esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Passive Actuation Schemes for Seismic Protection of Multibuilding Systems
## 2 Mathematical Model
### Plain Building Model  
Let us consider a MBS system formed by a row of \(m\) adjacent \(n\)-story buildings with identical dynamic characteristics. In the plain configuration, we assume that the buildings damping is only due to the effect of the structural damping, and the dynamical response of building \(\mathcal{B}^{(j)}\) can be modeled in the following form:  
\[\mathbf{M}_{b}\bar{\mathbf{q}}^{(j)}(t)+\mathbf{C}_{b}\bar{\mathbf{q}}^{(j)}(t )+\mathbf{K}_{b}\mathbf{q}^{(j)}(t)=-\mathbf{M}_{b}[\mathbf{1}]_{n\times 1} \bar{w}(t), \tag{1}\]  
where \(\mathbf{q}^{(j)}(t)=[q_{1}^{j}(t),\ldots,q_{n}^{j}(t)]^{T}\) is the vector of story displacements with respect to the ground (see Figure 2); \(\mathbf{M}_{b}\), \(\mathbf{C}_{b}\) and \(\mathbf{K}_{b}\) are the building mass, damping and stiffness matrices, respectively, which are common to all the buildings; \([\mathbf{1}]_{n\times 1}\) is a column vector of size \(n\) with all its entries equal to one; and \(\hat{w}(t)\) is the acceleration of the seismic ground disturbance. The building mass matrix has the diagonal form  
\[\mathbf{M}_{b}=\text{diag}[m_{1},\ldots,m_{n}]=\left[\begin{array}{ccc}m_{1} &&\\ &\ddots&\\ &&m_{n}\end{array}\right], \tag{2}\]  
where \(m_{i}\) is the mass of the \(i\)th story. The stiffness matrix has the following tridiagonal structure:  
\[\mathbf{K}_{b}=\left[\begin{array}{cccccc}k_{1}+k_{2}&-k_{2}&&\\ -k_{2}&k_{2}+k_{3}&-k_{3}&&\\ &\cdots&\cdots&\cdots\\ &&&-k_{n-1}&k_{n-1}+k_{n}&-k_{n}\\ &&&-k_{n}&k_{n}\end{array}\right], \tag{3}\]  
where \(k_{i}\) is the stiffness coefficient of the \(i\)th story. The stiffness matrix can be computed in the form  
\[\mathbf{K}_{b}=\mathbf{P}\,\text{diag}[k_{1},\ldots,k_{n}]\,\mathbf{P}^{T}, \tag{4}\]  
where \(\text{diag}[k_{1},\ldots,k_{n}]\) is the diagonal matrix defined by the story stiffness coefficients and \(\mathbf{P}\in\mathbb{R}^{n\times n}\) is the upper band-diagonal matrix  
\[\mathbf{P}=\left[\begin{array}{cccccc}1&-1&&&&&\\ &1&-1&&\\ &&\cdots&\cdots&\\ &&&1&-1\\ &&&&1\end{array}\right], \tag{5}\]  
with the following elements:  
\[\begin{cases}[P]_{i,j}=1,&i=1,\ldots,n,\\ [P]_{i,j+1}=-1,&i=1,\ldots,n-1,\\ [P]_{i,j}=0,&\text{otherwise}.\end{cases} \tag{6}\]  
When the story damping coefficients \(c_{i}\), \(i=1,\ldots,n\) are known, the structure of the building damping matrix \(\mathbf{C}_{b}\) is similar to the structure of the stiffness matrix in Equation (3) and can be computed in the following form:  
\[\mathbf{C}_{b}=\mathbf{P}\,\text{diag}[c_{1},\ldots,c_{n}]\,\mathbf{P}^{T}. \tag{7}\]  
Frequently, however, the damping coefficients \(c_{i}\) cannot be properly determined and an approximate damping matrix \(\mathbf{C}_{b}\) is computed by setting a suitable damping ratio on some of the building vibration modes [32]. Specifically, for the five-building model used in the numerical examples discussed in Sections 4 and 5, the matrix \(\mathbf{C}_{b}\) has been computed as a Rayleigh damping matrix by setting a 2% of relative damping in the first and fifth modes (see Equation (70)).
