esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 4 Simulation and Analysis
### Monte Carlo Simulation Verification  
To prove the effectiveness and reliability of the reconstruction decision-making algorithm for satellite swarms with different degrees of faults, this section uses the Monte Carlo method. The steps are as follows:  
1. Set the number of faulty satellites randomly distributed in the range [1; 30], i.e. \(N_{F}\in[1,30]\).
2. Randomly select \(N_{F}\) satellites in a satellite swarm with \(N\) satellites and generate fault type \(\mathbf{S_{F}}\).
3. Set the health state indicator of the objective function in Section 3.2 and the rest of the indicators as constraints, solving the optimal reconfiguration strategy.
4. Calculate the optimal health state and average health state of the satellite swarm corresponding to the reconstruction strategy.
5. Repeat the above steps 300 times.  
After collecting health state data 300 times, the health state data are sorted according to the severity of the fault from low to high, producing the simulation results in Figure 8. The ratio of faulty satellites to the total number of satellites (\(N_{F}/N\)) is calculated, and the corresponding Monte Carlo optimal health state value is selected, yielding the simulation results in Figure 9.  
Figure 8: Satellite swarm health state curve after Monte Carlo simulation.  
Figure 9: Satellite swarm health as a function of failure level.  
From the simulation results in Figure 7:  
1. For a satellite swarm with 61 satellites, when the number of faulty satellites is in the range [1; 30], the reconstruction decision algorithm restores the satellite swarm health state to 0.95-0.4. The optimal health is very close to the average health, and the difference is within \(10^{-3}\), which shows that the algorithm has high precision.
2. The health state curve is a typical ponytail curve, and nearly 1/3 of the data show that the health state value of the restored satellite swarm is below 0.7; when the health state is less than 0.7, the health state begins to decrease rapidly and fluctuate.  
Figure 8 is obtained from Figure 7 by using the fault proportion as the abscissa, which makes the Monte Carlo simulation results more intuitive. It is obvious that the higher the proportion of faulty satellites, the lower the health of the satellite swarm and the more severe the health state jitter, i.e., the health state after fault reconstruction is very uncertain. At the same time, when the health state is 0.7, the proportion of faulty stars is 38%. That is, for the mission satellite swarm when the number of faulty satellites exceeds 38%, the health state of the entire swarm becomes unstable. Therefore, it is possible to obtain the fault threshold of the satellite swarm, which can be used to manage it.
###### Abstract  
A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.  
ant colony optimization; critical obstacle influence factor; fuzzy control; global path planning; mobile service robot +
Footnote †: journal: Applied Sciences  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control
## 4 Methods for Calculating the Dynamic Response of Crankshaft System Substructures
### Verification of Dynamic Response Calculation Results  
This paper introduces a dynamic relaxation factor, integrating it with an energy gradient-based Newmark adaptive integration algorithm to calculate dynamic responses. To verify the accuracy of the established substructure dynamic model, a six-cylinder diesel engine at 1200 RPM is used as a test case. Under the same boundary and loading conditions, the dynamic programme based on the substructure method is compared with the coupled vibration response from the ABAQUS finite element model. The programme's computation time is reduced by 75% compared to ABAQUS. The calculation results are shown in Figures 5-7, with the following findings:  
1. The results from both methods align well regarding the main frequencies and amplitudes, indicating that the programme is relatively reliable.
2. The substructure dynamic calculation programme provides richer frequency components with a more detailed description of high-frequency harmonics.
3. The programme's results show noticeable interference in the torsional response for certain time periods, smoothing one peak. This phenomenon arises because the substructure model includes components beyond the observed direction, resulting from torsional, bending, and longitudinal vibration coupling.
4. Due to the proximity of the bearing installation positions to the flywheel end, the bending vibration amplitudes in all directions at the flywheel end are relatively small compared to the torsional vibration amplitude. This is because the nearby bearing support provides greater structural constraint, effectively limiting bending  
response, and the lateral rotational angle of the flywheel, which represents the bending vibration response.
