esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightning Surge Analysis for Overhead Lines Considering Corona Effect
## 6 Conclusions  
The corona characteristics have been measured experimentally in a corona cage under damped oscillation and double exponential surges. The measured results shows that a basic difference between the \(q\)-\(u\) curves under the two types of surge voltage appears after their respective maximum charges. In these curve parts, the \(q\)-\(u\) curves under double exponential surges descend monotonically, whereas those under damped oscillation surges behave as the minor loops. The area of the minor loops is significantly larger under negative polarity than under positive polarity. In order to characterize the complicated trajectory feature of the minor loops, an extended approach has been presented by considerably improving the traditional approach. The extended approach is adaptable not only to double exponential surges but also to damped oscillation surges. Based on the extended approach, an efficient method has been proposed for calculating the distortion and attenuation of damped oscillation surges on the overhead lines with corona. A better agreement between the calculated and field test results confirms the validity of the proposed method. Furthermore, special emphasis is put on the fact that the influence of the minor loops on the distortion and attenuation of damped oscillation surges is obviously stronger under negative polarity than under positive polarity in the subsequent oscillation cycles. The calculated results under negative polarity indicate that the calculated errors due to neglecting the minor loops may reach 10.2-10.6% at the first wave trough and second crest, respectively. Since most lightning surges have the negative polarity, it is of practical significance to take into account the influence of the minor loops in the transient calculation. In future work, a further improvement will be made to the proposed method in the lightning transient calculation of multiphase overhead lines with bundle conductors.  
Conceptualization, X.Z.; methodology, X.Z. and K.H.; software, K.H.; validation, K.H.; formal analysis, X.Z.; investigation, K.H.; resources, K.H.; data curation, K.H.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; visualization, K.H.; supervision, X.Z.; project administration, X.Z. Both authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51777007.  
No applicable.  
The data present in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Routing Issues and Challenges in WBSNs
### Network Topology  
Network topology describes the logical way in which the different communicating devices communicate with each other. Efficient routing protocol development requires a proper network topology as it effects the overall performance of the communication system [19]. Proper network topology is very important for WBSNs because of the energy constraint, body postural movements, heterogeneous nature of the sensors and short transmission range. Some researchers use single hop communication, where each node communicates directly with the destination, while others use cluster based multi-hop routing, and are discussed later in this paper.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 1 Introduction
Also, FL is a promising technique for WSNs since it allows the combination and evaluation of several network parameters in an efficient manner. Fuzzy logic is able to improve overall network performance as its execution requirements can be easily supported by sensor nodes. In WSNs, this approach is used in localization techniques, cluster formation, and CH selection process, security, and data routing methods [14]. In this study, a routing scheme with an efficient CH selection technique is proposed to provide a balanced energy consumption in the whole network. Additionally, a weight function is formulated on the basis of a fuzzy membership function. An intra-cluster communication cost function is also proposed to choose an energy-efficient and delay tolerant path from the source to destination node using [PERSON]'s algorithm. The fuzzy membership function chooses the relay node with maximum energy among the other neighboring nodes. On the contrary, the intra-cluster communication cost function ensures to minimize the communication cost within a cluster. As a result, the nodes closer to the CH do not deplete their energies faster. This technique induces a high degree of energy efficiency in the network. The main contributions of this paper are as follows:  
1. To formulate a multi-objective weighted sum function that would select an efficient CH node in a cluster and reduce energy consumption imbalance in the network. 2. To address the node isolation issue in the network by presenting the weight function which is influenced by two functions, i.e., a fuzzy membership function and an intra-cluster communication cost function to reduce the energy depletion during data communication from the source to the destination node within a cluster. 3. To conduct a series of experiments to evaluate the performance of the proposed routing scheme and demonstrate that the proposed algorithm performs better than the other existing routing protocols in terms of energy efficiency and network throughput. The remainder of this paper is organized as follows: Section 2 provides an overview of the related work for the cluster-based energy efficient routing schemes. Section 3 explains the system including the network and energy model. The proposed fuzzy logic Dijkstra-based routing scheme is explained in detail in Section 4. The performance analysis and evaluation and the simulation results are presented in Section 5. The paper concludes in Section 6.
