esa-sceva/satcom-chunk-collection huggingface dataset:
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Vehicle Operations Management  
Vehicle operations management in C-V2X is an example of a use case focused on leveraging C-V2X to enhance and optimize vehicle operations within a connected environment. Vehicle operations management is an intelligent platform utilizing C-V2X communication to coordinate and improve various driving and vehicle performance aspects.  
The Vehicle Operations Management use case can be split into several steps:  
1. Traffic and Coordination: The vehicle operations management system receives real-time traffic information and communicates with other vehicles and traffic infrastructure through C-V2X. It optimizes routes and adjusts speeds to enhance traffic flow.
2. Collision Prevention: Using C-V2X data, the vehicle operations management system detects potential collision situations with other vehicles and alerts the driver. It takes actions such as speed adjustment to avoid collisions.
3. Emergency Response: Vehicle operations management employs C-V2X to alert nearby vehicles and emergency services about the situation in emergencies. This improves response times and road safety.
4. Fleet Optimization: In commercial fleets, vehicle operations management uses C-V2X to track vehicle locations, monitor efficiency, and coordinate routes and maintenance schedules.
5. Dynamic Traffic Light Control: Using C-V2X, the vehicle operations management system communicates with traffic lights to optimize their timing based on traffic conditions, reducing congestion, and improving travel efficiency.
6. Parking Assistance: Through C-V2X, the vehicle operations management system guides drivers to available parking spaces, simplifying parking search and enhancing traffic flow.
7. Remote Maintenance and Software Updates: Utilizing C-V2X, the vehicle operations management system conducts real-time remote diagnostics and sends software updates without requiring a physical workshop visit.
8. Critical EvaluationFeasibility in 6G: 6G's capabilities to support a vast array of IoT devices and ensure high-reliability communication lay a robust foundation for advanced vehicle operations management. With features such as network slicing, 6G can allocate dedicated network resources to manage vehicle operations efficiently and reliably.  
Advantages: Implementing 6G would facilitate enhanced real-time monitoring, control, and optimization of vehicle fleets, thereby improving logistical operations and reducing operational costs.  
Challenges: Ensuring secure and consistent communication across various geographic locations and terrains presents a significant hurdle, alongside managing the voluminous data generated by vehicle fleets.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement
### Types of Multi-Path Routing: FMP and MMP  
Although FMP and MMP are not directly comparable, SD-WAN offers application-aware properties (DPI, encryption, and firewalling) that can also be used with SCION. However, SCION offers a super-set of SD-WAN properties at the underlay level: enhanced path selection, trust in the network, and real end-to-end control (not only the first hop). As the core difference, MMP is a public global Internet infrastructure with end-to-end control, whereas FMP providers typically build private networks within the first hop (see Figure 2). An MMP customer can obtain a connection from any ISP offering MMP, whereas an FMP customer is typically locked to one provider (Though there exist certain alliances, it is definitely not an open network). Typically, SD-WANs are operated using intra-domain routing protocols.  
It is, therefore, possible to distinguish FMP for one customer controlled using SD-WAN from MMP for any customer in a network dynamically controlled through a public infrastructure, such as SCION. SCION has a much more powerful multi-path routing. For instance, consider the case of a customer with an AS who has a regular Internet connection and an SD-WAN connection. The SD-WAN system can then send a packet over one of two connections. However, a single SCION Internet connection can enable 10 or more paths (depending on the topology) between the source and destination. The path \(p_{n}\) list without the routing loop in Figure 2 is shown below.  
* \(p_{0}\): \(Src\to A\to B\to C\to Dst\)
* \(p_{1}\): \(Src\to A\to B\to C\to D\to Dst\)
* \(p_{2}\): \(Src\to A\to C\to Dst\)
* \(p_{3}\): \(Src\to A\to C\to D\to Dst\)
* \(p_{4}\): \(Src\to A\to D\to Dst\)
* \(p_{5}\): \(Src\to A\to D\to C\to Dst\)
* \(p_{6}\): \(Src\to B\to A\to C\to Dst\)
* \(p_{7}\): \(Src\to B\to A\to C\to D\to Dst\)
* \(p_{8}\): \(Src\to B\to A\to D\to Dst\)
* \(p_{9}\): \(Src\to B\to A\to D\to C\to Dst\)
* \(p_{10}\): \(Src\to B\to C\to Dst\)
* \(p_{11}\): \(Src\to B\to C\to D\to Dst\)
* \(p_{12}\): \(Src\to B\to C\to A\to D\to Dst\)  
Figure 2: Source AS \(Src\) can choose a link between/among first hops to mitigate a link congestion/failure; however, FMP cannot choose a path after the first hops even if there are network hazards before the destination \(Dst\). MMP can control all ASes between \(Src\) and \(Dst\), allowing a path to be chosen by considering the link status.
