esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Seismic Hazard Analyses for the Ugandan Region
## 6 Conclusions  
A preliminary PSHA was performed for the Ugandan region. The analyses utilized an earthquake catalog that when mapped, suggested four seismic zones as sources of seismicity. One of these seismic zones represented the western branch of East African Rift Valley System relevant to Uganda. Applying a recurrence rate model confirmed that the seismic zone representing the western branch of the East African Rift Valley System recorded the largest number of earthquakes with \(M_{w}\geq 4\) out of the four seismic zones.  
Figure 8: Seismic hazard map of PGA contours for a probability 10% exceedance in 5 years.  
A comparison of two different but appropriate GMPEs revealed significant disparities between them. The results of one of the Idriss GMPE at \(M_{w}=7\) was similar to the results from the Toro GMPE at \(M_{w}=6\). Combining these components in the hazard integral and applying a logic tree revealed some interesting findings.  
When two sites designated for nuclear infrastructure projects were compared, the site further away from the western branch of the East African Rift Valley System showed slightly higher seismic hazard than a closer site. However, using seismic hazard maps for PGA with a 10% probability of exceedance in 5 years showed PGA to be approximately 0.28 g for regions within the rift and 0.09 g away from the rift system. The event return period corresponding to a 10% probability of exceedance in 5 years is 50 years. This range of PGA is in agreement with a previous seismic hazard analysis for Uganda. Seismic hazard maps for PGA with return periods of 100, 200, 500, 1000, and 5000 years were also computed and showed a significant increase from 0.28 g up to 0.95 g. The PGA hazard maps were also in agreement with previous studies that showed the seismic hazard in or near the western branch of the East African Rift Valley was highest as represented by PGA and low west of Uganda as well as the northeastern region of Uganda.  
Conceptualization, D.C. and E.Y.; methodology, D.C. and E.Y.; software, E.Y.; formal analysis, D.C.; writing--original draft preparation, D.C.; writing--review and editing, E.Y.; visualization, E.Y. and D.C.; supervision, E.Y.; project administration, E.Y.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2021 Research Fund of the KEPCO International Nuclear Graduate School (KINGS), Republic of Korea.  
Not applicable.  
Publicly available datasets were analyzed in this study. Any generated data can be found within this article.  
The authors declare no conflict of interest.
# Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery
## 4 Convergence Analysis of CGHTP
At the same time, to verify the advantages of the proposed algorithm in convergence speed, we select a fixed sparse signal and record the relative reconstruction errors of different algorithms under different number of iterations. As can be seen from Figures 6 and 7, as the number of iterations increases, the relative reconstruction errors of the signals reconstructed by HTP and CGHTP decrease, and becomes stable at a certain error value where the convergence is reached. However, the relative  
Figure 4: Number of iterations for HTP with different step size, CGIHT, and CGHTP algorithms. Figure 5: Time consumed by HTP with different step size, CGIHT, and CGHTP algorithms. reconstruction error of CGIHT algorithm decreases very slowly with the increase of iteration times. Figures 6 and 7 show that the number of iterations for convergence of CGHTP algorithm is smaller than that of HTP algorithm with an optimal step size and CGIHT algorithm, which reflects that CGHTP algorithm converges faster than HTP algorithm and CGIHT algorithm. The acceleration strategy chosen by the gradient descent method and the conjugate gradient method results in a better performance of the CGHTP algorithm in convergence rate. The recovery ability of the proposed algorithm is evaluated for different sparsity levels and different sampling rates. In each experiment, it is recorded as a successful reconstruction if \(\left\|\mathbf{x}_{n}-\mathbf{x}\right\|_{2}<10^{-4}\left\|\mathbf{x}\right\|_{2}\). Figure 8 describes the exact reconstruction rate of different algorithms with different sparsity levels at a fixed sampling rate of \(\tau=0.5\) while Figure 9 depicts the recovery performance of these algorithms at different sampling rates at a fixed sparsity level \(s=10\). Figures 8 and 9 show that the CGHTP is outperforms other algorithms. In addition, for the same rate of exact reconstruction, CGHTP requires fewer samples than other algorithms. Figure 6: Relative reconstruction error vs. iteration number, (\(s=10,M=120\)). Figure 7: Relative reconstruction error vs. iteration number, (\(s=20,M=80\)).
