esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design of Experiment (DOE)
### Graphical User Interface (GUI)  
To control the deformation of the morphing wing, the LABVIEW 2021 software was used that allowed for the design of a graphical user interface (GUI) and communication with external devices via USB or serial ports. LABVIEW is typically used with data acquisition cards provided by National Instruments. However, in this study, an Arduino Uno was utilized, which had a resolution of 10 bits, suitable for this case, and which can handle PWM signals on certain GPIO (General Purpose Input Output) pins. The LINX library was utilized to control the Arduino with the LabVIEW interface.  
Since each elephant trunk mechanism is independent and therefore has its own servo motor, the tension between the cables cannot be the same without calibration. The program includes an adjustment section with which each motor can be controlled separately, while the flap is managed via the Global Angle Control section. The serial port setting allows the selection of the communication port for the Arduino board. Finally, the servo pin number option allows the selection of the PWM GPIO terminals where each motor is connected. Figure 14 illustrates this GUI.  
A precision dial indicator was used to calibrate the ETMs. The calibration process was the following:  
* The wing was placed flat on the lower skin on a flat surface (measuring marble), with the flap section extended on a cantilever.
* It was then clamped to the marble, and the precision dial was positioned vertically on the trailing edge.
* The wing was then connected to the Arduino board and the power supply, and the Morphing Wing Controller program was initiated.
* The value of the slider corresponding to the ETM being measured with the precision dial was adjusted.
* This value was incremented by the minimum amount until the needle on the dial moved.
* The value was then incremented backwards, and the reading was recorded.  
The final value was the calibration value used to zero the deflections of their corresponding ETM. Each ETM must have its own calibration value. To calibrate each ETM, all the other ETM adjustment sliders should be set to zero, which ensures that only the tension of the measured ETM is considered when reading the dial. There should be four calibration values for the wing; however, disassembling the servo motors cancels the calibration and, for this reason, the calibration process must be repeated.  
Figure 13: Attachment of the flexible skin on the lower surface of the SMTE flap using the pretension tool.  
To test the Morphing Wing Controller program and make its adjustments, the wiring harness is connected to the Arduino board as shown in Figure 15.
## 4 Results
### Aeroacoustics Measurements and Predictions
#### 4.2.2 Choice of Integration Surface  
A permeable surface calculation was conducted to quantify the influence of quadrupole sources. [PERSON] et al. posited that this source was significant for the broadband noise component, due to wake interactions that would not be captured with a surface integration [7]. It is also hypothesized here that the permeable surface should capture the pressure scattering predicted by the trailing edge model in UCD-Quietfly. Therefore, the Helios/PSU-WOPWOP + UCD-Quietfly results for the impermeable surface are compared with the standalone Helios/PSU-WOPWOP permeable surface calculations. The results for an in-plane and above-plane observer listening to the isolated rotor in hover at 6000 RPM are plotted in Figure 16. The experimental measurements are included for reference.  
Focusing first on the in-plane results, there is a noticeable overall increase in noise at the first BPF. However, the broadband shape and amplitude are predicted with reasonable agreement. The permeable surface prediction reflects the relative flat broadband of the experiment, compared to the UCD-Quietfly predictions. The permeable surface also demonstrates excellent agreement for the above-plane observer. Previously, [PERSON] et al. showed much better agreement with the predicted BPF, using the two methods [7]. The large difference at the BPF shown here suggests the influence of hydrodynamic waves, which may be caused when the permeable surface is too close to the body. In addition, the comparison with experiments makes it even more difficult to validate either method because the impermeable integration matches in-plane but the permeable integration matches above-plane. It is unlikely that the computed noise at the BPF could be greater than that of the experiments because of the lack of recirculation in the simulations. Therefore, the current study will continue using the impermeable surface integration method for predictions. Future work will focus on identifying the effect of placement and topology of the permeable surface.  
Figure 16: Impermeable and permeable surface calculations of the single rotor in hover at 6000 RPM using Helios/PSU-WOPWOP, compared to experimental data.
