esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
As we can notice from Figures 2-7, when CCI at the relay increases from \(-5\) dB to \(+5\) dB, there is clearly a notable increase in average SER of the considered HSTS for a given modulation scheme. We see that the increase in SER is more prominent for higher-order modulation, such as 8-PSK/16-QAM, than that for the lower-order modulation scheme of BPSK/8-QAM/4-PAM. This can be seen from Figures 2-4, e.g., by comparing the plots for 8-PSK and BPSK for the same value of given CCI. The same line of reasoning is also applicable for comparing combinations of 8-PSK/QPSK and QPSK/BPSK modulations. When source-relay LMS channel suffers an increase in the amount of shadowing, as shown by the sequence of Figures 2-7, respectively, we notice that the average SER of the HSTS also increases correspondingly. The reader can view the effect of shadowing (in the source-relay LMS channel) on the considered system by comparing the curves for particular modulation (M-PSK) with the same given CCI from Figures 2-4, and also, the same observation is valid for M-QAM from Figures 5 and 6. We also comment here about the computational complexity of the expression for average SER in Equation (28), since it contains multiple integrals. We tested in MATLAB that for different SNRs (dB), e.g., 10, 20, 30; a modern personal computer takes approximately \(0.1\) s to calculate Equation (28). SNR region of 25-30 dB, and when SNR is over 30 dB, the magnitude of the difference between exact and asymptotic values is unnoticeable. Another example of the difference between exact average SER and asymptotic average SER (high SNR approximation) is presented in Figure 9. The analysis is done for QPSK-modulated HSTS with different values of CCI over average shadowed LMS source-relay channel. It can be noticed clearly from Figure 9 that with the increasing value of SNR, the gap between the exact SER curve and asymptotic SER curve keeps on decreasing. The difference between the exact and asymptotic values is very small in the high SNR region of 25-30 dB, and when SNR is over 30 dB, the magnitude of the difference between exact and asymptotic values is unnoticeable. As discussed above, Figures 8 and  
Figure 5: Average SER versus SNR for M-QAM with CCI in infrequent light shadowing. Figure 6: Average SER versus SNR for M-QAM with CCI in average shadowing. Figure 7: Average SER versus SNR for M-PAM with CCI in infrequent light shadowing.
## 3 Proposed Procedure
### Texture Mapping Process
#### 3.3.4 Texture Transformation  
The texture transformation algorithm emphasizes maintaining visual quality while optimizing the performance of the 3D model. After extracting UV coordinates, it encompasses crucial processes including texture image resolution optimization, mipmapping (multi-resolution texture) generation, compression, and storage.  
* Resolution Adjustment: resolution adjustment involves modifying the original texture size to achieve optimal resolution.
* Since UV mapping denotes the positional ratio of each pixel in the image, having sufficient resolution for appropriate color information mapping is advantageous for optimization.
* However, texture sizes used in 3D models can impact performance and quality if they are either too large or too small, necessitating appropriate resolution settings.
* The formula for adjusting texture size follows specific mathematical principles that balance visual quality with computational efficiency.  
\[W_{new}=W_{origin}\cdot S\]  
\[H_{new}=H_{origin}\cdot S\]  
S represents the scaling ratio (0 < S \(\leq\) 1, e.g., S = 0.5 indicates 50% reduction).  
When reducing texture resolution, interpolation techniques must be employed to minimize quality degradation.  
Multi-Resolution Texture: Mipmapping is a technique that optimizes GPU performance by pre-generating multiple resolution versions of the original texture. This approach enables the application of low-resolution textures to distant objects while maintaining high-resolution textures for nearby objects. The mipmapping process generates textures at half-size for each successive level.  
\[W_{i}=\frac{W_{i-1}}{2},\ \ H_{i}=\frac{H_{i-1}}{2}\]  
Each mipmap is created at half the resolution of its predecessor, maintaining smooth downscaling effects through interpolation techniques. For instance, given the original image size of 1024 \(\times\) 1024, mipmap levels are generated as follows in Table 10.  
The generated mipmaps are stored in GPU memory. During rendering and visualization services, the camera can select appropriate texture levels based on depth (pixel depth).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Mipmap Level** & **Width \(\times\) Height** \\ \hline
1 & Level 0 & 1024 \(\times\) 1024 \\
2 & Level 1 & 512 \(\times\) 512 \\
3 & Level 2 & 256 \(\times\) 256 \\
4 & Level 3 & 128 \(\times\) 128 \\
5 & Level 4 & 64 \(\times\) 64 \\
6 & Level 5 & 32 \(\times\) 32 \\
7 & Level 6 & 16 \(\times\) 16 \\
8 & Level 7 & 8 \(\times\) 8 \\
9 & Level 8 & 4 \(\times\) 4 \\
10 & Level 9 & 2 \(\times\) 2 \\
11 & Level 10 & 1 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mipmap Level Hierarchy and Texture Resolutions.  
