esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Framework  
### Overall Network Architecture  
Figure 1 presents the overall network architecture of our proposed framework. This framework globally comprises three modules: GAN modules of two types and a semantic segmentation module.  
For conventional semantic segmentation methods [81; 82; 83] based on supervised learning, source images and corresponding label images were prepared in advance. Training images including validation images and test images were extracted from these image sets. Our proposed framework provides image augmentation using GANs of two types. Subsequently, source and augmented images and labels were provided to the semantic segmentation module for pixel-wise segmentation. The utilization of both GAN modules provides not only data augmentation based on image generation, but also automatic labeling for the limited number of source images. The conditional GAN module is applied if a source image and its corresponding label are present. By contrast, the unconditional GAN module is applied if source images are solely present without label images.
## 3 Near-Space Passive Radar Remote Sensing
### Radar Configurations
Interferences from nadir and/or direct-path signals can then be suppressed by appropriate null-steering. A further potential is moving target indication (MTI). This becomes possible by a combined spatial and temporal processing of the recorded signals that allows for a directly selective suppression of narrow Doppler frequency bands from stationary clutter [51]. Furthermore, optimum processing schemes for MTI may be derived from the theory of space-time adaptive processing (STAP) [52, 53]. Figure 4: Near-space passive radar with digital beamforming on receive. Figure 3: Configurations of near-space passive radar system: **(a)** one receiver with two channels; **(b)** multiple receivers.
# Parallel Computation for Inversion Algorithm of 2D ZTEM
## 2 2D ZTEM Forward Modeling and Inversion
### 2D Inversion Modeling  
The forward model is as follows:  
\[d=F(m)+e \tag{6}\]  
\(d\) is the data vector, \(m\) is the model vector, \(e\) is the error vector, and \(F\) is the forward function [9; 10; 11].  
The data vector \(d=\left[d^{1},d^{2},\cdots,d^{N}\right]^{T}\), \(d^{i}\) is the tipper in an observation point. The model vector is \(m=\left[m^{1},m^{2},\cdots,m^{M}\right]^{T}\). The number of the grid is \(M\), and \(m^{j}\) is the resistivity of the unit.  
The objective function is as follows:  
\[\psi(m)=(d-F(m))^{T}V^{-1}(d-F(m))+\lambda m^{T}L^{T}Lm \tag{7}\]  
\(\lambda\) is the regularization parameter; \(V\) is the variance of error vector \(e\); the matrix \(L\) is the Laplace operator. The first item is the data-fitting difference, and the second item is the model-smoothing item [12; 13; 14].  
Because a great deal of time is spent computing the Jacobi matrix, in order to save run time, the non-linear conjugate gradient inversion algorithm avoids computing the Jacobimatrix. It computes the product of the Jacobi matrix (or the Jacobi transpose matrix) and a vector x. We need to perform two pseudo-forwards and then obtain \(A^{T}V^{-1}e\) and \(Ap\). Figure 1 shows the flow chart of inversion.  
This requires a great deal of time to compute the conjugate of the object function \(g=-2A^{T}V^{-1}e+2\lambda L^{T}Lm\) and \(f=Ap\). When we obtain the value of \(A^{T}V^{-1}e\) and \(Ap\)[15], we can obtain the step length \(\alpha\) and update the model \(m=m+\alpha p\).
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 2 Research Status and Development Trend at Home and Abroad  
To establish a prediction method for ultra-low cycle fatigue damage of steel bridge piers during earthquakes, scholars have explored the life, damage locations, and damage processes of ultra-low cycle fatigue in steel and steel structures. Currently, there are three primary methods for predicting ultra-low cycle fatigue damage in steel structures: the empirical formula method, the CVGM, and the CDM model. This section reviews the current research on these three prediction methods, focusing on their principles, applicability, and prediction accuracy.
