esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
### Net/Mesh, Non-Rotated Square, Armour Efficiency  
The theoretical probability \(P_{theo}\) for a correct functioning of a net cage armour solution based on a non-rotated, square mesh composed of round wires is both dependent on the impact angle in the vertical (elevation) and the horizontal (azimuth) plane. For a non-rotated mesh, i.e., composed of horizontal wires and vertical wires, the probability of correct functioning can, however, be decomposed in the horizontal and the vertical direction:  
\[P_{theo}=P_{theo, horizontal wires}\cdot P_{theo, vertical wires}=P_{theo}\langle\alpha\rangle\cdot P_{theo}\langle\beta\rangle\] (A16)  
With the two right-hand terms given by the solution for a round bar armour system. These terms are described by the same expressions and as they are independent of one another, their influence corresponds, respectively, to the influence of the horizontal wires and hence the impact angle \(\alpha\) in the vertical plane (elevation), and the vertical wires and hence the impact angle \(\beta\) in the horizontal plane (azimuth). In the following equations, \(\langle\alpha,\beta\rangle\) should be interpretated as \(\alpha\) or \(\beta\), with each equation both valid for the vertical and horizontal impact angle (but depending upon the specific combination of values different equations should possibly be used) and where the total theoretical probability is then calculated according to the right-hand side of Equation (A16).  
Case 1: \(I<C_{eff}\), \(L\cdot cos\langle\alpha,\beta\rangle>D\) until \(P_{theo}=0\):  
\[P_{theo}\langle\alpha,\beta\rangle=\frac{I\cdot cos\langle\alpha,\beta\rangle+D -(D+P)}{I\cdot cos\langle\alpha,\beta\rangle+D}\] (A17)  
Figure 10: Geometric analysis for the round bar armour system.  
When \(I\cdot cos\langle\alpha,\beta\rangle+D\leq(D+P)\):  
\[P_{theo}\langle\alpha,\beta\rangle=0 \tag{18}\]  
Case 2: \(I>C_{eff}\), \(L\cdot cos\langle\alpha,\beta\rangle>D\) until \(P_{theo}=0\):  
If \(C_{eff}\leq I\cdot cos\langle\alpha,\beta\rangle\):  
\[P_{theo}\langle\alpha,\beta\rangle=\frac{C_{eff}-P}{I\cdot cos\langle\alpha, \beta\rangle+D} \tag{19}\]  
When \(C_{eff}\geq I\cdot cos\langle\alpha,\beta\rangle\) :  
\[P_{theo}\langle\alpha,\beta\rangle=\frac{I\cdot cos\langle\alpha,\beta\rangle-P}{ I\cdot cos\langle\alpha,\beta\rangle+D} \tag{20}\]  
When \(I\cdot cos\langle\alpha,\beta\rangle\leq P\) :  
\[P_{theo}\langle\alpha,\beta\rangle=0 \tag{21}\]  
Case 3: \(L\cdot cos\langle\alpha,\beta\rangle<D\) until \(P_{theo}=0\):  
Not applicable to actual net/mesh armour systems.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 3 GNSS Orbit Repeat Period  
GNSS satellites are designed to orbit Earth for a regular period. However, the repeat period of a GNSS satellite observed from the ground does not match the orbital period owing to the Earth's rotation effect. Moreover, even satellites in the same navigation satellite system have slightly different orbital periods owing to the effects of gravity variability and other perturbations. Therefore, a GNSS's orbital period needs to be calculated for each satellite, considering the location of satellites observed from the ground, in order to perform modeling based on past PRC data. Here, we obtain a repeat time for when the satellite has the same aspect [20; 21].  