###### Abstract
The melting curve of chromium was measured experimemtally in [6], along with that of vanadium and other transition metals. It appeared to be the so-called \"low\" melting curve which virtually flattens out at P\(\sim\)1 Mbar and for which the value of \(T\) at the melting point (\(T_{m}\)) on the shock Hugoniot is much lower than that extracted from the shock compression data. Moreover, it appeared to virtually coincide with the melting curve of V. The latter was very thoroughly re-measured recently [7] and shown to be actually the so-called \"high\" melting curve for which the value of the shock \(T_{m}\) is in agreement with the shock data. Thus, the melting curve of Cr of Ref. [6] is most likely erroneous, just like that of V, and therefore needs to be corrected via being carefully re-measured experimentally and/or calculated theoretically. Even the value of the ambient \(T_{m}\) of Cr is ambiguous. \(T_{m}=2136\) K can be found in [PERSON]'s book [8]. Very accurate experimental measurements of Refs. [9; 10] give, respectively, \(T_{m}=2136\) and \(2131\pm 5\) K. According to [11], the experimental values of the ambient \(T_{m}\) of Cr that can be found in the literature span an interval of 2085(10)- 2180(20), i.e., \(\sim\)2130 \(\pm\) 50. The value \(T_{m}=2180\) K which is offered by several public web resources (\"Wikipedia,\" \"WebElements,\" \"It's Elemental,\" etc.) comes from Ref. [12] and is most likely incorrect. Specifically, in [12] this value was assessed, along with that of the melting enthalpy \(\Delta H_{m}\) (latent heat of melting), based on some literature data. The latter turns out to be \(\Delta H_{m}=21\) kJ/mol [12]. The value of \(\Delta H_{m}\) from the experiment of Ref. [9] is 29,674(574) J/mol, and that calculated in [13] based on the accurate literature data is \(\approx\)28,000 J/mol. Thus, \(\Delta H_{m}\)\(\sim\)(29 \(\pm\) 1) kJ/mol, so that the assessment of Ref. [12] leading to 21 kJ/mol is very likely incorrect, and so is their value of \(T_{m}\) of 2180 K. In the experiment of Ref. [11], Cr melts at \(T\) between 2120 and 2150 K, i.e., at 2135 \(\pm\) 15 K. Our own theoretical results on the melting of Cr discussed below lead to \(T_{m}=2135\) K.  
To address all of the above points of concern related to the phase diagram of chromium, in this work we carry out its extensive theoretical study to a pressure of 2.5 TPa (25 Mbar). This work continues a previous combined experimental and theoretical study of Cr in which its equation of state and melting curve were determined to a pressure of 150 GPa [14]. It is necessitated by the importance of the knowledge of the physical properties of Cr, specifically its phase diagram, to higher \(P\).
# Model for Quantifying the Quality of Secure Service
## 2 Goals and Approach  
The current literature suggests three primary characteristics that define the security of traditional Information Technology (IT) systems. These are confidentiality, integrity, and availability, i.e., the CIA triad [2, 3]. To quantify an overall level of security, we must have objective metrics to represent each of these individual characteristics. While objective metrics for availability are well established as QoS metrics, confidentiality and integrity [4] remain rather subjective and without commonly accepted quantifiable definitions. In addition, the user requirements for security may change based on changing operational conditions. Then, what are the appropriate measures for communication security?  
To address the need, we propose a model to quantify the security characteristics of point-to-point communication between two devices2. The model is patterned after existing quantification models [5, 6], and helps to define security requirements that, in the presence of adversarial actions, would enable communications to be successful. By comparison, this model does not rely on the application of security controls [7], but rather the analysis of the system architecture and probabilistic aspects of the network.
