esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Available Studies
#### 3.3.2 Chile  
An artificial neural network was employed by [PERSON] et al. [20] for the Chile region, to predict the earthquake, which obtained 79.7% accuracy. The Genetic Programming and Adaboost (GP-AdaBoost) methods were used [11] for the mentioned region, for seismic classification, attaining 84.4% accuracy. Table 9 illustrates that the proposed model outperformed, compared to the models presented in the literature. For the Chile region, the proposed model achieved the highest accuracy (98.28%), precision (98.69), and recall (98.68).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{**Training on Hindukush**} \\ \hline  & \multicolumn{6}{c}{**Testing on Chile**} & \multicolumn{6}{c}{**Testing on California**} \\ \hline
**Approach** & **DNN** & **RF** & **SVM** & **LR** & **DNN** & **RF** & **SVM** & **LR** \\ \hline Accuracy & 85.75 & 89.4 & 73.4 & 93.7 & 86.35 & 95.8 & 84.6 & 98.1 \\ Precision & 88.1 & 57.35 & 89.05 & 92.3 & 88.24 & 51.24 & 91.21 & 92.3 \\ Recall & 85.77 & 58.58 & 74.48 & 74.62 & 86.35 & 51.19 & 68.44 & 74.62 \\ F1-Score & 83.89 & 49.09 & 73.86 & 78.83 & 81.36 & 25.93 & 73.86 & 78.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training on Hindukush, and testing on California and Chile.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{4}{c}{**Region: Hindukush**} \\ \hline
**Authors** & **Accuracy** & **Precision** & **Recall** \\ \hline [PERSON] et al. [11] & 78.7 & 74.3 & 89.2 \\ [PERSON] et al. [9] & 65 & 61 & 36 \\ [PERSON] et al. [14] & 79 & 69 & 86 \\ Proposed Model & 95.13 & 93.15 & 85.56 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of extant studies to proposed model for Hindukush region.
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 2 Synthetic Strategies of Mn-Zn Ferrites  
There are different preparation techniques for the synthesis of ferrite nanoparticles that exhibit novel properties which are dependent on their structural characteristics, like particle size and shape. Various methods, described below, such as co-precipitation, thermal decomposition, hydrothermal methods and sol-gel, are used to prepare spinel manganese-zinc ferrite nanocrystals.
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 2 Finite Element Analysis of the I-Shaped RC Beam under the Blast Load
### Structural Model  
The experimental beam has an I-shaped cross-section. The height, width, and span of the beam are 300 mm, 300 mm, and 1500 mm, respectively. The thickness of the top plate, vertical ribs, and bottom plate are all 50 mm. Considering possible flexural damage, 10 longitudinal reinforcements with a diameter of 12 mm are arranged on the top and bottom plates, and the reinforcement ratio is 2.50%. At the same time, transverse reinforcement ratios of 0.75% and 1.41% are arranged in the mid-span and supporting point to strengthen the shear resistance under the impact loadings. During the experiment, laminated rubber bearings were placed under the bottom plate of the I-shaped RC beam to provide simple support for the structure. The explosive uses the hexogen (RDX) trimmed into a cube to detonate in contact with the mid-span position of the top plate. The explosives in this experiment are mixed by RDX and adhesives, of which 86% is RDX. In the finite element numerical model, solid elements are used for concrete and supports, and beam elements are used for steel bars. Figure 1 shows the experimental and numerical model. According to the literature [24], for concrete materials with a compressive strength between 20-58 Mpa, the recommended mesh size is 8-32 mm. Considering that the concrete of about 50 MPa is selected for the experiment, after preliminary calculations, different grid sizes mainly affect the calculation time. In order to ensure calculation efficiency and accuracy, the grid size selected for the numerical model is 10 mm, the number of nodes in the model is 79,809, and the number of units is 62,092.
## 3 Types of Supplementary Dampers and Their Characteristics
### Tuned Mass Dampers  
#### 3.5.1 Operating Principle  
Tuned mass dampers (TMDs) are passive control devices that reduce structural vibrations by adding an auxiliary mass-spring-damper system. Figure 12 depicts typical configurations of TMDs.  