## 3 EMS Selection and Description
### EMSs for FCEVs: Brief State of the Art
#### 3.1.3 Learning-Based Strategies  
Learning-based strategies leverage machine learning and artificial intelligence to adapt EMS to changing conditions and to improve performance over time.  
Reinforcement Learning (RL)  
RL algorithms have the ability to learn optimal control policies through trial and error, receiving feedback from the environment [17]. RL can adapt to dynamic conditions, but it requires extensive training and exploration.  
This approach uses historical data to train models that predict optimal control actions. While it is less adaptive than RL, supervised learning can provide robust performance with lower computational requirements [18].  
Neural networks can model complex relationships between inputs and control actions, enabling advanced EMS capabilities. They are very versatile and may also be combined with other optimization or rule-based strategies [16].  
Learning-based strategies offer high adaptability and the potential for continuous improvement. They can handle complex, nonlinear systems and adapt to changing conditions over time. However, these methods require large datasets for training and have high computational requirements for both training and execution. Despite these challenges, learning-based strategies represent the future of EMS for FCEVs, offering the potential for significant improvements in efficiency and performance [16].  
The previous resume has highlighted the importance of choosing the right EMS for FCEVs based on the specific application and available resources. Future research should focus on hybrid approaches that combine the strengths of various strategies to develop robust and efficient EMSs for FCEVs.
# Realizing Efficient Security and Privacy in IoT Networks
## 4 System Model
### Formulated Optimization Problem  
In this subsection, our objective is to boost the privacy capacity of the scheme using power and rate constraints with cooperative power distribution between \(T_{x}\) (source) and \(R_{x}\) (destination). As compared with the conventional physical layer security techniques, the proposed SCEO and swift privacy rate optimization algorithms are suitable for the Internet of Things, because the optimization algorithms are energy efficient; therefore, the low-energy consumption necessities of IoT is enormously satisfied. Specifically, in a wireless channel transmission scheme, intrinsic noise is deployed which degrades the quality of the eavesdropper's received signal; thus, privacy in transmission is guaranteed with no cost of supplementary power. In summary, the application of the proposed technique in IoT technologies is low power capable as it does not necessitated the use of additional energy to guarantee privacy in transmission. The authors in [38] attempted to solve this low power problem through the bisection approach by swapping the source and destination powers optimizations, iteratively. However, in this  
Considering the KKT conditions as  
\[\left\{\begin{array}{l}\forall m\in\psi_{2}\left\{\begin{array}{l}\frac{ \partial l}{\partial y_{m}}=-\varphi_{m}(y_{m},z_{m})-\frac{1}{M}\frac{A_{m}}{1+ B_{m}z_{m}A_{m}y_{m}}-\mu_{y}(m)+v_{y}=0\\ \frac{\partial l}{\partial z_{m}}=-\varphi_{m}(y_{m},z_{m})-\frac{1}{M}\left( \frac{B_{m}}{1+B_{m}z_{m}A_{m}y_{m}}-\frac{B_{m}}{1+B_{m}z_{m}}\right)-\mu_{z} (m)+v_{z}=0\\ y_{m}\geq 0,\mu_{y}(m)0,y_{m}y_{m}y_{m}(m)=0,\forall m\in\aleph_{z}\\ z_{m}\geq 0,\mu_{z}(m)0,z_{m}\mu_{z}(m)=0,\forall m\in\aleph_{z}\\ v_{y}\geq 0,\sum_{m}y_{m}\leq\mathbf{p}_{s},v_{y}\left(\sum_{m}y_{m}-\mathbf{p}_ {s}\right)=0\\ v_{z}\geq 0,\sum_{\in\psi y}y_{m}\leq\mathbf{p}_{d},v_{z}\left(\sum  
Finding solution for the KKT conditions which contains these two nonlinear equations appears to be unnerving; therefore, we consider applying the approach of sequential convex approximation.
