esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of Voice Conversion Methods Based on Deep Learning
## 2 Voice Conversion Process
### Speaker Identity Extraction
In addition, they assumed that the speaker characteristics were not time-dependent, and thus, they used average pooling on them by suggesting a suitably modified 1D InstanceNorm method [24]. In addition to VAE, adversarial networks, namely, GANs, are commonly used in voice conversion. The developers of StarGANv2-VC [15] extracted information about the speaker's speech style and identity from the mel spectrogram. The system removes the speaker's characteristics by adding a cost function from the discriminator, which classifies a sound's authenticity and speaker identity. Furthermore, to induce the generator to create samples with different styles, a cost function calculates the mean absolute error between examples of different styles and maximizes it. The style encoder minimizes the difference between the style obtained from the actual sample and the style generated by the entire pipeline. FreeVC [17] tested the performance of two speaker feature extraction methods: pre-trained and non-pre-trained. The developers of the first type of method trained them on a large set of speakers, similar to those using GE2E. However, they utilized a different model instead, namely, BNE-Seq2 seqMoL [25]. The authors noted that implementing the pre-trained method in this algorithm did not produce significantly better results than training the speaker encoder with the other parts from scratch. These results indicate that if the linguistic representation is extracted correctly, the speaker encoder learns the missing parts describing the identity.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 5 Overview of the Original SCA  
The Sine Cosine algorithm (SCA) is a recently introduced stochastic heuristic scheme, which is developed based on the mathematical sine and cosine functions [33; 34]. The SCA starts with initial candidate solutions and improves them through fluctuating outwards and toward the targeted global solution, using sine and cosine functions as follows:\[x_{j,\ t+1}=\left\{\begin{array}{ll}x_{j,\ t}+\omega\times\sin\left(rand\right) \times\left|rand\ P_{j,\ t}-x_{j,\ t}\\ x_{j,\ t}+\omega\times\cos\left(rand\right)\times\left|rand\ P_{j,\ t}-x_{j,\ t}\\ \end{array}\right|\quad rand\leq 0.5\\ \omega=a-t\frac{a}{T_{max}} \tag{27}\]  
where \(x_{j,\ t}\) is the current solution at \(t\)th iteration in \(j\)th dimension, \(P_{j,\ t}\) is the best solution, \(\omega\) is a control parameter which decreases linearly from a constant value \(a\) to 0 by each iteration, and \(T_{max}\) is the total number of iteration.
## 6 Discussion  
Although the proposed method has shown promising potential for accurately and efficiently modeling structural planes in tunnel engineering, some limitations and avenues for further improvement remain. Firstly, the accuracy and robustness of the approach can be influenced by challenging underground conditions, such as low lighting, humidity, dust, severe occlusion, or motion blur, which may affect image quality and subsequent feature recognition. Secondly, despite the inherent adaptability of the B-spline interpolation to arbitrary tunnel profiles--including elliptical, polygonal, or irregular shapes--it inherently relies on sufficient and uniformly distributed input data. Thus, in scenarios where cross-sectional data are partially incomplete or missing, the reconstructed surface essentially represents an extrapolation based on adjacent available sections. As a consequence, unique geological features or abrupt discontinuities within missing segments may be overly smoothed or underrepresented.  
To address these challenges in future research, we plan to further enhance the robustness of our deep learning-based feature extraction models through more diverse data augmentation and advanced training strategies, specifically targeting realistic underground disturbances. Additionally, adaptive and locally refined interpolation methods or multi-source data fusion strategies will be explored to better capture sharp geological changes and minimize information loss caused by incomplete datasets. Comprehensive field validations across a broader range of geological conditions, tunnel geometries, and data availability scenarios will also be prioritized. These efforts aim to ensure that the proposed workflow becomes more generalizable and reliable for routine practical applications, potentially complementing or even partially replacing traditional geological survey methods in underground engineering.