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 6 Investigation into CFST Arch Supports with Different Section Parameters  
### GSED-Based Stressing State Modes of the Different Supports  
The GSED values with the increase of load for the different CFST arch supports are plotted in Figure 10, so that the common and different working characteristics under section parameters could be further investigated. It can be seen in Figure 10 that the stressing state of each support are divided into four stages by three characteristic loads through applying M-K criteria, respectively elastic, elastic-plastic, plastic and failure stages, and the mutation characteristics around these characteristic loads are similar to the narrative in Section 5.1, which would not be repeated here. In addition, under  
Figure 9: The strain and stress fields of around characteristic load \(R\): (**a**) The strain fields of steel tube around load \(R\); (**b**) the strain fields of core concrete around load \(R\); (**c**) the stress fields of steel tube around load \(R\).  
the same load, the larger the wall thickness or diameter is, the lower the GSED values keep, indicating that the increase of the section size or wall thickness could improve the bearing capacity of the supports to some extent. As well, the supports have small difference of section parameters, they have similar GSED values at the same type of characteristic load, namely \(0.65\times 10^{3}\) J/m\({}^{3}\) at load \(L\), \(2.1\times 10^{3}\) J/m\({}^{3}\) at load \(Q\) and \(5.5\times 10^{3}\) J/m\({}^{3}\) at load \(R\) (updated failure load), which reflects the stability of determination for characteristic loads. However, GSED values of each support vary a lot at the ultimate load, revealing a certain uncertainty of the ultimate load determined by the existing method. The common mutation characteristics and GSED values could also prove the efficiency of adopting characteristic parameter GSED to analyze these supports and the rationality of M-K criteria.
## 3 Proposed Method
### Facial Emotion Description Point Location Based on AAM
#### 3.2.1 Marking Emotional Description Points  
Emotional description points refer to the specific set of points that can effectively reflect the changes in human facial expressions, and are mainly scattered in the eyes, nose tip, nostril, mouth, mouth corner, eyebrows, and facial contours. The basic principle of emotion description point marking is to use as few points as possible to describe facial expression information. In order to efficiently express the features of facial expressions, 49 emotion description points were proposed to mark the face image, as shown in Figure 3.  
If there are \(N\) images in a video, the emotional description points of the \(i\)-th image can be expressed as follows:  
\[S_{i}=(x_{i,1},y_{i,1},x_{i,2},y_{i,2},\ldots,x_{i,49},y_{i,49})=(s_{i,1},s_{i,2 },\ldots,s_{i,49}),\ 1\leq i\leq N \tag{1}\]  
where \(s_{i,j}=(x_{i,j},y_{i,j})\) is the two-dimensional coordinates of the \(j\)-th emotional description point in the \(i\)-th image.
## 3 Results
### Behavioral Metrics
#### 3.2.3 Decision Time  
In Figure 6, the duration of the decision phase is shown across conditions and risk levels. The presented data were first cured from outliers using the interquartile range approach. The main origin of the outliers was sporadic Unity freezing, where the recorded decision time was abnormally long. This occurred a maximum of once per experimental session. Note that, in contrast to other behavioral metrics where there is one data point for each participant per experimental session, there are multiple points for the decision time for each participant as each individual trial is considered. The cured data were normalized using a logarithmic function. The respective ANOVA results are summarized in Table 3. There was a significant effect of condition (\(F=32.838,p<0.001\)) and risk (\(F=14.092,p<0.001\)). A post hoc Tukey test showed a significant difference between the decision time across \"not revealed\" and \"revealed\" conditions for both low risk (\(p_{adj}<0.05\)) and high risk (\(p_{adj}<0.001\)). Furthermore, there was a significant difference between the  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Factor** & **DF** & **Sum Sq** & **Mean Sq** & \(F\)**-Value** & \(p\)**-Value** \\ \hline Risk & 1 & 49,135 & 49,135 & 119,076 & \(1.76\times 10^{-15}\) \\ Condition & 1 & 14,947 & 14,947 & 36.223 & \(1.42\times 10^{-7}\) \\ Risk-Condition & 1 & 1984 & 1984 & 4.808 & 0.0325 \\ Residuals & 56 & 23,108 & 413 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The summary table for ANOVA results for the total score. DF is degrees of freedom, Sum Sq contains values for the sum of squares, Mean Sq are mean squares, and \(F\)- and \(p\)-values are respective ANOVA statistics values.  
Figure 5: The total score in the experimental sessions. The data are visualized using bar plots with superimposed individual data for each participant. The error bars indicate confidence intervals of 95% computed using bootstrapping. The indicators of significant differences obtained from the ANOVA analysis are *: \(p<0.05\), ***: \(p<0.001\).  
decision time in the \"not revealed\" condition across risk levels (\(p_{adj}<0.005\)). The respective difference for the \"revealed\" condition was not significant.
## 6 Pitfalls and Potentials
### Virtualization Feasibility of Network Functions
#### 6.2.1 Core Functions Easy for Virtualization  
The UDM manages user data and subscription information such as credentials, user identification, and access authorization. The UDM works with the Unified Data Repository (UDR), which is a centralized storage in a 5G network for storing user-related data, network information, and service-related parameters to provide access to various 5G network functions [129; 130]. These network functions are basically deployed in a cloud-based infrastructure, and they could be virtualized [131]. The Authentication Server Function (AUSF) handles user authentication, security key management, and ensuring secure communication between the network and the user equipment [129; 130]. This network function could be easily virtualized [132].  
