esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p3: Diagnostics
SVMs exhibit high classification precision, but similar to ANNs and DNNs, do not provide a physical interpretation of the classification; conversely, DTs, KNN, and fuzzy predictive models provide an interpretation, which is based on a set of rules. [PERSON] et al. in 2012 [214] compared three classifiers for the fault diagnosis of a journal bearing based on vibration: Fisher linear discriminant (FLD), K-nearest neighbor (KNN), and support vector machine (SVM). The results demonstrated that the performance of SVM was significantly better in comparison to FLD and KNN. A key issue in using supervised ML methods is the need of labels, namely to assign a specific category to each training instance. To create labeled vectors is time-consuming, error prone, and may result in an unbalanced number of classes [270]. Different solutions have been studied to address the unbalanced classes problem [27], e.g., by removing instances belonging to the majority class (under-sampling) or by sampling more instances from minority class (oversampling), or by removing points in the majority class that are considered borderline, noise or redundant [271]. In the class \"Others\", in Table 4, some particular methods, which do not belong to the other classes, are collected. With reference to the problem of labels, [PERSON] et al. in [267] propose a method that combines self-supervised learning with supervised learning, making full use of unlabeled data to learn fault features, and transforms the data into three-channel vibration images. [PERSON] et al., in [265], present a method based on the matching pursuit algorithm (MPA), a new finding in signal processing, which proves the possibility of performing fault diagnosis of bearings, even in case of multiple defects. [PERSON] et al. in [199] show the results of the application of the AdaBoost algorithm (which belongs to fusion algorithms class) to diagnose faults of bearings. In the paper, the performances of a two-layer AdaBoost, SVM and a KNN, are compared under different numbers of kinds of features, and AdaBoost performed best, but for a few kinds of features.
## 3 Results
### Assessment of Accuracy and Agreement for %BF, WC, and WHR  
Based on the regression analysis for %BF, the R\({}^{2}\) and error values (except MAPE) allow us to conclude that both methods accurately estimate %BF (Table 3). A weaker correlation was observed for WC measured manually and by BIA, and error values (except MAPE) permit the inference that both methods were not accurate in estimating WC (Table 3). WHR calculated from WC and HC measured manually and estimated by BIA showed a very weak correlation, and there was no linear relationship between the values (Table 3). %BF determined by the BIA method is statistically different from ADP for the total group and males. There was no significant bias for females, which is small and equal to 0.219 (Table 4, Figure S1). The Bland-Altman plots revealed that InBody 770 underestimated of %BF in the total group, and in males, the bias is 0.600 and 1.237, respectively. In addition, the regression line indicates that bias for males and females was not distributed homogeneously (for men: \(\beta\) = 0.372, \(p\)\(\leq\) 0.001; \(\beta\) = 0.183, \(p\) = 0.039). The mean difference in %BF for the ADP and BIA methods was 0.600 percentage points for the total group. LOA was from \(-\)6.222% to 7.422%, and the Bland-Altman index was 6.4%, which indicates low compliance. Results of the Bland-Altman index for females and males were 5.5% and 6.6%, respectively. Moreover, in the subgroup with normal body weight, the Bland-Altman index
## 5 Results and Discussion
### Polymethyl Methacrylate (PMMA)
In this PMMA plate, the calculated attenuation and observed values were in better agreement than other cases, especially for the asymmetric case, as the following deviation statistics indicate. Here, percent deviation for the symmetrical case is larger, but this is due to low \(\alpha\)-\(S_{\text{o}}\) values below 100 kHz, which are comparable to measurement error. The \(\alpha_{\text{T}}\) value of this 6.3-mm plate was measured using four samples from four corners, 600 mm apart. Results were within \(\pm\)5%, indicating a good uniformity. As was the case for the 1.8-mm sheet  
Figure 15: (**a**) Observed \(\alpha\)–\(S_{\text{o}}\) values for 6.3-mm PMMA plate along with the calculated attenuation and dashed lines for \(\alpha_{\text{L}}\) and \(\alpha_{\text{T}}\). Three test series are plotted using blue symbols. Dot and triangle data used Olympus V101 Tx and PAC Pico Rx, and + symbols used Olympus V101 Tx and KRN-BB-PC Rx. All with short-pulse excitation. Vertical bar shows 2 \(\times\) dev. (**b**) Observed \(\alpha\)–\(A_{\text{o}}\) values for 6.3-mm PMMA plate along with the calculated attenuation and dashed lines for \(\alpha_{\text{L}}\) and \(\alpha_{\text{T}}\). Three test series are plotted using red symbols. Dot and triangle data used Olympus V103 Tx and NDT C16 Tx at 60\({}^{\circ}\) incident angle, respectively, with PAC Pico Rx, and + symbols used Olympus V221 Tx and KRN-BB-PC Rx. The poling direction of V221 was oriented to match the plate surface normal. All with short-pulse excitation. Vertical bar shows 2 \(\times\) dev. discussed above, the observed discrepancies cannot be attributed to bulk-wave attenuation, leaving the sensor-plate coupling as the main cause of the measurement error. From the above examination of Lamb-wave attenuation of PMMA plates, a satisfactory agreement between theory and experiment is found for \(\alpha\)-S\({}_{0}\) and \(\alpha\)-A\({}_{0}\).