## 3 Development of Thermal Condition Monitoring System
### Remedial Action for ZigBee Signal Challenges
#### 3.5.2 Line of Sight and Signal Sensitivity  
Although ZigBee device may be challenged by the line of sight range and signal latency, there are options available to improve on its expected range. It can be improved with front end range amplifier as an extender, during the configuration setup assessment stage. The provision exists to be applied, for designs during link budget analysis. ZigBee device is applicable for all assignments including rural and remote areas on ISM frequency band. The choice of components should conform to specifications [59]. The installation configuration of ZigBee depends on the type of module in use as the development relies on the manufacturer circuit board specifications and the module compatibility. Factors such as structure obstacles, reflections, background noise, temperature, and humidity influence the throughput of the system performance. Datasheets of specification and configuration schemes are provided per the following references [58; 60; 93; 94; 95; 96; 97] to achieve optimal range and signal sensitivity. Equation (2) supports the link budget assessment:  
\[d=\frac{\lambda}{4\pi}\sqrt{\frac{P_{t}G_{t}G_{r}}{P_{r}}} \tag{2}\]  
where, \(P_{r}=\) the receiver antenna power, \(P_{t}=\) the transmitting power, \(G_{r}=\) the gain of the receiver antenna, \(G_{t}=\) the gain of the transmitter antenna, \(d=\) the distance in meters, Lambda (\(\lambda\)) = the wavelength carrier frequency of the ZigBee.
## 2 Methodology and Data Collection
### Measurements and Data Collection Procedure
In each location, this method yields a circle with \(\lambda/2\) separation distance between successive antenna positions because 0.04 m \(\equiv\lambda/2\). According to [22], the spatial average of \(\lambda/2\) is in accordance with the shadow-fade correlation distance. Figure 1: An example of the measurement layout. Thus, to average out the small-scale fades, the resulting average power (\(\overline{P_{\text{rx}}}\)) at specific locations corresponded to the mean of the 60 samples recorded, which was calculated as follows:  
\[\overline{P_{\text{rx}}}=10\log\biggl{(}\frac{1}{60}\sum_{i=1}^{60}10^{P_{\text {rx}_{i}}/10}\biggr{)}, \tag{1}\]  
where \(P_{\text{rx}_{i}}\) is the received power measured in dBm and i is the number of the sample recorded. An overview of the measurement conditions is presented in Table 3.  
Figure 3: Separation distance according to the angular increment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Type of O2I links & OS NLOS and SS NLOS \\ Frequency, \(f\) & 3.5 GHz \\ Transmit power, \(P_{\text{rx}}\) & 17.8 dBm \\ Transmit gain, \(G_{\text{rx}}\) & 10.2 dB \\ Receive gain, \(G_{\text{rx}}\) & 2.4 dB \\ Receive distance range, \(d\) & 5–40 m \\ Receiver noise floor & \(-123\) dBm \\ Number of spatial positions at each receive location & 60 \\ Number of O2I NLOS links at USM & 308 \\ Number of O2I NLOS links at UDP & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement Parameters. Figure 2: An example of the OS NLOS measurement configuration with the on-axis 0.4 m rotating system.
## 2 Formulations
### Transfer Matrix Method
\begin{array}{c}a_{N+2}\\ b_{N+2}\end{array} \right)=M_{N+1}\cdots M_{p}\cdots M_{1}\left( \begin{array}[] {c}a_{1}\\ b_{1}\end{array} \right)=\left( \begin{array}{cc}M_{11}&M_{12}\\ M_{21}&M_{12}\end{array} \right)\left( \begin{array}{c}a_{1}\\ b_{1}\end{array} \right), \tag{19}\]  
where for TE modes:  
