esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Performance under Mixed Anomaly Scenarios  
In Section 5.2, we investigate the detection results of the proposed model under a single anomaly scenario. In a practical way, we expect to develop a model or system that can conquer various anomaly situations with different in-vehicle sensor signals. Therefore, in this section, we take into consideration the mixed sensor signals and anomaly scenarios mentioned in Section 3.4 to construct an overall detection model. The dataset used to train the CNN model in this section contains five sensor signals with five anomaly scenarios.  
The detection results are shown in Table 8; it can be seen that the proposed model performs well at detecting various anomaly scenarios of different in-vehicle sensor signals, with 96.28% accuracy, 95.30% sensitivity, 97.21% precision, and 96.24% F1 score. Though the complexity and diversity of the datasets increase the difficulty of feature extraction, the experimental result shows that our proposed model perform reasonably well at detecting vehicle anomaly behaviors, which means that our model can deal with various complex anomaly situations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Acc** & **Sens** & **Prec** & **F1** \\ \hline
1 & 95.65 & 94.79 & 96.44 & 95.61 \\
2 & 95.55 & 94.64 & 96.39 & 95.51 \\
3 & 95.38 & 94.46 & 96.23 & 95.34 \\
4 & 94.61 & 93.69 & 95.45 & 94.56 \\
5 & 94.84 & 93.92 & 95.68 & 94.79 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detection performance of the proposed model under the replay scenario.  
Figure 5: Detection performance of the proposed model of different in-vehicle sensor signals under different anomaly scenarios.
# Evaluation of the Effects of Locally Applied Resveratrol and Cigarette Smoking on Bone Healing
## 3 Results
### Vascularization  
Preparations stained with PSMA were evaluated for vascularization and scored according to the degree of vascularization as follows: 0, no neovascularization was shown; 1, a slight amount of vascularization was shown; and 2, a moderate level of vascularization was shown. The difference between the groups in terms of vascularization levels was found to be statistically significant (\(p<0.05\)). When the difference between the groups was examined by pairwise comparison tests with Bonferroni correction, it was determined that the level of no neovascularization (Score 0) was significantly lower in the RES group (36.4\(\%\)) than in the control group (100\(\%\)). Although it was not statistically significant, moderate vascularization was not seen in any group, but it was seen in the RES group (Figure 2, Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Vascularization** & **Control** & **RES** & **CS + RES** & **CS** & **P** \\  & **Scores** & **(_n, \%_)** & **(_n, \%_)** & **(_n, \%_)** & **(_n, \%_)** & **(_n, \%_)** & **P** \\ \hline \hline \multirow{3}{*}{**Vascularization**} & Score 0 & 10 (100.0) a ** & 4 (36.4) b  & 6 (60.0) a,b  & 8 (80.0) a,b  & \\ \cline{2-5}  & Score 1 & 0 (0.0) a  & 5 (45.5) a  & 4 (40.0) a  & 2 (20.0) a  & 0.02 * \\ \cline{2-5}  & Score 2 & 0 (0.0) a  & 2 (18.2) a  & 0 (0.0) a  & 0 (0.0) a  \\ \hline \hline \end{tabular} Fisher–Freeman–Halton Exact Test: 11.84. RES: resveratrol; CS: cigarette smoking. Different letters on the same line indicate a significant difference. Statistical differences are indicated by letters such as a, b, ab. There is a statistically significant difference between groups indicated with different letters (\(\lx@math@degree\)\(p<0.05\)). There is no statistically significant difference in groups with the same letters.  
\end{table}
Table 2: Comparison of vascularization levels by groups.  
Figure 1: **(A)** While there were no osteoblasts in the defective area in the control group, 1–2 osteoclasts (blue arrow) were observed. **(B)** In the smoking group, 3–4 osteoclasts (red arrow) were observed. **(C)** An increase in the number of osteoblasts and osteoblastic activity (green arrow) was observed in the resveratrol group. **(D)** A lower number of osteoblasts (yellow arrow) were observed in the CS + RES group compared to the RES group ((**A–D**): H&E 200\(\times\)).  
Figure 2: **(A)** In the sample scanned at 200\(\times\) with Microvisioneer manual whole slide scanner equipment, SATB2 expression was digitally evaluated from the hotspot area using QuPath open-source software. **(B)** A small number of new vessel formations were observed with PSMA in the resveratrol group. **(C)** In the resveratrol group, 2–3% proliferation was observed in the hotspot area with Ki-67. (QuPath open-source digital pathology; A: SATB2 DAB 200\(\times\), B: PSMA DAB 200\(\times\), C:K i-67 DAB 200\(\times\)).