## 1 Introduction
### Related Works
The authors presented an RL-based actor-critic scheme for channel estimation without the assumption of ideal CSI. The authors mainly depend on the agent to bring and utilize the pilot symbols into the estimation process and then employ the estimated channel parameters to create downlink beamforming matrices. To satisfy the purpose of maximizing the sum rate reward, network parameters are adjusted based on the deep policy gradient method. The results proved that the suggested channel estimation algorithm can provide convergence and stable performance under various channel statistics and can perform better than the typical MMSE procedure when the sum rate metric is examined. In [16], the authors developed a Deep Reinforcement Learning (DRL) method for device-to-device pairing to understand the correlation patterns between wireless networks. The introduced RL algorithm is adopted to explore the joint channel selection and power control problem for device-to-device pairing and to boost the weighted sum rate. Based on the suggested DRL learning procedure, each device-to-device pair can make use of the outdated and local information to understand the network parameters and perform decisions independently. Results showed that without a global CSI, the suggested DRL scheme is capable to attain a stable performance close to that achieved using standard analytical approaches. The combination between a DNN as a tool for channel prediction and an optimized power scheme is explored in [17] for the purpose of multiuser detection in the NOMA system. The DNN based Long Short-Term Memory (LSTM) network is developed for channel prediction based on complex data processing. The DNN network is trained on the basis of both the correlation between successive training sequences and the normalised channel statistics. The efficiency of the suggested DNN based LSTM for channel prediction is inspected using different fading models and simulation outcomes, in terms of different performance metrics, have proved that the presented DNN scheme for channel estimation can provide a consistent performance compared to the MMSE procedure even when cell capacity is expanded.
## 2 Principles of Improved Wavelet Energy Entropy and LSTM Neural Network
### LSTM Neural Network  
LSTM neural network is a special type of recurrent neural network, which is a type of neural network with memory and suitable for processing sequences with time scales [18]. Especially for the problem of time series with long span, it is able to memorize longer historical information. The structure of LSTM neural network is shown in the following Figure 1[19].  
The key to LSTM is the cell state, which is analogous to a transmission band, a straight line located at the top of the structural map. The state of the cell moves along this line with only a small number of linear operations. In this way, cellular information is not overly modified after passing through a cell. The superiority of the LSTM is its ability to selectively modify the state of the cell, and this is achieved by a gate which selectively allows information to pass through. The gate consists of a sigmoid neural network layer and a point multiplication operation.  
The operation of a single LSTM unit can be divided into four steps as follows:  
(1) Step 1  
First, the LSTM must decide to discard some of the information in the cell state, i.e., the \"forgetting layer\". The sigmoid function is used to determine the amount of information to be retained by using the output, ht-1, of the previous moment and the input, \(x_{t}\), of the current moment.  
Figure 1: LSTM neural network structure diagram.  
(2) Step 2  
The amount of information stored in the cell state is determined. First, the sigmoid layer, which is the \"input gate layer\", also outputs a value between 0 and 1 to determine the amount of information to be remembered; second, the information passes through the tanh layer to create a candidate vector, Ct'. This vector is then fed as additional information to the cell's state.  
(3) Step 3  
Update the value of Ct. First, multiply the forgetting factor, ft, obtained in the first step to obtain the information to be forgotten. Add the candidate vector to get the new cell state, Ct.  
(4) Step 4  
Require the new cell state to pass through the tanh layer to normalize the value between \(-1\) and 1; pass the two external inputs through the sigmoid layer to determine the amount of cell information output. The product of the two quantities is used as the final output quantity.