The generated mipmaps are stored in GPU memory. During rendering and visualization services, the camera can select appropriate texture levels based on depth (pixel depth).
## 6 Discussion
**RQ3**: _How do developers rate the usefulness of intelligent source code completion assistants?_  
Following the findings in [8], our survey revealed insights into the use of intelligent assistants. The main conclusions are the following:  
The intelligent assistants' **usefulness** was rated as **OK**, with the **user experience** being **average**. Interestingly, the **hedonic aspect** (pleasure of use) was rated higher than the **pragmatic** one, suggesting the assistant is enjoyable to use but not always effective. From Table 9, those participants familiar with the assistants found them more beneficial, rating both the pragmatic aspect and overall user experience higher. In contrast, **non-experts** gave neutral to modest ratings, especially for usability. A significant factor affecting the ratings was the relevance of the assistant's suggestions. The **NPS** value was \(-\)**11**, indicating dissatisfaction. However, **82%** would continue using AI-based assistants, hinting at their potential. **Experienced developers** found the assistant more practical, appreciating its hedonic qualities. They likely discerned relevant suggestions better and valued non-functional aspects like ease of use. In contrast, less experienced users, relying more on the assistant, might have been misled by irrelevant suggestions. When comparing ratings based on prior knowledge of AI assistants, there were notable differences. Those familiar with such technology rated it positively, whereas those unfamiliar with it were more critical. In summary, while intelligent assistants are seen as promising, their effectiveness, especially in suggestion relevance, needs improvement. Their appeal seems higher among experienced developers and those familiar with AI-based tools.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Pre-Processing  
In this study, the Daubechies 6 wavelet transform [27] performs a nine-level wavelet decomposition on the original ECG signal, eliminates the D1, D2, and A9 components, and reconstructs the remaining components to obtain the filtered signal. We used the down-sampling strategy [20] to down-sample all the ECG signals in the two databases to 120 Hz. Down-sampling is the process of reducing the sampling rate of ECG recordings, which helps to reduce the complexity of the model.  
The short-term ECG recordings used in this study had unequal signal lengths. In the CinC2017 database, the shortest recording was 9 s, and the longest was 61 s. In the CSPC2018, the shortest recording was 6 s, and the longest was 144 s. Thus, we use a scheme of overlapping slices [28]. The length of the overlap is calculated as follows:  
\[ol=\lfloor l-\left(L-l\right)/\left(n-1\right)\rfloor \tag{1}\]  
where \(L\) is the length of an ECG recording, \(n\) is the number of segments, \(l\) is the length of each segment, and \(ol\) is the length of the overlap between two adjacent segments.  
Different people demonstrate different amplitudes of ECG recordings. Even for the same individual, the amplitude of ECG recording varies. Standardizing the input data can prevent the MCNN-BLSTM model from experiencing a gradient explosion during training, and it can make the model converge faster. In this study, the z-score normalization technology is used to make the magnitude of the ECG signal subject to uniform data distribution. Z-score can be defined as follows:  
\[x^{*}=\frac{x-E(x)}{\sqrt{Var(x)}} \tag{2}\]  
where \(E(x)\) is the mean of the ECG samples, and \(Var(x)\) is the variance of the ECG samples.  
Finally, we split 20% of the processed database into the testing set, 10% of the remaining data into the validation set, and the rest into the training set.
## 3 Results and Discussion  
Figure 2 shows examples of SAXS images, fluorescence spectra, and ion TOF spectra from Xe clusters recorded in coincidence for each FEL pulse. Under our experimental conditions, the hit rate determined by the ratio of observed SAXS signals in the octal MPCCD sensor to the total number of FEL shots was less than 0.1%. Therefore, the recorded data of each FEL shot were considered to originate from a single cluster in the focus of the FEL pulse. In the SAXS images, we observed clear concentric diffraction rings which suggest a spherical shape of the Xe clusters. We analyzed the SAXS images with respect to the size of the Xe clusters as well as the fluence of XFEL pulses at the actual interaction point, described in detail in the following section. The obtained values of cluster size and XFEL fluence are summarized below in the panels (a), (d), and (g) in Figure 2. Further, Figure 2a-c shows the SAXS, fluorescence, and TOF spectra recorded at the highest fluence. Here, the diffraction pattern had a high intensity. The fluorescence spectrum shows an evident peak at around 5 keV, at the high energy side of the dark signal peak of the MPCCD sensor around 0 keV. Highly-charged ions were observed in the TOF spectrum. As the XFEL fluence decreased, as shown in Figure 2d-i, the SAXS signals got weaker, the yield of fluorescence photons decreased, and less highly charged  
Figure 1: Schematic of the experimental setup. X-ray free-electron laser (XFEL) pulses were focused by a pair of Kirkpatrick–Baez (KB) mirrors. The cluster beam was generated through adiabatic expansion and introduced through two skimmers to the reaction point. The scattered photons were collected by an octal multiport charge-coupled device (MPCCD) sensor installed 1.5 m from the reaction point in the direction of XFEL beam. Fluorescence photons were detected by two single MPCCD sensors, whose distances from the reaction point were 0.6 m and 0.8 m, respectively. Ions were collected by an ion time-of-flight (TOF) spectrometer, set on the upper side of the chamber.  