## Appendix
If colleges choose from contract set \(B\), just like from all contracts, they would set score limit to \(\underline{t}\); so, \(G(B)=S\). It is easy to see that \(A\cup B=E,A\cap B=S\); therefore, \(S\) is three-stable. Proof of Statement 7.: In Figures 2 and 4 and in the following Figures 9 to 13, the upper nodes symbolize the colleges, the lower nodes the applicants and the edges between them are the possible contracts. If we write \(a>b\) to a node, that means the given applicant prefers contract \(a\) to \(b\). In all examples, every student gets a score of zero everywhere, except in Figure 13, where contract \(b\) has a score of one; so, \(b\) is better for the college. If a node has only one incident edge, it always chooses that, if it is available. Figure 11: Example graph 5. Figure 12: Example graph 6. Figure 9: Example graph 3. Table 1 describes the choice functions, usually as a direct sum of the choice functions of individual colleges/applicants. Notation \(Q_{1}(a,b)\) means a college chooses from equally good contracts \(a\) and \(b\) and its quota is one. We use the abbreviation \"path ind.\" for path independence. Table 2 shows the stable sets for each notion for the seven examples. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Figure** & **Three-Stable** & **Four-Stable** & **Dominating Stable** & **Score-Stable** & \(\underline{t}\)**Fixed** \\ \hline
2 & \(\emptyset\) & \(\emptyset,\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\emptyset\) & \(\emptyset\) \\
4 & \(\emptyset,\{a\}\) & \(\emptyset,\{a\}\) & \(\{a\},\{b\}\) & \(\{a\}\) & \(\emptyset,\{a\}\) \\
9 & \(\{a,d\},\{c,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) \\
10 & \(\{a\},\{c\}\) & \(\{a,c\}\) & \(\{a,c\}\) & \(\{a\}\) & \(\{a\}\), \\
11 & \(\emptyset,\{a\}\) & \(\{a\}\) & \(\{b\},\{a,c\},\{a,d\}\) & \(\{a\}\) & \(\{a\}\) \\
12 & \(\emptyset,\{a,d\}\) & \(\emptyset,\{a,d\}\) & \(\{a,d\},\{b,c\}\) & \(\emptyset,\{a,d\}\) & \(\emptyset,\{a,d\}\) \\
13 & \(\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\{a\}\) & \(\{a\},\{b\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stable sets in case of different stability notions. Figure 13: Example graph 7. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Figure** & **Three-Stable** & **Four-Stable** & **Dominating Stable** & **Score-Stable** & \(\underline{t}\)**Fixed** \\ \hline
2 & \(\emptyset\) & \(\emptyset,\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\emptyset\) & \(\emptyset\) \\
4 & \(\emptyset,\{a\}\) & \(\emptyset,\{a\}\) & \(\{a\},\{b\}\) & \(\{a\}\) & \(\emptyset,\{a\}\) \\
9 & \(\{a,d\},\{c,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) & \(\{a,d\}\) \\
10 & \(\{a\},\{c\}\) & \(\{a,c\}\) & \(\{a,c\}\) & \(\{a\}\) & \(\{a\}\), \\
11 & \(\emptyset,\{a\}\) & \(\{a\}\) & \(\{b\},\{a,c\},\{a,d\}\) & \(\{a\}\) & \(\{a\}\) \\
12 & \(\emptyset,\{a,d\}\) & \(\emptyset,\{a,d\}\) & \(\{a,d\},\{b,c\}\) & \(\emptyset,\{a,d\}\) & \(\emptyset,\{a,d\}\) \\
13 & \(\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\{a\},\{b\}\) & \(\{a\}\) & \(\{a\},\{b\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The choice functions for the seven examples.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results under Event Overlap  
The results obtained for the Overlapped set are depicted in Figure 7. The Macro \(F_{1}\) performance of the single-resolution systems decreases to 20% or below, whereas the multiresolution fusions _3 res_ and _5 res_ offer a slight improvement, reaching 22% Macro \(F_{1}\). However, this decrease of performance is not equally observed in each event category. Attending to the relative difference between the results with overlap and over the original data, some classes (Alarm/bell/ringing, Dishes, or Running water) present between 60% and 70% relative decrease in \(F_{1}\), while other categories (Speech or Vacuum cleaner) only suffer a relative decrease of around 25%.  