Tuned mass dampers (TMDs) are passive devices widely implemented in the field of structural engineering to mitigate vibrations caused by dynamic forces, including seismic events. These systems consist of a mass, a spring, and a damper, all of which are carefully calibrated to resonate at a specific frequency that generally corresponds to the fundamental mode of the structure [98,99]. When subjected to excitation, the TMD moves in opposition to the main structure, effectively absorbing and dissipating energy. This mechanism leads to a reduction in the overall structural response, resulting in lower displacements, accelerations, and internal forces. TMDs have been successfully utilized in various structures, from skyscrapers to bridges, demonstrating their versatility in enhancing seismic performance. Nevertheless, their efficacy relies on accurate tuning and a comprehensive understanding of the dynamic properties of the structure [100,101].  
Unlike traditional tuned mass dampers (TMDs), which are designed for optimal performance in a singular mode, recent developments have led to the emergence of multi-tuned mass dampers (MTMDs) and adaptive tuned mass dampers (ATMDs) [102, 103, 104, 105, 106]. MTMDs consist of multiple TMDs, each possessing unique dynamic characteristics, facilitating concurrently managing various structural modes [107,108]. This approach significantly improves the system's resilience and effectiveness across a broader range of excitatory  
Figure 12: (**a**) Multi-degree-of-freedom system with TMDI at the anti-node position of the first bending mode. (**b**) Conventional TMDI with target structural mode. (**c**) Classical TMD with target structural mode. (**d**) TMDI with serial arrangement of stiffness, inerter, and viscous damper with target structural mode. (**e**) Two half TMDs with target structural mode [97].  
forces [109; 110; 111]. In contrast, atmds incorporate active control systems that permit real-time modifications to the TMD's properties, allowing for adaptation to changing dynamic conditions or alterations in structural characteristics [112; 113; 114]. These innovations have expanded the applicability of TMDs, making them more adept at managing the complex dynamic responses of structures exposed to seismic activity and enhancing their performance during unpredictable earthquake scenarios [115; 116; 117; 118; 119].
## 3 Simulation
### Wheel Irregularities  
#### 3.3.1 Characterization  
New wheels in railway vehicles are not completely perfect and smooth, as they present irregularities along their surface. Polygonal wheels can be characterized by various geometric parameters, including the wavelength (\(\Lambda\)), which varies based on the harmonic order (\(\theta\)) and the wheel radius (\(R_{w}\)). This relationship is defined by the following function:  
\[\Lambda=\frac{2\pi R_{w}}{\theta},\theta=1,2,3\ldots,n \tag{1}\]  
The wheel irregularity level spectrum \(Lw\) (in dB re 1 \(\upmu\)m) is defined by:  
\[L_{w}=20\ \text{log}_{10}\left(\frac{\hat{\mathbf{w}}}{w_{ref}}\right) \tag{2}\]  
where \(\hat{\mathbf{w}}\) is the root mean square value of the irregularity profile \(w(x_{w})\), evaluated for wavelengths corresponding to each OOR order, and \(w_{ref}=1\ \upmu\)m.  
To characterize the geometry of this type of imperfection, the average experimental measurements of four new wheels by [PERSON] et al. [5] are considered. The same procedure was adopted to characterize polygonal wheel defects, where the irregularity level spectrum \(Lw\) associated with each of the harmonics is based on experimental measurements [56; 57; 58]. Figure 4 depicts the level of irregularity related to each dominant harmonics. To cover a wide spectrum of harmonics, profiles with dominant harmonics between 6-8, measured by [PERSON] et al. [56]; between 12-14, measured by [PERSON] et al. [57]; and between 17-18, measured by [PERSON] et al. [58], are considered. Additionally, the Figure also includes the wheel profile based on the experimental measurements of [PERSON] et al. [5] nominated new wheel in this study.  
The polygonal wheel profile results from the superposition of all waves of harmonics and levels of irregularity associated with each harmonic. Then, different polygonal wheel profiles are generated based on the sum of sine functions (\(H\) = 30) as follows:  
\[w(x_{w})=\sum_{\theta=1}^{H}A_{\theta}\text{sin}\left(\frac{2\pi}{\Lambda}x_{ w}+\psi_{\theta}\right) \tag{3}\]  
where \(x_{w}\) is the distance along wheel circumference and \(A_{\theta}\) is the amplitude of the sine function for each \(\Lambda\), which is computed using the following function:  
\[A_{\theta}=\sqrt{2}\cdot 10^{\frac{Lw}{20}}\cdot w_{ref} \tag{4}\]  
Figure 4: Wheel irregularity amplitude spectra (\(Lw\)) and harmonic order (\(\theta\)) for new and polygonal wheels.  
whereas \(w_{ref}=1\)\(\upmu\)m. The wheel irregularity level (\(Lw\)) values are selected based on the irregularity spectrums of Figure 4, for all scenarios: new wheels or polygonal wheels.  
