esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method
## 2 Background and Related Work
### Operation on Hexagonal Fuzzy Numbers  
Consider two HFNs \(M^{-}\) = (\(m_{1}\), \(m_{2}\), \(m_{3}\), \(m_{4}\), \(m_{5}\), \(m_{6}\)) and \(N^{-}\) = (\(n_{1}\), \(n_{2}\), \(n_{3}\), \(n_{4}\), \(n_{5}\), \(n_{6}\)), then the operation on these two HFNs are as follows [50]:  
* Addition: \(\mathrm{M^{-}}\oplus\mathrm{N^{-}}\) = (\(m_{1}+n_{1}\), \(m_{2}+n_{2}\), \(m_{3}+n_{3}\), \(m_{4}+n_{4}\), \(m_{5}+n_{5}\), \(m_{6}+n_{6}\));
* Subtraction: \(\mathrm{M^{-}}\) = (\(m_{1}-n_{6}\), \(m_{2}-n_{5}\), \(m_{3}-n_{4}\), \(m_{4}-n_{3}\), \(m_{5}-n_{2}\), \(m_{6}-n_{1}\));
* Multiplication: \(\mathrm{M^{-}}\times\mathrm{N^{-}}\) = (\(m_{1}\times n_{1}\), \(m_{2  
* Threat 3: Attack from host applications communicating with VMs. This includes attacks that exploit vulnerabilities caused by improper network isolation and improper configuration to application privileges of the host machine;
* Threat 4: Attack to VMs from remote management path. This includes eavesdropping, tampering, DOS attack, and Man-in-the-Middle attack;
* Threat 5: Attack to external communication with third party applications. This includes illegal access to API and DOS attacks to API;
* Threat 6: Attack from external network via network edge node. This includes attacks against Virtualized Firewalls and Residential gateways;
* Threat 7: Attack from host machines or VMs of an external network domain. This includes attacks against the VNF migration and VNF scaling.  
From our analysis, besides the traditional network, IoT, and cloud attack surfaces that are inherited to the 5G networks, there are additional attacks enabled by the integration of mobile Edge computing (MEC) and 5G networks, as depicted in Figure 3, namely [31]:  
1. **(I): Insecure mobile backhaul network.** Data exchanged between MEC nodes often traverse insecure shared backhaul that is vulnerable to MITM attacks, including eavesdropping and spoofing. Such attacks can also come from edge nodes connected to the public internet through the edge Firewall Interfaces (e.g., SGi/N6);
2. **(S): Shared infrastructure with third-party applications.** MEC nodes can be opened to allow authorized participants to deploy applications/services to other users. However, poorly designed applications can create opportunities for attackers to invade the system and pose threats to the network applications running on the platform;
3. **(P): Privacy leakage illegitimate access to the Multi-access MEC system.** In this case, an attacker can compromise the service infrastructure and the network hampering information privacy, and accessing the information stored at the edge system's upper layers that in turns poses a serious concern for privacy leakage. In this paper, we mainly target these attacks using the VAA.  
Figure 2: Attack surfaces of the 5G Network.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 3 Experimental Results and Discussion
### Evaluation Indices  
Four evaluation indices are used to evaluate the performance of the methods, that is, the success rate (\(Sr\)), the tracking recall rate (\(TRe\)), precision (\(Pr\)), and the \(F1\)-score.  
The success rate is the ratio of successfully tracked frames to the total number of frames at a specific IoU threshold, \(\beta\). If the IoU value of a frame is greater than or equal to \(\beta\), the tracking of that frame is successful. \(Sr\) can be defined as  
\[Sr=\frac{1}{N}\sum_{i=1}^{N}\text{I}(IoU_{i}\geq\beta), \tag{16}\]  
\[IoU_{i}=\frac{\left|p_{i}\cap g_{i}\right|}{\left|p_{i}\cup g_{i}\right|}, \tag{17}\]  
where \(N\) is the total number of frames in a video, \(IoU_{i}\) is the IoU value of the \(i\)-th frame, and \(\text{I}(\cdot)\) is the indicator function whose value is 1 if the condition is satisfied and 0 otherwise. \(p_{i}\) is the predicted target position, box and \(g_{i}\) is the label position box.  