Additionally, not all categories obtain higher \(F_{1}\) scores when applying multiresolution: For Alarm/bell/ringing and Frying, the best performance is obtained with \(T_{++}\). In the case of Running water, \(T_{+}\) slightly outperforms the multiresolution systems in terms of \(F_{1}\) score. However, in these cases the performance of multiresolution is near the best  
Figure 5: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and the multiresolution systems (_3 res_, _5 res_) over DESED Public Evaluation set applying dynamic range compression. “Macro F1” shows average score over 10 target categories. Best viewed in color.  
Figure 6: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and multiresolution systems (_3 res_, _5 res_) over DESED Public Evaluation set applying clipping saturation. “Macro F1” shows average score over 10 target categories. Best viewed in color.  
single-resolution result, and above other resolution points that obtain lower \(F_{1}\) scores. Only for the Dishes category multiresolution supposes a decrease in performance with respect to every single-resolution system.
# Gravitational Particle Production and the Hubble Tension
## 6 Conclusions  
Gravitational particle production (GPP) of scalar particles and its contribution to Hubble parameters are studied in the era after decoupling till the present, and their phenomenological implications in the context of the Hubble tension are discussed. No new physics is employed in the present study. The model used here is just the standard \(\Lambda\)CDM. The only new element is the inclusion of the effect of GPP that was neglected in previous studies. It is observed that the effect of GPP is to raise the value of the Hubble constant in direct measurements. This effect may be significant if production of extremely heavy scalar particles is allowed phenomenologically at sufficiently high energies (even when they do not exist at present). The raised value of the Hubble constant (due to GPP) is the value of the Hubble constant that is measured in direct local measurements such as the Type Ia supernovae measurements. On the other hand, the value of the Hubble constant relevant to recombination calculations is the one without the effect of GPP. The Hubble parameter after inclusion of GPP is an overall constant times the Hubble parameter before inclusion of GPP. Therefore, the evolution of the Hubble parameter after inclusion of the effect of GPP is the same as its form before inclusion of the effect of GPP. In other words, no new physics is introduced here; only an explanation for the presence of two different classes of measurements of the Hubble constant (from direct and indirect measurements) is given. This may be a clue towards the solution of the Hubble tension. In future, further studies on this topic may be helpful to see all implications and details of the scheme introduced here. In particular, study of possible limitations of the use of gravitational particle production in cosmology (in the view that gravity is studied in a classical setting while matter particles and forces are studied and are quantized) may be useful.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** Author declares no conflict of interest.
## 1 Introduction
Cellular evolution mechanism is also a key issue in CA model construction, including seed selection, seed planting, and seed growth mechanism [29]. In the dynamic iteration process of the CA model, many studies use random algorithms to simulate the randomness and decisiveness of LUCC in real scenes when selecting seeds [30], random algorithms help determine the precise location of LUCC [29]. [PERSON] et al. [31] used a random selection algorithm and threshold to determine the starting seed point of patch growth inthe iterative process of the CA model. [PERSON] et al. [14] used the Monte Carlo algorithm to generate change seeds on the surface of land use/cover growth probability and used the decline threshold to control the organic growth and natural growth of patches. However, the existing random algorithms cannot fit the complex geographical phenomena in nature well but will cause a loss of simulation accuracy and a waste of time. To solve this problem, we propose the high-quality seed selection iterative algorithm (HQSSIA), which uses the existing land conversion probability to select the changed seed points, and locates the cells with higher conversion probability faster and more accurately. In this paper, a RF-CNN-LSTM-CA land use change simulation model based on HQSSIA is proposed. The model mainly includes three important parts: (1) using CNN-LSTM model to extract spatio-temporal features from land use/cover data and inputting them into RF model as one of the driving factors; (2) using RF model to establish the relationship between spatial variables and LUCC observation data, so as to extract the change suitability of LUCC; and (3) completing the simulation of LUCC in the dynamic iteration module of the CA model based on HQSSIA. This model not only retains the interpretability of the traditional RF-CA model, but also gives full play to the strong spatio-temporal feature mining advantages of deep learning. In order to make the land use model better mine the neighborhood effect in the process of long-term land use change in the study area, the CNN-LSTM model is introduced to improve the learning of the spatio-temporal characteristics of neighborhood drivers in the traditional RF-CA model. In order to solve the problem of low accuracy and efficiency of seed selection in the dynamic iterative module of CA model, a CA model based on HQSSIA is designed. Using the proposed model, this paper analyzes the influencing factors of land use change in Chongqing main city metropolitan area from 2015 to 2020, and predicts the land use pattern of Chongqing main city metropolitan area in 2025.