In general, GNSS users can choose almanac, broadcast navigation message, or precise orbits in order to calculate satellite positions and predict their orbits. Almanac consists of Keplerian elements, which can propagate GNSS satellite orbits in Earth-centered Earth-fixed (ECEF) coordinates. The broadcast ephemeris, on the other hand, provides a more accurate description of the satellite trajectory in real time. These data are essentially similar to almanac, but provide a better representation of the GNSS orbit by including secular and periodic perturbations [22]. In the case of International GNSS Service (IGS) orbits whose accuracy is higher than the previous two kinds, coordinates of satellites are directly recorded as X, Y, and Z values so that orbit propagation based on Keplerian elements is not necessary. Instead, a proper interpolation scheme should be adopted to compute the satellite position at a desired epoch.  
We used precise SP3 orbit files provided by the IGS. SP3 files are classified as ultra-rapid, rapid, and final products according to accuracy and update period. In the present study, rapid products were used, where accuracy is similar to that of final products and updated once a day [23].  
Figure 1: PRC observed at a base station in Incheon, Korea: (**a**) PRC of all GPS satellites for 24 h (23 April 2016). Each satellite is displayed with a different color; (**b**) PRC of GPS PRN 1 for a week (1–7 April 2016). Each color line is a different day.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 5 Discussion
Without such validation, there is a risk of misestimating landslide susceptibility, potentially leading to inadequate risk management strategies and jeopardizing safety in areas where the model is applied. Moreover, while the study emphasized the importance of factor engineering and multi-model integration, the intricate interactions between factors remain an area for further exploration. Future studies could investigate advanced techniques, such as deep learning and hybrid modeling approaches, to capture complex relationships among the factors that traditional ensemble methods may overlook. Additionally, incorporating uncertainty quantification methods could provide a more comprehensive understanding of the reliability of the predictions, enabling better informed decision making in landslide risk management. Finally, the reliance on historical data for model training and validation may introduce biases, particularly in regions with limited or inconsistent data availability. This could affect the model's ability to generalize to future scenarios, especially under changing environmental or climatic conditions. Future research should consider integrating dynamic data sources and adaptive modeling frameworks to address these challenges and improve the model's applicability to evolving conditions.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 5 Simulation Results
Figures 8a,b show the effect of the cache size on the cache hit ratio in a network with 50 nodes with a mobility rate of 1 m/s with Topology 1 and in a network with 500 nodes moving randomly with 10 m/s with Topology 2. The cache size is varied in chunks of 10 kb each. Because of trendiness-based caching, T-Move results in a better hit ratio compared to Vanilla NDN. The interesting point is that larger networks benefit better from caching due to a greater number of interests, which results in more cached data.
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 4 Comparative Performance Analysis
### Mitigation Strategies  
Two mitigation strategies to face the PVLAN attack were evaluated in this analysis. In order to describe them, 192.168.1.0/24 is used as the network address of a LAN segment and 192.168.1.1 is the IP address of the network gateway.  
Figure 4a shows the rules that compose an ACL that can be configured in the gateway in a conventional solution. They block all communications that have source and destination IP addresses within the same network, except those where the destination IP address matches that of the gateway. Note that this ACL must be applied to process inbound traffic in every interface (both hardware and software) that is acting as the gateway, or aggregation point, of a LAN segment.  
A solution based on the use of SDNs was also evaluated. The OpenFlow protocol version 1.3 was used in these tests in combination with the _Ryu_[37] SDN controller. The OpenFlow communications between the controller and the switches were performed through an out-of-band (OOB) management network. In this approach, the controller uses the OpenFlow protocol to proactively install the necessary rules on the flow table of the switches to discard all traffic that matches the pattern of a PVLAN attack. The controller only manages the access level switches because, in a corporate network, the access level is the first line of defence against internal attacks, such as the PVLAN attack. Thus, this prevents traffic from reaching other areas of the network, resulting in better network performance as malicious traffic is dropped from as close to the attacker as possible [38].  
Figure 4b shows the rules that compose the flow table of the switches. The communications with source and destination IP addresses within the same network are discarded, except those where both the destination IP address and the destination MAC address match those of the network gateway. To do so, two flows are installed on the flow table of the access level switches. First, the flow with priority value 4 permits legitimate traffic from the LAN segment to the network gateway. If this flow is matched, the packet is transmitted with no further processing since it is the highest priority flow. Second, the flow with priority value 3 blocks traffic with source and destination IP addresses within the LAN segment and the destination MAC of the network gateway, since this corresponds with the PVLAN attack pattern.
