esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Fluorescent-Material-Based Simple Method for Sunscreen Evaluation
## 4 Validation of Proposed Method  
### Validation Procedures  
While the amount of fluorescence emission is proportional to the amount of absorbed UV energy, the amount of absorbed UV energy depends on the distance or thickness that the UV radiation travels through the fluorescent layer. To include the physical distance and characteristics of the material, a dimensionless parameter called the distance index or thickness index \(\tau\) was introduced, which is the product of the extinction coefficient \(\beta\) and the physical distance of radiation traveling through the fluorescent layer \(x\), as shown in Equation (6). The units of \(\beta\) and \(x\) are mm\({}^{-1}\) and mm, respectively:  
\[\tau=\int_{0}^{x}\beta\,dx \tag{6}\]  
The distance of the UV radiation traveling through the fluorescent layer is determined by the incident angle of the UV radiation. Thus, the measured fluorescence intensity could differ depending on the incident angle of the UV radiation, even at the same incident energy. For this method to be effective, irrespective of the incident angle, when the amount of energy incident on the fluorescent layer is the same, the same fluorescence intensity must be measured.  
In practical applications, UV radiation is incident on the fluorescent layer in various directions because it is transmitted through scattering materials, such as sunscreen. Therefore, the validity of this system was evaluated by both numerical analysis and experimental measurement of the amount of fluorescence generated when the UV radiative energy \(E_{el}\) was incident on a fluorescent layer with a thickness index \(\tau_{f}\) at different incident angles.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
###### Abstract  
Most current unmanned aerial vehicles (UAVs) primarily use a global positioning system (GPS) and an inertial measurement unit (IMU) for position estimation. However, compared to birds and insects, the abilities of current UAVs to recognize the environment are not sufficient. To achieve autonomous flight of UAVs, like birds, the UAVs should be able to process and respond to information from their surrounding environment immediately. Therefore, in this paper, we propose a direct visual servoing system for UAVs, using an onboard high-speed monocular camera. There are two advantages of this system. First, the high image sampling rates help to improve the ability to recognize the environment. Second, the issue of control latency can be effectively solved because the position control signals are transmitted to the flight controller directly. In the experiment, the UAV could recognize a target at update rates of about 350 Hz, and a target tracking task was successfully realized.  
U
## 4 Discussion
The proposed method produces images that exhibit a significant improvement in image quality when compared to those processed using the conventional TV and NL-TV methods. However, there are also drawbacks associated with the method that need to be addressed. First, there is an increase in computational cost owing to the large number of operations. We found that we could obtain reasonable results in terms of resolution and noise, unlike the results from the original single search window. The proposed method is computationally intensive compared with existing methods because it performs conventional NL-TV and Gaussian filtering, followed by NL-TV filtering. For imaging devices that perform diagnostics after reconstruction, such as CT, we found that the computation of our proposed method is not a significant issue. However, in fluoroscopy, where images are checked in real time, necessitating immediate and efficient filtering, the proposed method is limited by its computation time. It is expected that this problem can be overcome through the incorporation of enhanced computational capabilities, such as parallel processing or GPU-based image processing. Additionally, real-time image processing is expected to be realized by combining the proposed method with lightweight noise-reduction algorithms [70]. Second, the appropriate number of search windows must be set. In this study, we set the search window to vary by three bins. However, this is variable, and the number of bins may affect the image quality and image processing speed. By setting many bins and performing image processing on an image slice with many smooth regions, we can achieve similar image results using more computation time than when using a single search window. In the case of clinical CT, the characteristics of the imaged object are constant; therefore, it is easier to apply the proposed method in clinical practice if the bins of the search window are set according to the body part to be radiographed and imaging conditions in advance. In addition, optimization is required for empirically set parameters (e.g., the value of beta, the Gaussian kernel size, and the value of weight factor-alpha). There are plans to perform parameter sensitivity analysis in the future, as this will affect the performance of the results. Finally, the challenge is to identify patches of similarity accurately, even at a low radiation dose. Accurate identification of similar patches is important for nonlocal based noise-reduction methods [71], as low-dose-induced noise can make it difficult to distinguish between texture and noise, which can lead to difficulties in image quality improvement. This can be overcome by exploiting the similarity of nonlocal patches [43] or leveraging structural tensors to optimize the detection of similar patches during image restoration [72]. This subject has been the focus of research efforts for a significant amount of time, and we intend to continue our endeavor to improve the image quality produced by the proposed algorithm for low-dose CT images.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 4 Conclusions  
This paper analyzes the composition of DFIG short circuit current. Moreover, using data of real fault cases, this paper illustrates the fault characteristics of DFIG-based wind farms connected to transmission lines and the corresponding applicability of the conventional distance protection based on the Fourier algorithm.  