## 1 Introduction
Finally, the performance benefits from the multi-level signaling with joint-PPE scheme are validated and compared with MMSE based post-equalization only (PE-only) with the help of an experimental setup.
## 3 The Proposed Approach
### Proposed Navigating Arm Movement Pattern  
Consider the position (solid lines) obtained from three participants performing the task using the conventional approach as shown in Figure 3. The dotted lines in the center of the figures are the target positions. Because the operator is allowed to move freely within the path, it can be seen that the variations in Figure 3a-c are the different combinations of lack of accuracy and lack of precision, which are difficult to predict. Therefore, any given movement must be specified in advance.  
If we consider Figure 3c, the human-made line has a fluctuating pattern around the target that is similar to the noise in an electrical signal, which is usually smoothened out using a linear estimator. Thus, we came up with the concept that there must be a movement pattern that allows an operator to move more comfortably. Then, we added a tool into the process, such as a linear estimator (in case of a straight path), to improve the accuracy and precision. Therefore, we drew on this concept to address the variation in the position of the master controller by allowing the operator to move the master controller in a wave-like pattern around the target, as shown in Figure 4.  
Figure 3: The nozzle position obtained from three participants performing the task using the conventional approach: (**a**) a participant who lacks accuracy (**b**) a participant who lacks precision (**c**) a participant who performs best but the positions still show the lack of both accuracy and precision.  
However, bias could occur in the positions such that the mean of the positions is far away from the target. In this case, the linearly estimated line will be far away from the target line too. To prevent such bias, the upper and lower boundaries of the path are set as targets for the crests and troughs of the waves.  
The shape of the guideline is formed from a connection of multiple waves of the same size (amplitude and wavelength). As the task is performed by a human operator, human arm movement error causes differences in the amplitude and wavelength. The positions, therefore, form a bell-shape histogram, which imitates the random noise behavior in an electrical signal.  
After this step, a jitter line is ready for the Kalman filter. Then, the Kalman filter is applied to the jitter line to filter it into a straight line.
## 4 Discussion
Therefore, the lack of correlations between the external load and the physical parameters is due to the tactical deteriorations of this playing position [23; 39; 42; 43]. For the wide defenders, a high correlation was only found between vRER and MPWEF, but in other studies there is also correlation between AnT and HSR, DIS6, DIS5, and between VO2 max and DIS6 [47]. A high aerobic level can be beneficial for the repeated high intensity actions that are necessary for these players [40], but AnT is a better indicator to discriminate the aerobic ability of the players [45]. The correlation between MPWEF and vRER can be explained because the external load (using the metabolic power approach) has a high correlation with the physical parameters [28; 71]. The midfielders had correlations between vRER and ACC2, MPWEF, DEC2 and MPW5, and also between VO2 max and DEC3, MPW4, MPW5, MPWEF, but no correlation was found for vVO2 max. For the players of the axon, VO2 max is positively correlated with DIST, DIS < 4 and negatively with DIS5 [47]. A small correlation was found for DIST and medium speed distance (16.1-19 km/h) in the field tests for young players [12]. These correlations are related to the demands of this position, which requires high numbers of accelerations, decelerations and medium speed running, but this is not the case for higher speed running [17]. For the wide attackers, there was a correlation between vVO2 max and ACC2, and MPW5 and MPWEF, but no correlation for VO2 max and vRER. Contrary results from our study showed that AnT was correlated with HSR, DIS5 and DIS6 [47]. The ability to perform repeated high intensity actions is crucial for these players, and this is correlated with MAS (from field tests) [31; 32; 33; 34; 35]. These actions consisted of ACC and high metabolic energy actions [29], which can probably explain the correlations that found in our study. For the strikers, there was correlation between vRER and MPWEF, and between vVO2 max and ACC3, but no correlation for VO2 max. [PERSON] et al. [47] only found a correlation between DIS < 4 with VO2 max and AnT. Also, different correlations were found by [PERSON] et al. [12] in young football players. This can be explained by noting that the strikers together with the central defenders have lower external load demands [68]; therefore, they do not use their maximal physical potential [23; 39; 42; 43]. This is possibly the reason why they do not have as many correlations. The study has some limitations. The main limitation of this study is that the data were collected from only one team, which was one of the top teams of the relevant league. Therefore, the results could be generalized only for similar samples or subjects. Additionally, the data concerning only the first semester for each of the two seasons were collected, so it is possible the data from the rest of the season could influence the results. Furthermore,the physical testing was conducted at the beginning of the season and the observed data lasted four months, so it is possible that the physical performance of the players would have changed during this period.
