esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Detecting Chalking Position by Photographing and Analyzing Wall Surfaces
### Chalking-Position Detection Model
#### 4.2.3 Novelty and Effectiveness of Method  
In this study, a nondestructive investigation system using a CCD line camera was constructed, along with a model for detecting chalked areas surrounding deteriorated areas. As mentioned previously, the survey system employs two corrections to identify the shooting position of the camera: correction using acceleration data and soft correction using the tunnels' joints, which allows continuous image capture with little distortion, camera shake, or overlap. This is a unique feature of the system. Furthermore, the model is novel in that it not only employs a high-sensitivity CCD camera with low minimum object illuminance, similar to previous studies [22], but also employs an illuminance meter to automatically adjust the camera's aperture according to the measured illuminance. The chalked-area detection model analyzes all the captured images of the entire tunnel; thus, the number of images and the data size are large. For this reason, YOLOv7, which has high detection accuracy and high detection speed, is used. There are no examples of object detection of chalked areas shown in images of tunnel walls, and the detection of chalked areas is novel because the characteristics of the objects differ from those of cracks.  
Figure 5: Losses for the training and validation of the chalking detection model.  
The use of the proposed system in headrace tunnels can reduce costs by approximately 13.74 million per year and reduce the amount of time spent by approximately one-fifth compared with visual inspection and investigation. It is necessary to cut off the water supply during the inspection and investigation of headrace tunnels. Therefore, the introduction of the system will lead to a significant cost reduction by shortening the survey time. As infrastructure facilities are aging, this system is effective for reducing maintenance and management costs.  
Figure 6: Example of detection results. In the lower part of the image, the chalking positions are separated from each other; thus, many positions were detected with high confidence. Meanwhile, in the upper center part of the image, there is a large amount of chalking overlap, and although some of the positions had low confidence, most of them were detected.
## 2 Materials and Methods
### Flow Equation  
This study assumes that the virtual fracture includes two parallel plates that enable the fluid to pass through the aperture of the plates at relatively high velocity. The virtual space between the two parallel plates consists of the void space. Therefore, the virtual fracture can be treated as a two-dimensional (2D) porous medium [21; 22; 30; 31; 32; 33; 34]. If the groundwater is assumed as an incompressible fluid, [PERSON]'s law can be applied to formulate the steady-state flow equation:  
\[\mathrm{u}+\mathrm{K}\
abla\mathrm{h}=0,\text{ in }\Lambda \tag{1}\]  
\[\
abla\cdot\mathrm{u}=\mathrm{q},\text{ in }\Lambda \tag{2}\]  
where \(\mathrm{u}\) is the Darcy velocity (m/s), \(\mathrm{K}\) is the hydraulic conductivity (m/s), \(\mathrm{h}\) is the hydraulic head (m), \(\mathrm{q}\) is the source/sink term (1/s), and notation \(\Lambda\) is the equidimensional model domain (m\({}^{3}\)). The boundary conditions on the boundaries \(\partial\Lambda\) have the following types:  
\[\mathrm{h}|_{\partial\Lambda_{h}}=\overline{\mathrm{h}},\text{ on }\partial \Lambda_{h} \tag{3}\]  
\[\mathrm{u}\cdot\mathrm{n}|_{\partial\Lambda_{u}}=\overline{\mathrm{u}},\text{ on } \partial\Lambda_{u} \tag{4}\]  
where \(\overline{\mathrm{h}}\) is the hydraulic head on the boundary \(\partial\Lambda_{h}\) (m) and \(\overline{\mathrm{u}}\) is the Darcy velocity perpendicular to the boundary surface \(\partial\Lambda_{u}\) (m/s) concerning the outer unit normal vector n (-).  