###### Abstract  
Aim: This systematic review and meta-analysis evaluated the adjunctive use of hyaluronic acid (HA) in non-surgical periodontal therapy to improve clinical outcomes in patients with moderate-to-severe chronic periodontitis. The aim was to assess its effectiveness in reducing probing pocket depth (PPD), improving clinical attachment level (CAL), and decreasing bleeding on probing (BOP). Methods: Four electronic databases were searched (PubMed, Cochrane Library, Scopus, Web of Science, Grey Literature) for randomized controlled trials (RCTs) published until January 2025 and involving otherwise healthy patients treated with scaling and root planing (SRP) plus HA after at least 12 weeks of follow-up. Risk of bias was assessed using RoB 2.0; data were collected on clinical assessment, microbiological, and biochemical analysis and then analyzed using a fixed-effects or random-effects model considering the high heterogeneity of the included studies, which calls for caution when interpreting the results. Results: Fourteen of the 21 RCTs reviewed underwent a meta-analysis. Compared with SRP alone, the addition of adjunctive HA \(\geq\) 0.8% led to an improvement in PPD, but the reduction in BOP was less consistent. Biochemical markers indicated less inflammation, oxidative stress, and selective antimicrobial activity. Although studies on 0.2% HA are still limited, improvements have been observed in clinical parameters, along with better biochemical and microbiological outcomes in the experimental group compared to the control group. Conclusions: Despite moderate heterogeneity and methodological limitations, the evidence supports the use of HA combined with SRP in periodontal treatment.  
hyduronic acid; non-surgical periodontal therapy; topical agent +
Footnote †: journal: Applied Sciences
## 5 Validation Results and Discussion
### Steady-State Simulation of Different Wall Configurations  
The second set of experimental results, which was used to validate the numerical models, was taken from the paper by [PERSON] and [PERSON] [40].  
During tests in steady-state conditions, the chambers were maintained at a constant temperature and the wall was exposed to a steady differential temperature. The temperatures were kept constant for a proper time until the steady-state condition occurred. In the Simulink model, the indoor chamber air temperature was controlled at 22 \({}^{\circ}\)C; the outdoor air was kept at a constant value to obtain a difference of temperatures equal to \(-\)45.6, \(-\)28.9, \(-\)17.8, 1.7, and 26.7 \({}^{\circ}\)C.  
Figure 7 shows the comparison between the experimental values and the calculated ones, for a steady-state temperature difference and the wall configuration number 4 (block brick cavity).  
The temperature of the indoor chamber was 22 \({}^{\circ}\)C, while the outdoor temperature was 50 \({}^{\circ}\)C [40]. The _MAPE_ for this simulation was equal to 0.72%.  
Figure 8 shows a steady-state condition of heat flow rate to the indoor chamber for (a) hollow block, (b) block brick, and (c) wood frame and brick veneer. As expected, the simulations showed that insulation leads to a significant reduction of energy consumption. Moreover, the figures present good correspondence between the numerical and experimental results.  
Figure 7: Steady-state block brick cavities.  
For these three steady-state simulations, described in Figure 8, the _MAPE_ was equal to 17.04%, while \(\phi\) was equal to 0.786.  
Table 3 illustrates the calculated and measured [40] value of resistance and transmittance for six different types of wall. The last column of the table reports a measured and calculated transmittance values ratio. The calculated data agreed with techniques recognized by the American Society of Heating and Air-Conditioning [41].  
According to the table, the transmittance of the insulated hollow block wall was 50% less than that of the uninsulated hollow block. The data obtained showed a close matching between insulated and uninsulated block-brick. In addition, Simulink values indicated the transmittance of the veneer wall was around 90% of the wood frame.
