esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Takeover Experiment Results and Discussion
### Accident Rate of Scenario  
In this section, the accident rate that occurred during a total of 641 takeover situations in single and multimodality takeover alarm experiments is discussed according to each scenario. When an accident occurred within 3 s after the requested driver started manual control, it was assumed that the takeover process had failed (Figure 15a). Scenarios with a takeover failure rate of more than 5% included B3 (small dark obstacle on the forward driving road), A1/B2 (combined scenario: inability to drive autonomously due to road construction/worker crossing from the side), A5 (small wild animals dashing into the roadway), and A2 (unexpected pedestrian crossing at the bus stop). Scenarios with a high accident rate had a common point in that a small obstruction that was not conspicuous directly obstructed the path of the autonomous vehicle. The driver who received the warning had difficulty understanding the situation even though the same amount of time was available as in scenarios such as loss of lanes and construction sections that recorded a low accident rate. Therefore, the system must decide whether to execute the emergency behavior depending on both the time to disengagement and the type of cause. For high-risk cases, advancing the timing of the minimum risk maneuver (MRM) is necessary when the driver does not respond to the takeover request, and an emergency maneuver (EM) is required to avoid imminent collision risk.  
Among the 641 takeover cases, 37 (5.8%) accidents occurred, and in the case of multimodality notification, it took approximately 2 s to takeover. Therefore, there was some margin compared to the time the notification was provided (TTD < 5 s). Nevertheless, about 47.6% of drivers assessed that the notification time was insufficient, and they requested an additional time of about 1.67 s on average (Figure 15b).
## 3 Results and Discussions
### Photocatalytic Activity
#### 3.5.1 Degradation of Bentazon Herbicide
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pollutants**} & \% & **Radiation** & **K** & **R2** & **Y = a + bx** \\  & **Degradation** & **Source** & **(min\({}^{-1}\))** & **R2** & **Y = a + bx** \\ \hline BZ & 99\% & UV & 0.1110 & 0.9769 & \(-\)0.2805 + 0.0110x \\ BZ & 75\% & sunlight & 0.03491 & 0.9987 & \(-\)0.0100 + 0.3491x \\ NB & 98\% & UV & 0.0762 & 0.8859 & \(-\)0.5997 + 0.0762x \\ NB & 100\% & sunlight & 0.0355 & 0.8157 & \(-\)0.8987 + 0.0355x \\ BZ & 68\% & UV & 0.00679 & 0.9757 & \(-\)0.0531 + 0.00679x \\ (in BZ + NB mixture) & 60\% & Sunlight & 0.0059 & 0.9575 & \(-\)0.10543 + 0.0059x \\ NB & 78\% & UV & 0.0093 & 0.9669 & \(-\)0.1092 + 0.0093x \\ NB & 95\% & sunlight & 0.0209 & 0.9289 & \(-\)0.4136 + 0.0209x \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Photodegradation kinetic parameters for TiO\({}_{2}\) catalyst against NB and BZ.
## 2 System Model
### Multi-User Systems  
The last scenario studied in this paper is that of a single transmitter communicating with multiple recipients. Each recipient is only interested in a subset of the information being transmitted, but can overhear everything. Each receiver has its own data and feedback channel, with independent SNR and decoding process. When a receiver is unable to decode its desired information, it reports the failures to the transmitter and requests IR. The transmitter compiles the failure reports from all the receivers and uses the proposed algorithm to optimize the set of IR bits that should be broadcast in order to ensure that none of them suffers a probability of error above a pre-fixed value \(\gamma\). This optimization is formulated as a convex optimization problem, albeit with the number of variables increasing exponentially with the number of failures reported. In any case, if the number of failures is too large, it is usually better to re-transmit the whole bundle anyway.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 4 Results
### Patent Portfolio Positioning Analysis
In addition, International Business Machines Corporation, Intel Corporation, and Microsoft Technology Licensing have similar positions in the machine learning in photonics patent portfolio, and their patents are mainly related to electric digital data processing (G06F), the transmission of digital information (H04L), computer systems based on specific computational models (G06N), commercial data processing methods (G06Q), and coding (H03M). AT&T Intellectual Property is related to transmission (H04B), wireless communication networks (H04W), antennas (H01Q), and waveguides (H01P); it focuses on technologies related to wireless communication. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Main Group Member** & **Main Patent Application Field** \\ \hline \multirow{2}{*}{I} & International Business Machines Corporation, Intel Corporation, & G06F, H04L, G06N, G06Q, H03M \\  & Microsoft Technology Licensing & G06F, H04L, G06N, G06Q, H03M \\ \multirow{2}{*}{II} & AT\&T Intellectual Property & H04B, H04W, H01Q, H01P \\  & Align Technology Inc., California Institute of Technology, & G06T, G02B, A61B, G06K, G16H \\ \multirow{2}{*}{III} & HeartFlow, Inc., Magic Leap, Samsung Electronics Co., Ltd., & G06F, H04L, G06N, G06Q, H03M \\  & Leland Stanford Junior University & G06F, H04L, G06N, G06Q, H03M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main members of each group. Figure 3: Grouped patent portfolios of machine learning in photonics.