\[M_{p}=\frac{1}{2}\left[ \begin{array}{cc}\exp(jk_{p+1}z_{p})&0\\ 0&\exp(-jk_{p+1}z_{p})\end{array} \right]\cdot\left( \begin{array}{cc}\tfrac{ p_{p}}{\mu_{p+1}}+\tfrac{k_{p}}{k_{p+1}}&\tfrac{p_{p}}{\mu_{p+1}}-\tfrac{k_{p}}{k_{ p+1}}\\ \tfrac{p_{p}}{\mu_{p+1}}-\tfrac{k_{p}}{k_{p+1}}&\tfrac{p_{p}}{\mu_{p+1}}+\tfrac{k_{ p}}{k_{p+1}}\end{array} \right).\]  
\[\left[ \begin{array}{cc}\exp(-jk_{p+1}z_{p})&0\\ 0&\exp(jk_{p+1}z_{p})\end{array} \right], \tag{20}\]  
and for TM modes:  
\[M_{p}=\frac{1}{2}\left[ \begin{array}{cc}\exp(jk_{p+1}z_{p})&0\\ 0&\exp(-jk_{p+1}z_{p})\end{array} \right]\cdot\left( \begin{array}{cc}\tfrac{ \varepsilon_{p}}{\varepsilon_{p+1}}+\tfrac{k_{p}}{k_{p+1}}&\tfrac{\varepsilon _{p}}{\varepsilon_{p+1}}-\tfrac{k_{p}}{k_{p+1}}\\ \tfrac{\varepsilon_{p}}{\varepsilon_{p+1}}-\tfrac{k_{p}}{k_{p+1}}&\tfrac{ \varepsilon_{p}}{\varepsilon_{p+1}}+\tfrac{k_{p}}{k_{p+1}}\end{array} \right).\]  
\[\left[ \begin{array}{cc}\exp(-jk_{p+1}z_{p})&0\\ 0&\exp(jk_{p+1}z_{p})\end{array} \right]. \tag{21}\]  
Using Equation (19) with \(a_{1}=1\), \(b_{1}=r\), \(a_{N+2}=t\), and \(b_{N+2}=0\), the reflection and transmission amplitudes, \(r\) and \(t\), can be obtained, respectively, by  
\[r=-\frac{M_{21}}{M_{22}} \tag{22}\]  
and  
\[t=\frac{M_{11}\cdot M_{22}-M_{12}\cdot M_{21}}{M_{22}}. \tag{23}\]  
The reflection and transmission coefficients, \(R\) and \(T\), can be implicitly represented by \(S\)-parameters, \(S_{11}(dB)\) and \(S_{12}(dB)\), as a function of the operating frequency,, for TE modes  
\[S_{11}(dB)=10 log\left|\tfrac{M_{21}}{M_{22}}\right|^{2}\quad\text{and}\quad S _{12}(dB)=10 log\tfrac{\mu_{1}k_{N+2}}{\mu_{N+2}k_{1}}\left|\tfrac{M_{11} \cdot M_{22}-M_{12}\cdot M_{21}}{M_{22}}\right|^{2}, \tag{24}\]  
and for TM modes  
\[S_{11}(dB)=10 log\left|\tfrac{M_{21}}{M_{22}}\right|^{2}\quad\text{and}\quad S _{12}(dB)=10 log\tfrac{\varepsilon_{1}k_{N+2}}{\varepsilon_{N+2}k_{1}}\left| \tfrac{M_{11}\cdot M_{22}-M_{12}\cdot M_{21}}{M_{22}}\right|^{2}. \tag{25}\]
# Internet Traffic Prediction with Distributed Multi-Agent Learning
## 2 Related Work
### Statistical Prediction Models  
Statistical models are featured by their explicit mathematical formulas based on a set of statistical assumptions for the data generation or distribution patterns. Their prediction performance can be analyzed and quantified in theory. Some representative statistical prediction models include ARIMA and Holt-Winters (HW) models. Other models include the simple exponential smoothing model, which is used for cellular traffic prediction [19], and the particle filtering method, which is used for 5G traffic demand prediction [20]. However, statistical prediction models are less common in recent years for network traffic prediction problems, because their prediction performance is worse than machine learning and deep learning models.
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 1 Introduction  
Time-interleaved analog-to-digital converters (TIADCs) perform high-throughput A/D conversions without loss of dynamic performance if all channels have identical electronic characteristics [1]. In reality, electronic mismatches, which periodically modulate the input signal and degrade the output signal's dynamic performance, are inevitable. In recent years, methods have been put forward to mitigate timing mismatches [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], bandwidth mismatches [18; 19], frequency response mismatches [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] (frequency response mismatch contains the gain, timing and bandwidth mismatches altogether), and nonlinearity mismatches [31; 32; 33; 34]. To consider all kinds of mismatches, ref. [35; 36] propose the joint calibration methods. This paper is an extended version of our paper published in 2018 41 st International Conference on Telecommunications and Signal Processing (TSP) [37].
