esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis in Composable Finite-Size Regime
### Without Clusterization  
We first perform a security analysis without clusterization, i.e., \(C=1\). Figure 4 shows the composable finite-size rate \(R_{\text{com}}\) with no clusterization versus the satellite altitude \(h\) and the zenith angle \(\theta\). The simulation parameters related to composable finite-size security are shown in Table 2. Here, we ignored the effect of the instantaneous zenith angle to show the gap between the composable finite-size rate and the asymptotic key rate through a joint analysis of Figures 2 and 4. The block size of each simulation point is set to \(N=10^{7}\).
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Infrastructure-Integrated Photovoltaics
#### 3.4.4 Roads and Railroads  
Photovoltaics can be integrated into the transport infrastructure, e.g., noise or crash barriers along roads, highways and railroads, road pavements, dikes, flyovers, road roofing, and rest areas. The infrastructure element in these applications, in addition to its main functionality (like noise or crash protection), incorporates PV modules for the simultaneous generation of electricity. The most common infrastructure applications are on noise barriers and rest areas.  
Research on PV on transport infrastructure (roads and railways) has shown that the potential installed capacity in the EU is 401 GWp, translated into 280-391 TWh depending on the PV technology employed (monofacial vs. bifacial PV modules). The above-mentioned electricity generations cover between 11% and 16% of the EU's total electricity consumption in 2022 [53]. In the Netherlands, the potential installation capacity on dikes has been identified to be 11 GWp [54].  
Depending on the specific application, TRLs vary between 6 and 7 for landfills, road roofing, and noise barriers, and 4-5 for crash barriers and dikes. Infrastructure-integrated photovoltaics are set to ramp up when the designed integrated solutions become more mature in terms of performance and safety as well as cost effectiveness [19].
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Quantitative Analysis of Fault Tree
#### 3.4.2 Calculation of the Fuzzy Probability of the Top Event
The probability intervals of the top event according to the different values of \(\lambda\) are presented in Table 4.  
It can be seen from Table 4 that when \(\lambda\) = 1, the fuzzy degree of the occurrence probability of the top event is the smallest, and its occurrence probability is approximately 0.000115. When \(\lambda\) = 0, the probability of occurrence of the top event is the fuzziest, and its probability interval is [0.000095, 0.000135], which shows that the system has a better reliability performance than the traditional fault tree.
## 2 Numerical Calculation Method for Telecommunication Towers  
### Numerical Calculation Model for Telecommunication Towers  
The subject of the study was an actual single-pipe telecommunication tower, with a height of \(H=35.0\) m. The diameter of the circumscribed circle at the top is \(D_{1}=0.5\) m, and at the bottom is \(D_{2}=1.0\) m. Through surveying, it was found that the tower can accommodate up to three layers of telecommunication antennas, with each layer exhibiting the same antenna arrangement number and antenna arrangement form, up to a maximum of six per layer. Therefore, the total antenna arrangement number on the tower can range from 1 to 18. The top surface elevation of the first layer of antennas is at \(1.0H\), the second at \(0.85H\), and the third at \(0.71H\). Figure 1 shows the mesh of the telecommunication tower and the schematic of the wind loads when 18 antennas are arranged [31].  
Since the antennas were symmetrically arranged, the wind loads on the surface of the telecommunication tower structure were calculated for all wind directions from \(0^{\circ}\) to \(180^{\circ}\). This primarily included the drag force \(F_{D}\) acting in the windward direction on the entire tower, the lateral force \(F_{S}\) in the crosswind direction, the windward base overturning moment \(M_{D}\), and the crosswind base overturning moment \(M_{S}\), with the moment center located at the center of the tower's base. By non-dimensionalizing, we obtained the drag coefficient \(C_{D}\), the lateral force coefficient \(C_{S}\), the windward base overturning moment coefficient \(C_{MD}\), and the crosswind base overturning moment coefficient \(C_{MS}\), as specifically shown in Equations (1)-(4):  
Figure 1: Schematic of the telecommunication tower mesh and wind loads [31].  