The AF supports traffic routing and interacts with the policy framework [129; 130]. AF is primarily software-based, which makes it ideal for virtualization. The NRF is responsible for service discovery and maintaining network function profiles [129; 130]. As it primarily deals with control plane information, it can be virtualized without impacting QoS and QoE [133]. Functions like the Network Slice Selection Function (NSSF) and Network Exposure Function (NEF), not directly engaged in data plane operations, are also suitable candidates for virtualization without compromising performance metrics [134].
## 2 The FSPR System and Temperature Measuring Method  
### The Pipe Roof of FSPR  
Gongbei tunnel is a critical part for the Zhuhai link of the Hong Kong-Zhuhai-Macao bridge and it crosses the prosperous Gongbei port, which has the highest people flux in the world. It is designed as a double-decker integral tunnel with a large section constructed by the shallow tunneling method. The total length of the tunnel is 255 m and the excavation section is about 345 m\({}^{2}\) (20 m high and 19 m wide). The tunnel is buried in water-rich strata within soft sandy and silty clay, about 4 to 5 m below the ground surface.  
Figure 1 shows the cross-section of the tunnel, and the five excavation layers are also illustrated in the figure, which are marked with letters A to E and divided into 14 small sections. It is shown that 36 steel pipes with a diameter of 1.62 m are arranged along the quasi-circular cross-section of the tunnel to form the pipe roof. The odd-numbered roof pipes are shifted inward by about 30 cm along the radial direction of the tunnel and the space between two adjacent pipes is about 35 cm.
## 6 Discussion and Conclusions  
### Observations  
* _QoS vs. QoE_: The majority of the existing ICN literature has studied QoS metrics. Some of the most commonly optimized QoS metrics include delay, throughput, cache hit ratio, and packet loss. Compared to QoS, fewer studies have measured QoE metrics for performance comparison. Among other factors, this could be due to the difficulty in measuring QoE accurately without human subjective tests. Some studies [75] have used QoE prediction models to approximate human perceptual quality, while others have defined QoE as a function of satisfied interests [115]. [PERSON] et al. [116] have utilized both the QoE prediction model as well as mean opinion scores (MOSs) from human subject tests.
* _Popular Strategies_: In ICN, the focus on optimizing QoS or enhancing QoE seems to predominantly center around in-network caching. This is because ubiquitous in-network caching is one of the key differences between the current Internet structure and ICN, and this caching impacts many aspects of a network, including throughput, congestion, delay, load balancing, routing, security, and consumer QoE. Another frequently studied area in the existing literature is name resolution and routing. This focus is justified by ICN's unique content naming and multi-path forwarding capabilities, which facilitate the development of novel QoS and QoE optimization techniques.
* _Commonly Studied ICN Architecture_: Among the literature we reviewed, CCN or NDN is the most commonly studied ICN architecture. In total, 32 out of the 48 studies listed in Tables 2-6 introduced solutions specifically for either CCN or NDN. This is consistent with the fact that NDN is considered the most prevalent among the ICN architectures [13].
* _Commonly Optimized QoS/QoE Metrics_: Among the most frequently optimized QoS and QoE metrics across the various strategies we examined are latency, cache hit rate, throughput, and mean opinion score (MOS). Figure 6 shows the number of research studies where each of these metrics was assessed. Aside from cache hit rate, the low counts of other ICN-specific QoS and QoE metrics in Figure 6 are due to the use of different evaluation metrics across studies, depending on the specific strategy employed.
###### Abstract  
Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China's fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies.  
highway rockfall; risk assessment; rockfall risk map; Guizhou Province; GIS 2020
## 4 Experiment Verification
### Experiment Results and Discussion  
#### 4.2.1 Static Characteristics Experiments  
To verify the sealing and flow characteristics of VCM-DHPV, the static characteristics of the valve is examined. Firstly, VCM-DHPV is closed by the VCM reverse power, and the supply pressures of the valve are set to 2, 4, 6 and 8 MPa, respectively. Then, the pressure sealing performance of the on/off valve is checked under the pressure holding time of 2 min. The experimental results show that the pressure sealing performances of VCM-DHPV are very good under the different supply pressures. The flow rate of VCM-DHPV under different supply pressures and is illustrated in Figure 11a. It can be seen that, with the gradual increase of supply pressure, the flow begins to increase rapidly. As the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Notation** & **Value** & **Unit** \\ \hline Orifice diameter of the valve & \(D\) & 24 & mm \\ Spool diameter & \(d_{r}\) & \(16\) & mm \\ Half cone angle & \(\alpha\) & \(45\) & \({}^{\circ}\) \\ Inlet diameter & \(d_{i}\) & \(11\) & mm \\ Outlet diameter & \(d_{o}\) & \(11\) & mm \\ VCM electromagnetic force & \(F_{e}\) & \(376\) & N \\ Test pressure & \(P_{s}\) & \(8\) & MPa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the proposed VCM-DHPV prototype.  