ions were observed, while ions in the lower charge state appeared more prominently. These results indicate that XFEL-cluster interaction can be studied more accurately using size- and fluence-selective measurements, i.e., by eliminating the washout of characteristics of single targets due to their size distribution and laser profile [18; 22; 33; 34].
# Implementation of Fault-Tolerant Control for
## 7 Conclusions  
In this paper, an active fault-tolerant control for a robot manipulator based on synchronous sliding mode was proposed. To verify its effectiveness, experimental implementation of the proposed control algorithm for a three degree-of-freedom FARA-AT2 robot were carried out and compared with the active fault-tolerant control with conventional sliding mode control in the both single and multiple fault cases. The results indicate that the active fault-tolerant control with synchronous sliding mode control has better fault-tolerant capability and results in better trajectory tracking performance when compared to the active fault-tolerant control with conventional sliding mode control algorithms. This fault-tolerant capability comes from synchronous sliding mode control, because each joint error is coupled and constrained due to the synchronization errors and newly defined synchronous sliding surface. Future work includes the optimal tuning of synchronization parameters by following some methods (e.g., the genetic algorithm, neural network technique, etc.) In addition, the synchronization technique will be applied to finite-time control such as terminal sliding mode control, non-singular terminal sliding mode control, and so on, in fault-tolerant control for a serial robot manipulator.  
All authors contributed equally to this article and accepted the final report. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, grant number (NRF-2019R1D1A3A03103528).  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1D1A3A03103528).  
All authors announce that they have no conflict of interest in relation to the publication of this article.  
Figure 15: The synchronization error with multiple faults. (**a**) Joint 1; (**b**) Joint 2; (**c**) Joint 3.
# Real-Time Detection of Hole-Type Defects on Industrial Components Using Raspberry Pi 5
## 3 Materials and Methods
The experimental setup Figure 3 for the real-time visual defect detection system comprises an integrated arrangement optimized for industrial inspection of metal components. It includes a high-resolution camera strategically positioned above an inspection area to capture clear, consistent images. The dedicated lighting system ensures uniform illumination,  
Figure 2: Workflow diagram illustrating the real-time visual defect detection system for metal components, detailing image acquisition, preprocessing and augmentation, and deep learning model development and training, followed by validation and deployment. reducing shadows and reflections, thereby enhancing defect visibility. Captured images are directly transferred to a Raspberry Pi-based edge computing unit, where they undergo immediate processing and classification using pre-trained deep learning models. The real-time results, indicating the presence or absence of defects, are promptly displayed through a monitoring interface, enabling rapid decision-making and timely intervention, ultimately supporting efficient and reliable quality assurance in manufacturing environments.
# Fatigue Performance of Tunnel Invert in Newly Designed Heavy Hauilway Tunnel
###### Abstract  
The Haoji railway in China is the longest heavy haul railway in the world, including 235 tunnels located along the 1837 km railway. With the increasing axle load of the new line and the basal deterioration of the existing heavy haul railway in China, studying the fatigue performance of the newly designed tunnel structure is essential. To study the coupling effect of the surrounding rock pressure and 30 t axle load train, in this study, we combined three-dimensional numerical simulation and three-point bending fatigue tests to investigate the fatigue performance of the basal structures. The results of numerical simulation indicate that the center of the inverted arch secondary lining is the position vulnerable to fatigue in the lower tunnel structures; the surrounding rock pressure performance exerts a stronger influence on the stress state of the vulnerable position than the dynamic train loads. The \(S\)-\(N\) formula obtained from the experiment showed that the fatigue life of tunnel bottom structures decreases with increasing surrounding rock pressure and dynamic load. In typical grade V surrounding rock and 30 t axle loads, fatigue failure will not occur in the newly designed tunnel bottom structures within 100 years if bedrock defects are lacking and pressure of surrounding rock is not excessive.  