Precision measures the degree of overlap between predicted and real bounding boxes. It can be defined as  
\[Pr=\frac{1}{N}\sum_{i=1}^{N}IoU_{i}, \tag{18}\]  
The tracking recall rate (\(TRe\)) is also used as an evaluation index to evaluate the tracking method. Unlike traditional recall, \(TRe\) measures the tracker's ability to correctly detect targets in visible frames. It can be defined as  
\[TRe=\frac{\sum_{t=1}^{N}\text{I}(\vartheta_{t}>\beta,v_{t}=1)}{\sum_{t=1}^{N} \text{I}(v_{t}=1)}, \tag{19}\]  
where \(v_{t}\in\{0,1\}\) is an annotation variable that determines whether the target is visible in frame \(t\), and \(\vartheta_{t}\in[0,1]\) is the confidence score for frame \(t\).  
Generally, a large \(Pr\) will lead to a low \(TRe\) and vice versa. For a better comparison, a unified index, i.e., the \(F1\)-score, combining \(Pr\) and \(TRe\) is introduced to evaluate the performance of a method. It can be defined as  
\[F1=2\times\frac{Pr\cdot TRe}{Pr+TRe}. \tag{20}\]
## 2 Current Elastography Methods
The speed of the shear waves propagating through the tissue are estimated using B-mode imaging tracking, and it is proportional to tissue stiffness. Using these methods, liver stiffness can be expressed in m/s displaying the speed of the shear wave through the tissue, or in kPa, based on the Young's modulus. 2-dimensional shear wave elastography (2D-SWE) uses a large field of view with pixel color coding of the stiffness values, while p-SWE is based on the assessment of a fixed region of interest (ROI) of approximately 1 mL. Regarding the actual technique, several specifics are important, such as the transducer's position parallel to the liver capsule, the ROI parallel to the transducer and perpendicular to the ARFI pulse, as well as the observer's focus on avoiding artefactual liver stiffening by taking the measurements 1.5-2 cm under the liver capsule. In order to obtain a value as accurate as possible, ten measurements are recommended in case of p-SWE and 5 for 2D-SWE [60]. Nevertheless, the quality criteria depends on the recommendation of each vendor [61]. The variability of measurements performed with p-SWE and 2D SWE was found to increase with liver stiffness [47]. It is worth mentioning that most of the data available in the literature focused on TE and several studies consider FibroScan as a reference for shear wave-based techniques [62; 63]. Cut-off values for ultrasound-based elastography in children identified in the literature were summarized in Table 2.
## 2 Antenna Design
### Optimization of Ground Slot Dimensions  
The proposed single-element antenna was analyzed according to the ground cut, varying two parameters in two steps, to achieve the upper mid-band conveniently. In the first step 1, from Figure 5a, the ground cut width (gcl) was varied to analyze the frequency shift, but by varying the width from 4.8 mm to 5.0 mm, there was a minor shift for both values not in the desired range. And in step 2, the Pi-shaped patch antenna's ground slot length (gcw) varied from 5.5 mm to 6.5 mm; the reflection coefficient is shown by a solid black color in Figure 5b for the desired range, where increasing the value shifted to higher frequencies and decreasing the ground cut length showed a minor shift to lower frequencies.  
Figure 4: Progression of \(S_{1,1}\) to Pi-shaped from simple T shape without ground cut to Pi-shaped with ground cut.