## 2 Strategies and Device Designs for Efficient PEC Water Splitting
### Absorption of Solar Radiation
The BiVO\({}_{4}\) with the octahedral Au NPs showed the best PEC performance (Figure 3i) and the enhancement could be explained as above. Moreover, the Mott-Schottky plot under dark conditions (Figure 3j) showed a lower flat band potential for the BiVO\({}_{4}\) with octahedral Au NPs than that of bare BiVO\({}_{4}\), which implies a more favorable band position for the PEC water splitting. Therefore, the improvement of PEC performance was attributed to the LSPR-induced enhanced light absorption by the plasmonic Au NPs on the BiVO\({}_{4}\) film. [PERSON] et al. investigated the size-dependent LSPR effect of Au NPs on the BiVO\({}_{4}\) film. They prepared Au NPs with diameters of 10, 20, 30, 40, 60, and 80 nm. All samples with Au NPs showed enhanced PEC performance over the pristine BiVO\({}_{4}\) film, and 20 and 30 nm diameters exhibited the highest photocurrent densities. In order to elucidate the reason for the enhancement, they performed FDTD simulation so that the LSPR-EFs in the different sizes of the Au NPs were simulated. The results indicated that the electric field enhancement was increased an increase in the diameter of Au NP. Therefore, photoexcited holes in the BiVO\({}_{4}\) could diffuse to the surface of BiVO\({}_{4}\) film and participate in the water redox reaction. Meanwhile, the visible light absorption was also improved by the presence of the Au NPs. Light absorption enhancement exhibited the maximum at around 530 nm, regardless of the size of the Au  
Figure 3: Finite-difference time-domain simulation results of BiVO\({}_{4}\) with (**a**) hemispherical Au NP and (**b**) octahedral Au NP. The simulations are performed under wavelengths of (**c,d**) 550 nm, (**e,f**) 430 nm, (**g,h**) 350 nm. (**i**) PEC performances of the samples. (**j**) Mott-Schottky plot of bare BiVO\({}_{4}\) and BiVO\({}_{4}\) with octahedral Au NPs under dark conditions (Frequency: 1 kHz, amplitude: 10 mV). Reprinted with permission from [23]. Copyright 2017, John Wiley and Sons. NPs. Thus, it can be inferred that the enhancement of PEC performance is attributed to the improved visible light absorption and effective carrier separation by the LSPR-EFs at the interface.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 2 Related Work
### Learning-Based Dehazing Methods with Training Datasets
In conclusion, there are three primary issues with learning-based dehazing methods: (1) the need for large-scale training datasets, (2) the complexity of extensive training time, and (3) poor generalizability.
## 5 Conclusions  
This article has proposed a scalable/flexible finite field polynomial multiplier architecture for NIST-standardized binary elliptic fields. Initially, a dedicated digit-parallel architecture is proposed for each binary field recommended by NIST, i.e., \(163,233,283,409\) and \(571\). Then, a scalable architecture having support for all variants of binary fields of elliptic curves is proposed. Towards performance investigation, we have compared dedicated multiplier architectures with scalable design. After this, the dedicated and scalable architectures are compared with the most relevant state-of-the-art multipliers. All multiplier architectures are implemented in Verilog HDL using the Vivado IDE tool. The implementation results are reported on a \(28\) nm Virtex-7 FPGA technology. The obtained results demonstrate that the dedicated multipliers utilize FPGA slices of \(1182\), \(1451\), \(1589\), \(2093\) and \(3451\) for \(m=163,233,283,409\) and \(571\). These can operate at a maximum frequency of \(500\), \(476\), \(465\), \(451\) and \(443\) MHz. Due to the higher clock frequency, the power consumption is relatively higher (\(3.201\) W for \(163\) and \(3.726\) W for \(571\)). Similarly, for all supported binary fields, our scalable architecture (i) utilizes \(3753\) slices, (ii) achieves \(305\) MHz clock frequency, (iii) takes \(0.013\)\(\upmu\)s for one finite field multiplication and (iv) consumes \(3.905\) W power. Our scalable digit-parallel architecture is comparatively \(8\), \(6\) and \(14\) times more area-efficient than most recent state-of-the-art multipliers of [17; 21; 23]. Moreover, our scalable design is \(403.89\) times faster in terms of computational time than [23]. The implementation results and comparison to the state of the art show the significance of this work.  
Conceptualization, M.R. and A.A.; Data extraction, S.Z.K. and S.S.A.; results compilation, M.R. and S.Z.K.; validation, M.R. and S.S.A.; writing--original draft preparation, S.Z.K. and H.K.; critical review, M.R. and I.B.; draft optimization, S.Z.K. and H.K.; supervision, H.K. and I.B.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. We are thankful for the support of the Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia for funding this work under grant number R.G.P.2/132/42.  