The DFIG short circuit current consists of a decaying DC component, a decaying AC component at rotor speed frequency, and a forced AC component at synchronous frequency. Wind farm is a weak power supply system. The short circuit current of wind farm side is much smaller than that of the power grid side. Additionally, the zero-sequence current will be the main component of the wind farm side short circuit current when a ground fault occurs. The wind farm side may show current frequency deviation during disturbances. The wind farm side voltage of transmission line is nominal frequency due to the grid support, whereas its frequency may no longer be nominal due to the existence of rotor speed frequency component. The conventional ground distance protection based on the Fourier algorithm is still relatively reliable, whereas the phase-to-phase instantaneous distance protection is no longer reliable for the transmission line connected to a DFIG-based wind farm. Finally, a time-domain distance protection method based on the \(R\)-\(L\) model and least-squares algorithm is proposed to solve the problem. The time-domain method accurately and quickly identified fault distances when faults occur on the transmission line connected to a DFIG-based wind farm. The efficacy of the proposed method is validated with real fault cases and simulation. The proposed method is immune to the frequency variation. Thus, it overcomes the defect of traditional distance protection. With the help of the proposed method, Relay Protection Equipment Manufacturers can improve the equipment level of technology, and the grid companies can improve the safe operation level of the power system.  
This work was supported by the National Science Foundation for Excellent Young Scholars of China (Grant No. 51422703).  
[PERSON] conceived the study and wrote the paper; [PERSON] performed the experiments and analyzed the data and wrote the manuscript; [PERSON] analyzed the topology and fault characteristics of DFIG; [PERSON] contributed to the data of wind farms and DFIG.  
The authors declare no conflict of interest.
## 2 The Development Stages of Grain-Drying Technology
### The Current State of Research on the Control Systems of Grain Dryers in China
#### 3.2.4 Problems and Solutions  
Despite the continuous innovation of technical means, traditional grain dryer control systems still have some issues with poor control effects. In response, researchers have designed new control systems that improve control accuracy while enhancing stability and applicability.  
[PERSON] et al. [100] developed an intelligent control system for grain dryers and touch screen technology, and used fuzzy PID algorithm reasoning rules to control the moisture content of the grain after leaving the machine in the process of drying with large lag; at the same time, they elaborated on the design methods and steps of the input fuzzy process, offline fuzzy control query table input program, and PID parameter output program. System performance testing and simulation results show that this intelligent control system has good control performance and a high degree of automation, and that using fuzzy PID control method for dynamic parameters in the grain-drying process can achieve excellent control effects. [PERSON] et al. [101] designed a grain dryer control system controlled by the Mitsubishi FXSU series PLC, achieving the automatic and manual full process of grain drying, focusing on the analysis of the temperature and humidity control module and the drying module. Through simulation debugging, it is shown that the grain dryer is easy to operate, stable, and reliable, and can improve efficiency in the actual production process, with broad development prospects.  
With the development and application of these technologies, it can be seen that the control systems for grain dryers are moving towards a more intelligent and efficient direction, which is of great significance for ensuring food security and improving production efficiency.  