# Effect of Lateral End Plates on Flow Crossing a Yawed Circular Cylinder
## 3 Results and Discussion
### Frequency Spectra of Local Lift Coefficient
#### 3.4.1 Nonslip Boundary Condition  
At a yaw angle of \(15^{\circ}\), the power spectra of the lift coefficient for 9 segments are shown in Figure 14. For Segment 9 at the base end of the cylinder, the frequency spectra are broadbands, which are evenly distributed from \(Str=10^{-2}\) up to 0.3 with an energy level of approximately \(10^{-4}\). Therefore, it convinces that no vortex is shed in the wake. In Segment 8, a narrowband peak appears at around \(Str=0.2\), the value of which is in agreement with that predicted by the independence principle (\(Str_{\text{IP}}=0.2075\)). From Segment 7 to Segment 4, two narrowband peaks appear respectively at \(Str=0.025\) and 0.2. The energy level at \(Str=0.2\) is in the order of \(10^{-3}\), which is one order of magnitude higher than that of Segment 8. The low-frequency narrowband peak at \(Str=0.025\) is likely to be associated with the separation of the secondary wall-attached axial flow because the location of Segment 4 corresponds to  
Figure 12: Pressure profiles under periodic boundary condition on the two end-plate walls at \(\alpha=45^{\circ}\).  
Figure 13: Time histories of lift coefficient for nonslip boundary condition and periodic boundary condition.  
the separation point of a secondary axial wall-attached flow as shown in Figure 8. For Segments 3 to 1, only the high-frequency peak consistent with that of the independence principle remains.  
At a yaw angle of 30\({}^{\circ}\) (as shown in Figure 15), the broadband spectra spanning from \(Str=10^{-2}\) to 0.16 with an almost even energy distribution are extended from Segment 9 to Segment 5, where no vortex is shed in the wake. In Segment 4, two narrowband peaks appear. The higher one with \(Str=0.16\) is consistent with the value of the independence principle, and the lower one with \(Str=0.035\) is thought to be generated from the separation of the secondary wall-attached axial flow. For Segment 3 to 1 at the tip end, the narrowband corresponding to the value of the independent principle becomes prominent.  
Figure 14: Power spectra of segmental lift coefficient at yaw angle of 15\({}^{\circ}\).  
Figure 15: _Cont._  
When the yaw angle is increased to 45\({}^{\circ}\) (as shown in Figure 16), the most distinguished feature of the lift coefficient spectra is that the second harmonic of the primary mode appears for all 9 segments, the energy level of which is higher than that of the primary mode (approximately \(Str=0.0968\) as predicted by the independence principle). The third peak appears in Segment 6 to 3, which should be ascribed to the contribution of the separation of the secondary wall-attached axial flow.  
Figure 16: Power spectra of segmental lift coefficient at yaw angle of 45\({}^{\circ}\).  
Figure 15: Power spectra of segmental lift coefficient at yaw angle of 30\({}^{\circ}\).
## 2 Technology Forecasting Methodology
### Concepts of Technology Forecast
It was first developed for military use, such as the Internet, GPS, computers for decryption, microwave ovens, and drones that we use in our daily life, but it is a case that has spread to the public. On the other hand, considering that civil-military compatibility is great at the stage of low technological maturity, there is an investment efficiency of applying technologies led by the private sector, such as commercial satellite communication technology, for military use. Therefore, predicting technologies with great military applicability starts with identifying promising technologies that can grow into necessary technologies. However, if the dependence on the general market grows, it is necessary to be able to receive the military-necessary products continuously and stably, and this needsto be considered. In addition, the competition for technological hegemony between the United States and China is accelerating in fields such as semiconductors, 5G, quantum computing, and artificial intelligence. Therefore, to adopt commercial technology that can be used for military purposes, it is necessary to understand the growth of companies that develop products. The technology prediction approach proposed in this study to develop military products based on commercial technology is as follows. First, as an exploratory study, TFDEA is used to measure the rate of technological change. Then, a candidate product that can satisfy the performance of the product to be launched at the time of the military target is selected. Finally, it is to prepare basic data for product selection decision-making by analyzing the growth trend of companies that produce products. The TFDEA technique applied to measure the rate of technological change is introduced in Sections 2.2 and 2.3. Figure 1 shows the approach in this study to select future commercial equipment that can meet the performance level of smartphones required for military purposes through technology prediction. It can be used for basic analysis to select an alternative that can satisfy military needs among products from A to E. The Malmquist index was used for the analysis of productivity, which is the core of the growth potential of a company that develops products, and the introduction of the methodology is described in Section 2.4.