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 3 Algorithm Process and Program Implementation
### Inelastic Correction
#### 3.2.3 Elastoplastic Damage Coupling Correction  
If \(F^{\omega}\) and \(F^{p}\) are both greater than the error value, then the current state is that of elastoplastic damage coupled loading, requiring elastoplastic damage coupling correction. When rock materials are subject to plastic damage coupled loading, the last updated values \(\sigma^{(i)}+d\sigma^{p(i)}\) and \(Y^{(i)}+Y^{p\omega(i)}\) should be retained on the yield surface \(F^{p(i+1)}\) and damage surface \(F^{\omega(i+1)}\). Then, based on Taylor expansion, the yield and damage criteria can be linearized under the current state variables:  
\[\left\{\begin{array}{l}F^{p(i+1)}=\left(F^{p}+\frac{\partial F^{p}}{ \partial\sigma}:d\sigma^{p\omega}+\frac{\partial F^{p}}{\partial\gamma^{p}}: d\gamma^{p}+\frac{\partial F^{p}}{\partial\omega}:d\omega\right)^{(i)}=0\\ F^{\omega(i+1)}=\left(F^{\omega}+\frac{\partial F^{\omega}}{\partial T}:d \gamma^{p\omega}+\frac{\partial F^{\omega}}{\partial\omega}:d\omega\right)^{( i)}=0\end{array}\right. \tag{66}\]  
During the coupled loading process of plastic flow and damage evolution, the above formulas should be calculated jointly, resulting in:  
\[\left\{\begin{array}{l}d\lambda^{p(i)}\\ d\lambda^{\omega(i)}\end{array}\right\}=\frac{1}{\left(H^{p}H^{\omega}-H^{p \omega}H^{\omega p}\right)^{(i)}}\left[\begin{array}{cc}H^{p}&-H^{p\omega} \\ -H^{\omega p}&H^{\omega}\end{array}\right]^{(i)}\left\{\begin{array}{l}F^{p} \\ F^{\omega}\end{array}\right\}^{(i)} \tag{67}\]where \(H^{P}\) is expressed as in Equation (63); \(H^{\omega}\) is expressed as in Equation (47); \(H^{P\omega}\) and \(H^{\omega P}\) are the coupled inelastic moduli, reflecting the interaction between damage evolution and plastic flow, expressed as:  
\[H^{P\omega}=\left(\frac{\partial F^{P}}{\partial\sigma}:D_{0}:\epsilon^{e}- \frac{\partial F^{P}}{\partial\omega}\right)\frac{\partial G^{\omega}}{ \partial Y} \tag{68}\]  
\[H^{\omega P}=\frac{\partial F^{\omega}}{\partial Y}\left[\epsilon^{e}:D_{0}: \frac{\partial G^{p}}{\partial\sigma}-\left(p\frac{\partial G^{p}}{\partial p }+q\sqrt{\frac{2}{3}\frac{\partial G^{p}}{\partial S}:\frac{\partial G^{p}}{ \partial S}}\right)\right] \tag{69}\]  
Substitute the plastic multiplier increment \(d\lambda^{P}\) and the damage multiplier increment \(d\lambda^{\omega}\) obtained from Equation (67) into Equations (57) and (61), respectively, to ultimately derive the stress-strain relationship for the elastoplastic damage coupled model:  
\[\sigma^{(i+1)}=\sigma^{trial}-d\sigma^{p\omega(i)} \tag{70}\]  
\[d\sigma^{p\omega(i)}=-d\lambda^{p(i)}\left(D:\frac{\partial G^{p}}{\partial \sigma}\right)^{(i)}-d\omega^{(i)}D_{0}:\epsilon^{e(i)} \tag{71}\]  
Substitute the stress and state variables obtained from Equation (70) in the current iteration step into the yield function to obtain \(F^{P(i+1)}\) and \(F^{\omega(i+1)}\), and then determine whether the yield conditions are met. If met, output the stress obtained from Equation (70); if not, proceed to the next iteration.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### CCC Retrieval Model
#### 2.6.1 Linear Regression Analysis  
The performance of the combined VIs was assessed using simple linear regression. Firstly, univariate linear regression (ULR) was employed to model the relationship between the CCC and MTIC, MTCI \(\times\) NDVI, MTCI \(\times\) LNDIVI, MTCI \(\times\) SNDIVI, MTCI \(\times\) RDVI, and MTCI \(\times\) MTVI2. Binary linear regression (BLR) models were then constructed in Matlab based on the relationship between CCC and the MTCI and NDVI, MTCI and LNDIVI, MTCI and SNDIVI, MTCI and RDVI, and MTCI and MTVI2. We used the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), bias (Bias), and normalized RMSE (NRMSE) to evaluate the fitness and predictive power of the models, respectively. They were calculated as follows:  
\[\text{R}^{2}=\frac{\sum_{i}^{n}(\hat{y}_{i}-\overline{y})}{\sum_{i}^{n}(y_{i}- \overline{y})} \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{2}\]  
\[\text{Bias}=\frac{\sum_{i}^{n}(y_{i}-\hat{y}_{i})}{n} \tag{3}\]  
\[\text{NRMSE}=\frac{\text{RMSE}}{y_{max}-y_{min}} \tag{4}\]  
where \(\hat{y}_{i}\) is the predicted CCC, \(y_{i}\) is the measured CCC, \(\overline{y}\) is the average measured CCC, \(y_{max}\) is the maximum value of the CCC, \(y_{min}\) is the minimum value of the CCC, and n is the number of measurements used. While linear regression analysis is simple to implement, particularly in uncomplicated variable spaces, complex vegetated areas are likely to require more advanced methods for their analysis.
