma_{z}\). There are many influencing factors here, such as the arching effect of the soil and the difference between the \(\sigma_{z}\) and \(\sigma_{x}\) (total normal stress in the lateral direction). In this case, the average total normal stress is 160 kPa (according to Figure 12, the average total normal stress is higher than 160 kPa, but the data are obtained from 100 m from the launch shaft, and there is a slight decrease in the frictional resistance along the longitudinal direction).  
Figure 12: The total normal stress distribution on the sectional interface.  
Third, by using the formula presented later, the relationship between the average total normal stress and the frictional resistance is established.  
\[f_{k}=\sigma\times R_{inter}\times tan\varphi=\sigma\times tan\varphi_{inter} \tag{6}\]  
In this formula,  
\(\sigma\) is the average total normal stress;  
\(\varphi\) is the internal friction angle of the soil stratum around the pipe.  
In this case, the average internal friction angle is 16.7\({}^{\circ}\), and there are six different values of \(R_{inter}\) to calculate. The results are shown in Table 5.  
Finally, apply the improved \(f_{k}\) to the Formula (4) to obtain the new answer. And the total jacking force is obtained using Formulas (1) and (2) as the previous theoretical methods. The results are shown in Table 5.  
The previous theoretical results, improved theoretical results, and numerical results of the jacking force are summarized in Figure 13.  
In Figure 13, it is evident that the improved theoretical results exhibit a linear relationship based on different interface reduction coefficients. This trend aligns better with the numerical results, and the discrepancy between the improved theoretical results and the numerical results is significantly smaller compared to the difference between the previous theoretical results and the numerical results.
# Surface Charging on Insulating Films with Different Thicknesses in UPS
## 3 Results and Discussion  
### Time-Dependent Residual Thickness  
Stratified material information can be obtained by the analytical capability of depth profiling. This approach enables the sequential spectral characterization of varied film thicknesses on a single specimen through controlled material removal. For a thin film with a known thickness, a prolonged sputtering duration until the detection of the substrate's characteristic spectral signatures allows for the retrospective determination of the residual film thickness at intermediate sputtering stages.  
Given the identical XPS spectral evolution during sputtering removal observed in both samples, the 93 nm thick SiO\({}_{2}\) film (Sample 1) was selected as a representative case to illustrate the time-dependent residual thickness of the SiO\({}_{2}\) film. As shown in Figure 1a,b, in the early stage of sputtering (0-120 min), both the O 1s (531.2 \(\pm\) 0.05 eV) and Si 2p (102.1 \(\pm\) 0.06 eV) spectra maintained stable intensity profiles and binding energy positions, confirming unaltered SiO\({}_{2}\) stoichiometry. Subsequently (120-145 min), a two-stage interfacial transition occurred: (1) 120-130 min marked a +1.5 eV shift for O 1s and a 1.1 eV shift for Si 2p, respectively, indicative of SiOx (0 \(<\) x \(<\) 2) interfacial species formation [24]; (2) 130-145 min saw the progressive attenuation of Si-O bonding signals alongside the emergence of Si-Si bonding signals. Finally (\(\geq\)145 min), complete SiO\({}_{2}\) removal was evidenced by oxide signal extinction. Combined with the evolution of atomic concentration (Figure 1c), the sputter rate was calculated as 0.64 nm/min (93 nm/145 min). Therefore, the remaining thickness of the film after different sputtering times could be obtained (as shown in Figure 1d). Consistent application of the sputter rate calculation method yielded a sputter rate of 0.56 nm/min (58.3 nm/105 min) for the 58.3 nm thickness SiO\({}_{2}\) film (Sample 2), enabling the determination of time-dependent residual thickness throughout the measurement process.  
Furthermore, despite the different sputtering rates of O and Si atoms, the system maintains stable stoichiometric ratios during the first 120 min of sputtering (as seen in Figure 1c), as evidenced by the correspondingly stable O 1s and Si 2p spectral features (Figure 1a,b). This temporal stability confirms that changes in chemical state do not dominate the structural tuning of the valence band spectra in this thickness range. However, during the sputtering period of 120-145 min, the observed stoichiometric deviation transforms into a key factor influencing the evolution of the XPS/UPS valence band spectra.