## 3 Proposed MDM-MIMO-IIR Photonic Radar Modeling
By performing this multiplication process, the optical coupler facilitates the mixing of the signal and reference waveforms, leading to the generation of intermediate frequencies. These intermediate frequencies carry valuable information about the detected targets, enabling accurate target analysis and tracking. The reflected signal is detected by means of a photodiode that has a responsivity, represented by the symbol \(\Re\). In the homodyne configuration, the signal-to-noise ratio is presented as Equation (5) [22]:  
\[SNR_{\rm homo}=\frac{\beta^{2}\Re^{2}P_{lo}P_{r}/2}{q\Re\beta^{2}P_{lo}B_{rx}+4k_ {b}T_{r}\frac{B_{rx}}{R_{L}}}\ (\rm{in\ short\ noise\ limited\ case})\ \approx\ \frac{\Re P_{r}}{2 qB_{rx}} \tag{5}\]  
where the receiver bandwidth is \(B_{rx}\), the electrical charge is \(q\) (approximately equal to \(1.6\times 10^{-19}\ c\)), the Boltzmann constant is \(k_{b}\) (approximately equal to \(1.38\times 10^{-23}\) J/K), the receiver noise temperature is \(T_{r}\), and the load resistance is \(R_{L}\). As previously discussed, it is common practice to use an IIR filter after the photodiode in a photonic radar system to compensate for any potential atmospheric uncertainties that may affect the received signal. The IIR filter is a type of digital filter that involves recursion. In the z domain, the transfer function can be represented as Equation (6) [25]:  
\[H(z)=\alpha\frac{\sum_{n=0}^{N}a_{n}z^{-n}}{\sum_{m=0}^{M}b_{m}z^{-m}} \tag{6}\]
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
[22] proposed the support vector machine residual kriging method (SVMR Kriging), which first fits the magnetic trend changes using the support vector machine then interpolates the residual component via the ordinary Kriging and finally adds the two parts together to construct a regional magnetic map to improve the regional accuracy of magnetic trend changes. [PERSON] et al. [23] proposed a magnetic reference map construction method based on the combination of the Kriging method and the back propagation neural network (BP neural network). In the case that the interpolation error, Kriging in the region with a large gradient is greater than that of the BP neural network, and the interpolation error in the region with a small gradient is smaller than that of the BP neural network. The predicted value of the BP neural network is used to replace the interpolation result of the Kriging method in the large gradient region to improve the interpolation accuracy. (3) Interpolation processing based on different variograms. Variogram is the core of Kriging interpolation algorithm, which can describe not only the spatial structural changing of the regionalized variable but also its randomness changing. Commonly used variograms include the spherical variogram, exponential variogram, Gaussian variogram, and so on. Most of these papers are in the field of mathematics and geostatistics, and few are in the field of magnetic map construction. The available literature uses relevant algorithms to optimize the parameters of the variogram. For example, [PERSON] et al. [24] used the vertical integration method with the particle swarm optimization algorithm (PSO) and genetic algorithm (GA) to optimize the parameters of the variogram until the specified accuracy was reached or the maximum number of iterations was reached, thus obtaining better optimization performance and globality. Although the interpolation algorithms based on different variograms have a certain universality, the selection or construction of what kind of variogram is more suitable for the construction of sparse magnetic maps has not been involved in relevant literatures. Therefore, based on previous studies, this paper proposes an interpolation processing method for sparse data with an improved variogram, and it uses the variogram to establish an optimal adaptive parametric interpolation method for different features of sparse data so as to improve the interpolation accuracy of sparse data and the spatial resolution of network data. At the same time, the algorithm is used to construct high-precision magnetic maps of simulation data and measured data, respectively. The results show that the interpolation accuracy of this method is better than that of Gaussian, spherical, and exponential variogram models, and the maximum absolute error, mean absolute error, and root-mean-square error are greatly reduced. It can be used to construct the local magnetic reference map with high precision and high resolution.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 5 Experiment and Result Analysis
### SDADM Effectiveness Experiment
#### 5.1.2 Effectiveness of Resisting Eavesdropping Attacks
As can be seen from the figure, in the network, except for node 10, the amount of data detected by all other nodes will not exceed 40%, and the effect of resisting attacks is significantly better than DHC and DPW and is more secure.
