esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
## 3 Bootstrap Methodology and Its Improvement
### Assessment of Reliability Indicators
In this study, the jackknife resampling method [26] is used to estimate the value of \(a\), as follows:  
\[a=\frac{\sum\limits_{i=1}^{n}\Big{(}\hat{\theta}_{(-i)}-\overline{\theta} \Big{)}^{3}}{6\bigg{(}\sum\limits_{i=1}^{n}\Big{(}\hat{\theta}_{(-i)}- \overline{\theta}\Big{)}^{2}\bigg{)}^{3/2}}, \tag{21}\]  
where \(n\) is the sample size of the original sample, \(\hat{\theta}_{(-i)}\) is the parameter estimate of the jackknife sample after excluding the \(i\)th observation, and \(\overline{\theta}\) is the average of the parameter estimates of all the jackknife samples, i.e.,  
\[\overline{\theta}=\frac{1}{n}\sum\limits_{i=1}^{n}\hat{\theta}_{(-i)}. \tag{22}\]  
Using the bias correction \(z_{0}\) and the acceleration value \(a\), the upper and lower corrected quartiles of the confidence interval \(\alpha_{1}\) and \(\alpha_{2}\) are calculated as  
\[\alpha_{1}=\Phi\Bigg{(}z_{0}+\frac{z_{0}+\Phi_{(a/2)}^{-1}}{1-a\Big{(}z_{0}+ \Phi_{(a/2)}^{-1}\Big{)}}\Bigg{)}\alpha_{2}=\Phi\Bigg{(}z_{0}+\frac{z_{0}+\Phi _{(1-a/2)}^{-1}}{1-a\Big{(}z_{0}+\Phi_{(1-a/2)}^{-1}\Big{)}}\Bigg{)}, \tag{23}\]where \(\alpha\) is the level of significance and is assumed to be 0.05. Then, the confidence interval is  
\[CI=\big{(}\text{percentile}\big{(}\hat{\theta}^{*},100a_{1}\big{)},\text{ percentile}\big{(}\hat{\theta}^{*},100a_{2}\big{)}\big{)}. \tag{24}\]
## 3 Findings
### Microstructure Characteristics (SEM, TG, DTA)  
The thermal decomposition of brucite, first with the release of physical water, causes a continuous mass loss until around 500-550 \({}^{\circ}\)C [37; 38; 39; 40; 52]; then, the dehydroxylation of layered silicates and the disruption of the crystal lattice occur [43; 53]. Another significant thermogravimetric analysis (TGA) confirmed mass loss. The endothermic peak in the differential thermal analysis (DTA) curve and the major exothermic derivative (DTG) peak  
Figure 5: Discriminating diagrams of serpentine minerals.  
occurred between 610 and 700 \({}^{\circ}\)C (Figure 6). Sharp and highly exothermic DTA peaks at approximately 810-830 \({}^{\circ}\)C indicate the formation of new minerals such as olivine, pyroxene forsterite or enstatite [40; 41; 42; 46]. In the case of sample 6, similar TGA steps are seen, except that the second step is divided into two steps, with an extra step forming between 610 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. Also, in sample 6, the sharp and high exothermic DTA peak after 810 \({}^{\circ}\)C disappeared (did not form), so no new mineral formation occurred in this sample.  
The SEM images of the samples are given in Figure 7. Sample 2 contained Fe, Si, Al, Mg, O and C. Sample 5 contained Fe, Ca, Si, Mg and O, and sample 6 contained Fe, Si, Al, Mg and O. When we compare the samples to each other, the atomic percentages of both Mg and O are high compared to other elements. All the samples contained Fe, Si, Mg and O elements in their structures. The SEM images of samples show chemical color contrasts between dark and very compatibly colored schist serpentine minerals and light gray serpentinite clasts. The SEM examinations show the morphology and changes of serpentine crystals with small crystal sizes (0.1-0.3 \(\upmu\)m), depicted in Figure 7a-c. As shown in Figure 7a, chrysotile consists of thin and flexible fibrils with a length/diameter ratio of >3 [38], and lizardite has a plate-like morphology, as shown in Figure 7c. The X-Ray diffraction patterns are shown in Figure 8. When the analysis results are evaluated, the types of serpentine minerals can be identified as lizardite, a small amount of chrysotile, and forsterite. The X-Ray results support those of EDX analyses.  