This study assumes that a virtual fracture has two main characteristics, including (1) The width of the fracture is uniform in an element and can be represented as fracture aperture, which is extremely smaller than the fracture size, and (2) The hydraulic conductivity of a virtual fracture is relatively higher than that of the rock matrices. Therefore, the fracture can be composed of 2D triangular meshes, whereas the host rock is composed of 3D tetrahedron meshes. The 2D triangular meshes share the nodes and faces of 3D tetrahedron meshes. Such approximation can significantly reduce the required small meshes near the interfaces between fractures and the rock matrix. A mass balance equation based on [PERSON]'s law for calculating the flow parallel to the fracture plate could be written as follows [21; 22]:  
\[\frac{1}{\varepsilon_{2}}\mathrm{u}_{2}+\mathrm{K}_{2}^{\mathrm{eq}}\
abla_{ 2}\mathrm{h}_{2}=0,\text{ in }\Omega_{2} \tag{5}\]\[\
abla_{2}\cdot\mathrm{u}_{2}-\sum\left(\mathrm{u}_{3}\cdot\mathrm{n}\right|_{ \Gamma_{2}}\right)=\mathrm{q}_{2},\ \mathrm{in}\ \Omega_{2} \tag{6}\]  
where \(\mathrm{u}_{2}\) is the Darcy velocity (\(\mathrm{m}/\mathrm{s}\)), \(\mathrm{K}_{2}^{\mathrm{eq}}\) is the effective hydraulic conductivity perpendicular to the fracture plate (\(\mathrm{m}/\mathrm{s}\)), \(\mathrm{h}_{2}\) is the pressure head (\(\mathrm{m}\)). \(\
abla_{2}\) is the gradient in the tangent direction (-), and notation \(\mathrm{q}_{2}\) is the source/sink term (\(1/\mathrm{s}\)).
## 3 Proposal
### ARGoS Simulations and Scenario Modelling  
The formation scenarios were modelled in ARGoS [6], a robot simulator capable of efficiently simulating large-scale swarms of robots of any kind. The selected robot model was the Spiri UAV [23] (a \(47\times 47\times 9\)-centimetre quadrotor), while the communications were implemented using the ARGoS' Range and Bearing model (Figure 3). Each UAV only has access to the relative distances and angles to the other swarm members and to the rogue drone, calculated from the received beacon signals. The UAVs start at different initial positions and move towards the rogue drone, avoiding collisions and arranging in a stable formation. During their journey, they are subject to many iterations which make the final positions hard to calculate without using a simulator.  
Figure 2: Block diagram of the distributed formation algorithm3 (DFA3).  
The DFA3 is executed onboard each UAV and was parameterised using the aforementioned formation parameters, i.e., \(D_{THRESHOLD}\), \(D_{MIN}\), \(F_{CENTRE}\), and \(SPEED\). Obtaining a stable formation depends on the values of these parameters, requiring an optimisation process which takes into account the distance to the centre (\(D_{CENTRE}\)) and the number of UAVs. In this article, we propose the study of swarms of three, five, ten, fifteen, and thirty UAVs, tripling our previous studies. This can only be possible if we use surrogate models to replace the costly simulations.
## 2 Test Materials and Equipment
### Pile Material  
The indoor half model was used for this test ([PERSON] et al., 2016). The model piles were made of aluminum alloy tubes cut along the center of the longitudinal axis, with a length of 700 mm, an outer diameter (D) of 30 mm, an inner diameter of 27 mm, and a wall thickness of 1.5 mm. The piles were buried into the model foundation by 600 mm. The continuously solidified part was made of aluminum alloy with the same material as that of the model piles and also cut along the central axis of the longitudinal axis. The length of the continuously solidified part had a pile spacing of +60 mm + 40 mm, a width of 30 mm, and a height of 60 mm. At the same time, semi-circular cross-section holes of 30 mm diameter were cut in the continuously solidified part, and the base pile was tightly bonded to the continuously solidified part using structural adhesives. Strain gauges were pasted on the inside of the pile and filled with wax. To simulate the surface roughness of the pile body in the actual situation, threads with a depth of 0.5 mm and a spacing of 1 mm were machined on the outer surface of the pile body. The completed model pile is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Maximum Dry** & **Minimum Dry** & **Specific Gravity** & **Water Content** & **Angle of Internal** & **Cohesion** \\
**Density** & **Density** & **of Soil Particles** & **(\%)/\%** & **Friction** & **(\%/\({}^{c}\))** & **(\%/\({}^{c}\))** \\ (\(\rho_{\text{dmax}}\)g\(\cdot\)cm\({}^{-3}\))** & **(\(\rho_{\text{dmin}}\)/g\(\cdot\)cm\({}^{-3}\))** & **(Gs)** & **(\%)** & **(\%)** & **(\%/\({}^{c}\))** \\ \hline
1.62 & 1.31 & 2.67 & 0.042 & 38.39 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of quartz sand.  
