esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm
### Peak Detection  
The fingerprint algorithm is based on the detection of prominent frequencies called _peaks_ in the linear-scale spectrogram. In doing so, the prominence of peaks, i.e., the height of a peak with reference to the closest local minimum, is essential to identify prominent frequencies with relation to respective frequency bands and independently of overall peak amplitudes. As a preparatory step, peaks were identified separately in each linear-scale frequency spectrum.  
First, an estimate of the overall number of peaks \(N_{\hat{p},all}\) was derived by using the averaged linear-scale spectrogram  
\[\hat{S}_{av}(k)=\frac{1}{L_{\mathrm{G}}}\sum_{i=1}^{L_{\mathrm{G}}}\hat{S}_{i _{i}}(k) \tag{6}\]  
on a logarithmic scale, i.e., \(\hat{S}_{av,log}=log_{10}\hat{S}_{av}\). The overall number of peaks \(N_{\hat{p},all}\) was determined in the signal \(\hat{S}_{av,log}\) by detecting peaks with a minimum prominence of 0.1. This means that the height of peaks had to exceed the corresponding local minima by at least 0.1. In addition, only peaks with a minimum distance of 10 frequency bins \(k\) to the next peak were considered. Thereby, the high variability of the signal \(\hat{S}_{av,log}\) was taken into account and only one peak per relevant component was detected.  
The overall number \(N_{\hat{p},all}\) was then used to select the number of single peaks, which should be identified per linear-scale spectrum. The best trade-off between minimum noise and a maximum number of prominent peaks had to be found empirically for each measurement setup. A reduction factor of \(p_{red}\) was introduced and the most prominent \(N_{\hat{p},single}=p_{red}\cdot N_{\hat{p},all}\) peaks were detected for each linear-scale spectrum. In the following analysis, peaks \(\hat{p}_{i,n}\) are described by their prominence \(p_{i,n}\), width \(w_{i,n}\) and location \(l_{i,n}\), with \(i=1,...,L_{\mathrm{G}}\) corresponding to the reference grid \(\hat{p}_{1},\hat{p}_{2},...,\hat{p}_{L_{\mathrm{G}}+1}\) and peak numbers \(n\) corresponding to discrete frequency bins \(k\) with \(n\in\{1,...,N\}\). Figure 6 (right) displays detected peak locations with colours corresponding to their properties (i.e. related to Type 1 and Type 2 vibrations or noise) as derived subsequently in Sections 3.3-3.5.
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 3 Experimental Procedures  
The effect of different admixtures and variable activator content on compressive and flexural strengths and water absorption of the fly ash-based geopolymer mortar was investigated in this study. The ratio of activator to fly ash was variable (AC/FA = 0.5, 0.6, and 0.7). The ratio of admixtures was held constant at 0.8% by weight of fly ash, based on the manufacturer's recommendations. To prepare the mortar, fly ash was mixed with sand for 2 min in the mixer. The activator and specific admixture were then added, and the mixing continued for another 3 min.  
Based on those assumptions, the mix proportions of geopolymer mortars were prepared and presented in Table 4. Additionally, three series of control samples were prepared without admixtures.  
For each mortar series twelve 40 \(\times\) 40 \(\times\) 160 mm mortar specimens were cast. Samples were compacted for 20 s on a vibrating table. Then they were cured for 24 h at a temperature of 65 \({}^{\circ}\)C. For each series, compressive and flexural strengths, and water absorption tests were performed after 28 days of curing.  
Each compressive and flexural strength test was performed on six specimens in accordance with EN 196-1 [27]. Six specimens were used for water absorption and density tests, following the PN-B-04500 standard instructions [28]. Statistical analyses were performed using Tibco Software Inc. Statistica version 13.3 software [29].
