esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Study and Error Quantification
### Error Measurement Metrics  
This section summarizes key metrics for quantifying prediction accuracy and computational efficiency, along with their comparative advantages and limitations.  
Root Mean Square Error (RMSE) measures the magnitude of prediction errors by averaging squared residuals and taking their root. It shares the same unit as the target variable but amplifies outlier effects due to squaring. A higher RMSE indicates larger deviations between predictions \(\hat{y}_{i}\) and true values \(y_{i}\).  
MSE computes the average squared error, emphasizing large discrepancies. While sensitive to outliers, it avoids the unit conversion required by RMSE. Mean Absolute Error (MAE) quantifies average absolute deviations, offering robustness to outliers. Unlike RMSE/MSE, MAE is scale-dependent and interpretable.  
Mean Bias Error (MBE) indicates systematic bias by averaging prediction errors. A positive/negative MBE reflects overestimation/underestimation. R-squared captures the proportion of variance in \(y_{i}\) explained by the model. Ranging from 0 to 1, it assesses overall fit quality and is dimensionless, enabling cross-model comparisons.  
Moreover, Runtime represents the total time for training/testing cycles. It compares models' balance of accuracy and resource demands.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Name (Abbreviation)** & \begin{tabular}{c} **Advantages of Hybrid** \\ **Modeling** \\ \end{tabular} & **Hyperparameter** \\ \hline \multirow{4}{*}{SMA-RF} & \
\begin{tabular}{c} Reduce computational cost \\ and improve generalization \\ ability \\ \end{tabular} &
\begin{tabular}{c} Number of decision trees: \\ [10; 300] \\ Minimum number of leaf \\ nodes: [10; 300] \\ Initial learning rate: \\ \([1\times 10^{-4},1\times 10^{-1}]\) \\ Number of neurons in \\ GRU layer: [3; 50] \\ L2 regularization \\ parameter: \\ \([1\times 10^{-6},1\times 10^{-2}]\) \\ Penalty factor: [1; 2000] \\ Radial basis function \\ kernel parameter: \\ \([1\times 10^{-2},10]\) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Detailed information about comparative items (hybrid models).  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{10}\] \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right|\] (11) \[\text{MBE}=\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y}_{i}-y_{i}\right)\] (12) \[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right) ^{2}}{\sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{13}\]
## 3 Results and Discussion
### Test Results of the Field Demonstration  
To verify the accuracy of the function relationship between the vibration energy values and the grout filling degree, as fitted in Figure 9, for field experiments, this section conducted field tests using the pre-embedded sensor method to measure the grout filling degree after grouting. The precast shear wall used in the experiment contained multiple sets of sleeve connections. According to the design information of the shear wall, the internal structure and volume of the sleeves were known. The field experiment set the grout filling degrees at 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, and 100%, a total of nine test conditions.  
To avoid deviations in the grout filling degree test results for each set of sleeve connections due to factors such as internal structure and residual debris, the grout filling degree detection instrument was used to test each set three times, and the average value was taken.  
After the experiment, the grout filling degree was calculated based on the test results and the function relationship in Equation (1), using the vibration energy values. The set grout filling degree and the experimentally calculated values are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pre-Embedded Sensors** & **Impact-Echo Technique** \\
**Vibration Energy Value (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{   }}}}}}}}}}}}}\))** & **Fullness Detection Value (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{  }}}}}}}}}}}}}}}\))** \\ \hline
255 & \(<\)70 \\
174 & 70 \\
152 & 75 \\
140 & 80 \\
131 & 85 \\
125 & 90 \\
120 & 95 \\
118 & 100 \\ \hline \hline \end{tabular} \end{table 2: Test results of pre-embedded sensors and impact-echo technique.  
Figure 9: Fitting analysis of vibration energy value and grouting fullness.  
The analysis of Figure 10 shows that in the field experiment, the grout filling degree calculated from the vibration energy values measured using the pre-embedded sensor method, through the fitted function in Equation (1), has a small deviation from the set grout filling degree and a high reliability. This also indicates that Equation (1) can be used to represent the relationship between the vibration energy values and the grout filling degree within the sleeve, i.e., the grout filling degree can be directly evaluated through the pre-embedded sensor method.
