esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results for Inclined Cracks in Ferro-Magnetic Steel  
For experimental investigations of cracks with defined depths and inclination angle, two ferro-magnetic steel samples with artificial defects were produced. The first one is a laser-sintered sample, where four artificial cracks with a length of 10 mm and depths of 1 mm and 0.6 mm (see Figure 16a) were manufactured [23]. The cracks had a width of 0.3 mm and thus were closer in appearance to a kind of notch instead of natural cracks, which usually have a width of only a couple of \(\upmu\)m (see Figures 8a and 11a). However, these artificial cracks or notches also showed results that are very similar to those presented by the simulation predictions.  
Two of the cracks of the sintered sample were vertical and two of them had an inclination angle of \(\theta\) = 30\({}^{\circ}\). Figure 16b,c show the phase image after a 0.1 s heating pulse and a profile through the phase image. As this is in good agreement with the previous sections, one can observe  
* that the deeper crack has a higher phase contrast than the shallower one;
* that the phase profile of the inclined cracks is asymmetric and the mean value of the phase minima at the left and right sides is almost equal to the minimum phase of the vertical crack.  
Figure 16: (a) CAD data for four artificially created cracks in a laser-sintered sample; (b) phase image after a 0.1 s heating pulse; (c) profile through the phase image at the red marked position.  
Figure 15: Phase difference (a) and phase contrasts (b) for an asymmetrical phase distribution depending on the inclination angle of the crack with a 1 mm depth. For the definition of the displayed values, see Figure 14a.  
The second sample is a 30 \(\times\) 60 \(\times\) 80 mm\({}^{3}\) ferro-magnetic steel sample, where five defects were cut with the EDM technique (electro-discharge machining). Each of them had a depth of 1 mm, but the inclination angles varied from 75\({}^{\circ}\) up to 0\({}^{\circ}\). Figure 17 shows the phase images after 0.1 s and 0.6 s heating pulses and different phase profiles. Like the simulation results, also shown in Figure 14, the following can be observed:  
* For a short heating pulse (100 and 200 ms), the phase is asymmetric, and the wider the inclination angle is, the greater the difference is between the phase at the left and right sides of the crack.
* For longer pulses (400 and 600 ms), asymmetry decreases.
* The larger the inclination angle, the higher the phase value at the crack position.  
It is important to note that, in both of the samples with artificial defects, the defect width was about 0.3 mm, which is much larger than the width of the natural cracks that are usually not visible at the surface. It is very difficult to manufacture artificial defects with a given depth and angle, but with a very small width. Even so, these artificial samples can be used well as test objects to investigate different behaviors.
## 4 Size and Target Pointing Accuracy of Antennas  
The size of any antenna mounted on a fixed-wing UAV should comply with the ITU-R Recommendations and Report [17; 18]. In particular, the maximum equivalent isotropically radiated power (EIRP) [17], taking into account the antenna pattern and transmitting power, should be lower than the specified values. At the same time, smaller antennas are desirable from the viewpoint of driving the inertia moment. Using recommendations applied to the extremely high frequency (EHF) [19] bands at 31.0-31.3 GHz, the antenna size can be calculated (Figure 12).  
As a result, the size of \(D_{\text{a}}\) is 22 cm, and its beam's width is 2.6 degrees. If the antenna is smaller than 22 cm, then it will exceed the line specified by ITU Recommendations. By allowing less than 0.7 dB of deviation in the EIRP, the antenna's pointing accuracy was determined to be 0.13 degrees, or 1/20 of the beam's width.
## 4 Discussion and Results
### Evaluation of Beam Profiles on the Defect Types
In the case of the \"Axial\"-type defect, where the crack is in the same direction as the propagation of the Rayleigh waves, the amplitude is about 1.8-2.4 times that of the \"Circum\"-type defect. In the case of the \"Shear\"-type defect, where the Rayleigh waves and the crack location intersect at an angle of 45\({}^{\circ}\), the peak-to-peak amplitude is in the middle of the \"Circum\" and \"Axial\" defects depending on the crack depth. However, even though the amplitude values of the \"Axial\" and \"Shear\" defects overlap when the crack depth is 100 \(\upmu\)m, this does not significantly affect the defect type. The amplitude of the Rayleigh waves decreases as the crack depth increases, which shows that there is a close correlation with the defect type. The test results also show that the receiving transducer receives signals with a high reliability in the range of about 5-10 mm. However, the amplitude of the Rayleigh waves is expected to decrease rapidly beyond this section.