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 3 Experimental Validation  
In order to demonstrate the effectiveness and superiority of the proposed DIES method, this section utilizes two experimental datasets. That is, the IMS Incipient Bearing Faults dataset [30] and the HIT Inter-shaft Bearing Faults dataset [1]. The former is primarily employed as a benchmark to assess the effectiveness of the proposed method in extracting  
Figure 9: A flowchart of the proposed DIES method.  
weak fault signals obscured by strong background noise. The latter is utilized to verify the effectiveness and generalization of the proposed method in addressing challenges posed by high background noise and dual-frequency excitation problems.
## 2 Theoretical Models
### PSO-SVM Prediction Model  
For seeking the mapping relationship between input and output, SVM is employed as a learning machine based on the statistical learning theory and the principle of structural risk minimization. The SVM possesses excellent computational capabilities and strong generalization ability, making it suitable for the simulation requirements of this study. The core concept of SVM involves transforming the input variables into a high-dimensional space through a nonlinear transformation and subsequently identifying the optimal linear classification surface. This process is primarily accomplished by defining various kernel functions.  
Different kernel functions have different characteristics, and for arbitrary function, \(h(x)\) and \(\psi(x)\) can be written as  
\[\int\int G(x,y)h(x)h(y)dxdy>0\ \ \int g^{2}(x)dx<\infty \tag{4}\]  
\[G(x,y)=\sum_{i=1}^{\infty}\alpha_{i}\psi(x)\psi(y)\ \ \ \ \alpha_{i}\geq 0 \tag{5}\]  
where the kernel function \(G(x,y)\) is an inner product in the feature space. The commonly used kernel function in this work refers to the Gaussian radial one, which can be expressed as  
\[G(x,y)=\exp\left(-\gamma\|x-y\|^{2}\right)=\exp\left(-\frac{\|x-y\|^{2}}{2\theta ^{2}}\right) \tag{6}\]  
where \(\theta\) is the telescopic scale and \(\gamma\) is a kernel function parameter.  
PSO is a simple algorithm with strong global search ability, which can be used as an intelligent optimization algorithm for swarm algorithms to optimize the SVM parameters, resulting in the creation of the PSO-SVM prediction model. This model combines the global  
Figure 3: BPNN prediction process.  
search capability of the PSO algorithm with the ability of the optimized SVM to avoid complex genetic operations. The formulas for the PSO algorithm are presented below:  
\[v_{id}=v_{id}+c_{1}r_{1}(p_{id}-x_{id})+c_{2}r_{1}\left(p_{gd}-x_{id}\right) \tag{7}\]  
\[x_{id}=x_{id}+v_{id} \tag{8}\]  
where \(i=1,2,\ldots,D\); \(d=1,2,\ldots,D\); \(c_{1}\) and \(c_{2}\) are learning factors, whose values are generally non-negative; \(r_{1}\) and \(r_{2}\) are random constants, ranging from 0 to 1; and \(v_{id}\in[-v_{\max},v_{\max}]\).  
The specific calculation process of the PSO-SVM prediction model is illustrated in Figure 4.
# Reliability by Using Weibull Distribution Based on Vibration Fatigue Damage
## 2 Fatigue Damage
### Weibull Analysis  
A two-parameter Weibull distribution is used to statistically analyze fatigue behaviors [23; 24; 25]. It allowed us to perform accurate fatigue failure analysis [26; 27]. The probability density function \(f(t)\) and cumulative distribution function \(F(t)\) are described by Equations (6) and (7), respectively.  
