esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### CNN and Parallelization  
In this study, we chose 1D-CNN as the student model. The structure of the 1D-CNN is shown in Figure 3. Unlike a GRU, a 1D-CNN is a feedforward neural network in which each unit separately processes information about the current moment [7; 17]. In other words, it does not respond well to correlations between moments. The 1D-CNN in Formula (2) shows that \(\phi\) is the nonlinear activation function, \(x\) is the input, \(k\) is the weight and \(b\) is the bias. Obviously, it only responds to the current input. However, CNNs have some advantages over other parallelizable feedforward neural networks, such as MLPs, because MLPs lack design time processing and do not take into account the sequence of data points, which limits their ability to effectively model the dynamics of time series.  
\[y_{i}^{f}=\phi(\sum_{n=1}^{n_{i}}\sum_{j=1}^{n_{k}}x_{i+j-1,n}^{in}\cdot k_{j, n}^{f}+b^{f}) \tag{2}\]  
However, since each structure processes information individually, without the need for recursive operations such as GRUs, 1D-CNNs provide the conditions for parallelization. The GRU cannot be parallelized because it requires recursion, and each moment requires information from the previous moment. In computers, a network structure that can be parallelized can make more efficient use of the computing power of the CPU or GPU, and accelerate the training and use of neural networks. Parallelization also plays an extremely important role in practical applications: parallel computing improves computational efficiency and reduces the time required for equalizers to process information. More importantly, parallelization allows for neural network-based equalizers to be applied in  
Figure 2: Structure of a GRU cell.  
real hardware. In particular, in optical networks, high-speed data processing and latency can be a key factor in determining the equalizer's performance.
## 5 Experiments
### Time-Frequency Feature Extraction of the AE Signal  
Time-frequency analysis is a signal analysis method that transforms time-domain signals into time-frequency images. It describes the spectral components of the signal at different times, so it contains more information [35]. In this paper, the Hilbert transform is used to convert the denoised one-dimensional time-domain signal into a three-dimensional time-frequency image, and the spectrum of typical AE signals in the four stages is selected, as shown in Figure 12.  
Figure 11: Waveform and spectrum of noise-containing coal AE signals. (**a**) Original signal. (**b**) Signal after noise reduction.  
Figure 12 shows the distinct spectral characteristics of the AE signal in different stages of coal uniaxial compression. The main frequency range of AE during coal failure is predominantly between 100 and 300 kHz. During the initial 20% \(\sigma_{c}\) of the test, the main frequency band is narrow, centered around approximately 296 kHz. As the sample continues to be loaded, the dominant frequency of AE spans between 100 and 300 kHz at 50% \(\sigma_{c}\) and 80% \(\sigma_{c}\), with a longer duration. When the sample reaches peak strength, the main frequency is approximately 92.5 kHz. At this time, the main frequency bandwidth decreases, and the amplitude increases. Therefore, the AE frequency bandwidth gradually widens during the coal loading process, but the main frequency bandwidth suddenly decreases, and the main frequency decreases when the coal is destroyed. There are differences in the spectral characteristics of AE in the four typical stress stages. Thus, we can try to extract the spectral characteristics of AE by using the multifractal spectrum.
