esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Thresholding  
After obtaining the processed coefficient \(W_{y}\) through the preprocessing steps, the synchrosqueezed transform was performed according to Equation (4) to obtain the synchrosqueezed-CWT (SS-CWT) coefficients \(T_{y}\).  
\[T_{y}(\omega_{\ell},\tau)=\Delta\omega^{-1}\sum_{a_{k}|\omega(a_{k},\tau)- \omega_{\ell}|\leq\frac{\Delta\omega_{\ell}}{2}}W_{y}(a_{k},\tau){a_{k}}^{- \frac{3}{2}}\Delta a_{k}, \tag{4}\]  
where \(\omega\) is the discrete frequency and \(\omega_{\ell}\) represents the \(\ell\)th discrete frequency, \(a_{k}\) represents the \(k\)th scale, \(\tau\) is the time shift, and \(\Delta\) represents the increment symbol, where \(\Delta\mathrm{a}\ =\ a_{k}-a_{k-1}\).  
Then, we used the hard threshold scheme (Equation (5)) proposed by [PERSON] et al. [29; 30] to provide a threshold for the SS-CWT coefficients \(T_{y}\).  
\[\eta_{\lambda}^{h}\left(W_{y},\lambda\right)=\left\{\begin{array}{ll}W_{y}&,\left|W_{y}\right|>\lambda\\ 0&,\left|W_{y}\right|<\lambda\end{array}\right., \tag{5}\]  
where \(\lambda\) is the selected appropriate threshold; \(\eta\) is the selected threshold rule, which can be divided into a hard threshold and a soft threshold; \(\eta_{\lambda}^{h}\) represents the hard threshold rule; and \(W_{y}\) represents the wavelet coefficients of observation \(y\).  
The optimal threshold \(\lambda\) was automatically determined by the GCV method, which was proposed by [PERSON] et al. [31]. The GCV function is defined as follows:  
\[\mathrm{GCV}(\lambda)=\frac{1}{N}\frac{\|T_{y}-\widetilde{T}_{\lambda}\|^{2}} {\|\frac{N_{0}}{N}\|^{2}}, \tag{6}\]  
where \(\widetilde{T_{\lambda}}\) is the threshold coefficient using the threshold \(\lambda\), and \(N_{0}\) is the number of coefficients using the threshold \(\lambda\) to return to zero. This function simulated the error between the estimated signal and the real signal, so its minimum value was used to select an optimal threshold. By selecting thresholds for the main components of the signal, the initial estimation of the signal was obtained using Equation (7):  
\[y_{k}(t)=2C_{\psi}^{-1}Re\left(\sum_{l\in L_{k}(t)}T_{y}\big{(}\where \(L_{k(t)}\) denotes a small frequency band around the ridge of the \(k\)th component in the SS-CWT, the symbol \(Re\) represents the real part, and the constant \(C_{\psi}\) is given by [PERSON] et al. (2018):  
\[C_{\psi}=\int_{0}^{\infty}\xi^{-1}\hat{\psi}^{*}(\xi)d\xi, \tag{8}\]  
where \(*\) represents the complex conjugate; \(\psi\) is the mother wavelet, which is a square integrateable function; \(\xi\) is the angular frequency; and \(\hat{\psi}\) is the Fourier transform of \(\psi\).
## 6 Conclusions and Future work  
In this paper, we proposed a new path mapping approach that uses path identification for mapping virtual service requests. We formulated the NFV-RA problem to be solved based on path identifications instead of mapping them node by node. Then, the proposed approach was implemented by ILP-based scheme and heuristic scheme--namely ILP-P and DcPSM. Four schemes were simulated in 16 scenarios of long simulation runs and 6 scenarios of short simulation runs. The results show that increasing the number of physical links might reduce the execution time for back track mechanism with low number of physical nodes, however, increasing the number of nodes will always increase the execution time. The proposed ILP-P and DcPSM have overcome the other compared schemes especially in the cost to revenue ratio in most cases. Additionally, the proposed path mapping schemes have efficiently reduced the execution time and embedding cost with high acceptance ratio taking into account the high cost to revenue ratio. As for the number of virtual links (edges) and the number of end-to-end paths, it was found that increasing the number of virtual links and paths might lead to a low acceptance ratio in all of the studied schemes. For this reason, our recommendation for decomposed mobile core network functions is to reduce the cross connections and to relay more on protecting the end-to-end paths, which can be implemented by locating state-full functions or load balancing functions at the beginning or at the end of the chains. Furthermore, it is worth noting that the path mapping approach is more efficient than back-track mechanism and can be deployed for multiple virtualization environment to enhance the resiliency and high availability for mobile networks.  