\[f(t)=\frac{\beta}{\eta}\left(\frac{t}{\eta}\right)^{\beta-1}exp\left\{-\left( \frac{t}{\eta}\right)^{\beta}\right\} \tag{6}\]  
\[F(t)=1-exp\left\{-\left(\frac{t}{\eta}\right)^{\beta}\right\} \tag{7}\]  
where, \(\beta\) is the shape parameter, \(\eta\) is the scale parameter, and \(t\) is the selected random variable (damage or fatigue life). The corresponding reliability function \(R(t)\) is given as  
\[R(t)=exp\left\{-\left(\frac{t}{\eta}\right)^{\beta}\right\} \tag{8}\]  
From [28], the Weibull fatigue damage \(\beta_{D}\) and \(\eta_{D}\) parameters are determined as  
\[\beta_{D}=\frac{-4\mu_{y}}{0.995\times ln\left(\frac{\sigma_{1}}{\sigma_{2}} \right)} \tag{9}\]  
\[\eta_{D}=exp(\mu_{x}) \tag{10}\]  
where \(\mu_{y}\) represents the mean of the \(Y\) vector (see Equation (13)) determined by using Equation (5). \(\mu_{x}\) represents the log-mean of the failure-time data, which is determined here directly from the addressed maximum \(\sigma_{1}\) and minimum \(\sigma_{2}\) stress values of Section 2.1. Thus, \(\mu_{x}\) is determined as  
\[\mu_{x}=ln(\sigma_{1}\sigma_{2})^{\frac{1}{2}} \tag{11}\]Here, notice that the efficiency of the Weibull parameters \(\beta_{D}\) and \(\eta_{D}\) only depends on the accuracy with which the \(\sigma_{1}\) and \(\sigma_{2}\) values are determined by Equation (1). In this paper, they were determined from an electrodynamic shaker acceleration response and by the method performed in [17], Section 3.1.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 3 Distance Protection Methods
### I/N Protection Threshold  
In the distance protection method, there is a maximum permitted interference at the input to the FSS ES. The long-term interference criterion, defined in [13], is considered herein to access the interference from the cellular systems at the FSS ES receivers. For the long-term interference, however, two cases can be considered depending on the types of scenarios noted in recommendation ITU-R S.1432:  
* In-band sharing: 1. I/N = \(-\)12.2 dB, corresponding to the total interference received from other systems having co-primary statuses during 100% of the worst month. 2. I/N = \(-\)10 dB, corresponding to the aggregate interference received from the co-primary allocations during 20% of any month.
* Adjacent band sharing: 1. I/N = \(-\)20 dB, corresponding to the aggregate interference received from all other sources during 100% of the time.  
Based on the above criteria, in our simulation, we set I/N = \(-\)12.2 dB as the in-band interference threshold.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
###### Abstract  
This paper presents a dual-mode low-density parity-check (LDPC) decoding architecture that has excellent error-correcting capability and a high parallelism design for fifth-generation (5G) new-radio (NR) applications. We adopted a high parallelism design using a layered decoding schedule to meet the high throughput requirement of 5G NR systems. Although the increase in parallelism can efficiently enhance the throughput, the hardware implementation required to support high parallelism is a significant hardware burden. To efficiently reduce the hardware burden, we used a grouping search rather than a sorter, which was used in the minimum finder with decoding performance loss. Additionally, we proposed a compensation scheme to improve the decoding performance loss by revising the probabilistic second minimum of a grouping search. The post-layout implementation of the proposed dual-mode LDPC decoder is based on the Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm complementary metal-oxide-semiconductor (CMOS) technology, using a compensation scheme of grouping comparison for 5G communication systems with a working frequency of 294.1 MHz. The decoding throughput achieved was at least 10.86 Gb/s without evaluating early termination, and the decoding power consumption was 313.3 mW.  
error correction code; low-density parity-check code; min-sum algorithm; ASIC implementation
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 4 HIL Results and Comparison
### Switching Frequency Comparison  
Finally, the switching frequency (\(f_{sw}\)) of each control algorithm has been analyzed in Figure 19, given that one of the key characteristics of DC and MPC is their variable switching frequency operation. To assess this, a Fast Fourier Transform (FFT) was used to estimate the spectrum of the voltage between phase \(a\) of the converter and the negative terminal of the DC bus, \(V_{uN}\), which value commutates between 0 and \(V_{DC}\).  
The first two graphs, corresponding to the FBC and VFOC, exhibit similar harmonic profiles since both use PWM for voltage modulation, which enforces a fixed switching frequency. As seen in both cases, the dominant harmonic components, aside from the \(DC\) component and the fundamental frequency, appear at the predefined switching frequency of 3000 Hz, as well as in its neighboring frequencies and higher multiples, albeit with lower intensity. This behavior is industrially accepted [41].  