Figure 6: The DTA and TGA thermograms of the serpentinite samples: (**a**) Sample 2, (**b**) Sample 5, (**c**) Sample 6.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 4 Weight-Based Feature Selection Methods
### Correlation  
We may think of correlation as a technique for selecting characteristics by how similar they are. Correlation coefficients can take on values between \(-1\) and \(1\), with the sign indicating the nature of the relationship (negative or positive) [58]. As soon as there is no correlation between the attributes, its quantity reaches 0. The \(r\)-value of a pair of variables (X, Y) indicates their level of resemblance. It is characterized by Equation (11):  
\[r=\frac{\sum\limits_{i=1}^{n}\left(X_{i}-\overline{X}\right).\left(Y_{i}- \overline{Y}\right)}{\left(n-1\right).\sqrt{\frac{\sum\limits_{i=1}^{n}\left( X_{i}-\overline{X}\right)^{2}}{n-1}.\sqrt{\frac{\sum\limits_{i=1}^{n}\left(Y_{i}- \overline{Y}\right)^{2}}{n-1}}}} \tag{11}\]  
where \(i\) designates the augmentation parameter and \(n\) denotes the amount of examples of the features X and Y.  
The correlation coefficient is an efficient method in feature selection processes [66]. If the correlation feature is extended in a graphical format, and the points at each instant tend to form a straight line, it is termed linear correlation. If they cluster around a curve other than a straight line, then the correlation is non-linear.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Modular Structure  
InnoCube is a 3U+ CubeSat (354 \(\times\) 113 \(\times\) 113 mm\({}^{3}\)) with a mass not exceeding 4 kg. One of the benefits of a wireless bus system is the lack of the data harness and the ability to test hardware in the laboratory as well as integrated in the satellite. To utilize those benefits, the structure is designed to allow quick access and exchange of subsystem modules, allowing for variable circuit board size, enabling COTS and custom designs, which provide a certain radiation protection for the wireless bus modules.  
The structure concept is to install the subsystem circuit boards individually from the front with little assembly effort. For this purpose, boards can be inserted from the front on evenly spaced rails in the sidewalls with clamping mechanisms, wedgelocks, retaining each board in its inserted rail slot as shown in Figure 9. Wedgelocks are pin-shaped components that expand when tightened, building a resistance against the side of the rail and board. This way, the circuit boards are secured in place providing resistance to shock and vibration, as well as enabling a thermal transfer to the main structure. The power supply to the modules is realized with a backplane.  
Figure 8: InnoCube avionics overview.
## 2 \(\Phi\)-Otdr Signal Storage Model
### Signal Storage  
In practice, the \(\Phi\)-OTDR technique often results in a huge amount of vibration signals. Taking the NBX-S3000 distributed vibration monitoring device (Nebreux, Kobe, Japan; sampling rate, 4000 Hz; spatial resolution, 0.1 m; monitoring range, 10 m; format, double-precision floating-point) for example, the monitoring process generates 3.2 MB of data per second and 270 GB of data each day. The massive amount of data adds to the difficulty in data operations, such as storage and analysis. Similar to those  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Length (m)** & **SNR (dB)** & **No.** & **Length (m)** & **SNR (dB)** \\ \hline
1 & 4.4 & 0.5061 & 10 & 5.3 & 6.7691 \\
2 & 4.5 & 1.7981 & 11 & 5.4 & 6.3194 \\
3 & 4.6 & 3.5218 & 12 & 5.5 & 5.9771 \\
4 & 4.7 & 5.0084 & 13 & 5.6 & 5  
shown in Figure 2, \(\Phi\)-OTDR signals are displayed regularly. This naturally associates the image-based approach with the data reduction of the \(\Phi\)-OTDR technique. Clearly, the image-based approach only works if the hard drive space can be released substantially without losing a significant quantity of signal features.  
Before generating images from \(\Phi\)-OTDR signals, it is necessary to pre-process the original signals through the following steps:  
* Step 1: During the acquisition process, the \(\Phi\)-OTDR signals appear as a slow and low-amplitude sine wave due to the features of the acquisition card. Therefore, a high pass filter (threshold: 1 Hz) was added to filter the signals.
* Step 2: The signals were further screened by a sliding window filter (window: 10 Hz) to eliminate noises and possible error points.
* Step 3: The signals were magnified exponentially to obtain a better signal-to-noise ratio (SNR).  
To save storage space, the pre-processed signals were saved in an image model through the following steps:  
* Step 1: The distributed vibration signals were split into one-second segments.
* Step 2: Taking time as the horizontal axis and length as the vertical axis, the signal amplitude was normalized into the greyscale range between 0 and 1.