## 2 Materials and Methods
### Algorithm  
Now, we can consider the basic algorithms for signal fault localization and classification, which are based on the use of previous methods and algorithms.  
Mathematically, we can describe the problem to be solved as follows:  
\(\arg\min_{(s,t,a)\in S\times T\times A}\text{MSE}(y,\hat{y})\).  
\(\arg\min_{(s,t,a)\in S\times T\times A}\text{MSE}(y,\text{ErrorFunction}(s,t,a))\).  
Here, the following applies:  
1. \(y\): the original signal represented as a time series. This signal is the reference signal and is used to evaluate the accuracy of the approximation.  
2. \(\hat{y}\): the approximated signal obtained by applying an error function with certain parameters. The goal is to match this signal as closely as possible to the original signal \(y\).  
3. ErrorFunction: a function that takes stretch, shift, and amplitude parameters as input and returns an approximated signal. Formally, \(\text{ErrorFunction}(s,t,a)\) denotes the approximated signal obtained given the parameters \(s\), \(t\), and \(a\).  
4. \(S\): the set of possible values of the signal stretching parameter along the OX axis. Each element of this set represents a stretching factor that can be applied to the original signal.  
5. \(T\): the set of possible values of the signal shift parameter. Each element of this set represents a shift value that can be applied to the original signal.  
6. \(A\): a set of possible values of the signal amplitude parameter. Each element of this set represents an amplitude coefficient that can be applied to the original signal.  
7. \(S\times T\times A\): The Cartesian product of sets \(S\), \(T\), and \(A\) that is the set of all possible combinations of stretch, shift, and amplitude parameters.  
The purpose of the method is to find such a combination of parameters \((s,t,a)\) that minimizes the MSE error between the original signal \(y\) and the approximated signal \(\hat{y}\).  
Thus, this formula defines a procedure for finding the optimal stretch, shift, and amplitude parameters that provide the best approximation of the original signal in the sense of minimizing the MSE error.  
The main steps of the algorithm are as follows (Figure 1):  
1. Input signal: Obtaining a raw signal containing potential anomalies (failures).  
2. Signal preprocessing: Normalization, the removal of unwanted noise components, and peak detection.  
3. Wavelet analysis: Wavelet transform (decomposition of the signal into wavelet coefficients for analysis at different scales) and coefficient analysis (examination of wavelet coefficients to identify features associated with failures).  
4. Error Classification: Using optimization methods (Nelder-Mead method, BFGS) to minimize the MSE between a failure in the signal interval and a possible failure from error sample libraries.  
5. Results output: Error classes (defined failure types), error estimation (metrics output), temporal localization (determining when the failure occurs).
## 1 Introduction
Current fire simulations for converter transformers typically assume a fixed fire source power, which does not change with the addition of fire suppression methods. This assumption overlooks the dynamic characteristics of fire source power, which varies over time in real fires and fails to couple fire evolution with the effect of water mist spray of different particle sizes. Furthermore, the shell structure of the converter transformer is assumed to be steel, which may fail in a fire [36]. However, there is a lack of simulation studies on the performance of thermal insulation coatings applied to the converter transformer's shell [25; 26; 27; 28; 29; 30]. \begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Location** & **Consequence** \\ \hline
2019 & Yinan, China & The Yinan Converter Station, Pole II, low-end Y/Y-C phase converter transformer experienced a sudden fault and caught fire, with no casualties reported. \\
2020 & Jinan, China & The converter transformer burst into flames and later caused a fire. The accident has resulted in one death and two injuries. \\
2022 & Nevada, USA & The converter transformer exploded and caught fire at the Hoover Dam hydroelectric plant. There were no injuries or fatalities as a result of the incident. \\
2024 & Ishikawa, Japan & The converter transformer exploded and burned, causing a loss of 500,000 volts of power and shutting down the nuclear reaction. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of converter transformer fire incidents. Therefore, this study will systematically investigate the effect of water spray with different particle sizes on the surface temperature and fire development of the converter transformer, and at the same time, comparatively analyze the temperature barrier effect of the thermal insulation coating to provide the theoretical basis and technical support for the optimization of fire protection technology of the converter transformer.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## Appendix A The Structure of the Model
### Canopy Radiative Transfer  
We applied the two-stream approach to develop a transmission model that accounts for multiple scatterings and reflection in the finite and infinite canopy to better estimate radiant energy inside and outside the canopy [44]. It is assumed that radiation is scattered equally in an upward and downward direction and that the scattering direction is along the same path as the incoming radiation. This model considers incoming radiation to be either transmitted through the canopy, reflected by the canopy, or absorbed by the canopy.  