## 4 Method
### Regional Storage Deficit  
The maximum regional storage capacity can be estimated by identifying the recurrent peaks in the GRACE-derived TWSA series [46]. Consequently, water storage deficit \(S_{D}\) is defined as the volume of water that can still be stored before reaching this maximum capacity. The estimation of \(S_{D}\) can be expressed as follows:  
\[S_{D(t)}=S_{MAX}-S_{(t-1)} \tag{6}\]  
where \(S_{MAX}\) represents the maximum GRACE-derived TWSA value observed between 2002 and 2023, \(S_{(t-1)}\) denotes the TWSA from the previous month, and \(S_{D(t)}\) signifies the maximum allowable relative change in water storage. It assesses the regional water deficit, which is essential for effective water resource management and planning. By identifying the gap between current storage levels and maximum capacity, policymakers and water managers can better understand the potential impacts of drought, evaluate water supply reliability, and implement strategies to mitigate deficits. This insight is vital for sustainable water management practices, particularly in regions facing variability in precipitation and increasing water demand.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 2 Methodology
### Model Construction and Simulation Scheme for Numerical Simulation
#### 2.3.3 Simulated Excavation Plan  
The excavation procedure was carried out as follows:  
The coal seam, with a mining height of 8 m, was excavated in 50 m increments from a starting point 450 m from the left boundary. After each excavation step, the model was allowed to reach equilibrium before proceeding to the next step. This cycle continued until the total excavation length reached 250 m. Throughout this process, joint and mesh parameters were optimized to minimize the deviation between simulated displacements and the model's predefined displacements.  
During the coal seam excavation, the dynamic movements of the sandy soil layer at the top of the model and the heavy conglomerate layer within the model were monitored, with a focus on the settlement of the sandy soil layer. A total of 12 displacement monitoring points were placed at 55 m intervals in both the sandy soil layer and the heavy conglomerate  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Lithology** & **Density** & **Cohesion** & **Friction** & **Shear** & **Poisson** & **Elastic** & **Extension** & **Bulk Ratio** \\  & **(kg/m3)** & **(MPa)** & **(°)** & **(GPa)** & **Ratio (GPa)** & **(MPa)** & **(GPa)** \\ \hline sandy soil & \(1.92\times 10^{3}\) & 1.6 & 15 & 0.41 & 0.21 & 0.35 & 0.2 & 0.8 \\ sandy silt & \(1.57\times 10^{3}\) & 0.75 & 35.3 & 0.4 & 0.17 & 0.2 & 0.18 & 0.9 \\ conglomerate & \(2.72\times 10^{3}\) & 21 & 38 & 7.53 & 0.32 & 19.57 & 4.62 & 9.56 \\ sandy & \(2.54\times 10^{3}\) & 0.85 & 29 & 3.67 & 0.269 & 9.32 & 0.87 & 4.44 \\ mudstone & \(2.62\times 10^{3}\) & 2 & 32 & 8.49 & 0.25 & 19.34 & 7.23 & 8.05 \\ siltone & \(2.56\times 10^{3}\) & 3 & 32 & 7.87 & 0.2 & 18.79 & 2.29 & 8.3 \\ fine sandstone & \(1.4\times 10^{3}\) & 2.8 & 31 & 1.14 & 0.23 & 2.59 & 1.23 & 1.07 \\ mudstone & \(2.59\times 10^{3}\) & 3.5 & 23 & 0.96 & 0.22 & 2.5 & 2.92 & 1.22 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical characteristic parameters of coal and rock mass.  
Figure 8: UDEC numerical model.  
layer. Monitoring data were recorded using the history command, and the locations of the monitoring points are shown in Figure 8.
## 2 Image Dataset  
The \"Spot the Jellyfish\" citizen science campaign provides an interactive interface that shows a map of the Maltese Islands with all the validated jellyfish sighting reports received by connecting to the central campaign database [24]. In order to train the developed classification model, all of the jellyfish photos submitted through the campaign were initially organised in different directories, according to species. Additional photos for each jellyfish species were also obtained from online sources retrieved through the Google image search feature. A mixture of different submitted jellyfish image typologies was availed of, consisting of images that show jellyfish within the water column, jellyfish beached on shores, jellyfish photographed against complex backgrounds as well as a mixture of images with and without watermarks. Figure 1 shows images of _Pelagia_ _noctiluca_, _Cotyldriza tuberculata_, _Carybdea marsupialis_, _Velella velella_ and salps, while Table 1 highlights the number of images defined in each class. In total, 602 images were used to train the classification model, while 200 images were used for its testing. The 40 images used to test the classification accuracy of each species were chosen randomly from the available set of photos.  
