esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Data Augmentation  
To compensate for the relatively small number of available cases and to improve the robustness of the classification models, data augmentation was performed through geometric transformations of the original imaging data. Specifically, each MRI volume was rotated in three-dimensional space along the axial plane by six angles: \(-30^{\circ}\), \(-20^{\circ}\), \(-10^{\circ}\), \(+10^{\circ}\), \(+20^{\circ}\), and \(+30^{\circ}\). These transformations aimed to simulate variations in patient positioning while preserving the anatomical structure of the tumour.  
Radiomic features were extracted from each augmented volume using the same PyRadiomics pipeline applied to the original data. The features from the rotated images were then integrated into the training set, effectively increasing the sample size and variability. This augmentation strategy enabled the models to learn more robust and rotation-invariant patterns and helped reduce overfitting, particularly in the presence of class imbalance.
## 2 Results and Discussion
### Expression of Pc-FPS in Pichia pastoris (P. pastoris)  
A 1086-bp _fps_ was integrated in frame with the _S. cerevisiae_\(\alpha\)-factor secretion signal sequence under the control of the AOX1 promoter in pPICZ\(\alpha\)A to obtain the expression vector pPICZ\(\alpha\)A-_fps_. Plasmid pPICZ\(\alpha\)A-_fps_ was transformed into _P. pastoris_ X33. To select the FPS producing clones, we randomly picked 20 positive colonies and inoculated them into 500 mL shaking flasks containing 50 mL Buffered Glycerol-complex Medium (BMGY). SDS-PAGE was used to observe the expression of the recombinant protein (Figure 5). SDS-PAGE was carried out on a 12% running gel and stained with Coomassieie Blue. As shown in Figure 5, the molecular weight of rPc-FPS was about 42 kDa, which was same as the calculated molecular weight. After purification by Ni\({}^{2+}\)-chelating chromatography, the purified rPc-FPS showed a single band on SDS-PAGE with molecular weight of 42 kDa (Figure 5).
## 3 Damage Detection Case Studies
### Simulated Simulved Supply Supported Beam 1 (Case 1)  
The simulated beam has a rectangular cross-section of 0.25 \(\times\) 0.20 m and a 6-m length (Figure 1a). It was equally divided into 10 two-dimensional beam elements. The values for other material properties corresponding to reinforced cement concrete were taken as 2500 kg/m\({}^{3}\) for density and 32 GPa for elastic modulus. The constitutive laws assumed for the beam were linear perfectly elastic. Case 1 with the single damage scenario is considered (Figure 1b). A number of approaches can be used to model cracks in beams. In this research, the cracks were simulated by reducing the stiffness of a particular element of the structure locally [46]. This approach can approximate the stiffness changes for beams with open cracks under low frequency vibrations, as compared to breathing cracks, which open and close and produce non-linear dynamics [47]. In the beam, only the sixth element of the beam was considered to be damaged. A 20% reduction in the moment of inertia (MOI) has been assumed as compared to the reference model for damage detection at different noise levels. This leads to a value of 0.8 for the ratio \(EI_{d}/EI_{r}\) in Equation (12) and a corresponding damage index of 0.2. Only the first three lower natural frequencies and mode shapes have been used in the current research. The mode shapes were expanded and then mass normalized using Equation (13). No modeling or mesh errors were assumed in the simulated study (Case 1 and Case 2) as the simulations were aimed at observing the performance of different objective functions on damage assessment.  
The general background theory of GA can be found elsewhere [48]. GA implemented in MATLAB was used in this research. GA was used to minimize the Objective Functions I, II and III with noise. Eight noise cases have been considered: noise both in frequencies and mode shapes at 1%, 3%, 5% and 10% and noise only in mode shapes at 1%, 3%, 5% and 10%. A population of 100, maximum iterationsof 500, tournament selection with tournament size of 4, two-point cross over with 70% probability, mutation with a probability of 1% and a threshold for minimum change in five consecutive generations was set as 10\({}^{-10}\).The upper bound for all the parameters was set at 1.15-times the initial model, and the lower bound was set as 0.25-times the initial model parameter values as it can be assumed that the frequencies decreased due to damage in the elements.