)3 State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 34 National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257347, China 4  
###### Abstract  
The Guangxi Zhuang Autonomous Region, a vital strategic geographic entity in southern China, is prone to frequent road collapse disasters due to its complex topography and high rainfall, severely affecting regional economic and social development. Existing risk assessments for these collapse disasters often lack comprehensive analysis of the combined influence of multiple factors, and their predictive accuracy requires enhancement. To address these deficiencies, this study utilized the ResNet18 model, a convolutional neural network (CNN)-based approach, integrating 10 critical factors--including slope gradient, lithology, and precipitation--to develop a risk assessment model for road collapse disasters. This model predicts and maps the spatial distribution of collapse risk across Guangxi. The results reveal that very high-risk areas span 49,218.94 km\({}^{2}\), constituting 20.38% of Guangxi's total area, with a disaster point density of 8.67 per 100 km\({}^{2}\); high-risk areas cover 56,543.87 km\({}^{2}\), representing 23.41%, with a density of 3.38 per 100 km\({}^{2}\); and low-risk areas encompass 61,750.69 km\({}^{2}\), accounting for 25.57%, with a density of 0.29 per 100 km\({}^{2}\). The receiver operating characteristic (ROC) curve yields an area under the curve (AUC) value of 0.7879, confirming the model's high reliability and predictive accuracy in assessing collapse risk. This study establishes a scientific foundation for the prevention and mitigation of road collapse disasters in Guangxi and offers valuable guidance for risk assessments in similar regions.
## 4 Impossibility Results
### Synchronization and State Observation Problems
#### 4.1.3 Termination Detection (TD)  
The TD problem is specified as follows.  
* **Safety specification of TD** states global termination--all processes passive and no in-transit application messages in a (transitless) consistent global state--should not be declared unless global termination has occurred.
* **Liveness specification of TD** states that some process should eventually be able to detect global termination once it has occurred.  
Theorem 4.4: _In a system with even one Byzantine process, the distributed TD problem is subject to the same limitations (exposure to false positives and false negatives) as the CD problem, resulting in safety and liveness violations._  
**Proof.** Solving TD requires satisfying  
\[\Phi_{TD}(e_{i}^{\vartheta})\stackrel{{ def}}{{=}}\bigwedge_{h}(e_{h} ^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}=1)\wedge\phi(e _{i}^{\vartheta}),\]  
where \(e_{h}^{x_{h}}\) is an event where the process becomes passive, \((\forall h)\,y_{h}\leq\tilde{y}\), \(\phi\) is a predicate capturing the other requirements of the TD problem--other processes are passive in a transitless consistent global state--besides the first predicate, \(e_{i}^{\vartheta}\) is an event where global termination is detected, and \(\to\) is defined on termination detection algorithm messages.  
As \(e_{h}^{x_{h}}\) is an internal event, from Theorem 1 for the CD problem, detecting \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}\) is susceptible to false positives and false negatives. Thus, it cannot be guaranteed that the predicate \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}\) in the formula \(\Phi_{TD}\) can be satisfied. Hence, TD cannot be solved.  
A false positive of the CD problem is a safety violation--no real global termination has occurred--in the TD problem. A false negative of the CD problem is a liveness violation--real global termination is not detectable--in the TD problem.
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 4 The Correlation between the Strength and Ultrasonic P-wave Velocity
### Correlation between Compressive Strength and P-wave Velocity  
The ultrasonic p-wave velocities and compressive strengths measured in nine groups of mix proportions are plotted in Figure 7.  
Figure 7 highlights a simultaneous increment in ultrasonic p-wave velocity and sample strength, peaking in the fifth group of mix proportions. It was observed that at this peak, the ultrasonic p-wave velocity and compressive strength reached 2.357 km/s and 5.521 MPa, respectively.  
