esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Estimation of \(R_{s,k}\) by Maximum Likelihood
\begin{split}\frac{\partial\ln\text{L}}{\partial\beta}=0\;\; \Rightarrow\;\;\frac{-2(n+m)}{\beta}+\frac{1}{\beta^{2}}\sum\limits_{i=1}^{n} x_{i}+\frac{1}{\beta^{2}}\sum\limits_{j=1}^{m}y_{j}-(\alpha_{1}-1)\sum\limits_{i=1}^{n} \frac{\frac{x_{i}^{2}e^{-\frac{x_{i}}{\beta}}}{\left(1-\left(1+\frac{x_{i}}{ \beta}\right)e^{-\frac{x_{i}}{\beta}}\right)\beta^{3}}}{\left(1-\left(1+ \frac{x_{i}}{\beta}\right)e^{-\frac{x_{i}}{\beta}}\right)\beta^{3}}\\ -(\alpha_{2}-1)\sum\limits_{j=1}^{m}\frac{y_{j}^{2}e^{-\frac{y_{i} }{\beta}}}{\left(1-\left(1+\frac{y_{i}}{\beta}\right)e^{-\frac{y_{j}}{\beta}} \right)\beta^{3}}=0.\end{split}  \tag{10}\]From Equations (8) and (9), we obtain the following:  
\[\hat{\alpha}_{1} =\frac{-n}{\sum\limits_{i=1}^{n}\text{ln}\left(1-\left(1+\frac{\gamma _{i}}{\beta}\right)\text{e}^{-\frac{\gamma_{i}}{\beta}}\right)}, \tag{11}\] \[\hat{\alpha}_{2} =\frac{-m}{\sum\limits_{j=1}^{m}\text{ln}\left(1-\left(1+\frac{ \gamma_{i}}{\beta}\right)\text{e}^{-\frac{\gamma_{i}}{\beta}}\right)}, \tag{12}\]  
and \(\hat{\beta}\) is the solution of the following non-linear equation:  
\[ \begin{array}{c}g(\beta)=0\Leftrightarrow\\ \frac{-2(n+m)}{\beta}+\frac{1}{\beta^{2}}\sum\limits_{i=1}^{n}\frac{\sum\limits _{i=1}^{n}\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right)\text{e}^{-\frac{ \gamma_{i}}{\beta}}\right)}{\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right) \text{e}^{-\frac{\gamma_{i}}{\beta}}\right)}+\frac{n}{\sum\limits_{k=1}^{n} \text{ln}\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right)\text{e}^{-\frac{ \gamma_{i}}{\beta}}\right)}\sum\limits_{i=1}^{n}\frac{\frac{x_{i}^{2}e^{-\frac {\gamma_{i}}{\beta}}}{\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right)\text{e} ^{-\frac{\gamma_{i}}{\beta}}\right)}}{\left(1-\left(1+\frac{\gamma_{i}}{\beta }\right)\text{e}^{-\frac{\gamma_{i}}{\beta}}\right)}+\\ \frac{1}{\beta^{2}}\sum\limits_{j=1}^{m}\frac{y_{j}\left(1-\text{e}^{-\frac{ \gamma_{j}}{\beta}}\right)}{\left(1-\left(1+\frac{\gamma_{j}}{\beta}\right) \text{e}^{-\frac{\gamma_{j}}{\beta}}\right)}+\frac{m}{\sum\limits_{k=1}^{n} \text{ln}\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right)\text{e}^{-\frac{ \gamma_{i}}{\beta}}\right)}\sum\limits_{i=1}^{n}\frac{y_{i}^{2}e^{-\frac{ \gamma_{i}}{\beta}}}{\left(1-\left(1+\frac{\gamma_{i}}{\beta}\right)\text{e} ^{-\frac{\gamma_{i}}{\beta}}\right)}^{3}}=0,\end{array}  \tag{13}\]  
where \(\hat{\beta}\) is a simple iterative solution of the non-linear equation \(g(\beta)=0\).
## 4 Results and Discussion
### Model Fitting  
According to the analysis of variance (ANOVA) results, many \(p\)-values are above 0.05, which mean that these factors or second order effects do not have statistically significant effects on the response studied. The fitting of the model of each response have to avoid terms of the factor that do not make a significant effect in order to simplify the Equation (2) of the mathematical model. Table 6 shows p-values from ANOVA table to test the acceptability of the model from a statistical perspective.  