## 2 Rock Blasting Using Long and Large-Diameter Uncharged Holes
### Comparison of Traditional Cut Methods and the LLB Method  
Figure 4 shows a schematic of the three types of cut methods. The traditional V-cut method shown in Figure 4a is popularly applied to reduce blast-induced vibration. Additionally, it is suitable for short hole blasting. In contrast, the traditional cylinder-cut method shown in Figure 4b is commonly used for relatively long hole blasting. In this method, uncharged holes with a diameter of 102 mm are utilized to reduce blasting vibration. Traditional cut methods generally concentrate explosives at the end of the blast holes in the cut area to completely break rocks; this results in high blasting vibration.  
Figure 3: Effect of free surface in the rock blasting process.  
Figure 2: Photograph of field application of the LLB method at a tunnel construction site located in South Korea.  
The LLB method, which utilizes an LLB hole to maximize the vibration reduction efficiency, is illustrated in Figure 4c. The amount of explosives can be reduced by approximately 20% compared to the traditional cut method in the cut area because of a 382 mm diameter LLB hole; thus, blasting vibration can be decreased. Therefore, one of the advantageous features of this method is the increase in the advance per round due to tensile failure being induced in front of the explosives while minimizing the blasting vibration.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 3 Possible Solutions to Reduce the Electric Field
### New Expert System Based on Dynamic Line Rating (DLR)
If the temperatures are equal, there is no possibility to increase the transfer capacity without breaching the safety and security requirements. If the temperature is not close to the upper limit, the ampacity should be set as high as the thermal limit of the conductor. Figure 5: Process drawing of the first step of the new dynamic line rating (DLR)-based expert system. This can be achieved by applying Equation (6) with all the necessary data measured from the weather stations [6]. \[P_{I}\ +\ P_{S}\ +\ P_{M}\ +\ P_{I}\ =\ P_{C}\ +\ P_{R}\ +\ P_{W}\ +\ P_{P} \tag{6}\]  
where \(P_{I}\) is the Joule heating, \(P_{S}\) the solar heating, \(P_{M}\) the magnetic heating, \(P_{I}\) the corona heating, \(P_{C}\) the convective cooling, \(P_{R}\) the radiative cooling, \(P_{W}\) the evaporative cooling and \(P_{P}\) the precipitation cooling. Transforming Equation (6) to Equation (7), the new current limit until the next sensor measurement is available [6]. \[I\ =\ \sqrt{\frac{P_{C}\ +P_{R}+\ P_{W}+P_{P}-\ P_{S}-\ P_{M}}{R_{AC}(T)}} \tag{7}\]  
where (\(I\)) means the ampacity limit, and the \(R_{AC}\) is the temperature-dependent alternating current (\(AC\)) resistance of the conductor. Applying DLR can results in 20-30% surplus capacity contrarily to the SLR approach. In favorable cases, DLR's surplus current can substitute for the ampacity loss from the reduced temperature. This way, it is possible to  
Figure 6: Process drawing of the second step of the new DLR-based expert system. increase the line's safety and security, meet the electric field legal requirements, and not cause economic loss. This algorithm is repeated continuously to ensure that the transfer capacity limit is always the theoretical maximum adjusted to whatever weather conditions allow. In summary, the significant benefits and strength of the new expert system are the following;  
* the electric field's peak is within the boundaries of the legal limits all the time;
* with DLR the full utilization of the line is ensured all the time;
* this method does not require high upgrade cost or other significant investment. It is important to note that this expert system can be combined with other possible solutions mentioned before. Reconductoring the power line with HTLS conductors and applying the expert system on the line can result in higher ampacity limits, which can be vital from the transmission system operator point of view.
## 3 Numerical Simulations
### Path Planning with Ocean Currents  
When ocean currents are considered, the actual velocity of the AUV is a vector sum of the AUV's velocity \(\mathbf{v_{1}}\) in stationary water and the ocean current \(\mathbf{v_{2}}\). The total travel time is the sum of the cruising times that the AUV completes each action. If \((\mathbf{v_{1}}+\mathbf{v_{2}})\)-\(\mathbf{n^{a}}\) is negative, the AUV cannot fulfill the current action in its present grid.