## 5 Testbed Demonstration Setup  
Emulation of the SATCOM part of the testbed are not enough to prove the whole functionality of the testbed development. It requires real over-the-air tests with commercial satellites to analyze the complete transmission chain. This section describes in detail the first over-the-air demonstration tests conducted with the SATis5 testbed in November 2018. The purpose of these tests was to demonstrate key benefits of satellite integration into 5G as well as to pave the way for the further development of the SATis5 testbed based on the lessons learned. In particular, two over-the-air demonstrations are elaborated here after:  
* Seamless Satellite Integration into Standard 3 GPP Network Architecture
* Mobile Edge Node Demo: Connectivity on the Move  
Figure 10: Satellite forward- and return-link emulator modules.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 4 Experimental Verification
### Experimental Platform Characteristics and Cp Design  
As shown in Figure 10, the FFSM experiment platform consists of an adjustable base, a flexible mirror, a voice coil motor, a position sensor, a control cabinet, and a communication bus. By adjusting the adjustable base, different characteristics of the controlled object can be obtained. The flexible mirror is driven by a voice coil motor to track the reference input signal. The mirror is made of metal with softer material properties, and it will be slightly deformed during the movement, which will bring uncertainties to the system. The position sensor outputs the mirror position in real time. The communication bus is responsible for the signal transmission, and the control cabinet implements the control operation.  
Through frequency sweep processing, the model of the controlled object is obtained. The transfer function \(G_{0}(s)\) (Equation (26)) consists of two second-order oscillation elements (SOOE), one second-order differentiation element (SODE), one first-order inertial element (FOIE), and a delay element (DE). The data in Table 2 are obtained after identification. The identification effect comparison is shown in Figure 11a.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SOOE 1** & **SOOE 2** & **SODE** & **FOIE** & **DE** \\ \hline \(\omega\) & 241.9026 & 333.0088 & 282.7433 & - & - \\ \(\xi\) & 0.228 & 0.165 & 0.220 & - & - \\ **others** & - & - & - & \(T_{0}\) = 0.0005 & \(T_{1}\) = 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification parameters 2.  
The position controller Cp is designed with a simple and practical zero-pole cancellation method. The open-loop transfer function of the forward path \(Gcp(s)\) is obtained as Equation (27).  
The measured closed-loop bandwidth under Cp control is 37.83 Hz (Figure 11b), which meets the bandwidth requirements under actual application conditions.  
\[G_{0}(s)=k*\frac{\left(\frac{s}{w_{1}}\right)^{2}+\frac{2\mathcal{D}_{3}}{w_{ 3}}+1}{\left(\left(\frac{s}{w_{1}}\right)^{2}+\frac{2\mathcal{D}_{1}}{w_{1}}+ 1\right)\left(\left(\frac{s}{w_{2}}\right)^{2}+\frac{2\mathcal{D}_{2}}{w_{2}}+ 1\right)\left(T_{0}s+1\right)}*e^{-T_{1}s} \tag{26}\]  
\[G_{cp}(s)=K*\frac{1}{\left(T_{0}s+1\right)\cdot s}*e^{-T_{1}s} \tag{27}\]
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 2 Model Principles and Methods
### Elm  
As a matter of fact, the feedforward neural network algorithm that is in line with a single hidden layer can be denoted as the extreme learning machine (ELM). Compared to the traditional neural networks, the ELM is equipped with a faster learning speed and a more favorable generalization performance under the premise of ensuring a greater learning accuracy [37; 38; 39]. Therefore, its principle is as follows.  
Assuming that there is a single hidden layer neural network with \(N\) arbitrary samples \((x_{i},t_{i})\), \(x_{i}=[x_{i1},x_{i2},\cdots,x_{in}]^{\top}\epsilon R^{n}\) and \(t_{i}=[t_{i1},t_{i2},\cdots,t_{in}]^{\top}\epsilon R^{m}\) signify the input and output data, respectively. Accordingly, the ELM network structure diagram is displayed in Figure 2.  
Figure 1: PSO algorithm flowchart.  