The Reasons for the Performance Gap Between China and Other Countries in the Field of Grain Dryer Control Systems  
In summary, although China has made significant progress and development in the field of automatic control technology for the grain-drying process in recent years, there is still a certain gap between the research and development of grain dryer control systems and the international advanced level. Foreign systems are known for their high degree of automation, advanced technology, and excellent drying efficiency and grain quality control capabilities. The reasons behind this gap can be attributed to various factors:
## 4 Attack Filtering Performance
### Effectiveness of Credit-Based Enhancement  
To check the efficacy of the credit-based enhancement, we repeat the simulation for Figure 6 with the employment of the credit-based check. Figure 9 shows the result.  
We see from the figure that the additional credit-based check completely shuts out the road-side attacker, as it fails to accumulate enough credit, just as intended by Equation (2). A crucial observation in the issue of position plausibility check is that the risk of vehicle collisions is higher between those that are in close proximity. Therefore, it is imperative that the nearby vehicle positions should be ascertained more than those of farther vehicles. It is why the SAE J2945/1 standard stipulates that only the vehicles within 100 m of the ego vehicle be tracked [24]. From this viewpoint, the credit-based double check is highly recommended due to its usefulness in checking the positions within short, safety-critical distances, \(d_{W}\) in particular.  
We can summarize the significance of the proposed scheme as follows. In connected/autonomous cars, V2X is no longer an option, but a mandatory component that allows vehicles to sense larger distances and non-line-of-sight (NLoS) situations. Without the proposed solution, the attackers can safely position themselves at a significantly larger distance from the victims (connected/autonomous cars). The hardware sensors such as cameras, radars, and lidar are all line-of-sight (LoS) sensors, and their coverage is limited. Therefore, with only the sensors, we could not cope with the attacks that use longer range and non-LoS technology that is the V2X. By using a smaller Tx power to make \(d_{W}\) narrow, we can  
Figure 8: Channel busy percentage (CBP) increase due to whispers.  
Figure 9: Attack success probability with additional credit-based check.  
create an opportunity for the cooperation between the LoS sensors and the communication for the message contents validation.
## 1 Introduction
ITBS was found to be associated with increased knee internal rotation and hip adduction in female runners [15], however, other previous studies have established that it is related to maximum knee flexion, maximum knee internal rotation velocity, maximum foot inversion, and maximum ankle extension [21; 22]. Several studies have concluded that shot runners prone to ITBS, may use abnormal segmental coordination patterns, particularly, with couplings involving thigh adduction/abduction and tibial internal/external rotation [21; 22]. The biomechanical cause of MTSS is still inconclusive. One case-control study found that individuals with a history of shin splints demonstrated a significantly greater frontal plane pelvic tilt, peak hip internal rotation, and less knee flexion compared to a matched group of runners without pain [23]. In contrast, [PERSON] et al. in their systematic review, found no correlation between biomechanical factors and shin splints in runners, and an increased hip external rotation range of motion (ROM) and navicular drop as statistically significant risk factors of MTSS [24]. According to the above findings, treatment strategies have addressed these faulty biomechanics by altering running techniques and applying functional training corrections [25; 26; 27]. [PERSON] et al. reported that lower limb running injuries have been linked to several abnormal biomechanical factors [27], however, currently, there is no consensus for a specific biomechanical deviation leading to injury. Moreover, previous studies have not found any evidence to support a correlation between abnormal biomechanics and running [15; 17]. The inconsistency regarding the relationship between deviations and injury might be due to a combination of several biomechanical deviations and kinematic variables that must be taken into consideration as a cause of injury. Three-dimensional kinematics quantify the overall deviation of an individual's gait or run from a normal gait or run. A biomechanical running analysis, used in sports as a diagnostic tool, assists in the understanding of the kinematics of a wide range of variables throughout the running cycle, as well as detecting abnormal biomechanics. Currently, as yet, no single score has been ascertained to identify running deviations. Such a score can be used for classifying running deviations with a single numeric value, based on pelvis lower limb kinematics, as well as identifying movement patterns which are risk factors for running injuries. We hypothesized that injured long distance runners might present with kinematic differences during running that would most likely be reflected by differences in GPS and GDI, subsequently identifying runners who are more prone to developing sport-related injuries.