## 7 Discussion and Future Work
### Android Devices and Scenario Impact  
Analysis of a different scenario showed a clear impact on the measurements and positioning quality. S2 was expected to give the largest errors, given the challenging nature of the dense urban canyoning. Yet, the scenario that registered the highest errors is S3. While it also contained challenging sections, the highest error values could be due to the longer survey time, which increases the probability of erroneous measurements in the dataset. Despite the increase in measurement noises, the positioning results stayed consistent for all devices throughout all scenarios and regardless of the device platform (i.e., smartphone/smartwatch). This supports our hypothesis of using smartwatches for research applications in positioning, and possibly other topics (e.g., health applications).  
Devices have shown clear positioning abilities to maintain a suitable positioning solution throughout the scenarios, yet with challenges for some devices (see Section 7.4). These discrepancies can partially be explained due to the environment and possible unaccounted multipath, yet would require a deeper analysis to better understand their exact origin. While we focus mainly on the outdoors for this study due to GNSS positioning, the additional indoor scenarios would be interesting to review in a future analysis. More specifically, the transition from outdoor to indoor and/or light indoor environment (e.g.,glass ceiling) would be beneficial to assess the sensitivity of the antenna/receiver embedded in smart devices.  
Similarly, future research should provide deeper attention to the carry mode effects on the device measurements. While we only performed acquisition texting and pocket mode, new modes like swinging should be considered, especially for the smartwatch review.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 3 Feature-Level Camera Style Transfer
### Adaptive Batch Normalization  
In order to make camera style transfer while preserving identity information, this paper proposes a novel Adaptive Batch Normalization (ABN) layer. When trying to add camera style information into feature generation, the authors first tried to simply add a one-hot camera label to the original identity feature. However, they found that the one-hot camera label is merely a label, and it does not contain enough camera style information for the network to learn from. Then, the authors tried to use the concatenation of identity feature and camera feature, but the result is still not ideal. The authors believe the reason is that the network sees a feature as a whole, the identity part and the camera style part are not clearly separated, leading to semantic confusion. Inspired by Adaptive Instance Normalization [29], in which the style code can be injected through linear affine parts in image style transformation, the authors proposed an Adaptive Batch Normalization (ABN) layer that can effectively embed camera information into an identity feature and accomplish camera style transfer in a feature level. ABN layers are applied to replace the corresponding normalization layers in generator \(G\) by a similar yet more concise way:  
\[ABN(x,y)=m_{1}(y)\frac{x-\mu(x)}{\sigma(x)}+m_{2}(y), \tag{8}\]where \(\mu(\cdot)\) and \(\sigma(\cdot)\) denote the mean calculation and the standard deviation calculation, \(x\) represents features that normally pass through the layer, and \(y\) represents style features that are mapped into the appropriate dimensions through mapping functions \(m_{1}(\cdot)\) and \(m_{2}(\cdot)\). Note that the normalization layers are placed into the feature level, so the related statistics are calculated only along the batch dimension. The ablation study shows the effectiveness of the proposed ABN layer, and the detailed experimental results are shown in Section 5.6.2.
## 3 Formalization of Dima Optimization
### Aircraft Constraints
#### 3.2.3 Connectivity Constraints  
Connectivity of virtual links allows the communication between functions attached on devices. Such a connectivity has its own constraint in that the inflow of a device should be equal to the outflow of the device. As such, we need to model the relationship between the inflow and the outflow of each device. Notice that there can be multiple destinations for the outflow. We define a set of auxiliary variables \(\mathbf{W}=\{W_{ij}^{st}\}\) to model the paths between any two functions, where \(W_{ij}^{st}=1\) if the following conditions are satisfied:  
* \(D_{i}\) and \(D_{j}\) are connected with respect to \(\mathcal{V}(F_{s})\);
* \(F_{s}\) and \(F_{t}\) are mapped to \(D_{i}\) and \(D_{j}\), respectively;
* \(F_{t}\) is one of the destinations of the virtual link \(\mathcal{V}(F_{s})\),that is, \(F_{t}\in\mathcal{V}(F_{s})\).  