A neural network with L neurons in a single hidden layer can be represented as follows.  
\[\sum_{i=1}^{L}\beta_{i}g(\omega_{i}\times x_{i}+b_{i})=o_{j},j=1,\cdots,N \tag{4}\]  
In the formula, \(g(x)\) depicts the activation function, \(W_{i}=[\omega_{i1},\omega_{i2},\cdots,\omega_{in}]^{T}\) symbolizes the input weight value, \(\beta_{i}\) indicates the output weight value, \(b_{i}\) refers to the threshold of the \(i\)th neuron of the hidden layer, and \(o_{j}\) denotes the calculated output value.  
Admittedly, the purpose of training an ELM network is to minimize the errors between the calculated output values and the expected output values, which can be expressed as \(\sum_{j=1}^{N}\left\|o_{j}-t_{j}\right\|=0\). Hence, Equation (4) can be simplified to the matrix form.  
\[H\beta=T \tag{5}\]  
In the formula, \(H\) indicates the hidden layer output matrix, \(\beta\) represents the output weight values, and \(T\) symbolizes the expected output values.  
Consequently, the definitions of \(H\), \(\beta\), and \(T\) can be determined as the following.  
\[H=\begin{pmatrix}g(w_{1}x_{1}+b_{1})&\cdots&g(w_{1}x_{1}+b_{L})\\ \vdots&\ddots&\vdots\\ g(w_{1}x_{N}+b_{1})&\cdots&g(w_{L}x_{N}+b_{L})\end{pmatrix}_{N\times L} \tag{6}\]  
\[\beta=\begin{pmatrix}\beta_{1}^{T}\\ \vdots\\ \beta_{L}^{T}\end{pmatrix}_{L\times m} \tag{7}\]  
\[T=\begin{pmatrix}t_{1}^{T}\\ \vdots\\ t_{L}^{T}\end{pmatrix}_{L\times m} \tag{8}\]  
\(\hat{\beta}=H^{+}T\). Among them,\(H^{+}\) signifies the Moore-Penrose generalized inverse matrix of the output matrix \(H\).  
Figure 2: ELM network structure diagram.
## 5 Experimental Outcomes
### Multi-Class Classification Experiment
However, notable results are also shown by the FA and the SSA algorithm. The confusion matrix of the best performing model for multi-class classification can be observed in Figure 6.  
As can be observed, the algorithm struggles to identify slowITe and flood attacks often confusing slowing for legitimate data and flood attacks for denial-of-service attacks. However, flood and denial-of-service attacks are fairly similar in practice, so detection is still within acceptable margins. Additionally, malformed data can often be classified as DOS attacks. It can be deduced that the introduced approach performs significantly better when tackling the problem of anomalous traffic detection as a multi-class classification challenge, rather than a simple binary challenge. This is likely due to the confusion between flood and slowITe attacks with legitimate data. Nevertheless, the introduced method shows great potential for real-world implementation. Furthermore, the introduced optimization metaheuristic demonstrates improvements over existing techniques as well as the original base algorithm. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Method** & **Metric** & **Benign** & **DOS** & **Malformed** & **Brustorce** & **SlowITe** & **Flood** & **Accuracy** & **Macro Avg** & **Weighted Avg** \\ \hline \multirow{3}{*}{CNN-XG-GIRSA} & precision & 0.878533 & **0.867624** & **0.728448** & 0.616408 & 0.794728 & 0.967742 & **0.859764** & **0.808922** & **0.856535** \\  & recall & **0.950442** & 0.83392 & 0.15467 & **0.638934** & 0.515205 & 0.163043 & **0.859764** & **0.551955** & **0.859764** \\  & f1-score & 0.913074 & 0.86049 & 0.255195 & **0.627469** & 0.650984 & 0.279070 & **0.889764** & 0.597701 & 0.849664 \\ \hline \multirow{3}{*}{CNN-XG-RSA} & precision & **0.879449** & 0.866883 & 0.662388 & 0.616214 & 0.7878948 & 0.967742 & 0.859321 & 0.796746 & 0.853417 \\  & recall & 0.949254 & 0.854416 & 0.155893 & 0.630660 & 0.584147 & 0.163043 & 0.859321 & 0.551184 & 0.859321 \\  & f1-score & 0.913019 & 0.86064 & 0.251976 & 0.623353 & 0.650649 & 0.270700 & 0.859321 & 0.596445 & 0.849402 \\ \hline \multirow{3}{*}{CNN-XG-GA} & precision & 0.878810 & 0.86639 & 0.656366 & 0.619056 & 0.795276 & 0.769231 & 0.859231 & 0.764264 & 0.852741 \\  & recall & 0.950281 & 0.854032 & 0.161989 & 0.618938 & 0.548714 & 0.163043 & 0.859231 & 0.549500 & 0.859231 \\  & f1-score & 0.913149 & 0.860240 & 0.259984 & 0.619152 & 0.64 To encourage experimental repeatability, hyperparameter selections made by each algorithm are shown in Table 8.
