esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classic Numerical Scheme Test Cases
### Numerical Simulation of the Double Mach Reflection Problem  
The double Mach reflection problem (DMRP) is a classic scenario in compressible fluid dynamics that involves the complex reflection of shock waves propagating within a fluid medium. This phenomenon occurs when a shock wave interacts with a rigid wall, resulting in two distinct Mach reflections within the fluid. The study of DMRP is crucial for understanding shock wave dynamics and fluid behavior under extreme conditions. In our computational study, we consider two-dimensional Euler equations with specific initial conditions defined over a domain of \([0,4]\times[0,1]\). The specific initial conditions are as follows:  
\[(\varrho,p,u,v,\gamma)^{T}=\begin{cases}(8,116.5,7.14471,\ -4.125,1.4)^{T},&x \geq\sqrt{3}\Big{(}x-\frac{1}{6}\Big{)},\\ (1.4,1.0,0,0,1.4)^{T},&x<\sqrt{3}\Big{(}x-\frac{1}{6}\Big{)}.\end{cases} \tag{9}\]  
The boundary conditions are carefully prescribed to capture the reflective and shock wave interactions accurately. At the left boundary \((x=0)\), we impose inflow conditions to initiate shock wave propagation. The right boundary \((x=4)\) is set as a reflective boundary to simulate the wall interaction. The bottom boundary \((y=0)\) presents a unique scenario where an exact solution is provided for \(0\leq x\leq 1/6\). This serves as a reference region for validating the numerical results. For \(1/6\leq x\leq 4\), symmetric boundary conditions are employed to maintain flow symmetry. At the top boundary \((y=1)\), a Mach 10 shock wave is introduced to induce complex wave interactions within the fluid domain. This shock wave poses a significant challenge and provides insights into high-speed flow dynamics typical of supersonic conditions.  
We performed computations on a grid of \(960\times 240\) using improved finite-volume WENO schemes of the third, fourth, fifth, and sixth orders, and obtained results at \(t=2\), as shown in Figure 2. It can be observed from the results that the fourth-order finite-volume WENO scheme exhibits superior analytical capabilities comparable to a sixth-order scheme on the same grid, particularly in resolving vorticity of flow and shock waves.  
This test contrasts the performance of improved finite-volume WENO schemes of different orders in complex flow fields. Particularly on high-resolution grids, it demonstrates the outstanding numerical stability and analytical performance of the improved finite-volume WENO scheme, offering a more effective and reliable numerical simulation tool for solving complex wave systems and flow structures.  
Figure 1: The results of solving the Lax problem using different numerical schemes. (**a**) Full range; (**b**) interrupted position.
## 5 All-dielectric-DBR GaN-based VCSELs Fabricated by Epitaxial Lateral Overgrowth
The obvious threshold in the I-L curve and the fact that only a single longitudinal mode was observed above Ith demonstrate that lasing was achieved. The spacing of the longitudinal modes obtained below Ith was 6.7 nm (Figure 5), which is corresponding to cavity length of 4.5 \(\upmu\)m by calculation for the following parameters: \(n=2.45\), \(\lambda=450\) nm, \(dn/d\lambda=-0.001\). The obtained cavity length is consistent with the value measured by SEM (Figure 3). As noted previously in this section, GaN-based nitrides have associated challenges in terms of obtaining DBRs having high reflectivity and a wide stopband and precise control of the fabrication process is required to obtain cavity lengths shorter than 10 \(\upmu\)m. The VCSELs introduced in the present section met all these criteria. The DBRs at both ends of the cavity were made of dielectric materials that exhibit high reflectance over a wide spectral band width. It is preferable to determine the cavity length using the precise epitaxial growth afforded by MOCVD rather than to employ a more delicate process such as polishing, which was used in previous studies [8; 9; 10; 11; 12; 13]. Figure 5a shows that the cavity length of 4.5 \(\upmu\)m gives a diffraction loss per round trip of much less than 1% at a variety of aperture sizes. Figure 4: I–V and I–L curves generated by an all-dielectric-DBR GaN-based vertical-cavity surface-emitting laser (VCSEL). Here, Ith was 18 mA and the maximum output power was 1.1 mW, the highest value reported to date for this type of device. The kinks in the I–L curve suggest multimodal operation for the lateral mode, presumably due to the absence of lateral optical confinement. Figure 5: Spectra obtained from the all-dielectric-DBR GaN-based VCSEL above and below the threshold current (18 mA). Spectrum (**a**) was obtained at 20 mA and shows a single longitudinal mode at 453.9 nm and a half width at maximum of 0.05 nm. The latter value is equivalent to the resolution limit of the apparatus used to acquire the data (ADCMT8341). Spectrum (**b**) was acquired at 10 mA using a different spectrum analyzer (Ocean Optics USB4000) and shows a mode spacing of 6.7 nm. The ELO process that is primarily responsible for the formation of the cavity can be implemented uniformly over a large area of the wafer. Thus, this process enables the fabrication of multiple devices on a single wafer, which makes it very suitable for mass production.