## 5 Results and Discussion
### Enhanced Consistency of DL Model Performance Using Monte Carlo Dropout
The heatmap displays the \(p\)-values for pairwise comparisons of classification accuracy between all tested models. Darker colors indicate a statistically significant difference (\(p<0.05\)). performance difference between _EEGNet_ and _TCNet Fusion_ was not statistically significant (\(p=0.6191\)), indicating they are competitively matched without MCD. However, after applying our framework, the regularized models demonstrate a clear advantage. For instance, _MCD-EEGNet_ significantly outperforms the baseline _TCNet Fusion_ (\(p=0.0053\)). This is a powerful result, as it shows that a simpler architecture (_EEGNet_) equipped with our proposed uncertainty-aware regularization can surpass a more complex baseline model. Conversely, the simplest model, _ShallowConvNet_, was often significantly outperformed by the more advanced regularized models, underscoring the combined benefit of advanced architecture and robust regularization. To assess the practical significance of our framework, we calculated [PERSON]'s \(d\) for all pairwise model comparisons, with the results visualized in Figure 11. The analysis confirms the consistent positive impact of our MCD-based regularization, yielding small-to-medium positive effect sizes when comparing each MCD-enhanced model to its baseline counterpart: _MCD-EEGNet_ vs. _EEGNet_ (\(d=0.24\)), _MCD-ShallowConvNet_ vs. _ShallowConvNet_ (\(d=0.18\)), and _MCD-TCNet Fusion_ vs. _TCNet Fusion_ (\(d=0.17\)). These values indicate that the integration of channel dropout yields a reliable and practically meaningful improvement in classification accuracy across all architectures, demonstrating that the benefits are consistent and not merely statistical artifacts. Furthermore, the matrix highlights more nuanced relationships, such as the notable positive effect size of the MCD-enhanced _ShallowConvNet_ when compared against the baseline _EEGNet_ (\(d=0.29\)), suggesting that a simpler architecture, when properly regularized with our uncertainty-aware technique, can outperform a more complex baseline model. Overall, this effect size analysis complements our significance testing by providing a quantitative measure of the practical value of our proposed framework for enhancing MI-EEG classification. Figure 11: Pairwise effect size matrix showing [PERSON]’s \(d\) for classification accuracy comparisons between all tested models. The color intensity corresponds to the magnitude of the effect size, providing a measure of the practical significance of the performance difference.
###### Abstract  
The growth of using wind energy on a large scale increases the demand for wind energy conversion machines (WECMs), among these converters, the doubly-fed induction generator (DFIG) is the favorite choice. However, DFIG is very sensitive to wind speed variations and grid faults during operation. In order to overcome these undesirable characteristics, this paper proposes a hierarchical fault tolerant control (FTC) to improve the performance of DFIG. The hierarchical fault tolerant control (FTC) approach consists of pitch angle control (PAC) and maximum power point tracking (MPPT). This hierarchical approach demonstrates the robust response under various (low, rated, and high) wind speed ranges and reduces the undesirable DC voltage overshoots during short-circuit disorder. The simulation results are summarized in a logical table, which depicts the order of controlling scheme and operation for a sustainable energy generation system. The proposed control scheme achieved the healthy and the robust dynamic response without deteriorating the grid power quality or stressing the converters, and approved the effectiveness to suppress the DC voltage overshoots and tolerate the lower down short-circuit disorder to its rated range.  
renewable resources; wind energy; double fed induction generator (DFIG); fault-tolerant control (FTC) +
Footnote †: journal: Applied Sciences
## 3 Results
### High-Temperature Creep Test  
Figure 5a,b provides the creep strains and strain rates at a constant applied load of 80 MPa at 760 \({}^{\circ}\)C as a function of creep time in the Alloy 800H BM and the weldment specimens. The onset of tertiary creep was defined by the 0.2% strain offset from linear secondary creep (Figure 5b), following the criteria in [16]. Table 2 lists the minimum creep strain rate, creep strain at rupture, time to tertiary stage (\(T_{t}\)), and creep time at rupture (\(T_{R}\)). The apparent minimum creep strain rates of the weldment specimens were about one order of magnitude lower than that of the BM specimen. The time to tertiary stage and the time to rupture of the BM specimen were much shorter than those of the weldments. Furthermore, the apparent creep strains of the weldment specimens at rupture were only about 10%; in contrast, the creep strain of the 800H BM specimen reached as high as 45%. In the weldment specimens, the ratios between the time to tertiary stage and time to rupture were close to 0.3, and the ratio for the Alloy 800H BM specimen was only 0.13, indicating that the majority of the creep time was in the tertiary stage in both the BM and the weldment specimens.  