## 2 Mechanical System
### Ideal Impact Model  
Equation (3) describes the motion of the vibro-impacting mass \(M\). Since this paper aims to present a preliminary investigation into the effectiveness of the proposed protection system in reducing the seismic effects on a frame structure and does not delve into the details of the design of the protection system, an ideal model of the impacting mass was adopted. In the scientific literature, many refined models of impacting mass exist, such as the nonlinear elastic (Hertz) and viscoelastic (Tsuji, Kuwabara) models [33; 34; 35]. A comparative study was conducted in [36] to assess the effectiveness of a vibro-impacting nonlinear energy sink (VI-NES) in reducing seismic effects when it is directly connected to a frame structure story. The comparison involved different impact models, including the ideal one.  
The main differences between an ideal model and a refined model is the time required to perform an impact. In the ideal model, the impact occurs instantly, whereas the refined models propose different ways through which to evaluate the duration of an impact. Such an estimated duration, in material that is sufficiently stiff, is of a small length, and it is always significantly less than a second. For this reason, it was deemed that the ideal model provides satisfactory results for a preliminary study. The results discussed in [36] confirmed such an assumption.  
As represented in Figure 1, the mass \(M\) with velocity \(\dot{u}_{M}\) impacted the frame structure that possesses the mass \(m_{1}+m_{e}\) and velocity \(\dot{u}_{1}\). In the adopted ideal impact model, the post-impact velocities can be obtained as a function of the pre-impact velocities by solving the equations  
\[\dot{u}_{M}^{+}-\dot{u}_{1}^{+}=-r(\dot{u}_{M}^{-}-\dot{u}_{1}^{-}), \tag{5}\]  
\[(m_{1}+m_{e})\dot{u}_{1}^{+}+M\dot{u}_{M}^{+}=(m_{1}+m_{e})\dot{u}_{1}^{-}+M\dot {u}_{M}^{-}, \tag{6}\]  
where the superscript (\(-\)) denotes the pre-impact quantities, and the superscript (\(+\)) denotes the post-impact quantities. Equation (5) provides the post-impact relative velocity between the impacting mass \(M\) and the external structure \(\dot{u}_{M}^{+}-\dot{u}_{1}^{+}\) as a function of the corresponding pre-impact relative velocity through the restitution coefficient \(r\). It should be noted that, due to the rigid connection, the external structure has the same velocity as the first story of the main structure. Equation (6) accounts for the conservation of horizontal momentum before and after the impact.
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 2 Methodology
### Cae  
Autoencoder (AE) is an artificial neural network to realize data representation learning. At first, AE was studied to solve the \"encoder problem\" in representation learning. In 1985, [PERSON], [PERSON] and [PERSON] first tried the AE algorithm on the Boltzmann machine. Its effect of representation learning was discussed [18]. After the backpropagation (BP) was proposed, AE was studied as one of the implementations of BP [19]. Until 1987, AE was formally proposed in a study by [PERSON] [20]. In the research, an AE was constructed by MLP to reduce data noise. In addition, the research on data dimensionality reduction using MLP AE also received attention [21]. The schematic of AE is shown in Figure 1.  