Finally, the performance evaluation that carried out in this paper encourages us to continue the work and to study the path mapping with VNFs life-cycle dynamic operations for scaling and end-to-end path protection and migration. Since the DcPSM is proposed in this work to measure the possibility of mapping virtual paths to exact match of simple physical paths, we think that further efforts to improve the DcPSM could enhance the performance of heuristic schemes based on path mapping which may lead to a highly competitive scheme.  
All authors of this article have contributed to the work as follows: conceptualization, validation, formal analysis, methodology, software, visualization, and original draft preparation have been done by B.R. and [PERSON]; project administration and supervision have been done by I.A.A.-B.; technical review, writing the final draft and funding acquisition have been done by M.A.A.; proofreading and editing have been done by M.A.-M.  
This work is funded by Universiti Putra Malaysia under RMC general grant.  
This work is supported by Universiti Putra Malaysia and the Ministry of Higher Education--Yemen.  
The authors declare no conflict of interest.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 2 Preliminaries
### Signal Strength Model  
The CC2530 module is utilized as a signal source in Figure 2. The corresponding received signal strength model is given by  
\[\left\{\begin{array}{l}Z_{k}=\left\lfloor 10(\log_{10}c-\alpha\log_{10}d_{k}+ \log_{10}\Phi(\phi_{k}))\right\rfloor\\ d_{k}=\sqrt{(x(k)-p)^{2}+(y(k)-q)^{2}}\\ \phi_{k}=\text{atan}(y(k)-q,x(k)-p)-\theta(k)\end{array}\right. \tag{2}\]  
where \(Z_{k}\) is the expected signal strength; \(d_{k}\) is the distance between the robot and the signal source at time \(k\); \((p,q)\) is the source position; \(\phi_{k}\) is the bearing angle; and \(c\) and \(\beta\) are parameters whose specific values are based on the used hardware. \(\Phi(\phi_{k})\) is calculated by  
\[\Phi(\phi_{k})=\left\{\begin{array}{cl}1&d_{k}\leqslant d_{a}\\ \cos^{2}(\lambda\phi_{k})&\phi_{k}\in[\pm\varphi^{\circ}],d_{k}>d_{a}\\ \cos^{2}(\lambda\varphi^{\circ})&\textit{otherwise}\end{array}\right. \tag{3}\]  
where \(d_{a}\) is the antenna length. The specific values of parameters \(\varphi\) and \(\lambda\) are based on the used directional antenna.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions
### Throughput Comparison  
Figure 4 illustrates the throughput of the MBMQA approach with respect to the speed of devices. The obtained results indicated that the proposed approach has outperformed the other approaches. It can be observed from the figure that the overall throughput with all the stated schemes slightly reduced through an increase in the speed of the nodes. This is attributed to the increase in the difficulty of finding a stable route in cases where the node speed is high. The superiority of the proposed MBMQA scheme as compared to both MP-OLSRv2 and MEQSA-OLSRv2 lies in the back-pressure algorithm employed, which does not solely rely on the speed and battery levels of the nodes when making routing decisions but caters to the queue length of packets (commodities) as well in order to be processed by each node. The incorrect selection of nodes based solely on the speed and battery level will force the nodes to be part of the route, which results in higher queue length, and nodes will constantly discard the packets when the queue length is full. Avoiding devices with higher queue lengths reduces network traffic, as the load is equally distributed among the devices. Therefore, only nodes with low queue lengths are selected to forward data. This distinction provides leverage to the proposed protocol and enhances throughput compared to both MP-OLSRv2 and MEQSA-OLSRv2. Furthermore, the MBMQA scheme proved to be more stable than its counterparts due to its optimized approach in the selection of multiple relays and avoiding high mobility nodes.
