esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Data Analysis
### Time Domain Analysis
#### 3.1.2 Empirical Mode Decomposition Analysis  
Empirical mode decomposition is an adaptive signal time-frequency processing method, which is more suitable for the processing of nonlinear and non-stationary signals. The essence is to decompose the noisy signal into each component intrinsic-mode function step by step, from high frequency to low frequency, based on the time scale [20].  
This method decomposes complex signals into a limited number of high-frequency and low-frequency components with meaningful instantaneous frequencies, whose frequency or amplitude can be modulated, and has the advantages of no prior knowledge and strong adaptability [21; 22]. Since the acceleration of the tunnel wall is the most concerned, the empirical mode decomposition method is used to decompose the acceleration time-domain curves of the tunnel wall under the four working conditions, as shown in Figure 12, among them, IMF1-11 refers to the intrinsic mode function of the 1 st-11 th order. It can be seen that the acceleration time-domain curves of the tunnel wall under the four working conditions can be decomposed into 11 orders, and each order intrinsic-mode function of the tunnel wall at steel-spring-floating slab track vibration is smaller than the corresponding intrinsic-mode function curve of the ordinary monolithic track tunnel wall vibration. Therefore, the steel-spring vibration isolator has a weakening effect on the vibration in all frequency bands.  
Comparing Figure 12a,c, it can be seen that there are 6 peak envelopes on the decomposed intrinsic-mode function curve for the ordinary monolithic-track tunnel wall acceleration, corresponding to the 6 carriages of the B-type subway car, and the peak envelope of the curved segment is more obvious. Except for intrinsic mode function 3, intrinsic mode function 4, intrinsic mode function 5, and intrinsic mode function 11, the intrinsic- mode function amplitudes of other orders in the curved segment are significantly larger than those in the straight line segment, especially intrinsic mode function 1. It can be seen that the amplitude of the high-frequency signal in the curved segment increases significantly.  
Comparing Figure 12b,d, it can be seen that the intrinsic mode function 2, intrinsic mode function 3, intrinsic mode function 5, intrinsic mode function 10 of the tunnel wall at the steel-spring-floating slab track acceleration in the curved segment have increased,especially in the intrinsic mode function 2 and intrinsic mode function 10. It can be seen that the high-frequency and low-frequency signal intensities in the curved segment have increased.  
Figure 12: Comparison of vertical acceleration intrinsic-mode function of tunnel wall. **(a)** Straight line segment—ordinary monolithic track; **(b)** Straight line segment—steel-spring-floating slab track; **(c)** curved segment—ordinary monolithic track; **(d)** curved segment—steel-spring-floating slab track.
## 5 Results
### Question IV--Lighting Positioning Rating  
The next part of the questionnaire focused on lighting positioning rating within the in-vehicle limited space. Compared to Table 2, we asked the participants to rate nine different position settings with a constant color cyan, color code in RGB: (6, 206, 179), within two driving contexts according their preferences: The first one asked about the positioning rating if the person is sitting at the second row and the vehicle is driving, Q4a, whereas the second context focused on the first row, where the participant is driving actively, Q4b. The rating was performed on the same four and five items on Likert-like scales and statistical evaluation followed the same two test procedures: For independent groups, we used the Mann-Whitney-U test and for dependent groups the Wilcoxon signed rank test. Figure 7 shows the results of both driving scenarios, where a + b focus on the second row and c + d focus on the first row.  
Figure 7: Light-positioning rating: (**a**,**b**) Sit at the second row and not active driving, vehicle is driving; (**c**,**d**) Sit at the first row and active driving. Statistical significances \(p<0.05\) marked with *, separated between the Chinese and European groups and genders.  
