esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of Tracking Reliability
#### 3.3.2 Test Site 2: Open track  
This test site consists of an open track with a cable that is directly mounted on the rail, which leads to a very different scenario from the first test site. In our test period, we were not observing any lost tracks of moving trains on the open track. However, due to the cable installation method, we observed a higher amount of noise in the signal, which leads to a number of false positive detections of trains during the evaluation period. Of the 1071 total tracks, we found 12 to be artifacts, which leads to a total of 99% correct tracks. Generally, mounting the optical cable directly on the rail leads to more noise in the signal that is not caused by trains. This is not surprising since such a cable is exposed to natural elements, such as wind and rain. Furthermore, we observed that certain sections of the cable seem to be coupled, meaning that if a train enters such a section, all the cable segments in that section will vibrate. An illustration of this phenomenon, as well as of the increased noise in the signal, can be found in Figure 5. The coupling of fiber segments leads to decreased accuracy in estimating the train length because the detected leading and trailing edge do not coincide with the start and end of the train but, rather, with the start and end of the coupled segments.
# Maximally Deficiency: Treatments and Applications for Adolescents
## 2 Orthopedic Approaches
### Middle and Late Adolescents
#### 2.3.1 Bone-Borne Maximillary Expander  
The pure bone-borne expander does not utilize teeth for anchorage. Instead, only two or four miniscrews are directly inserted into the alveolar bone, located 6-8 mm palatal from the teeth's gingival margins, between the roots. For the maxillary skeletal expander (MSE) with four miniscrews, the front implants are symmetrically positioned in the spaces between the first and second premolars, while the implants at the back are set between the second premolars and the first molars. In an adolescent population, this bone-based technique with four miniscrews results in a nearly threefold increase in expansion at the mid-palatal suture compared to methods that anchor to the teeth [36]. Moreover, this approach significantly enhances the vertical alignment of the maxillary posterior teeth and markedly improves support for the outer alveolar bone [36; 89]. However, for the bone-borne expander equipped with two miniscrews, which are positioned between the first and second premolars, there is no significant difference in mid-palatal suture separation compared to the HYRAX expander [87].  
The C-expander is a tissue-bone-borne expander. It features four or six miniscrews implanted on both sides of the palatal slope, connected to expansion screws via an acrylic base. In late adolescent patients, C-expanders are demonstrated to have superior orthopedic outcomes and exhibit fewer dentoalveolar complications when compared to HYRAX expanders [37].  
Recently, a new pure bone-borne maxillary expander known as the ATOZ expander has been introduced [38]. This expander utilizes a thermoplastic installation guide to help accurately position and install the device. One of the distinctive features of the ATOZ expander is its anteroposterior jackscrew design, which results in a narrower width, making it particularly suitable for patients with relatively narrower plates. Moreover, both the C-expander and the ATOZ expander influence the sutural width in the nasomaxillary zygomatic region. The C-expander reduces the width of the circumzygomatic suture, while the ATOZ expander increases the width of the frontozygomatic suture without affecting other surrounding sutures [90].
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 1 Introduction
The transformation process involves converting the time-series data into a Markov transition field (MTF) [11] image. The MTF image represents the probability of transitioning between different price states at different points in time, providing a unique perspective on the data's inherent patterns and temporal dependencies. Following the transformation, we enhance the MTF image using histogram equalization [12]. This technique improves the image's contrast, making the patterns within the data more discernible and, thus, more suitable for the subsequent classification process. Finally, we employ Wide ResNets [13], a CNN known for its performance in image classification tasks, to classify the enhanced MTF images. This classification serves as the basis for our Bitcoin price predictions. The main contributions of this paper are as follows:  
* We introduce Wide-TSNet, a novel image-based approach that leverages convolutional neural networks (CNNs) to predict Bitcoin prices. Wide-TSNet provides a powerful and accessible methodology for analyzing and forecasting complex financial data. * We demonstrate the effectiveness of Wide-TSNet in predicting Bitcoin price movements. By integrating innovative data conversion techniques with an efficient CNN architecture, we contribute valuable insights for cryptocurrency price prediction. This will empower researchers, particularly those with limited computational resources, to further explore this challenging domain. * We investigate the impact that varying the number of pixels in the generated images has on the classification results. This exploration provides insights into the use of computer vision approaches for time-series analysis. * Unlike conventional approaches that dichotomize time-series data into 'increase' or 'decrease,' Wide-TSNet introduces a third class (i.e.,'stable'), providing a more nuanced representation of Bitcoin price trends. This tripartite classification forms the basis of our image-generation process. The remainder of this article is structured as follows: Section 2 describes the related work, while Section 3 explains the Wide-TSNet architecture, including data preparation, image generation, as well as image classification. Furthermore, Section 4 gives details on the experiment setup for the research. Section 5 summarizes and discusses the results of the study, concluding the work in Section 6.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues  
In this section we identify several key challenges and open issues from the aspects of network architecture, performance enhancement, and optimization.
