esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Fault Diagnosis  
In traditional fault diagnosis, methods are typically focused on the operational mechanisms and theoretical analysis of the target system, such as approaches based on fault physics and fault tree analysis. However, with the development of IoT technology enabling real-time collection of extensive equipment operational data, more data-driven methods for fault diagnosis are being proposed [19; 20; 21; 22; 23]. For instance, artificial neural networks (ANNs) are employed to analyze fault data of rotating machinery by collecting various operational parameters such as current, voltage, temperature, vibration, and speed via sensors. After performing feature extraction and selection on these data, a corresponding fault classification model is constructed. This approach effectively identifies and classifies potential faults in rotating machinery, providing scientific evidence and support for fault diagnosis and prediction [24]. A method for \"fault diagnosis of complex electronic systems\" involves using machine learning algorithms to analyze operational data of electronic systems, achieving automated fault diagnosis [25]. RNNs were utilized to handle the time-dependency of signal transmission in bearing fault diagnosis, thereby enhancing diagnostic accuracy [26]. An aircraft fault diagnosis method based on a knowledge graph has been used, combining knowledge and data to enhance diagnostic efficiency [27]. A hybrid Bayesian network approach for fault diagnosis was developed based on residuals, knowledge, and data [28]. This method overcomes the weaknesses of using these components individually and demonstrates superior application performance. These emerging data-driven fault diagnosis methods analyze and model operational data for different fault types and equipment systems, facilitating automated fault detection and improving diagnostic accuracy and efficiency.  
Based on the above content, it can be concluded that the new, integrated fault diagnosis method based on digital twin has broader applicability in electric screw press systems. Many recent fault diagnosis methods often neglect the temporal information of fault occurrences and issues related to data imbalance. Therefore, proposing a novel fault diagnosis method specifically for electric screw press machines is significant. This method comprehensively considers data balance, data features, and temporal information, aiming to enhance fault diagnosis accuracy and efficiency in these systems.
## 2 Solution for the Elastoplastic Bending of the Cantilever Beam
### Elastic Regime  
The cantilever beam is in the elastic regime until the value of the maximum normal stress in the cross-section reaches the yield stress of the material (\(f_{y}\)). When the material is in the linear elastic regime, the bending moment in the arbitrary cross-section is proportional to the intensity of the force and the perpendicular distance between the force and the cross-section (lever arm); therefore, the function of the bending moment along the cantilever beam axis (Figure 1) can be written as:  
\[M(x)=F\cdot(h-x). \tag{1}\]  
The maximum bending moment is in the cross-section at the clamped end of the cantilever beam:  
\[M_{max}=F\cdot h. \tag{2}\]  
Based on Figure 1, the function of the circular cross-section diameter change and the function of the moment of inertia of the cross-section, along the axis of the cantilever beam, can be derived as follows:  
\[d(x)=d_{f}+(d_{c}-d_{f})\cdot\frac{h-x}{h}, \tag{3}\]  
\[I_{z}(x)=\frac{d(x)^{4}\cdot\pi}{64}=\frac{\left[d_{f}+(d_{c}-d_{f})\cdot \frac{h-x}{h}\right]^{4}\cdot\pi}{64}. \tag{4}\]  
The maximum value of the normal stress in the cross-section is not necessarily in the section at the clamped end, because the normal stress is not only the function of the bending moment, but also the function of the section modulus of the cross-section, which itself is a third-degree function of the coordinate along the cantilever beam axis:  
\[\sigma(x)=\frac{M(x)}{I_{z}(x)}\cdot\frac{d(x)}{2}=\frac{32\cdot F\cdot(h-x)}{ \left[d_{f}+(d_{c}-d_{f})\cdot\frac{h-x}{h}\right]^{3}\cdot\pi}. \tag{5}\]  
Figure 1: A mathematical model and characteristic deformation of the cantilever beam. The position of the cross-section (\(\rm{x_{e}}\)) with the maximum value of the normal stress can be defined based on the definition of the extremum of the function, i.e., via equalization of the first derivative of the normal stress function with zero:  
\[\frac{\rm{d}\sigma(\rm{x})}{\rm{dx}}=\frac{96\cdot(\rm{d_{c}-d_{f}})\cdot F \cdot(h-x)}{\left[d_{f}+(\rm{d_{c}-d_{f}})\cdot\frac{h-x}{h}\right]^{4}\cdot \pi\cdot h}-\frac{32\cdot F}{\left[d_{f}+(\rm{d_{c}-d_{f}})\cdot\frac{h-x}{h} \right]^{3}\cdot\pi}=0, \tag{6}\]  