AE can achieve self-supervised learning with its symmetrical model structure: encoder and decoder [22]. The encoder compresses the high-dimensional input \(x\) into a latent spatial representation \(h\) (Formula (1)), and then restores the low-dimensional representation to the high-dimensional output \(\hat{x}\) which is similar to the input (Formula (2)).  
\[h=f(W\cdot x+b) \tag{1}\]  
\[\hat{x}=f\big{(}W^{\prime}\cdot h+b^{\prime}\big{)} \tag{2}\]  
where \(f\) is the nonlinear activation function, \(W\) and \(b\) represents the weights and bias of the encoder and \(W^{\prime}\) and \(b^{\prime}\) represents the decoder. It is a learning algorithm of connectionist model based on the dimensionality reduction problem of neural networks.  
Due to the reason that the dimension of \(h\) is generally much smaller than \(x\), AE is widely used for dimensionality reduction. Moreover, the encoder could be used to provide efficient representation of inputs for other tasks.  
The traditional AE generally uses fully connected layers (FC). Hence, there are too many redundant parameters introduced in the model. In addition, spatial information of the input cannot be effectively learned. As a result, the learned representation is often not good enough for multi-dimensional data. By replacing the FC layer with the convolution layer, CAE can effectively solve the above problems. The principle of CAE is the same as AE. It could downsample the input to provide a smaller dimensional potential representation. The spatial information can be effectively learned through convolution, and the model parameters can be reduced at the same time.
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 4 Discussion
### Indirect Benefits of the Green Trombe Walls
#### 4.4.2 Sustainability Prospects  
Achieving the perfectly sustainable green Trombe wall, as described in the introduction (Section 1.3), currently presents several challenges that can be improved upon. Among them, we can cite:  
1. Initial Setup Costs: Establishing the system involves significant upfront expenses, including materials such as wood, bottles, and appliances (e.g., fans and water reservoirs). While these costs are unavoidable, they can be offset by the long-term benefits and durability of the system.
2. Operational Efficiency: * Ventilation systems require energy input but can be optimized through improved space design and effective passive cross-ventilation techniques. * Water pumping systems can be enhanced by incorporating gravity-fed designs with elevated reservoirs and overflow-compatible components, reducing energy consumption and increasing the reliability and robustness of the system.
3. Resource Recycling: The system generates minimal waste, and most components are reusable. Plants can be propagated from cuttings instead of seeds, and under optimal conditions, annual plants may sustain growth for multiple years with minimal intervention, limited to occasional pruning and cleaning to remove dust and debris.
4. Nutrient Supply: The primary challenge that is difficult to deal with lies in the nutrient delivery system. The Kratky hydroponic method, while efficient, depends on a continuous supply of synthetic chemical nutrients, which are environmentally  
Figure 14: Daily total energy gains and losses, by components, per average day of each season before (BY) and during the experiment (EY). The ventilation-induced energy losses are so small that they don’t appear visible at the scale of the figure.  
costly [91]. Bulk procurement and on-site preparation of these nutrients might slightly mitigate their environmental impact, but sourcing such a complex chemical mix locally is unfeasible. Although organic nutrient solutions have been explored [54], they present challenges such as higher susceptibility to disease, mold, and algae growth, making them less reliable for long-term use. Without significant advancements in sustainable plant nutrition, chemical nutrients remain the primary limitation to achieving true sustainability for this initiative.
* Alternative Cultivation Methods: Traditional systems like aquaponics or media-based cultivation offer potential alternatives, but require significant adaptation to function effectively within a Trombe wall environment. Current designs and practices are not yet capable of seamlessly integrating these methods.  
In summary, while the green Trombe wall concept shows promise as a sustainable and productive architectural feature, overcoming its reliance on synthetic nutrients and refining its design for efficiency and ecological integration are critical steps toward achieving its full potential.