Figure 6 provides images of the ruptured BM and weldment specimens after the creep tests. The base metal specimen was relatively uniform within the gauge section, with slight necking observed near the fracture surface (Figure 6a). Surface cracks were evenly distributed along the gauge section and were perpendicular to the applied loading direction. In both weldment specimens, the FZ and the HAZ adjacent to the fusion boundary experienced relatively uniform deformation, with no surface cracks in these areas. Necking and rupture occurred closed to the unaffected base metal, as shown in Figure 6b,c. This localized deformation was attributed to the lower hardness of the unaffected base metal compared to the FZ and HAZ, as illustrated in Figure 3. The materials were cut from different locations in the tested weldment specimen, labeled as 1, FZ; 2, HAZ adjacent to the fusion boundary; 3, base metal region close to rupture surface; and 4, un-deformed region in the threaded section. Subsequent sections in this paper will present and compare the microstructures of as-received 800H BM and as-welded materials, as well as the post-creep weldment specimens.  
Figure 5: Creep curves of the Alloy 800H BM and WM specimens with Inconel 625 and Haynes 230 filler materials at 760 \({}^{\circ}\)C and 80 MPa. **(a)** Creep strain. **(b)** Creep strain rate as a function of time.
###### Abstract  
In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.  
Keywords:cross-validation; data localization; distributed data; model selection; variable selection +
Footnote †: This work was performed while at the University of Illinois at Chicago and is not associated with Amazon.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 4 Simulation Results
### VTB Decoding Simulation  
This scheme uses Matlab 2017 software to simulate the module of VTB decoding. The relationship curve between the Viterbi decoding bit error rate and the normalized signal-to-noise ratio is shown in Figure 25.  
It can be concluded from the figure that when the bit error rate is \(10^{-3}\), \(10^{-5}\), and \(10^{-7}\), respectively, the \(\mathrm{E_{b}}/\mathrm{N_{0}}\) and decision gain are as shown in Table 4.  
It can be concluded from the means of evidence above: (1) For (2, 1, 7) convolutional codes, when the free distance is 10, the upper bound of the coding gain is 7 dB; (2) The coding gain increases as the bit error rate decreases, but it will not rise infinitely.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Pattern-Based Privacy
Figure 13: Application of Obfuscation Techniques to Conceal Private Patterns in a Data Stream. The key strengths and weaknesses of pattern-based privacy approaches are the following:  
Strengths:  
Pattern-based privacy does not degrade the data quality of the measurement data. Due to the public and private patterns, sensitive information can be filtered out in a target-oriented manner. Weaknesses:  
The computation of an optimal configuration, i.e., the maximization of the quality metric, is very complex. A pattern-based privacy approach requires full control over incoming and outgoing data streams of a data processing system in order to effectively apply the required obfuscation techniques.
## 2 Related Work and Motivation
### Motivation  
The motivation for our research paper stems from the observation that much of the existing research in the field has predominantly focused on two key areas: the utilization of renewable energy sources to power base stations and the development of smart grids through hardware solutions. These approaches focus on changing the energy resources but do not significantly address curtailing energy consumption in cellular communication. Some authors have also explored technologies such as mMIMO, mmWave, HetNet, and dynamic base station management strategies based on traffic patterns. While these areas have seen significant attention, we noted that the potential of technologies like C-RAN, SDN, and NFV remains underexplored in the context of cellular network sustainability.  
Although a few papers have introduced the fundamentals of C-RAN, SDN, and NFV, there is a notable gap in fully harnessing their capabilities within the broader cellular network architecture. This research gap serves as a source of inspiration for our work. Our objective is to make these networks more sustainable and energy efficient, leveraging the full potential of these technologies to address the evolving needs of the telecommunications industry. The following sections explore these technologies, beginning with the role of SDN in enabling programmable and energy-efficient networks.