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 4 Simulation Results and Analysis
### Simulation Results and Analysis
Therefore, the scheme has optimal performance, which can reduce the collision and congestion in the environment of high number of stations, and improve the stability and reliability of communication. Figure 4: Performance comparison of system throughput for different schemes. Figure 5: Performance comparison of system packet loss probability for different schemes. Figure 6 shows the delay performance comparison of different schemes under different channel average SNRs. As can be seen from the figure, the two-hop NOMA scheme has better delay performance, because NOMA reduces the collision probability of packets, thus shortening the average time required for the node retreat process. The curve is not smooth because the number of competing stations in Formula (25) needs to be an integer based on the function corresponding to the transmitting power. In addition, compared with \(SNR=5\)dB, the delay performance of \(SNR=8\)dB is slightly improved. This is because increasing the average signal-to-noise ratio will improve the communication reliability at the physical layer, thus improving the channel utilization efficiency at the MAC layer. However, the delay performance of the system is not significantly affected by the parameters at the physical layer, and its performance is mainly affected by the number of stations at the MAC layer.
## 6 Results
### Machine Learning  
The results of the analysis showed that the XGBoost model had the highest R\({}^{2}\) value of 0.80, followed by Random Forest (0.79), and Decision Tree (0.55). The R\({}^{2}\) value indicates the proportion of variation in the amount spent at local businesses in Saint Louis that can be explained by the features used in the model (Table 2). Additionally, the root mean squared error (RMSE) values for the three models were 334.0 for Random Forest, 403.71 for Decision Tree, and 297.58 for XGBoost (Table 2). The RMSE value measures the difference between the predicted values and the actual values, with lower values indicating a better fit.  
The results also showed that removing the longitude and latitude features from the model resulted in a decrease in the R\({}^{2}\) values for all three models, with the lowest R\({}^{2}\) value of 0.39 for Decision Tree. The RMSE values also increased, with the highest value of 595.07 for Decision Tree.  
The feature importance analysis within our Random Forest model highlights the significant role of geographic coordinates, with longitude (0.35) and latitude (0.32) as key predictors in determining the economic impact of MLB games on local businesses. This analysis suggests that longitude, in particular, serves as a critical indicator due to its representation of the east-west positioning of businesses within the broad geographic landscape of Saint Louis (Figure 8). Such positioning may influence consumer behavior and spending patterns, given the city's layout and the distribution of commercial areas.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **Evaluation** & \
\begin{tabular}{l} **With Spatial** \\ **Information** \\ \end{tabular} &
\begin{tabular}{l} **Without Spatial** \\ **Information** \\ \end{tabular} \\ \hline \multirow{2}{*}{\(RF\)} & R\({}^{2}\) (Test) & 0.79 & 0.42 \\ \cline{2-4}  & RMSE & 334.0 & 594.20 \\ \hline \multirow{2}{*}{\(DT\)} & R\({}^{2}\) (Test) & 0.55 & 0.39 \\ \cline{2-4}  & RMSE & 403.71 & 595.07 \\ \hline \multirow{2}{*}{\(XGBoost\)} & R\({}^{2}\) (Test) & 0.80 * & 0.45 * \\ \cline{2-4}  & RMSE & 297.58 * & 545.94 * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the machine learning models. * Indicates the best model results for each variable.  
Figure 8: Random Forest feature importance scores. D/N refers to a day or night game.
## 2 Materials and Methods
### Characterization of MSCs
#### 2.2.1 Flow Cytometry  
CD73 FITC, CD90 BV421, and CD105 APC as positive markers and CD34 Pe, CD19 Pe Cy-7, CD45 V500, and HLA-DR APC-H7 (all from Becton Dickinson (BD, Milan, Italy) were evaluated by flow cytometry [14]. Briefly, cells (1 \(\times\) 10\({}^{5}\) cell/tube) were incubated for 20 min at +4 \({}^{\circ}\)C with conjugated monoclonal antibodies. After washing, 10,000 events were acquired by FACS Canto II (BD). Analysis was performed by Diva software v.9.0. (BD). The cells were gated (gate A) for physical parameters (FSC for cell sizes and SSC for cell complexity), and the percentage of positive cells was calculated within this gate.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 2 State of the Art
### Geocast Routing  
On one hand, protocols such as the Geocasting Spray And Flood (GSAF) [11] and the Geocasting For Opportunistic Networks (Geoopp) [12] focus on distributing messages by all nodes located inside a region set remotely, relative to the position of the nodes that generate such messages. This implies, however, that these algorithms break down the routing process into the two following phases: (1) forwarding (and carrying) the messages to the destination region; (2) delivering the message to all nodes inside the ROI. While both protocols mentioned, regarding the latter phase, follow an epidemic-based behavior, their strategy differs with respect to the first phase. For instance, when using the GSAF protocol, a spraying procedure is applied in the first phase. Messages are assigned a predetermined number of tickets \(T\) that denote the number of times a given message can be forwarded to encountered nodes outside the destination region. Thus, each time a message is replicated, the \(T\) field is sequentially decreased on both messages until \(T\) equals zero.  