## 4 Results and Discussion
### Basic Statistical Analysis
This methodology enhances the reliability of the analysis, ensuring that both short- and long-term impacts are appropriately captured, even in the presence of structural changes. As presented in Table 3, the results from both the F-test and \(t\)-test indicate that the respective test statistics exceed the critical values for the upper bound. Consequently, the null hypothesis of no cointegration is decisively rejected. This suggests the existence of a stable long-run relationship among the variables under consideration in this study. The rejection of the null hypothesis confirms that the variables are cointegrated, signifying that despite short-term fluctuations, they tend to move together in the long run. Following this, the analysis proceeds to examine the long-run and short-run dynamics as estimated by the FARDL model. The FARDL approach allows for a detailed decomposition of the long-term equilibrium relationships and short-term adjustments, enabling a more nuanced understanding of the interactions between the variables. The subsequent section presents the estimated coefficients for both the long-run and short-run relationships, offering insights into the extent and direction of the effects, as well as the speed of adjustment toward long-run equilibrium. These findings provide a robust basis for policy recommendations, as they illuminate the differential impacts of the considered variables over various time horizons.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review  
This section presents a historical review of SUHI studies via satellite data from their origin nearly 50 years ago to the present. The characteristics of research output including the historical trend, geographic areas, study time-of-day and season, and research foci were examined using a set of descriptive statistics of a carefully screened collection of the SUHI literature. The key milestones in remote sensing of SUHIs are then summarized.
## 3 Results Simulation Analysis and Optimization
### Optimization of Bow-Tie Air Holes Parameters  
The bow-tie air holes were introduced between the multi-ring elliptical core and the bow-tie stress-applying areas. We optimized the parameters of the bow-tie air holes of the DBT-MREC-MMF to increase min \(\Delta n_{\text{eff}}\) and reduce modal coupling. Figure 10 shows the colormaps of min \(\Delta n_{\text{eff}}\) versus \(\epsilon_{0}\) and \(a_{5}\), with \(\theta_{0}\) taking 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\), respectively. The influence of structure parameters \(\epsilon_{0}\), \(a_{5}\), and \(\theta_{0}\) on bending loss along the direction of the \(x\)-axis and \(y\)-axis at bending radius \(R\) = 30 mm is shown in Figure 11. It can be seen from Figure 11 that \(B_{\text{m}}\) increases with the decrease of \(\epsilon_{0}\) and the increase of \(a_{5}\) and \(\theta_{0}\). This is because decreasing the ellipticity \(\epsilon_{0}\) and increasing \(a_{5}\) and \(\theta_{0}\) implies an increase in the geometric asymmetry and area of the bow-tie air hole. That leads to an increase in the contribution of geometric birefringence to overall birefringence. Considering larger min \(\Delta n_{\text{eff}}\), appropriate manufacturing tolerances, and lower bending loss, \(\epsilon_{0}\) = 1, \(a_{5}\) = 18.5 \(\upmu\)m, and \(\theta_{0}\) = 45\({}^{\circ}\) were selected as the parameters of the bow-tie air holes, as shown in the red dot in Figure 10b. At this point, the min \(\Delta n_{\text{eff}}\) value increases from 3.289 \(\times\) 10\({}^{-4}\) to 3.515 \(\times\) 10\({}^{-4}\), and the modal birefringence value increase from 3.26 \(\times\) 10\({}^{-4}\) N/m\({}^{2}\) to 3.627 \(\times\) 10\({}^{-4}\) N/m\({}^{2}\). The results show that the introduction of bow-tie air holes effectively increases the min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes.  
In summary, we optimized the parameters of the fiber structure to support 22 eigenmodes. The results indicate that the DBT-MREC-MMF exhibits a large min \(\Delta n_{\text{eff}}\) between adjacent eigenmodes. We selected the optimal parameters to achieve a larger min \(\Delta n_{\text{eff}}\) and better-designed tolerances.  
Figure 9: Variations of \(B_{\text{m}}\) (**left side**) and bending loss (**right side**) along the \(x\)-axis or \(y\)-axis direction at bending radius \(R\) = 30 mm as a function of (**a**) \(h\), (**b**) \(r_{1}\), and (**c**) \(\theta\).
