esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The morphology of the joint surface is multi-scale, and it can be divided into first-order asperity (waviness) and second-order asperity (unevenness). At present, the joint roughness characterization formula considers only the morphology contribution of waviness and unevenness components and does not fully consider their mechanical contribution. At same time, the relationship between the mechanical contribution and the morphology contribution is still unclear. Thus, the characterization formula considering the mechanical contribution of waviness and unevenness needs to be further studied. In this study, the standard joint roughness coefficient (JRC) profiles were first decomposed into waviness and unevenness. Then, three types of joint specimens with different asperity orders (flat, the standard JRC profile, and the profile containing only waviness) were prepared by the 3D engraving technique. Finally, direct shear tests were carried out on 39 sets of red sandstone joint specimens under three normal stresses. The mechanical contributions of waviness and unevenness were studied, the relationship between the mechanical contribution and the morphology contribution of waviness and unevenness was analyzed, and the characterization formula considering the mechanical contribution of waviness and unevenness was established. The results showed that the following: (1) the method combining the ensemble empirical mode decomposition (EEMD) and the critical decomposition level could be used to separate the waviness and unevenness from the joint surface; (2) the mechanical contribution of the waviness and unevenness decreased with the increase in normal stress; (3) the relationship between the mechanical contribution ratio and the statistical parameter ratio of the waviness and unevenness can be describe by power function; and (4) the roughness characterization formula considering the mechanical contribution and morphology contribution was established. This study will enhance the accurate evaluation of the roughness coefficient and shear strength of the joint specimen.  
joint roughness coefficient; asperity order; mechanical contribution; shear test; 3D carving
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Simulation Parameters
#### 5.1.2 MAC Layer Simulation Parameters  
Regarding the configuration of the superframe, both BO (macBeaconOrder) and SO (macSuperframeOrder) parameters are set with the same value, so that there is no inactive period in the superframe. Since this work is intended to evaluate the performance of uplink communications during the CAP, we decided not to assign any slot of the superframe to the CFP. Thus, the entire superframe is available to perform transmissions using the CSMA/CA based slotted random algorithm. The value assigned to both the BO and SO parameters is:  
\[BO=SO=9 \tag{10}\]  
We performed several simulations using larger values for both \(BO\) and \(SO\) parameters, up to the maximum limit allowed by the standard when the superframe is used (\(SO=BO=14\)). We checked that network performance tends to improve as the value used for both BO and BO increases. The rest of the MAC layer configuration parameters were chosen according to the default values defined in the standard. Table 2 shows the values used for the main configuration parameters of the MAC layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission rate & 1.25 Mbps \\ Optical clock rate & 3.75 MHz \\ Coordinator optical transmission power & 1500 mW \\ Devices optical transmission power & 30 mW \\ Half Power Angle \(\theta_{1/2}\) & 60\({}^{\circ}\) \\ Field of Vision (FOV) & 60\({}^{\circ}\) \\ Photo detector area \((A)\) & 1 cm\({}^{2}\) \\ Photo detector responsivity \((R)\) & 0.54 A/W \\ Optical concentrator gain \((G)\) & 15 \\ Optical filter transmission coefficient \((T)\) & 1 \\ Clear Channel Assessment (CCA) function duration & 8 clock cycles \\ _aTurnaroundTime-TX-RX_ & 0 clock cycles \\ _aTurnaroundTime-RX-TX_ & 8 clock cycles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main configuration parameters of the physical (PHY) layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline _macMaxBE_ & 5 \\ _macMinBE_ & 3 \\ _macMaxCSMABackoffs_ & 4 \\ _macMaxFrameRetries_ & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main configuration parameters of the medium access control (MAC) layer.
# Statistical Multipath Model Based on Experimental GNSS Data in Static Urban Canyon Environment
## 4 Statistical Model Analysis
### Multipath Time Delay
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Function** & **Gamma** & **Burr** & **Rayleigh** & **Rice** & **Log-Logistic** & **Weibull** \\ \hline Result & 34.2 & 61.6 & 362.2 & 361.8 & 106.1 & 100.9 \\ \hline \end{tabular}
\end{table} 
Table 5: Chi-square test result of time delay distribution fitted by six different functions. each distribution function is credible. In addition, it is difficult to calculate the chi-square test when the raw experimental data are insufficient. However, the overall trend of the histogram is consistent with each distribution. Figure 7 shows the mean of multipath time delay with different elevation angles. The mean value is nearly linearly decreasing with elevation angle increases. The fitted distribution is given as follows:  
\[f(x;a,b)=a\cdot x+b \tag{9}\]  
where \(x\) is the elevation angle. The parameters \(a=-3.3\) (\(-3.9\), \(-2.7\)) and \(b=358.3\) (\(325.3\), \(391.3\)), with 95% confidence bounds. This illustrate that the mean of multipath time delay decreases approximately 3 m when the satellite elevation increases 1 degree. The mean of multipath time delay for all elevation angles is approximately 200 m.  