The original images were of varying sizes. In the preprocessing stage, the containment area within each image that showed jellyfish individual/s, was defined by recording the lower left coordinate, the width, and the height (in terms of pixel numbers), of the bounding box. During this stage, some of the images were manually cropped to remove unnecessary image background so that the species occupied around 60 percent of the image. The process is demonstrated in Figure 2.  
In a second processing stage, all images and the corresponding boundaries that specify the regions that contain the jellyfish, were resized so that the maximum width was that of 224 pixels. These dimensions were applied in order to apply transfer learning protocols and take advantage of models that were pretrained using the same image sizes. The rasters were downscaled using a bicubic interpolation scheme with antialiasing. Up to this stage, the image height was left to vary proportionally, so not to distort the same images.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 2 Related Surveys
They indicated approaches to execute experiments and to measure the convergence time on the Internet and also organized the inter-domain convergence works into five categories: efficient policy configuration; speeding up; limiting path exploration; centralized control; multi-path. Despite the fact that approaches to improve the BGP convergence time of inter-domain routing affects the control plane of BGP routing systems, the focus of this survey is primarily on those approaches that seek to evolve the control plane behavior of inter-domain routing. Security is another field that is affected in inter-domain routing. The design of BGP and its wide deployment on the Internet have been frustrating efforts to change the inter-domain systems. Those systems are notoriously susceptible to myriad types of attacks from incorrect configuration [27] to well-orchestrated initiatives, such as botnet [28]. Thus, BGP systems can be used to directly or indirectly produce anomalies in the inter-domain [16], because the BGP has no effective mechanisms to check the accuracy of routing information. For example, the standard BGP does not provide an efficient authentication measure for advertising routes. [PERSON] et al. [17] compiled the proposed security extensions to the BGP and discussed the difficult trade-off between acceptable costs and practical and suitable improved security measures. Although security plays a paramount role in the inter-domain routing system, it is not the primary aim of this survey. Other initiatives to overcome BGP limitations also affect the control plane. For example, the BGP follows a destination-based forwarding paradigm [6], and that approach does not allow one to explore the path diversity of the current Internet topology. An extensive survey about multi-path routing and provision on the Internet is provided by [PERSON], [PERSON] and [PERSON] [15]. They depicted the multi-path problem using the entire TCP/IP model (layers: application, transport, network, link and physical), where all network layers present particular challenges and issues. For inter-domain routing, the focus will be on the network layer and how approaches to change the control plane can incorporate the multi-path capabilities. Therefore, this survey presents a more comprehensive literature review about approaches to evolve the control plane of inter-domain routing. Moreover, it provides readers with insights into the promising efforts and how the problem of evolving the control plane of inter-domain routing may be better addressed.