Figure 19: Switching frequencies.  
Figure 18: Instantaneous currents during the transition to islanded mode.  
On the other hand, for the DC and MPC models, the harmonic concentration is more dispersed, indicating that the switching frequency varies dynamically. In the case of DC, the switching frequency predominantly concentrates around 4000 Hz, with the presence of low-frequency harmonics, which could potentially introduce operational issues. Meanwhile, for MPC, the harmonic content in the voltage fluctuates around 5000 Hz. The fact that these control strategies operate at a higher switching frequency is a notable drawback, although both control schemes can be adjusted to operate at other switching frequencies, particularly MPC, where modifications to the cost function could control the switching frequency and voltage spectrum [46; 47]. Higher switching frequencies lead to increased switching losses in the power semiconductors and impose greater computational demands on the control hardware.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Extreme Gradient Boosting Classifier  
In crop recommendation, the extreme gradient boosting classifier predicts suggested crops based on decision trees, repeatedly. Features like soil, climate, season, and economic factors shall be used as inputs. Here, the trees are generated in parallel. They handle the complex relationships in the data and regularize them to stop the overfitting problem. Extreme gradient boosting (XGBoost) has higher performance in classification than gradient boosting.  
Consider \(Y_{i}\) as the output predicted for the \(i_{\text{th}}\) observation, \(Q\) is the count of trees observed in the model, \(F_{q}\) shows the \(q^{th}\) tree in the ensemble approach, and \(X_{i}\) is the feature for the \(i_{th}\) observation.  
Then, the Equation (17) for XGBoost can be represented as follows:  
\[Y_{i}=\sum\limits_{q=1}^{Q}F_{q}(X_{i}) \tag{17}\]  
Here, \(F_{q}\) is a tree that gives out results for each observation. Then, the final prediction shall be found by the summation of all the predictions.
## 3 Results and Discussion
### GC--MS Investigation of Microcapsules  
The yarrow essential oil composition in its free form and following microencapsulation is presented in Table 7.  
In the case of the microencapsulated essential oil, the same major components could be identified, but instead of the initial 45 components, only 33 were present. The loss of some components might be attributed to the microencapsulation technology applied which comprises an emulsification step of essential oil in the gelatin solution performed at approx.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Compound** & \multicolumn{2}{c}{**Free AMO**} & \multicolumn{2}{c}{**Microencapsulated AMO**} \\  & & **Peak\%** & \multicolumn{2}{c}{**Peak\%**} \\ \hline
1. & thujene & 1.08 & 1.12 \\
2. & alphapinene & 4.47 & 4.68 \\
3. & camphene & 0.60 & 0.51 \\
4. & **sabinene** & **8.31** & **9.05** \\
5. & **beta pinene** & **8.59** & **10.81** \\
6. & beta myrcene & 0.41 & - \\
7. & alpha terpinene & 0.47 & - \\
8. & p cyprene & 1.55 & 1.44 \\
9. & D limonene & 2.14 & 1.79 \\
10. & **eucalypt** & **5.09** & **2.73** \\
11. & gamma-terpinene & 2.18 & 1.88 \\
12. & artemisinin ketone & 1.93 & 1.87 \\