Drag coefficient:  
\[C_{D}=\frac{F_{D}}{0.5\rho{U_{10}}^{2}A} \tag{1}\]  
Lateral force coefficient:  
\[C_{S}=\frac{F_{S}}{0.5\rho{U_{10}}^{2}A} \tag{2}\]  
Windward base overturning moment coefficient:  
\[C_{MD}=\frac{M_{D}}{0.5\rho{U_{10}}^{2}DH^{2}} \tag{3}\]  
Crosswind base overturning moment coefficient:  
\[C_{MS}=\frac{M_{S}}{0.5\rho{U_{10}}^{2}DH^{2}} \tag{4}\]  
where \(A\) represents the windward surface area of the telecommunication tower (m\({}^{2}\)); \(\rho\) is the air density (kg/m\({}^{3}\)); \(U_{10}\) is the reference wind speed at a height of 10.0 m (m/s); \(D\) is the average value of the diameters of the circumscribed circles of the top and bottom surfaces of the telecommunication tower (m); \(H\) is the height of the telecommunication tower (m).  
By superimposing the drag coefficient and the lateral force coefficient, the overturning force coefficient \(C_{F}\) was obtained; by superimposing the windward base overturning moment coefficient and the crosswind base overturning moment coefficient, the overturning moment coefficient \(C_{M}\) was derived, as specifically shown in Equations (5) and (6):  
Overturning force coefficient:  
\[C_{F}=\sqrt{{C_{D}}^{2}+{C_{S}}^{2}} \tag{5}\]  
Overturning moment coefficient:  
\[C_{M}=\sqrt{{C_{MD}}^{2}+{C_{MS}}^{2}} \tag{6}\]
## 6 Experimental Results and Simulations
### Results with Feature Selection
#### 6.3.2 Performance of WOA with V-Shaped TFs  
In order to perform further analysis on the obtained results, Table 11 presents a statistical analysis using the Wilcoxon test with a significance level of 0.05. We compared all transferred functions with WOA-V4 since WOA-V4 outperforms all S-shape and V-shapes transfer functions to simplify the comparison. It is clear that the performance of WOA-V4 is not similar to all S-shape transfer functions.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 4 Case Study
### Contrast Analysis
The SSA-LSTM model is almost completely consistent with the real curve, which effectively predicts the complex nonlinear variation trend between dam deformation and impact factors in the short term and accurately grasps the variation pattern, further illustrating the high efficiency of the model prediction performance of the SSA algorithm for optimization. This validation shows that the dam monitoring data analysis based on the artificial intelligence algorithm has a good prediction effect and can be used in the later work of dam monitoring data analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Monitoring Point** & **Model** & **MAE (mm)** & **RMSE (mm)** & **MAPE (\%)** \\ \hline \multirow{3}{*}{EX3-4a} & LSTM-SSA & \(5\times 10^{-5}\) & 0.027 & 0.152 \\  & BP & \(6.5\times 10^{-4}\) & 0.233 & 0.939 \\  & LSTM & \(7.3\times 10^{-4}\) & 0.932 & 2.130 \\ \hline \multirow{3}{*}{EX3-3a} & LSTM-SSA & \(4.1\times 10^{-5}\) & 0.05 & 0.402 \\  & BP & \(2.4\times 10^{-4}\) & 0.11 & 1.791 \\  & LSTM & \(4.0\times 10^{-4}\) & 0.496 & 1.173 \\ \hline \multirow{3}{*}{EX2-7a} & LSTM-SSA & \(1.3\times 10^{-4}\) & 0.207 & 0.097 \\  & BP & \(1.3\times 10^{-4}\) & 0.196 & 0.278 \\  & LSTM & \(1.4\times 10^{-4}\) & 0.011 & 0.670 \\ \hline \multirow{3}{*}{EX2-6a} & LSTM-SSA & \(1.2\times 10^{-4}\) & 0.191 & 0.376 \\  & BP & \(1.7\times 10^{-4}\) & 0.247 & 0.481 \\ \cline{1-1}  & LSTM & \(1.9\times 10^{-4}\) & 0.250 & 0.502 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of performance indicators of different measurement points. Figure 7: Training and prediction results chart of different model. **(a)** EX3-4a. **(b)** EX3-3a. **(c)** EX2-7a. **(d)** EX2-6a.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 2 Related Work
A random back-off mechanism is used to improve the efficiency of sending hello messages. The design effectively finds the ratio of neighbors per cycle and reduces packet collision probability and throughput in the network. An energy-efficient and position-aware routing protocol for wireless sensor networks (WSN) is proposed by [23], which uses energy and hop-distance-based formulation for routing mechanism. The protocol selects a neighbor with maximum energy level and minimum hop distance for data forwarding, impacting network lifetime, packet delay, and throughput. An efficient route selection routing information protocol (ERS-RIP) is proposed by [24] for multihop WSNs under a jamming environment. The design uses energy cost for route selection and performs routing on optimal paths using RIP that occasionally exchanges network updates. The system achieves better results in terms of end-to-end delay, throughput, packet delivery rate, and network lifetime compared to DSR and other protocols. The designs mentioned above rely on other layer parameters to yield routing and slot assignment information incorporated in control frames [25; 26]. This increases the control overhead in the execution of route discovery, channel, and slot assignment. In contrast, our design excels in radios' autonomous behavior and performs all the mentioned operations in a distributed manner. The design fairly schedules collision-free simultaneous data transmission to all network nodes using virtual sub-nets. It employs a distributed neighbor and routes discovery algorithm for multihop routing using cross-layer design capabilities. The proposed VSCL-MAC design is unique in network organization and route establishment.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 GCPs and Checkpoints  