## 2 Materials and Methods
### Sample Preparation and Push-Out Test  
From the apical side of each retreated tooth, one 2 mm thick dentin disk was taken perpendicularly to the long axis of the root, with a diamond disk of 0.5 mm thickness on a water-cooled cutting device (Microtome, Mecatom T180; Presi SA, Angonnes, France) rotating at a low speed (250-300 rpm). The section's thickness was measured with a digital caliper. Within the limitations of the precision cutting device, it was adjusted so that the desired dimensions could be achieved with minimal loss. Every single dentin disk was assigned a number to avoid confusion among the groups. The specimens were tested for bond strength using the Shimadzu universal testing machine (Autograph AGS X; Shimadzu Co., Kyoto, Japan) [21]. In the experimental groups, each specimen was placed in contact with a 0.8 mm diameter stainless steel cylindrical tip through the cement, which was prepared to perform the push-out test only. The pushing force was performed at a rate of 1 mm/min, coronally to apically, until bond failure occurred. The forces were registered in Newtons (N). Coupling strength values in MPa were calculated with the formula below.  
\[\text{Bond strength}(\text{MPa})=\frac{Force\ (N)}{Surface\ Area=2\pi rh},\]  
(bonding surface area of the cement mm\({}^{2}\)), (\(\pi\) = 3.14, r = radius of the inner channel cavity, h = height in mm).
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 5 Performance Optimization of the PPWJ Device
### Optimization Process
#### 5.1.3 Constraint Condition  
The hydraulic pump is rated at 15 MPa, and the oil inlet pressure must not exceed 14 MPa due to pressure loss from the hydraulic oil during flow. To minimize wear on dynamic seals, the piston movement speed should be kept below 1 m/s. Considering manufacturing challenges, the length to diameter ratio of the cylinder's inner hole should be less than 6.  
\[p_{\text{in}}\leq 14\,\text{MPa} \tag{24}\]  
\[\left|\frac{\text{d}x_{p}}{\text{d}t}\right|\leq 1\,\text{m/s} \tag{25}\]  
\[\frac{L}{d_{\text{p2}}}\leq 6 \tag{26}\]  
where \(L\) is the inner hole length of the cylinder.
## 4 Optimal Frame Generation Time Threshold Calculation
### Overall Structure
#### 4.1.2 Action Space  
After each time slot, the agent makes decisions based on the environment state and the reward function. The action space in the current time slot is \(A=[a_{1},a_{2},\cdots,a_{t}]\), where the action \(a_{t}\) represents the frame generation threshold for the RTFG algorithm. The decision on the threshold value determines when the real-time data packets in the queue will be framed. The action impacts the environment and interacts with the environment based on the chosen threshold. The range of actions is \(a_{t}\in[0,T_{m}]\), where \(T_{m}\) is the maximum allowed average queue delay for real-time business. In time slot \(t\), after the algorithm's decision, a fixed frame generation threshold \(T_{th}\) is set, and a fixed frame length \(L_{th}\) is also established. According to the rules of the real-time frame generation algorithm, the data packets in the real-time data service queue are combined into a data frame and sent only when at least one of these two conditions is met. Obviously, the threshold value of one round will affect the decision of the next round.  
In continuous action space, the standard deviation \(\sigma\) of the action decays gradually to a minimum value \(\sigma_{\text{min}}\). This parameter controls the randomness of action selection. The update rule for the standard deviation is as shown in Equation (13):  
\[\sigma_{t+1}=\max(\sigma_{t}-\text{Y},\sigma_{\text{min}}) \tag{13}\]  
where \(\sigma_{t}\) is the standard deviation of the action after the \(t\) time interval update, \(\text{Y}=\alpha t\) is the decay rate, \(\alpha\) is the proportional coefficient of the decay rate, and \(\sigma_{\text{min}}\) is the minimum standard deviation of the action. The constraint condition for the standard deviation of the action is  
\[\sigma_{t}\geq\sigma_{\text{min}} \tag{14}\]  
This ensures that the standard deviation of the action will not be lower than the set minimum value, thus ensuring continuous exploration of the agent.  
The definition of the action space, including the mapping between the selected threshold and frame generation behavior, is tailored to reflect the control objectives of our system model.