###### Abstract  
This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), hybrid networks, generative adversarial networks (GANs), and transformer networks. The studies analyzed diverse datasets from panoramic, periapical, and hybrid imaging techniques, assessing diagnostic accuracy, sensitivity, specificity, and interpretability. CNN models like Deetal-Perio and YOLOv5 achieved high accuracy in detecting alveolar bone loss (ABL), with F1 scores up to 0.894. Hybrid networks demonstrate strength in handling complex cases, such as molars and vertical bone loss. Despite these advancements, challenges persist, including reduced performance in severe cases, limited datasets for vertical bone loss, and the need for 3D imaging integration. AI-driven tools offer transformative potential in periodonontology by rivaling clinician performance, improving diagnostic consistency, and streamlining workflows. Addressing current limitations with large, diverse datasets and advanced imaging techniques will further optimize their clinical utility. AI stands poised to revolutionize periodontal care, enabling early diagnosis, personalized treatment planning, and better patient outcomes.  
2025
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 3 Experimental Results and Analysis
### Quantitative Evaluation
The confidence intervals for SSIM, PSNR, DE, and ALME were determined as [0, 1], [0, 50] (with values above 30 considered good and above 40 considered excellent for PSNR), [0, 40], and [0, 30], respectively. As shown in Figure 6, an in-depth analysis of the Root Mean Square Error (RMSE) for the MECI, NLM, and AB-NLM algorithms on various images reveals noteworthy observations. On the Art image, AB-NLM exhibits a significantly lower RMSE (3.297) compared to MECI (6.291) and NLM (5.294). Similarly, in the Teddy image, AB-NLM demonstrates superior performance, with an RMSE of 1.873, exhibiting a significant difference compared to NLM (2.358) and MECI (3.185). This trend persists across other images (Moebius, Recycle, and Jadeplant), showcasing the comparatively more accurate depth image restoration capabilities of AB-NLM. Examining the Peak Signal-to-Noise Ratio (PSNR) results in Figure 7 reveals the consistent superiority of the AB-NLM algorithm across diverse images. For instance, on the Art image, AB-NLM achieves a remarkable PSNR of 45.148, surpassing MECI (42.156) and NLM (43.153). This pattern extends to other images (Teddy, Moebius, Recycle, and Jadeplant), underscoring the consistently higher PSNR values and reaffirming AB-NLMs robust performance in image reconstruction tasks. Figure 6: Displays a comparative analysis of RMSE results among the MECI, NLM, and AB-NLM algorithms based on the Middlebury dataset. Figure 7: Displays a comparative analysis of PSNR results among the MECI, NLM, and AB-NLM algorithms based on the Middlebury dataset. Figure 8 presents a scientific analysis of the Structural Similarity Index (SSIM) results for the MECI, NLM, and AB-NLM algorithms on various images. Notably, AB-NLM consistently demonstrates higher SSIM values, indicating its excellence in image quality assessment. Taking the Art image as an example, AB-NLM achieves an SSIM of 0.986, significantly surpassing MECI (0.982) and NLM (0.983). This consistent trend across different images (Teddy, Moebius, Recycle, and Jadeplant) emphasizes AB-NLMs ability to provide higher structural similarity, indicative of a more accurate reconstruction of the original images. In Table 1, when ndisp = 256, it implies that the depth map's disparity values do not exceed 256 during the restoration evaluation. Increasing ndisp results in higher DE and ALME. For instance, in the Jadeplant image, both DE and ALME values are notably higher, yet this does not necessarily mean poorer restoration. Notably, within the five images in Table 1, all DE values surpass their respective ALME values, indicating most restoration outcomes closely approximate true depth values, with a small fraction exhibiting suboptimal results. Analysis of the MECI, NLM, and AB-NLM algorithms in Table 1 reveals that AB-NLM outperforms others in terms of DE and ALME values across various images, indicating superior performance in-depth image repair.