Figure 3: Relationship between the drop distance and the relative density.
## 2 Existing ASs and the Related Market Design
### Categories of ASs
#### 2.1.2 Voltage Control  
Essential for maintaining the power system's voltage within specified operating limits, voltage control is a mandatory service. The management of voltage in the electrical PS predominantly relies on reactive power control. To facilitate the transmission of reactive power demanded by the grid over extensive distances, strategic placement of reactive power generation resources and compensatory investments at suitable locations is imperative. The comprehensive regulation of voltage across the entire electrical PS is executed through a three-level hierarchical system:  
* Primary voltage control;
* Secondary voltage control;
* Tertiary voltage control.  
Primary voltage control relies on controllable devices like Automatic Voltage Regulators (AVR) and Static VAR compensators. This aspect of control involves automatic, localized adjustments made by these devices to bring the voltage within a specific feeder to its designated value. On the secondary side, a centralized automatic control system, often employed by regulatory agencies, manages local reactive power flows. Tertiary voltage control takes a manual approach to optimize reactive power flow across the grid. In this phase, equipment capable of generating and consuming reactive power is incorporated based on the relationship between voltage and reactive power.  
The reactive power required for voltage control is locally supplied at the points of need. Reactive power from generators with dynamic sources, such as synchronous compensators (STATCOM), fulfills this requirement. Additionally, static sources like capacitor banks from static voltage controllers and Flexible Alternating Current Transmission Systems (FACTS) contribute to the provision of reactive power [18].
# Sensitivity Analysis for Microscopic Crowd Simulation
## 3 Results and Discussion
### Sensitivity Ranking  
In Figure 12, we present the normalized indices for visual comparison. According to [26], we also show the first eigenvector as a sensitivity metric additionally to the Sobol' indices and the activity scores. The parameters are ranked consistently for all three indices. In addition, Sobol' indices and activity scores assign the control parameter a ranking that is close to zero, reflecting that it has no impact on the outcome.  
The agreement between the results of both Sobol' indices and activity scores support the hypothesis that activity scores with finite difference approximation of the gradients can be applied to crowd simulation, and more specifically the Optimal Steps Model.  
All the metrics assign small indices with the control parameter. This indicates that they are able to recover that this parameter has no impact on the chosen quantity of interest. We use the non-zero sensitivity index assigned with the control parameter as indication: each parameter that is assigned an index smaller than the control parameter's index is classified as a non-influential parameter. If the control parameter is assigned a sensitivity index of zero, a different threshold must  
Figure 11: Sufficient summary plots of the active variable in the active subspace to the maximum Voronoi density (quantity of interest) using the gradient samples for 81 samples (\(\alpha=20\)).  
Figure 12: Normalized activity scores, first eigenvector entries, and Sobol’ indices for the bottleneck scenario. The parameter index is mapped as follows: 1—control parameter, 2—free-flow speed mean, 3—free-flow speed standard deviation, 4—number of agents, 5—bottleneck width, 6—personal space strength.  
be found. In our setup, the mean of the free-flow speed, as well as the number of agents are--within their respective ranges--non-influential for the maximum Voronoi density in front of the bottleneck. The most influential parameter is clearly the personal space strength. This is plausible because the mean free-flow speed is not directly related to the density, neither is the number of agents. If resources are available to determine uncertain factors (and it is possible), the personal space strength should be studied first in order to reduce the uncertainty in the prediction.