## 3 Data and Methodology
### Importance Analysis of Landslide Conditioning Factors  
Many studies have suggested that the importance of landslide conditioning factors should be evaluated because factors with noise or poor quality may jeopardize landslide spatial prediction results [5,23]. With the purpose of assessing the landslide susceptibility in the study area, we prepared twelve conditioning factors in the initial step. Considering that the contribution of each factor to the landslide occurrence may not be equal, the information value (IV) evaluation method was utilized in this step to detect the factors appropriate for modeling. The IV is known as an effective feature selection method in the field of credit risk scorecards [56,57], which is designed to describe the ability of an input variable to distinguish whether a target variable occurs or not. A higher IV value indicates the variable is more important. The IV can be calculated using the following equations:  
\[\mathrm{IV}(x_{i})=(n_{i1}/n_{1}-n_{i0}/n_{0})\ln\frac{n_{i1}/n_{1}}{n_{i0}/n_ {0}} \tag{1}\]  
\[\mathrm{IV}(x)=\sum_{i=1}^{N}\mathrm{IV}(x_{i}) \tag{2}\]  
where \(n_{1}\) is the total number of the landslide pixels, \(n_{0}\) is the total number of the non-landslide pixels, \(n_{i1}\) is the number of landslide pixels in the class \(x_{i}\) of variable \(x\), and \(n_{i0}\) is the number of landslide pixels in the class \(x_{i}\) of variable \(x\).  
The prepared landslide conditioning factors contain continuous variables (e.g., elevation) and discrete variables (e.g., land use). The IV analysis implemented by R software can automatically split continuous variables into several intervals based on supervised discretization [58,59]. The parameter p (minimum allowing percentage of samples per interval) was set as 0.01 to control the discretization.
## 3 Proposed Solution
### Lightweight IO Visor-Based Packet Tracing for Security-Enhanced Visibility  
IO Visor ([[https://www.ivovisor.org/technology/use-cases](https://www.ivovisor.org/technology/use-cases)]([https://www.ivovisor.org/technology/use-cases](https://www.ivovisor.org/technology/use-cases)), accessed on 22 June 2024) is an open-source project built based on the extended Berkeley Packet Filter (eBPF) [21]. The original BPF is designed to analyze and filter packets for network monitoring, while eBPF is designed to enable any in-kernel I/O modules, so it can help IO Visor to accelerate the innovation, development, and sharing of virtualized kernel I/O services. While eBPF need a C program to be translated into instructions, and loaded and executed into the kernel, IO Visor provides capability through the BPF compiler collection (BCC) tool to execute eBPF instructions from other high-level languages such as Python. As a result, IO Visor is able to trace different information such as network packets, kernel processes, and user applications.  
By developing IO Visor-based packet tracing, we can trace the entire payload of the packet or only specific information in the packet header. Also, IO Visor-based tools can call other eBPF programs (i.e., chaining) and pass traced information between them. For example, a packet-tracing program can be hooked and call a kernel-tracing program to collect specific kernel-related information (e.g., application name and process identifier) of respective traced packet data.  
To enable flow-centric visibility for assisting networking control, we designed IO Visor-based packet tracing tailored for two different collections, each capable of collecting security-related information. First, base collection collects 5-tuples packet header information for grouping and in addition extracts an associated number of bytes for packet-based statistics. Second, TCP-sync collection for security detection, which collects a TCP header with a SYNC connection type. These 5-tuples encompass IP source, IP destination, protocol, TCP or UDP source port, and TCP or UDP destination port. The collection process commences with the specification of these 5-tuples within the user-space program as tracing criteria. Next, this user-space program is converted to eBPF bytecode and hooked to the Linux networking socket. Consequently, only packets containing those 5-tuples are send to the user space for packet statistics measurement. TCP-sync collection is designed to leverage the TCP socket as the hook point and only takes a packet with TCP-sync in the header as its tracing criteria. It sends the packet information, such as TCP-sync sender IP address and TCP port number, to its associated application running in the user space. The overall design of the base collection and TCP-sync collection is depicted in Figure 3.  
Figure 3: IO Visor-based packet tracing design for security-enhanced visibility.