esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the efficient evaluation of operational reliability and the critical component identification based on the PCE method are investigated for the PV-based new energy power system. The subsequent conclusions are reached:  
1. The distribution characteristics of PV output are modelled using GMM-HMM, and the model is able to reflect the correlation of PV output more accurately.
2. On the basis of SE and PCE methods, the analytical function relationship between the PV output and the operation reliability levels of the power system is established. This eliminates the repeated calculation of the OPF model when the PV output varies and realises the dynamic operation reliability evaluation of the new energy power system as the PV output varies in real time. Compared to the SE method of the same order, the proposed method drastically reduces calculation time under the assumption that the error is insignificant.
3. This paper also examines the effectiveness of the HTPCE method in the proposed dynamic reliability evaluation. The HTPCE method can effectively minimise the time required during offline model training.  
The method proposed in this paper can be used not only to deal with the uncertainty of photovoltaics but can also be extended to other uncertain variables in new energy power systems, such as wind power and load. When too many uncertain variables are taken into consideration, the method proposed in this paper still has the issue of relatively lengthy polynomial coefficient calculation times. In future research, the sparse polynomial method can be applied to the offline model to reduce the calculation time required offline.  
H.H.: Conceptualisation, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Original draft, and Writing--review and editing. L.G.: Conceptualisation, Supervision, and Writing--review and editing. P.H.: Funding acquisition, Conceptualisation, Supervision, and Writing--review and editing. C.S.: Conceptualisation, Methodology, Supervision, and Writing--review and editing. T.X.: Conceptualisation, Supervision, and Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Component risk responsibility allocation results.  
**Funding:** This research was funded by the Open Fund of the State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems (China Electric Power Research InstituteInstitute), grant number NYB51202201699.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusion of this article will be made available by the authors without undue reservation.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Literature Analysis  
Our literature review on the scheduling problem encompasses more than 100 papers that were published between 2016 and November 2022. For a better overview, we classified them in Tables 12 and 13. As can be seen, the number of works mostly increased every year, showing an increasing interest in the research community to solve this problem. An important driver of this interest is the need by the industry to develop solutions for their use cases that can be deployed in real networks as TSN standards became more mature over the recent years and products started to evolve.
## 2 Preliminaries
### Information Geometry  
With the explosive growth in the variety and number of electronic devices, driven by advancements in wireless communication technology, the electromagnetic environment for wireless communication has become increasingly complex. The complexity and uncertainty posed by noise and interference in communication signals have become prominent issues, posing new challenges to technologies such as channel coding. Scholars have attempted to solve problems in the field of information and communication by leveraging modern differential geometry, a mathematical theory. This emerging theoretical framework is known as information geometry and is regarded as a revolutionary information theory. The main idea of information geometry is to define a family of probability distribution functions as a geometric manifold and then convert problems in the field of information and communication into mathematical problems on the geometric manifold by defining metrics and connections. Various methods from modern differential geometry are then employed to solve these problems [21; 22]. This paper mainly deals with the concepts of statistical manifolds, metric matrices, and distances.
## 4 Case Study  
### Background  
The highway tunnel under construction studied in this paper is located in Guangdong Province in southern China. It is over 6300 m long and has a maximum depth of 740 m. The tunnel site area is located in a tectonic uplift erosion and denudation low mountain landscape. The stratigraphic lithology of the tunnel site area is mainly Jurassic felsic tuff, Jurassic andesite, and Yanshanian granite. The stratigraphy ranges from new to old, as follows: the Quaternary Pleistocene alluvium (Q\({}_{4}\)\({}^{4+1}\)), the Quaternary deluvial soils and eluvial soils (Q\({}^{4+1}\)), and the Upper Jurassic Gaojiiping group (J\({}_{2+3}\)gj\({}^{5}\)), etc. The site area is influenced by the Lotus Mountain deep rift zone. The tunnel crossed a zone of fracture tectonic development with secondary fractures and joints. The rock in the tunnel site area is fractured, which is shown in Figure 2[36].  
The local water resources are abundant, with an average annual rainfall of 1865.6 mm. The results of the hydrogeological survey show that the groundwater in the tunnel site area is mainly supplemented by rainfall. The groundwater types are divided into loose rock pore water and bedrock fracture water. During the construction, a large amount of high-pressure water gushing poured into the tunnel and threatened construction safety, as shown in Figure 3.