To connect a source function \(F_{s}\) and a destination function \(F_{t}\), we have the following constraints for any pair of functions. First, the outflow of device \(D_{i}\) should sum to 1 if \(F_{s}\) is mapped to \(D_{i}\), and 0 otherwise, that is,  
\[\sum_{D_{i}\in\mathcal{D}}W_{ij}^{st}=\mathbb{I}^{c}(F_{s},D_{i}),\quad\forall D _{i}\in\mathcal{D},\,F_{s}\in\mathcal{F},\,F_{t}\in\mathcal{L}(F_{s}). \tag{3}\]  
Second, the inflow of device \(D_{j}\) should sum to 1 if \(F_{t}\) is mapped to \(D_{j}\), and 0 otherwise, that is,  
\[\sum_{D_{i}\in\mathcal{D}}W_{ij}^{st}=\mathbb{I}^{c}(F_{t},D_{j}),\quad\forall D _{j}\in\mathcal{D},\,F_{s}\in\mathcal{F},\,F_{t}\in\mathcal{L}(F_{s}). \tag{4}\]  
In addition, for intermediate node \(k\), the inflow and the outflow of the mapped devices should be equal, that is,  
\[\sum_{D_{i}\in\mathcal{D}}W_{ijk}^{st}=\sum_{D_{j}\in\mathcal{D}}W_{k,t^{\prime }}^{st}, \tag{5}\]  
Notice that the connectivity of the virtual links is established using auxiliary variables \(\mathbf{W}\) instead of \(\mathbf{V}\). Hence, additional constraints are needed to enforce the relationship between \(\mathbf{W}\) and \(\mathbf{V}\). Intuitively, \(\mathbf{W}\) can be regarded as a \"transformation\" of \(\mathbf{V}\) onto a single pair of functions. For a pair of devices \(D_{i}\) and \(D_{j}\), if \(W_{ij}^{st}=1\) for any \(F_{s}\) and \(F_{t}\), the corresponding \(V_{ij}^{s}\) should be set to 1. In other words, \(V_{ij}^{s}\) equals to the disjunction of \(W_{ij}^{st}\) for given \(D_{i}\) and \(D_{j}\), that is,  
\[V_{i,j}^{s}=\bigcup_{t}W_{i,j}^{st}. \tag{6}\]  
Standard ILP techniques could represent these disjunctive conditions by a set of linear constraints. As our focus of the paper is DIMA optimization, we will pass the tedious details.
## 3 Model Evaluation and Implementation
### Training and Implementation  
To optimize the model's performance, the parameters for each ML technique were calibrated based on values from previous studies--neural network parameters were adapted from [15] and those for random forest and SVR were adapted from [21]. The best result obtained from each weak model was then used in the hybrid model, in which the meta-learner (another neural network) was calibrated. Table 2 shows the evaluated hyperparameter values for each model.  
The key hyperparameters for SVR include the following: Gamma (\(\gamma\)), which defines the influence of a single training example; Epsilon (\(\varepsilon\)), which specifies the margin of tolerance where no penalty is given to errors; C, a regularization parameter that controls the trade-off between training error and model complexity; and the Kernel, which determines the function used to map data into higher-dimensional space (e.g., linear, polynomial, radial basis function). These parameters were tuned to optimize performance on each dataset.  
Early stopping with a patience of 10 epochs was applied during training, allowing the process to stop if no improvement was observed after 10 consecutive epochs. Each epoch corresponds to one complete pass through the training dataset, with a default batch size of 32 samples per iteration. Note that the length of each dataset may vary, which affects the exact duration of an epoch. This helps prevent overfitting. In the case of the random forest, different numbers of trees were tried to achieve a balance between accuracy and computational efficiency. Finally, the SVR parameters were adjusted by exploring different values for gamma, epsilon, C, and various kernel types to adapt the model to the complexities of other datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{Support Vector Regression} & Gamma & 0.1, 0.2, 0.3, 0.4, 0.5 \\ \cline{2-3}  & Epsilon & 0.1, 0.2, 0.3, 0.4, 0.5 \\ \cline{2-3}  & C & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 \\ \cline{2-3}  & Kernel & Polynomial Degree 1, Polynomial Degree 2, Polynomial Degree 3, Linear, Sigmoid, RBF \\ \hline \multirow{6}{*}{Neural Network} & Epochs & 250 \\ \cline{2-3}  & Hidden Layers & 1 \\ \cline{1-1} \cline{2-3}  & Hidden Layer Neurons & 5, 10, 15, 20 \\ \cline{1-1} \cline{2-3}  & Regularization & Early Stopping \\ \hline Random Forest & Trees & 10, 20, 30, 40, 50, 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluated hyperparameters for each model.