###### Abstract  
This research investigates the deployment and effectiveness of the novel Pre-Signature scheme, developed to allow for up-to-date reputation being available in Vehicle-to-Vehicle (V2V) communications in rural landscapes, where the communications infrastructure is limited. We discuss how existing standards and specifications can be adjusted to incorporate the Pre-Signature scheme to disseminate reputation. Addressing the unique challenges posed by sparse or irregular Roadside Units (RSUs) coverage in these areas, the study investigates the implications of such environmental factors on the integrity and reliability of V2V communication networks. Using the widely used SUMO traffic simulation tool, we create and simulate real-world rural scenarios. We have conducted an in-depth performance evaluation of the Pre-Signature scheme under the typical infrastructural limitations encountered in rural scenarios. Our findings demonstrate the scheme's usefulness in scenarios with variable or constrained RSUs access. Furthermore, the relationships between the three variables, communication range, amount of RSUs, and degree of home-to-vehicle connectivity overnight, are studied, offering an exhaustive analysis of the determinants influencing V2V communication efficiency in rural contexts. The important findings are (1) that access to accurate Reputation Values increases with all three variables and (2) the necessity of Pre-Signatures decreases if the amount and range of RSUs increase to high numbers. Together, these findings imply that areas with a low degree of adoption of RSUs (typically rural areas) benefit the most from our approach.  
V2V; SCMS; RSUs; reputation; trust; cryptographic signatures; certificates; vehicular communication; SUMO; disconnected areas +
Footnote †: journal: Future Internet
# Quantifying Auditory Presence Using Electroencephalography
## 4 Discussion
### Correlation between Questionnaires and EEG Indices  
In Section 3, the McNemar test was used to evaluate the correlation between presence score and EEG indices, and revealed no significant difference between these two metrics in distinguishing which program stimulated a higher presence level. Another statistic metric, Cohen's kappa could also be utilized. It is commonly used to measure the agreement between different observers on the same judgement. Kappa is standardized to a \(-1\) to \(1\) scale. A kappa of \(1\) indicates perfect agreement; a kappa of \(0\) indicates agreement equivalent of chance; negative values indicate agreement less than chance [53]. We could take kappa as a tool to evaluate the agreement between questionnaires and EEG measurements.  
Kappa values between the presence score and each EEG index (all indices in Experiment 1 and T/B in Experiment 2) were calculated as shown in Table 6. It can be seen that only the kappa values between presence score and T/B were significantly different from \(0\), proving the consistency between them to be significant. Kappa was \(0.636\) in Experiment 1 and \(0.56\) in Experiment 2, indicating substantial agreement between presence score and T/B. In addition, kappa in Experiment 2 was a little lower than that in Experiment 1. This is consistent with the fact that the difference between Programs A and C is smaller than that between Programs A and B.
## 2 Materials and Methods
### Testing Procedures  
For data collection, each participant was simultaneously equipped with the previously described marker set and the IMU. According to the manufacturer's instructions, the IMU was placed approximately at the L5/S1 vertebrae level using a customized elastic belt.  
Starting from a neutral standing position, each participant was then required to perform the following tasks (Figure 2):  
* Trunk flexion (Figure 2a): participants bent forward at the waist level while maintaining a neutral spine, aiming to reach toward the floor without knee flexion;
* Trunk extension (Figure 2b): participants extended the trunk backward while ensuring hip stability;
* Lateral bending toward right/left (Figure 2c): participants performed a lateral bending movement at the waist, lowering one arm toward the corresponding leg while maintaining pelvic stability;
* Trunk rotation toward right/left (Figure 2d): participants rotated the upper body to one side while keeping the hips oriented forward.  
Each task was performed in two sets of six repetitions (per side for bilateral movements). Participants were instructed to perform the movements at their natural, self-selected pace.  
Figure 2: Visual representation of the performed trunk motion tasks. Flexion (**a**); extension (**b**); lateral bending (**c**); and rotation (**d**).