## 7 Challenges, Opportunities, and Future Trends
### Future Trends in Energy Harvesting and Security  
#### 7.3.1 Emerging Technologies  
The emerging technologies in EH-WSNs are as follows:  
* Advanced Energy-Harvesting Materials: Research into novel materials, such as nanomaterials and meta-materials, has the potential to improve the efficiency and scope of energy-harvesting technology [125; 126]. These compounds can enhance the performance of solar cells, thermoelectric generators, and other energy-harvesting systems.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Challenge** & **Description** \\ \hline Energy Efficiency [116] & \begin{tabular}{c} Manage limited energy resources \\ while ensuring reliable data transmission \\ and network functionality \\ \end{tabular} \\ \hline Network Scalability [117] & \begin{tabular}{c} Addressing issues related to performance \\ and lifetime as the network grows in size \\ \end{tabular} \\ \hline Data Security [118] & \begin{tabular}{c} Ensuring secure data transmission \\ and protection against unauthorized access \\ \end{tabular} \\ \hline Dynamic Topologies [119] & \
\begin{tabular}{c} Handling changes in node availability \\ and network structure \\ \end{tabular} \\ \hline Integration with & Ensuring compatibility and interoperability \\ Existing Technologies [119] &
\begin{tabular}{c} with existing systems \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key challenges in EH-WSNs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Opportunity** & **Description** \\ \hline Advanced Energy & Innovations in harvesting technologies \\ Harvesting Technologies [120] & \begin{tabular}{c} for better energy capture \\ \end{tabular} \\ \hline Smart Energy & Systems that dynamically manage energy \\ Management Systems [121] & \begin{tabular}{c} based on availability and network needs \\ \end{tabular} \\ \hline Enhanced Routing & \begin{tabular}{c} Development of routing protocols that \\ optimize data transmission and network \\ performance \\ \end{tabular} \\ \hline Integration of AI and & \
\begin{tabular}{c} Use of AI and ML for adaptive energy \\ management and routing \\ \end{tabular} \\ \hline Improved Security &
\begin{tabular}{c} Enhancing security protocols to \\ protect data and resist attacks \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: The opportunities for improvement and innovation.  
* Integration of IoT and EH-WSNs: Innovation in EH is anticipated to be fueled by the expanding Internet of Things (IoT) [12; 127]. Improved network efficiency and innovative energy management techniques are expected outcomes of integration with IoT devices.
* Smart Grids and EH-WSNs: The combination of EH-WSNs and smart grid technologies can improve energy distribution and utilization [128; 129]. Smart grids will provide EH-WSNs more control over energy supplies, allowing them to adjust to fluctuating energy availability.
# Hard and Soft EM in Bayesian Network Learning from Incomplete Data
## 2 Methods
### Missing Data  
A data set \(\mathcal{D}\) comprising samples from the random variables in \(\mathbf{X}\) is called _complete_ when the values of all \(X_{i}\) are known, that is, observed, for all samples. On the other hand, if \(\mathcal{D}\) is _incomplete_, some samples will be completely observed while others will contain missing values for some \(X_{i}\). The patterns with which data are missing are called the _missing data mechanism_. Modelling these mechanisms is crucial because the properties of missing data depend on their nature, and in particular, on whether the fact that values are missing is related to the underlying values of the variables in the data set. The literature groups missing data mechanisms in three classes [4]:  
* _Missing completely at random_ (MCAR): missingness does not depend on the values of the data, missing or observed.
* _Missing at random_ (MAR): missingness depends on the variables in \(\mathbf{X}\) only through the observed values in the data.
* _Missing not at random_ (MNAR): the missingness depends on both the observed and the missing values in the data.  
MAR is a sufficient condition for likelihood and Bayesian inference to be the valid without modelling the missing data mechanism explicitly; hence, MCAR and MAR are said to be _ignorable patterns_ of missingness.  