## 6 Analysis and Discussion of the Results
#### 6.2.1 HF Concentration  
Figure 13 shows that the burning BEB causes a substantial increase in the HF levels along the tunnel length compared to the ICEB fire, in both cases of ventilation investigated.  
By considering the scenario of the naturally ventilated tunnel, Figure 13 also shows that when the BEB catches fire, the HF concentrations are, except in the proximity of the two tunnel portals, always above the threshold value of 30 ppm [7], achieving a maximum value of approximately 85 ppm and 80 ppm downstream and upstream of the fire, respectively. During the burning of the ICEB, the HF concentrations are above 30 ppm at a distance from the fire center in the range of 110-295 m downstream and 100-290 m upstream of the bus in flames, with a peak value of approximately 40 ppm both downstream and upstream of the fire. These results indicate that the natural ventilation alone is not enough to prevent the back-layering phenomenon.  
With reference to the mechanical ventilation, from Figure 13, it can be noted that for both types of bus fires, the HF levels are above the tenability limit only immediately downstream of the fire, while they are almost equal to zero upstream of the bus in flames. These results prove that longitudinal mechanical ventilation can prevent the back-layering phenomenon, pushing toxic gases out of the tunnel through Portal \(B\). It is also worth saying that, in this case, the maximum HF concentration measured for the burning BEB is much higher than that corresponding to the ICEB fire (i.e., approximately 80 ppm versus about 40 ppm).
## 2 Problem Setting and the Related Results
For any permutation \(\pi_{k}\in S\) and any scenario \(p\in T\), the difference \(\gamma_{p}^{k}-\gamma_{p}^{t}=:r(\pi_{k},p)\) is called the regret for permutation \(\pi_{k}\) with the objective function \(\gamma\) equal to \(\gamma_{p}^{k}\) under scenario \(p\). The value \(Z(\pi_{k})=\max\{r(\pi_{k},p)\,:\,p\in T\}\) is called the worst-case absolute regret. The value \(Z^{*}(\pi_{k})=\max\{\frac{r(\pi_{k},p)}{\gamma_{p}^{t}}\,:\,p\in T\}\) is called the worst-case relative regret. While the deterministic problem \(1||\sum C_{i}\) is polynomially solvable [14], finding a permutation \(\pi_{t}\in S\) minimizing the worst-case absolute regret \(Z(\pi_{k})\) or the relative regret \(Z^{*}(\pi_{k})\) for the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\) are binary NP-hard even for two scenarios [19; 21]. In [6], a branch-and-bound algorithm was developed to find a permutation \(\pi_{k}\) minimizing the absolute regret for the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum w_{i}C_{i}\), where jobs \(J_{i}\in\mathcal{J}\) have weights \(w_{i}>0\). The computational experiments showed that the developed algorithm is able to find such a permutation \(\pi_{k}\) for the instances with up to 40 jobs. The fuzzy scheduling technique was used in [7; 8; 9; 22] to develop a fuzzy analogue of dispatching rules or to solve mathematical programming problems to determine a schedule that minimizes a fuzzy-valued objective function. In [23], several heuristics were developed for the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum w_{i}C_{i}\). The computational experiments including different probability distributions of the processing times showed that the error of the best performing heuristic was about 1% of the optimal objective function value \(\sum w_{i}C_{i}\) obtained after completing the jobs when their factual processing times became known. The stability approach [5; 11; 12] was applied to the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum w_{i}C_{i}\) in [15], where hard instances were generated and solved by the developed Algorithm MAX-OPTBOX with the average error equal to 1.516783%. Algorithm MAX-OPTBOX constructs a job permutation with the optimality box having the largest perimeter. In Sections 3-6, we continue the investigation of the optimality box for the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\). The proven properties of the optimality box allows us to develop Algorithm 2 for constructing a job permutation with the optimality box having the largest relative perimeter and Algorithm 3, which outperforms Algorithm MAX-OPTBOX for solving hard instances of the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\). Algorithm 3 constructs a job permutation \(\pi_{k}\), whose optimality box provides the minimal value of the error function introduced in Section 5. Randomly generated instances of the problem \(1|p_{i}^{L}\leq p_{i}\leq p_{i}^{U}|\sum C_{i}\) were solved by Algorithm 3 with the average error equal to 0.735154%.