# Texture Analysis of Near-Infrared Vein Images During Reactive Hyperemia in Healthy Subjects
## 4 Discussion
### Composite Parameters and Their Physiological Meaning  
To better capture how venous texture evolves with morphological changes, we developed composite parameters that integrate both dimensions [48]. Rather than assessing whether isolated parameters change, these indices explore how texture behaves relative to vein expansion. Among them, the entropy-to-width ratio reflects the informational complexity per unit of venous cross-sectional size, essentially, how \"densely disorganized\" the signal is. Its significant decrease during occlusion and incomplete recovery thereafter suggest that while entropy remained stable, the enlarged vein diluted the local complexity. This may reflect a more homogeneous, less chaotic signal within the distended structure, consistent with smoother intraluminal filling. Similarly, the correlation-to-width ratio declined with occlusion. Although absolute correlation increased, indicating more regular pixel relationships, this index adjusts for the simultaneous growth in vein size. The decline implies that regularity did not scale proportionally with expansion, suggesting that the additional volume was more uniform but not more ordered at the microtextural level, which may reflect different venous filling profiles across veins of varying diameters [49].  
In contrast, total entropic content increased significantly during occlusion and remained elevated during recovery. This metric captures the total informational complexity across the entire venous cross-section. The rise suggests that while local entropy per pixel did not change, the overall signal complexity increased, likely due to the greater tissue volume or variability between vein lumens. Finally, the correlation-to-entropy ratio rose modestly, reaching statistical significance only during the recovery phase. This may indicate a physiological shift toward a more structured organization relative to disorder, possibly reflecting a reduction in flow velocity. Although we did not directly measure venous flow, one possible interpretation is that the delayed rise in this index reflects gradual reorganization of the venous signal as congestion resolves. Interestingly, the pattern observed in Figure 3 mirrors this temporal behavior--the ratio declines during baseline, possibly reflecting reduced venous return due to immobility [50], then increases with occlusion-induced volume accumulation, and rises further after cuff release, approaching baseline only after several minutes. Taken together, these composite indices revealed phase-dependent changes not detected by single features. By expressing texture in relation to size and complexity, they offered a more sensitive and physiologically meaningful characterization of venous adaptation to congestion and reperfusion.