On the other hand, geocast protocols can be applied to distribute content in ROIs whose center is defined by the position of the nodes responsible for creating the respective messages at the generation time instant. In this sense, the authors of the Floating Content algorithm [13] include in the messages' headers not only the center coordinates and radius of the circularly shaped ROI but also an additional radius parameter to determine the extension of a buffer zone. The buffer zone is defined as an extension of the ROI where carrier nodes may keep the messages stored beyond the boundaries of the ROI. This allows for a more efficient delivery of messages to nodes that approach the ROI.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 3 Numerical Results
### Homogeneous Material  
#### 3.1.1 Mesh Sensitivity  
The dependence of damage response on the resolution of the underlying discrete grid is a well-known problem for non-regularized continuum damage models. As described in Section 2.3.2, the proposed time-delay damage model introduces an inherent length scale proportional to the relaxation time and longitudinal elastic wave speed, i.e., \(l_{d}\propto c_{\mathrm{d}}\tau_{\mathrm{c}}\). To show mesh-objectivity of the results, we compare the damage evolution for coarse and medium meshes in Figure 7. For this numerical example, material properties are homogeneous and listed in Table 1, and the loading magnitude is \(P_{\mathrm{peak}}=13.5\) MPa.  
Figure 6: Initial meshes used for the simulations: (**a**) Coarse, (**b**) Medium, and (**c**) Fine.  
Figure 7: Damage responses at different times for two different meshes. Figures (**a,b**) correspond to the coarse mesh and figures (**c,d**) correspond to the medium mesh. The results are shown on the deformed mesh with a magnification factor of 300.  
Figure 7 shows an excellent agreement between the solutions of the two meshes at early and evolved stages of damage evolution. We also refer the reader to [31] for a more detailed study of mesh objectivity for a tensile fracture problem where damage localization zone converges to a region of finite width. We reiterate that the time-delay formulation addresses the mesh-objectivity problem with much less computational difficulty than the non-local integration-based and gradient-based damage models. Moreover, it does not violate the hyperbolicity of the problem. This facilitates the use of the aSDG method and is consistent with the physical observation that damage propagates with a finite speed [66].
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 5 Virtual and Real Mapping
### Motion Behavior Mapping  
The motion transformation of the twin model is based on the geometric transformation matrix. In contrast to the ground coordinate system \(W\)[24], which typically employs a right-handed convention, Unity3D utilizes a left-handed world coordinate system with the Y-axis pointing vertically upwards [25]. Therefore, the base of the radio telescope is defined to rotate around the \(Y\) axis, the coordinate transformation matrix is \(R_{Y}(\phi)\), the elevation body rotates around the \(Z\) axis, and the coordinate transformation matrix is \(R_{Z}(\theta)\), where \(\phi\) and \(\theta\) are the azimuth and elevation angles, respectively. The coordinate relationship of the motion model is shown in Figure 10.  
\[R_{Y}(\phi)=\left[\begin{array}{cccc}\cos(\phi)&0&\sin(\phi)&0\\ 0&1&0&0\\ -\sin(\phi)&0&\cos(\phi)&0\\ 0&0&0&1\end{array}\right]\quad R_{Z}(\theta)=\left[\begin{array}{cccc}\cos( \theta)&-\sin(\theta)&0&0\\ \sin(\theta)&\cos(\theta)&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right] \tag{18}\]  
The coordinate transformation matrix \(T_{H}\) between the primary reflector and the base is obtained by multiplying the above transformation matrix in turn:  
\[T_{H}=R_{Y}(\phi)\cdot R_{Z}(\theta)=\left[\begin{array}{cccc}\cos(\phi) \cos(\theta)&-\cos(\phi)\sin(\theta)&\sin(\phi)&0\\ \sin(\theta)&\cos(\theta)&0&0\\ -\sin(\phi)\cos(\theta)&\sin(\phi)\sin(\theta)&\cos(\phi)&0\\ 0&0&0&1\end{array}\right] \tag{19}\]