esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Stability Analysis
With the definition of the generalized vector \(\boldsymbol{G}=\left[s,\hat{W}_{c}^{\top}\boldsymbol{\theta}_{1}\right]^{\top}\), the inequality (47) can be rewritten as:  
\[\hat{V}\leq-\boldsymbol{G}^{\top}\boldsymbol{\Pi}\boldsymbol{G}+\boldsymbol{G}^ {\top}\boldsymbol{\Pi}_{\theta}+\epsilon_{v1} \tag{48}\]  
where \(\boldsymbol{\Pi}=\text{diag}(\boldsymbol{Q},\boldsymbol{I})\), \(\boldsymbol{\Pi}_{\theta}=\left[\boldsymbol{0},\aleph_{3}/\left(\boldsymbol{ \theta}^{\top}\boldsymbol{\theta}+1\right)\right]^{\top}.\) Given that \(\boldsymbol{\Pi}_{\theta}\), \(\hat{W}_{a}\) are bounded vectors, it can be concluded that the \(\hat{W}_{a}\) is a bounded vector and \(||\boldsymbol{\Pi}_{\theta}||\leq\hat{\Pi}_{\theta}\) and \(|\epsilon_{v1}|\leq\epsilon_{v1}\). Therefore, \(\hat{V}\) is negative if  
\[||\boldsymbol{G}||>\frac{\hat{\Pi}_{\theta}\sqrt{\hat{\Pi}_{\theta}^{2}+4\lambda_{ \min}(\boldsymbol{\Pi})\epsilon_{v1}}}{2\lambda_{\min}(\boldsymbol{\Pi})}, \tag{49}\]  
which implies that the sliding variable \(\boldsymbol{s}\) and \(\hat{W}_{c}^{\top}\boldsymbol{\theta}_{1}\) are UUB. Furthermore, in view of the assumption that \(\boldsymbol{\theta}_{1}\) is PE, the weight estimation error \(\hat{W}_{c}\) is also UUB [14]. Once the sliding variable converges to the vicinity of origin, the gradient vector \(\
abla\sigma[\mathbf{s}(t)]\) together with \(\tilde{M}_{a}^{*}\) will also be small enough. Defining \(\mathbf{d}=\mathbf{\Xi}+\mathbf{D}_{1}+\mathbf{B}_{1}\tilde{\mathbf{M}}_{a}^{*}\), and assuming that there exists a small positive constant \(\bar{d}\) satisfying \(||\mathbf{d}||\leq\bar{d}\), the dynamic of the sliding variable can be represented as:  
\[ \begin{bmatrix}\dot{\mathbf{s}}\\ \dot{\mathbf{M}}_{\bar{d}}\end{bmatrix} = \begin{bmatrix}\mathbf{d}-\mathbb{C}\Big{[} \lambda_{1i}\text{sig}^{1/2}(s_{i})\Big{]}-\mathbf{M}_{\bar{d}}\\ -\mathbb{D}(\lambda_{2i})\text{sign}(s_{i})\end{bmatrix} . \tag{50}\]  
According to the deduction lines in [30], the sliding variable can reach a small neighborhood of origin; thus, the equivalent unconstrained errors \(\eta_{1i}\) are bounded. Recalling the error transformation method in (9), it can be derived that  
\[z_{i}=T(\eta_{1i})=\frac{\exp(\eta_{1i})-\exp(-\eta_{1i})}{\exp(\eta_{1i})+ \exp(-\eta_{1i})}. \tag{51}\]  
Note that \(T(\eta_{1i})\) is a smooth, strictly increasing, function w.r.t. \(\eta_{1i}\) and satisfies \(\lim\limits_{\eta_{1i}\rightarrow-\infty}T(\eta_{1i})=-1\), \(\lim\limits_{\eta_{1i}\rightarrow\infty}T(\eta_{1i})=1\); thus, it can be derived that \(-1<z_{i}<1\) and \(-\rho_{i}<e_{1i}<\rho_{i}\) hold true if \(\eta_{1i}\) is bounded. Following the above analysis, it can be deduced that \(e_{1i}\) never violates the fixed-time prescribed performance constraints in (8) for all \(t>0\), and \(|e_{1i}|<\rho_{i\infty}\) for \(t>T\). This completes the proof of Theorem 1.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 5 Experimental Results