Considering different phase angles (\(\psi_{\theta}\)) in the sinusoidal functions uniformly and randomly distributed in the range from 0 to \(2\pi\), five cases are generated for each wheel defect amplitude based on each polygonal wheel irregularity spectrum.
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 4 Experiment
### Discussion  
Comprehensive empirical evaluations demonstrate that the proposed methodology has yielded efficacious outcomes in style transfer for a preponderance of facial images, particularly in addressing intricate details within facial highlights and mitigating image edge artifacts. Nevertheless, our experimental investigations have revealed certain limitations in specific scenarios, primarily encompassing the following aspects:  
While our approach exhibits robust performance in processing images of Caucasian and Asian subjects, it demonstrates a propensity for detail attenuation when confronted with images of individuals possessing darker skin tones. This phenomenon can be primarily attributed to inherent biases in training data, as numerous facial style transfer models are predominantly trained on datasets comprising Caucasian or Asian subjects, resulting in suboptimal recognition and processing of facial features characteristic of darker-skinned individuals. Furthermore, the complexities involved in processing diverse skin tones are exacerbated by the more nuanced tonal and textural variations present in darker skin, thereby intensifying the challenges associated with the accurate capture and transformation of these subtle details.  
Our method achieves commendable results in style transfer for adult facial images; however, its performance is suboptimal when applied to children's facial images. This discrepancy can be attributed to several factors: firstly, the significant morphological differences between children's and adults' facial features, such as more rounded face shapes, proportionally larger eyes, and distinct forehead-to-face ratios, which may lead to inadequate performance of models trained exclusively on adult data. Secondly, the inherently smoother texture of children's skin, lacking the fine lines and textural complexities present in adult faces, may result in either loss or excessive addition of texture during processing. Lastly, the style transfer process may inadvertently introduce age-inappropriate features, effectively \"aging\" children's faces and diminishing age-specific characteristics.  
Additionally, our current methodology exhibits limitations in terms of personalization and user control. The existing framework autonomously learns and selects styles corresponding to facial images through the model, without providing users the opportunity to actively participate in style selection during the transformation process. This constraint may not adequately address the diverse preferences and requirements of all users.  
These identified limitations provide valuable insights for future research directions and potential enhancements to our proposed method.
## 3 Description of System Performance Indicators
### Performance Metrics for Complete Systems
#### 3.2.1 System Reliability  
In wireless sensor networks, system reliability is viewed as the probability that, even under the worst channel conditions, the packet transmission time would not exceed the timeout threshold. The expression is as follows:  
\[\mathscr{R}_{k}^{(v)}=\Pr\Bigl{\{}T_{\max}^{(v)}<t_{\text{out}}\;\Bigr{\}}, \tag{14}\]  
where \(T_{\max}^{(v)}=\max\limits_{k\in\{1,2,\dots,K\}}\Bigl{\{}T_{k}^{(v)}\Bigr{\}}\).
## 2 Basic Principles
### Improved Whale Optimization Algorithm  
The Whale Optimization Algorithm (WOA) is a population-based intelligent optimization algorithm with a simple structure, few adjustable parameters, and strong global optimization capabilities. However, its random selection of initial positions and use of constant weights lead to slow convergence and a tendency to become trapped in local optima. Research indicates that the weight is a critical parameter in WOA. Using constant weights reduces WOA's efficiency: large weights hinder local exploitation, while small weights are detrimental to global optimization. To address this, this paper proposes an adaptive weight strategy to ensure appropriate nonlinear weights during iterations. The weight \(w\) decreases as the number of iterations increases, facilitating global search in the early stages and local optimization in the later stages. The significant decrease in \(w\) enhances local optimization, improving convergence accuracy and speed. The adaptive weight is defined as follows:  
Figure 3: Whale spiral position update.  