* Step 3: In the generated image, the number of pixels on the horizontal axis (\(Pixel_{x}\)) is the number of sampling points per second, also known as sampling rate (\(Fs\)) in Equation (1):  
\[Pixel_{x}=Fs \tag{1}\]  
Since the natural frequencies of large structures usually fall between 0 Hz and 60 Hz, the sampling rate should reach 120 Hz according to the Nyquist-Shannon sampling theorem. As for concrete structures, the frequency of vibrations signals varies from hundreds to thousands of hertz. Thus, the sampling frequency for concrete structures should fall between 1 kHz and 4 kHz.  
* Step 4: In the generated image, the number of pixels on the vertical axis (\(Pixel_{y}\)) is the ratio of length (\(L\)) to the spatial sampling rate (\(R\)): \[Pixel_{y}=\frac{L}{R}*20\] (2)
* Step 5: The image was saved in the jpg or bmp format.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 6 Experimental Verification
### Fault Feature Dimensionality Reduction  
Feature information extraction is a crucial step in gearbox fault diagnosis. Using permutation entropy, fuzzy entropy, and sample entropy, the entropy features and time-frequency domain features of the main intrinsic mode functions (IMFs) were extracted to construct the initial feature vector. However, the obtained initial feature vector may have high dimensionality and could contain redundant features. Therefore, normalization was applied to the extracted data, and the t-SNE algorithm was utilized to reduce the dimensionality of high-dimensional feature vectors to obtain low-dimensional feature vectors. Additionally, the PCA and LLE methods were employed for dimensionality reduction, and the results of the dimensionality reduction are illustrated in Figure 10.  
Figure 9: Visualization of dimensionality reduction methods: (**a**) t-SNE; (**b**) PCA; (**c**) LLE.  
From Figure 10, Figure 10b illustrates the dimensionality reduction effect of LLE, showing poor performance as it fails to effectively separate different fault types. Although PCA can separate the fault types, its clustering is poor, and it cannot perfectly isolate the fault types. Through the comparison of the three dimensionality reduction methods, t-SNE exhibits the best dimensionality reduction effect. It can completely separate the five states, with the best clustering, distinct feature differentiation, and the ability to maximize the preservation of the original matrix's characteristics. Therefore, t-SNE was chosen for dimensionality reduction, resulting in two-dimensional feature vectors.
# Antibacterial and Antiproliferative Potential of Green, Roasted, and Spent Coffee Extracts
## 3 Materials and Methods
### Antibacterial Activity  
Minimal inhibitory concentration (MIC) of coffee extracts was performed according to the method reported by [PERSON] et al. [60], with slight modification.  
Antibacterial activity was evaluated against a reference sensitive to antibiotic American Type Culture Collection (ATCC) strains _Escherichia coli_ 25922; _Staphylococcus aureus_ 25923; _Enterococcus faecalis_ 29212; _Enterobacter_ cloacae 700323; and _Staphylococcus dublin_ 9676 and clinically isolated methicillin-resistant strains including _Staphylococcus haemolip-tycus_ 1129, _Staphylococcus hominis_ 433, _Klebsiella pneumoniae_ 189, and _Escherichia coli_ 1042. Briefly, a stock extract solution was prepared with 200 \(\upmu\)L DMSO and 800 \(\upmu\)L of sterilewater. Serial dilutions were made to obtain concentrations of the extracts at 1, 2, 4, 6, and 8 mg\(\cdot\)mL\({}^{-1}\), adding 100 \(\upmu\)L of Mueller-Hinton broth (MH) to each sterile 96-well microplate. Then, 3 \(\upmu\)L of the inoculum of each bacterium (standard 0.5 of the McFarland scale 1.5 \(\times\) 10\({}^{8}\) CFU\(\cdot\)mL\({}^{-1}\)) was added. Microplates were incubated at 37 \({}^{\circ}\)C for 24 h. After incubation, the MIC was determined, according to the coloration generated by the reaction with 30 \(\upmu\)L of 3-(4,5-DiMethylThiazol-2 yl)-2,5-diphenylTetrazolium Bromide (MTT) added to each well at 37 \({}^{\circ}\)C for 15 min. The antibiotic utilized as a positive control (C+) was amikacin (100 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)). The negative control (C\(-\)) was a mixture of MH, DMSO, and the inoculum. Evaluations were performed in triplicate.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Network Architecture  
Based on ten studies on deep learning on ultrasound muscle images, it was found that the U-Net and CNN architectures were the most used network architectures in segmentation [34; 35; 37; 38; 39]. Meanwhile, the RAN was the latest development of the region-based convolutional neural network (RCNN) [36]. The classification entirely uses a CNN based on AlexNet, Deep-CNN, and VGG [30; 31; 32] (Figure 4).