The significant feature points inside and outside the pit rim structure were selected as the ground control points (GCPs) and the checkpoints, respectively. The GCPs were adopted to construct the 3D model, and the LiDAR point-cloud data results and the measurement accuracy of the 3D model were comprehensively analyzed using the checkpoints. Fourteen GCPs and 26 checkpoints were acquired using the Hi-Target V90 RTK connected to the continuously operating reference stations (CORS) network (Figure 2e,f) with a CGCS 2000. It achieved a horizontal accuracy of \(\pm\)8 mm + 1 ppm and an elevation accuracy of \(\pm\)15 mm + 1 ppm.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 4 Security Threats in Modern Agriculture
### Cybercime and Cybersecurity in Agriculture  
A cyber-attack on an agribusiness or food industry is increasingly likely today thanks to automation and the widespread use of internet-connected equipment, which give potential (cyber) criminals greater chances in fields that were previously too remote or difficult to physically access [61]. Consequently, a potential cyber-attack or security problem in an agricultural organization could have serious human or financial repercussions. This implies that pertinent security concerns brought about by the widespread and ongoing usage of IoT, as well as the emergence of agro-terrorism, require adequate attention, better equipment, and solutions [62].  
The bulk of system operations in the agriculture sector are network-based, and frequently these systems are not protected from cyber threats, which can have substantial financial and security ramifications [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86]. Five important elements, as identified in [87], strengthen these risks in the agricultural sector:  
* Increasing the farm consolidation highly depends on technology;
* The joint ventures of the food supply chains, allow manufacturers to conduct processes and trade products directly;
* Food-related technologies in intelligent markets depend on more components, which increases their vulnerability to errors and malfunctions.
* Effective monitoring of food-related systems, social networks, and industries in a safe, dynamic, and almost real-time manner is lacking, making it difficult to identify serious digital and security vulnerabilities that could be the root of significant data breaches and system defects.  
Due to the heightened risk of cybercime, the agriculture sector may be impacted by such threats in a number of ways. Categorize these methods as in [84]. Threats of tampering include delivery disruption, confidential information interception, formula modification, and delivery disruption. These risks are undoubtedly closely related to the adoption and application of technological advancements in the agriculture and food sectors.  
[PERSON] et al. and [PERSON] et al. identify the following as the primary technical advancement areas that are commonly used in the agri-sector and contain vulnerable places for harmful cyber-attacks [86; 87; 88]:  
* Radio Frequency Identification (RFID); wireless communications (such as Wi-Fi);
* Sensors for the infrastructure, soil, and crops;
* Drones and other unmanned aerial vehicles (UAVs);
* PA-specific automation solutions (such Real Time Kinematic Technology);
* Portable electronics (such as laptops, smartphones, and GPS trackers);
* Smart agriculture and vertical agriculture;
* The use of AI in conjunction with biotech and nanotech (AI).  
Table 4 provides a summary of the study performed on different attacks in agricultural 4.0 due to cyber security concerns.
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 2 Related Work
### Routing-Based Approaches  
Routing-based approaches require globally updating FIBs to advertise the new location of a mobile node. This imposes a great signaling overhead under high-mobility environments, and the mobile producer remains inaccessible until convergence of the MM scheme, which introduces high delay. On the positive side, in this approach, direct access to a mobile node is achieved without any redirection or path stretch required. However, the complexity is obvious in propagating the location update information in a minimal time.  