\[\tau =\frac{\tau^{\prime}(1-(\beta^{\prime})^{2})}{1-(\beta^{\prime})^{2} (\tau^{\prime})^{2}} \tag{111}\] \[\beta =\frac{\beta^{\prime}(1-(\tau^{\prime})^{2})}{1-((\beta^{\prime})^ {2})(\tau^{\prime})^{2}} \tag{112}\]  
where \(\tau\) and \(\beta\) are the corresponding transmission and reflection factors for a finite canopy, respectively, and \(\tau^{\prime}\) and \(\beta^{\prime}\) are the corresponding transmission and reflection factors for an infinitely deep canopy, respectively.  
\[\tau_{d}^{\prime} =(1-\sqrt{1-\alpha}\times G\times LAI)e^{-\sqrt{1-\alpha}\times G \times LAI}\] \[\quad+(\sqrt{1-\alpha}\times G\times LAI)^{2}\] \[\quad\times E_{i}(1,\sqrt{1-\alpha}\times G\times LAI) \tag{113}\] \[\tau_{b}^{\prime} =e^{-\sqrt{1-\alpha}\kappa_{b}\times LAI}\] (114) \[\beta^{\prime} =\frac{1-\sqrt{1-\alpha}}{1+\sqrt{1-\alpha}} \tag{115}\]  
where \(\alpha\) is the leaf-scattering coefficient (\(\alpha_{b}=0.1\), \(\alpha_{d}=0.65\)). Since the approach for diffuse radiation is just an integral of single-beam components over the hemisphere, the reflection factors for an infinitely deep canopy (\(\beta_{b}^{\prime}\) and \(\beta_{d}^{\prime}\)) are estimated using the same equation.  
\[G =\phi_{1}+\phi_{2}cos\theta \tag{116}\] \[\phi_{1} =0.5-0.633\chi-0.33\chi^{2}\] (117) \[\phi_{2} =0.877(1-2\phi_{1}) \tag{118}\]  
where \(G\) is the leaf orientation factor depending on the solar zenith angle [107] and is an empirical leaf angle distribution parameter ranging from \(-1\) to \(1\) (\(-1\) for vertical distributed leaves, \(0\) for a spherical leaf angle distribution with randomly distributed leaves, and \(1\) for horizontally distributed leaves).  
\[K_{b}=G/cos\theta \tag{119}\]  
where \(K_{b}\) is the extinction coefficient of black leaves.  
\[E_{i}(n,x)=\int_{1}^{\infty}\frac{e^{-xt}}{t^{n}}\,dt \tag{120}\]  
where \(E_{i}(n,x)\) is an exponential integral with n a non-negative integer [47].  
Since the exponential integral cannot be computed directly within the GPU, the exponential integral is solved by approximation [108; 109; 110] as follows:  
\[part1 =\frac{e^{-x}}{2}ln(1+\frac{2}{x}) \tag{121}\] \[part2 =e^{-x}ln(1+\frac{1}{x})\] (122) \[E_{i}(n,x) =\frac{part1+part2}{2} \tag{123}\]
## 3 Materials and Methods
### Performance Evaluation Metric  
Six evaluation measures were utilized to assess the prediction performance of the proposed classifiers based on the counts of True positive (TP): This means a person is classified as breast cancer; True Negative (TN): This means a healthy person is classified as healthy; False Positive (FP): This means a healthy person is classified as breast cancer; False Negative (FN): This means a breast cancer person is classified as being healthy.  