# Key Factors of the Initiation and Development of Polygonal Wear in the Wheels of a High-Speed Train
## 2 Characteristics of Polygonal Wear of High-Speed Wheels
From the statistical results of the tracking test in the field, the most prominent feature of polygonal wear is that there is nearly a one-to-one relationship between the wave number (or dominant order) of the polygonal wear and wheel diameter. For different wheel diameters, the wavelengths (or passing frequencies) of polygonal wear are considerably near at a constant operation speed, which suggests that the vehicle system in operation generates a vibration at nearly the same resonant frequency, thereby causing polygonal wear on the wheels. The resonance of the vehicle leads to polygonal wear. The smaller the diameter, the smaller the wave number.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 1 Introduction
We analyzed the correlation between the execution time, cache miss, and the BIU request and then confirmed that the L2 cache misses and the BIU requests had a significant effect on the execution time. When applying the TDMA and AER models, the TDMA model's execution time decreased by more than 60% due to the reduction of the L2 cache misses and BIU requests. The AER model reduced the interference rather than the contention but showed higher interference than TDMA. The AER model has more context switches as inter-partition communication for phase control. This caused more cache misses. Moreover, AER increased the execution time by 30% due to the control of execution flow between partitions on each core. The proposed multi-TDMA has an execution time similar to TDMA. Nevertheless, the L2 cache misses of multi-TDMA were about five times higher than that of TDMA, due to simultaneous memory access on two cores. Conversely, the BIU requests occurred similarly to TDMA, signifying that the execution time did not increase. In summary, we demonstrate that our proposed multi-TDMA can reduce the interference caused by memory sharing and increase multicore utilization. The contributions of this paper to the literature are as follows:  
* We analyzed the interference caused by memory sharing in a commercial operating system (OS) and commercial off-the-shelf (COTS) hardware adopted in avionics systems. We measured the interference by measuring the execution time, L1 and L2 cache miss, and the BIU request counts. Experiments have shown that L2 cache misses and the BIU requests have a significant impact on the execution time. * We applied an execution model to reduce interference. Then, we propose a method to implement the execution model with pseudo-partition and message queuing of commodity OS, which has limited source code modification. Experimental results show that TDMA can reduce the execution time by reducing the interference due to memory sharing. In contrast, the interference of AER was reduced, but its execution time increased by about 20% due to phase control between cores. * We propose a multi-TDMA model that takes into account the characteristics of the target hardware. We demonstrate that multi-TDMA can reduce interference due to memory sharing by 60%. Multi-TDMA has twice the utilization of TDMA and can reduce the execution time by 30% compared to AER. The remainder of this paper is organized as follows. Section 2 describes the digital avionics system and the execution model used in this study. Section 3 discusses the methods used to implement the execution model in commercial RTOS and the proposed execution model. Section 4 shows the results of measuring the interference caused by memory sharing in the multicore architecture and the analyses of the reductions by applying the execution model. Section 5 discusses related studies that reduce the interference caused by hardware resource sharing in multicores. Finally, Section 6 provides the conclusion of the study.
## 6 Conclusions  
This study proposed a high-speed algorithm for the calculation of the propagation time and attenuation rate distributions of the acoustic waves, which is applicable to onsite quick check of the aeroacoustic measurement in wind tunnel testing. Firstly, the propagation time and attenuation rate distributions, including the refraction of sound by the shear layer, were calculated over 100 patterns of combinations of the wind speed and the microphone position as training data. Subsequently, the data-driven sparse sampling was applied to these training data, and dominant modes and optimum sampling points were discovered. The reconstructed propagation time and attenuation rate distributions can be quickly calculated at sparse points of the sound source localization area. The error of the reconstructed result is 1% or less and significantly smaller than that of the conventional interpolation methods, and the calculation time is 1 s or less. In other words, the proposed method can significantly reduce the computational time of the steering vector in the onsite quick check of the experiment with minimizing the error. Therefore, the proposed method is concluded to be an onsite powerful tool for the aeroacoustic measurement in wind tunnel testing.  
In the future, we will focus on the training data dependency of the data-driven sparse sampling, and we would like to enable more accurate calculations with low computational cost.  
Figure 11: Distributions of error in the sound source grid estimated by TIN interpolation (\(p=r\), 10 sampling points).  
