esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 2 Quasi-Static Modeling of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrication
### The Geometric Approach Relationship Between Rollers and Races  
As shown in Figure 2, the interaction between rollers and races is derived from the bearing's geometry. An inertial coordinate system O\({}_{\rm i}\)x\({}_{\rm i}\)y\({}_{\rm i}\)z\({}_{\rm i}\) is established, with analysis confined to the x\({}_{\rm i}\)O\({}_{\rm i}\)z\({}_{\rm i}\) plane. The outer ring is stationary, while the inner race undergoes translational motion along the z\({}_{\rm i}\) axis.  
Let the position vector of the center of the inner ring relative to the center of the coordinate system be v\({}_{\rm r}\); the position vector of the center of the cylindrical roller relative to the center of the coordinate system be v\({}_{\rm b}\); and finally, the position vector of the center of the cylindrical roller relative to the center of the inner ring be v\({}_{\rm br}\):  
\[{\rm v}_{\rm br}={\rm v}_{\rm b}-{\rm v}_{\rm r} \tag{7}\]  
The geometric approach \(\Delta\) between the roller and inner race is expressed as follows:  
\[\Delta=\pm\bigg{(}|{\rm v}_{\rm br}|-\frac{D_{m}}{2}\bigg{)} \tag{8}\]  
Figure 2: Relationship between displacement and deformation of the loaded bearing.  
where \(\rm D_{m}\) is the bearing pitch diameter; \"+\" is used when indicating the outer ring of the bearing; and \"-\" is used when indicating the inner ring of the bearing.  
As illustrated in Figure 3, based on [PERSON]'s conclusions [24], under the assumption of elastohydrodynamic lubrication conditions for the contact in a roller bearing, the relationship among geometric approach \(\Delta\), elastic deformation \(\delta\), and film thickness \(h\) is as follows:  
\[\rm H=\frac{\delta}{R_{x}}-\frac{\Delta}{R_{x}} \tag{9}\]  
where \(\rm H\) is the dimensionless oil film thickness, with \(\rm H=\frac{hR_{x}}{b^{2}}R_{x}\), where \(R_{x}\) is the equivalent radius of the rolling direction.  
The determination of elastic deformation \(\delta\) serves as a prerequisite for calculating the contact load Q. Its value can be solved through Equation (9) based on the functional relationship between oil film thickness and the geometric approach quantity. The precise computation of oil film thickness requires the establishment of an elastohydrodynamic lubrication theoretical model and its equations governing the relationship between cylindrical rollers and raceways.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 1 Introduction
A major repair is often associated with high costs due to the replacement of those defective components that cannot be repaired and the involvement of a range of maintenance resources, such as a large crane vessel that has powerful lifting capability, a large team to deliver the major repair, etc. Since so many maintenance resources are already in use it is wise to take the opportunity to conduct a full inspection of the wind turbine once the major repair is completed. To investigate the impact of such an approach upon the safety and reliability of the turbine system, in the PN model developed a full inspection of the entire wind turbine system is conducted following performing a major repair. Such an approach would benefit wind farms in the long run as they could run at the lowest cost. Since the relevant cost would be covered in the contract for performing the major repair, it will not be considered separately in the PN model developed. However, it is worth noting that this measure will affect the health state of the wind turbine components in the model. Measure (3)--Equip the wind turbine with a CMS that has a more powerful fault detection capability. The study of this measure will investigate the impact of the application and quality of the CMS on the O&M of the OWT. Today, CMSs have been widely accepted as a valid tool to aid the O&M of OWTs [20; 21; 22]. Therefore, most of the existing maintenance models have considered them. However, a diversity of wind turbine CMSs has currently been developed. They are different in hardware cost and fault detection capability. Such differences are not taken into account in the existing models, resulting in inaccurate prediction results. This issue has been overcome by the new PN model developed in this paper. The remaining part of the paper is organised as follows. In Section 2, an OWT composed of six critical subsystems is defined; In Section 3, the PN-based modelling technique is briefly reviewed; In Section 4, four types of PN models are developed for describing the maintenance activities in offshore wind farms; In Section 5, the downtime and maintenance costs in different scenarios are calculated using the PN models developed and the obtained results are discussed; In Section 6, the paper concludes with key research findings.
## 5 Experiments and Results  
### Platform and Datasets  
The algorithm proposed in this paper is only suitable for video satellites working in staring mode. Thus, we used remote sensing videos from commercial video satellites to perform experimental validation of the proposed algorithm.  