1. Classification Accuracy: demonstrates the performance of the classification system. \[\text{ACC}=\frac{\text{TN}+\text{TP}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}*100\%\] (2)
2. Recall: The ratio of accurately predicted positive observations to all observed positive observations in the actual class. \[\text{SEN}=\frac{\text{TP}}{\text{TP}+\text{FN}}*100\%\] (3)
3. Specificity shows that a prediction is negative and the individual is healthy. \[\text{SPE}=\frac{\text{TN}}{\text{TN}+\text{FP}}*100\%\] (4)
4. F1-Score: This is the harmonic mean of precision and recall. \[\text{F1}-\text{score}=2*\frac{\text{precision}*\text{recall}}{\text{ precision}+\text{recall}}*100\%\] (5)
5. Precision refers to the accuracy with which the model makes a correct diagnosis. \[\text{PRE}=\frac{\text{TP}}{\text{TP}+\text{FP}}*100\%\] (6)
6. Receiver Operator Characteristics (ROC) is a curve that draws the TPs against the FPs at various threshold values for evaluation of binary classification results.
7. The precision-recall (PR) curve is used to assess binary classification algorithms' performance. PR curves, like ROC curves, provide a graphical depiction of a classifier's performance by calculating and plotting precision against recall for a single classifier over a range of thresholds rather than a single number.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 2 System Model and Selective PNC
### Fading Models  
In the proposed network, non-identical and independent fading channels are assumed. The satellite source--mobile terminal link is modeled as a Shadowed-Rician fading channel with the following PDF  
\[f_{h}(x)=2 xae^{-\mu x^{2}}{}_{1}F_{1}(m;1;\delta x^{2}),\ x>0, \tag{8}\]  
where \(\alpha=0.5(2 bm/(2 bm+\Omega))^{m}/b\), \(\mu=(0.5/b)\), \(\delta=0.5\Omega/(2b^{2}m+b\Omega)\), the parameter \(\Omega\) is the average power of LOS component, \(2b\) is the average power of the multipath component, and \(0\leq m\leq\infty\) is the Nakagami parameter, for \(m=0\) and \(m=\infty\), the envelope of \(h\) follows the Rayleigh and Rician distribution, respectively; and \({}_{1}F_{1}(a;b;z)\) is the confluent hypergeometric function.  
The channel of the mobile terminal--destination user link is assumed to follow the Rayleigh distribution \(g\sim\mathcal{CN}(0,\Omega)\), where \(\Omega\) is the average power.
## 6 Conclusions  
The example presented in this article shows that the GPC algorithm applied to MISO objects may be unstable due to numerical problems caused by the high order model. It may be helpful to solve numerical problems of high order models by using an order reduction technique. However, this solution has limitations and may not be effective with models that have a large number of input signals.  
In addition to order reduction, this problem can be solved by using longer sampling times. A longer sampling time improves numerical stability, but this approach also has limitations for processes that exhibit fast dynamics. Another solution is to identify the model as a MISO model with a predefined model order instead of identifying the SISO models and changing the model to a common denominator model.  
A completely different approach could be to use an estimator to calculate the change in output regardless of each input. This would eliminate the need to reduce the internal model to a common denominator and avoid high order models. Such research goes beyond the framework of this article and may be further investigated.  
The problem described for the GPC controller is not applicable to other predictive control structures such as Dynamic Matrix Control (DMC). This is due to the structure of the model, which in the case of the DMC controller is the step response coefficients. Based on our industrial experience of applying GPC, we have found that if the order of the internal model, after the reduction operation, exceeds 9 th order, the resulting GPC controller will be subject to numerical issues. In cases where this occurs, we have found that the best course of action is to utilize another control structure such as DMC.  
An alternative interesting direction would be to investigate the model predictive controller algorithm, in which the internal model is described by a state space model (MPCs). Checking and comparing the sensitivity of GPC and MPCs algorithms could be an interesting continuation of the problem shown.  
Conceptualization, S.P. and R.W.K.; Validation, R.W.K.; Formal analysis, S.P.; Investigation, S.P.; Writing--original draft preparation, S.P.; Writing--review and editing, R.W.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external founding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript  
Figure 15: GPC with reduced internal model closed loop simulation of 300 time-steps, trend for MVs signals (u\({}_{1}\), u\({}_{2}\) and u\({}_{3}\)). Controller parameters: _control horizon_\(N_{u}=20\), _prediction horizon_\(N=400\), and the \(R\) matrix = _identity matrix_.