## 4 Results and Discussion
### Routing Overhead  
Routing overhead denotes the number of control packets generated while generating the transmission path. Table 6 shows the performance analysis of the routing overhead. Table 7 shows the performance analysis of network lifetime.  
Figure 7 shows the comparison of routing overhead for the CEELBRP [24], EECRP-PSO [25], and ECTSA. The routing overhead of the ECTSA is varied from 2561 packets to 3510 packets. From the analysis, it is concluded that the ECTSA achieves less routing overhead than the CEELBRP [24] and EECRP-PSO [25]. The ECTSA-based cross-layer routing does not require a high amount of control packets, because it uses effective fitness metrics to identify the transmission path.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{3}{c}{**Routing Overhead**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **Proposed ECTSA** \\ \hline
50 & 6009 & 5028 & 3510 \\
75 & 6125 & 5120 & 3160 \\
100 & 6530 & 5356 & 2190 \\
125 & 6530 & 4789 & 2357 \\
150 & 6781 & 5010 & 2561 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of Routing Overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{3}{c}{**Network Lifetime (s)**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **Proposed ECTSA** \\ \hline
50 & 1583 & 1533 & 1603 \\
75 & 1517 & 1501 & 1588 \\
100 & 1476 & 1438 & 1550 \\
125 & 1433 & 1399 & 1505 \\
150 & 1390 & 1371 & 1490 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of Network Lifetime.  
Figure 6: Analysis of EED.
## 4 Results and Discussion
### Micro-Analysis
#### 4.5.1 XRD  
Figure 17 shows the XRD diffraction patterns of RPSG. According to Figure 17, compared with the specimen RP, the diffraction peaks of chlorite, mica and feldspar in RP-S0-M1.3N6 and RP-S30-M1.3N6 are significantly lower. This shows that the alkaline environment provided by the modified sodium silicate can dissolve the layered silicon-minate minerals of chlorite in the RP. The modified sodium silicate can provide reaction conditions for the RPSG. The specimen RP-S30-M1.3N6 showed some broader diffraction peaks between 20\({}^{\circ}\) and 40\({}^{\circ}\), but the phenomenon was not significant. This is because the slag prompted the generation of poorly crystalline hydration products CASH and NASH from the RPSG. The amorphous nature of these hydration products, coupled with their overlap with the diffraction peaks of the more crystalline minerals in the XRD diffraction pattern, means that they do not have a distinct diffraction peak in Figure 17. In addition, specimen RP-S30-M1.3N6 has a weakened diffraction peak in the calcite phase compared to specimen RP-S0-M1.3N6, which has a significant effect on drying shrinkage of the calcite relative to the slurry [68]. It should be noted that the diffraction peak intensity of quartz decreases with the addition of activator or slag, and this is mostly caused by the dilution effect [69]. In fact, the amount of crystalline quartz dissolved in an alkaline environment is so small that it has a negligible effect on the variation of its diffraction intensity.  
Figure 16: PH values of RPSG with different moduli.  
Figure 17: RD patterns of RPSG with different slag contents.
## 3 Results
### Regression Analysis of Rainfall Erosivity Parameters with Splash Erosion
#### 3.4.1 Splash Erosion and Kinetic Energy Per Rainfall Duration  
The relationship between \(KE_{h}\) and splash erosion resulted in a non-linear (power) regression function, with an \(R^{2}\) of 0.75 for MI soil and 0.76 for ZW and BK soil (Figure 6a, Table 4). The results indicated a less scattered distribution compared to results for \(KE_{sum}\) (Figure 4). However, several observations in Figure 6a present a difference of 97% between the lowest and the highest splash erosion rate for a similar range of \(KE_{h}\) (220-280 J m\({}^{-2}\) h\({}^{-1}\)). The splash erosion measurement with \(KE_{h}\) of 220 J m\({}^{-2}\) h\({}^{-1}\) (E\({}_{1}\) on Figure 6a) was the result of a rainfall event with short duration. Peak intensity of 42 mm h\({}^{-1}\) was reached at the beginning of a rainfall event, with a short duration of 3 min. This might indicate that the peak intensity duration was too short to produce higher splash erosion rates. High splash erosion rates up to 1044 g m\({}^{-2}\) h\({}^{-1}\) were measured for the rainfall event with a \(KE_{h}\) of 223 J m\({}^{-2}\) h\({}^{-1}\) (E\({}_{2}\)). Maximum intensity for this rainfall event reached 60 mm h\({}^{-1}\), where a high percentage of large drops (drop diameter \(>\) 3 mm) with high velocities (\(>\)6 m s\({}^{-1}\)) was measured. The splash erosion rates for the event with \(KE_{h}\) of 280 J m\({}^{-2}\) h\({}^{-1}\) (Figure 6a, E\({}_{3}\)) were remarkably low considering a high amount of the total rainfall of 44 mm.  