Within all genders and origins and for both scenarios there were similar tendencies observed. The door and the foot area, compared in Figure 7, were highly rated but with significant differences between the participants. For the seat, top and a-pillar position there was a general more dislike tendency observed within all groups. For the center and screen illumination there was again a slightly higher preference level shown. Finally, the steering wheel and the combination of all lighting positions together were rated significantly worst within all groups. The strongest effect was between _China:female_ and _Europe:male_ with \(z=-4.02\), \(p=5.78\)\(\times\)\(10^{-5}\) and \(r=0.40\) for the foot position in Q4a and \(z=4.12\), \(p=3.83\)\(\times\)\(10^{-5}\) and \(r=0.41\) for all positions at Q4b between _China:female_ and _Europe:female_, indicate different expectations about in-vehicle light positioning. In addition, four major position groups could be identified with strong significant differences between Chinese and European participants marked as high, medium-high, medium and opposite preferences represented by a statistical effect power \(r\) between 0.34 and 0.86. Detailed statistical tables for the lighting position relations are placed in the Supplementary Materials of this study, Section S2, Tables S9-S12.
# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 3 Approach
This process continues until the maximum number of iterations is reached, after which the algorithm returns the global best feature subset found, representing the most optimal selection of features according to the fitness function used. ```
1:Input: Dataset \(\mathcal{D}\), Number of particles \(N\), Number of features \(F\), Maximum iterations \(I_{max}\)
2:Output: Best feature subset gbest
3: Initialize particles with positions and velocities
4: Initialize global best position gbest and score \(\textit{gbest}\_score\) to infinity
5:for each particle \(i\) in the swarm do
6: Compute Gini, ANOVA, and Relieff positions for the dataset
7: Divide particles equally among these positions
8: Initialize feature subset \(\textbf{fs}_{i}\) using the knee point method
9:endfor
10:for each iteration \(t\) from 1 to \(I_{max}\)do
11:for each particle \(i\) in the swarm do
12: Evaluate fitness of \(\textbf{fs}_{i}\) using the fitness function
13:if fitness is better than particle's best score then
14: Update particle's best position and score
15:endif
16:if fitness is better than global best score then
17: Update global best position and score
18:endif
19:endfor
20: Sort swarm based on particle best scores
21: Update elite particles
22: Transfer knowledge from elite particles to others
23:for each particle \(i\) in the swarm do
24: Update velocity and position of the particle
25:endfor
26:endfor
27:returngbest
```  
**Algorithm 1** Guided Particle Swarm Optimization for Feature Selection  
In terms of the complexity analysis, the time complexity is as follows:  
\[O(I_{max}\cdot N\cdot M\cdot F)\]and the space complexity is as follows:  
\[O(N\cdot F)\]  
where \(N\) is the number of particles in the swarm, \(F\) is the number of features in the dataset, \(M\) is the number of samples in the dataset, and \(I_{max}\) is the maximum number of iterations. The algorithm is computationally intensive for large datasets (large \(M\) and \(F\)) and large swarm sizes (\(N\)), but its linear scaling with respect to \(I_{max}\) and \(N\) makes it manageable for large dataset sizes.
## 2 Materials and Methods
### Near-Infrared Characteristic Spectrum Dimension Reduction Method  
To improve the classification effect of the NIR spectrum and the generalization ability of the classification model, it was necessary to further reduce the dimension of the feature spectrum. KPCA is the best algorithm for this purpose. KPCA is a dimension reduction method for nonlinear data, which solves the problem of linear inseparability of the nonlinear data. Through a nonlinear mapping function \(\phi(\cdot)\), the original space data \(X=(x_{1},x_{2},\ldots,\)\(x_{m})\) is projected into a high-dimensional feature space, and data processing based on PCA is performed in the high-dimensional feature space. The nonlinear mapping function is not easy to solve, and the inner product of the mapping function is expressed by the polynomial kernel function \(K\).  
\[X^{\prime}=[\phi(x_{1}),\phi(x_{2}),\cdots,\phi(x_{n})] \tag{9}\]  
\[K(x,y)=<\phi(x),\phi(y)> \tag{10}\]  
\[K(x,y)=(ax^{T}y+c)^{d} \tag{11}\]  
where \(d\) is the exponent of the polynomial kernel, with \(a>0\) and \(c\geq 0\).