## 6 Simulation-Based Implementation, Experimental Results and Discussion
### Comparative Analysis of E-BIOSARP and ESPA  
E-BIOSARP and a recent ESPA [29] were compared in an ns-2-based simulation analysis. The network topology was defined using the simulation parameters given in [29]. ESPA exhibits much better performance than EEBAR [46] and SERP [15], as presented in [29]. A WSN with 300 wireless sensor nodes was deployed in a 300 m \(\times\) 300 m region, with a base station with ID 0 in the center of the network. The real-time traffic scenario was configured by using a UDP transport layer to handle the generated CBR traffic. The average difference in energy consumption between E-BIOSARP and ESPA is presented in Table 10, revealing a 7.6% increase in energy consumption when E-BIOSARP is utilized in a WSN. The advantage of E-BIOSARP based on random key encryption is illustrated in Figure 14. As the level of malicious activity increases, the performance of ESPA dramatically decreases, whereas that of E-BIOSARP remains more stable.  
Figure 13: Packet overhead comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Packet Rate (Packets/s)**} & \multicolumn{3}{c}{**Detection Rate and Accuracy in Percentage**} \\ \cline{2-4}  & **SRTLD** & **E-BIOSARP** & **Difference** \\ \hline
1 & 85/110 = 77.3\% & 109/128 = 85.2\% & 7.9\% \\
2 & 128/161 = 79.5\% & 137/154 = 89\% & 9.5\% \\
4.84 & 196/243 = 81\% & 134/151 = 88.74\% & 7.74\% \\
6.38 & 187/239 = 78.24\% & 140/151 = 92.72\% & 14.48\% \\
10.64 & 144/176 = 82\% & 411/429 = 95.8\% & 13.8\% \\ Average Accuracy & 79.608\% & 90.292\% & 10.684\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Detection rate and accuracy comparison.
## 3 Statistical Methods
### Goodness-of-Fit Test
#### 3.2.3 Log-Normal Distribution  
The log-normal distribution is used for performing statistical analysis on high-voltage engineering data to analyze the characteristics of insulating materials [23; 24]. This distribution is a probability distribution in which the natural logarithm of the data follows a normal distribution. In other words, if a variable \(x\) follows a log-normal distribution, the logarithm of that variable, \(ln(x)\), follows a normal distribution. The log-normal distribution is mainly suitable for data that only have positive values and is used when the data are asymmetric with a right-skewed tail. The pdf and cdf of the log-normal distribution are expressed as follows [39]:  
\[D_{LN}(x)=\frac{1}{\sigma x\sqrt{2\pi}}\text{exp}\left(-\frac{(\ln(x)-\mu)^{2 }}{2\sigma^{2}}\right)\text{ for }x>0 \tag{13}\]  
\[F_{LN}(x)=\int_{0}^{\infty}D_{LN}(x)dx \tag{14}\]  
where \(\mu\) represents the mean of the logarithmic values and \(\sigma\) represents the standard deviation of the logarithmic values. This distribution, similar to the normal distribution, allows for the calculation of statistical metrics such as mean and variance. Unlike the normal distribution, however, it provides a more suitable model when the data distribution is skewed to one side. For a more detailed understanding of the log-normal distribution, readers are encouraged to refer to the relevant literature [23; 39].