# Future Wireless Networking Experiments Escaping Simulations
## 2 Theoretical Comparison of the Testbeds
### Machine Learning Capability  
The application of machine learning algorithms represents a key component in 5G and beyond networks. The data generated by the testbed platforms can be used to apply machine learning algorithms in new emerging fields. Several works show preliminary advances in this field.  
Using data collected via POWDER, the authors of [22] investigate the challenge of radiofrequency fingerprinting leveraging neural networks. Testbed COSMOS can be leveraged to collect data via its sensors to feed a deep learning algorithm and create a tracking of all objects in the intersection [25]. The algorithms use images from a bird's eye view to detect objects of different sizes.  
Based on data measurements from CityLab, the work in [20] trains four different supervised learning algorithms using a limited set of measured Wi-Fi performance indicators. The work in [21] uses eleven gateways in CityLab to characterise the interference and design a neural network to predict the interference using RSSI measurements.  
As mentioned in Section 2.3, using W-iLab.t, it is possible to collect Wi-Fi data, such as throughput and Round-Trip Time (RTT). From a machine learning point of view, the main advantage of controlled environments such as W-iLab.t is ensuring the repeatability and reproducibility of experiments.
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 4 Formation Control with Virtual Leader
### Phase II  
The aim of the second part is mainly to test the effect of the algorithm of local trajectory planning. The position of the obstacle is located randomly at \([-10,-5,60,6]\) (x, y, width, height) and agents are from the box \([-50\) m, \(-40\) m] \(\times[-60\) m, \(-50\) m] as Figure 8a shows; the acceleration, velocity, and the angle of drift are in intervals \([0\) m/s\({}^{2}\), \(2\) m/s\({}^{2}]\), \([1\) m/s\({}^{1}\), \(15\) m/s\(]\) and \([-\pi/2,\pi/2]\), the velocity of the virtual leader is \(v^{l}=[5\) m/s\({}^{,}5\) m/s\(]\). The formation has started at the nominal configuration and encounters the obstacle along the desired path \(x_{d}\), as Figure 8b shows. The state X\({}_{el}\) of the virtual leader is the desired state for agents, as Figure 8b shows, and the formation would generate Algorithm 1 to steer agents to avoid obstacles. The velocity and its error related to the virtual leader are given in Figure 8c,d; as can been seen, the velocity fluctuates greater at \(t=10\) s and \(t=20\) s moving through the obstacle and the velocity change interval is from \(v=5\) m/s to \(v=[1\) m/s\(,14\) m/s\(]\). Figure 8d illustrates the change in velocity error during the same time interval. The primary reason for this is that the desired trajectory, as moved by the virtual leader, would directly pass through the obstacle. However, the formation manages to avoid collision and maintain its configuration due to the forces exerted by the virtual leader. In Figure 8e, the trajectory error of all agents is depicted, with the note that the X and Y axes have different scales. It can be observed that the leaders and followers exhibit smaller average position errors, while the overall formation has a relatively larger error. The desired trajectory is not fully matched due to the influence of uninformed followers on the leaders, but it remains within an acceptable range. Lastly, Figure 8f demonstrates the total velocity error for all agents, indicating that the protocol will converge eventually.  
Figure 8: _Cont._  
Figure 7: Affine-based predefined configurations patterns for illustrations: (**a**) pre-defined path; (**b**) pre-defined configuration. The leader is red dot, followers blue dot, and obstacles are gray. Red and blue dotted lines show the desired trajectory for leaders and followers, respectively.
## 2 Objective Function Formulation
### The Proposed Environment-Constrained Economic Dispatch (\(Eced\))  
Both of the aforementioned CEED strategies have as their primary goal the lessening of atmospheric pollution. The system's generating cost increases substantially, surpassing the highest value attained by economic dispatch. Equation (6) shows how to combine two objective functions with distinct aims in order to achieve a better quality compromise solution [50; 51]. Whether a system is unimodal or multimodal is determined by (1) and (2). The characteristics of the economic dispatch equation and the emission dispatch equation are given in accordance with this equation.  
\[ECED=\mu\bigg{[}\frac{ECD-ECD_{\min}}{ECD_{\max}-ECD_{\min}}\bigg{]}+(1-\mu)\bigg{[} \frac{EMD-EMD_{\min}}{EMD_{\max}-EMD_{\min}}\bigg{]} \tag{6}\]  
where \(\mu\) is between 0 and 1, \(ECD_{\min}\) is the minimum generation cost, \(EMD_{\min}\) is the minimum amount of pollutants emitted, \(ECD_{\max}\) is the maximum generation cost, and \(EMD_{\max}\) is the maximum volume of pollutants released attained by replacing the optimal constraints of \(EMD_{\min}\) in Equations (1) and (2). The data acquired [51] also suggest the following three procedures and presumptions:  
1. Setting \(\mu\) = 0.5, or providing equal weight to both goal functions, is known to provide the fastest and most effective steps towards obtaining the best compromised solution.