Figure 10: Pictures of VCM-DHPV prototype and test system: (a) VCM-DHPV prototype; and (b) test bench.  
Figure 9: Schematic diagram of the pneumatic circuit test bench.  
supply pressure continues to be increased to a certain value, the flow rate of VCM-DHPV could reach a stable value of about 5500 L/min, as shown in Figure 11b.
## 4 Experimental Results and Discussions
### Choose Watermark Embedding Strength  
To balance the robustness and fidelity, the part discusses the embedding strength \(Q\). We set the watermark embedding strength \(Q\in(10,1,500)\).  
Figure 13 shows that the value of \(Q\) is increasing, PSNR is decreasing, and NC is increasing, indicating that the robustness of the watermark is improved, whereas the image quality is deteriorated. When the value of \(Q\) reaches 410, NC is close to 1, and the watermark can be completely extracted without being attacked. To balance robustness and fidelity, \(Q=1160\) and PSNR = 40.413. Figure 14 shows the PSNR of the eight watermarked images, consisting of \"Lena\", \"Castle\", \"Baboon\", \"Barbara\", \"Boats\", \"Fruit\", \"Airplane\", \"Houses\", and a watermark \(w_{0}\).  
Figure 11: The IQDFT results of the real component \(A_{IQDFT}\), red channel \(R^{{}^{\prime}}\), green channel \(G^{{}^{\prime}}\), and blue channel \(B^{{}^{\prime}}\).  
Figure 12: Pseudo-Zernike moments expression.  
It can be seen from the Figure 14 that \(PSNR\) is larger than 40, which indicates our scheme has better fidelity.
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
## 2 System Model
Thus, with the blockage, the received signal on the BS can be expressed as  
\[\mathbf{y}(t)=\text{diag}(\mathbf{b})\mathbf{G}^{BR}\text{diag}( \overline{\mathbf{b}}\circ\boldsymbol{\theta}(t))\mathbf{g}^{RU}x(t)+\mathbf{ n}(t). \tag{5}\]  
We define that \(\mathbf{b}=\mathbf{1}_{N}+\mathbf{s}\) and \(\overline{\mathbf{b}}=\mathbf{1}_{M}+\overline{\mathbf{s}}\), where \(\mathbf{s}\) and \(\overline{\mathbf{s}}\) denote the offset coefficients of BS and RIS, respectively. Usually, the RIS or BS is partially blocked. Thus, the offset coefficients \(\mathbf{s}\) and \(\overline{\mathbf{s}}\) are usually sparse, that is, there are a small number of non-zero elements in vector \(\mathbf{s}\) and \(\overline{\mathbf{s}}\). If we can estimate the offset coefficients, the blockage coefficients can be obtained. Then, (5) can be rewritten as  
\[\mathbf{y}(t)=\text{diag}(\mathbf{1}_{N}+\mathbf{s})\mathbf{G}^{ \text{BR}}\text{diag}(\mathbf{g}^{\text{RU}}\circ\boldsymbol{\theta}(t))( \mathbf{1}_{M}+\overline{\mathbf{s}})+\mathbf{n}(t). \tag{6}\]Then, combining \(T\) time slots, we have  
\[\mathbf{y}=\left[ \begin{array}{c}\text{diag}(\mathbf{1}_{N}+\mathbf{s})\mathbf{ H}(1)(\mathbf{1}_{M}+\mathbf{\bar{s}})\\ \vdots\\ \text{diag}(\mathbf{1}_{N}+\mathbf{s})\mathbf{H}(T)(\mathbf{1}_{M}+\mathbf{ \bar{s}})\end{array} \right]+\mathbf{n}, \tag{7}\]  
where \(\mathbf{y}=[\mathbf{y}^{\mathsf{T}}(1),\cdots,\mathbf{y}^{\mathsf{T}}(T)]^{ \mathsf{T}}\in\mathbb{C}^{TN\times 1}\) and \(\mathbf{n}=[\mathbf{n}^{\mathsf{T}}(1),\cdots,\mathbf{n}^{\mathsf{T}}(T)]^{ \mathsf{T}}\in\mathbb{C}^{TN\times 1}\). The equivalent cascaded channel \(\mathbf{H}(t)\) can be expressed as \(\mathbf{H}(t)=\mathbf{G}^{\text{BR}}\text{diag}(\mathbf{g}^{\text{RU}}\circ \boldsymbol{\theta}(t))\). In the next section, we propose a compressed-based algorithm for joint estimation of the RIS blockage coefficients and the BS blockage coefficients according to the received signal \(\mathbf{y}\) shown in (7).