esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Analyzing Pore Structure Characteristics Using the Rock Physics Model  
The relationship between the velocity and porosity of the rock samples in the study area becomes very complicated because the tight sandstone reservoirs have undergone a series of diagenetic transformations. Figure 6 shows the cross plot of P-wave velocity (Vp) versus porosity in the dry rock samples.  
Figure 5: Anisotropic rock physics modeling flow chart of a fractured tight sandstone reservoir.  
It can be seen from Figure 6 that although V\({}_{\text{P}}\) generally decreases with increasing porosity from the overall trend, the data points present a certain degree of scattering distributions, so the porosity is not conclusive to subsequent seismic quantitative interpretation.  
The established rock physics model was used to simulate the variation trend of the P-wave velocity, it shows that the velocity depends on the aspect ratio (AR) of the rock pores, itself depending on the feldspar content. As the dashed lines in Figure 6 show, we found that the scatter was mainly ascribed to the variation of pore structure. Samples with high feldspar content are close to the trends of spherical or tubular pores, while primary matrix pore samples were near the crack-like trend. At a given porosity, the greater the number of secondary dissolution pores, the higher the velocity of the sample.  
Additionally, we used the established rock physics model to explore the relationship between the pore structure parameters and the rock elastic properties. As shown in Figure 7, the velocity difference of three rock samples A1-A3 with the same lithology and similar porosity reached 800 m/s. By observing the thin sections of the rock samples, it was found that sample A1 only contained dissolved pores, sample A2 had intergranular pores and dissolved pores together, and sample A3 developed micro-fractures.  
Figure 6: Cross plot of velocity versus porosity in the dry rock samples.  
Figure 7: The relationship between pore structure parameters and rock elastic properties. (**a**) Cross plot between pore aspect ratio and P-wave velocity. (**b**) Cross plot between pore connectivity coefficient and P-wave velocity.  
Due to the development of micro-fractures and dissolved pores increasing the connectivity between pore systems, the interaction between the fluid and the rock matrix is also enhanced, which greatly reduces the velocity. The pore aspect ratio divides the velocity-porosity relationship into three regions with different characteristics of pore structure which can be used to characterize reservoirs with different pore types.
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## 2 Static Voltage Stability Boundary  
### The Definition of Static-Voltage-Stability Boundary  
Without a loss of generality, the power flow equation can be written as follows:  
\[\mathbf{f}(\mathbf{x},\mathbf{y})= \mathbf{0}, \tag{1}\]  
where \(\mathbf{x}\) is the voltage variable, i.e., amplitude and phase angle of the node voltage; \(\mathbf{y}\) is the nodal injection power variable, i.e., the active and reactive power at different buses. The static-voltage-stability region (SVSR) \(\mathbf{\Omega}\) indicates the area where the power system can maintain stable operation, and its boundary \(\partial\Omega\) is composed of several SNB points as follows:  
\[\partial\Omega:=\left\{(\mathbf{x},\mathbf{y})\left|\mathbf{f}(\mathbf{x},\mathbf{y})= \mathbf{0},\frac{\partial\mathbf{f}}{\partial\mathbf{x}}=\mathbf{0}\right\}. \tag{2}\]  
Geometrically speaking, the SVSR \(\Omega\) is a binary tuple (\(\mathbf{x}\), \(\mathbf{y}\)) consisting of the power system state variable \(\mathbf{x}\) and the control variable \(\mathbf{y}\). Given that high-dimensional boundaries are difficult demonstrated, dimensionality reduction is usually achieved through the boundary function \(\mathbf{h}\):  
\[\mathbf{h}:\mathbf{y}\to\mathbf{x},\mathbf{y}\in\partial\Omega. \tag{3}\]  
According to Equation (3), the SVSRB is equivalent to a stable boundary binary tuple (\(\mathbf{x}\), \(\mathbf{y}\)) by characterizing it in the power injection space, i.e., the space of coordinate \(\mathbf{y}\), as shown in Figure 1.  