In [40], MobiCCN was proposed. This is a greedy routing protocol that supports producer mobility by greedily selecting the candidate routers that a mobile node is likely to be accessible through. The candidate routers were selected based on distance measurements of the neighbors of the current router to the destination.  
In addition, in [41], mobility support was achieved through a hierarchical name-based scheme. The proposed architecture allows any meaningful space of the name hierarchy to be mobile. Entities under the name space are accessible anywhere in the network. However, location information is propagated through flooding, which imposes a high signaling overhead.  
In [42], the authors proposed a producer MM solution for dynamic IoT environments. The proposal is based on two phases: the FIB construction phase and the link recovery phase. In the FIB construction phase, the FIB is modified to include the faces through which a prefix has been requested to be called (out). In the recovery phase, upon producer mobility, a recovery packet is forwarded from the new PoA of the mobile producer. It propagates by applying LPM operation and using the associated (out) face in the FIB, if any. Otherwise, the recovery packet is broadcasted through all available faces. As it traverses, it adds the new forwarding information to the nodes which it visits.
# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 2 Materials and Methods
### Statistics
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Indicator** & **Mean** & **IQR** & **Model** & \(\mathbf{\alpha}\) & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Neurophysiological Indicators & & & & & & & \\ ADAS-11 & 8.9 & 7.2 & Random forest & 0.75 & 5.91 & 4.22 & 0.18 \\ ADAS-13 & 13.9 & 10.3 & Random forest & 0.5 & 8.25 & 6.20 & 0.30 \\ AV45 & 1.2 & 0.4 & Decision trees & 0.5 & 0.20 & 0.15 & 0.15 \\ CDR-SB & 1.4 & 1.5 & SVM Radial & 0.8 & 2.32 & 1.61 & 0.19 \\ FAQ & 3.3 & 3.0 & Random forest & 0.8 & 3.53 & 2.76 & 0.66 \\ FDG & 1.2 & 0.1 & Multilayer & 0.5 & 0.09 & 0.07 & 0.08 \\ MMSE & 27.5 & 3.0 & Linear & 0.8 & 3.72 & 2.83 & 0.12 \\ MOCA & 24.1 & 6.0 & Gradient boosting & 0.75 & 4.09 & 3.12 & 0.25 \\ RAVLT & 40.2 & 20.0 & Decision trees & 0.5 & 11.9 & 9.70 & 0.29 \\ \hline Neurobiological Indicators & & & & & & & \\ Entorhinal Vol & 3940.6 & 1031.0 & Decision trees & 0.8 & 864.96 & 659.14 & 0.11 \\ Fusiform Vol & 17,953.6 & 3295.0 & SVM Radial & 0.8 & 2352.72 & 1841.73 & 0.17 \\ Hippocamp Vol & 7060.3 & 1531.6 & Random forest & 0.8 & 885.20 & 702.23 & 0.46 \\ Mid Temporal Vol & 26,163.5 & 3979.0 & Gradient boosting & 0.75 & 2622.42 & 2151.96 & 0.23 \\ \hline \end{tabular}
\end{table} 
Table 6: Best prediction models of neuropsychological and neurobiological indicators. All four models exhibited nearly identical performance metrics across the evaluation scheme. The accuracy for each model was observed to be between 0.4836 and 0.4985, with a 95% confidence interval ranging from 42.89% to 55.33%.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### 5G and IoT Sensor Technologies for UAVs
#### 2.3.2 Data-Acquisition Sensors  
Drones are equipped with several sensors to capture information and data that are required to perform certain tasks. Depending on the application, the payload sensor suite can be arranged during the development of the drones.  
* For military use cases, UAVs may be equipped with high-end electro-optical sensors, and radars for airborne systems providing resolutions from submillimeters to a few centimetres.
* In surveillance and monitoring applications, we can have sensors at the lower end of the spectrum, such as low- or high- (e.g., 4K) resolution RGB (Red Green Blue) cameras, NDVI (Normalized Difference Vegetation Index) cameras for precision farming, LIDAR (Light Imaging, Detection, And Ranging) for simultaneous localization and mapping, and ultrasonic sensors for sense and obstacle-avoidance methods.
* We can also have hyperspectral depth and thermal sensors [32]. Applications that monitor environmental and weather conditions and are deployed in disaster relief and management require sensors to measure or detect liquefied petroleum gas (LPG), butane, methane (CH4), hydrogen, smoke, oxygen, temperature, and humidity.  