## 3 Simulation Model  
### Simulation Arrangements  
With reference to [PERSON]'s near-field underwater explosion SHG experiment [23], the simulation arrangements are shown in Figure 2. The draft depth of the structure was 0.08 m. The TNT explosive was located in the water directly below the center of the structure. To avoid the influence of Euler boundary reflection on bubble results, the Euler domain should be large enough. Referring to the simulation settings of similar cases in reference [23], the flow field size was modeled as 5 m \(\times\) 5 m \(\times\) 2.5 m (length, width, height, sequentially), while the water field was 1.9 m high and the air field was 0.6 m high. According to experience, the mesh within three times the bubble radius should be densified. Considering the size of the bubble radius, SHG structure, and SHG moving range, the mesh in the 1.7 m \(\times\) 1.2 m \(\times\) 2.5 m (length, width, height, sequentially) area around the explosive was the main focus. After the mesh sensitivity inspection, a model containing 2.9 million elements (the minimum mesh size is 0.01 m) was selected within the allowable range of computational power. The comparison results of the different meshes are shown in Figure 3. The hexahedral Euler elements were used for the flow field. S4R elements were used for structures. The Euler boundary setting was the same as the actual situation, with surrounding walls to prevent the medium from flowing out.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\rho_{0}\)/(kg/m\({}^{3}\)) & \(A\) (GPa) & \(B\) (GPa) & \(R_{1}\) & \(R_{2}\) & \(\omega\) & \(E_{m}\) (J/kg) \\ \hline
1630 & 371.2 & 3.21 & 4.15 & 0.95 & 0.3 & \(4.29\times 10^{6}\) \\ \hline \end{tabular}
\end{table}
Table 1: TNT explosive parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Yield Stress/Pa** & **Plastic Strain** & **Strain Rate /(I/S)** \\ \hline
2.35 \(\times\) 10\({}^{8}\) & 0 & 0 \\
3.75 \(\times\) 10\({}^{8}\) & 0.3 & 0 \\
5.17 \(\times\) 10\({}^{8}\) & 0 & 100 \\
8.25 \(\times\) 10\({}^{8}\) & 0.3 & 100 \\
8.51 \(\times\) 10\({}^{8}\) & 0 & 5000 \\
1.358 \(\times\) 1
## 3 Deep Learning-Based Methods
### Deep Learning-Based Classifiers
#### 3.2.1 Gated Recurrent Unit (GRU)  
A gated recurrent unit (GRU) is a recurrent neural network (RNN) structure. This development helps RNNs to learn long-string dependencies more effectively. GRU networks have two gates: a reset gate (r) that adjusts the incorporation of new input with the previous memory and an update gate (z) that controls the preservation of the previous memory. Figure 1 shows the transition functions in the hidden units of GRUs.  
The update gate assists the model in determining the previous information amount (from previous time steps). Equation (1) is employed for the current update gate.  
\[Z_{t}^{j}=\sigma(W_{z}X_{t}+U_{z}h_{t-1})^{j} \tag{1}\]  
When Xt is plugged into the network unit, its weight W(z) is multiplied. Likewise, it can be applied to h\({}_{t-1}\) with the information for the previous t - 1 units, and its weight U(z) is multiplied. A sigmoid activation function squashes the outcome between 0 and 1. Figure 2 demonstrates the function's behavior.  
Figure 1: LSTM and GRU recurrent neural networks [23].  
Figure 2: The overall framework of the model.  
The reset gate ensures that the model decides how much of the previous information to forget. Equation (2) is utilized to update the rest of the gates.  
\[r_{t}^{j}=\sigma(W_{r}X_{t}+U_{r}h_{t-1})^{j} \tag{2}\]  
This equation is similar to the equation of the update gate. The difference arises from the weights and the use of the gate, which will be demonstrated in the subsequent parts to reveal how the gates will impact the final output precisely. First, the reset gate is used. fit denotes novel memory content, utilizing the reset gate to store the related information from the previous time. It is calculated in the equation below:  
\[\hat{\Pi}_{t}^{j}=\tanh(WX_{t}+r_{t}\odot Uh_{t-1}) \tag{3}\]  
Ultimately, the network must compute the ht vector with information for the current unit. The update gate finds what must be collected from the current memory content \(\hat{\text{ht}}\) and the previous steps \(\text{ht}-1\), with the final memory at the current time step.  
\[h_{t}^{j}=\tanh\left(z_{t}\odot h_{t-1}+(1-z_{t})-\hat{\Pi}_{t}\right) \tag{4}\]  
Figures 1 and 3 show the GRU and LSTM memory block architectures.