As is shown in Figure 1, the SVSR under an active power injection space with the selected system key nodes \(i\) and \(j\) can be depicted by the shaded areas.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 3 Materials and Methods
### Quantitative Evaluation Metrics  
The performance of the various methods employed was quantified using two measures. First, using the \"clean\" image as reference, the structural similarity index measure (SSIM) [26] was calculated:  
\[\text{SSIM}(\text{x},\text{y})=\frac{\left(2\mu_{\text{x}}\mu_{\text{y}}+\text{C }_{1}\right)\left(2\sigma_{\text{xy}}+\text{C}_{2}\right)}{\left(\mu_{\text{x}} ^{2}+\mu_{\text{y}}^{2}+\text{C}_{1}\right)\left(\sigma_{\text{x}}^{2}+\sigma _{\text{y}}^{2}+\text{C}_{2}\right)} \tag{4}\]  
where x and y are signals from input and reference, and \(\mu_{\text{x}}\), \(\mu_{\text{y}}\), \(\sigma_{\text{x}}\), \(\sigma_{\text{y}}\), and \(\sigma_{\text{xy}}\) denote mean of input, mean of reference, variance of input, variance of reference, and covariance. C\({}_{1}\) and C\({}_{2}\) are two constants used to prevent the denominator from becoming zero.  
The second performance measure was the peak-signal-to-noise ratio (PSNR). The PSNR is the ratio of the maximum possible power (MAX) of a signal and the power of distorting noise that affects the quality of its representation (i.e., the mean square error (MSE) between the denoising result from a DL model and the clean reference):  
\[\text{PSNR}=10\text{log}_{10}\left(\frac{\text{MAX}^{2}}{\text{MSE}}\right) \tag{5}\]  
When the numerical difference between a predicted image and the reference approaches 0, the PSNR approaches infinity and the SSIM approaches 1.
## 3 Testing Results
### Energy Characteristics  
The location of AE signals are indicated in Figure 10. Blue and red balls represent low and high frequency AE, respectively, and large balls represent the AE with high energy. It can be clearly observed that most of high energy AE in dried specimens are the ones with high frequency. In contrast, high energy AE are mostly with low frequency in saturated specimens (except SM4).  
Figure 9: Cont.  
As shown in Table 3, the range of dried specimens' percentage of energy of high frequency waveforms is from 71.21% to 80.20% in comparison with 13.99% to 27.03% for the range of low frequency waveforms. On the contrary, the percentage of energy of high frequency AE in saturated specimens ranges from 15.02% to 56.97% compared to 39.04% to 84.71% for the low frequency AE. Due to the saturation, the rock specimens' average percentage of energy of low frequency waveforms was raised from 18.96% to 64.43%.  
Figure 10: The space distribution of AE events. (**a**) DM1; (**b**) DM2; (**c**) DM4; (**d**) SM1; (**e**) SM2; (**f**) SM4.  
Figure 9: Curves of average frequency corresponding to the normalized applied stress. (**a–c**) DM1–DM4; (**d–g**) SM1–SM4.
## 3 Case Research
### Bayesian Inference Analysis
#### 3.3.2 Sensitivity Analysis  
Bayesian network sensitivity analysis refers to quantifying the effect of small changes in local conditional probability parameters on the posterior probability distribution of the target node [42]. By calculating the change in the expected effect under continuous perturbation of a specific parameter, both key parameters and structures are identified and evaluated. Based on the above inference results, the occurrence probabilities of M\({}_{3}\) (corrosion perforation) and M\({}_{1}\) (tank cracking) are relatively high. Sensitivity analysis is performed on these two risk events separately to deduce the root causes of their occurrence, providing targeted decisions for risk control, as detailed in Table 10.  
As observed in Table 11, in the ethane leakage risk M\({}_{3}\) (corrosion perforation), the risk nodes with high sensitivity include X\({}_{25}\), X\({}_{29}\), X\({}_{30}\), X\({}_{31}\), and X\({}_{32}\). These risk factors are all related to corrosion, directly affecting the rate and extent of corrosion, which may lead to leakage in the ethane storage tank. According to Table 12, in the ethane leakage risk M\({}_{1}\) (tank cracking), the risk nodes with high sensitivity are X\({}_{4}\), X\({}_{5}\), X\({}_{6}\), and X\({}_{7}\), all of which are related to the internal load stress of the storage tank.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Risk Event** & **Sensitivity** & **Risk Event** & **Sensitivity** \\ \hline X\({}_{24}\) & 0.104 & X\({}_{29}\) & 0.675 \\ X\({}_{25}\) & 0.675 & X\({}_{30}\) & 0.650 \\ X\({}_{26}\) & 0.336 & X\({}_{31}\) & 0.668 \\ X\({}_{27}\) & 0.274 & X\({}_{32}\) & 0.665 \\ X\({}_{28}\) & 0.378 & & \\ \hline \end{tabular}
\end{table}
Table 11: Sensitivity analysis for ethane storage tank leakage risk M\({}_{3}\) (corrosion perforation).  