2. The choice with the largest quality compromise will have a smallest \(CPI\)-\(EPI\) difference, where the \(CPI\) is cost performance index and \(EPI\) is emission performance index. The formulae for \(CPI\) and \(EPI\) are denoted by Formulas (7) and (8). \[CPI=\bigg{[}\frac{ECD-ECD_{\min}}{ECD_{\max}-ECD_{\min}}\bigg{]}\times 100\%\] (7) \[EPI=\bigg{[}\frac{EMD-EMD_{\min}}{EMD_{\max}-EMD_{\min}}\bigg{]}\times 1 00\%\] (8)
3. The generating cost of the higher quality compromise will be closer to \(ECD_{\min}\), while the pollution output will be lower than \(EMD_{\min}\).
# Mixed Reality-Based Robotics Education--Supervisor Perspective on Thesis Works
## 3 Results
### Student D--Augmented Reality-Based Robotics Education
#### 3.4.1 Methodology  
Approach: Student D employed a theoretical and experimental approach to develop an AR-based educational platform aimed at enhancing the learning experience in robotics. The development involved creating an AR application that integrates real-time interactions with robotic models.  
Tools and Technologies: The application was developed using Vuforia Studio and Creo for creating and visualizing 3D models. The AR content was designed to be compatible with mobile devices, allowing students to interact with the virtual model of an industrial robot, specifically the ABB IRB 120.  
Data Collection and Analysis: The effectiveness of the AR application was evaluated through user studies involving students. Data collection methods included questionnaires and performance metrics. The data were analyzed using both quantitative and qualitative methods to assess usability, engagement, and learning outcomes.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 3 Results and Discussion  
This section presents the numerical results of the optimal design for the cantilever composite beam seen in Figure 1. [PERSON] et al. [24] previously studied this beam for single-mode shunt damping. The host beam is made of aluminum, and the piezoceramics are PIC151 and are placed 0.5 mm from the fixed end. The material and geometric properties of the structure are given in Table 2. In this study, the second and third mode of the cantilever beam are damped using the multimode resonant shunt shown in Figure 3b. The reason for only addressing the second and third modes is that, due to the position of the piezoelectric patch, these modes have greater authority than other structural modes. For the determination of the frequency response, the proposed FE model is used, and the harmonic force is applied at the tip of the beam. The system response is determined at the same position.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Beam** & **PZT** \\ \hline Length \(L\) (mm) & 170 & 25 \\ Width \(b\) (mm) & 20 & 20 \\ Thickness \(h\) (mm) & 2 & 0.5 \\ Young’s modulus \(E\) (GPa) & 72 & 66.7 \\ Shear modulus \(G_{12}\) (GPa) & 27.48 & 25.46 \\ Poisson’s ratio \(v_{12}\) & 0.31 & 0.31 \\ Density \(\rho\) (Kg/m\({}^{2}\)) & 2800 & 8500 \\ Piezoelectric constant \(\widetilde{e}_{31}\) (C/m\({}^{2}\)) & - & \(-14\) \\ Dielectric constant \(\widetilde{e}_{33}\) (nF/m) & - & \(2068\varepsilon_{0}\) \\ \hline \(\varepsilon_{0}=8.854\times 10^{-12}\) F/m is the free space permittivity. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric and material parameters of the compound structure.  
Figure 4: Flowchart of the optimization procedure.  
Three different optimization problems are studied, as already discussed in Section 4. In each problem, the particle swarm optimization method is applied with the number of particles in the swarm set to be 150 and the maximum number of iterations to be 250. The numerical experiment is carried out in the Windows 10 Pro environment running MATLAB 2019b. The computer processor is 11 th Gen Intel(R) Core (TM) i7-11370H @ 3.30 GHz, and the RAM is 16.00 GB. Initially, the parameters of the model 1 are chosen with the trial-and-error method by taking into account the commercial availability of the electrical components, the size of the problem and the desired accuracy. The upper and lower bounds of the design variables are given in Table 3.