# Adjustable Stiffness-Based Supination-Pronation Forearm Physical Rehabilitation
## 4 Electronic Interface  
To operate the adjustable stiffness device with the implementation of the proposed control strategy, a Simulink(r) model was created in order to use the rapid control prototyping method presented in [43,44], which made use of an embedded coder to translate control diagrams into a C program that can be imported as a function into the System Workbench for STM32 IDE that allows programming the microcontroller with C code that compiles and runs on an STM32F767 ZI board.  
Additionally, the activities performed by each of the electronic components and the control strategy are described next:  
1. The master board NUCLEO-F767 ZI [45] is in charge to manage the control loop process at 1 kHz sample rate and the signal processing. It contains a low/high-level (general purpose input/output and trajectory/tension controllers) control strategy.
2. Two linear actuators (TGA-Y-300-24-5-405) of 24 VDC and 900 N of maximum load are implemented. Additionally, it includes an internal micro switch to make the actuator's stem stop automatically after it ends. Its displacement corresponds to 300 mm. Moreover, an external encoder (LPD3806-400 BM-G5-24C) of 400 pulses per revolution (ppr) has been adapted to each actuator. Based on a quadrature method, the number of ppr has been incremented to 1600 in such a manner that the respective resolution equals 37,600 pulses per each displaced millimeter.
3. To move the actuators, an L298N dual H-bridge DC motor driver module has been implemented. The speed and direction of each actuator is controlled by means of applying 12 bits pulse width modulation (PWM).
4. Two load cells (JLBM-1-100) of 100 N, with \(2.0\pm 0.1\)\(\mathrm{m}\mathrm{V}\) of sensitivity, were implemented to convert the mechanical force of the cables-springs into analogue values to measure/calculate the system stiffness by means of two 12-bit ADC.
5. In order to measure the angular position \(\theta\) of the handle and to close the feedback trajectory loop, another incremental encoder with 1600 ppr was implemented.  
Once the physical platform has been instrumented and its electronic interface has been implemented, the control strategy described in Section 5 can be experimentally validated. It is worth mentioning that the dynamic properties of both actuators are not considered in this paper.
## 4 Control-Law Design  
Based on a simplified model (Equation (3)) or the reduced-order model (Equation (5)), we devised four controllers and conducted three sets of simulations for comparisons. Drawing insights from the first section of the literature, we formulated MPC and SMC schemes, constituting the first set of comparative experiments to assess their performance in joint-angle tracking. Subsequently, we combined the SMC scheme with a designed Nth-order LADRC algorithm, formulating the second-order LADRC-SMC scheme. This scheme was then subjected to the second set of comparative experiments against the SMC scheme, evaluating its joint-angle and contact-force tracking performance. Additionally, during the practical implementation of controllers, delays may arise because of the influence of control and sensor factors, potentially affecting the stability of the controllers. Hence, we propose a multi-loop control strategy (LADRC-SMC-SP scheme) and conduct the third set of comparisons against the LADRC-SMC scheme. This strategy maximizes the advantages of individual sub-loop controllers to enhance the force-position tracking performance of the single-leg system and mitigate the risk of instability caused by system delays. Next, we will systematically design these algorithms following this logical sequence.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Increased Accuracy in the Permittivity
#### 3.1.2 Influence of the Quality Factor  
There is a trade-off between temporal resolution and spectral resolution in \(f_{\text{res}}\) considering a fixed amount of measurement time. A resonant mode that can respond fast has a broad peak and finding the spectral position of a wide peak accurately is more difficult than finding that of a narrow peak. The characteristic time \(\tau\) of a resonant mode is proportional to the width of the peak and therefore to the error in the position. In the previous paragraph, it was shown that the error in the determination of the permittivity decreases with \(N^{0.5}\). For this reason, the duration of an experiment scales inverse quadratically with the change in temporal resolution while maintaining the same resolution in \(f_{\text{res}}\).  
In the description above, it is assumed that the position of the resonance frequency barely changes. This is true for experiments at atmospheric pressure as discussed in references [18; 19; 20]. However, in experiments in which the shift of the resonance frequency is significant--with respect to the width of a peak--temporal resolution becomes less expensive. A hand waving argument for this is to accommodate an increase in temporal resolution, the \(Q\) must decrease, which causes the peak to widen. As the ideal number of probing frequencies over the width of a peak is fixed, the total number of probing frequencies within the frequency range over which the resonance peak shifts decreases.  