### Base Model (Using All Features)  
Table 4 shows the evaluation results of the classification using the algorithm described in Section 4.2.3, applying all 55 extracted features indicated in Table 3. As a result, KNN, an instance-based learning method, achieved the lowest accuracy of 70.038% and an F1-score of 77.96%. In contrast, the stacking ensemble, RF methods, and ensemble learning methods achieved higher scores. Stacking showed the highest score, with an accuracy of 97.264% and an F1-score of 96.991%. The base model tested in this section was then compared with the experiment using feature selection, as described in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** \\ \hline DT & 81.289\% & 87.272\% & 87.258\% & 93.081\% & 94.868\% \\ \hline RF & 86.537\% & 91.012\% & 90.709\% & 94.513\% & 95.911\% \\ \hline KNN & 70.038\% & 78.863\% & 77.96\% & 84.879\% & 90.055\% \\ \hline Stacking & 97.264\% & 97.609\% & 96.992\% & 96.47\% & 97.951\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multiclass classification performance with all 55 extracted features.
## 3 Results and Discussion  
### Tensile Strength  
The tensile test of the newly developed knitted fabrics was performed in order to determine the influence of the elastomeric element in plain structure and loop length on the tensile strength, i.e., breaking force and elongation at break. The results of the breaking tests are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Loop** & **Wale Density,** & **Course** & **Loop Area,** & **Area Density,** & **Thickness,** \\
**Code** & **Length, mm** & **cm\({}^{-1}\)** & **Density, cm\({}^{-1}\)** & **mm\({}^{2}\)** & **g/m\({}^{2}\)** & **mm** \\ \hline WP1 & 7.8 & 4.6 & 7.2 & 3.02 & 343 & 1.41 \\ WP2 & 8.3 & 4.4 & 6.8 & 3.34 & 337 & 1.31 \\ WP3 & 8.8 & 4.2 & 6.4 & 3.72 & 323 & 1.22 \\ WPE1 & 8.6 & 6.4 & 7.0 & 2.23 & 715 & 1.80 \\ WPE2 & 9.1 & 6.0 & 6.4 & 2.60 & 569 & 1.65 \\ WPE3 & 9.6 & 5.0 & 6.0 & 3.34 & 433 & 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of knitted fabrics.  
Figure 1: Images of knitted structure (technical face and technical backsides): (**a**) weft plain (WP); (**b**) weft fleece pattern (WPE); (**c**) knitting diagram of WP; and (**d**) knitting diagram of WPE.  
The obtained results clearly demonstrate that the loop length as well as elastomeric element in the knitted pattern have significant influence on the tensile characteristics of the knits. This was also proved by other researchers [10; 12]. By increasing the loop length, the breaking force linearly decreases, while elongation at the break linearly increases. The breaking force leads to a densification of the structure due to the redistribution of the yarn in the neighbouring loops. It was found that the increase in the loop length of the plain knits with elastomeric tuck-miss laid yarn (WPE) had significantly higher influence on the decrease of the breaking force and increased the elongation at break than of the weft plain knits without the elastomeric yarn (WP). The 11.4% increase of the loop length in the WP group knits leads to the 20.8% decrease of the breaking force and 10.3% increase of the elongation at break, whereas the similar (10.4%) loop length increase in the WPE group knits leads to even 81.8% decrease of the breaking force and 38.9% increase of the elongation at break. It means that even small changes in the loop length of such fabrics with elastomeric fleece yarn in the structure can give high impact on the tensile strength.
## 6 MSE of Direct Position Determination Method in Presence of Array Model Errors
### Perturbation Analysis on the Eigenvalues of Positive Semidefinite Matrix  
Because the cost function in (22) is expressed as the maximal eigenvalue of some positive semidefinite matrix, an eigenvalue perturbation result is formally stated in a proposition as follows.  