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 3 Methodology
### Loss Setting
#### 3.5.2 Stealthiness Loss  
The purpose of stealthiness loss is to control the invisibility of the generated adversarial perturbations. The VGG [14] network is a well-known deep convolutional neural network architecture that is widely used in computer vision tasks such as image recognition. The network's core idea is to extract hierarchical image features by stacking multiple convolutional and pooling layers. It utilizes smaller convolutional kernels and deeper layers to increase the depth of the network, enabling it to better capture details and local features in images, thereby improving the accuracy of image classification. The network's shallow convolutional and pooling layers are mainly responsible for extracting low-level features, such as edges and textures. These features are highly sensitive to the local structure and details of the image, but relatively weak in representing overall semantics and high-level features. The deeper convolutional and pooling layers gradually increase the network's depth to extract higher-level semantic features. By stacking multiple convolutional and pooling layers, the network can gradually expand its receptive field and learn more abstract feature representations. These higher-level features can capture more global image information.  
This study utilized the shallow outputs of the VGG network to capture low-level features in both clean images and adversarial samples, thereby computing the VGG loss to control the invisibility of UAPs. Specifically, the adversarial samples and clean images are fed separately into the VGG network, extracting only the shallow-level information to obtain low-level features. By comparing the differences in feature extraction between the two, we can derive the invisibility loss, which controls the visibility of the generated perturbations. The definition of stealthiness loss is shown in the following equation:  
\[\mathcal{L}_{\text{ste}}=\sum_{x^{(i)}\in\mathcal{D}}\big{(}\Big{\|}\varphi_{ j}\Big{(}\mathbf{x}^{(i)}\Big{)},\varphi_{j}\Big{(}\mathbf{x}^{(i)}_{\text{adv}} \Big{)}\Big{\|}_{2}\big{)}, \tag{10}\]  
where \(\varphi_{j}\) is the feature map of the \(j\)th layer of the VGG network. The imperceptibility of the adversarial samples is improved by reducing the difference between the VGG shallow features of both the adversarial samples and the clean images.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 5 Performance Evaluation
### Synthetic Datasets
#### 5.1.1 Effect of Dimensionality  
To study the effect of dataset dimensionality, we used datasets with the cardinality of 100K, skyband size of \(K=5\), and dimensionality \(m\) ranging from 2 to 6. The runtime results for this experiment are presented in Figure 7(a)-c. The results reveal that as dimensionality increases, the performance of the proposed method gradually decreases. This degradation in performance was caused by growth in the number of non-dominant objects in high dimensions. It should be noted that the data distribution factor also influences algorithm performance. As the number of non-dominant objects in the correlated dataset is fewer than that in the anti-correlated and independent datasets, the algorithm with the correlated data generally requires less execution time than those with the other data distributions. Execution on the correlated dataset is 10 times faster than on the independent dataset and 12 times faster than on the anti-correlated dataset.
## 5 Results and Discussion
### Results
#### 5.1.3 Surface Displacement of BHR 1513  
The change law of the surface displacement of monitoring points D1, D2, and D3 of the BHR 1513 before and during the mining of the working face 1511 is shown in Figure 11.  