Not applicable.  
The authors declare no conflicts of interest.
## 4 Experiments and Results
### Experimental Settings
The hyperparameters \(\beta_{1}\), \(\beta_{2}\in[0,1)\) control the exponential decay rates of these moving averages, as shown in the following equations:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{3}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{4}\]  
where \(g\) is the current gradient value of error function for the neural network training. ```
1: Declare the parameters Objective function \(f(\theta)\), hyperparameter learning rate \(\alpha\), exponential decay rates \(\beta_{1}\), \(\beta_{2}\) for moment estimates, tolerance parameter \(\epsilon>0\) for numerical stability
2: Initialize first moment vector \(m_{0}=0\), second moment vector \(v_{0}=0\) and timestep \(t=0\)
3:while\(\theta_{t}\) has not converged do
3:1 update timestep \(t=t+1\)
3:2 compute gradient of objective using \(g_{t}=\bigtriangledown_{\theta}f_{t}(\theta_{t}-1)\)
3:3 update first moment estimate and second moment estimate using Equations (3) and (4), respectively. 3:4 compute unbiased first and second moment estimate using Equations (5) and (6), respectively. 3:5 update objective parameters using Equation (7). endwhile
4: return final parameter \(\theta_{t}\)
```  
**Algorithm 1** Adam, our proposed algorithm for the training process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training-Testing Partition (\%)** & **Total Training Records** & **Total Testing Records** \\ \hline
**70–30** & 1748 & 750 \\
**10-fold cross validation** & 2248 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Class distribution of beach visitors. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Specification** \\ \hline
**Number of hidden layers** & 6 \\
**Number of neurons per hidden layer** & 300 \\
**Optimizer** & Adam \\
**Learning rate** & 0.001 \\
**Activation function** & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters. Moving averages are initialized as 0. The moment estimates are biased around 0, especially during the initial timesteps. This initialization bias can be easily counteracted resulting in bias-corrected estimate. \[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{5}\]  
\[\hat{o}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{6}\]  
Finally, we update the parameter \(\theta_{t}\) as shown below:  
\[\theta_{t}=\theta_{t-1}-\frac{\alpha\hat{m}_{t}}{\sqrt{\hat{v}_{t}+\varepsilon}} \tag{7}\]  
We used in our experiments for the Adam optimizer a learning rate \(\alpha=10^{-3}\) and two decay parameters \(\beta_{1}\)= 0.9 and \(\beta_{2}\) = 0.999 [42]. Experiments were carried out on a laptop running 64-bit Windows 10 Home on an Intel Core i5-8265U and using 8 GB of memory.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
## 4 Discussion  
Numerical simulation provides an efficient, flexible, and economical means to simulate the dynamic response of rock under complex impact loads. In this study, the numerical simulation of cyclic impact tests on rock materials under confining pressure was successfully realized through LS-DYNA by first applying static circumferential confining pressure before impact load with dynamic relaxation, then conducting cyclic impact tests with full restart analysis. Subsequently, the cyclic impact damage evolution of rock materials under confining pressure was analyzed based on simulation results. As only the HJC model is employed for rock materials in numerical simulation, the applicability of this study may be limited. Follow-up studies may consider more constitutive models for comparative studies to improve the applicability of simulation results. Moreover, the parameters of the constitutive model may also vary to represent various kinds of rocks.  
Based on numerical simulation, both confining pressure and amplitude of half-sine incident stress waves show a significant effect on the damage evolution of rock materials. As the dynamic response of rock material under an impact load is closely related to the waveform of impact load, follow-up studies may consider various waveforms of incident stress waves, such as rectangular stress waves, trapezoidal stress waves, and triangular stress waves. The damage variable is characterized by volume change induced by failure elements in the finite element model. Therefore, the grid of the rock specimen has an influence on the accuracy of calculated damage. However, small element size consumes much time for numerical simulation. Therefore, follow-up studies may use the discrete element method, such as DEM and PFC, to investigate crack generation and propagation during cyclic impacts under confining pressure.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 2 Proposed Hierarchical Lempel-Ziv Complexity
### Hierarchical Lempel-Ziv Complexity  
In this subsection, a novel method called HLZC is proposed by combining the hierarchical decomposition and Lempel-Ziv complexity. The hierarchical decomposition can decompose an arbitrary time series into high-frequency components and low-frequency components [29]. Figure 3 gives an example of the structure of hierarchical components decomposed by hierarchical decomposition. The detailed calculation procedures of our proposed HLZC method can be summarized into four steps as follows.  