The resolution in \(Q\) does not change with a change in \(Q\) as long as the number of probed frequencies within the width of a peak is kept constant. However, in the analysis, the parameter \(\Delta(1/Q)\) is used and the resolution of \(\Delta(1/Q)\) is better for high \(Q\). For small changes in \(Q\), the resolution in \(\Delta(1/Q)\) is inversely proportional to \(Q\).  
Figure 10: The exponent \(\beta\) which indicates the noise reduction in \(\Delta(1/Q)\) (red) and \(\Delta f\) (blue) as a function of the number of averages (square) and the frequency step size (circle) with respect to the reference measurement with 4096 averages and a frequency step size of 1 kHz. The results indicated by triangular symbols show the effect of averaging 20 consecutive time steps. The theoretically expected \(\beta\) is indicated by the horizontal dashed line.  
A very important property is the prominence of a resonance peak, i.e., the depth which is determined by the efficiency of the coupling of the microwaves into the cavity. Experimentally, a coupling of at least 10 dB appeared to be required to obtain decent results.
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
## 4 Numerical Verification and Comparison
### Solution from the LOWGA Operator Method  
When input arguments are semantic variables, to deal with the semantic information, [PERSON] [42] proposed the LOWGA operator. The LOWGA operator is an extension of OWGA operator used to aggregate semantic information. The assessment of criterion weights and criterion rating values for expert-provided information requires certain information in the LOWGA operator method. In this case, some information included uncertain or incomplete information from experts D2 and D4. Therefore, this method considered only the complete information of experts D1 and D3. For example, the criterion rating values for alternative \(A_{1}\) were 4.5000, 7.000, 4.5000, 3.000, 3.5000, and 5.500, respectively. Sorting from largest to smallest gave 7.000, 5.500, 4.5000, 4.5000, 3.5000, and 3.000.  
Based on the LOWGA operator method, using Equation (3), Table 1, and Table 2, we calculated the overall \(z_{i}\) value of alternative \(A_{i}\).  
\[z_{1}=LOWGA_{w}(s_{4.5},s_{7},s_{4.5},s_{3},s_{3.5},s_{5.5})\]  
\[=(s_{7.000})^{0}\otimes(s_{5.500})^{0.067}\otimes(s_{4.500})^{0.333}\otimes(s_{4.500})^{0.333}\otimes(s_{3.500})^{0.267}\otimes(s_{3.000})^{0}\]  
\[=s_{(7.000^{0}\times 5.500^{0.067}\times 4.500^{0.333}\times 4.500^{0.333}\times 3.50 0^{0.267}\times 3.000^{0})}\]  
\[=s_{4.265}\]  
The other values were obtained through the LOWGA operator method.  
\(z_{2}=s_{5.901},z_{3}=s_{4.564},z_{4}=s_{5.000}\), and \(z_{5}=s_{5.901}\).  
According to the LOWGA operator method, the ranking order of the alternative was  
\[A_{5}=A_{2}>A_{4}>A_{3}>A_{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Decision-Makers**} & \multirow{2}{*}{**Alternatives**} & \multicolumn{6}{c}{**Criterion**} \\ \cline{3-8}  & & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** \\ \hline \multirow{8}{*}{\(D1\)} & \(A1\) & M & VG & M & B & MB & MG \\  & \(A2\) & G & VG & MB & VG & MG & G \\  & \(A3\) & VG & MB & G & M & MB & G \\  & \(A4\) & G & M & MG & MG & G & G \\  & \(A5\) & VG & G & VG & MG & MG & G \\ \hline \multirow{8}{*}{\(D2\)} & \(A1\) & G & M & * & M & * & G \\  & \(A2\) & G & G & * & MG & MG & [G, VG] \\  & \(A3\) & VG & MB & MG & M & M & G \\  & \(A4\) & G & MB & MG & G & MG & [MG, G] \\  & \(A5\) & G & VG & G & VG & * & [MG, G] \\ \hline \multirow{8}{*}{\(D3\)} & \(A1\) & MG & VG & MG & M & M & G \\  & \(A2\) & VG & G & MB & G & MG & G \\  & \(A3\) & VG & MG & VG & MB & M & G \\  & \(A4\) & VG & M & MG & MG & M & MG \\  & \(A5\) & G & VG & VG & MG & M & G \\ \hline \multirow{8}{*}{\(D4\)} & \(A1\) & [MB, M] & G & M & M & MB & G \\  & \(A2\) & G & VG & M & G & G & MG \\  & \(A3\) & VG & MB & MG & [MB, M] & M & MG \\  & \(A4\) & G & M & MG & G & MG & VG \\  & \(A5\) & [G, VG] & G & VG & G & [M, MG] & G \\ \hline \hline \end{tabular} The missing values are shown with *.  