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 2 Servomotors: Structure, Operating Method, Types, Main Characteristics  
### Structure and Operating Method  
A servomotor is a type of electomotor, the shaft of which can be controlled with high accuracy. A shaft of a servomotor can rotate at the required angle or with constant rotational speed. Servomotors have become widespread in robotics for these properties [22; 23].  
A servomotor consists of a DTC motor, gearbox with shaft, and controller with necessary sensors (encoder, position sensor, etc.). A draft of the construction of a servomotor is presented in Figure 1.  
The gearbox in servomotors is used to reduce speed and increase torque on the output shaft. A potentiometer or encoder is used to track the rotation angle or speed of the shaft, thus creating a closed-loop control system with feedback [23; 24; 25].  
A popular method of controlling a servomotor is pulse-width modulation (PWM). This method is based on determining the angle of rotation or speed of the output shaft based on the pulse length at a given frequency. The use of PWM for controlling servomotors is based on the following principles [26; 27; 28]:  
1. PWM generates pulses of varying width (duration) with different periods. \[\mathrm{D}=\frac{\mathrm{T_{ON}}}{\mathrm{T_{period}}}\times 100,\] (1) where D--pulse duration, \(\mathrm{T_{ON}}\)--the ON time of the signal, and \(\mathrm{T_{period}}\)--the total period of one PWM cycle. \[\mathrm{f}=\frac{1}{\mathrm{T_{period}}},\] (2) where f--PWM frequency.  
Figure 1: The draft of the servomotor.  
2. To control the speed of the servomotor, the width of the pulses is changed, allowing the regulation of the power supplied to the motor. \[\text{PW}=\text{D}(\text{PW}_{\text{max}}-\text{PW}_{\text{min}})+\text{PW}_{ \text{min}},\] (3) where PW--pulse width and PWmax and PWmin--the maximum and minimum pulse width supported by the servomotor.
3. Control of the position of the servomotor is possible using feedback. To adjust the position, the voltage applied to the motor, after the PWM signal is converted, is compared to the desired voltage, resulting in a control signal. \[\alpha=\text{D}(\alpha_{\text{max}}-\alpha_{\text{min}})+\alpha_{\text{min}},\] (4) where \(\alpha\)--angular position of the servomotor and \(\alpha_{\text{max}}\) and \(\alpha_{\text{min}}\)--the maximum and minimum angles of the servomotor.
4. PWM is also used to regulate a smooth trajectory of movement from one point to another for the servomotor.
5. In addition to PWM, PID controllers and microcontrollers are applied to enhance the efficiency of regulation and control.  
This method has gained popularity due to its simplicity of implementation and low cost. PWM also provides high efficiency in speed and positioning control, making it applicable in applications requiring a high response to control input and precise control. Examples of such applications may include non-dynamic systems such as fans, pumps, or conveyors [29; 30]. However, to achieve the best results, other principles of controlling servomotors are applied, which will be discussed below.
## 2 Literature Review
### SC Network Resilience and SC Capabilities
Lastly, SC velocity is built based on the efficiency of the SC's response and recovery [20,64]. With the increasing size of supply networks, greater attention has been given to undermining alertness and awareness practices. Despite the current communication information systems and routines of network members, having a means of alerting each other or a monitoring process as part of awareness practice is sometimes considered to be of little significance. Alertness is defined as the SC capability to detect changes, either from the surrounding business environment or from the internal SC network, in a timely manner [11,65, 41]. Both the buyer and supplier firms should be aware of various levels of risks, such as risks related to assets, processes, organizations and the environment [66] and should collaboratively work to improve knowledge transfer among SC partners [67]. Awareness enhances preparation for emergencies, which consequently improves the supplier's resilience capabilities [68].