## Secret Sharing in a Multi-Cloud Environment  
The secret sharing methods show potential in being used in Cloud-based infrastructures, as they inherently preserve the data without the requirement for private keys. Figure 2 outlines an example of a proposed architecture known as SECRET which supports a secret sharing scheme in a multi-cloud environment. It has four main elements:* **Application platform**. Its function is to: determines the access structure; encodes secrets; sends secrets to the main multi-cloud proxy server for distribution to multi-cloud service providers, as well as keeping the secret shares when recovered.
* **Main multi-cloud proxy server with router**. This splits and distributes encoded shares to multi-cloud based on pre-determined access structure and manages the fail-over protection of shares.
* **Metadata server**. This includes the functionality of: User management; Server management; Session management; and File metadata management [3].
* **Multi-cloud proxy server**. This Gathers shares and reconstruct secret as well manages break-glass data recovery.
* **Sub-routers.** This creates a path between the Cloud Service Provider (CSP) (considered here as front-end) with other Cloud Service providers (considered here as the Back-ends) thereby creating a quick and alternative recovery path for all the shares. For example: R4 connects with R3 + R2 + R1, so R4 is a path for CSP1 + R3 + R2 + R1 + S1, and so on.
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 3 Contact with Liquid Phases that Separate from Solution Reshapes Membranes at Phase Boundaries
### Deviations from Budding: Nanotubes and Nanodroplets  
Budding is in competition with other reshaping phenomena, depending on the spontaneous curvature of the vesicle membrane [36]. Internal membrane nanotubes have been observed in deflated vesicles that contained a PEG/dextran ATPS; the appearance of these tubules implies spontaneous curvature must have been induced [33]. These tubes preferentially located to the liquid-liquid phase boundary [33; 37]. Spontaneous curvature in nanotube-forming vesicles is a result of membrane-polymer interactions and the magnitude of this induced curvature depends on the phase of the vesicle membrane: Liquid-disordered membranes obtained far more curvature than liquid-ordered ones [37].  
A parameter known as the intrinsic contact angle (\(\theta_{\text{in}}\)) can inform the arrangement of phases within the vesicle. At the site of budding, a \"kink\" appears at optical resolution to exist in the membrane which allows the measurement of three effective contact angles between the two membrane segments and the liquid-liquid phase boundary (Figure 3a). In reality, the membrane surface must still be locally smooth at its intersection with the phase boundary, and so an intrinsic contact angle exists between the phase boundary and the membrane (Figure 3b) [38]. This parameter, \(\theta_{\text{in}}\), is a material property (unaffected by factors such as vesicle shape) if spontaneous curvature is negligible [38; 39].  
Nanometer-scale droplets formed through liquid-liquid phase separation can experience different physical interactions with membrane surfaces than the larger droplets discussed so far. The line tension of a droplet-surface system has an increasing impact relative to the surface tension as a droplet's size decreases; when droplets with diameters on the scale of nanometers interact with a surface, the line tension at the contact line between droplet and surface contributes significantly to the system's configuration [40]. In simulations, such droplets were engulfed as membrane tension is lowered, but a negative line tension broke the symmetry of the system as the membrane neck takes on a linear, flipped shape around the droplet to lengthen the contact line (Figure 3c) [41].  
Figure 2: **(a)** Wetting transition leading to inversion of liquid phases (first branch) or budding. Budding may eventually lead to vesicle fission. “Raspberries” of smaller droplets (second branch) may form during dehydration, eventually coalescing into a single bud; (**b)** In the presence of a heterogeneous membrane, the “red” lipid phase has an affinity for the dark blue liquid phase, and the two bud and separate from the mother vesicle together; (**c**) “Inward” budding induced by a dark blue liquid droplet external to the vesicle. Light and dark blue represent liquid phases of an ATPS (e.g., a PEG-rich and dextran rich phase), black the lipid bilayer.