The proposed adjusted model fits satisfactorily to the results obtained, with correlation coefficients shown in Table 7 for each response.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & \(p\)**-Values Y\({}_{1}\)** & \(p\)**-Values Y\({}_{2}\)** & \(p\)**-Values Y
## 4 Simulation Results  
### Deep Neural Network (DNN) Structure and Simulation Environments  
In this paper, five types of digital communication signals BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM are considered. Additionally, the nine characteristic values consisting of the second, fourth and sixth-order cumulant are used. The structure of the DNN algorithm consists of an input layer with nine feature in a fully connected layer structure, a hidden layer consisting of three layers, 40 nodes, 20 nodes, and 10 nodes, and finally an output layer for classifying signals. In the hidden layer, the Rectified Linear Unit (ReLU) function [21] is used and in the last output layer, each modulation type is classified by Softmax [22]. Table 6 shows the nonlinear activation functions considered in this paper. Since the Softmax function can produce the output in terms of probability, we can calculate the accuracy for each classified signal. The DNN structure for the first set of simulations is shown in Figure 2.  
In both sets of simulations, we trained the DNN algorithm several times for hyperparameter optimization. Since the DNN is a very complex structure, it is difficult to find the optimal weighting coefficients in one calculation. Therefore, in this paper we set up the hyperparameters and trained the DNN algorithms through the following standard procedures. In the first step, we adjusted the hyperparameters and trained the DNN using the backpropagation algorithm based on the gradient descent, and applied the batch normalization to prevent the overfitting during the training. Next, the validation errors were counted and the training was stopped to prevent overfitting when the validation error started to increase. Also, when the validation errors did not decrease anymore, we continued to train by cutting the learning rate in half. We utilized 20% of the input data for the validation.  
In order to train the above DNN structure, the epoch was set to 200, batch size to 64, and a total of 50,000 units of data (10,000 digital modulation symbols by each of 5 modulation schemes considered) were generated in various SNR environments. Then, 9 features (\(C_{20}\), \(C_{21}\), \(C_{40}\), \(C_{41}\), \(C_{42}\), \(C_{60}\), \(C_{61}\), \(C_{62}\), \(C_{63}\)) were considered for each digital modulation symbol, yielding 450,000 features used as the input data. In other words, the number of training data units is 450,000, and that of the test and the validation data is 90,000 each, which is 20% of the training data. The parameters of the first DNN obtained through the above process are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline ReLU & \(f(x)=\max(x,0)\) \\ Softmax & \(f\big{(}x_{j}\big{)}=\frac{\sqrt{c^{j}}}{\sum_{i}e^{x_{i}^{i}}}\) \\ \hline \end{tabular}
\end{table}
Table 6: Definitions of the non-linear activation functions used in the deep neural network (DNN). The ReLU was used for all hidden layers, while the Softmax was used for the output layer.  
Figure 2: DNN structure for the first set of simulations.
# Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures
## 1 Introduction  
The growing demand for fast, powerful, scalable, and reliable computing and communication infrastructures has driven the evolution of the computation paradigm from in-house solutions towards shared infrastructures, such as the cloud computing paradigm (which in the long term can provide a reduction in the total cost of ownership by up to 66%), building up to the most innovative distributed infrastructures being proposed for edge/fog computing. The result is the advent of large distributed infrastructures composed of multiple data centers, where virtualization is applied at both the computing and networking levels, to the point where we can describe the infrastructure as being composed of Software-Defined Data Centers.  
These infrastructures are becoming increasingly popular, especially to support modern applications which are characterized by huge volumes of data (in the order of peta or exabytes--the so called big data), possibly collected from thousands of sensors (in the Internet of Things scenario) and may be deployed on tens of thousands of Virtual Machines (VMs), possibly interacting through a virtualized network spread over a geographic area. For example, more than 60% of the Apache Spark installations are deployed on the cloud. However, the management of these large and complex infrastructures represents a major problem. On one hand, there is the need to maximize resource utilization and to minimize the energy consumption of the infrastructure, to reduce costs and for environmental reasons. On the other hand, there is the need to guarantee the resources required by the applications hosted on the infrastructure in terms of computing power, storage, and communication. These requirements may be expressed directly or may be directed via a Service Level Agreement at the level of the application behavior.  
The challenge of infrastructure management in this scenario is clear. Even the monitoring of such infrastructure is difficult, but we must consider that the complexity of the problem is further exacerbated by the need for the infrastructure to react in a timely and unsupervised manner with an ever-changing workload, characterized by the unpredictable oscillations in application demands through the joining and leaving of new applications that are deployed or dropped. The goal is to devise models, techniques, and algorithms that can support a self-management system that is able to adapt, manage, and cope with changes, without the need for human supervision.