## Characteristics of Surface Waves Propagating on Grounded Anisotropic Slab
### Infinitely Large Anisotropic Grounded Slab
#### 2.1.3 Hybrid Mode Surface Wave  
Based on the above discussions, it is concluded that if the pure TM or TE surface wave exists on the grounded dielectric slab, the field distribution along the \(y\) direction must be uniform. In other words, if \(\partial f(y)/\partial y\
eq 0\) or \(Ry\
eq 0\), hybrid mode surface waves that consist of quasi-TM mode with small \(H_{x}\) and quasi-TE mode with small \(E_{x}\) propagate along the slab. In this case, the continuity of tangential fields across the interface \(z=t\) leads to:  
\[\begin{array}{l}\beta_{x}^{2}R_{y}^{2}k_{0}^{2}(1-\varepsilon_{z}\mu_{y}) \left(1-\varepsilon_{y}\mu_{z}\right)\\ =\left[\varepsilon_{z}k_{z}\left(k_{0}^{2}-\beta_{x}^{2}\right)+k_{0z}\left( \varepsilon_{z}\mu_{y}k_{0}^{2}-\beta_{x}^{2}\right)\tan(k_{z}t)\right]\left[ \mu_{z}k_{z}\left(k_{0}^{2}-\beta_{x}^{2}\right)-k_{0z}\left(\varepsilon_{y} \mu_{z}k_{0}^{2}-\beta_{x}^{2}\right)\cot(k_{z}t)\right].\end{array} \tag{19}\]  
By applying [PERSON]'s equation, the dispersion relations in both regions are:  
Region I  
\[\begin{array}{l}\beta_{x}^{2}R_{y}^{2}k_{z}^{2}(\frac{1}{ \varepsilon_{z}\mu_{y}k_{0}^{2}-\beta_{x}^{2}}-\frac{1}{\varepsilon_{y}\mu_{ z}k_{0}^{2}-\beta_{x}^{2}})^{2}=-k_{0}^{2}(\varepsilon_{x}+\frac{\varepsilon_{y}R_{y}^ {2}}{\varepsilon_{y}\mu_{z}k_{0}^{2}-\beta_{x}^{2}}-\frac{\varepsilon_{z}k_{z} ^{2}}{\varepsilon_{z}\mu_{y}k_{0}^{2}-\beta_{x}^{2}})\\ (\mu_{x}+\frac{\mu_{y}R_{y}^{2}}{\varepsilon_{z}\mu_{y}k_{0}^{2}-\beta_{x}^{ 2}}-\frac{\mu_{z}k_{z}^{2}}{\varepsilon_{y}\mu_{z}k_{0}^{2}-\beta_{x}^{2}}). \end{array} \tag{20}\]  
Region II  
\[\beta_{x}^{2}-R_{y}^{2}-k_{0z}^{2}=k_{0}^{2}. \tag{21}\]  
From Equations (19)-(21), it is seen that three equations with four unknowns \(\beta_{x}\), \(R_{y}\), \(k_{z}\), and \(k_{0z}\) are obtained, which implies that there is no unique solution for \(\beta_{x}\), unless \(R_{y}\) is determined. It is worth mentioning that for pure TM and TE surface waves, \(\beta_{x}\), \(k_{z}\), and \(k_{0z}\) can be solved for given \(k_{0}\), slab thickness \(t\), and relative permittivity \(\overline{\varepsilon}\) and permeability \(\overline{\mu}\) by substituting \(R_{y}=0\) into Equations (19)-(21). The above analyses can also be applied to the grounded isotropic slab, in which case we can use the relative permittivity \(\varepsilon_{r}=\varepsilon_{x}=\varepsilon_{y}=\varepsilon_{z}\) and permeability \(\mu_{r}=\mu_{x}=\mu_{y}=\mu_{z}\), instead of \(\overline{\varepsilon}\) and \(\overline{\mu}\).