## 5 Results and Analysis  
The fidelity of the finite element method was corroborated through comparison with the experimental findings presented in Figure 6 from the referenced study [25]. In reference [25], the bearing had a length of 75 mm, a width of 30 mm, eight supply orifics with depths of 2 mm, and a diameter of 0.4 mm; the supply gas pressure was 0.4 MPa. The relative error between the simulation data obtained in this study and the experimental data from this paper is within 10%, indicating the reliability of the proposed method. The finite element results closely align with the experimental findings from the literature, albeit  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of** & **Load Capacity** & **Mass Flow Rate \(Q\) (kg\(\cdot\)s\({}^{-1}\))** & **Maximum Speed \(V\) (m\(\cdot\)s\({}^{-1}\))** \\
**Grids** & **\(W\)(N)** & **Mass Flow Rate \(Q\) (kg\(\cdot\)s\({}^{-1}\))** & **Maximum Speed \(V\) (m\(\cdot\)s\({}^{-1}\))** \\ \hline
2,324,007 & 419.91 & 3.44 \(\times\) 10\({}^{-5}\) & 118.24 \\
2,849,645 & 419.93 & 3.44 \(\times\) 10\({}^{-5}\) & 118.00 \\
3,382,857 & 420.08 & 3.47 \(\times\) 10\({}^{-5}\) & 118.66 \\
3,947,919 & 420.07 & 3.49 \(\times\) 10\({}^{-5}\) & 119.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of mesh independence analysis.  
Figure 5: (**a**) Overhead pressure contour map of aerostatic rectangular thrust bearing; (**b**) velocity contour map at junction of throttle hole and gas film.  
with some inherent error. The sources of error encompass the assumption of ideal laminar flow in simulations, whereas practical applications may involve turbulent flow and other influential factors.  
The three-dimensional pressure contour surface of a rectangular throttle orifice array with a diameter of 0.2 mm, a gas film thickness of 20 \(\upmu\)m, a length \(a\) of 40 mm, and with a supply gas pressure of 0.6 MPa was calculated and is illustrated in Figure 7a.  
Figure 7b shows the transverse pressure distribution across the flow direction at differing gas supply pressures, with \(P_{\mathrm{s}}\) spanning from 0.2 to 0.6 MPa. The diagram clearly indicates that as the supply gas pressure increases, the pressure distribution escalates, highlighting a pressure drop phenomenon.  
To examine the effects of diverse structural parameters and operational factors on the load capacity, mass flow properties, stability, and stiffness of porous aerostatic bearings, we analyzed changes in these four attributes under varied operating conditions. Variables included gas pressure, orifice diameter, gas film thickness, and the distribution of orifices.
## 4 Materials and Methods
### Methodology  
#### 4.2.1 Geochemical Analysis  
Pyrolysis analysis is the most often used analytical technique for assessing the kerogen type, thermal maturation, and potential for production [38; 39]. Pyrolysis analysis was established first to determine the age of coal macarels [40; 41]. It is a highly successful technique for discriminating between source rock and kerogen quality and has developed into a critical tool for oil and gas exploration, giving geologists information about source rock attributes and reservoir potential. It is based on the progressive heating of crushed source rock in a source rock analyzer oven (SRA). At temperatures below 300 \({}^{\circ}\)C, the free hydrocarbons vaporize and are designated as the S\({}_{1}\) peak. At temperatures between 300 and 600 \({}^{\circ}\)C, the kerogen begins to break down and releases its genetic potential at optimal maturity, which is designated as the S\({}_{2}\) peak [42], which is also measured in milligrams of hydrocarbon per gram of rock (mg/g). The involved CO\({}_{2}\) is designated as the S\({}_{3}\) peak, which results from the breakdown of carboxyl groups and other oxygen-containing compounds in kerogen, obtained at 300-390 \({}^{\circ}\)C and measured by mg CO\({}_{2}\)/g rock [11]. The temperature at which the S\({}_{2}\) peak occurs is referred to as the Tamax, which is a maturity indication [43]. The hydrogen index (HI = S\({}_{2}\)/TOC wt.%), the oxygen index (OI = S\({}_{3}\)/TOC wt.%), the production index (PI = S\({}_{1}\)/[S\({}_{1}\) + S\({}_{2}\)]), and the generating potential (GP = S\({}_{1}\) + S\({}_{2}\)) are four critical pyrolysis parameters. Tamax can be plotted against HI to assess the kerogen quality, against OI to determine the depositional environment and kerogen type, against PI to determine maturity, and against GP to determine potentiality.