Figure 10: Distributions of error in the sound source grid estimated by IDW interpolation (\(p=r\), 10 sampling points).  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and T.N. developed the computational codes. S.K. performed the numerical experiments under the guidance of [PERSON] and T.N. S.K. analyzed the data under the guidance of [PERSON], T.N., [PERSON], T.N. and H.U. contributed to the data interpretation. S.K., Y.O., [PERSON], [PERSON], T.N. and K.A. contributed to writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The present work was supported by the Japan Society for the Promotion of Science KAKENHI (No. 19H00800), CREST(JPMJCR1763), and ACTX(JPMJAX20 AD) of Japan Science and Technology Agency.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Audio Feedback with the Use of a Smartphone in Sailing Training among Windsurfers
## 2 Methods
### The Application Operation According to Basics of Sailing Theory  
The operation of the application SoniSailing covers tactical situations during sailing upwind, including tacking. The primary function of the application is based on the so called \"no-go zone\", which is the angle between the close-hauled lines on portside tack and on starboard tack. The angle depends on several factors (e.g., wind speed, the type of the hull, etc.) and ranges predominantly from 60\({}^{\circ}\) to 120\({}^{\circ}\). The greater the deviation from the no-go zone, the faster the sailing speed, but at the cost of a longer distance. Maintaining the optimal course during tacking as an adequate ratio of speed to distance covered is one of the most important skills during tacking. Primarily, the competitor must distinguish any changes in wind direction, as this shifts the whole no-go zone (Figure 1). In option A, the line of wind direction is aligned with the line between the start and windward buoy, whereas option B presents a shift of 20\({}^{\circ}\) in wind direction. If the competitor does not notice the change, he or she can unconsciously bear away to less beneficial position (option B). The second competitor, when continuing port tack course with changed wind direction (W2), will face a longer distance to the windward buoy in the case of consecutive unfavorable change.  
Slow changes are often difficult to notice, especially on large bodies of water. The SoniSailing application can detect and report the unfavorable change of the current motion vector of the competitor in relation to the line between the start and windward buoy. The report can be audibly provided in real time with the use of a speech generator (the spoken deviation angle value in degrees). The system was set to communicate the deviation from the optimal course with an accuracy of 5\({}^{\circ}\). Furthermore, a 5 s break in audio transmission was set to eliminate the effects of dynamic changes of the GNSS signal to prevent users from getting bored or irritated with frequent messages. In case of the windsurfer taking the optimal course, no speech messages were provided by the application, even though the system continued to collect data.  
In addition, the application may inform the competitor about the moment when they reach the so called layline (\"Layline\" sound message). This information can be helpfulin deciding to change tack because, theoretically, this last change allows the competitor to reach the windward buoy most effectively (Figure 2). Assuming a constant speed of both boards, a delayed tack change by the second competitor and, as a result, significantly overstepping layline, may result in unfavorable position and extended distance to the windward buoy.
## 3 Related Work
### Software-Based Countermeasures  
Software-based solutions focus on enhancing cryptographic algorithms and protocols to improve resistance against SCAs without extensive hardware requirements:  
* Dynamic Key Replacement: Mechanisms such as those proposed by [PERSON] et al. (2014) use MTD to periodically update cryptographic keys, reducing the risk of key reuse and subsequent SCAs.
* Lightweight Cryptographic Algorithms: Algorithms like PRESENT and SPECK are designed to minimize computational overhead, making them suitable for IoT applications ([PERSON] et al., 2017). However, their security against advanced SCAs, such as template attacks, remains a topic of ongoing research.  
These solutions provide flexibility and scalability but often involve trade-offs between performance and security, particularly in real-time applications with stringent resource constraints.
## 1 Motivation  
### Related Notations  
Software fault localization contains some concepts, including fault location based on the program spectrum, coverage matrix, etc. To clearly illustrate our proposed approach, we will describe these concepts.  
* Program spectrum-based fault localization: The program spectrum-based method is an efficient fault localization method. The program spectrum refers to a collection of runtime code coverage information. [PERSON] et al. [1] proposed four factors: \(N_{CF}\): the number of times the statement is covered by the fail test case, \(N_{US}\): the number of times the statement is not covered by the fail test case, \(N_{CS}\): the number of times the statement is covered by the pass test case, and \(N_{US}\): the number of times the statement is not covered by the pass test case. [PERSON] et al. uses these four factors to construct a formula to calculate the suspiciousness of each statement in the program and then generates a list of suspicious rankings.
* Coverage matrix: Suppose a program \(P\) has a total of \(M\) statements, and the program \(P\) contains single or multiple faults. Using \(N\) test cases to execute the program \(P\), the execution set of the program \(P\) based on the test cases will be obtained. This set will form a coverage matrix. The behavior of the matrix is the coverage of all statements when each test case is executed. If a statement is covered once during execution, the corresponding value in the matrix is incremented by 1. If a statement is not covered during execution, the corresponding value in the matrix is 0. The columns of the matrix are the set of all statements in program \(P\).
* Result vector: The result vector refers to the test judgment value of the executed test case. The value of the test result includes 1 and 0. The value of 1 stands for the failed test case, and the value of 0 stands for the successful test case.
* Test Case Matrix: The test case matrix refers to a square matrix whose dimension is equal to the number of statements in the program \(P\). The diagonal elements of the square matrix are all 1, and the remaining elements are all 0.