In the context of BNs, we can represent the missing data mechanism for each \(X_{i}\) with a binary latent variable \(Z_{i}\) taking value 0 for an observation if \(X_{i}\) is missing and 1 otherwise. \(Z_{i}\) is included in the BN as an additional parent of \(X_{i}\), so that the local distribution of \(X_{i}\) in (1) can be written as  
\[X_{i}\,|\,Pa(X_{i}),Z_{i}\sim\left\{\begin{array}{ll}\mathrm{P}^{(O)}\left( X_{i}\,|\,Pa(X_{i}),\Theta_{X_{i}}^{(O)}\right)&\text{for }Z_{i}=1\\ \\ \mathrm{P}^{(M)}\left(X_{i}\,|\,Pa(X_{i}),\Theta_{X_{i}}^{(M)}\right)&\text{ for }Z_{i}=0\end{array}\right..\]  
In the case of MCAR, \(Z_{i}\) will be independent from all the variables in \(\mathbf{X}\) except \(X_{i}\); in the case of MAR, \(Z_{i}\) can depend on \(X_{j}\,|\,Z_{j}=1\) (\(Z_{i}\perp\!\!\!\perp X_{j}\,|\,Z_{j}=1\)) but not on \(X_{j}\,|\,Z_{j}=0\) (\(Z_{i}\
ot\perp\!\!\!\perp X_{j}\,|\,Z_{j}=0\)) for all \(j\
eq i\). In both cases, the data missingness is ignorable, meaning that  
\[\mathrm{P}^{(O)}\left(X_{i}\,|\,Pa(X_{i}),\Theta_{X_{i}}^{(O)}\right)=\mathrm{ P}^{(M)}\left(X_{i}\,|\,Pa(X_{i}),\Theta_{X_{i}}^{(M)}\right)=\mathrm{P}\left(X_{i} \,|\,Pa(X_{i}),\Theta_{X_{i}}\right).\]  
However, this is not the case for MNAR, where the missing data mechanism must be modelled explicitly for the model to be learned correctly and for any inference to be valid. As a result,different approaches to handle missing data will be effective depending on which missing data mechanism we assume for the data.
## 2 Methodology
### Change Point Detection  
Change point detection is crucial in detecting the early signs of deterioration to prevent industrial equipment from unexpected disruptions [54].  
The PwL fitting method [55] is used for change point detection in generating the target labels. It fits PwL functions to predict nonlinear or multiple trends in the data. By optimizing segment positions and associated slopes, PwL fitting provides a broad perspective and capacity to model changes in data patterns. This makes it particularly valuable in applications such as change point detection and trend analysis.  
The Python library pwlf [55] performs a least squares fit, which solves for the \(\beta\) parameters that minimize the sum-of-squares error of the residuals for any given set of breakpoint locations \(b\) if the change points are known. The sum of the squares of the residuals can be expressed as a function dependent on the change point locations \(SSR(b)\), and the optimization problem is as follows:  
\[\begin{array}{l}\text{\emph{minimize}}\hskip 5.690551 ptSSR(b),\hskip 5.690551 ptb= \left[b_{2},\hskip 2.845276 pt\ldots,\hskip 2.845276 ptb_{n_{b}-1}\right]^{T}\\ \text{\emph{subject to}}\hskip 5.690551 ptx_{1}\leq b_{k}\leq x_{n},\hskip 5.690551 ptk=1,\hskip 2.845276 pt2,\hskip 2.845276 pt\ldots,\hskip 2.845276 ptn_{b}\end{array}\]  
where \(x\) is the 1D data, \(n_{b}\) is the number of change points, and \(b_{n_{b}}\) is the last breakpoint. \(n_{b}-1\) is the number of line segments. The library assumes that \(b_{1}\) is the first value and \(b_{n_{b}}\) is the last value of the one-dimensional dataset. Differential-evolution-based global optimization is used to find the best change point locations when the change points are unknown but the desired number of segments is known.  