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 4 Numerical Simulations
### Fault Parameter Estimation for Linear System  
The wind turbine pitch hydraulic system model is considered as a linear system. The hydraulic pitch system is modeled as a closed-loop transfer function between the pitch angle \(\beta\) and its reference \(\beta_{r}\). This is a piston servo system which can be modeled by a second order transfer function as follows [17]:  
\[\frac{\beta(s)}{\beta_{r}(s)}=\frac{\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+ \omega_{n}^{2}} \tag{19}\]  
where \(\zeta\) is the damping factor and \(\omega_{n}\) is the natural frequency. In normal cases, these parameters are considered as \(\zeta_{0}=0.6\) and \(\omega_{n0}=11.11\) rad/s. The pitch system may have a fault due to the drop in hydraulic pressure, which will result in the change of system dynamics (i.e., variations of the values of \(\zeta_{0}\) and \(\omega_{n0}\)) [18]. The parameters \(\zeta\) and \(\omega_{n}\) in the pitch system can be reformulated as convex functions for the normal situation and the low-pressure situation as follows:  
\[\begin{split}&\omega_{n}^{2}=\omega_{n0}^{2}+(\omega_{nf}^{2}- \omega_{n0}^{2})f\\ &\zeta\omega_{n}=\zeta_{0}\omega_{n0}+(\zeta\omega_{nf}^{2}- \zeta_{0}\omega_{n0})f\end{split} \tag{20}\]  
Figure 3: Membership function for adaptive fuzzy fading Kalman filter: (**a**) input fuzzy variables \(\alpha_{k}\) and \(\Delta\alpha_{k}\), (**b**) output fuzzy variable \(\Delta\lambda_{k}\).  
where \(\zeta\) and \(\omega_{n}\) are the current values, \(\zeta_{0}\) and \(\omega_{n0}\) are the nominal values, and \(\zeta_{f}\) and \(\omega_{n\!f}\) are the values under the low pressure fault. \(f\) represents the fault indicator, that is, \(f=0\) denotes the normal pressure and \(f=1\) denotes the low-pressure fault with \(\zeta_{f}=0.9\) and \(\omega_{n\!f}=3.42\)[19]. The process noise and the measurement noise \(w_{k}\) and \(v_{k}\) have the following statistics:  
\[\begin{array}{l}E[w_{k}]=E[v_{k}]=0\\ E[w_{k}w_{j}]=0.01\delta_{k-j}\\ E[v_{k}v_{j}]=0.01\delta_{k-j}\end{array} \tag{21}\]  
The reference signal, \(\beta_{r}\), is a sinusoidal wave with the amplitude of 2 and the frequency of 5 Hz. The maximum value of \(\alpha_{k}\) is set to 2, because a large \(\alpha_{k}\) could cause a divergence. To compare the performance of each method, the following index is adopted:  
\[J(\sigma)=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(\sigma_{i}-\hat{\sigma}_{i})^{2}}} \tag{22}\]  
where, \(\sigma\) is the true parameter value, \(\hat{\sigma}\) is the estimated parameter value. In this work, equation 22 is computed for the period of 5 s: (a) t = 0-5 s for the estimation of normal parameter, and (b) t = 5-10 s for the estimation of fault parameter.
## 4 Data Quality Checks and Experimental Corrections
### Strouhal Scaling (for AC-2 TsAGI Data)  
The resulting noise spectra correspond to landing gear noise measured for small-scale and large-scale models in the same conditions. Then, the AC-2 TsAGI data are transferred to the full scale, thus allowing the results from the two facilities to be directly compared. Since the flow speeds \(V\) in both facilities were the same, there was no problem of spectra scaling with respect to flow speed as the 6 th or 7 th power of \(V\) (or something else), which has been extensively discussed in a number of studies on landing gear noise [14,15,22]. Thus, noise spectra of small-scale landing gear models \(L_{small}\) are scaled to the FL-17 CARDC  
Figure 12: Interpolation of noise levels measured in AC-2 TsAGI to azimuthal angles \(\theta_{0}\), corresponding to FL-17 CARDC for **(a)** 2000 Hz, **(b)** 12,000 Hz.  
data (frequency \(f_{full}\) and sound pressure level \(L_{full}\)) with the use of the Strouhal number, as follows:  
\[f_{full}=f_{small}/D,\,L_{full}=L_{small}+20\,\log_{10}\,(l) \tag{2}\]  
where \(l=10.9\) is the scale ratio of landing gear models in AC-2 TsAGI and FL-17 CARDC. The resulting spectra can be directly compared.  