## 6 Methodology
### Case (1): Constant Output Voltage Control
#### 6.1.2 Turn-On Angle Control  
PSO is used for tuning the PID parameters, which generates the turn-on angle while the turn-off angle is kept constant. The tuned absolute PID parameters of PID are \(k_{p}=0.568\), \(k_{i}=16.67\), and \(k_{d}=0.003\). Implementing the control system to generate the turn-on angle at different reference output voltages indicates that the time for peak value is different and depends on the reference output voltage, as shown in Figure 10. The turn-off angle is fixed at \(45^{\circ}\), while the generated turn-on angle from the PID is responsible for generating the required voltage. The steady-state settling time of the turn-off angle is 0.15 s, while the peak overshoot value is 75% for 100 V output and decreases to 20% for 250 V output.  
Figure 8: The transient response of turn-off angle control, (**a**) different reference output voltage (v); (**b**) the corresponding generated turn-off angle in deg.  
Figure 9: Maximum generated output voltage in volt at (**a**) different initial turn-on angle in deg. (**b**) different reference maximum input current.  
The generated turn-on angle at different values of turn-off angles and different reference voltages is shown in Figure 11a, where at V\({}_{\text{ref}}\) = 200 V, the minimum turn-off angle is 25\({}^{\circ}\), which gives \(\theta_{\text{on}}\) = 5\({}^{\circ}\). Meanwhile, at V\({}_{\text{ref}}\) = 100 v, the minimum turn-off angle is 20\({}^{\circ}\), and the corresponding turn-on angle is 0\({}^{\circ}\). The phase current peak value at different values of turn-off angles and V\({}_{\text{ref}}\) is shown in Figure 11b. The output voltage and the turn-on angles' response at different reference voltage values are illustrated in Figure 12a,b. The output response has a peak overshoot at the beginning, while there is no peak overshoot after that since the output voltage has an initial value from the previous step. The system can also generate a maximum output voltage at different turn-off angles and different maximum excitation currents, as shown for the turn-off angle control in Figure 13a,b.  
Upon comparing the turn-off angle control and turn-on angle control for a constant voltage, the turn-off angle control has a 0.03 s steady state settling time, less than the steady-state settling time for the turn-on angle control, which is 0.15 s. The turn-off angle control for a constant output voltage has less peak overshoot value than the latter. The two techniques have the same steady-state settling time, which is 0.055 s, and the system response is overdamped in the case of generated maximum output voltage.  
Figure 11: The effect of the initial turn-off angle on (**a**) the generated turn-on angle and (**b**) the maximum generated current at different reference output voltages.  
Figure 10: Output voltage response at turn-on angle control (at turn-off = 0.0).
## 3 Computational Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Instance** \\ **ID** \\ \end{tabular} } & \multirow{2}{*}{**BFS (60 s)**} & \multicolumn{4}{c}{**MST2 (60 s)**} & \multicolumn{4}{c}{**_r-Flip (60 s)**} \\ \cline{3-8}  & & & **OFV** & **TB (s)** & **OFV (_r = 1_)** & **TB (s)** & **OFV (_r = 2_)** & **TB (s)** \\ \hline p3000\_1 & 3,931,583 & 3,931,583 & 10 & 3,931,583 & 3 & 3,931,583 & 8 \\ p3000\_2 & 5,193,073 & 5,193,073 & 25 & 5,193,073 & 2 & 5,193,073 & 2 \\ p3000\_3 & 5,111,533 & 5,111,533 & 52 & 5,111,533 & 8 & 5,111,533 & 4 \\ p3000\_4 & 5,761,822 & 5,761,437 & 10 & 5,761,822 & 2 & 5,761,822 & 2 \\ p3000\_5 & 5,675,625 & 5,675,430 & 24 & 5,675,625 & 7 & 5,675,625 & 17 \\ p4000\_1 & 6,181,830 & 6,181,830 & 40 & 6,181,830 & 3 & 6,181,830 & 4 \\ p4000\_2 & 7,801,355 & 7,797,821 & 12 & 7,801,355 & 13 & 7,801,355 & 4 \\ p4000\_3 & 7,741,685 & 7,741,685 & 31 & 7,741,685 & 5 & 7,741,685 & 8 \\ p4000\_4 & 8,711,822 & 8,709,956 & 58 & 8,711,822 & 5 & 8,711,822 & 11 \\ p4000\_5 & 8,908,979 & 8,905,340 & 27 & 8,908,979 & 4 & 8,908,979 & 13 \\ p5000\_1 & 8,559,680 & 8,556,675 & 56 & 8,559,680 & 21 & 8,559,680 & 7 \\ p5000\_2 & 10,836,019 & 10,829,848 & 34 & 10,836,019 & 59 & 10,836,019 & 11 \\ p5000\_3 & 10,489,137 & 10,477,129 & 28 & 10,489,137 & 20 & 10,489,137 & 16 \\ p5000\_4 & 12,251,710 & 12,245,282 & 52 & 12,251,710 & 54 & 12,251,520 & 42 \\ p5000\_5 & 12,731,803 & 12,725,779 & 56 & 12,731,803 & 