# Global Formulation of the Synthetic Storm Technique Oriented to Satellite Link-Budget Design
## 5 Overall Error and Discussion  
From the scatter plots shown in Figure 4 for Spino d'Adda and in Appendix A for the other sites, it is useful to calculate the average value and standard deviation of the error versus attenuation, in two disjoint ranges of probability, namely 10% to 0.1% and 0.1% to 0.01%, and in two disjoint ranges of elevation angle, \(\theta\leq 30^{\circ}\) and \(30<\theta\leq 60^{\circ}\), without distinguishing frequency, which plays a very minor role in error statistics, as the parameter \(m\) depends very little on frequency (see the modelling in Section 3, or [38]).  
These different ranges are useful because communication systems may be designed in the upper range (10% to 0.1%) instead of the more common lower range (0.1% to 0.01%), or with low elevation angles (in low satellite orbits, such as in LEO constellations). Our choice is due to the extremely large values of rain attenuation found at low probabilities or in low elevation angles, which would require, therefore, unavailable system power margins, if site diversity or other fade countermeasures are not used.  
Figure 5 (probability range 10% to 0.1%) and Figure 6 (probability range 0.1% to 0.01%) show these statistics, calculated by considering all sites, in the range \(0<A_{SST}\leq 40\) dB, which should be a realistic range of tolerable attenuations and therefore power margins to provide to the link budget. From these results, we can derive the following conclusions.  
In the probability range 10% to 0.1% (Figure 5):  
1. The error is always negative; in other words, the global SST tends to underestimate the attenuation.
2. There is a significant difference between the elevation angle ranges, with the lower elevation angle range more accurate.
3. The relative error is approximately \(-10\%\) for \(\theta\leq 30^{\circ}\) and about \(-20\%\) for \(\theta>30^{\circ}\).
4. The standard deviation is about 5% for both elevation angle ranges.  
In the probability range 0.1% to 0.01% (Figure 5):  
1. The error is always negative; the global SST tends to underestimate the attenuation.
2. There is not a significant difference between the two elevation angle ranges.
3. The relative error is approximately \(-15\%\).
4. The standard deviation is about 5% for \(\theta>30^{\circ}\) and reaches 15% for \(\theta\leq 30^{\circ}\).  
Finally, notice that, for any probability, the error is negligible in the range \(70^{\circ}\leq\theta<90^{\circ}\) and it is zero for \(\theta=90^{\circ}\) because at the zenith, the global SST coincides with the full SST [1].  
Figure 5: (a) Average (continuous lines) and standard deviation (dashed lines) of the error \(\varepsilon\) versus \(A_{SST}\); (b) \(\varepsilon_{r}\). Probability range 10% to 0.1%.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
###### Abstract  
Incremental learning is a methodology that continuously uses the sequential input data to extend the existing network's knowledge. The layer sharing algorithm is one of the representative methods which leverages general knowledge by sharing some initial layers of the existing network. To determine the performance of the incremental network, it is critical to estimate how much the initial convolutional layers in the existing network can be shared as the fixed feature extractors. However, the existing algorithm selects the sharing configuration through improper optimization strategy but a brute force manner such as searching for all possible sharing layers case. This is a non-convex and non-differential problem. Accordingly, this can not be solved using powerful optimization techniques such as the gradient descent algorithm or other convex optimization problem, and it leads to high computational complexity. To solve this problem, we firstly define this as a discrete combinatorial optimization problem, and propose a novel efficient incremental learning algorithm-based Bayesian optimization, which guarantees the global convergence in a non-convex and non-differential optimization. Additionally, our proposed algorithm can adaptively find the optimal number of sharing layers via adjusting the threshold accuracy parameter in the proposed loss function. With the proposed method, the global optimal sharing layer can be found in only six or eight iterations without searching for all possible layer cases. Hence, the proposed method can find the global optimal sharing layers by utilizing Bayesian optimization, which achieves both high combined accuracy and low computational complexity.  
Bayesian optimization; incremental learning; layer sharing algorithm +
Footnote †: journal: Applied Sciences