## 4 Experiments
### Dataset Construction  
#### 4.2.1 Acquisition of Raw Images  
The quality of the dataset is highly relevant to the training and improvement of the model [42]. Since there is no publicly available dataset for IGBT solder layer defects, this paper establishes a standardized IGBT production line solder layer defect detection dataset. The images of this dataset were randomly collected from the standardized IGBT production line using an industrial X-ray device with a collection size of 1536 \(\times\) 1536 images and a total of 400 images with high image quality. To ensure the diversity and completeness of defect types, the data images are randomly collected from different production batches. The principle is to use the penetrating property of X-ray for precise imaging to detect the internal solder layer structure, solder layer defect location, size, and dimension of IGBT without damaging the IGBT.
## 5 Eliminating B-O LID during Cell and Module Fabrication
### Natural Recovery from B-O Related LID during Operation in the Field  
With an internal hydrogen source distributed throughout the bulk of the silicon from cell fabrication, normal operating conditions in the field can enable a natural recovery from B-O related defects [15; 16; 92]. During operation, modules can easily reach temperatures in excess of 50 \({}^{\circ}\)C, which is sufficient to enable a recovery of module performance. [PERSON] et al. reported that within 700 h of operation at 50 \({}^{\circ}\)C with maximum power-point operation, the open circuit voltage (VOC) loss due to LID had been reduced by 40%. An acceleration of this recovery can be achieved by leaving modules in open circuit, which is typical for modules during initial stages of installation. In open circuit condition at 60 \({}^{\circ}\)C, full recovery can be observed within 200 h of continuous illumination [15; 48; 149]. However, the time for a module to recover is heavily dependent on the mounting construction, geographical location of mounting and the time of year when the modules are installed, all of which can heavily influence the operating temperatures of the module [16]. These dependencies can result in recovery times ranging from several days to several years. The uncertainty of module outputs by relying on a natural recovery of the modules in the field could potentially create issues with financing projects. It is much more desirable that the long-term efficiency is known, which lends towards the implementation of LID mitigation processes during industrial solar cell or module fabrication.
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 3 Proposed SMTrust Model
### System Model
#### 3.1.5 Trust Value Update  
A trust value needs to be updated regularly, that is, after particular time intervals. To update trust after regular intervals, SMTrust employs the trickle timer functionality in RPL, referred to as periodic trust update. The trickle timer is basically a timing mechanism used for optimizing the broadcast of DIO messages for preserving nodes' resources. Similarly, another mechanism is adopted from RPL routing operation for trust value update, which is according to the node behavior change, referred to as reactive update of trust value.  
The reactive update takes place when the behavior of the node changes and new parent needs to be selected from potential parents list. For instance, when the rank of the parent node changes, the child node has to align itself in the topology and hence tends to change its rank, as well as it needs to recompute the trust of the parent node according to its new position and behavior. Hence, the child node follows the trust computation, trustworthy parent selection, and attack detection process again to maintain a trustworthy topology.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 5 Numerical Results
* Base station at the origin \((x,y)=(0,0)\);
* 100 drops of 8 UEs placed on a disk of radius 150 m centered at \((x,y)=(0,200\,\mathrm{m})\); the locations of the UEs are iid with a uniform distribution on the disc;
* 8 \(\times\) 10 uniform rectangular antenna array at the base station with half-wavelength dipoles at \(\lambda/2\) spacing;
* 5 MHz bandwidth at center frequency 2.53 GHz;
* No Doppler shift, shadowing and pathloss. Figure 6 shows the average GMIs for LP-ZF and MAGIQ. At high SNR, there is a slight decrease in the slope of the MAGIQ GMI as compared to LP-ZF. This suggests that one might need a larger \(N\) or \(b\). The performance for the Rayleigh fading model is better than for the Winner2 model but otherwise behaves similarly. Figure 4: Average GMIs for System A with 64-QAM and \(b=3\). Figure 7 shows BERs for the LDPC code with 64-QAM. Each codeword is interleaved over 4 OFDM symbols, all 396 subcarriers, and the 6 bits of each modulation symbol by using bit-interleaved coded modulation (BICM). The interleaver was chosen randomly with a uniform distribution over all permutations of length 9504. The solid curves are for  
Figure 5: Average GMIs for System B.  