13. & terpinolene & 0.32 & - \\
14. & **linalool** & **6.78** & **6.00** \\
15. & alpha thujone & 2.84 & 3.04 \\
16. & beta thujone & 0.46 & - \\
17. & **2-bornanone (camphor)** & **7.84** & **6.68** \\
18. & 1-methone & 0.27 & - \\
19. & endo borneol & 1.77 & 1.75 \\
20. & terpinen 4-ol & 1.66 & 1.16 \\
21. & alpha terpineol & 0.42 & - \\
22. & cis dihydrocarvone & 0.56 & 0.43 \\
23. & pulgeone & 0.30 & - \\
24. & D-carvone & 0.60 & 0.53 \\
25. & **piperitone** & **5.85** & **4.72** \\
26. & sabinil acetate trans & 0.32 & - \\
27. & bornyl acetate & 2.68 & 3.74 \\
28. & thymol & 1.31 & 2.25 \\
29. & diosphenol & 0.40 & - \\
30. & carvacol & 1.41 & 1.27 \\
31. & piperitone & 0.52 & - \\
32. & 4-acetyl-1-methylcyclohexene & 1.86 & 0.98 \\
33. & beta bourbonene & 0.76 & 0.71 \\
34. & beta elemene & 0.54 & 0.43 \\
35. & **caryophyllene** & **7.74** & **9.82** \\
36. & humulene & 1.57 & 1.74 \\
37. & beta farnesene & 1.07 & 0.73 \\
38. & gamma muurolene & 0.28 & - \\
39. & **germacene D** & **6.10** & **7.10** \\
40. & trans alpha bergamotene & 0.63 & 0.33 \\
41. & cis-\(\gamma\)-bisabolene & 0.38 & - \\
42. & cadinene & 0.79 & 0.90 \\
43. & caryophyllene oxide & 1.09 & 1.65 \\
44. & bisabolon oxide A & 0.20 & 0.39 \\
45. & **chamazulene** & **3.80** & **5.14** \\ \hline  & **Total** & **99.94\%** & **97.37\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: GC-MS results of components for free and microencapsulated AMO.  
50 \({}^{\circ}\)C as well as the drying phase, which is accomplished in reduced pressure conditions, during which some highly volatile components could be lost.
## 5 Experimental Results  
Given the noise caused by torque ripple, EMI, and economic efficiency, the 110-degree model was adopted and manufactured as a prototype to prove the validity of design via experiments. For noise, it is assumed that higher inherent torque ripple causes more acoustic noise according to the literatures [7; 8; 9; 10; 11; 12]. Figure 13 is the manufactured prototype of V-shaped IPMSM of which the included angle is 110 degrees. Two sorts of experiments were performed to compare the results from FEA. Back-EMF and MTPA trajectory were identified, which are respectively no-load and under-load tests.  
The experimental testbed is equipped with dynamometer, torque sensor, power analyzer, three-phase inverter and control board with DSP. The entire testbed is described as shown in Figure 14. The dynamometer is connected to the target motor through the torque sensor coupled with both in between them. Three-phase inverter and its control board (DSP) are integrated on the same board  
Figure 12: Mean value of torque, torque ripple, and efficiency with magnet volume.  
Figure 13: stator and rotor manufactured for experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Angle** & **Magnet Volumes** & **Mean Value of Torque** & **Torque Ripple** & **Efficiency** \\
**[degree]** & **[cm\({}^{3}\)]** & **[Nm]** & **[\%]** & **[\%]** \\ \hline
70 & 15.98 & 8.04 & 10.93 & 94.2 \\
80 & 14.31 & 7.68 & 10.34 & 93.8 \\
90 & 13.16 & 7.53 & 7.97 & 93.7 \\
100 & 12.53 & 7.31 & 7.05 & 93.4 \\
110 & 11.80 & 7.16 & 6.25 & 93.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Magnet volumes and resulting performances under load.  