Figure 8 gives the cumulative distribution probability (CDF) of multipath time delay with different elevation angles. It illustrates that the cumulative probability is larger as the satellite elevation angle increases. The multipath time delays corresponding to 50% and 90% cumulative probability are marked in this figure. More than half of the delay is less than three hundred meters, and most of the delay is less than six hundred meters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elevation** & **(0, 15)** & **(15, 30)** & **(30, 45)** \\ \hline \(\chi^{2}/\chi^{2}_{0.01}/k-1\) & 31.6/46.9/27 & 28.1/46.9/27 & 34.7/46.9/27 \\ \hline
**Elevation** & **(45, 60)** & **(60, 75)** & **(75, 90)** \\ \hline \(\chi^{2}/\chi^{2}_{0.01}/k-1\) & 32.2/42.9/24 & 37.0/41.6/23 & - \\ \hline \end{tabular}
\end{table} 
Table 6: Chi-square test result of distribution with different elevations. Figure 8: Cumulative distribution of time delay with different elevation angles. Figure 7: The fitted distribution for mean of multipath time delay with different elevation angles.
## 3 Latest Applications in Distribution Systems
### Topology Identification  
Key functionalities, such as state estimation, power flow analysis, and voltage control, are built on known network topology. With the expanding integration of DERs, the network topology changes more frequently, making it difficult for real-time topology identification in distribution systems. On the other hand, the recent surge in smart grid technologies has led to the development of AMIs and PMUs, increasing the availability of high-resolution real-time measurements. Thus, it is of interest in distribution networks to utilize this information to develop topology identification methods [7].  
Conventional model-based approaches for topology identification assume complete observability from available nodal voltage and line measurements [71; 72]. Furthermore, works that consider systems with missing nodal voltage measurements, such as [73; 74], leverage relevant historical measurements for data imputation. Realistic distribution systems are featured by partial observability due to limited available sensors that should be considered for applicable topology identification [56]. The metrics of voltage sensitivity are exploited [55] to reconstruct the network topology with partial observability. Based on node-to-node distances, candidate topologies are generated, and the optimally matched topology is determined by GL to minimize a multi-criteria objective regarding voltage sensitivity, magnitudes, and correlation coefficients. A Bayesian network (BN) is adopted in [57] for its robustness against missing data, as BN can capture the probability of partial observability of the network. Then, the switching relationship between different topologies is exploited to develop a topology graph bank. Specifically, by exploiting a limited number of possible posterior topologies produced from switch positions, an estimated topology is obtained within the according bank based on real-time measurements, and any unobservable nodes can be estimated by power flow analysis or state estimation techniques from the estimated topology. A similar approach, in regard to utilizing the set of possible topologies that can be achieved from a specific topology, is adopted in [58].  
References [59, 60] combine GL with a deep attention mechanism to overcome the traditional scalability and generalizability limitations of GL. Considering different features of voltages and power, [59] implements a dual-graph attention-based framework that applies separate convolution layers for voltage and power measurements to learn their features at a deeper level. Then, an attention pooling layer is adopted to select the most relevant nodes for topology identification. A graph transformer network (GTN), which introduces the concept of transformers [12] to GNN, is adopted in [60] mainly to establish meta-paths and extract useful correlations within these paths that traditional GNNs fail to capture.
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler  
MPTCP mainly has two important flow managing mechanisms, namely, congestion control and packet scheduler. The congestion control is in charge of controlling induced network load, and to avoid network congestion. The traditional single path TCP utilizes the congestion avoidance algorithm, which includes the slow start scheme and additive increase/multiplicative decrease (AIMD) scheme. The MPTCP congestion control mechanism was designed [45] and standardized [46]. On the other hand, the scheduler is responsible for distributing data over the multiple paths. A proper scheduler can improve the goodput (application-level throughput). However, unlike MPTCP congestion control, the scheduler has not been standardized yet. The most commonly used schedulers are round robin (RR) scheduler and shortest smoothed RTT (SRTT). The Linux kernel MPTCP implementation [47] scheduler is based on SRTT. Many studies regarding MPTCP congestion control and scheduler have been conducted in recent years. Meanwhile, there are also some studies that combine MPTCP congestion control and scheduler together to improve transmission performance. For example, [PERSON] et al. [48] proposed a solution to improve the transmission efficiency of short TCP flows. The main idea is to create a path cluster with similar characters based on RTT. Meanwhile, some research efforts that prevent long flows from preempting resources have been made. In the following section, we introduce the studies regarding MPTCP congestion control and scheduler.