## 2 Vibration Signal Processing Methods
### Multi-Domain Feature Extraction and Sensitive Feature Selection
#### 2.2.3 The Fusion of Sensitive Features  
Using the Kernel Principal Component Analysis (KPCA) [35] method to reduce the dimensionality of the selected features, resulting in health indicators, the detailed steps are as follows [33]:  
(1) The sample matrix X, composed of feature vectors and b dimensions, is as follows [33]:  
\[X_{cd}=\begin{bmatrix}X_{11}&X_{12}&\dots&X_{1b}\\ X_{21}&X_{22}&\dots&X_{2b}\\ \dots&\dots&\dots&\dots\\ X_{a1}&X_{a2}&\dots&X_{ab}\end{bmatrix} \tag{10}\]where \(X_{cd}=\{X_{c1},X_{c2},\ldots,X_{cb}\}\) corresponds to the feature vector value of the vibration signal in the c-th row of the matrix.
2. Implement the mapping of \(X\) into the high-dimensional space \(\Re\), samples in the input space are transformed via \(\Phi\), denoted as \(X\rightarrow\Phi(X)\), and become sample points within the high-dimensional feature space \(\Re\), where \(\Phi(X)=[\Phi(x_{1}),\Phi(x_{2}),\cdots,\Phi(x_{b})]\), satisfying the centrality condition [33]: \[\sum_{k=1}^{n}\Phi(x_{k})=0\] (11) The covariance matrix in \(\Re\) is given by C [35]: \[C=\frac{1}{b}\sum_{j=1}^{b}\Phi(x_{j})[\Phi(x_{j})]^{T}\] (12) where \(\Phi(x_{j})\) represents the feature sample in \(\Re\).
3. Calculate the eigenvalues and eigenvectors of the covariance matrix C. The eigenvectors thus obtained represent the principal component directions of the original sample space in the feature space \(\Re\)[33]: \[\lambda v=Cv\] (13) where \(\lambda\) represents the eigenvalue of C, and \(v\) denotes the corresponding eigenvector in the feature space \(\Re\).
4. Define the \(M\times M\) matrix, \(K_{cd}=K_{cd}(x_{c},x_{d})=\Phi(x_{c})\Phi(x_{d})\), the normalized feature vector is represented as \(v^{\Re}\), \((v^{\Re},v)\) = 1, the expression for the \(f\)-th principal component \(S\) of the original sample is [33]: \[S=(v^{m},\Phi(x))=\sum_{c=1}^{M}K(x,x_{c})\] (14)
5. Zero-mean processing is performed on matrix \(K\)[33]: \[\bar{K}=K-P_{M}K-KP_{M}+P_{M}KP_{M}\] (15) where \(P_{M}=\frac{1}{M}I\), \(I\) is the identity matrix.
6. \(\lambda_{k}\) is an eigenvalue of matrix \(\bar{K}\), the first \(l\) eigenvalues, whose contribution rates satisfy the following formula, are selected as principal components [33]: \[\sum_{k=1}^{l}\lambda_{k}\sum_{d=1}^{g}\lambda_{d}\geq 85\%\] (16)  
Based on the eigenvalues and eigen components obtained from the above formulas, the value obtained by fusing various features is used as an indicator of the bearing's degradation trend.