# Exercise Suppresses Appetite in Obesity: A Biochemical, Metabolic, and Molecular Approach
## 4 Exercise, BDNF and Appetite
The complex behavior of food intake is not only coordinated by homeostatic mechanisms in the brain but is also regulated via hedonic factors involving the mesolimbic dopamine reward pathway (MDP) [446; 477]. MDP regulates motivated and reward-seeking behaviors and contains dopamine neurons in the VTA, which intrude into the nucleus accumbens and medial prefrontal cortex. This neural circuit coordinates the behavioral effects of drug abuse and rewards such as food and initiates synaptic dopamine transmission in mesolimbic targets [477; 478; 479; 480; 481]. BDNF and its receptors are expressed in the MDP and nucleus accumbens (an area with relatively low endogenous BDNF expression), where they regulate hedonic feeding [482; 483; 484] (Figure 5). Increased BDNF levels in the VTA (mesolimbic pathway) of mice with high-fat-diet-induced obesity lowered the intake of food with a high fat content and reduced adiposity in these animals. Central deletion of BDNF has similar effects to failed dopamine transmission due to high-fat diets [481]. In essence, BDNF modulates the excitability of dopaminergic neurons in the VTA and potentiates dopamine binding to its receptor (D1R), which it in turn reduces hyperphagic behaviors and appetite and dampens responses to food reward [481; 485]  
Figure 5: Factors that are altered by central BDNF to suppress appetite. Abbreviations: 5-HT, 5-hydroxytryptamine; \(\alpha\)-MSH, alpha-melanocyte stimulating hormone; AC, adenylate cyclase; alpha-melanocyte stimulating hormone; AMPK, adenosine monophosphate-activated protein kinase; CRH, corticotropin-releasing hormone; CRHR, corticotropin-releasing hormone receptor; ERK, extracellular signal-regulated kinase; LEPR, leptin receptor; MAPK, mitogen-activated protein kinase; MC4R, melanocortin 4 receptor; MDP, mesolimbic dopamine reward pathway; NPY, neuropeptide Y; PI3K, phosphatidylinositol 3-kinase; PKA, protein kinase A; PKC, protein kinase C; PLC, phospholipid C; POMC, pro-opiomelanocortin; TrkB, tropomyosin receptor kinase B; TSP, thrombospondin; UCN, urocortin. (Figure 5). Intensifying the signaling pathways of other anorexigenic factors, such as leptin and \(\alpha\)-MSH, in the hypothalamus is another mechanism through which BDNF can suppress appetite [330, 402, 417, 433, 436, 440, 486]. These anorexic effects induced by BDNF are mediated, in part, by upregulating their receptors (LEPR and MC4R), as well as by increasing the release of POMC and \(\alpha\)-MSH hypothalamic POMC neurons [330, 453, 454] (Figure 5).
## 4 Results and Discussion
### Feature Extraction and Selection  
The raw vibration signals were decomposed for extracting vital information as features. Thirteen sets of vital statistical information were mined from the simulated vibration signals. Tables 3 and 4 show the sample feature values extracted from the simulated and experimental vibration signals.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Natural Frequencies (Hz)**} & \multicolumn{2}{c}{**\% Deviation**} \\ \cline{2-7}
**Modes (m,n)** & \multicolumn{2}{c}{**Laminated Plate**} & \multicolumn{2}{c}{**Delaminated Plate**} & \multicolumn{2}{c}{**\% Deviation**} \\ \cline{2-7}  & **Ref. [38]** & **Present** & **Ref. [38]** & **Present** & **Laminated Plate** & **Delaminated Plate** \\ \hline
1,1 & 21.1 & 20.8 & 20.6 & 20.1 & 1.42 & 2.49 \\
1,2 & 39.4 & 40.4 & 38.2 & 39.1 & 2.54 & 2.3 \\
2,1 & 102.2 & 100.4 & 97.5 & 96.6 & 1.76 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the natural frequencies of a tapered composite plate derived by utilizing the present FEM with those presented in [38].  
Figure 5: Vibration response of composite plates. (**a**) Experimental response of the laminated composite plate. (**b**) Finite element response of the laminated composite plate. (**c**) Experimental response of the delaminated composite plate. (**d**) Finite element response of the delaminated composite plate.  