From Section 3 we know that the smaller the GSD (ground sample distance) of remote sensing images, the better the detection performance of the algorithm. Considering the size differences of different types of aircraft, we suggest that the GSD of video remote sensing images should be approximately 1 m or less than 1 m. Therefore, two video instances of high resolution with a GSD of less than 1 m (0.92 m), and low resolution with a GSD of larger than 1 m (1.13 m), are used for verification here. Table 2 outlines the main parameters of the two remote sensing videos. These two videos have a frame rate of 25 fps but are of different image sizes. The scenes in both videos are at the end of an airport runway and its surrounding environment.  
The complete algorithm was deployed and tested on an embedded AI processor called NVIDIA Jetson AGX Xavier.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Support Vector Machine (SVM) Classifier Based Machine Learning Approach [66]:  
SVM classifiers are fed multiple features extracted from the system parameters such as voltage. The classifiers then determine the islanding condition of the system. Linear, polynomial and Gaussian Radial Basis Function kernels are used to train the machine and further islanding and non-islanding data are used to fine-tune the system. The proposed method is tested with various data with all the possible combinations of active and reactive power imbalances. The resulting method yields exceptional results when compared with some passive methods of islanding detection such as ROCOF and over/under frequency.
# A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining
## 2 Literature Review
### Pre-Large Concept  
The mining of useful and meaningful information is a critical target in data mining [1; 2; 43], and most of the studied algorithms have focused on mining information from a static database.  
Thus, when the size of the database changes, a conventional approach is to re-process the updated database and re-mine the required information again to reveal the most up-to-date information. The fast updated (FUP) concept [44] was presented to handle dynamic data mining, particularly for a transaction insertion. The FUP concept is efficient at updating discovered information, but it still needs to rescan the original database again in certain cases. The pre-large concept [45; 46] was presented to more efficiently update the mined information, and can avoid the need for multiple database scans if the number of newly inserted transactions does not achieve the pre-defined safety bound. It uses two thresholds to maintain large (frequent) and pre-large itemsets, and treats pre-large itemsets as a buffer for later maintenance. Thus, movement of a large itemset directly to a small itemset can be significantly reduced, and vice versa. Although this approach needs extra memory use for a pre-large itemset, it is efficient at handling the problem of dynamic data mining, particularly for a transaction insertion. Figure 1 shows nine cases of the pre-large concept.  
Figure 1 indicates that the itemsets in cases 2, 3, 4, 7, and 8 will not affect the final results of the frequent itemsets. In case 1, some frequent itemsets may be removed, and, in cases 5, 6, and 9, the newly frequent itemsets may be generated. If all large and pre-large itemsets are maintained, then cases 1, 5, and 6 can be easily handled. For a special case, such as case 9, an itemset cannot be a large itemset in the updated database if its support value satisfies the following equation:  
\[f\leq\frac{(S_{u}-S_{l})\times|D|}{S_{u}}, \tag{3}\]  
where \(S_{u}\) is the upper-bound value, which is the same as the traditional minimum support threshold; \(S_{l}\) is the lower-bound value; and \(|D|\) is the size of the original database. If the number of deleted transactions satisfies the above condition, which is smaller than the safety bound (\(f\)), the itemset in case 9 cannot be a large itemset; it can only remain as a pre-large or small itemset. Thus, the original database needs to be rescanned and the computational cost can be reduced.
# Enhancing Feature Selection for Imbalanced Alzheimer's Disease Brain MRI Images by Random Forest
## 4 Results and Analysis
### Experimental Results and Evaluation
#### 4.3.1 The Effect of the Parameter K on the Proposed Algorithm  
To prove the validity of the proposed feature selection algorithm, we applied it to the data of NC-AD, NC-MCI, and MCI-AD, respectively. In the feature selection process, it is necessary to determine whether to continuously select the \(K\) with the highest ranked features, or the consecutive \(K\) features that have the minimum redundancy with the highest score feature that was selected last time. For each dataset, the influence of different  
\(K\) represents the number of features that need to be selected in the feature selection process and have the minimum redundancy with the previous highest-ranked feature. When \(K\) equals 0, the redundancy among features is not considered. Features are sorted by the AUC and added to the feature subset each time, and the top \(K\) features are finally selected as the feature subset. When the value of \(K\) becomes larger and affected by the redundancy between features, the classification performance will be affected. For the NC-AD group data, when \(K\) equals 1, the classification performance is maximized, and the maximum value is 93.7%. In the case of the NC-MCI group data, when \(K\) equals 2, the classification performance is maximized, and the maximum value is 87.6%. For the MCI-AD group data, when \(K\) equals 1, the classification performance is maximized, and the maximum value is 85.7%. For these three datasets, when we observe the results at the overall level, it can be seen clearly that when \(K\) becomes larger and reaches a certain value, the classification performance will decrease. This is because as \(K\) increases, the redundancy between features is over-considered, and the importance of the features themselves may be ignored.