## 4 Simulations Results and Discussions
### Cavitation Characteristics  
When the local pressure reduced down to the vapor pressure, cavitation generally occurs, consequently, the gas-filled or gas and vapor-filled bubbles are formed [39; 40]. Usually, jet-induced cavitation occurs in the jet shear layers primarily, at the same time the cores of vortex are present in the jet flow, and the cavitation number is usually defined as Equation (20) for submerged waterjets [5; 41]. The occurrence of cavitation, following the difference between the inlet pressure, the confining pressure and the vapor pressure of the fluid, had been assessed through the cavitation number, the vapor pressure set as \(P_{c}=3169\) Pa in the simulation, and combining with experimental parameters. In this study, the cavitation number is \(\sigma=0.085\),  
\[\sigma=\frac{\overline{P}_{a}-P_{c}}{\overline{P}_{in}-\overline{P}_{a}}. \tag{20}\]  
The vapor pressure was defined as \(P_{c}=3169\). Pa was an absolute pressure, so that we need to change it to gauge vapor pressure as \(P_{\rm g}=P_{c}-\)atmospheric pressure \(=(3169-1.013\times 10^{5})\) Pa \(=-97131\) Pa. As mentioned before, the condition of cavitation is that the simulation flow field pressure is lower than the gauge vapor pressure. The errors ranges reult was shown in Figure 10, the minimum pressure of the simulation flow field was distribution at the throat of the nozzle, the value is \(-1.371\times 10^{6}\), which is lower than the gauge vapor pressure \(P_{\rm g}\), and it can be concluded that strong cavitation occurs at the throat of the nozzle. However, in other places of the simulation domain, the dynamic process of local pressures m did not observe obvious cavitation phenomenon, for the reason that the pressure was higher than the gauge vapor pressure \(P_{\rm g}\). The volume fraction of the vapor phase, as shown in Figure 11, ranges from 0 to 0.676. It can be seen that the strongest cavitation occurs at the start point of the straight section of the nozzle throat, which is clearly shown in the local zoom of Figure 11. As we know, erosion characteristics have a strong correlation with the cavitation intensity, and we can through the cavitation characteristics to realize the control and modulation of the erosion characteristics.  
Figure 10: Pressure contours at the outlet of the nozzle.
## 3 Research Methodology
### Solar Panel Fault Detection Approach  
This section outlines the methodology for solar panel fault detection, with a focus on the architectures of ResNet-50 and Convolutional Neural Networks (CNNs), along with the metrics used for performance evaluation and training, implemented in Python.  
CNNs are frequently utilized in image processing and are very good at locating solar panel defects. Convolutional layers, one of the several layers that make up CNN architecture, are capable of automatically identifying elements in images, such as edges, textures, and patterns that point to defects like fractures or hotspots. The network's efficiency is increased by further processing these features through pooling layers, which lower the dimensionality of the data. The retrieved features are then interpreted by the fully connected layers at the end of the network to determine whether a problem is present in the solar panel photos.  
By adding residual connections, ResNet-50, a more sophisticated deep learning model, expands upon the fundamental CNN architecture. The network can go deeper thanks to these connections without running into the vanishing gradient issue. ResNet-50, with its 50 layers, can extract more complicated characteristics and improve the accuracy of complex defect detection. Compared to a standard CNN, the residual connections allow the model to learn more abstract and detailed features while maintaining its efficiency as it goes deeper.  
Important measures like data augmentation are done to the training dataset to increase the variety of input images and boost the models' capacity to generalize to new data in the training process for both models. Color corrections, flips, and rotations of images may be part of this procedure. The models are trained across a number of epochs using optimization methods to minimize the loss function, such as the Adam optimizer or stochastic gradient descent. To prevent overfitting and guarantee that the models can correctly identify errors in fresh, unviewed photos, the training process is closely watched.  
Several measures are utilized to assess and compare CNN's and ResNet-50's performance. Precision shows if the flaws that have been recognized are accurate, whereas accuracy evaluates the percentage of faults that have been identified correctly. The model's recall evaluates its capacity to find all potential errors, and the F1-score offers a trade-off between recall and precision. Since faster models are preferred for real-time monitoring, inference time--the amount of time needed for the model to analyze an image and create a prediction--is also taken into account. When combined, these measures offer a comprehensive assessment of the two architectures' efficacy and efficiency in solar panel fault detection.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 1 Introduction
### Related Works  
To the best of our knowledge, this work is the first to focus on GAN-based noise modeling to create training sets for speech enhancement. Although similar ideas appear in image enhancement, these are not applied in speech enhancement. In addition, although data augmentation, as a method of preparing training sets, has been widely used to process images and speech [16; 17], it is still based on label preserving transformations. Data augmentation schemes typically deform existing training sets and expand them to create more training sets. This strategy enhances the adaptability of DNN-based networks and solves the problem of small or limited training sets. Moreover, data augmentation is commonly used in image recognition, where transformations such as translation, rotation, scaling, and reflection can significantly improve recognition accuracy [18]. However, speech enhancement tasks commonly involve limited numbers of noisy speech samples or no training set.  