All the features may not be required for classification. Hence, feature selection was initiated using the attribute evaluator and the effect of the number of features studied. The attribute evaluator ranks the contributing features. The first feature suggested by the attribute evaluator was selected and classified by the considered algorithms. The corresponding accuracy was noted. Then, the top two ranked features were selected for classification and the corresponding accuracy was noted. In the same way, all the features were clubbed one by one based on their ranking, and their corresponding accuracies are noted in Table 5. Referring to Table 5, all the algorithms produced the maximum classification accuracy with the top five feature combinations. These were the (1) Mean, (2) Standard error, (3) Sample variance, (4) Kurtosis, and (5) Skewness. However, the decision tree and naive Bayes algorithms produced the maximum accuracy with the top four features. Based on model-building time, the decision tree algorithm is suggested for further study.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 1 Introduction
### Conventional Shift Construction Structures
#### 1.1.2 Diaphrag Walls  
The traditional open-cut method, using long and deep diaphragm walls as the retaining structure, occupies a large amount of UUS. In shaft construction, particularly when the ground consists of very dense sand with a high-water table, conditions often make other shaft types impractical for achieving the required safety factors. However, due to costs, panel geometry, and time constraints, shafts constructed with cast-in situ diaphragm wall panels have become limited. Moreover, the labor-intensive construction process, which requires maintaining trench stability through consistent circulation of bentonite slurry, verticality control via guide wall templates, specialized machinery, highly skilled operators, and heavy reinforcement for crack control [8], makes this method less feasible in UUS.
## 3 Absorption Characteristics
### Absorbance Comparison with the Different Dielectric Layers and Metal  
In this simulation, a dielectric layer and metal were used to simulate our proposed design (resonator and back-layer). The combination of tungsten and silicon dioxide shown in Figure 7a,b ensures the best result. On the other hand, as it is widely known that a high absorbance level is not always required for an application, several other applications using various dielectrics or metal layers have been covered in this article. In the first step, Figure 7a displays the results of replacing silicon dioxide with four other materials that serve as dielectrics. They are gallium arsenide (GaAs), silicon nitride, quartz, and silicon. It is clear from this comparison that silicon dioxide has a much higher average absorbance rate compared to other materials. The various refractive indices of such materials are the main cause of this sort of difference in the results. It is widely known that a lower refractive index also results in more excellent absorbance and broader bandwidth. The refractive index of Si\({}_{3}\)N\({}_{4}\), GaAs, Silicon, Quartz, and SiO\({}_{2}\) is 2, 3.9, 3.88, 1.55, and 1.50, correspondingly. On the other hand, Figure 7b depicts a simulation of the unit cell using a variety of metals, including aluminum, copper, platinum, silver, and tungsten. Tungsten is suitable for the proposed cell due to its complete optical area and excellent impedance match. Due to the incompatibility in impedance with this structure, aluminum, platinum,and copper exhibited poor absorbance. Here, silver and tungsten denote a high absorbance level of more than 80% between 450 and 650 nm.
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.7 Bias Calculation  
Even though this approach can perform beamforming without any calibration/modeling, the RF chain/cabling bias information is useful to show the performance of interference rejection of the CRPA receiver by angle-frequency response. Hence, these biases are estimated in the software receiver. This bias information is not necessary for the beamforming process as previously explained. The bias estimation process starts from obtaining the differential ICPs and unit vectors to satellites through positioning. The differential ICP for \(k^{\text{th}}\) satellite between \(i^{\text{th}}\) and first antenna is given by:  
Figure 10: Architecture of weight and sum including data formatting and used SIMD instructions.  
\[\Delta\varphi_{i1}^{k}=\frac{2\vec{\varphi}_{i}\cdot\hat{r}^{k}}{\lambda}+\Delta \gamma_{i}+N_{i}^{k}+\varepsilon_{i}^{k} \tag{18}\]  
where \(N_{i}^{k}\) is the integer associated to \(\Delta\varphi_{i1}^{k}\), and \(\varepsilon_{i}^{k}\) is the phase error. Then, with known baseline vectors, the fractional part of bias can be estimated by:  
\[f\!r(\Delta\gamma_{i})=\frac{1}{K}\sum_{k=1}^{K}\mathrm{mod}(\Delta\varphi_{i 1}^{k}-\frac{2\vec{\varphi}_{i}\cdot\hat{r}^{k}}{\lambda},\!2\pi) \tag{19}\]  
where \(K\) is the number of channels. Only the fractional part of bias is required to calculate the angle-frequency response because integer part of bias can be ignored inside exponential function. This bias estimation does not consider the biases from mutual coupling and phase-center variations.