## 2 System Model  
The objective of the proposed work is to design a model for the compensation of FWM effect. The fundamental concept and network architecture of LHOCS is explained in this section. A 32 channel-based proposed framework considered in this work is shown in Figure 1. The transmitter of the proposed setup is using 32 continuous wave (CW) laser array having 12.5, 25, and 50 GHz channel spacing is shown in Figure 1. The spectral width of the optical signal at transmitter side is narrow down by employing advanced DB modulation scheme. Moreover, by implementing DB, the chromatic dispersion effect is compensated due to its three level signal (electrical form) property [37]. Modulated DB optical pulses are achieved by using a mach-zehnder modulator (MZM). The output signals of filtered pseudo random bit sequence (PRBS) generator are used to drive MZM. These filtered signals are generated by DB precoder after introducing one bit delayed line in electrical domain in order to set the levels of correlated signals for DB modulation. Moreover, MZM driver ensures the availability of \(2V_{pi}\) full swing and controls the bias voltage for DB modulation. Then, the modulated signals are fed to a WDM multiplexer, having 3 dB insertion loss, for transmitting over SMF. SMF of length 500 km is used for data transmission. Since the attenuation of SMF is 0.2 dB/km, therefore, to manage such losses erbium doped fiber amplifiers (EDFAs) are used after each 100 km fiber of length. At the receiver side, after demultiplexing, the optical to electrical conversion is performed using photo detector. Thus, DB modulated signals are processed as a simple NRZ demodulation process with only two decision levels. The received electrical signal is then passedthrough an electrical filter and RF amplifier to reduce the amplified spontaneous emission (ASE) issues. In addition, to counter the effect of FWM, IDSP is applied, which is described in Section 2.2. The list of different parameters used in the proposed model are summarized in Table 1, where most of the elements are kept constant to simulate the system presented in Figure 1. On the other, side parameters such as channel spacing, number of channels, linear and nonlinear dispersion, transmission range, nonlinear effective area, nonlinear refractive index, dispersion slope and input power are varied within a limited range for analyzing the performance against FWM. Likewise, values of the parameters used in this work are listed in Table 1.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions  
Any communication system or telecommunication system is not free of challenges. The technical aspects of any communication system face major drawbacks and challenges that need to be overcome in the process of development. WSN and IoDs are equally prone to errors--there cannot be a fully fledged solution to the communication problems. However, scientists work tirelessly to improve the infrastructures. Currently deployed 5G networks include channel characterisation and path planning for drones. Integration of drones and IoT devices is also a solution to the transmission problems in IoDs. Another challenge is to determine the number of sensor nodes, CHs, and drones for effective communication in the network. For the deployment of drones in city areas, path loss and attenuation models need to be considered. The battery power of drones present a problem in the networks. The drones must have an optimum battery time so that the stationary and mobile components of the network can all sustain corresponding data transmission and reception from the drones. Table 2 gives a summary of characteristic improvement areas that one comes across in an IoD.  
The following outlined challenges occur when designing and operating a UAV-based WSN. All the areas are given equal importance as the UAV problem is approached and outsourced.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 5 Singular Pulses
### Cosine Pulse  
A bounded cosine pulse is an example of a continuous pulse (unlike the rectangular one), where the discontinuity at the edges of the pulse occurs on the amplitude's derivative level.  
It should be stressed that in terms of the pulse's intensity, the derivative is continuous as well. Therefore, this is an important pulse because, for many practical purposes, it mimics a continuous pulse, but at the same time, it is (initially) temporally bounded.  
In this case, if the initial pulse is  
\[A(z=0,t)=\begin{cases}A\cos(\pi t/\theta)&|t|<\theta/2\\ 0&|t|\geq\theta/2\end{cases} \tag{37}\]  
which can be rewritten as  
\[A(z=0,t)=\tfrac{\delta}{2}\{[\exp(i\tfrac{\pi}{\theta}t)+\exp(-i\tfrac{\pi}{ \theta}t)]u(-t+\theta/2)-[\exp(i\tfrac{\pi}{\theta}t)+\exp(-i\tfrac{\pi}{ \theta}t)]u(-t-\theta/2)\} \tag{38}\]  
then, using Equation (24), the pulse profile at the medium's end is  
\[\begin{array}{l}A(z,t)=\tfrac{A_{0}}{4}\exp(i\tfrac{\delta z^{2}}{2}(\tfrac {\pi}{\theta}t)^{2})\biggl{[}\exp(i\pi\tfrac{t}{\tfrac{t}{T}})erfc\biggl{(} \tfrac{t-T/2+\pi\beta z/T}{\sqrt{-2i\beta z}}\biggr{)}+\exp(-i\pi\tfrac{t}{T} )erfc\biggl{(}\tfrac{t-T/2-\pi\beta z/T}{\sqrt{-2i\beta z}}\biggr{)}-\\ \exp(i\pi\tfrac{t}{T})erfc\biggl{(}\tfrac{t+T/2+\pi\beta z/T}{\sqrt{-2i\beta z ^{2}}}\biggr{)}-\exp(-i\pi\tfrac{t}{T})erfc\biggl{(}\tfrac{t+T/2-\pi\beta z /T}{\sqrt{-2i\beta z}}\biggr{)}\biggr{]}\end{array} \tag{39}\]  
Figure 12 illustrates the dynamics of the pulse presented by Equation (39).  