In conclusion, our research presents some originality in solving the DNN-based SE problem with a limited noisy speech dataset. The proposed method increases the robustness of the SE model training when applied to actual speech and for blind speech denoising tasks when the noise type is unlabeled, and the clean speech signal is unavailable.  
The remainder of this paper is organized as follows: Section 2 introduces the basic principles of GAN and the architectures that evolved from the original network. Section 3 analyzes the NM-GAN architecture and the proposed method for building paired training sets, while Section 4 presents the information regarding the noise modeling and evaluation experiments, together with the experimental results and their analysis. Finally, Section 5 concludes this work and suggests some future research directions.
## 3 Method
### N-Gram Sequence Feature Extraction  
This section describes how n-gram sequence features are extracted from the domain names using the LSTM model. After analysis, we select 1-gram and 3-gram sequence features; 1-gram sequence features are mainly used for the detection of character-based AGDs, and 3-gram features are mainly used for the detection of dictionary-based AGDs.  
According to the RFC specification of the DNS [50], the common characters in the domain names are digits (0-9), letters (a-z and A-Z), hyphen (\"-\") and dot (\".\"). Since the domain name resolution process is case-insensitive, a total of 38 common characters are considered. In addition, some domain names in the dataset may contain underlines (\".\"). To calculate the string embedding vector without throwing an exception, 39 valid characters are considered here. Therefore, the number of distinct 1-grams is 39, 2-grams may be 39\({}^{2}\) = 1521, 3-grams may be 39\({}^{3}\) = 59,319, and 4-grams may be 39\({}^{4}\) = 2,313,441. Since the LSTM model needs to calculate the embedding vector for each n-gram, there are too many distinct 4-grams, so only 1-grams, 2-grams, and 3-grams are considered here. When the value of \(n\) increases, the n-gram better describes the character fragment sequence, which is suitable for the detection of dictionary-based AGDs; in contrast, the  
Figure 3: Process of word distribution feature extraction.  
\(1\)-gram can better describe the character sequence, which is more suitable for the detection of character-based AGDs. Therefore, for character-based AGDs, the relative detection performance is as follows: 1-gram > 2-gram > 3-gram. For dictionary-based AGDs, the detection performance is as follows: 3-gram > 2-gram > 1-gram. This is demonstrated in experiments. We fuse 1-gram and 3-gram sequence features to detect both character-based and dictionary-based AGDs, as shown in Figure 2. We experimentally show that the fusion of 1-gram and 3-gram sequence features exhibits the best performance.  
For each n-gram sequence, the character sequence of the domain name without TLD is split according to the step size of 1 character and then truncated or padded to a fixed length. According to the RFC specification of the DNS, the maximum length of each label in a domain name is 63, and the random string of an AGD mostly appears at the first label of the domain name, so we set the sequence length to 64. Last, based on the sequences obtained by 1-gram and 3-gram, we extract sequence features, although the LSTM model and output 128-dimensional features separately.
## 3 Linearized System
### Slip Boundary Condition
As \(k=0\) is a critical wavenumber with neutral stability, it follows that for the problem to be well posed in the sense of [PERSON] and [PERSON] [13], \(Im\{c(k)\}<0\) as \(k\rightarrow\infty\). Thus, there must be a cut-off wavenumber for the long wave instability, where \(Im\{c(k_{cutoff})\}=0\) for \(k>0\) and higher wavenumbers decay, presumably due to the viscous and heat dissipation modes. Conveniently, the exponential growth rate with slightly supercritical \(M\) grows quadratically in \(k^{2}\) (Marangoni pumping) and decays as \(k^{4}\), since the growth constant is \(-kIm\{c\}\). As the odd order contributions in \(k\) are purely imaginary, it is possible that the long wave instability is limited to a wave packet, with higher wavenumbers than \(k_{cutoff}\) being damped. Figure 1 demonstrates this occurrence for slightly supercritical \(M=12.1\). It can be seen that the growth rate is positive for wavenumbers less than approximately 0.007, but that for larger wavenumbers, the growth rate is negative, which is indicative of decaying modes. An analytic form for the cutoff wavenumber can be computed from Equation (28), which also shows the supercritical nature of the long wave packet of instability. Ref. [8] assumed the occurrence of just this form of dispersion relation as the basis for their multiple scale perturbation theory. They assumed \(k_{cutoff}<<1\) as the formal perturbation parameter. Given that \(k_{cutoff}=0.00724\) for the conditions in Figure 1, their intuition has been proved correct here.