**Proposition 1**.: _Let \(\mathbf{Z}\in\mathbf{C}^{N\times N}\) be a positive semidefinite matrix with eigenvalues \(\lambda_{1}\leq\lambda_{2}\leq\cdots\leq\lambda_{N}\), associated with unit eigenvectors \(\mathbf{u}_{1}\,,\,\mathbf{u}_{2}\,,\,\cdots\,,\,\mathbf{u}_{N}\), respectively. Moreover, \(\lambda_{n}\) differs from the other eigenvalues. Assume \(\mathbf{Z}\) is corrupted by a Hermitian error matrix \(\widetilde{\mathbf{Z}}\in\mathbf{C}^{N\times N}\), and the corresponding perturbed matrix is denoted \(\mathbf{\hat{Z}}\); i.e., \(\mathbf{\hat{Z}}=\mathbf{Z}+\widetilde{\mathbf{Z}}\in\mathbf{C}^{N\times N}\). If the eigenvalues of matrix \(\mathbf{\hat{\lambda}}\) are denoted \(\tilde{\lambda}_{1}\leq\tilde{\lambda}_{2}\leq\cdots\leq\tilde{\lambda}_{N}\), then the relationship between \(\tilde{\lambda}_{n}\) and \(\lambda_{n}\) can be described by_  
\[\tilde{\lambda}_{n}=\lambda_{n}+\mathbf{u}_{n}^{\text{H}}\widetilde{\mathbf{Z}}\mathbf{u}_ {n}+\mathbf{u}_{n}^{\text{H}}\widetilde{\mathbf{Z}}\mathbf{U}_{n}\widetilde{\mathbf{Z}}\mathbf{u}_ {n}+o(||\widetilde{\mathbf{Z}}||_{2}^{2})\,, \tag{26}\]  
where  
\[\mathbf{U}_{n}=\sum_{\begin{array}{c}i=1\\ i\
eq n\end{array}}^{N}\frac{\mathbf{u}_{i}\mathbf{u}_{i}^{\text{H}}}{\lambda_{n}- \lambda_{i}}\,. \tag{27}\]  
The proof of Proposition 1 can be found in [21]. Note that Proposition 1 plays a fundamental role in our subsequent analysis.
## 5 Data Collection
### Data Set Construction
#### 5.2.2 Data Pre-Processing  
After data collection, this paper carries out format conversion and segmented filtering on the original data. The specific process is as follows:  
1. Make the necessary format conversion of the original data. The original data from server are in CSV format, so we need to use python for pre-processing and we use the tensor to store the data.
2. Put each sensor data into the MongoDB database by sensor type to identify them easily.
3. After the data import, a joint index is established on two attributes of sensor data, which are the \"timestamp\" and \"sensorName\" to speed up the search speed when segmenting the data.  
After importing the sensor data into the database and establishing the index, we traverse the activity labels in the questionnaire and search the sensor data in the corresponding time period according to the time when the label is generated.  
In the process of data search, for all activity labels, Algorithm 1 is used to search the data of each sensor. For the searched data, if the length is greater than 200, the part greater  
Figure 8: The data sequence.  
Figure 7: Accelerometer axes on the mobile phone.  
than 200 will be cut off and only the previous data will be retained. If the length is less than 200, zeros are added after the data until the length is equal to 200. If the length of the actual available data for a label is less than 180, they will be excluded from the training dataset to prevent too many zeros from affecting the accuracy of the model.  
After the data of the seven sensors are searched, the data collected by seven sensors are merged in the sensor dimension and form the sensor data to the corresponding activity label. The data shape after merging is \([7,300,3]\), where 7 is the number of sensors, 200 is the length of sensor data, and 3 is the number of sensor axes.  
The search process for certain sensor data is shown in Algorithm 1.  