\[w=w_{\min}+(w_{\max}-w_{\min})\cdot r\cdot e\bigg{(}-\frac{t}{Max_{\text{iter}}} \bigg{)} \tag{17}\]  
where \(w_{\min}\) is the minimum weight, \(w_{\max}\) is the maximum weight, \(r\) is a random number within [0, 1], \(t\) is the current iteration, and \(Max_{\text{iter}}\) is the maximum number of iterations. The position update formulas are as follows:  
Encircling Prey:  
\[X(t-1)=w\cdot X^{*}(t)-A\cdot D \tag{18}\]  
Spiral Update:  
\[X(t+1)=D\cdot e^{bl}\cdot\cos(2\pi l)+w\cdot X^{*}(t) \tag{19}\]  
Search for Prey:  
\[X(t+1)=w\cdot X_{\text{rand}}(t)-A\cdot D \tag{20}\]  
The improved WOA, referred to as IWOA, is implemented as shown in Algorithm 1 (pseudocode).  
```
Input: \(Max_{\text{iter}}\) is the maximum iteration, \(m\) is the number of whales; Output: \(w\), \(b\) function IWOA (\(Max_{\text{iter}}\), \(m\)) \(Max_{\text{iter}}\)\(\prec\)30, \(m\)\(\leftarrow\)30 \(w_{\min}\)\(\leftarrow\)0.1, \(w_{\max}\)\(\leftarrow\)0.55 for \(t=1\)\(\rightarrow\)\(Max_{\text{iter}}\) do /* Calculate initial position \(D^{*}\)/ a\(\leftarrow\)2 - t \(\times\) (2/\(Max_{\text{iter}}\)) for \(i=1\)\(\rightarrow\)size (\(D\), 1)do \(r1\), \(r2\)\(\leftarrow\)rand() \(A\)\(\leftarrow\)2 \(\times\)\(a\)\(\times\)\(r1\) - \(a\)\(C\)\(\leftarrow\)2 \(\times\)\(r2\) /*Calculate weight using Equation (17) */ if \(p<0.5\) then if \(|A|<1\) /* Encircling prey using Equation (18) */ else / * Search for prey using Equation (20) */ end if else / *Spiral position update using Equation (19) */ end if end for end for return \(w\), \(b\) end function
```  
**Algorithm 1**IWOA
## 5 Results and Discussion
### Discussion  
Based on the experimental results above, the following advantages of GPRL can be stated: Compared with traditional entity relationship extraction models, it does not require a significant amount of manual involvement in feature extraction, thus saving valuable time and resources. In the comparative experiments, GPRL outperformed the traditional model NovelTagging. Additionally, compared with the common pointer annotation entity relationship extraction model, GPRL can identify overlapping triples and nested entities with a global view and performs entity relationship extraction accurately. In the nested data experiment, GPRL outperformed the pointer-based model CasRel in terms of results. The model is specifically designed for Chinese text, maximizing contextual learning and inference within Chinese language data, and presents advantages such as superior efficiency, cost-effectiveness, and better results compared with other baseline models.  
However, GPRL faces challenges and limitations that require further improvement. It may have difficulty in extracting specialized domain knowledge, particularly in domains with complex and unique entity relations, which require more meticulous handling and  
Figure 3: Parameter change curve. (**a**) The threshold for determining relation filtering; (**b**) the threshold for adding after relation filtering to the original model.  
learning. The main reasons are as follows: GPRL's performance may suffer when dealing with niche or specialized domains due to a lack of sufficient training data in those areas. Complex entity relationships that are ambiguous or context-dependent can pose challenges for GPRL in accurately capturing and understanding the underlying connections. Additionally, the model may encounter limitations in handling long-distance dependency relationships in lengthy texts or complex contexts, necessitating enhanced mechanisms for capturing global contextual information. The model's performance may also be impacted by data scarcity and annotation quality, requiring enhanced robustness and generalization for low-frequency relations or noisy data.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Sensor Fusion
#### 3.6.2 Particle Filters  
The basic assumption of the Kalman filters is that the noise experienced by the system is Gaussian [101]. However, in most cases, the noise experienced by the system will not have a Gaussian characteristic [119]. Particle filters (PF) have been proposed for processing non-Gaussian noise (see, e.g., [120] and the references therein). When applying PF there is always a trade-off between precision and computational resources [111; 121]. Therefore, an extended particle filter (EPF) [122] and an unscented particle filter (UPF) [111] have been proposed. Both EPF and UPF use the sigma points, making them computationally inefficient and difficult to implement in real time [122]. Recently, particle-aided unscented Kalman filter (PAUKF) has been proposed [101] which does not update the sigma points resulting in fewer computational resources. For vehicle localisation, the PAUKF algorithm has been shown to perform consistently better compared to existing methods including PF and UKF and yields an RMSE of approximately 1.5 m [101].