heavy haul; railway tunnel; bottom structure; fatigue +
Footnote †: journal: Applied Sciences
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 7 Conclusions  
In this paper, we presented a statistical performance analysis of an ultra-dense network by considering NDN as a network layer protocol. The position of this paper was threefold. First, a four-way factorial design method was presented, which was applied to generate the dataset, including various network parameters, in the ndnSIM simulator. Second, we evaluated these network parameters based on mean plots and interaction plots and used multiple comparison tests to analyze how the main effects and interaction effects influenced the ISR. Third, we selected an adequate MLR model to fit the ISR based on both the AIC and the coefficient of determination \(R^{2}\). Results showed that theresponse variable ISR was strongly dependent on NN, NI, and RB. Any variation in these variables may result in a significant change in the ISR. An NN of less than 10 had a more significant effect on the ISR than an NN greater than 10. The NI had a negative effect on the ISR. Log(RB) worked better than RB itself to fit the ISR by reducing the significant amount of AIC. In the final model, \(R^{2}\) was 88.95%, which was very high and indicated that the model explained almost all of the variability of the response data around its mean.  
The current study considered the communication between WAPs and edge devices, as deployed in a crowded area, to analyze the behavior of ISR. In future work, we aim to extend the statistical performance analysis to wireless communications between end-users and WAPs to develop a suitable experimental design that could be helpful when designing a wireless network infrastructure for crowded areas.  
Conceptualization, M.A.U.R. and K.C.; methodology, M.A.U.R., K.C., and B.S.K.; validation, M.A.U.R., R.U., D.K., K.C., and B.S.K.; formal analysis, M.A.U.R., D.K., and R.U.; investigation, B.S.K., K.C., M.A.U.R., and R.U.; resources, B.S.K.; writing, original draft preparation, M.A.U.R.; writing, review and editing, M.A.U.R., R.U., K.C., and B.S.K.; supervision, K.C. and B.S.K.; funding acquisition, B.S.K. This research was supported by the National Research Foundation of Korea (NRF) through the Korean Government (2018R1A2B6002399).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.3.2 Climatological Footprint  
To better interpret flux tower measurements, flux footprint models have been used to estimate the size and position of the surface area contributing to the measured flux [63]. The knowledge of this area, also known as flux footprint, is of extreme importance in upscaling the model from a single site flux measurement to an ecosystem or regional scale. Wind and turbulence conditions, and atmospheric conditions and surface characteristics contribute to the flux measured at a specific point in time. To estimate the flux footprint, we used the Flux Footprint Prediction (FFP) model of [PERSON] et al. [63] available in Python code ([[https://geography.swansea.ac.uk/nkljun/ffp/wwwv/](https://geography.swansea.ac.uk/nkljun/ffp/wwwv/)]([https://geography.swansea.ac.uk/nkljun/ffp/wwwv/](https://geography.swansea.ac.uk/nkljun/ffp/wwwv/)), accessed on 12 December 2022). We derived two aggregated footprints, also known as footprint climatology, three months of half-hourly input data for 2020 (October to December 2020), and six months of half-hourly input data for 2021 (January to July 2021).  
Most of the input variables required for the FFP model were collected as a part of the EC system at the Duque Fuente flux tower. The variables included mean wind speed, measurement height above displacement height, equal to the anemometer sensor height minus the displacement height (3.75 m), the Obukhov length, the standard deviation of lateral velocity fluctuations and the friction velocity. The boundary layer height was estimated with the expressions described by [PERSON] et al. [63] for atmospheric near-neutral and stable conditions and here reported for completeness (Equation (1))  
\[\mathrm{h}=\frac{\mathrm{L}}{3.8}\left[-1+\left(1+2.28\frac{\mathrm{u}*0.5}{ \mathrm{fL}}\right)\right] \tag{1}\]  
where L is the Obukhov length, \(\mathrm{u}^{*}\) is the friction velocity and \(f\) is the Coriolis parameter calculated using the site's latitude. The boundary layer is set to be at 1500 m at convective conditions defined as records in the eddy covariance data set when Obukhov length \(<\) 10 m [64]. The FFP model was computed to derive 20%, 40%, 60%, and 80% isolines. The 100% was not calculated with the FFP model but set as the fetch of the Duque Fuente tower according to the 1:100 displacement height-fetch ratio [65]. The derived footprints can be seen in Figure 4. The climatological footprints did not differ significantly in shape and size, having a surface of 5.72 and 5.30 ha within the 80% contour lines for 2020 and 2021, respectively.
## 3 Evaluation Experiments
### Summary of the Evaluation Experiments  
From the results of the evaluation experiments given above, the effectiveness of the proposed method can be demonstrated, as follows:  
* The proposed method reproduced the sound pressure transition with higher precision than the precision of the other comparison patterns.
* The proposed method achieved lower error in frequency characteristic than other comparison patterns.
* The proposed method reproduced the DRR transition with higher precision than the other comparison patterns.
* The performance of the proposed method was robust against the VSS position in the above-mentioned relevant metrics.
* The proposed method provided high-precision distance perception. In particular, when the VSS was constructed far from the listener, the proposed method achieved equivalent performance to that of a real sound source.