1. There is an increase in the displacement of the roof and two coal sides during the mining of the working face 1511 towards the right. The solid coal sidewall sees the largest displacement increase (from 658 mm to 876 mm, an increase of 218 mm), followed by the roof (from 182 mm to 391 mm, an increase of 209 mm) and solid coal sidewall (from 467 mm to 490 mm, an increase of 23 mm);
2. When the mining distance of working face 1511 is within 60 m, the roof displacement shows a linear growth trend with a rapid growth rate, while the displacement of coal pillar sidewall grows slowly. When the mining distance of working face 1511 exceeds 60 m, the roof displacement velocity slows down, without obvious displacement, while the displacement velocity of the coal pillar sidewall increases significantly.
###### Abstract  
Several recent studies have found that measuring the dielectric permittivity of soil can be used to determine the level of environmental pollution. However, there is limited research on the measurement of dielectric properties in soil contaminated with biodiesel-diesel blends from Thailand. This paper presents to monitor the dielectric properties of soil contaminated with biodiesel-diesel blends. Specifically, we use the commercial grade diesel B7 to contaminate a sample of sand soil. We also study the measurement of dielectric properties in contaminated soil with the diesel B7 using a dual electrode plate-based radio frequency (RF) heating system. This allows us to observe the behavior of the contaminated soil before and after RF heating treatment. The experimental result showed that the dielectric properties of uncontaminated and contaminated soil were different. In addition, the RF heating system utilizing the electric field intensities of 450 kV/m resulted in the dielectric properties of the contaminated soil becoming similar to those of uncontaminated soil. These findings suggest that using RF heating on contaminated soil samples improve the air in the pore space compared to unheated contaminated soil. This approach may be effective for the treatment of soil in Thailand using an RF heating system with dual electrode plates.  
dielectric properties; contaminated soil; radio frequency heating; electric field intensity Article
## 5 Conclusions  
In this paper, a model based on constraint convex optimization is proposed for the radiation pattern synthesis in cylindrical and spherical microstrip conformal array antennas. The analysis of \(M\times N\) square array configurations, which represent a special case where both the rows and columns contain an equal number of radiating patches, is discussed. All the configurations of the conformal square arrays have been precisely designed at the central frequency of 28 GHz. The focus centers on the synthesis of radiation patterns in microstrip conformal square arrays of cylindrical and spherical shapes, while considering the impact of varying the number of elements within the array. The optimization algorithm is modelled to decrease the Euclidean distance between the obtained radiation pattern of the deformed array and the desired radiation pattern of the planar array by calculating the suitable values of amplitude and phase for the array elements. Four prototypes of \(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\) microstrip patch array antenna are used as the radiating elements which are deformed from the planar configuration to the prescribed spherical and cylindrical shapes with various radii of curvatures. The results reveal that the performance of optimization algorithm performs better in a \(5\times 5\) array, followed by \(4\times 4\) array, \(3\times 3\) array, and \(2\times 2\) array; the \(5\times 5\) array provides more degrees of freedom for tuning the radiation pattern in the desired form. The \(5\times 5\) array computes appropriate amplitude and phase values for the radiating elements of the array, which restore the radiation pattern in terms of mainlobe reconstruction, nulls locations, sidelobe levels, up to the deformation of 20 cm in both spherical and cylindrical configurations of conformal array.  
Conceptualization, T.A.K. and M.I.K.; Software, T.A.K.; Methodology, T.A.K.; Validation, M.I.K. and S.A.A.; Formal Analysis, T.A.K. and A.B.; Investigation, T.A.K., A.M.A. and A.A.A.; Writing--Original Draft preparation, T.A.K.; Writing-Review Editing, S.A.A. and A.B.; Supervision, M.I.K.; Visualization: A.B. and S.A.A.; Project Administration: A.B. and S.A.A.; Funding Acquisition: A.B. and S.A.A.; Resources, A.M.A. and A.A.A.; Data Curation: T.A.K. and S.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research. (IFKSURC-1-7111).  
The authors declare no conflict of interest.