# Real-Time Seismic Data from the Bottom Sea
## 3 Results  
The results of the validation deployment, done near the OBSEA underwater observatory, have shown that the system is capable of transmitting near real-time seismic data. The telemetry system, via underwater inductive link and surface radio link, is capable of providing a permanent two-way communication link with an acceptable packet loss of less than 3%. Moreover, thanks to the high sensitivity of the broadband marine seismometer, the data collected in these tests has shown that distant  
Figure 10: Fourier tension spectral analysis with extreme Mateo. Spectral density of the effective tension at the seabed (anchor) vs the frequency (Hs). (**a**) ratio 1.2 (**b**); ratio 1.4; (**c**) ratio 0.85 and (**d**) ratio 1.2.  
Figure 9: Fourier tension spectral analysis with normal Mateo. Spectral density of the effective tension at the seabed (anchor) vs the frequency (Hs). (**a**) Line 1 ratio of 1.2 (**b**) Line 1 ratio of 1.4; (**c**) Line 1 ratio of 0.85 and (**d**) Line 1 ratio of 1.2.  
earthquakes could be measured with this system. Figure 11 illustrates the seismic data registered by the seismometer from an earthquake located in the Hautes-Pyrenees, of magnitude 3.7, that occurred on 28 October at 19.06 UTC. As depicted in Figure 12, the travel time difference corresponds to an epicentral distance of the earthquake of about 218 km, which is approximately the distance between the place where the OBS has been deployed and where the earthquake occurred in the Hautes-Pyrenees, that has been recorded by all the seismic stations of the Institut Cartografic i Geologic de Catalunya (www.icgc.cat) and the cabled OBS installed at OBSEA observatory (www.obsea.es).  
In order to validate this seismic event, we compared it with the seismic data from the Garraf Seismic Station [15] (see Figure 14), which is located at approximately 18.4 km from the OBSEA. Moreover, the moored OBS has been validated with the cabled seismometer station of OBSEA [16] which detected the same seismic event as illustrated in Figure 13. This cabled seismometer station is based on the Trillium 120P/PA broadband seismometer together with the Taurus data logger provided by Nanometrics Inc, and a GPS emulator developed with a Stellaris microcontroller [4].  
Figure 11: Recording of a seismic event located at Hautes-Pyrenees, of magnitude 3.7, that occurred on 28 October at 19.06 UTC. A band filter between 4 to 10 Hz has been used to filter the raw seismic data.  
Figure 12: Epicentral distance of the seismic event located at Hautes-Pyrenees.
# Systematic Analysis of Risks in Industry 5.0 Architecture
## 4 Outcomes of the Studies
### Cybersecurity Risks  
Over the past few years, interest in cyber security has significantly increased. As our world becomes increasingly connected, real-time system availability is becoming increasingly important. As a result, enterprises must pay close attention to maintaining and preserving their information assets to prevent the effects that cyberattacks may have on them. The assets play a big role in critical corporate operations. Additionally, users and customers are increasingly appreciating the value of the information provided by various  
Figure 7: Frequency of identified risks based on Table 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Risks** & **Reference Studies** \\ \hline Workforce and training risk & [9; 34; 36; 44; 48; 49; 50; 51; 52; 53] \\ \hline Financial and investment risk & [9; 46; 48] \\ \hline Security risk & [9; 37] \\ \hline Cybersecurity risk & [38; 39; 40; 55; 32; 33; 34; 35; 36; 38; 39; 50; 51; 52; 53; 54; 55; 56; 57] \\ \hline Operational and implementation risk & [34; 35; 37; 43; 44; 49; 51; 53; 54; 55; 57] \\ \hline Technological risk & [36; 45; 56] \\ \hline Social and societal risk & [36] \\ \hline System integration risk & [34; 51] \\ \hline Information and Knowledge Gap Risk & [51] \\ \hline Technical risk & [37] \\ \hline Information security risk & [38; 45] \\ \hline \end{tabular}
\end{table}
Table 5: Types of risks present in reference studies.  