\end{table}
Table 2: Criterion rating values for five alternatives.
## 1 Introduction
In extreme sandstorm situations, the algorithm removes the red veil from the sand-dust images while maintaining stable and robust dust removal performance. * We propose a novel color correction method that is equally effective for red-dominant sand-dust images. Channel compensation is applied to restore the distribution of the compressed blue and red channels. Color transfer ensures the consistency of the mean and variance across the color channels. Dynamic range expansion is employed to enhance the image's contrast and brightness. * We propose a local-block-based atmospheric light estimation strategy and design a novel illuminance adjustment method, significantly reducing color artifacts. The remainder of this paper is structured as follows. Section 2 provides a concise review of existing sand-dust image enhancement and restoration methods, followed by a comprehensive analysis and summary. Section 3 outlines the motivations behind this work. Section 4 presents a detailed description of the components of the proposed sand-dust image restoration algorithm. Section 5 demonstrates the experimental results and an analysis comparing the proposed method with several state-of-the-art approaches. Finally, Section 6 concludes the paper with a summary of the findings.
## 4 Results
### Simulated Cyst Targets  
To analyze the performance of each algorithm on image contrast, we simulate a cyst phantom in a high speckle noise environment by Field II. The circular cyst with a radius of 5 mm is located at (x, z) = (0, 40) mm. Since the cyst mimics the lesions which are anechoic inside, the interior of the cyst has no scattering points while several scattering points with different coefficients are placed outside the cyst randomly to increase the speckle noise. Figure 5 shows the echo images of various algorithms. The lateral variation at the center of the circle is shown in Figure 6. Table 2 lists the contrast and generalized contrast-to-noise ratio (gCNR) of each image in detail. Compared with DS, SA, and GSC, contrast with GSC-CROSS is improved by 101%, 32.6%, and 63.5%, respectively. Because the interior of the cyst is anechoic, the lower the gray value of the cyst in the echo image is, the better the sidelobe suppression is. Compared with DS, SA, and GSC, GSC-CROSS can reduce the mean intensity inside the cyst by 47.83, 33.45, 34.65 dB, respectively. Moreover, observing the edge area of the cyst, the boundary between the cyst and background in the images of DS, SA, and GSC is blurred because of the interference, while GSC-CROSS results in a distinguishable edge. Observation index gCNR [34] is robust against dynamic range alterations. It is usually used to judge whether the algorithm can improve the contrast indeed, rather than the effect of dynamic range changes. The gCNR describes the success rate for an ideal observer to detect the lesion. The closer gCNR is to 1, the higher probability of lesion detection of the algorithm. In Table 2, the GCC-CROSS has the highest gCNR, which means that this method is easier for the detection of lesions and can improve the contrast. The cyst targets simulation experiment shows that GSC-CROSS can effectively suppress the interference and improve the contrast.  
Figure 4: Lateral variation of point at (**a**) z = 35 mm; (**b**) z = 40 mm.  
Figure 5: Contrast of beamforming responses for different beamforming algorithms. (**a**) DS; (**b**) SA; (**c**) GSC (L = 32, \(\Delta\) = 20); (**d**) GSC-CROSS (L = 32, \(\Delta\) = 20). The dynamic range of image is 80 dB.
# A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity
## 5 Numerical Results  
In this section, the proposed topology shaping method's validity is evaluated in different target topologies. In addition, its performance is also assessed in terms of energy consumption and computational complexity. All the simulation results are obtained from Matlab running on a personal computer (CPU 12 th Gen Intel(R) Core(TM) i9-12900K, 128 GB RAM, Windows 11 Pro).  
Two simulation scenarios are set to analyze the proposed topology shaping method. For the first scenario, the target topology shape is an aircraft with a length and wingspan of 350 m, and an aircraft height of 70 m. In total, 32 UAV nodes are randomly distributed in a 3D space of 1000 m \(\times\) 1000 m \(\times\) 1000 m. The specific simulation parameters are shown in Table 1. For the second scenario, the target topology shape is a cube with a side length of  
Figure 3: Flow chart of the proposed topology shaping method.  