## 4 Experimental Results of the Proposed Systems
### Augmentation and Balance of Data  
The lack of medical images is one of the limitations of CNN models. This limitation was addressed by a data augmentation technique. Additionally, the unbalanced dataset is one of the challenges, because the accuracy tends towards the majority class [49]. Therefore, this challenge was overcome by applying data augmentation for the minority classes more than for the majority classes [50]. It is noted that the first dataset is almost balanced, and so the limitation of data shortage is addressed by increasing the images by an equal amount for both classes. For the second dataset, it is noted that the dataset needs to be balanced, and we increased the number of X-ray images for the minority classes. The data were artificially augmented from the same dataset by many operations, such as shifting, flipping, rotating in several directions, and others. Table 2 shows the number of artificially increased X-ray images. For the first dataset, the number of images was increased by nine times for each image for both classes. For the second dataset, the number of images was increased by four times for tuberculosis, while the number of images in the normal class was not increased, and the image number remained the same. Thus, the lack of images and the imbalance of the dataset were overcome.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 4 Approaches
### Model-Driven Approaches  
Numerous early works attempted to tackle human engagement with rule-based methods. As a classic rule-based method, PERCLOS denotes the percentage of duration of eye closure. A 50% bar is commonly used to determine driver fatigue [36]. A low blink frequency can be a strong indicator of drowsiness as well. For instance, fewer than ten blinks per minute were regarded as tiredness in the experiments of [PERSON] et al. [129]. Head pose and body posture are commonly used cues. [PERSON] and [PERSON] [17] utilized a 30\({}^{\circ}\) nodding threshold in a short interval to decide driver drowsiness. They found a 94% accuracy from in-house experiments, which will likely degrade on unseen experiment settings and individuals. [PERSON] et al. [130] tested and contrasted a rule-based engagement estimator with machine learning models in a robot bartender scenario. Specifically, two rules were applied to distinguish human engagement level: (1) the human head was less than 30 cm from the bar, and (2) the human torso was facing the right side with a rotation angle less than 10\({}^{\circ}\). The authors noted that a rule-based classifier, despite being simple, showed equally competitive performance, a 98% engagement detection rate, in comparison to learning-based methods such as Support Vector Regression (SVR) and Naive Bayes. Nonetheless, all algorithms saw less than 68% accuracy and 0.69 F1-score with novel test data confirming their limitation.  
An apparent shortcoming of rule-based engagement estimation lies in a lack of robustness in generalization. The performance of rule-based systems degrades significantly when tested online and in the wild [130]. In multi-party interactions with strong social and spatio-temporal dynamics, the pre-defined structure and prior knowledge of the modelling is easily compromised. It is tough for models to discern how many human agents are potentially engaging and to keep track of them, not to mention predicting their engagement accurately.  
Rule-based methods not only fall short in adapting to different tasks but find it difficult to accommodate differences of individual users. Different tasks and environments require distinguishable levels and modes of engagement with varying criticality. For example, drivers are allowed substantial nodding and head turning to look at side mirrors whilst reversing, whereas this may indicate disengagement in soldering electronics. What is more, how humans perceive, receive, and express engagement differs significantly in demographics [1]. Researchers have noticed the effects of many individual factors such as age, gender, body type, proficiency in tasks, etc., on engagement modelling. These are important matters in engagement estimation but easily overlooked in a model-driven approach or essentially too difficult to model.