(1) For an arbitrary time series \(X[x(i),i=1,2,\cdots,N]\), the averaging operator \(Q_{0}\) and differential operator \(Q_{1}\) can be expressed as follows:  
Figure 3: The structure of hierarchical components with three hierarchical layers.  
Figure 2: The flowchart of multi-scale Lempel-Ziv complexity (MLZC).  
\[Q_{0}(x)=\frac{x(i)+x(i+1)}{2}\ \ \ i=1,2,\cdots,N-1 \tag{6}\]  
\[Q_{1}(x)=\frac{x(i)-x(i+1)}{2}\ \ \ i=1,2,\cdots,N-1 \tag{7}\]  
where \(Q_{0}(x)\) and \(Q_{1}(x)\) denote the low-frequency component and high-frequency component for a given time series, respectively.  
(2) Conducting of the operators \(Q_{j}\) matrix (\(j\) = 0 or 1) can be adaptively generated according to the length of the time series \(N\) as follows:  
\[Q_{j}=\left[\begin{array}{ccccccc}\frac{1}{2}&\frac{(-1)}{2}^{j}&0&0&\cdots &0&0\\ 0&0&\frac{1}{2}&\frac{(-1)}{2}^{j}&\cdots&0&0\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&0&0&\cdots&\frac{1}{2}&\frac{(-1)}{2}^{j}\\ \end{array}\right]_{N/2\times N} \tag{8}\]  
(3) Repeat step (2) to obtain the hierarchical components \(X_{k,e}\) as Equation (9).  
\[X_{k,e}=Q_{r_{k}}^{k}Q_{r_{k-1}}^{k-1}\cdots Q_{r_{1}}^{1}x \tag{9}\]  
where \(e\) is the number of hierarchical nodes. For hierarchical layer \(k\), \(e\) can be obtained as follows:  
\[e=\sum_{m=1}^{k}2^{k-m}r_{m} \tag{10}\]  
where \([r_{1},r_{2}\cdots,r_{k}]\) represents the unique vector corresponding to the integer \(e\), and \(\{r_{m},m=1,\ldots,k\}\in\{0,1\}\) indicates the averaging or differential operator at the \(k\)th layer.  
(4) Compute all the hierarchical components by repeatedly using steps (1)-(3). Then, calculate LZC values of all the nodes. The HLZC is the set of all LZC values expressed as  
\[HLZC(X,k)=LZC\big{(}X_{k,e}\big{)} \tag{11}\]  
The calculation process of HLZC is shown in Figure 4.  
Figure 4: Flowchart of the hierarchical Lempel-Ziv complexity (HLZC) method.
# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 6 Simulation Platforms for AV Traffic
### VISSIM (Visual Simulation System)
This platform aims to evaluate driving behavior in an immersive and interactive environment, offering new ways to study the effectiveness of collective intelligence countermeasures in improving traffic systems. The integration of VISSIM with game engines represents a significant advancement in simulation technology, providing more dynamic and realistic environments for traffic studies. [PERSON] et al. [245] used VISSIM to evaluate different expressway exit designs, comparing conventional designs with alternative approaches such as passenger vehicle and truck separations (PVTSs) and lane separations around interchanges. This study demonstrated how VISSIM can be applied to optimize road designs, reduce congestion, and improve operational performance on expressways. Additionally, VISSIM has been employed in disaster management scenarios, such as the work by [PERSON] and [PERSON] [246], who developed a metamodel to predict traffic delays and queue lengths in landslide zones. Their study focused on optimizing traffic control during landslides to enhance travel efficiency and safety, showcasing VISSIM's versatility in handling emergency situations. In summary, VISSIM is a powerful and adaptable tool that supports a wide range of applications in traffic modeling and urban planning. This tool's ability to simulate detailed traffic scenarios, coupled with its capacity for integration with other technologies, makes it an invaluable resource for researchers and practitioners aiming to improve urban mobility and transportation systems. The relationships between these studies illustrate VISSIM's broad applicability and the continuous efforts to refine and enhance its capabilities in various domains.