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## 1 Introduction
Therefore, besides diameter measurement in the cylindrical growth process, it is necessary to introduce a vision system to detect DHL in time, and alert the operator to promptly complete the following process operations such as remelting, fetching, and growth of end cone, to prevent waste of energy and reduce the cost of equipment operation. Due to high temperature and the closed environment in the CZ furnace, a vision system is installed at the observation window with a tilted angle and consists of two cameras and a set of optical filters to capture the image of the silicon crystal ingot. The filters are located under the camera due to high reflection in the furnace. In our system, monochrome industrial cameras of resolution \(2592\times 1944\) pixels and a camera lens of focal length 50 mm are used. A sample of images captured by phone and the imaging system are shown in Figure 3. Since the temperature of melt reaches about 1450 \({}^{\circ}\)C, it can be seen that the furnace is filled with orange and bright white lights. The crescent part on the melt surface is the reflected image of the bottom rim of the heat shield, which is projected as an incomplete ellipse. The habit line is the target that needs to be detected. Due to the limited small observation window and the heat shield, there exists occlusion in the scene as can be seen in Figure 3c,d. The ingot is rotating along its axis at a prescribed speed, when the habit line is in the occlusion area, it is hard to tell whether it is normal or disappears. Due to occlusion and to be compatible with furnaces without occlusion, the detection area as shown in Figure 3d is quite limited. A deep learning model avoids the cumbersome image processing steps of the traditional method by autonomously extracting high-dimensional information in the image. In recent years, target detection algorithms based on convolutional neural networks (CNNs) have achieved great success in many fields [6]. One of the application scenarios is defect detection in industrial environments, and it has advanced recognition performance. Thus, in this paper, a deep learning-based method is proposed to detect dislocation in the cylindrical step of the CZ crystal growth process. Figure 2: A finished silicon crystal ingot with a disappeared habit line. The remainder of this paper is organized as follows. In Section 2, firstly, the patterns of habit lines are discussed in detail, then, a detection model is designed based on the different patterns between normal and disappeared habit lines, and a tracking strategy is proposed to gain more comprehensive results. In Section 3, experimental results are shown to demonstrate the effectiveness and superiority of our approach. The main reasons for misdetection are discussed in Section 4. Finally, Section 5 concludes our paper.
# Target State Classification by Attention-Based Branch Expansion Network
## 5 Discussion
### Comparison with State-of-the-Art Results  
We verify the superiority of the \"ABE 2-4\" model on the public dataset Treshnet by comparing with the following relatively new technologies of recent years, including traditional machine learning methods and deep learning methods. We visually show the level of accuracy in the form of a statistical histogram graph in Figure 8. Our proposed model, ABE 2-4, is shown in the first line, while other deep learning methods are shown in orange, and the traditional machine learning methods are shown in blue.  
ResNet [23] gains an accuracy of 88.66%, and Inception [23] gains 87.71% accuracy, while Xception achieves 91.41% accuracy in our experiment. In 2020, The M_b Xception [37] modified from the Xception improved by 3% in which the number of convolution channels increased from 728 to 1024; their 728-channel model achieved 93.25%. The DenseNet [21] achieves 89% and the fine-tuned one achieves the highest accuracy of 95% among deep learning methods by fine-tuning from the pre-trained weights on the ImageNet data. The above two are similar to our results. [PERSON] et al. [43] compared the VGG-16 with traditional methods and reached an accuracy of around 93% on fine-tuned VGG-16. Among the traditional machine learning technologies, the KNN performs best with an accuracy of 88%. It can be seen that the precision of fine-tuning a model can be greatly improved when the premise is still big data.  
We show the confusion matrix of the original Xception and the final \"ABE 2-4\" model in Figure 9. The vertical axis represents the true label, the horizontal axis represents the predicted label, and the diagonal element is the perception of pictures predicted to the correct class. The confusion matrix of \"ABE 2-4\" in Figure 9 shows consistent improvements in classification performance on each class; in particular, \"Glass\", \"Metal\", and \"Trash\", which are difficult to predict, demonstrate the stability of the model on a small sample dataset.  
Figure 8: The statistical histogram graph of the comparison between the proposed “ABE 2-4” model and relatively new technologies in recent years.