## 3 Helix Polygon
### Construction of Helix  
An \(n\)-gon Helix polygon (i.e., \(H_{n}\)) is constructed by an incremental method using an \(n-1\)-gon Helix, \(H_{n-1}\). A Helix with three vertices is a triangle. By adding two new edges to \(H_{n-1}\) and also removing an edge of \(H_{n-1}\), \(H_{n}\) is constructed on the basis of \(H_{n-1}\), where \(n\geq 4\). The details are presented in Algorithm 1 and are illustrated in Figure 3.  
```
Input: Integer number \(n\geq 3\) as the number of vertices. Output: The Helix polygon \(H_{n}\).
1: Construct \(H_{3}=\triangle v_{1}v_{2}v_{3}\), which is an equilateral triangle, where \(v_{2}v_{3}\) is horizontal and the vertices are in counterclockwise order.
2: Choose a positive real number \(\delta\) so \(0<\delta<\frac{|v_{2}v_{3}|}{2\lfloor\frac{n-1}{3}\rfloor}\)
3:\(p_{1}=v_{1}\); \(p_{2}=v_{2}\); \(p_{3}=v_{3}\).
4:for\(i=4\); \(i\leq n\); \(i++\)do
5:\(q_{1}=p_{1}\); \(p_{1}=\) a point on \(v_{1}v_{2}\) such that \(|p_{1}q_{1}|=\delta\), \(a=l_{13}(p_{1})\);
6:\(q_{2}=p_{2}\); \(p_{2}=\) a point on \(v_{2}v_{3}\) such that \(|p_{2}q_{2}|=\delta\), \(b=l_{12}(p_{2})\);
7:\(q_{3}=p_{3}\); \(p_{3}=\) a point on \(v_{1}v_{3}\) such that \(|p_{3}q_{3}|=\delta\), \(c=l_{23}(p_{3})\);
8:if\(i==4\)then
9:\(l=b\);
10:else
11:\(l=l_{24}(v_{i-2})\);
12:endif
13:if\(i==5\)then
14:\(l^{\prime}=c\);
15:else
16:\(l=l_{35}(v_{i-2})\);
17:endif
18:if\(i==3k\)then
19:\(v_{i}=a\cap c\);
20:endif
21:if\(i==3k+1\)then
22:\(v_{i}=c\cap l\);
23:endif
24:if\(i==3k+2\)then
25:\(v_{i}=b\cap l^{\prime}\);
26:endif
27:Add edges \(v_{i}v_{i-1}\) and \(v_{i}v_{i-2}\).
28: Remove \(v_{i-1}v_{i-2}\) to obtain \(H_{i}\)
29:endfor
30:Return \(H_{n}\).
```  
**Algorithm 1** Constructing Helix Polygon  
It is worth noting that the length of a line segment \(pq\) is denoted by \(|pq|\), and for an arbitrary point \(p\), \(l_{ij}(p)\) denotes a line parallel to \(v_{i}v_{j}\) which passes through \(p\).  
Figure 2: (**a**) A Helix with \(11\) vertices. (**b**) A Helix with \(12\) vertices.
## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.3 Features Captured from Explicit Signals  
Feature engineering is used to create additional insights to find the learner's score improvement by using Equation (2), which uses the difference between post-test and pre-test. This identifies a learner's progress in the specific area of the domain. After preprocessing the explicit data, the cleaned, transformed, and featured data will be updated as presented in Table 9.  
Score improvement:  
Score Improvement \[=\text{Post-test}-\text{Pre-test}\] (2)  
Insights from the dataset:  
Learners 1, 7, 9, and 10 showed consistent or exceptional improvement with high satisfaction ratings, benefiting from tailored content and valid predictions.  
Learners 2, 5, and 8 demonstrated steady improvement, though they could benefit from advanced challenges or personalized support.  
Learners 3, 4, and 6 had lower improvements or incomplete modules. These learners require additional support through foundational reinforcements, intermediate modules, or engaging content.
# Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study
## 2 Material and Methods
### Mathematical Model and Numerical Mode
#### 2.4.3 Numerical Model and Verification
The overbreak of the tunnel was obvious, and the amount of underbreak was very small. Figure 10: Comparison of section contour between numerical simulation and field monitoring: (**a**) massive integrity structure; (**b**) massive structure; (**c**) layered structure; and (**d**) cataclastic structure. Figure 9: Tunnel contours with different rock mass structures. Table 3 shows the tunnel smooth blasting section area of numerical simulation and field monitoring. The designed tunnel section area was 63.48 m\({}^{2}\). For the massive integrity structure rock mass, the tunnel section area of the field test was 65.81 m\({}^{2}\), and the section overbreak rate was 3.67%. The tunnel section area of the numerical simulation was 65.94 m\({}^{2}\) and the overbreak rate was 3.88%. For the massive structure rock mass, the tunnel section contour area of field test was 66.45 m\({}^{2}\), and the section overbreak rate was 4.68%. The numerical simulation tunnel section contour area was 66.59 m\({}^{2}\), and the section overbreak rate was 4.88%. For the layered structure rock mass, the tunnel section contour area of field test was 66.95 m\({}^{2}\), and the section overbreak rate was 5.47%. The numerical simulation tunnel section contour area was 66.85 m\({}^{2}\), and the section overbreak rate was 5.32%. For the cataclastic structure rock mass, the tunnel section contour area of field test was 71.51 m\({}^{2}\), and the section overbreak rate was 12.65%. The numerical simulation tunnel section contour area was 71.63 m\({}^{2}\), and the section overbreak rate was 12.84%. Comparing the overbreak rate of different rock mass structure, the overbreak rate increased with the decrease in the integrity of rock mass structure. It also can be seen that the difference between the tunnel smooth blasting section area of numerical simulation and field monitoring was very small, indicating that the numerical model adopted in this paper had high reliability.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
###### Abstract  
Network Functions Virtualization (NFV) is a key technology for network automation and has been instrumental to materialize the disruptive view of 5G and beyond mobile networks. In particular, 5G embraces NFV to support the automated and agile provision of telecommunication and vertical services as a composition of versatile virtualized components, referred to as Virtual Network Functions (VNFs). It provides a high degree of flexibility in placing these components on distributed NFV infrastructures (e.g., at the network edge, close to end users). Still, this flexibility creates new challenges in terms of VNF connectivity. To address these challenges, we introduce a novel secure link-layer connectivity platform, L2S. Our solution can automatically be deployed and configured as a regular multi-site NFV service, providing the abstraction of a layer-2 switch that offers link-layer connectivity to VNFs deployed on remote NFV sites. Inter-site communications are effectively protected using existing security solutions and protocols, such as IP security (IPsec). We have developed a functional prototype of L2S using open-source software technologies. Our evaluation results indicate that this prototype can perform IP tunneling and cryptographic operations at Gb/s data rates. Finally, we have validated L2S using a multi-site NFV ecosystem at the Telefonica Open Network Innovation Centre (5 TONIC), using our solution to support a multicast-based IP television service.  
ETSI NFV; Management and Orchestration (MANO); multi-site NFV services; NFV experimental infrastructure; secure link-layer connectivity Article  
A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 3 Results and Discussion
### Editing the YOLOv2 Output Data into a Convenient Form  
The output data, especially the pixel coordinates of the 224 \(\times\) 224 image block in the square grid and bounding box within a 224 \(\times\) 224 image block provided by the YOLOv2 detector, only referred to a local position, but did not reflect a location inside the whole orthomosaic nor a particular concrete slab. To determine the coordinates of each slab within the orthomosaic, it was necessary to detect its edges. The asphalt grout was used to seal joints between the slabs at Bechyne airport. The grout is characterized by a black color, which is clearly visible in the orthomosaic. This color feature was applied during the image segmentation, followed by morphological operations to detect the edges and compute slab coordinates within the orthomosaic. Image segmentation converts the true-color RGB image to a grayscale image, and thresholding makes the image easier to analyze, whereas morphological image processing removes imperfections introduced during segmentation. The process included the following steps:  
1. Conversion of the true-color RGB image to a grayscale image;
2. Thresholding by logical indexing;
3. Morphological image processing.