Figure 8: Comparison of positive and negative inference results of bayesian network models.
## 5 Conclusions  
This paper proposed a joint iterative semi-blind SI cancellation and intended channel estimation in 5G QC-LDPC-encoded short-packet FD transmissions, via the feedback of the known pilot symbols, named the JISB_DSICED3 scheme. The innovation of the proposed algorithm was taking advantage of iterative algorithms to design simultaneous SI cancellation and intended channel estimation in order to efficiently cancel the SI component and improve the simultaneous channel estimation in the next iterations. This semi-blind algorithm adds only a minimum of four pilot symbols to the information symbols while not requiring the feedback of temporary decoded messages. The numerical results showed that the proposed semi-blind algorithm JISB_DSICED3 was nearly optimal and efficiently increased the performance of the MSE and BER. The significant processing time and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Number of Computations** \\ \hline Demodulation & \(\mathcal{O}(N)\) \\ \hline De-Interleaver & \(\mathcal{O}(N)\) \\ \hline SPA decoding & \(j_{max}.((2.N.\bar{u}_{v}+(N-K)(3.\bar{u}_{c}-1)))\) \\ \hline RLS algorithm & \(\mathcal{O}(E^{\prime 2})\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the number of computations.  
computational complexity reduction of the semi-blind feedback algorithm JISB_DSICED3 were impressive as it only required the feedback of the channel estimate for \(i_{max}=4\) iterations and only one (\(j_{max}=1\)) decoding iteration where the decoding algorithm had a prohibitive computation cost. All these results indicate the efficient use of this semi-blind feedback algorithm, especially since the use or the insertion of these pilot symbols did not in practice really lead to a loss of the data rate because they were already generally required for time and frequency synchronisation.
# An Experimental Study of Turbulent Structures in a Flat-Crested Weir-Type Fishway
## 3 Results and Discussions
### Reynolds Stress Distribution  
On the _xoy_ and _xoz_ planes, Reynolds stress can be respectively expressed as follows:  
\[\eta_{xy}=-\frac{\overline{u\tau v\
u}}{U^{2}}\eta_{xz}=-\frac{\overline{u\tau v \
u}}{U^{2}}. \tag{2}\]  
Reynolds stress equation can be written as:  
\[\underbrace{\frac{\partial}{\partial t}\Big{(}\overline{V^{\prime}{}_{i}V^{ \prime}{}_{j}}\Big{)}}_{changing\ rate}+\underbrace{\overline{V_{l}\overline{V^{ \prime}{}_{i}V^{\prime}{}_{j}}}}_{adv_{l}}=\underbrace{\frac{\partial}{ \partial x_{l}}\Bigg{[}-\overline{V^{\prime}{}_{i}V^{\prime}{}_{j}V^{\prime} {}_{l}}-\frac{\overline{p^{\prime}}}{\rho}\Big{(}\delta_{jl}V^{\prime}{}_{i}+ \delta_{il}V^{\prime}{}_{j}\Big{)}+\overline{\frac{\partial V^{\prime}{}_{i}V^ {\prime}{}_{j}}{\partial x_{l}}}\Bigg{]}}_{diffusion},\]  
\[\underbrace{-\Bigg{(}\overline{V^{\prime}{}_{i}V^{\prime}{}_{l}\frac{\partial V _{j}}{\partial x_{l}}+\overline{V^{\prime}{}_{j}V^{\prime}{}_{l}}\frac{ \partial V_{i}}{\partial x_{l}}}\Bigg{)}}_{production}-\underbrace{2\overline{v \frac{\partial V^{\prime}{}_{i}}{\partial x_{l}}\frac{\partial V^{\prime}{}_{ j}}{\partial x_{l}}}}_{dissipation}+\underbrace{\overline{p^{\prime}} \Bigg{(}\frac{\partial V^{\prime}{}_{i}}{\partial x_{j}}+\frac{\partial V^{ \prime}{}_{j}}{\partial x_{i}}\Bigg{)}}_{stress-strain}. \tag{3}\]  
It follows from the Reynolds stress equation that variation in Reynolds stress depends upon comprehensive effects of diffusion, production, dissipation, and stress-strain of turbulent flow. On the _xoy_ plane, variation in Reynolds stress along flow direction is shown in Figure 10. We can see from the figure that amplitude of variation in Reynolds stress reaches the maximum in the surface layer, which provides a hydraulic condition for fish having a preference for jumping in the free surface. The amplitude of variation in Reynolds stress is smaller below the free surface, however, it increases with an increase in flow rate. In the impingement region of weir flow, Reynolds stress fluctuates intensively, and it was found combining with vertical turbulence intensity (shown in Figure 8) that the range that turbulence intensity was larger is the same as Reynolds stress.  