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 4 Preliminary Construction: TSBT-ORAM
### Storage Organization
#### 4.1.1 Server-Side Storage  
Two servers denoted as \(\mathcal{S}_{0}\) and \(\mathcal{S}_{1}\) are needed. Since the servers are almost identical to each other, we only describe how storage is organized in \(\mathcal{S}_{0}\) and call \(\mathcal{S}_{0}\) the \"server\". Later, we will show the differences between \(\mathcal{S}_{0}\) and \(\mathcal{S}_{1}\).  
The server storage is organized as a binary tree with \(L=\log N+1\) layers. Each node stores \(3c\log N\) blocks, where \(c\) is a system parameter related to the security parameter \(\lambda\). Within a node, each block has a unique position offset ranging from zero to \(3c\log N-1\).  
Recall that the total number of exported data blocks is assumed to be \(N\), but the capacity of the storage is larger than \(N\) blocks. Hence, dummy blocks, each a block of random bits, are introduced in order to fill up the storage. Hereafter, we call the aforementioned \(N\) blocks real blocks to differentiate them from the dummy ones. Before being stored in the server storage, each real data block is encrypted with a probabilistic symmetric encryption scheme; that is, each data block \(d_{i}\) is stored as \(D_{i}\gets E(d_{i})\), where \(E\) denotes the encryption function. To initialize the server storage, all the real blocks are randomly distributed to the leaf nodes while the dummy blocks are distributed to the rest of the storage space. Note that the blocks distributed to each node are randomly permuted before uploading, which hides the distribution of real blocks from the server.  
Each node also contains a symmetrically-encrypted index block that records the IDs of the data blocks stored at each position of the node; as the index block is also encrypted, the index information is not known to the server.  
Figure 1 shows an example, where \(N=32\) data blocks are exported and stored in a binary tree-based storage with six layers. Starting from the top layer, i.e., Layer 0, each node is denoted as \(v_{l,i}\), where \(l\) is the layer index and \(i\) is the node index on the layer.
## 1 Introduction
This approach reduces the external factors [18], such as for specialized sensors, for example the fusion of LiDAR and camera sensors [19], a driving model for the steering control of autonomous vehicles [20], a controller for robot navigation using a deep neural network [21], and autonomous driving decisions based on the deep reinforcement learning  
Figure 1: Conventional architecture for a path-planning generator. approach [22]. The aims of these developments are the minimization of the resources needed and the improved performance of their respective architectures. Likewise, one machine-learning solution is the generative adversarial network (GAN), composed of two kinds of networks, a generative and adversarial network. In this approach, both networks compete and generate new samples from a source of noise. Therefore, it is possible to relate a sample taken from one domain to another [23]. On the other hand, simulators, such as virtual reality, are capable of producing a model of reality. This technology consists of developing an immersive experience to explore a virtual world, which can bring us closer to the real world [24]. Subsequently, using an end-to-end approach, it is possible to reduce the computational time of the planner. Here, we propose three different solutions: a deep neural network [25], hierarchical clustering [26], and an auto-encoder [27], the aim of which is to map an input to a path generated by the conventional architecture. Once the interoperability between the authentic and the virtual environment is achieved, it is possible to reduce the features of the current system through the transfer-learning approach [28] to implement it in embedded devices such as the Jetson nano and mobile devices. As a consequence of the above, we made the following hypothesis. If a GAN allows connecting two domains, then it is possible to connect the real and virtual environments. Therefore, a dataset generated by a virtual simulator to estimate a path could be employed in an authentic environment, reducing the design time and costs. However, it is necessary to evaluate the performance of a problem that can be simulated and employed in the real world. This paper makes the following contributions. First, the interoperability coefficient determines the number of samples of an authentic environment on a 2D plane. Second, the performance of the system on a virtual and an authentic environment results in different path-planning solutions. Additionally, we built a dataset based on a virtual environment, using an end-to-end approach. Finally, the proposal was evaluated on embedded devices. The remainder of the manuscript is presented as follows. Section 2 reviews the background. The proposed work is described in Section 3. Likewise, the experiment and the analysis of the results are detailed in Section 4. Finally, conclusions and future work are presented in Section 5.