## 3 Notation  
We write the stretched exponential function in dimensionless form as  
\[f_{\beta}(t):=\exp\left(-t^{\beta}\right) \tag{3}\]  
Motivated by the relations between relaxation, linear response, and dynamic susceptibility (Appendix A), we define the Laplace transform of \(f_{\beta}\) as  
\[F_{\beta}(\omega):=\int_{0}^{\infty}\!\mathrm{d}t\,\mathrm{e}^{\mathrm{i} \omega t}\,f_{\beta}(t) \tag{4}\]  
In most applications, one is interested in either the cosine or the sine transform,  
\[\begin{split} Q_{\beta}(\omega)&:=\mathrm{Re}\,F_{ \beta}(\omega)\\ V_{\beta}(\omega)&:=\mathrm{Im}\,F_{\beta}(\omega) \end{split} \tag{5}\]  
The two-sided Fourier transform of \(f_{\beta}(|t|)\) is  
\[\int_{-\infty}^{\infty}\!\mathrm{d}t\,\mathrm{e}^{\mathrm{i}\omega t}\,f_{ \beta}(|t|)=2Q_{\beta}(\omega) \tag{6}\]  
To compute the convolution of Equation (6) with an experimental resolution function (Appendix B), it is sometimes advisable to use the primitive of \(Q_{\beta}(\omega)\),  
\[P_{\beta}(\omega):=\int_{0}^{\omega}\!\mathrm{d}\omega^{\prime}\,Q_{\beta}( \omega^{\prime})=\omega\int_{0}^{\infty}\!\mathrm{d}t\,\text{sinc}(\omega t)f _{\beta}(t) \tag{7}\]The function \(Q_{\beta}(\omega)\) is even in \(\omega\), with  
\[Q_{\beta}(0)=\Gamma(1/\beta)/\beta \tag{8}\]  
whereas \(V_{\beta}(\omega)\) and \(P_{\beta}(\omega)\) are odd. To simplify the notation, we restrict ourselves to \(\omega>0\) for the remainder of this paper.  
In physics, the stretched exponential function is almost always used with an explicit time constant \(\tau\),  
\[f_{\beta,\tau}(t):=\exp\left(-(t/\tau)^{\beta}\right) \tag{9}\]  
The corresponding transforms can be expressed quite simply by their dimensionless versions:  
\[\begin{array}{rcl}F_{\beta,\tau}(\omega)&=&\tau F_{\beta}(\tau\omega)\\ P_{\beta,\tau}(\omega)&=&P_{\beta}(\tau\omega)\end{array} \tag{10}\]
## 2 Materials and Methods
### Statistical Metrics  
Cross-correlation consists of the shifted dot product between two series and is generally used to measure the degree of similarity between two signals. It is also a crucial tool for determining the time delay. In the case of discretized records, the cross-correlation between two signals \(u\) and \(v\) with the same N samples length is provided in Equation (1) [53].  
Figure 3: Application examples performed with the designed system.  
Normalization was applied to the obtained cross-correlation results to ensure that the values were between \"-1\" and \"1\". Cross-correlation analysis is applied to detect leaks in liquid-filled pipes [54]. The true R-square is the proportion of the variance in Y that is explained by X. The R-square and root mean square error (RMSE) formulas are presented in Equations (2) and (3) [55] (pp. 152-290). In the literature, R-squared has been used to evaluate the performance of machine learning models in predicting hydrogen production [56]. In addition, the ANFIS method was used to predict the heat-affected zone, surface roughness, and the results were evaluated using RMSE [51].  
\[corr[u,v](t)=\sum_{n=n0}^{N}\left(u(n).v(t+n)\right) \tag{1}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y}_{i})^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{3}\]  
Equation (1) expresses the cross-correlation between the time series \(u\) and \(v\). In Equation (2), \(y_{i}\) represents the observations; \(\hat{y}_{i}\) represents the values predicted by the model; and \(\hat{y}_{i}\) represents the mean value of the observations. In Equation (3), \(y_{i}\) represents the observations, and \(\hat{y}_{i}\) represents the predicted values.
# Future Renewable Energy Communities Based Flexible Power Systems
## 8 Local Electricity and Flexibility Markets in Smart Cities  
Currently, small-scale resources, such as a small energy community or a prosumer, are unable to participate in the wholesale energy and flexibility markets. The existing energy and flexibility markets usually determine a minimum capacity for the participants. Thus, a smart prosumer in the smart city cannot play an active role to sell its production. This may decrease the prosumer's motivation to become active. Local markets are deployed to solve this issue.  