# Compact Trap-Assisted-Tunneling Model for Line Tunneling Field-Effect-Transistor Devices
## 2 Model Presentation
### Schenk Model
Both \(E_{\text{phon}}\) and \(S\) are known to influence \(\Gamma\), although the effect of \(E_{\text{phon}}\) has been found to be dominant over \(S\) on \(\Gamma\). Furthermore, the current can change if either \(S\) or \(E_{\text{phon}}\) change, even if \(\varepsilon_{\text{R}}\) remains constant [18]. \(E_{\text{tn(p)}}\) is the energy at which the tunneling probability for the carriers to the tunnel from \(E_{\text{T}}\) to the band edge is the maximum and \(\Theta_{n(p)}=\)(\(q^{2}E^{2}/2\hbar m_{n(p)}\))\({}^{1/3}\) is the electro-optical frequency [18]. \(m_{n}\) and \(m_{p}\) are the electron and hole tunneling masses, respectively. The model parameters including trap level \(E_{\text{T}}\), \(S\), \(E_{\text{phon}}\), and \({}_{n(p)}\) are the fundamental quantities that strongly influence the TAT process. The first three can be determined experimentally using Deep Level Transient Spectroscopy [19; 20]. The electro-optical frequency is dependent on the electric field and the tunneling masses. This restricts the model to being a local TAT model [18]. \(E_{0n(p)}\) is the energy of an optimum horizontal transition path which is dependent on the field strength and temperature. This determines the most probable recombination path [18], and is given by  
\[E_{0n(p)}=2\sqrt{\varepsilon_{Fn(p)}}\left[\sqrt{\varepsilon_{Fn(p)}+E_{tn(p) }+\varepsilon_{R}}-\sqrt{\varepsilon_{Fn(p)}}\right]-\varepsilon_{R} \tag{13}\]  
where \(\varepsilon_{Fn(p)}=\frac{\left(2\varepsilon_{F}kT\right)^{2}}{\left(\hbar \Theta_{n(p)}\right)^{3}}\). \(E_{\text{tn}}\) for electrons is given by  
\[E_{tn}=\frac{1}{2}E_{bgn}+\frac{3}{4}kT\ln\left(\frac{m_{n}}{m_{p}}\right)-E_{ T}-\left(32R_{c}h^{3}\Theta^{3}\right)^{\frac{1}{3}} \tag{14}\]  
and \(E_{\text{tp}}\) is given by  
\[E_{tp}=\frac{1}{2}E_{bgn}+\frac{3}{4}kT\ln\left(\frac{m_{n}}{m_{p}}\right)-E_{ T}+\left(32R_{c}h^{3}\Theta^{3}\right)^{\frac{1}{3}} \tag{15}\]  
where \(R_{c(\text{v})}\) are the effective Rydberg energies; \(R_{c(\text{v})}=m_{n(\text{p})}R_{\text{y}}/\varepsilon^{2}\) for electrons (holes), where \(R_{\text{y}}\) (= 13.6 eV) is the Rydberg energy and \(\varepsilon\) is the permittivity.
# Evaluation of Radio Communication Links of 4G Systems
## 4 Results and Discussion
### Reference Signal Received Quality (RSRQ)  
RSRQ is defined as the quality of the received signal. Measuring the RSRQ becomes mainly crucial near the cell's border when decisions must be made to transfer to the adjacent cell.  
The reception quality of the reference signal is used only during the connected states. The RSRQ can be measured with the following equation [7,21,22]:  
\[\text{RSRQ}=\text{N}_{\text{RB}}\times\text{RSRP}/\text{RSSI} \tag{12}\]  
where: \(\text{N}_{\text{RB}}\) is the number of resource blocks of measured RSSI.  
RSSI: Received Signal Strength Indication (average of the total received power).  
RSRP: Reference Signal Received Power.  
The obtained results of RSRQ with the drive test before dealing with the problems are presented in Figure 9.  
In this case, the value of RSRQ under the normal state is [\(-9\), \(-3\)] dB. In good condition, RSRQ is higher than \(-3\) dB. For these two states, no action will be done.  
When the value of RSRQ is less than \(-9\) dB, an action of optimization is necessary. These actions are possible: handover management (the process of transferring intercellular of UE), increase in the height of the transmitter, optimization of antenna tilt parameters, and implementation of new eNB if necessary.  
The obtained values of RSRQ before and after treatment are given in Table 2:  
Figure 9: RSRQ distribution: (**a**) before treatment; (**b**) after treatment.  
According to Figure 10 and Table 2, the amelioration in RSRQ is 43.76%. The amelioration is distributed as follows: amelioration of 00.12% in the range of [min, \(-\)19.5] dB; amelioration of 11.03% in the range of [\(-\)19.5, \(-\)14] dB and amelioration of 32.6% in the range of [\(-\)14, \(-\)9] dB. In this case, the amelioration is very significant.