An example of such a comparison is provided in Figure 13 for the same conditions as for Figure 10. It can be seen that the normalized spectra are in a good agreement.
## 2 Materials and Methods  
### Overview of the Proposed Method  
This paper aims to calibrate and compensate the marker-object offset matrix to realize a perfect alignment AR registration visual effect. To generalize the method, the coordinate systems used in this paper are presented in Figure 3.  
Figure 3: The coordinate systems of an uncalibrated AR system.  
In industrial AR applications, the default CAD modeling coordinate system (MDCS) is assumed to coincide with the marker coordinate system (MCS). In the real scene, the coordinate of the real object is denoted as LCS, which is defined on the supporting plane of the object. Because the MDCS usually locates on the first modeled plane of the product, the default registered virtual model at MCS exists as an offset from the real object at LCS. The undetermined transformation between MCS(MDCS) and LCS in the camera coordinate system (CCS) is then denoted as the offset matrix \(T_{\text{O}}\). Given the calibrated \(T_{\text{O}}\), the registration matrix \(T_{\text{R}}\) could be calculated by:  
\[T_{\text{R}}=T_{\text{M}}\cdot T_{\text{O}} \tag{1}\]  
The problem is then formulated as follows: given an image containing a tracking marker and the CAD model, \(T_{\text{O}}\) is estimated which minimizes a registration error function \(E(T_{\text{R}})\). In the Cartesian coordinate system, \(T_{\text{O}}\) is a \(4\times 4\) matrix that has 6 degrees of freedoms (DoFs); to simplify the problem, this paper transforms \(T_{\text{O}}=[R_{\text{O}},t_{\text{O}}]\) into a 6 DoF vector \(\mathbf{v}_{\text{O}}=[\theta v_{\text{x}},\theta v_{\text{y}},\theta v_{ \text{z}},t_{\text{x}},t_{\text{y}},t_{\text{z}}]^{\text{T}}\) using the exponential map minimal parameterization [23]. The mapping relationship between \(T_{\text{O}}\) and \(\mathbf{v}_{\text{O}}\) is given in Equations (2) and (3).  
\[\mathbf{R}_{\text{O}}=\left[\begin{array}{ccc}2(v_{x}^{2}-1)s^{2}+1&2v_{x}v_ {y}s^{2}-2v_{z}cs&2v_{x}v_{z}s^{2}+2v_{y}cs\\ 2v_{x}v_{y}s^{2}+2v_{z}cs&2(v_{y}^{2}-1)s^{2}+1&2v_{y}v_{z}s^{2}-2v_{x}cs\\ 2v_{x}v_{z}s^{2}-2v_{y}cs&2v_{y}v_{z}s^{2}+2v_{x}cs&2(v_{z}^{2}-2)s^{2}+1\\ \end{array}\right], \tag{2}\]  
\[\mathbf{t}_{\text{O}}=[t_{\text{x}},t_{\text{y}},t_{\text{z}}]. \tag{3}\]  
This paper then derives all six parameters by four procedures as shown in Figure 4, and the details are presented in the following subsections.
## 2 Problem Description
### Simulation Challenges
#### 2.1.1 Accurate Models of the Environment  
Signal fading is the primary manifestation of the effects of the wireless propagation channel. The fading manifests itself at multiple scales (large-scale fading due to path loss and shadowing effects and small-scale variations due to scattering effects). These fading manifestations might be correlated over time, space and frequency, ultimately determining the throughput and delay characteristics of the system. Furthermore, network simulation might assist system designers to assess the performance of the system under more diverse operating conditions for which real testbeds are available.