Local markets aim to put small-scale energy and flexibility resources at the heart of energy markets. In local markets, small-scale resources such as communities and prosumers can trade energy and flexibility with each other [28]. An active player in local markets is able to decrease its costs by purchasing cheaper energy when it needs energy and sell its surplus energy and flexibility and make profits accordingly. Figure 12 illustrates a local market within a smart city consisting of energy communities and prosumers. The EMS of a prosumer is in charge of making a bidding strategy for the prosumer to obtain the maximum revenues from participating in the local markets. Regarding the energy community, its central EMS builds its optimum bidding strategy and schedule the available resources accordingly [40].  
Figure 11: Flexibility services that can be provided by an energy community.  
Figure 12: An example of a local energy market within a smart-city.
## 7 Performance Analysis and Discussion
Finally, to confirm the consistent and dependable performance of the HRIS-DMA, taking into account the aforementioned validation metrics, we conducted 100 repeated tests with identical operating settings and features to assess the accuracy of the results. As depicted in Figure 9, the HRIS-DMA exhibits system dependability and stability of 95%, whereas the T/R RIS only maintained a system reliability of 11% during these tests. Implementing the HRIS-DMA in the wireless communication system led to a significant  
Figure 7: FSR comparison for different user densities and 300 GHz.
# A Study on the Seismic Performance of Steel H-Column and T-Beam-Bolted Joints
###### Abstract  
The finite-element model was developed using ABAQUS to investigate the hysteretic properties of space joints. This study examined the effects of axial compression ratio, T-plate stiffness, column wall thickness, and bolt-preload on the joint's hysteretic behavior. The model was verified by comparing the failure modes, hysteresis curves, and skeleton curves of the specimens with the test results of the relevant literature, ensuring the reliability of the research. The results reveal three primary failure modes: beam flange buckling, T-plate buckling, and column-wall buckling; increasing the thickness of the T-plate web or column wall significantly enhances joint stiffness and mitigates brittle failure. Specifically, the stiffness of T-plate 1 has a substantial impact on joint performance, and it is recommended that its web thickness be no less than 18 mm. In contrast, variations in the thickness of T-plate 2 have negligible effects on seismic performance. Increasing the column wall thickness improves the bearing capacity and stiffness of the joint, with a recommended minimum thickness of 12 mm, which should not be less than the flange thickness of the steel beam. While an increase in the axial compression ratio reduces the bearing capacity and stiffness, it enhances the energy dissipation capacity and ductility of the joint. Notably, variations in bolt-preload were found to have minimal influence on joint performance. These findings provide valuable insights for optimizing the design of unilateral bolted joints in steel structures to improve seismic resilience.  
square steel pipe column; seismic performance; numerical simulation; failure mechanism; node category +
Footnote †: journal: Applied Sciences
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 5 Responses of Segmental Tunnel
### Cross-Sectional Deformations of Model Tunnel  
Cross-sectional deformation can be represented with the measurements of displacements in diametral direction. Time histories of the cross-sectional deformation of the segmental tunnel are shown in Figure 20.  
Compared with vertical direction (0 degrees, line BD1) and horizontal direction (90 degrees, line BD3) of tunnel's cross-section, the diametral displacement of segmental tunnel is relatively large in the conjugate (45 degrees, line BD2 and 135 degrees, line BD4) direction of tunnel's cross-section under earthquake. This result reveal that the segmental tunnel shows shear deformation under the transverse excitation. Underground structures behave as flexible members that follow free-field deformations, as concluded in [PERSON]'s paper [28]. This is also consistent with the distribution law of diametral displacement of shield tunnels during Yokohama earthquake in Japan [29]. The deformation responses of symmetrical monitoring positions show almost 180 degrees out-of-phase.  
In the case of the Sin-2, response waveforms of cross-sectional deformation are approximately sine function. In the case of the Sin-3, the cross-sectional deformation response increases periodically with time. This is due to the superposition effect of seismic wave and resonance-induced oscillation. In the cases of the Sin-6, Sin-9, Sin-11, Sin-14, and Sin-17, the deformation response curves are irregular and present various waveforms. This is probably due to the fact that deformation responses may be obviously disturbed by high-order vibration modes of the ground.