```
Require: The timestamp of the label \(A\), the maximum length \(M\) of the window, the data quantity \(K\) inside the window, the current window \(C\) and the data length threshold \(T\). for\(i=1,\ldots,M\)do \(C=[A,A+i]\) \(K=getSensorDataLengthOfWindow(C)\) if\(K>T\)then  finish else  continue endif endfor
```  
**Algorithm 1** Search Certain Sensor Data According to Activity Labels  
Taking the label of \"running\" as an example, the corresponding accelerometer, gyroscope and gravity acceleration data are visualized as shown in Figure 9.  
It can be seen from Figure 9, different types of sensors have significantly different characteristics with a certain periodicity. Therefore, the neural network model can accurately classify the behavior corresponding to the sensor data through feature extraction and fusion.
## Materials and Methods
### Principle of Wavelet Threshold Denoising  
The current commonly used image denoising methods include median filtering, bilateral filtering [24], Block-Matching 3-Dimension (BM3D) [25], wavelet threshold denoising [26], etc. In the method of wavelet threshold image denoising, the key is to calculate the appropriate wavelet threshold. If the selected threshold is too large, the removed information will contain noise and valid information, which will reduce the imaging quality. Conversely if the threshold is too small, the noise in the image cannot be removed clearly. The noise in the image mainly comes from the electromagnetic wave interference from the environment and the photoelectric, mechanical, material, circuit and other noise of the imaging system. Assuming that the noise of the image is independent of the original image, the noise can usually be divided into additive noise and multiplicative noise:  
\[Y_{ij}=X_{ij}\cdot\delta+N_{p}, \tag{6}\]  
where \(X_{ij}\) is noise-free image; \(Y_{ij}\) is noisy images; \(\delta\) is multiplicative noise and \(N_{p}\) is additive noise.  
For a noisy image, the additive and multiplicative noises in the image need to be filtered to obtain a noise-free image. Wavelet transform can decompose the image on multiple scales, which is more conducive to separating noise. Therefore, wavelet transform has great advantages in removing image noise.
###### Abstract  
The wet carbonation of coal gangue-based backfilling slurry (CGBS) is considered to be an effective method for the resource utilization of coal gangue solid waste and CO\({}_{2}\) sequestration, but CO\({}_{2}\) sequestration has a negative impact on the rheological properties of CGBS. This investigation explores the effect of carbonization pressure on the rheological properties and CO\({}_{2}\) sequestration properties of CGBS by using a carbonization reactor, a rheometer, X-ray diffraction, a nitrogen adsorption-desorption instrument, a scanning electron microscope and other testing methods. The results show that increasing the carbonization pressure can increase the CO\({}_{2}\) sequestration capacity of CGBS, and the carbonization products produced make the pores of CGBS smaller and the structure more compact; however, increasing the carbonization pressure will reduce the rheological properties of the slurry, and the optimal carbonization pressure is 0.7 MPa. At this time, the yield stress, plastic viscosity and hysteresis loop area of CGBS are 171.66 Pa, 0.0998 Pa-s and 1376 Pa/s, respectively. However, when the carbonization pressure is further increased, the CO\({}_{2}\) sequestration capacity tends to remain unchanged. This is mainly because the carbonization pressure causes the carbonization reaction to intensify, forming a calcified layer on the particle surface, which hinders the penetration of CO\({}_{2}\) into the particles. This study is of great significance for improving the utilization rate of gangue solid waste and CO\({}_{2}\) sequestration.  
coal gangue; backfilling slurry; CO\({}_{2}\) sequestration; rheological properties; carbonization pressure
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
###### Abstract  
Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Beam-forming techniques using evolution algorithms have been studied for multi-user resource allocation in simultaneous wireless information and power transfer (SWIPT) systems. In a high-capacity broadband communication system there are many users with wearable devices. A transmitter provides simultaneous wireless information and power to a particular receiver, and the other receivers harvest energy from the radio frequency while being idle. In addition, the ray bounce tracking method is used to estimate the multi-path channel, and the Fourier method is used to perform the time domain conversion. A simple method for reducing the frequency selective effort of the multiple channels using the feed line length instead of the digital phase shifts is proposed. The feed line length and excitation current of the transmitting antennas are adjusted to maximize the energy harvest efficiency under the bit error rate (BER) constraint. We use the time-domain multipath signal to calculate the BER, which includes the inter symbol interference for the wideband system. In addition, we use multi-objective function for optimization. To the best of our knowledge, resource allocation algorithms for this problem have not been reported in the literature. The optimal radiation patterns are synthesized by the asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) algorithms. Both APCO and SADDE can form good patterns for the receiver for energy harvesting. However, APCO has a faster convergence speed than SADDE.  