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 6 Discussion  
To optimize diversity and coding gains over fading channels in a fully data-driven manner, we proposed a robust DL-based MC-AE system capable of learning both the encoder and decoder. The encoder uses a linear activation function for continuous outputs, while the decoder employs a GRU neural network to effectively manage temporal dependencies across subcarriers, enhancing the system's adaptability to varying channel conditions. In our investigation, we focused on evaluating the performance of the model, calculating key metrics such as BER, BLER, SE, and EE for different data configurations and DL model parameters. The results were compared with several conventional and DL-based models. While our system demonstrated superior performance in terms of these metrics, it exhibits higher complexity compared to some of the other models. Some techniques like model quantization, static graph execution, and simplifying the channel model through approximations for noise and fading can be employed to optimize performance while reducing complexity. These combined approaches enable faster inference with minimal performance loss. In a prior study [32], the authors designed an AE model with a DNN network and analyzed the BER and BLER for different scenarios, such as uplink-downlink and multiuser systems. Although this approach had lower complexity than ours, our model outperformed it in terms of performance. We intentionally avoid training with negative SNR levels, as excessive noise would overpower the signal, leading to overfitting and reduced information for learning. Our current work primarily focuses on static channel conditions, although we have explored varying training SNR values to account for dynamic channel conditions. Further work could extend this model to fully dynamic channel scenarios.
## 3 Experimental Results and Analysis
### Influence of Different Parameter Combinations on Model Accuracy
Compared with Model 5, which does not consider video quality fluctuation, Model 1 increases PLCC by 13.12% and SROCC by 17.44% on the Waterloo Streaming QoE Database-III, and 3.72% and 4.53%, respectively, on the LIVE-NFLX-II dataset, which indicates that video quality volatility has a crucial influence on the prediction accuracy of the model.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work  
Current GNSS receivers rely on state-of-the-art models to estimate the ionospheric delay. These models have been developed and refined over time to assess the impact of the ionosphere on GNSS signals, and by utilizing them, GNSS receivers can improve their PNT solution accuracy. Ionospheric models can be categorized into four types: mathematical, empirical, physical, and data-driven. Mathematical models rely on pure mathematical or numerical solutions to provide theoretical values of ionospheric parameters [29; 30]. Empirical models represent the ionosphere using equations derived from observational data [31]. Physical models are based on equations that simulate the chemical content and processes governing the ionosphere [31; 32]. Data-driven models use multiple methods (e.g., linear regression, autocorrelation, machine learning, neural networks, etc.) to learn from previous observational data to model the ionosphere [33]. We start this section by introducing some key concepts of ionospheric effects before diving into the related work and describing some of the empirical state-of-the-art models.
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 3 Popularity-Aware Service Provisioning Framework  
### Overall Framework  
Figure 1 shows the system model of this paper. The service provider operates \(N_{\text{S}}\) services for mobile devices (MDs). For the service provision, the service provider exploits the laaS of the cloud operator based on the pricing policy of pay-as-you-go. That is, if the service provider requests \(A_{i}\) resources (e.g., CPU, memory, storage, etc.) for the provision of service \(i\) during a contract time \(T_{D}\) (e.g., \(1\) h), the service provider should pay \(\rho A_{i}\), where \(\rho\) is the unit cost for using one resource during the contract time to the cloud operator. After receiving the resource request message, the cloud operator allocates \(A_{i}\) resources to the service instance.  
As shown in Figure 1, when the MD requests the service \(i\), the service request is enqueued in the service queue and handled in a first-in-first-out (FIFO) manner. In addition, the processing rate of each service is proportional to the requested resource \(A_{i}\). This means  
Figure 1: System model.  
that the response time of the request is determined by the number of requests in the service queue and the requested resources. Therefore, when lots of MDs request the service \(i\) simultaneously and insufficient resources are requested for that service, the response time of that service for some MDs cannot meet its requirement. This phenomenon can be avoided by requesting enough resources. However, since the cost of the IaaS is proportional to the requested resources \(A_{i}\), too excessive resource requesting causes a significant increased OPEX of the service provider. That is, there is a tradeoff between the service response time and the OPEX. To optimize this tradeoff, in the PASPF, the service provider requests the analytics on the popularity variation of each service to the NWDAF in 5G. As in standard [17], the NWDAF can provide the analytics (i.e., service popularity dynamics) that estimates how the congestion level and list of top services contributing most of the traffic change for the next time period. After receiving the analytics, the service provider can decide the optimal amount of resources for each service for the next time period. In short, the PASPF operates as follows:  
* The PASPF leverages the NWDAF, where the popularity estimation model runs to obtain the analytics on the popularity variations of each service.
* Based on the analytics, the PASPF decides the optimal amount of resources for the next time period and requests the corresponding resources to the cloud operator.  
Note that, for the optimal decision, we formulated the CMDP model, which will be described in the next section.