## 2 B-Spline Corner Smoothing Method
### B-Spline Curve
#### 2.1.1 Error Feasible Area  
As shown in Figure 1, \(\{C_{0},\ldots,C_{4}\}\) represents a linear tool path. The transition allowable error \(\varepsilon\) denotes the distance between the dotted line and the tool path. The area between two dashed lines is defined as the error feasible area. \(\theta\) is the angle between the unit vector \(\overrightarrow{T_{1}}\) and \(\overrightarrow{T_{2}}\). \(\overrightarrow{T_{1}}=C_{i}\overrightarrow{C_{i-1}}/\|C_{i}C_{i-1}\|\), \(\overrightarrow{T_{2}}=C_{i}\overrightarrow{C_{i+1}}/\|C_{i}C_{i+1}\|\), \(\overrightarrow{T_{3}}=(\overrightarrow{T_{1}}+\overrightarrow{T_{2}})/\| \overrightarrow{T_{1}}+\overrightarrow{T_{2}}\|\) and \(\overrightarrow{T_{4}}=(\overrightarrow{T_{2}}-\overrightarrow{T_{1}})/\| \overrightarrow{T_{2}}-\overrightarrow{T_{1}}\|\). To achieve the allowable error of tool path corner transition, the reconstructed tool path should be within the error feasible area. Depending on the convex hull characteristics and symmetry of B-spline, nine control points of the corner transition curve must be within the error feasible area. Therefore, \(P_{j,A}\) is located the angular bisector of \(\theta\). \(P_{j,3}\) and \(P_{j,5}\) are located at the boundary of error feasible area, respectively. \(\|P_{j,A}C_{j}\|=0.8\varepsilon\) and \(\|P_{j,3}P_{j,5}\|=2l\). \(l\) can be expressed as:  
\[l=\varepsilon/\cos(\theta/2)+1.1\varepsilon/\tan(\theta/2) \tag{10}\]  
In order to obtain better corner smoothness, the control points \(P_{j,0}\), \(P_{j,1}\) and \(P_{j,2}\) lie on the linear segment \(P_{j,3}P_{j-1,5}\); \(P_{j,6}\), \(P_{j,7}\) and \(P_{j,8}\) lie on the linear segment \(P_{j,5}P_{j+1,3}\). Additionally, when \(j=1,\ P_{0,5}\to C_{0}\). Meanwhile, when \(j=N,\ P_{N+1,3}\to C_{4}\). Thus, the control points of B-spline are calculated as follows:  
\[\left\{\begin{array}{l}P_{j,0}=P_{j,1}+b\overrightarrow{T_{1}}\\ P_{j,1}=P_{j,2}+c\overrightarrow{T_{1}}\\ P_{j,2}=P_{j,3}+d\overrightarrow{T_{1}}\\ P_{j,3}=C_{j}+1.1\varepsilon\overrightarrow{T_{3}}-l\overrightarrow{T_{4}}\\ P_{j,A}=C_{j}+0.8\varepsilon\overrightarrow{T_{3}}\\ P_{j,5}=C_{j}+1.1\varepsilon\overrightarrow{T_{3}}+l\overrightarrow{T_{4}}\\ P_{j,6}=P_{j,5}+d\overrightarrow{T_{2}}\\ P_{j,7}=P_{j,6}+c\overrightarrow{T_{2}}\\ P_{j,8}=P_{j,7}+b\overrightarrow{T_{2}}\end{array}\right. \tag{11}\]  
Figure 1: The distribution of corner-smoothing curve.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 4 Discussion
### Limitations  
This study analyzed the temporal and spatial variation trend of vegetation and drought in YRB, and analyzed the response of NDVI and SIF to meteorological drought using Pearson correlation analysis method. However, correlation only indicates the degree of correlation between two variables, and does not represent the causal relationship between them. With the growing interest in SIF emerging satellite products, more satellite products covering the global region will be available in the future. Future research can apply other sensors with higher spatial resolution and time frequency, such as GeoCARB [53], TROPOMI [54] and FLEX [55]. The response of vegetation to drought is related not only to the resistance of vegetation to water stress, but also to the resilience of vegetation at the end of drought events. Vegetation plays a critical role in hydrological cycle, terrestrial carbon cycle and energy exchange [56]. Future research should explore the internal driving mechanism between climate change, drought stress and vegetation change. In the context of climate change, an in-depth understanding of the impact of drought on vegetation is more important, which can provide a constructive reference for the formulation of land carbon cycle and biodiversity protection policies.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 2 Design of the Micro-Phasor Measurement System
### Voltage Sampling Circuit  
The voltage sampling circuit is designed by using operational amplifiers. First, the isolated voltage signal is input to voltage follower OP1 while OP2 is developed to generate a voltage reference based on resistors R4 and R5, a voltage divider of the DC power supply at the input. A reference could be output through OP2 with the capacitor C2 that stabilizes the voltage reference output. Since the signals of the 3-phase AC source comprise positive and negative cycles, OP3 is used to compare the input AC signal from OP1 and compare voltage reference signal from OP2 to generate a zero-crossing signal for the digital signal processor (DSP) with R6 pull-up resistor. The designed voltage sampling circuit is illustrated in Figure 2.