technologies. A cybersecurity risk is the result of the likelihood that a cybersecurity-related incident will occur and its possible effects. It includes a variety of hazards with different technology, attack routes, and techniques, but they all have two things in common: they might have a big impact, and people might think that they are improbable. To identify and manage these dangers, which were previously viewed as unlikely and hence received little attention, cyber security entails activities. Due to their unpredictable nature and the requirement for specialized ways to detect and classify them, cybersecurity risks require a different strategy for management than other categories of hazards [58].  
Confidentiality, integrity, and availability are the three main security objectives as shown in Figure 8, and in a cybersecurity attack, these objectives are violated, leading to attacks on digital systems, networks, and data. It considers the potential for unauthorized access, data breaches, system outages, and data theft.
# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 3 Results
### Application in Real Field  
To validate the versatility, the CNN-denoiser was also applied to the 1510 noisy signals recorded in Zijing tunnel. These noisy signals include various types and intensities of noise so that the SNRs ranged from 0 to 15 dB (Figure 6). SNR [28] is calculated as follows:  
\[\text{SNR}=20\times\log_{10}(S_{Amax}/N_{Amax}) \tag{4}\]  
where \(S_{Amax}\) and \(N_{Amax}\) are peak amplitudes of signal and noise, respectively. As shown in Figure 6, the proposed method provides an excellent denoising performance for microseismic signals with various noise in terms of the improvement of the SNRs, good shape recovery, and amplitude characteristics, even one noisy signal contains more than one microseismic waveform (Figure 6d). The average SNR of the 1510 noisy signals after denoising is increased by 8.48 dB, and the maximum improvement up to 36.21 dB (Figure 7). In addition, the shape and amplitude characteristics of the estimated noise are well preserved. Although the CNN-denoiser is trained on semi-synthetic data, it could well be extended to real noisy signals. This suggests that the proposed method in this study can be directly applied to actual engineering for denoising tasks.  
Figure 5: Distribution of max amplitude difference between real and estimated noise.  
Figure 6: _Cont._  
Figure 6: Denoising examples of different recorded noisy signals in Zijing tunnel (China). The signals in the time domain and time–frequency domain are noisy signal, estimated signal, and estimated noise from top to bottom of each sub-figure. (**a**,**b**) Micrososeismic signals with cyclic noises at different frequency; (**c**) microseismic signal with a mixture of cyclic noise and other noise; (**d**) noisy signal contains more than one microseismic waveform.
## 4 Simulation Results
### Process and Control System Configurations  
The dimensionless CSTR model was simulated with nominal parameter values of \(D_{a}=0.072\), \(\gamma=20\), \(H=8\), and \(\beta=0.3\). This study utilized a 3-20-1 RBFNN architecture: 3 inputs, 20 hidden neurons, and 1 output. The network inputs are the current input (\(u(k)\)), the previous input (\(u(k-1)\)), and the previous output (\(y(k-1)\)), represented as the input vector \(p=[u(k)\), \(u(k-1)\), \(y(k-1)]^{T}\). The RBFNN identifier was trained online using a learning rate (\(\eta_{x}\), \(\eta_{c}\), \(\eta_{h}\)) of 0.05 and a momentum factor (\(\alpha\)) of 0.01. The initial centers and widths of the RBFNN were randomly selected within the ranges \([-15,15]\) and \([0,20]\), respectively. The ANPID and APID controllers were configured with learning rates of 0.08, 0.03, and 0.02 for proportional, integral, and derivative gains, respectively. Both controllers began with the same initial gains of \(K_{p}=0.5\), \(K_{i}=1\), and \(K_{d}=0.2\), obtained through trial and error, to provide a challenging starting point.