Keywords:asynchronous particle swarm optimization; wireless transfer; information and power transfer; Beamforming; broadband; self-adaptive dynamic differential evolution algorithm +
Footnote †: journal:
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 2 Optimization of Induction Heating System for Concrete Curing  
### Induction Heating System for Concrete Curing  
In this study, an induction heating system capable of efficiently converting electric energy to heat was developed. Figure 1a shows a block diagram of the induction heating system connected to temperature-control devices. The converter delivers power from the alternating current (AC) source to the inverter after converting AC into direct current (DC). The inverter generates the desired frequency from the rectified DC using insulated gate bipolar mode transistor switches used for high current. The matching transformer between the inverter and induction coil enables the inverter output to be transmitted to the coil with low loss. It also transforms the inverter output current into a high current which is advantageous for induction heating. The magnetic flux generated by the current in the coil develops an eddy current on the surface of the work piece (metallic form in this study), as shown in Figure 1b. The eddy current functions as a source of heat for concrete curing. To alter the formwork surface temperature, the current of the coil that adjusts the eddy current intensity is controlled by the temperature sensor and the control device connected between the formwork and converter.
## 4 Simulation Results
However, since the other three proposed systems were specifically designed to enhance LLRs obtained from the SOVA detectors, comparing their performance with that of the cascaded MLP approach provides valuable insights for this study. Additionally, System II aimed to maximize LLR mutual data, resulting in higher reliability of data detection. By emphasizing the summation of LLR, the detection of incorrect bits became highly reliable. In contrast, System III prioritized the probability of flipping incorrect data bits to correct ones by considering the maximum probability of LLR mutual data. In summary, the MLP algorithm in System V, the LLR-based techniques in Systems II and IV, and the probability considerations in System III collectively contributed to the enhanced performance of the proposed systems over the conventional system. Figure 14: BER performance of the proposed systems with position and size fluctuations at 10%. Figure 15: FER performance of the proposed systems without position and size fluctuations. To evaluate the complexity of our proposed systems, we calculated the number of operations required for detecting and decoding a user bit. The operator count for each system can be summarized in Table 1, which provides a clear comparison of their computational complexities.
## Materials and Methods
### Path Loss Measurement Campaign  
The first step was to fix both transmitter and receiver on a height of \(H_{2}\) = 0.4 m from the ground. Measurements have been started with a separation distance of \(d_{1}\) = 1 m. The transmitter was kept stationary and the receiver was moved each time with 2 m. At each receiver antenna position, the received power was measured and recorded. The next step was to lower the height of both transmitter and receiver simultaneously to \(H_{1}\) = 0.2 m and apply the same procedure of the first step.  