## 4 Results of Vehicle-Bridge Coupling Analysis
### Impact Analysis of Damage Parameters
When the reduction coefficient increases from 0 to 0.5, the displacement growth rate of the midspan section is 335.86%, that of the one-fourth section is 316.9%, and that of the one-third section is 452.8%. It can be seen from these data that with the continuous damage to the bridge, the displacement of each section will continue to increase and that the growth trend is the fastest in the midspan. Figure 21: Stress of each section under different damage degrees. Figure 22: Peak displacement of each section under different damage degrees. As can be seen from Figure 23, the impact coefficient of the bridge also changes greatly, and the impact coefficient at the midspan increases the fastest. By comparing the change curves of the impact coefficients at the three cross-section locations, the approximate slope of each reduction coefficient curve is ordered as follows: k\({}_{0.65-0.5}\) > k\({}_{0.76-0.7}\) > k\({}_{0.7-0.65}\). When the reduction coefficient of stiffness reaches 0.65, the growth trend of the impact coefficient of each cross-section increases significantly compared to that before 0.65, and the three curves are almost linearly distributed. However, before the reduction coefficient reaches 0.65, the impact coefficient curves of each section fluctuate to a certain extent. Therefore, the impact coefficient obviously increases after the damage occurs. In summary, when the stiffness reduction coefficient is in the range of 0.65 to 0.6, the stress growth rate of each section does not change much. In other ranges, the stress growth rate of the different sections is different. According to the stress growth mode shown in the figure, the stress on the lower edge of the concrete in the midspan section will first reach the tensile strength and crack, followed by the one-fourth section and, finally, the one-third section. With the continuous damage to the bridge, the displacement and impact coefficient of each section will have an increasing trend, with the fastest growth in the midspan. Therefore, when these two indexes of the bridge structure increase significantly, it indicates that the structural stiffness is decaying, and certain reinforcement measures should be taken at this time.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction
#### 2.2.5 Modulation Spectrogram  
The modulation spectrogram is a frequency-frequency representation that helps us to study the temporal dynamics of spectral components. Features extracted from the modulation spectrum have been used widely in human speech analysis, as they provide increased robustness against environment noise; representative examples include speaker identification [63] and speech emotion recognition [64], to name a few. If we consider the spectrogram as \(X(t,f)\), the modulation spectrogram can be calculated as follows:  
\[X(f,f_{mod})=\mathcal{F}_{t}|X(t,f)|, \tag{8}\]  
where \(\mathcal{F}_{t}\) is a second Fourier transform computed across the time axis of the first time-frequency representation. The modulation spectrum is explored here as a potential tool for beehive context awareness.
## 2 Theoretical Framework and Methods
### Analysis Process
#### 2.3.3. Constructing the Uncertainty Measurement Matrix  
Substituting the observation value \(t_{ij}\) into the uncertainty measurement function \(\mu_{ijk}=\mu\left(t_{ij}\in C_{k}\right),\) we obtain the degree of uncertainty measurement \(\mu_{ijk}\) (a real number between \([0,1]\)) that \(t_{ij}\) belongs to the category \(C_{k}\), resulting in a single-index uncertainty  
Figure 3: Unscertained measure function.  
measurement vector \(\mu_{ij}=\left(\mu_{ij1},\mu_{ij2},\ldots,\mu_{ip}\right)\), which forms the single-index uncertainty measurement matrix \(\left(\mu_{ijk}\right)_{m\times p}\).  
Using measured information entropy to determine indicator weights  
\[v_{ij}=1+\frac{1}{\log p}\sum\limits_{k=1}^{p}\mu_{ijk}\log\mu_{ijk} \tag{20}\]  
\[w_{j}{}^{(i)}=\frac{v_{ij}}{\sum\limits_{j=1}^{m}v_{ij}} \tag{21}\]  
In the formula, \(0\leq w_{j}^{(i)}\leq 1\), \(\sum\limits_{j=1}^{m}w_{j}^{(i)}=1\), The weight vector obtained is as follows:  
\[w(i):w^{(i)}=\left(w_{1}{}^{(i)},w_{2}{}^{(i)},\ldots,w_{m}{}^{(i)}\right) \tag{22}\]  
Calculate the multi-indicator uncertain measurement vector and measurement matrix \(\left(\mu_{ik}\right)_{m\times p}\)  
\[\left(\mu_{ik}\right)_{m\times p}=\sum\limits_{j=1}^{m}w_{j}{}^{(i)}\mu_{ijk}= \sum\limits_{j=1}^{m}\begin{pmatrix}w_{1}\\ w_{2}\\ \ldots\\ w_{m}\end{pmatrix}^{T}\begin{pmatrix}\mu_{11}&\mu_{12}&\ldots&\mu_{1p}\\ \mu_{21}&\mu_{22}&\ldots&\mu_{2p}\\ \ldots&\ldots&\ldots&\ldots\\ \mu_{m1}&\mu_{m2}&\ldots&\mu_{mp}\end{pmatrix} \tag{23}\]  
In the above formula, the following is true:  
\(w_{j}{}^{(i)}\) is the weight of the \(j\)-th indicator \(y_{j}\) of \(x_{i}\). \(0\leq\mu_{ik}\leq 1\), \(i=1,2,\ldots n\); \(j=1,2,\ldots m\); \(k=1,2,\ldots p\).  