Excluding these observations from the regression analysis, the resulting relationship between splash erosion and \(KE_{h}\) was linear, with \(R^{2}\) values of 0.91 for ZW and MI soil, and 0.90 for BK soil (Figure 6b; Table 4). The difference of 62% between the RMSEs for linear and nonlinear models indicates better performance of the splash erosion-\(KE_{h}\) relationship without extreme observations. According to RMSE, correlation coefficients, and slope coefficients of regression equations, fewer minor differences between the soils were noted for the linear than for non-linear regression.
## 3 Numerical Simulation  
In this section, we demonstrate the effectiveness of CR-MRTM using a simple three-layer model and the Marmousi model, and we discuss its advantages over conventional RTM methods.  
In the numerical experiments presented in this section, we used the staggered-grid finite difference method to solve the wave equation. The specific first-order velocity-stress equation for elastic waves is given via the following formula [31,32]:  
\[\left\{\begin{array}{l}\frac{\partial v_{x}}{\partial t}=\frac{1}{\rho} \Big{(}\frac{\partial v_{xx}}{\partial x}+\frac{\partial v_{zz}}{\partial z} \Big{)}\\ \frac{\partial v_{zz}}{\partial t}=\frac{1}{\rho}\Big{(}\frac{\partial v_{xx}} {\partial x}+\frac{\partial v_{zz}}{\partial z}\Big{)}\\ \frac{\partial v_{xx}}{\partial t}=(\lambda+2\mu)\frac{\partial v_{xx}}{ \partial x}+\lambda\frac{\partial v_{zz}}{\partial x}\\ \frac{\partial v_{zz}}{\partial t}=\mu\Big{(}\frac{\partial v_{zz}}{\partial z }+\frac{\partial v_{zz}}{\partial x}\Big{)}\end{array}\right. \tag{11}\]  
Here, \(v_{x}\) and \(v_{z}\) represent the horizontal and vertical particle velocities, respectively; \(\tau_{xx}\), \(\tau_{xz}\), and \(\tau_{zz}\) are the components of the stress tensor, \(\rho\) is the medium density, and \(\lambda\) and \(\mu\) are the Lame parameters, which are determined by the medium's shear wave velocity, compressional wave velocity, and density.  
In addition, for cases in which boundary reflections must be eliminated, perfectly matched layer (PML) techniques were utilized for the numerical experiments in this study. After the imaging results were obtained, a Laplace filter was uniformly applied to process them and mitigate the effects of low-frequency noise.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods  
### Overview of Aquasafe Structure  
Aquasafe is a web-based platform created through a collaborative effort that brought together scientists and stakeholders. Aquasafe employs data from multiple sources to calculate important parameters, which are then utilized to derive indicators serving as warning signs for supporting aquaculture farm management. Aquasafe is structured into four main pillars: (a) input data, (b) estimated parameters, (c) indicators, and (d) web-based platform (Figure 1).  
1. The input data, which include information from both satellites and _in-situ_ measurements, are used to determine the relevant parameters (Figure 1a).
2. The estimated parameters, which include water temperature, dissolved oxygen, chlorophyll-a, biogenic oil film, and fish growth metrics, are derived from the processing of the input data and are then combined to determine the indicators or \"warning signs\" (Figure 1b).
3. Five indicators based on the aforementioned parameters, namely algal blooms, sea surface temperature, saturation of dissolved oxygen, fish growth, and wind speed/gusts,  
Figure 1: Aquasafe structure and the four main pillars: (**a**) input data, (**b**) parameters, (**c**) indicators, and (**d**) Aquasafe web-based platform.  
were identified. These indicators are used to alert the administrator or operator of the farm of potential threats and required actions (Figure 1c). All the indicators are a combination of two or more parameters, except for the sea surface temperature, which is used directly. More details on the combinations and the thresholds for the alerts are described in Section 2.4.
* All of the elements described above are integrated and visualized into the Aquasafe web-based platform (Figure 1d), providing forecasts and alerts to the administrator or operator of the farm.