Vertical distribution of Reynolds stress on the plane _xoz_ is shown in Figure 11. It follows from Figure 11 that Reynolds stress fluctuates intensively close to upstream baffle (impingement region of weir flow) and downstream baffle, which resulted from the formation of a vertical vortex after weir flow plunged into the fishway pool, and the existence of ascending and descending flows close to the upstream and downstream baffles.  
Figure 10: Variation in Reynolds stress along flow direction on the _xoy_ plane. (**a**) \(Q_{1}=20.79\) L/s; (**b**) \(Q_{2}=30.04\) L/s.
# Projected Wind Energy Maximum Potential in Lithuania
## 3 Results
### Energy Potential with Alternative Wind Turbines  
Four more wind turbines from Enercon were selected for additional analysis. Three of them had a capacity of \(\sim\)3 MW (E-82, E-101 and E-115). The other two were 4.2 MW (E-126 EP4) and 7.6 MW. Table 4 shows how much energy each of these plants would generate in different regions, based on the average wind speed prevailing in that region.  
Figures 5-7 show the energy profile in different regions. It should be noted that the wind in eastern Lithuania is not very favorable for wind power generation. Here, compared to the coastal or central regions, wind generation is almost two times lower. The most powerful (E-126 and E-126 EP4) of the selected plants are too powerful for any region, taking into account energy profiles. The graphs show that in Lithuania, there are many suitable wind power plants for any region, with a capacity of around 3 MW. The eastern region is the exception. Here, even a 3 MW plant does not reach peak capacity due to the low average wind speed at hub height (3.38 m/s), which makes wind energy development in this region questionable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Turbine** & **E-82** & **E-115** & **E-101** & **E-126 EP4** & **E-126** \\ \hline Rated power, kW & 3020 & 3000 & 3050 & 4200 & 7580 \\ \hline Energy production (**coastal**), kWh & 321 & 627 & 479 & 745 & 760 \\ \hline Energy production (**central**), kWh & 144 & 281 & 214 & 332 & 340 \\ \hline Energy production (**eastern**), kWh & 26 & 50 & 39 & 60 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Turbine comparison by rated power and energy production at different regions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **HAD** & **IPSL** & **MPI** \\ \hline
2021–2040 & 13.92–31.96 & 39.74–57.78 & 78.46–96.50 \\
2041–2060 & 38.32–56.35 & 21.04–59.08 & 44.06–62.09 \\
2061–2080 & 28.50–46.54 & 24.22–42.26 & 40.03–58.06 \\
2081–2100 & 24.14–42.18 & 19.33–37.37 & 28.63–46.67 \\  & Limits are set according to Figure 4. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated average energy production per year, TWh, for SSP2-4.5 scenario, with the assumption that wind power plants will be built in every available area.  
Figure 5: Energy profile of different wind turbines in the coastal region.  
Figure 6: Energy profile of different wind turbines in the central region.  
For all regions, E-115, which, like E-82 and E-101 (Table 5), has a capacity of 3 MW, is the best choice. It is best suited for this purpose because it will exploit the potential of the plant. Meanwhile, E-126 is too powerful for Lithuania, as the peak power is either unavailable or rarely available. It should be noted that in the central part of the country, the power generation is 2.2 times lower than in the coastal part, and in the eastern part, it is 12.5 times lower. This means that it is not advisable to develop wind energy in the east of the country.