The correlation between ultrasonic p-wave velocity and compressive strength was visualized in a heatmap depicted in Figure 8. The coloration of the ellipses in the heatmap corresponds to the correlation coefficients' signs, with red ellipses denoting a positive correlation. Ellipses that appear more elongated indicate a stronger correlation. It was found that the correlation coefficient between the ultrasonic p-wave velocity and compressive strength stood at 0.82, denoting a potent positive correlation. Hence, it is suggested that higher ultrasonic p-wave velocities in the coal based paste fill materials typically accompany higher compressive strengths, and vice versa.  
Figure 8: Heatmap for the correlation coefficient between the ultrasonic p-wave velocity and compressive strength.  
Figure 7: Changes in the ultrasonic p-wave velocity and compressive strength under different mix proportions.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 1 Introduction
### Sensors and Measurements  
Pressure sensors are suitable devices for measuring sea levels in an IoT network. However, commercially available devices can be very expensive, with single pressure-based tide gauges costing more than \(\pounds 1000\). For example, the Nortek AWAC used for comparison in this research project costs around \(\pounds 17,000\) (although it also measures waves and currents). While these types of commercial gauges are capable of long deployments and near-real-time data return, they have a high initial cost for self-contained devices and require additional electronics and software packages for the telemetry element.  
[PERSON] et al. [13] showed that inexpensive pressure sensors (e.g., MS5803, 2-bar and 5-bar pressure sensors from Measurement Specialties) could be used for long periods and produce reliable data when combined with low-cost micro-controllers. They recorded pressure measurements internally (on microSD cards), recovered the data many months later and demonstrated that the sensors could remain stable over long deployments. Also, [PERSON] et al. [14] used these types of sensors together with low-power micro-controllers to record high-frequency pressure data for long periods (e.g., MS5803, 10-bar pressure sensors from Measurement Specialties).  
The research presented here demonstrates that low-cost pressure sensors can provide accurate sea-level measurements, and when incorporated with a Sigfox ultra narrowband wide-area network with off-the-shelf Arduino micro-controllers, can be incorporated into an IoT network, which can then provide the necessary data to monitor the changes in coastal morphology.
## 3 Results and Discussion
### Scale Effect  
#### 3.3.1 Limestone  
Based on the correlations presented in Table 1 regarding the point load index and \(\sigma_{c}\), the scale effect curve \(\sigma_{c}\)-\(d\) was estimated for the limestone. The results of \(\sigma_{c}\) from all correlations are shown in Figure 6 as colored squares, while the experimental \(\sigma_{c(31)}\) is represented by a red triangle. It can be clearly observed that the estimated values of \(\sigma_{c}\) generate an ascending model, like in Figure 2d (\(k_{2}=-0.3126\)). Likewise, values that were outside the trend for \(d\) less than 50 mm were discarded, since in Figure 2d there are up to 60.9 mm included in the ascending model.  
After discarding the lowest values of \(d\) in the range of 54-57 mm (Equations (1), (11), (13), (15) and (16)), we had Figure 7, whose adjustment by least squares gives the \(k_{1}\) changed sign. Since \(k_{1}\) is known, it allowed us to calculate the \(\sigma_{c(50)}\) of the scale effect corresponding to Equation (27). The values of \(k_{1}\) and \(\sigma_{c(50)}\) were \(-0.2545\) and \(  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \(N\) & **Mean** & **Standard Deviation** & **Maximum** & **Minimum** \\ \hline \(\beta_{1}\) & 200 & 21.00 & 1.36 & 26.06 & 17.28 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the normal distribution of the linear fit for the \(\sigma_{c}\) and \(I_{s}\) pairs of the travertime.  
Figure 5: Histogram with distribution curve (**a**) and normal probability plot (**b**) of slope (\(\beta_{1}\)) between \(\sigma_{c}\) and \(I_{s}\) of the travertime, from the least squares fit of 10 pairs of data points selected from 200 random samples.  