17 & 12,731,803 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the MST2 algorithm and _r-flip_ strategy in Algorithm 5 within the CPU time limit of 60 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Instance** \\ **ID** \\ \end{tabular}  } & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Density**} & \multirow{2}{*}{**MST2**} & \multicolumn{2}{c}{_r-Flip with 60 s_} & \multirow{2}{*}{**MST2**} & \multicolumn{2}{c}{_r-Flip with 600 s_} \\ \cline{3-8}  & & & **with 60 s** & & **_r = 1_** & _r = 2_ & **with 600 s** & _r = 1_ & _r = 2_ \\ \hline p4000\_1 & 4000 & 0.5 & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p4000\_2 & 4000 & 0.8 & 10 (0) & 10 (0) & 10 (0) & 9 (0.004) & 10 (0) & 10 (0) \\ p4000\_3 & 4000 & 0.8 & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p4000\_4 & 4000 & 1 & 1 (0.033) & 10 (0) & 10 (0) & 10 (0) &When the CPU time limit was increased to 600 s, the MST2 algorithm matched 10 out of 27 of the best solutions. The 1-flip strategy matched 25 out of 27 of the best solutions, and the 2-flip strategy matched 23 out of 27 of the best solutions. The 1-flip and 2-flip strategies in Algorithm 5 performed well on the large, high-density instances. There was no clear pattern of the 2-flip strategy being used more often than the 1-flip strategy to find the same OFV. The 1-flip and 2-flip strategies in Algorithm 5 chose the initial solution randomly and independently. The 1-flip strategy had better performance when the CPU time limits were 60 and 600 s.
## 5 Case Study
### Scenario 1
#### 5.1.3 Results and Analysis of the Distributed Optimal Dispatching on Day 1 of Scenario 1
The SOC of BESS in each optimization period is presented in Figure 9. Combined with Figure 5 and 7, BESS can absorb the surplus renewable energy and discharge to meet the load demands of MGs. In the periods 1-17 and 36-61, the renewable resource is surplus, and MGs give priority to charging their BESSs. If there is energy left after a full charge, it will be delivered to other MGs. Though only expected exchange power is communicated in coalition during the distributed optimization process, BESSs can be effectively scheduled according to the operation state of MGs in coalition, which ensures operating economy. Having gained insight into the optimal scheduling, Figure 10 shows the optimal variables and net load in MG1. Obviously, after convergence, the sum of power purchased from the main grid, charge-discharge power of BESS and exchange power with other MGs is equal to the net load of MG1 with the consideration of power loss. Based on the satisfaction of load demands, each MG can choose to purchase electricity from the main grid, charge or discharge by BESS or exchange power with other MGs to reduce the total operation cost with a comprehensive consideration of purchasing electricity cost, energy storage cost, load characteristic and power loss cost. In order to illustrate the effectiveness and benefits of the proposed method in this paper, the power loss cost comparison between with and without optimization of power loss in each period is presented in Figure 11.  
Figure 10: The optimal variables and net load in MG1 on Day 1 of Scenario 1. Figure 9: The state of charge (SOC) of BESSs in MGs on Day 1 of Scenario 1. As shown in Figure 11, in Periods 18-32 and 71-90, the exchange power among MGs and the exchange power between each MG and the main grid are greater; thus, the power loss cost is higher in these time slots. After optimization of the power loss, the exchange power is lowered; therefore, the power loss cost in each period is decreased. From Table 3, it can be seen that the power loss cost is reduced by 22.56% compared to the case without optimization power loss cost. It is clear that the proposed method in this paper can lower power loss effectively. To clarify the efficiency of the proposed distributed optimization algorithm based on ADMM, we use a personal computer with Intel Core i7-6700K CPU 4.0 GHz (Intel Corporation, Santa Clara, CA, USA), 8 G memory and MATLAB 2015b, as the testing environment. The convergence of the primal residual and dual residual is shown in Figure 12. Figure 12 represents that the algorithm can achieve convergence in 40-60 iterations for most optimization periods, which indicates that the ADMM shows a faster convergence rate and better performance for the solution of this problem.