## 6 Conclusions  
In this paper, we have established a complete evaluation procedure of vulnerability analysis with ECPSs considering cyber attacks. Firstly, a MATLAB-based platform for the offline simulation and analysis of the vulnerability of ECPSs was proposed. This platform has advantages of easy programming, fast calculation, and no damage to the systems. The protection procedure of ECPSs under cyber attacks and algorithms about detection and protection when dealing with a cascading failure are embedded by functions. Compared with the existing power simulation platforms and approaches integrating existing simulators like Matpower, PSCAD, or PSASP, our platform has a higher performance when dealing with the propagation of cascading failures.  
Secondly, this paper has presented a comprehensive approach to vulnerability assessment. Existing indicators (such as node degree, cluster coefficient, node betweenness) are mainly proposed in the research area of complex networks. These indicators cannot represent the relationship of power flows and topologies. Our proposed indicators of vulnerability assessment include robustness, economic costs, the degree of damage, vulnerable equipment, and trip point. Associated with the simulation results obtained by our platform, we can have a thorough comprehension of the vulnerability in ECPSs. Additionally, decision-makers can get specific advice and defence strategies of this system against cyber attacks based on the results.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 1 Introduction
### The Imperative of High Availability  
In this digital age, the global populace expects uninterrupted, seamless online service, spanning leisure activities to mission-critical tasks like remote surgeries. The gravity of ensuring consistent availability grows as we further transition to a world deeply interwoven with digital threads. Here, downtime is not merely an inconvenience. For businesses, brief lapses can cause substantial financial dents, tarnish brand images, and erode consumer trust. The future internet is not merely a tool for browsing or entertainment; it is poised to be the linchpin in pivotal systems, from autonomous transit and smart city frameworks to healthcare and defense mechanisms.  
The pressing question now is: How do we guarantee this indispensable availability across the labyrinthine multi-tiered internet? As we integrate more layers into the internet, each with its own potential pitfalls, the challenge intensifies. The concept of service availability, pivotal to a system's operational effectiveness, emerges as a primary metric alongside others like throughput and latency. Yet conventional reliability modeling techniques falter when faced with these multi-tiered behemoths.
## 3 Theoretical Derivations
### Bolts
#### 3.1.3 Displacement of the Tunnel Wall Derivation
\[p_{i}\prime=\sigma_{r}(r_{0}+L)=(r_{0}+L)^{n-1}[\frac{p_{i}+C_{b}\cot\varphi_ {b}}{r_{0}^{n-1}}+\int^{L+r_{0}}_{r_{0}}f(r)r^{1-n}dr]-C_{b}\cot\varphi_{b} \tag{33}\]  
Combine Equation (33) with Equations (24), (25) and (27) to obtain  
\[p_{i}\prime=\sigma_{r}(r_{0}+L)=(r_{0}+L)^{n-1}[\frac{p_{i}+C_{b}\cot\varphi_ {b}}{r_{0}^{n-1}}-\frac{2\pi a_{b}}{D_{b}\theta}\cdot L\cdot t_{ult}\cdot t_{ 0}^{\int L+r_{0}}e^{-\frac{1}{2}tr^{2}}r^{1-n}dr]-C_{b}\cot\varphi_{b} \tag{34}\]  
\[\int^{L+r_{0}}_{r_{0}}e^{-\frac{1}{2}tr^{2}}r^{1-n}dr=-\frac{(\Gamma(-\frac{n -2}{2},\frac{(r_{0}^{2}+2 Lr_{0}+2)^{2}t}{2})-\Gamma(-\frac{n-2}{2},\frac{r_{0 }^{2}t}{2}))t^{\frac{n}{2}-1}}{2^{\frac{n}{2}}} \tag{35}\]  
From this, the radius of plastic zone \(R_{s0}\) of the tunnel supported by the system bolt is  
\[R_{s0}=(r_{0}+L)[\frac{(P+C_{b}\cot\varphi_{b})(1-\sin\varphi_{b})}{p_{i} \prime+C_{b}\cot\varphi_{b}}]^{\frac{1-\sin\varphi_{b}}{2\sin\varphi_{b}}} \tag{36}\]  