## 2 Research Objectives and Significance  
Since the modulus of asphalt layer is significantly affected by temperature, the temperature correction must be conducted for the asphalt layer when conducting FWD back-calculation in order to evaluate the modulus of the asphalt layer at the same temperature. Moreover, measuring the temperatures at various road depths in situ is difficult, and a model must be established for estimating the temperature at various depths of asphalt layers. Hence, this study conducted the FWD tests and collected the temperature data at various depths in roads at the FWD test site constructed by Taiwan's Freeway Bureau. The objectives of this study are:  
1. To establish a temperature-evaluation model for various pavement depths by performing the regression analysis of temperature measurements at various depths in the test site.
2. To perform in situ core drilling in the test site and conduct the dynamic modulus test for obtaining the master curve and the relationship between temperature and frequency.
3. To conduct FWD tests in the test site at different temperatures and to apply the temperature correction for FWD back-calculation.
4. To verify the effectiveness of the frequency-temperature correction by comparing the master curve of the dynamic modulus obtained in the laboratory and the back-calculated modulus for asphalt layers.
## 4 Convergence Analysis  
The local convergence of the \(4^{th}CON\) is proved in Theorem 1 and it is as follows.  
**Theorem 1**.: _Let \(\mathbf{F}:\mathbf{D}\subset\mathbb{R}^{n}\rightarrow\mathbb{R}^{n}\), be four times Frechet differentiable in a convex set \(\mathbf{D}\) of \(\mathbf{F}(\mathbf{x})=\mathbf{0}\). Then the scheme defined  
We use the approximate computational order of convergence \(p\) (see [14]) given by  
\[p_{c}\approx\frac{log(\|\mathbf{x}^{(k+1)}-\mathbf{x}^{(k)}\|)/(\|\mathbf{x}^{(k )}-\mathbf{x}^{(k-1)}\|)}{log(\|\mathbf{x}^{(k)}-\mathbf{x}^{(k-1)}\|)/(\| \mathbf{x}^{(k-1)}-\mathbf{x}^{(k-2)}\|)} \tag{25}\]  
We let \(Iter_{max}\) be the number of iterations required before convergence is reached and \(err_{min}\) be the minimum residual.  
**Test Problem 1** (TP1) [5]\(\mathbf{F}(x_{1},x_{2})=\mathbf{0}\), where \(\mathbf{F}:(4,6)\times(5,7)\rightarrow\mathbb{R}^{2}\), and \(\mathbf{F}(x_{1},x_{2})\)\(=(x_{1}^{2}-x_{2}-19\,x_{2}^{3}/6-x_{1}^{2}+x_{2}-17)\). The starting vector is \(\mathbf{x}^{(0)}=(5.1,6.1)^{T}\) and the exact solution is \(\mathbf{x}^{*}=(5,6)^{T}\). In addition, it is easy to see that \(IE_{2^{nd}NM}=\frac{\ln 2}{2+4}=0.1155,\quad IE_{3^{th}CON}=\frac{\ln 3}{2+2(4)} =0.1099,\quad IE_{4^{th}CON}=\frac{\ln 4}{2+2(4)}=0.1386\).  
**Test Problem 2** (TP2) [12] The test is as follows  
\[\begin{array}{l}\cos x_{2}-\sin x_{1}=0\\ x_{3}^{x_{1}}-\frac{1}{x_{2}}=0\\ \exp x_{1}-x_{3}^{2}=0\end{array} \tag{26}\]  
with the following solution \(x_{1}^{*}=0.9095694945200...\), \(x_{2}^{*}=0.6612268322748...\), \(x_{3}^{*}=1.5758341439070...\). We choose the starting vector \(\mathbf{x}^{(0)}=(1,0.5,1.5)^{T}\). Here, we have  
\[IE_{2^{nd}NM}=\frac{\ln 2}{12}=0.0577,\quad IE_{3^{th}CON}=\frac{\ln 3}{21}=0. 0523,\quad IE_{4^{th}CON}=\frac{\ln 4}{21}=0.0660\]  
Systems of four nonlinear equations:  
**Test Problem 3** (TP3) (see [12]) This example is in what follows:  
\[\begin{array}{l}x_{2}x_{3}+x_{4}(x_{2}+x_{3})=0\\ x_{1}x_{3}+x_{4}(x_{1}+x_{3})=0\\ x_{1}x_{2}+x_{4}(x_{1}+x_{2})=0\\ x_{1}x_{2}+x_{1}x_{3}+x_{2}x_{3}=1\end{array} \tag{27}\]  
We solve this system using the initial approximation \(\mathbf{x}^{(0)}=(0.5,0.5,0.5,-0.2)^{T}\). The solution is  
\[\begin{array}{l}x_{1}^{*}=0.57735026918963...\quad x_{2}^{*}=0.5773502691896 3...\\ x_{3}^{*}=0.57735026918963...\quad x_{4}^{*}=-0.28867513459482...\end{array}\]  
Note that  
\[IE_{2^{nd}NM}=\frac{\ln 2}{4+16}=0.0346,\quad IE_{3^{th}CON}=\frac{\ln 3}{4+2 (16)}=0.0305,\quad IE_{4^{th}CON}=\frac{\ln 4}{2+2(16)}=0.0385\]  
Table 1 gives the results for the TP1-3. It is observed that for all problems the fourth-order method converges in the least number of iterations. The computational order of convergence agrees with the theory. \(4^{th}CON\) gives the best results in terms of least residual and is the most efficient method.
# Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems
###### Abstract  
As we move toward increasing the grid integration of large-scale wind farms (WFs), reliable monitoring, protection, and control are needed to ensure grid stability. WFs are considered to be large and complex cyber physical systems owing to coupling between the electric power system and information and communication technologies (ICT). In this study, we proposed a framework for a cyber physical wind energy system (CPWES), which consists of four layers: a WF power system layer, data acquisition and monitoring layer, communication network layer, and application layer. We performed detailed network modeling for the WF system, including the wind turbines, meteorological mast (met-mast), and substation based on IEC 61400-25 and IEC 61850 standards. Network parameters and configuration were based on a real WF (Korean Southwest offshore project). The simulation results of the end-to-end delay were obtained for different WF applications, and they were compared with the timing requirements of the IEC 1646 standard. The proposed architecture represents a reference model for WF systems, and it can be used to enable the design of future CPWESs.  
wind farm; communication network; cyber physical wind energy system; grid integration; IEC 61850; IEC 61400-25
## 3 Proposed Approach
### Security Layer Attacks and Countermeasures  
As the final defense, the security layer incorporates technologies such as firewalls, IDS, and IPS, with optimal operation requiring proper configuration and real-time threat intelligence. Implementing AI-powered analysis and automated firewall configuration ensures minimal human error and real-time security policy application. Proactive training in social engineering and insider threat detection protocols further mitigates risks posed by human factors.  
These systems are designed to detect, prevent, and respond to unauthorized access, malware, and suspicious network activities. However, their effectiveness heavily depends on the proper configuration and up-to-date threat intelligence. If these systems are mis-configured or lack current security updates, attackers can exploit vulnerabilities, bypass defenses, and gain unauthorized access to critical infrastructure. For example, improperly configured firewalls and IDSs can allow malware to penetrate, leading to data breaches, service disruptions, or even total system control. These vulnerabilities often stem from human error during system configuration or failure to apply the latest security patches. The  
Figure 5: Attack vectors and response processes of the application layer.  
'cyber persona' factor is crucial in the security layer, where attackers use social engineering tactics to deceive administrators into weakening security configurations or disabling alerts.  
According to Figure 6, the diagram illustrates how a hacker attempts to bypass the firewall and IDS to infiltrate the system. If successful, the malware can compromise the system, leading to severe consequences. The diagram also shows how administrators receive reports on additional patches and countermeasures to strengthen the system's defenses. the human element plays a significant role in both configuring and managing security systems. Attackers often exploit social engineering tactics to manipulate security personnel into making poor decisions, such as disabling critical firewall rules or turning off security alerts. Additionally, insiders may unintentionally misconfigure settings due to lack of knowledge or intentionally compromise systems with malicious intent. Proper training for security personnel, combined with awareness of potential 'cyber persona' threats, is essential to maintaining the integrity of the security layer. Ensuring that staff are educated and aware of both external and internal threats helps to prevent vulnerabilities caused by human factors.  
* The IDS system should be enhanced by leveraging machine learning and artificial intelligence technologies to learn abnormal patterns and detect previously unknown threats.
* Firewall configurations should be automated to ensure consistent security policies are applied and to minimize configuration errors. Additionally, all network traffic should be monitored in real time, and AI-based analysis tools should be utilized to quickly identify potential attack patterns.
* Standardized procedures for configuring and managing security equipment should be established to reduce operational errors.