In all these applications, the IoT sensors collect data in real time and are either processed on board if enough power is available or transmitted to a base station.
## 3 System Model
### Energy Efficiency and Reliability  
The framework optimizes energy efficiency by dynamically adjusting, depending on the distance between nodes and the environmental conditions, the transmission power. This is expressed as follows:  
\[E_{i,t}=\eta\cdot P_{i,t}\cdot d_{i,t}^{2},\]  
where \(\eta\) is the energy efficiency factor, \(P_{i,t}\) is the transmission power of node \(i\), and \(d_{i,t}\) is the distance between node \(i\) and its communication partner.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Dynamic Traffic  
In addition to stations changing over time, we also evaluate the performance when the number of active stations does not change, but instead their transmission interval changes over time. For the HT scenario, the network consists of 1024 stations with a total traffic load of 0.85 Mbps at the start of simulation, while for LT there are 1024 stations with a total traffic load of 0.095 Mbps at the start of simulation. Traffic distribution among stations is done according to the method detailed in Section 6.2. Every second, a random set of stations is selected that will change their transmission interval, according to a Poisson distribution with rate \(\lambda=5\). For each selected station, the change in transmission interval \(\Delta\) is chosen uniformly at random as a percentage between \([-10,10]\), \([-20,20]\), or \([-50,50]\). Table 7 lists the average throughput for both EDCA/DCF as well as TAROA. The conclusions drawn in the previous section also apply here. Specifically, TAROA outperforms EDCA/DCF in all dynamic traffic scenarios if it also outperforms EDCA/DCF under similar static conditions (i.e., in all scenarios expect LT with 1024 stations and a starting traffic load of 0.095 Mbps). Moreover, changes in traffic load result in far less degradation of throughput than changes in topology, both for EDCA/DCF and TAROA. \begin{table}
\begin{tabular}{c|c c|c c} \hline \multirow{2}{*}{\(\lambda\)} & \multicolumn{4}{c}{**HT Scenario**} \\ \cline{2-5}  & \multicolumn{2}{c|}{**Without Hidden Nodes**} & \multicolumn{2}{c}{**With Hidden Nodes**} \\ \cline{2-5}  & **EDCA/DCF** & **TAROA** & **EDCA/DCF** & **TAROA** \\ \hline
0.1 & 0.63010 \(\pm\) 0.00142 & 0.83621 \(\pm\) 0.00119 & 0.51374 \(\pm\) 0.00237 & 0.83027 \(\pm\) 0.00134 \\
1 & 0.62942 \(\pm\) 0.00165 & 0.81656 \(\pm\) 0.00193 & 0.51080 \(\pm\) 0.00379 & 0.76800 \(\pm\) 0.00274 \\
5 & 0.60546 \(\pm\) 0.00209 & 0.74428 \(\pm\) 0.00305 & 0.48361 \(\pm\) 0.00313 & 0.67500 \(\pm\) 0.00328 \\ \hline \multirow{2}{*}{\(\lambda\)} & \multicolumn{4}{c}{**LT Scenario**} \\ \cline{2-5}  & **Without Hidden Nodes** & \multicolumn{2}{c}{**With Hidden Nodes**} \\ \cline{2-5}  & **EDCA/DCF** & **TAROA** & **EDCA/DCF** & **TAROA** \\ \hline
0.1 & 0.09706 \(\pm\) 0.00003 & 0.09568 \(\pm\) 0.00036 & 0.05440 \(\pm\) 0.00140 & 0.09476 \(\pm\) 0.00058 \\
1 & 0.09485 \(\pm\) 0.00004 & 0.09106 \(\pm\) 0.00045 & 0.04964 \(\pm\) 0.00191 \({}^{\star}\) & 0.08864 \(\pm\) 0.00053 \\
5 & 0.09712 \(\pm\) 0.00004 & 0.07749 \(\pm\) 0.00029 & 0.03284 \(\pm\) 0.00146 \({}^{\star}\) & 0.07431 \(\pm\) 0.00058 \\ \hline \end{tabular} \({}^{\star}\) indicates that simulation uses association time out 10 s instead of 0.5 s.  
\end{table} 
Table 6: Comparison of throughput of EDCA/DCF and TAROA under three different intensities of dynamic number of stations.