## 3 Results and Discussion
### Feasibility Analysis  
#### 3.5.1 Economic Feasibility  
According to data from the 2019 Shenzhen Municipal Bureau of Statistics, the average housing area of every person in Shenzhen city of China was 21.12 m\({}^{2}\), while the average area that required air conditioning was approximately 43 m\({}^{2}\) for a household of four family members. The National Refrigeration Department announced that the typical power of an air conditioner was 3000W for an area from 42 m\({}^{2}\) to 52 m\({}^{2}\). Given the weather conditions in Shenzhen, air conditioners are normally required in the months from April to November, and are expected to operate for a period of 16 h per day. However, an air conditioner is usually intermittently operated, so the empirical operating time was then assumed to be 40-50% of the usage time. Therefore, the annual power consumption by air conditioning per household was calculated as 3000 \(\times\) 16 \(\times\) 6 \(\times\) 30 \(\times\) 0.45/1000 = 3888 kW\(\cdot\)h, or 648 kW\(\cdot\)h monthly (during the 6 months of operating). The Shenzhen Power Grid Department announced that the cost of electricity would be 0.98 RMB/kW-h when the electricity consumption in the current month exceeds 600 kW\(\cdot\)h. As a result, the air conditioning cost per household would be 3810.24 RMB/year.  
Based on the household's annual air conditioning power consumption, the daily power consumption would be 10.65 kW\(\cdot\)h/day or 38.35 MJ/day. To absorb 38.35 MJ of heat, 2360 kg of DP is required, comprising 354 kg paraffin, 2006 kg diatomic, 20,060 kg gypsum and 200.6 kg CF. The market prices of paraffin, diatomite, gypsum and CF were 8.5 RMB/kg, 2.2 RMB/kg, 0.5 RMB/kg and 85.0 RMB/kg, respectively. It is worthwhile to point out that the efficiency of DP depends on temperature and weather conditions, so it was assumed that the PCM had a working efficiency of 50% [49]. Moreover, according to the authors' experience, it will take 30 working days to prepare 2660 kg DP, and 3 working days to fabricate DP/gypsum. The labor cost is approximately 300 RMB/day, and thus the total labor cost is RMB 9900. Based on the above discussions, the payback period of applying structural-functional integrated energy storage gypsum board was 23.31 years, as indicated in Table 4, which is much less than the service life of the building, and it is considered suitable to be used as building components.
## 2 Preliminaries
### IND-CPA Security  
A foundational security notion for modern public-key encryption schemes, including SKFHE schemes and MKFHE schemes, is indistinguishability under chosen plaintext attack (IND-CPA). This notion guarantees that any PPT adversary, even with access to an encryption oracle, cannot distinguish between the ciphertexts of any two messages of their choice with non-negligible advantage.  
Definition 2: **(IND-CPA Security [31]).** Let \(\mathcal{E}=(Setup,KeyGen,Enc,Dec)\) be a public-key encryption scheme, and let \(\lambda\in\mathbb{N}\) denote the security parameter. Let \(\mathcal{M}\) denote the message space. We say that \(\mathcal{E}\) is IND-CPA secure if for every PPT adversary \(\mathcal{A}\), there exists a negligible function \(\text{neg}(\cdot)\) such that:  
\[\left|\Pr\left[\text{Exp}_{\mathcal{E},\mathcal{A}}^{\text{IND-CPA}}(\lambda) =1\right]-\frac{1}{2}\right|\leq\text{neg}(\lambda),\]  
where the experiment \(\text{Exp}_{\mathcal{E},\mathcal{A}}^{IND-CPA}(\lambda)\) is defined as follows:  
1. The challenger runs \((\textbf{pk},\textbf{sk})\leftarrow KeyGen\left(1^{\lambda}\right),\) and sends the public key \(\textbf{pk}\) to \(\mathcal{A}\).
2. \(\mathcal{A}\) sends two equal-length messages \(m_{0},m_{1}\in\mathcal{M}\) to the challenger.
3. The challenger samples a uniform bit \(b\in\{0,1\}\), computes the challenge ciphertext \(\textbf{c}\leftarrow\text{Enc}(\textbf{pk},m_{b})\), and sends \(\textbf{c}\) to \(\mathcal{A}\).
4. \(\mathcal{A}\) outputs a guess bit \(b^{\prime}\in\{0,1\}\). The experiment outputs 1 if \(b^{\prime}=b\), and 0 otherwise.  
A scheme is IND-CPA secure if the adversary's advantage in distinguishing \(m_{0}\) from \(m_{1}\) based on \(\textbf{c}\) is bounded by a negligible function with respect to \(\lambda\). For FHE schemes, IND-CPA security is typically established under the RLWE assumption, ensuring that ciphertexts do not leak any partial information about the underlying plaintexts against passive adversaries.