Layered upward aggregation results in a comprehensive measure of uncertainty.
# Implementation Aspects in Regularized Structural Equation Models
## 5 Simulation Study 2: Two-Dimensional Factor Model with Cross-Loadings
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & \multicolumn{4}{c}{\(p=1\)} & \multicolumn{4}{c}{\(p=0.5\)} \\ \cline{4-13}  & & & & & & & & \multicolumn{2}{c}{BIC} & \multicolumn{2}{c}{\(\lambda=0.025\)} & \multicolumn{2}{c}{BIC} & \multicolumn{2}{c}{\(\lambda=0.025\)} \\ \cline{4-13}
**Par** & \(N\) & \(\delta\) & **DIR** & **CD** & **DA** & **CD** &  
Figure 4: Simulation Study 2: Absolute bias and root mean square error (RMSE) of the factor correlation \(\phi_{12}\) as a function of the regularization parameter \(\lambda\) for a cross-loading of \(\delta=0.2\) for sample sizes \(N=500\) and \(N=1000\) and powers \(p=1\) and \(p=0.5\) of the penalty function. The RMSE of the estimate obtained by the optimal BIC and coordinate descent (BIC-CD) is displayed by the blue line. The location of the average optimal regularization parameter obtained by BIC-CD is displayed by the blue triangle. Table 5 displays the average number of regularized cross-loadings. It turned out that the choice of the threshold parameter \(\tau\) was less critical for \(p=0.5\) than for \(p=1\). Furthermore, using \(\tau=0.02\) in the DA approach resulted in a similar average number of regularized parameters to the exact approach (CD). Figure 5: Simulation Study 2: Absolute bias and root mean square error (RMSE) of the factor correlation \(\phi_{12}\) as a function of the regularization parameter \(\lambda\) for a cross-loading of \(\delta=0.4\) for sample sizes \(N=500\) and \(N=1000\) and powers \(p=1\) and \(p=0.5\) of the penalty function. The RMSE of the estimate obtained by the optimal BIC and coordinate descent (BIC-CD) is displayed by the blue line. The location of the average optimal regularization parameter obtained by BIC-CD is displayed by the blue triangle. Finally, Table 6 displays the coverage rates and bias of the estimated factor correlation and the factor loading of the first item. Coverage rates were satisfactory for the DIR approach as well as for the DA approach with fixed regularization parameters. There was a tendency of overcoverage for the power \(p=0.5\) for a small regularization parameter \(\lambda=0.025\).
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
## 4 Validating the Prediction Accuracy of RBF Neural Network  
From Equation (1), it is known that output voltage \(U\) of electric eddy current sensor is related to the clearance \(x\) and the angle \(\alpha\) of blade cutting magnetic induction line. Adopting the method of controlling variables, first fix the angle \(\alpha\) at which the blade cuts the magnetic inductance, change the distance \(x\) between the blade and the BTT sensor, and obtain the \(U-x\) curves at different angles \(\alpha\). Similarly, fixing a value of the clearance \(x\) and then measuring the output voltage \(U\) at different pinch angles \(\alpha\), we can obtain the \(U-\alpha\) curve at different clearances.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 5 Implementation
### VNF Initialization  
Each enclave consists of a virtual function responsible for packet processing and a local handler for modifying internal parameters and external output. Therefore, when the virtual function performs initialization, multiple elements of the virtual function running inside the enclave also need to be initialized. The steps include creating routes, elements and port connections, and the initialization of each element. (Internal initialization does not need the lexer to analyze the configuration process because the connections inside each VNF are fixed). In addition, the packets in the enclave also need to be processed by cells.