Figure 6: Average GMIs for System C.  
data-aided channel estimation and the dotted curves show the performance of PAT when the fraction of pilots is \(S_{p}/S=10\%\). The pilots were placed uniformly at random over the four OFDM symbols and 396 subcarriers. A good blind detector algorithm that performs joint channel and data estimation should have BERs between the solid and dotted curves. The dashed curves in Figure 7 show the SNRs required for the different algorithms based on Figure 3. In particular, the rate 5.33 bpcu requires SNRs of 9 dB, 12.9 dB, and 15.2 dB for LP-ZF, QCM, and SQUID, respectively. SQUID is run with 300 iterations and QCM is run with 6 iterations. Each UE computes its log-likelihoods based on the parameters (20) of the auxiliary channel. The GMI predicts the coded behavior of the system within approximately 1 dB of the code waterfall region, except for SQUID, where the gap is about 2 dB. The gap seems to be caused mainly by the finite-blocklength of the LDPC code, since the smaller gap of approximately 1 dB is also observed for additive white Gaussian noise (AWGN) channels. The sizes of the gaps are different, and the reason may be that the slopes of the GMI at rate 5.33 bpcu are different, see Figure 3. Observe that LP-ZF exhibits the steepest slope and SQUID the flattest at \(R_{\rm a}=5.33\) bpcu; this suggests that SQUID's SNR performance is more sensitive to the blocklength. Figure 8 is for System A and shows how the GMI decreases as the CSI becomes noisier. The behavior of all systems is qualitatively similar. However, the figure shows that the QCM rate is more sensitive to the parameter \(\epsilon\) than the SQUID rate when \(\epsilon\) is small. Figure 7: BERs for System D and a 5G NR LDPC code. The dashed vertical curves show the SNRs required for long random codes, see Figure 3.
## 3 Results
### Optimal Arnlet Shape for Wet Circuit Measurement System: Key Findings
The force applied at the interface between the strap and the pipe was measured to be 1138.4 N. The proposed solutions for expanding the strap and implementing the clamping mechanism were illustrated for installation on a pipe with a diameter of 250 mm. Specifically, a Roman screw featuring opposing threads was conceptualized, fabricated, and subjected to testing (photographs are available for reference). The maximal deformations recorded were within the range of 0.2 mm. Further investigation revealed that the peak stress experienced by the strap was 4.46 MPa. When the pipe diameter was 160 mm, the stress level reached its maximum at 1.49 MPa (Figure 5). For a pipe with a diameter of 200 mm, the stress level measured 0.8 MPa, as illustrated in Figure 6. In the case of a 250 mm diameter pipe, the maximum stress level was recorded at 0.65 MPa, as depicted in Figure 7. When dealing with a 315 mm diameter, the maximum stress level dropped to 0.51 MPa (Figure 8). Finally, for a 400 mm diameter pipe, the maximum stress level was at its lowest, measuring 0.41 MPa, as indicated in Figure 9.  
Upon thorough analysis, it was concluded that the most suitable material should possess superior yield strength and optimal tensile strength at the point of fracture. Consequently, the recommended material for this application was identified as PC/ABS. Furthermore, Spearman correlation analyses were conducted to assess the relationship between different parameters of the selected PC/ABS material (Figure 10). Figure 8: Visualizing pressure distribution (MPa) in armlet with a wet circuit measurement system for a 315 mm diameter pipe. Figure 7: Visualizing pressure distribution (MPa) in armlet with a wet circuit measurement system for a 250 mm diameter pipe.