where \(P\) is the original rock stress of the tunnel. The total displacement of the tunnel wall under the bolt support is  
\[u_{2}=\frac{(P+C_{b}\cot\varphi_{b})\sin\varphi_{b}R_{s0}^{2}}{2G_{s}r_{0}} \tag{37}\]  
The released displacement of the surrounding rock before the bolt support is \(u_{0}\). Considering the construction sequence, it can be concluded that  
\[u_{1}=u_{0}-u_{2} \tag{38}\]  
where \(u_{0}\) can be determined by Equation (40). \[u_{0}=\frac{(P+C_{b}\cot\varphi_{b})\sin\varphi_{b}R^{2}}{2G_{s}r_{0}} \tag{39}\]where \(R\) is the radius of the plastic zone without support, calculated by the following equation:  
\[R=r_{0}[\frac{P+C_{b}\cot\varphi_{b}}{C_{b}\cot\varphi_{b}}(1-\sin\varphi_{b})]^{ \frac{1-\sin\varphi_{b}}{2\sin\varphi_{b}}} \tag{40}\]
## 5 Implementation and Evaluation
### Quality Testing with Different Levels of Noise  
#### 5.4.1 Quality Calculations with Noise and Fixed Windows Size  
To test the quality performance, we test the continuous quality calculations given a fixed window. We set a window size of 300 s (i.e., 20 data points) for all \(w_{ij}\ \in\ W_{ij}\), where  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Correlation Thresholds for Topic**} \\ \multicolumn{2}{c}{**(sensor\_1, sensor\_2, and sensor\_5)**} \\ \hline \hline \(\mathbf{v_{12}}\) & _0.81_ \\ \(\mathbf{v_{13}}\) & _0.86_ \\ \(\mathbf{v_{23}}\) & _0.86_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input parameters for quality testing.  
Figure 6: Time (s) taken by the smart broker to detect drops in \(D_{\tau}\) below 0.7, 0.5, and 0.3, respectively, as delays increase at a rate of 0.2\(\delta\), when \(\delta=15\) s.  
\(i,j\in R=\{\tau_{1},\tau_{2},\tau_{3}\}\). We consider the \(v_{ij}\) as the normal expected correlation value for each \(w_{ij}\) for corresponding pair of topics. This is shown in Figure 7a. We then introduce a topic \(\tau_{1}^{\mathrm{c}}\) which is the noisy version of the topic \(\tau_{1}\) and simulate the running quality calculations as if the noisy topic \(\tau_{1}^{\mathrm{c}}\) was received along with the \(\tau_{2}\) and \(\tau_{3}\). This is shown in Figure 7b. The setting of proper \(v_{ij}\) for each \(w_{ij}\) is very important, and the ability to detect deviations is used for quality calculations. Both figures plot the actual quality calculations at all intervals of \(\delta=15\) s, based on the last 300 s. But for a practical smart broker, the actual quality calculations will happen at the end of the window.  
The results in Figure 7c indicate that between 300-1200 s and 1900-2300 s, the quality of the \(\tau_{1}^{\mathrm{c}}\) is significantly lower than the other two for the _Normalized Group Specific Quality_ of \(\tau_{1}^{\mathrm{c}}\). Both \(\tau_{2}\) and \(\tau_{3}\) remaining close to 1. As the three-time series come closer to each other during 1200-1900 s, the quality for all of them goes closer to 1 before the \(\tau_{1}^{\mathrm{c}}\) dropping again. This shows that the smart broker is able to identify a rouge sensor in a group of three. However, if two sensors behave unexpectedly, the broker may not be able to identify a problem, in which case it will need a larger group.  
However, in any case, the smart broker can at least accurately characterize unreliable sensors in a group and accurately measure the closeness of the sensor relationship within that group.  
Figure 7: The smart broker quality calculation within a group. (**a**) the raw values of the time series data from each publisher topic, which are time-synchronized. (**b**) the quality calculations using Equation (12) for each of the time series. (**c**) the Normalized Group Specific Quality of each time series.