The same described method was used for each of the three frequency carriers, \(F_{1}\) = 868 MHz, \(F_{2}\) = 2.4 GHz and \(F_{3}\) = 5.8 GHz. Pictures in Figure 2 try to illustrate the experimental scenarios of the measurement campaign.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Kinematic Constraints
#### 2.3.1 Method Content  
Since the Euclidean distances matrix (EDM) is coordinate-free and invariant to in-plane image rotations and translations, [PERSON] et al. [23] use EDM to represent pairwise distances of body joints. Combining body structural information and capturing joint correlations, they formulate the 3D estimation task as a regression between matrices encoding 2D and 3D joint distances to reduce ambiguity. As shown in Figure 4, [PERSON] et al. [24] employ propagating LSTM networks (p-LSTMs) connected in series to elaborate the 3D pose progressively. They effectively overcome ambiguity by learning joint interdependency based on actual human behavior. [PERSON] et al. [25] propose to distinguish joints with different degrees of freedom (DOF) based on prior knowledge about physiological structure. In detail, the bi-directional dependencies among body parts with different DOFs make them supervise each other, yielding physically constrained and plausible pose-estimation results. [PERSON] et al. [26] propose an attention mechanism-based [27] model to combine the end-to-end training scheme in GNN and the limb length constraints in PSM. In addition, [PERSON] et al. [28] parameterize the human body by shape, and joint angles based on the SMPL model [29] to reduce the probability of generating an unreasonable body caused by ambiguity. As shown in Figure 5, [PERSON] et al. [30] split the 3D coordinate regression problem into length and direction estimation, which alleviates ambiguity and self-focusing contained in images by a large margin within a more compact space.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Dir.** & **Disc.** & **Eat** & **Greet** & **Phone** & **Photo** & **Pose** & **Purch.** & **Sit** & **SitD.** & **Smoke** & **Wait** & **WalkD.** & **Walk** & **WalkT.** & **Avg** \\ \hline [PERSON] et al. [17] & 48.4 & 50.7 & 57.2 & 55.2 & 63.1 & 72.6 & 53.0 & 51.7 & 66.1 & 80.9 & 59.0 & 57.3 & 62.4 & 46.6 & 49.6 & 58.3 \\ [PERSON] et al. [10] & 47.1 & 50.6 & 49 & 51.8 & 53.6 & 61.4 & 49.4 & 47.4 & 59.3 & 67.4 & 52.4 & 49.5 & 55.3 & 39.5 & 42.7 & 51.8 \\ [PERSON] et al. [10] & 45.2 & 46.7 & 43.3 & 45.6 & 48.1 & 55.1 & 44.6 & 44.3 & 57.3 & 65.8 & 47.1 & 44 & 49 & 32.8 & 33.9 & 46.8 \\ [PERSON] et al. [18] & 44.6 & 47.4 & 45.6 & 48.8 & 50.8 & 59.0 & 47.2 & 43.9 & 57.9 & 61.9 & 49.7 & 46.6 & 51.3 & 37.1 & 39.4 & 48.8 \\ [PERSON] et al. [19] & 28.5 & 33.5 & 28.1 & 28.9 & 32.6 & 35.5 & 33.3 & 30 & 37.4 & 39.9 & 31.4 & 30.2 & 29.5 & 23.9 & 25.5 & 31.2 \\ [PERSON] et al. [20] & 41.4 & 43.5 & 40.1 & 42.9 & 46.6 & 51.9 & 41.7 & 42.3 & 53.9 & 60.2 & 45.4 & 41.7 & 46 & 31.5 & 32.7 & 44.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of performance on Human3.6 Protocol #1 (MPJPE: mm).  
Figure 4: Illustration of [PERSON]’s approach with pose depth cues.
## 3 Modeling and Results Analysis
### Simulation Model Building  
On the basis of an analysis of the whole vehicle steering driving closed-loop control system, the mathematical models of the hydrostatic drive system, the steering kinematics, and the dynamics of the tracked vehicle in the previous section are used as sub-model modules in the tracked vehicle steering driving system, and the simulation model is built in MATLAB/Simulink, as shown in Figure 5.  
On the basis of Figure 5, it can be seen that the vehicle steering driving system simulation model includes a control strategy input module (\(v-w\)), a pump displacement controller module (Fuzzy-PID controller), a hydrostatic drive system module (PM), and a vehicle steering kinematics and dynamics module (vehicle). The control strategy module interprets the steering input in the form of the appropriate track speed required for both sides while avoiding vehicle rollover, and sends the corresponding variable pump displacement control signal to the variable pump controller; then, the signal from the motor speed sensor is fed to the controller to complete the closed-loop control of vehicle speed  
Figure 4: Control system diagram of tracked vehicle steering.  
Figure 5: Simulation model of vehicle steering driving system.  
and steering conditions. The main parameters of the tracked vehicle used in the simulation model are shown in Table 1, below.