## 2 System Model and Problem Formulation  
### System Model  
Figure 1 depicts the model of a communication system utilized in this paper. This model includes a set of wireless communication transceivers and a jamming device. The jamming signals from this device are capable of effectively enveloping the receiver. This paper makes the following assumptions:  
* Communication receiver  
The wireless communication transceivers operate in the frequency bandwidth of \(B\) with dynamic spectrum access and power control abilities. The channels available are denoted as \(f_{n}\in\{f_{1},f_{2},...,f_{M}\}\) and the \(M\) channels do not overlap with each other, and each of them has a bandwidth of \(\Delta B=B/m\). Their available power levels are represented as \(p_{n}\in\{p_{1},...,p_{D}\}\). The transmission channel gain for the communication system is denoted as \(g_{n}=g_{p}|h_{s}|\), where \(g_{p}\) represents the path loss at a given distance, and \(|h_{s}|\) is a Rayleigh distributed random variable. The total time duration for communication is denoted as \(T_{max}\). Equation (1) shows the signal-to-jamming-plus-noise ratio (SINR) \(H_{T}\) in time slot \(T\).  
\[H_{T}=\frac{P_{r,T}}{p_{k,T}^{\prime}+P_{n,T}} \tag{1}\]  
The SINR in the communication system needs to meet the requirement of \(H_{T}\geq H_{T}^{th}\) at any moment in time slot \(T\) to make sure the data frame transmitted in time slot \(T\) can be successfully decoded, where \(H_{T}^{th}\) represents the SINR threshold.  
The normalized throughput of the communication system's data transmission is defined as \(c_{T}=\delta\left(H_{T}\geq H_{T}^{th}\right)\), where \(\delta(\cdot)\) is the indicator function.  
* Communication transmitter  
The transmitter can transmit one subframe in each time slot, with each subframe containing the same amount of information. A packet, comprising \(l\) subframes, includes a Cyclic Redundancy Check (CRC) field. The transmitter is located far from the jammer, minimally affected by jamming, and can reliably receive control information transmitted by the receiver through a protocol-reinforced low-capacity control channel, enabling cooperative anti-jamming.  
* Malicious jammer  
The jammer preemptively acquires the communication frequency and time slot synchronization information of both the transmitter and receiver. The bandwidth of the pulse jamming signal can completely cover the transmission channel, and the duration of a single pulse is equal to the length of a time slot.  
The jammer can indiscriminately attack all nodes in the communication system and can dynamically adjust its jamming power and its targeted channels between different communication time slots. Its available power is denoted as \(p^{\prime}_{k}\in\{p^{\prime}_{1},...,p^{\prime}_{E}\}\), and the selectable channel as \(f^{\prime}_{k}\in\{f_{1},f_{2},...,f_{M}\}\), where \(k\in\{1,2,...,K_{T}\}\) represents the number of blocked channels, and \(K_{T}\leq M\) represents the number of channels blocked by the jammer during time slot \(T\). \(P_{n,T}\) represents the background noise during the time slot \(T\).
## 2 Materials and Methods
### Anomaly Extraction  
The relative power spectrum is a common method to extract seismic information from infrared remote sensing images and is used in some case studies with significant results. The process is shown in Figure 4 and was programmed using Python in the Spyder platform. The algorithm is introduced in detail as follows [35; 36]:  
1. Pre-processing: We could not gather complete earth surface radiation data because of cloud coverage and the limitation of the remote sensing system. Some pre-processing is necessary before extracting anomalies. Infrared radiation could not go through clouds, so the infrared images in the region covered by clouds reveal the temperature of the top of the clouds, which is far lower than that of the earth's surface. After obtaining brightness temperatures by the look-up table method, we remove the invalid values that are 1.5-times the standard deviation below the average to decrease the effect of clouds [27]. Then, the average temperature every day is calculated. Finally, the spatiotemporal data become continuous through the nearest-neighbor interpolation method.
2. Wavelet decomposition: The infrared radiation on the earth's surface is also affected by seasonal changes, weather conditions, geological activities, human activities, and so on. High-frequency information and low-frequency information are separated by wavelet decomposition and wavelet reconstruction. The low-pass part of seventh-order wavelet decomposition retains long-period information and is regarded as the background field. The low-pass part of second-order wavelet decomposition eliminates high-frequency information and is used to attenuate the influence of weather and human factors. The low-pass part of the second-order wavelet decomposition is subtracted by that of the seventh-order wavelet decomposition to eliminate the background field and high-frequency components.
3. Power spectrum: The power spectrum reflects the change of signal power with frequency. We take 64 days as the window length and 1 day as the step length to calculate the time-frequency distribution of the power spectrum in each window [37].
4. Characteristic frequency: We calculate the relative amplitude for each pixel and frequency, and select the frequency with the largest amplitude change as the characteristic frequency to obtain the spatiotemporal data of the relative power spectrum at the characteristic frequency.  
Figure 4: Flow chart of anomaly extraction algorithm.  
In this paper, the algorithm is used to process the data from every channel of FY-2G, respectively, to compare the difference between channels and improve prediction performance using multi-channel data.