## 3 Results and Analysis
### Bounding Box Loss Function Side-by-Side Comparison Experiment  
The effectiveness of the WIoU loss function for enhancing the bounding box loss function was evaluated through a cross-sectional comparison experiment. This experiment compared the performance of five bounding box loss functions--DIoU, SloU, GloU, CloU, and WIoU--using the Vanilla-Backbone lightweight network, as delineated in Experiment (2) in Table 2. Results are presented in Table 2. The CIoU loss function served as the baseline for this comparison. It was observed that the model's average accuracy decreased when the GIoU loss function was employed, suggesting that the prediction box and the actual box either shared an inclusion relationship or had overlapping dimensions, which were not suitable for the dataset in this study. The average accuracies of DIoU and SloU were comparable to that of CIoU, nearly maintaining the same level. However, DIoU did not account for the aspect ratio of the bounding box, and SloU lacked a strategy for gradient dynamic updating. While the GFLOPS slightly increased, the computational volume was larger. WIoU demonstrated the highest average accuracy, with [EMAIL_ADDRESS] reaching 93.9% and [EMAIL_ADDRESS]:0.95 at 75.8%, surpassing the other bounding box loss functions in the experiment.  
Figure 15 illustrates the training process performance comparison for the five bounding box loss functions, indicating WIoU's superior results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Bounding Box** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]:0.95\%** & **GFLOPS** & **Params/106** & **Size/MB** \\
**Loss Functions** & & & & & & \\ \hline +DIoU & 93.0 & 74.6 & 6.7 & 2.57 & 5.1 \\ +SIoU & 93.4 & 75.2 & 6.8 & 2.57 & 5.1 \\ +GIoU & 91.4 & 73.8 & 6.7 & 2.57 & 5.1 \\ +CIoU & 93.1 & 74.9 & 6.8 & 2.57 & 5.1 \\ +WIoU & 93.9 & 75.8 & 6.7 & 2.57 & 5.1 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of different bounding box loss functions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95\%** & **GFLOPS** & **Params/106** & **Size/MB** \\ \hline YOLOv8n (1) & 86.7 & 83.2 & 92.9 & 75.0 & 8.2 & 3.01 & 6.0 \\ YOLOv8n + Vanilla-Backbone & & 87.5 & 86.3 & 93.1 & 74.9 & 6.8 & 2.57 & 5.1 \\ YOLOv8n + Efficient-Neck (3) & 84.0 & 90.7 & 93.7 & 73.9 & 6.6 & 2.61 & 5.2 \\ YOLOv8n + Vanilla-Backbone & & 90.6 & 86.5 & 93.9 & 75.8 & 6.7 & 2.57 & 5.1 \\ YOLOv8n + Vanilla-Backbone & & 88.7 & 88.4 & 94.2 & 75.2 & 5.9 & 2.28 & 4.6 \\ +Efficient-Neck (5) & & & & & & & \\ YOLOv8n + Vanilla-Backbone & & 89.0 & 89.5 & 95.6 & 76.2 & 5.9 & 2.28 & 4.6 \\ + EfficientNeck + WIoU (6) & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Ablation experiments for improved processes.
## 4 LTC Architecture
### PoT Gateway
#### 4.2.5 PoT Gateway as an MQTT Broker  
Tracking a user's location is carried out using Message Queuing Telemetry Transport (MQTT) [9]. Following the typical MQTT system architecture, the PoT gateway embeds an MQTT broker, which acts as a transaction server that orchestrates message exchange between publishers (in this case, the door entry nodes) and subscribers (a cloud-hosted web server). The web server is hosted as a virtual machine on the same VPS instance that hosts the OpenVPN server, as depicted in Figure 2. The web server is used to update an instance of MongoDB Atlas [31], a cloud Database-as-a-Service (DBaaS) hosted by MongoDB. The database is made available to Dialogflow via webhook requests to the web server, as depicted in Figure 2.