Figure 2: A step-by-step example of the initialization process. (**a**) Example cost matrix; (**b**) After column reduction; (**c**) After reduction transfer of row 3; (**d**) After row reduction to row 3; (**e**) After reduce 2 in row 1; (**f**) After inverse column reduction on column 2; (**g**) After augmenting row reduction on row 4; (**h**) After augmenting row reduction on row 2.  
In the second simulation scenario, the target topology is a cube shape, and its topology shaping process is the same as in the first scenario. Simulation results are shown in Figure 5. The X-Y perspective of topology shaping in aircraft shape.  
Figure 5: The X–Y perspective of topology shaping in aircraft shape.  
Figure 6: The X–Z perspective of topology shaping in aircraft shape.  
Figure 7, where the green circles represent the initial positions of nodes while the red asterisks represent the target positions. The solid black line indicates the outline of target topology, and the blue dashed lines represent the mapping relationship between initial and target topology. It can be seen that the desired topology shaping is successfully achieved by the proposed method.
## 3 Internal Stability Analysis
The imaginary root \(\iota\cdot\omega\) can be solved by substituting the values of \(z_{1}\), \(z_{2}\) into (21a) or (21b). The Dixon resultant \(D(\psi_{1}(\omega),\psi_{2}(\omega))\) constitutes a closed-form description of the kernel and offspring hypersurfaces of the CTCR paradigm. Next, by utilizing the remaining procedure of the CTCR paradigm, the ETDMs \(<\overline{\tau}_{1},\ \overline{\tau}_{2}>\) are obtained. The remaining procedure of the CTCR paradigm is omitted here, and more details are presented in the literature [15; 32; 33]. **Remark 2**. : _The Dixon resultant is computationally more efficient than the well-known Sylvester resultant and other popular methods (e.g., ACE combined with Kronecker summation [22]). Especially, for the third-order vehicular platoon with sensing delay and communication delay, the size of the Dixon matrix is just \(3\times 3\), but the sizes of the Sylvester matrix and ACE matrix are \(6\times 6\) and \(9\times 9\), respectively. Therefore, the Dixon resultant consumes less computational burden and is preferable to compute the ETDMs of the vehicular platoon._
## 3 Design of Approximate QAM Demodulation Circuits
### Arithmetic Approximation Techniques  
We adopt both the floating-point (FLP) and fixed-point (FXP) arithmetic formats in our QAM demodulation circuits, and perform approximate arithmetic operations. Next, we describe the approximate arithmetic units used in our QAM designs.
# An Automated Behaviour-Based Clustering of IoT Botnets
## 4 The Proposed Approach
### Sandbox Execution and Analysis  
The IoT botnet samples are executed using the ELF DIGEST sandbox, presented in our previous work [8]. The ELF DIGEST sandbox supports ARMv5, ARMv7, MIPS, x86 and x64 CPU architectures, as well as older (v.3) and more recent (v.4+) Linux kernel versions. The sandbox can detect the use of anti-sandbox techniques. To increase the chances for a successful botnet sample execution, the VMs comprising the sandbox include software tools and libraries that may be used by IoT botnets [8]. The botnet samples are executed for 310 s to accommodate for a potential use of delayed execution.  
The on-host behaviour of the samples is recorded using the program execution tracing tools systemtap [29] and strace [30], while the network traffic is captured using tcpdump [31]. The tracing tools record the system calls made by the botnet sample. A system call is a request made by a program to the Linux kernel for a specific service. There are  
Figure 1: Steps comprising the proposed approach for automated behaviour-based clustering of IoT botnets.  
Figure 2: URLhaus malware tracking service.  
various system calls with different purposes [9], such as executing a program, allocating memory, writing to disk or network socket, killing processes, etc. However, a behaviour clustering using system call traces as input may be ineffective because the system call traces can vary significantly, as discussed in [27]. For instance, a program may invoke the'read' system call to read 256 bytes from a file at once, or may invoke the'read' system call 256 times, reading one byte per system call. Thus, the same behaviour may result in different system call traces. We argue that the behaviour of the botnet samples can be described more effectively at a higher abstraction level, as a set of actions and objects the actions were performed on.  
To identify the actions performed by a botnet sample, the sandbox performs behavioural and network analyses on the recorded system calls and the captured network traffic, respectively. The behavioural and network analyses are performed automatically, after the sample execution is finished. The behavioural analysis can identify actions such as reading, writing, and removing files, executing programs and shell commands, loading of kernel modules, use of persistence techniques, changes made to the firewall configuration, etc. The network analysis can identify DNS queries, HTTP requests, port scanning and C2 communication.