Although PwL fitting is not a change point detection algorithm per se, it has the same principle as the PwL target labeling approach. It is helpful in the early detection of the first change point with the practical usage of a line segment parameter. The change point detection method based on pwlf offers the advantage of the early detection of breakpoints through its line segment parameter. It also possesses the potential to address the challenges related to the HS division problem of engine degradation processes.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 1 Introduction
### The Need for Topological Accuracy
Recently, the application of persistence homology (PH) in learning topology-aware studies has gained attention [32; 33; 34]. [PERSON] et al. (2019) proposed TopoGAN, the pioneering GAN model that explicitly learns the topology of real images, such as connectedness and loops, by introducing a novel topological loss function based on PH. Their approach showed improvements in preserving the topological integrity of generated images [32]. In another study, [PERSON] et al. (2023) developed PHGAN that enhances image generation by incorporating topological features from PH into a GAN. These topological features capture global structural information, which is combined with convolutional features in the discriminator. The topological features extracted by a PH module transform into vectors that are fed into the network [35]. Although PH is a significant tool in topological data analysis (TDA) for addressing inaccuracies in GANs, its integration presents challenges. PH-based methods focus primarily on features such as connected components and loops, often overlooking other crucial aspects such as terminal nodes or disconnections in the generated masks. An example is a bicycle wheel with a missing spoke compared to one with a dangling spoke. In terms of TDA and PH, these configurations are treated as topologically identical because they present the same connected components and loops. However, from our perspective, the presence of a dangling spoke (i.e., a terminal node) is a critical difference that indicates an issue embedded in the structure. This limitation makes the application of PH in GANs less appropriate for certain cases where disconnections are considered important. In addition, the computational demands of PH can be substantial, especially with large datasets, potentially reaching \(O(n^{3})\) in complexity [32; 33; 34].
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 3 Proposed Methodology
### Training the VGG16 Model  
The Oxford University Visual Geometry Group created the convolutional neural network architecture known as VGG16. It contained 16 layers, including numerous convolutional layers and layers that maximized pooling. After that, the output of these convolutional and pooling layers was flattened to a one-dimensional vector and sent through a number of dense layers that were fully connected. Figure 10 represents the layered [30] architecture of VGG16 model. The VGG16 model was used as a feature extractor in the implementation, with just the convolutional layers being employed, and the output of the last convolutional layer was flattened and passed into a dense layer with four units of output. The last thick layer had four neurons, one for each of the four classes in the classification task. The VGG16 model was trained on a dataset for 20 epochs throughout the training phase. The performance of the VGG16 model was notable with the provided dataset, demonstrating high accuracy across both the training and validation sets. However, the accuracy did not improve significantly after epoch 18, indicating that further training may not have yielded significant improvements. The saved model weights could be used for further evaluation of test data or for deployment in a real-world application.  
Figure 11 shows the plot of loss vs. epochs for the validation and training dataset of the VGG16 model. Figure 12 shows the plot of accuracy vs. epochs for the Validation and Training Dataset of the VGG16 model. The results of the 20 epochs of training indicated a progressive increase in validation accuracy, rising from 0.9561 to 0.9737 during the final five epochs. The training accuracy of the VGG16 model illustrated its usefulness in boosting classification performance. This methodology was particularly effective for obtaining improved accuracy in the image classification task. The VGG16 model attained a training accuracy of 96%. This implies that the training process was successful in boosting the model's performance for the classification job.  
Figure 10: Layer architecture of VGG16 model.
# Adaptive Robust Fuzzy Impedance Control of an Electro-Hydraulic Actuator
## 1 Introduction
The two control schemes could be synthesized to improve the control performance of hydraulic systems. Therefore, an adaptive robust fuzzy impedance control (ARFIC) strategy was developed in our research that combines ARC and fuzzy impedance control. ARFIC can not only compensate the parameter uncertainties and uncertain nonlinearities, but also adjust the dynamic behavior between velocity and force. Fuzzy impedance control determines the velocity command. If the contact force lies in the regular range, the velocity command does not change, and high-accuracy velocity tracking performance can be realized. If the contact force exceeds the regular range, the contact force is regulated to an acceptable range by adjusting the velocity command, and an expected dynamic behavior between velocity and force is maintained. This article is organized as follow. In Section 2, a detailed nonlinear mathematic model of the electro-hydraulic actuator is derived. In Section 3, a fuzzy impedance algorithm is described to regulate output force and velocity. In Section 4, a nonlinear velocity controller based on adaptive robust control and backstepping is designed. In Section 5, the overall control system stability is analyzed. In Section 6, experiments are described to validate the effectiveness of the proposed control strategy. Finally, conclusions are drawn in Section 7.