Based on the above, the scale effect curve is represented in Figure 8 and the ascending model is given by the following equation:  
\[\frac{\sigma_{c}}{\sigma_{c(50)}}=\left(\frac{50}{d}\right)^{-0.2545}, \tag{38}\]  
where the value of \(k_{1}\) (\(-0.2545\)) does not correspond to the 0.18 proposed by [PERSON] and [PERSON] [33, 34], neither in sign nor in magnitude, since, as already indicated, the model of this curve was ascending (Figure 7) and not descending.  
Figure 6: Estimation of \(\sigma_{c}\) from the correlations with \(I_{s}\), \(I_{s(50)}\), and \(I_{s(50)}\) of Table 1 for limestone.  
Figure 7: Least squares adjustment to estimate the \(\sigma_{c(50)}\) of the scale effect of the limestone.  
Another issue is that the value of \(k_{1}\) did not correspond to that of \(k_{2}\) (\(-\)0.3126) obtained in the point load test. In this sense, neither [PERSON] [16] nor [PERSON] et al. [26] indicates that the two coefficients must be equal, but \(\sigma_{c(50)}\) was estimated by multiplying \(I_{s}\) or \(I_{s(50)}\) by a constant, so the exponent would not be modified. This suggests that, for the ascending model (Figure 7), the experimental \(k_{1}\) value would be in a range close to the values of \(-\)0.3126 and \(-\)0.2545.
## Appendix A Comparison of the Performances of GA1 and GA2 with Various Number of Features  
Table 1 shows the results of GA1 and GA2 with four machine learning models according to the number of features. The best result for each machine learning model is marked in bold among the results of GAs with the experimented number of features. In the table, CPU time spent on training and testing each machine learning model is also presented. For XGBoost and DT, GA2 performed better than GA1 when the number of features was 25 or 50. On the other hand, for SVM, GA1 performed better than GA2 when the number of features was 75 or 100. For DNN, GA1 outperformed GA2 when the number of features was 25, 50, or 100.  
Among the results of Table 1, the combination of DNN and GA1 with 100 features showed the best performance. On the other hand, the combination of DT and GA2 with 25 features showed the worst performance. For GA2, DNN showed the best performance when the number of features was 75.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### Jiangsu Power Grid
#### 5.2.2 Analysis of Economy and HVDC Consumption  
The operating costs and average HVDC power of both cases are shown in Table 5.  
Compared with Longzheng's HVDC full power operation in cases 0 and 1, the average power of Jinsu's HVDC is 6539.4 MW and 5382.5 MW. The reason is that due to larger capacity, if Jinsu's HVDC is full power operating, low frequency and overload of the transmission line problem will appear when Jinsu HVDC blocked. With relevant constraints of first and second frequency regulation capacity, Jinsu's HVDC power is decreased at different levels. The difference of Jinsu's HVDC power in cases 0 and 1 is caused by voltage stability constraints, explained in the last section. In addition, case 1 is has the most calculation iterations, 30 times and 189.6 s.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
###### Abstract  
This study integrates path planning, fuzzy theory, neural networks, image processing, range sensors, webcam, global navigation satellite system (GNSS), and real-time kinematic (RTK) positioning system into an intelligent wheeled mobile robot (WMR) for outdoor patrolling. The robot system uses ultrasound sensors, laser sensors, and fuzzy controllers to detect obstacles and avoid them. The starting position and the goal position of the WMR in an outdoor environment are given by the GNSS RTK positioning system. Real-time position correction of the robot is performed through the differential global positioning system. The robot system applies the ant algorithm and the Dijkstra algorithm to find the shortest path for patrol tasks. The convolutional neural network image processing is utilized to identify intruders that are appearing in the patrol path. When the WMR detects an intruder by the face detection and recognition methods, the robot captures the photo of this person by the webcam and acquires the location information of this person by the RTK positioning system. Then the WMR sends the location and photo of the intruder to the control center by Wi-Fi and asks for help.  
image processing; fuzzy theory; GNSS; range sensor; path planning +
Footnote †: journal: Applied Sciences