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Scan Plane Classification  
Scan plane classification differs from scan plane detection as, in this case, every image is already known to be a standard plane. Although this is not a common situation in clinical practice, it has received significant attention as ultrasound databases from hospitals (typically within a PACS system) only store frames corresponding to standard planes. Therefore, algorithms developed on these datasets have focused on the classification and categorization of each acquired plane.  
A representative example is given by the guided random forest method proposed by [PERSON] et al. [34] for the classification of fetal 2D ultrasound images into seven standard planes. The authors included an additional class (\"other\") in order to consider possible non-modeled standard views and make the model more general. A more recent work with CNN was developed by [PERSON] et al. [35] using six classes: four fetal anatomical planes, the mother's cervix, and an additional class (\"other\"). Fetal brain images were also classified into the three most common fetal brain planes. Their results indicate that the performance of these CNN-classifying standard planes in fetal examinations is comparable to that achieved by humans.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 3 System Model and Problem Definition
### Problem Definition  
Consider a vehicular edge-computing network with \(K\) vehicles and \(N\) servers. Each vehicle \(\text{v}_{\text{i}}(\text{i}=1,\dots,\text{ K})\) generates a local dataset \(\text{d}_{\text{i}}=\{(\text{x}_{\text{ij}},\text{y}_{\text{ij}})\}_{\text{j} =1}^{|\text{d}_{\text{i}}|}\) of raw observations (e.g., images) \(\text{x}_{\text{ij}}\) and labels \(\text{y}_{\text{ij}}\), and each server \(\text{s}\)\((\text{s}=1,\dots,\text{ N})\) serves only the vehicles currently within its radio coverage. Our goal is to enable this federation of servers and vehicles to collaboratively train a shared ML/DL model with high accuracy, while ensuring the following:  
* Ensuring robust aggregation, despite heterogeneous updates;
* Respecting vehicles' mobility, which causes frequent handovers and variable participation;
* Minimizing communication overhead, given limited edge-network bandwidth.  
In conventional cloud-based FL, a single server aggregates all client updates; here, each server s plays that role locally. However, as vehicles (e.g., migrating vehicle in Figure 1) traverse coverage areas, naive aggregation can incur lost updates (when a vehicle moves before uploading its model) and resource under-utilization (when a server has too few clients). To address this, we formalize client-server associations as follows: an association indicator  
Figure 1: System model for multi-server FL in vehicular edge computing. Each server broadcasts its global model to nearby vehicles that perform local training and upload their updates via V2X links. Servers exchange their models over RSU-to-RSU backhaul, applying inter-server aggregation using validation-weighted contributions. The framework also supports model handover as vehicles migrate between RSU coverage areas.  
\(\mathrm{a}^{\mathrm{t}}_{\mathrm{i,s}}\,e\,\{0,1\}\) that equals 1 if vehicle \(\mathrm{v}_{\mathrm{i}}\) is attached to server \(s\) during communication round \(\mathrm{t}\), otherwise \(\mathrm{a}^{\mathrm{t}}_{\mathrm{i,s}}=0\). The set of active clients of server \(s\) is therefore \(\mathrm{v}^{\mathrm{(s)}}(\mathrm{t})=\{\mathrm{i}\,|\,\mathrm{a}^{\mathrm{t} }_{\mathrm{i,s}}=1\}\). A handover simply corresponds to \(\mathrm{a}^{\mathrm{t}}_{\mathrm{i,s}}=1\), \(\mathrm{a}^{\mathrm{t+\varepsilon}}_{\mathrm{i,s}}=0\), and \(\mathrm{a}^{\mathrm{t+\varepsilon}}_{\mathrm{i,s}^{\mathrm{t}}}=1\). We enforce \(\sum\limits_{\mathrm{s}}\mathrm{a}^{\mathrm{t}}_{\mathrm{i,s}}=1\), so that each vehicle is attached to exactly one server in every communication round.  
With this formalism in place, we seek an FL protocol that (1) dynamically adapts to the evolving active client sets \(\mathrm{v}^{\mathrm{(s)}}(\mathrm{t})\), (2) assesses and aggregates model updates from both local vehicles and peer servers, and (3) maximizes global models in the face of high vehicular mobility.