being connected with PC and target motor respectively for a download of firmware and power supply to the target motor. The torque sensor measures while under-load test is ongoing and sends the data to the power analyzer such that power analyzer computes power and efficiency.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Parameters** & **Malignant** & **Benign** & **Control** & **Average** \\ \hline DT & precision & 100 & 76.9 & 100 & 92.3 \\ \cline{2-6}  & sensitivity & 90 & 100 & 80 & 90 \\ \cline{2-6}  & specificity & 100 & 85 & 100 & 95 \\ \cline{2-6}  & accuracy & 90 & 90 & 90 & 90 \\ \cline{2-6}  & F1-score & 94.7 & 87 & 88.9 & 90.2 \\ \hline SVM & precision & 81.8 & 76.9 & 100 & 86.2 \\ \cline{2-6}  & sensitivity & 9 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Parameters** & **Malignant** & **Benign** & **Control** & **Average** \\ \hline DT & precision & 90 & 90 & 100 & 93.3 \\ \cline{2-5}  & sensitivity & 90 & 90 & 100 & 93.3 \\ \cline{2-5}  & specificity & 95 & 95 & 100 & 96.7 \\ \cline{2-5}  & accuracy & 93.3 & 93.3 & 93.3 & 93.3 \\ \cline{2-5}  & F1-score & 90 & 90 & 100 & 93.3 \\ \hline SVM & precision & 100 & 100 & 62.5 & 87.5 \\ \cline{2-5}  & sensitivity & 80 & 60 & 100 & 80 \\ \cline{2-5}  & specificity & 100 & 100 & 70 & 90 \\ \cline{2-5}  & accuracy & 80 & 80 & 80 & 80 \\ \cline{2-5}  & F1-score & 88.9 & 75 & 76.9 & 80.3 \\ \hline KNN & precision & 90 & 77.8 & 63.6 & 77.1 \\ \cline{2-5}  & sensitivity & 90 & 70 & 70 & 76.7 \\ \cline{2-5}  & specificity & 95 & 90 & 80 & 88.3 \\ \cline{2-5}  & accuracy & 76.7 & 76.7 & 76.7 & 76.7 \\ \cline{2-5}  & F1-score & 90 & 73.7 & 66.7 & 76.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation, including precision, sensitivity, specificity, accuracy, and F1-score, for the DT, SVM, and KNN classifiers using t-SNE dimensionality reduction technique to show the saliva hormones performance from normal subjects, benign patients, and malignant patients. Figure 6: Confusion matrices for the DT, SVM, and KNN classifiers using FA dimensionality reduction technique to demonstrate the saliva hormones performance from the normal subjects, benign patients, and malignant patients. Figure 7 shows the confusion matrix for identifying normal subjects, benign patients, and malignant patients from the salivary levels of P, T, C, and HCG using statistical features after using the t-SNE dimensionality reduction techniques and KNN, SVM, and DT classifiers, respectively. The correct recognition is seen on the diagonal, while the substitution errors are seen off of the diagonal. Figure 7 depicts the percentage of the data that was properly identified by the DT, SVM, and KNN classifiers using t-SNE when the salivary level hormones were used as the variable of interest. The accuracy of the DT was 93.3%, while that of the SVM was 80%, and that of the KNN was 76.67%. Because of this, the DT was successful in correctly identifying 9 patients with cancer, 9 people with benign conditions, and 10 control patients. Thus, one malignant patient was incorrectly classified as benign, and one of the benign patients was incorrectly classified as a malignant patient.
## 1 Introduction
Based on the analysis, we concentrate on Seluge [12]. Security-wise, it is one of the most promising OTAP protocols. It uses the Merkle tree structure. Including the inefficiency of the Merkle tree, it suffers, due to: (i) an exponential growth in the number of overhead packets when a block size of the hash algorithm used in the design is increased; and (ii) the limitation of the hash algorithms with a larger block size. To handle these issues, we propose a new OTAP protocol named Seluge++, which complies with SMR and uses an efficient method compared to the Merkle tree. Analytical and simulation results show the potential of Seluge++ compared to Seluge. Previous efforts on securing the dissemination of code updates in WSNs are studied in Section 2. Section 3 analyzes the weaknesses of Merkle trees to show that previous works that have employed the Merkle tree in their design are vulnerable to attacks. Assumptions and threat model are presented in Section 4. Afterward, in Section 5, a list of prerequisites required in the design of a secure OTAP scheme is identified, which is correspondingly named the _Security Model Requirements (SMR)_. The design details of Seluge++ that address the current issues with OTAP schemes in scenarios where security must be at the highest level possible are given in Section 6. The behavior and application of the message-specific puzzle as a weak authentication mechanism used in Seluge++ is described in Section 7. The results of security analysis and performance evaluation in Section 8 show Seluge++ establishing a balance between energy consumption and the provided security level. The compatibility of Seluge++ with SMR will also be presented in this section. Finally, the conclusion comes in Section 9 to sum up our findings.