# Can Blockchain Strengthen the Energy Internet?
## 5 Decentralized Scalability
### Role of Blockchain  
Inherent features of blockchain enable the delegation of processing to edge nodes and cloud storage platforms through a trustless trust establishment. Low latency and high throughput can be achieved through distributed processing which further provides solutions to intermittent connectivity in remote areas in a store and forward manner. PETCON, a secure P2P trading mechanism for plug-in hybrid EVs [63], and Guardtime, a permissioned blockchain-based approach, are considered to be scalable solutions for complex data exchange in EI grids.  
Further, with the incorporation of off-chains, side-chains, sharding and edge computing to offload the computational burden, better results have been obtained to cater for the increase of nodes connected with minimum trust issues. This would be further empowered with the speculated advancements in communication infrastructure by deployment of 6G technologies, enabling edge intelligence [29]. DL and big data analysis applied on the data aggregated in cloud storage would be controlled and managed through a secure mechanism utilizing blockchain platforms. Leakage of information would be shielded and better control over the data can be achieved through such approaches [16].  
Scalability issues related to EI grid implementations, however, have not been fully overcome through the blockchain platforms. These can be identified as future research prospects for the improvement of the applicability of blockchain for energy grid management.
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 3 Conventional Beamformers
### Kronecker Product Beamforming  
Designing a single beamformer with different features is highly important, allowing different effects to be employed over a single array of sensors. Two methods to accomplish such a task are the Kronecker product (KP) [25; 26] and linear convolutional Kronecker product (LCKP) methods [23]. In these processes, the sensor array is split into subarrays for which we design separate beamformers and use the chosen technique to synthesize the whole sensor array's (or full array's) beamformer.  
The KP beamforming process is as follows: given steering vector \(\mathbf{d}_{\theta}\), we decompose it into two parts (namely, \(\mathbf{d}_{1;\theta}\) and \(\mathbf{d}_{2;\theta}\)) satisfying the relation \(\mathbf{d}_{\theta}=\mathbf{d}_{1;\theta}\otimes\mathbf{d}_{2;\theta}\), where \(\otimes\) represents the Kronecker product. By designing beamformers \(\mathbf{h}_{1}\) and \(\mathbf{h}_{2}\) for \(\mathbf{d}_{1}\) and \(\mathbf{d}_{2}\), respectively, we obtain the beamformer for the full array as \(\mathbf{h}=\mathbf{h}_{1}\otimes\mathbf{h}_{2}\)[32].  
LCKP beamforming is achieved similarly: given a uniform linear array's (ULA) steering vector \(\mathbf{d}_{\theta}\) of length \(M\), we define \(\mathbf{d}_{1;\theta}\) as the \(M_{1}\)-th first elements of \(\mathbf{d}_{\theta}\) and, similarly, \(\mathbf{d}_{2;\theta}\) with \(M_{2}\) elements, respecting \(M_{1}+M_{2}-1=M.\) By designing beamformers \(\mathbf{h}_{1}\) and \(\mathbf{h}_{2}\) for each subarray, the full array's beamformer is \(\mathbf{h}=\mathbf{h}_{1}*\mathbf{h}_{2}\)[23], where \(*\) denotes the linear convolution.  
For both the KP and LCKP methods, we have the following properties:  
\[\mathcal{B}(\mathbf{h},\mathbf{d}_{\theta})=\mathcal{B}(\mathbf{ h}_{1},\mathbf{d}_{1;\theta})\,\mathcal{B}(\mathbf{h}_{2},\mathbf{d}_{2; \theta}), \tag{16a}\] \[v_{\mathrm{d}}(\mathbf{h},\mathbf{d})\leq[1+v_{\mathrm{d}}( \mathbf{h}_{1},\mathbf{d}_{1})][1+v_{\mathrm{d}}(\mathbf{h}_{2},\mathbf{d}_{2 })]-1. \tag{16b}\]  
The first one shows that the beampattern of \(\mathbf{h}\) is the combination of the beampatterns of the subarrays. Through the second one, we can see that, if \(\mathbf{h}_{1}\) and \(\mathbf{h}_{2}\) are distortionless beamformers, \(\mathbf{h}\) also will be. From these properties, we can see that the beampattern properties (including the distortionless feature) from \(\mathbf{h}_{1}\) and \(\mathbf{h}_{2}\) are maintained for \(\mathbf{h}\).