Figure 11: Same as Figure 2, but for the intensity (\(|A(t,z)|^{2}\)) of the pulse presented by Equation (36).  
Figure 12: Similar to Figure 10, but for the bounded cosine pulse (Equation (39)) and for the final distance of \(\zeta=\beta_{2}z/\theta^{2}=0.3\).
## 6 Discussion  
It is worth noting that this is a high-level analysis, and further in-depth analysis that could provide more specific insights into compliance with the various legislation and requirements for cybersecurity. For example, one could look at the coverage of specific requirements across different standards (ISO/IEC 27001:2013, ISO/IEC 27001:2022, the GDPR, CER, CIS, NIS 2) and compliance levels, or analyze the data at a more granular level, such as examining coverage for individual mapping codes. The authors' work that has been completed generates research questions such as:  
* How can organizations develop tailored compliance roadmaps that consider their unique needs and the identified correlations between standards?
* How can organizations streamline their compliance efforts by understanding which parts of different legislation and standards often meet requirements together?
* How can organizations overcome the challenges and costs of integrating multiple cybersecurity standards?  
The correlations found between standards might reflect broader industry or regulatory shifts. Engaging with stakeholders--such as policymakers, industry experts, and compliance officers--could elucidate the practical implications and perceptions of these correlations. Based on the findings, future endeavors could focus on developing tailored compliance roadmaps for organizations, considering their unique needs and the identified correlations between standards.  
This paper would help organizations understand how well the Security Baseline covers the requirements of the existing standards, and it would also help organizations identify any gaps in the Security Baseline. This is because the Security Baseline is based on expert judgment and uses the NREN as the main assumption. However, the Security Baseline is a valuable tool for organizations that are trying to improve their security posture.  
The research questions could be used to develop a more in-depth analysis of the issues and proposed solutions. For example, the researchers could conduct interviews with policymakers, industry experts, and compliance officers to understand the practical implications and perceptions of the correlations between standards. The researchers could also develop a case study of an organization that has successfully integrated multiple cybersecurity standards.  
Given the moderate negative correlation between NIS 2 and other standards, it would be valuable to investigate why this standard is distinct. Does NIS 2 have fundamentally different requirements, or does its application diverge in certain industry contexts? Even when the relevance of standards varies depending on the context? The organization could be segmented in the future by size, industry, or region to assess if correlations persist or if new patterns emerge.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 2 Materials and Methods
### Research Workflow  
This study adopts manual Pavement Condition Index (PCI) inspection data as the basis for verifying the consistency and accuracy of results obtained by different inspectors. A standardized inspection process was developed to ensure the reproducibility and reliability of the PCI data. The research workflow consists of the following steps:  
1. Defining the research focus and reviewing relevant background information and standards, such as ASTM D6433-23.
2. Selecting road and inspectors for evaluation: determining the roads to be inspected and assigning inspectors to carry out the manual assessments.
3. Grouping and training inspectors: dividing the inspectors into groups and conducting comparative evaluations of their performance before and after training.
4. Analyzing and comparing inspection data: evaluating the consistency (repeatability) and accuracy of the inspection results using statistical analysis.  
5. Establishing validation standards: differentiating the validation into personnel verification, equipment verification, and software verification. Recommendations for standard validation protocols were made based on the results of the data analysis.  
This study also focuses on the effect of inspector training on the performance of manual PCI evaluations. The inspectors, labeled as A, B, C, and D, were assessed both before and after the training. By comparing the data from these two stages, this study evaluated their repeatability and analyzed the accuracy of the PCI inspection process. This approach aims to provide insights into the impact of training on inspection reliability and offers a basis for developing effective validation standards.