\[\rm{x_{e}}=\frac{(2\cdot d_{c}-3\cdot d_{f})\cdot h}{2\cdot(\rm{d_{c}-d_{f}}) }. \tag{7}\]  
Equalizing the relation (7) to zero, the condition in which the critical section is at the clamped end of the cantilever beam is defined as follows:  
\[\rm{x_{e}}=\frac{(2\cdot d_{c}-3\cdot d_{f})\cdot h}{2\cdot(\rm{d_{c}-d_{f}}) }=0\ \ \rightarrow\ \ 2\cdot d_{c}-3\cdot d_{f}=0\ \ \Rightarrow\ \ \frac{d_{c}}{d_{f}}=1.50. \tag{8}\]  
Based on the relation (8), it can be concluded that the normal stress has the extreme value in the cross-section at the clamped end when the diameter ratio of the cross-section at the clamped end and at the free end of the cantilever beam (hereinafter, cross-sections diameter ratio) is lower than or equal to 1.50, while in the case when this ratio is greater than 1.50, the extreme value of the normal stress is in one single cross-section located along the beam axis.
## 6 Cybersecurity
### Vulnerabilities and Threats for Photovoltaic Systems  
Photovoltaic system vulnerabilities are represented by insecure communication protocols, lack of access control, lack of parameter sanitization, backdoor and hard-coded accounts, and cross-site scripting [78]. The most popular types of cyber-attacks for photovoltaic systems are as follows:  
1. False data injection--once the intruders can change and manipulate the data collected from sensors, a cyber-attack named false data injection can be easily planned [79].
2. Man-in-the-middle (MITM)--an intrusive party embeds itself within a conversation between two communication devices with the intention of eavesdropping on either one of the devices. This is achieved by making it appear to be a normal exchange of information [80].
3. Denial of service (DoS)--the primary targets for this type of attack are the protocols, with the intention of overloading the communication channels [81].
4. Replay--those engaged in malicious activities capture network traffic and act as the primary source by forwarding the traffic to the intended recipient. The attacker creates delays in data transfer or the data are resubmitted [82].
5. Brute force attack--the objective of the attacker is to gain unauthorized access to the system by attempting to guess the usernames, passwords, or encryption keys on multiple occasions. In the event that malevolent actors have gained access to the system, they may remain undetected for an extended period, provided that they are able to masquerade as a legitimate user. Concurrently, they may establish clandestine access points, navigate the system undetected, comprehend its functionality and objectives, and procure confidential data. In addition, they are capable of configuring and executing malicious scripts [83].  
Three major security factors are commonly viewed as risks [84]:  
1. the attackers and the vulnerabilities in the system
2. the produced impact
3. the repercussions of the attack  
A total of 23 known vulnerabilities in PV systems were analyzed in the study [85], their root causes were investigated, and mitigation strategies were proposed. A vulnerability database [86] was used to determine the vulnerabilities.
## 1 Introduction  
Water is an essential resource for the sustenance of all living organisms. Being a significant resource, its quality needs to be monitored and managed properly. However, it is constantly being affected by anthropogenic activities caused by growing urbanization. Factors such as soil erosion and climate change can have a huge impact on the physical landscape of water bodies. These factors are usually ignored while assessing water quality, and traditionally, only the physico-chemical parameters, such as turbidity, conductivity and pH are used. However, such factors are not enough to accurately analyse the conditions that can have an impact on the water body. Thus, topographical and hydrological parameters, such as slope, aspect, lithology, geology and soil type, may have a direct/indirect impact on the overall quality of the water body. Similarly, the abundance of air pollutants such as nitrogen and carbon dioxide can cause water eutrophication [1], acidification [2] and nutrient pollution [3] that can be harmful for the aquatic ecosystem. Moreover, heavy precipitation can also directly affect the water cycle resulting in the deterioration of the water bodies [4]. In reality, the monitoring of multiple contamination sources is a tedious and expensive task that involves field visits and laboratory work. Utilizing remote sensing and machine learning technology to overcome such challenges is being used by many researchers [5]. This, in turn, can make the sampling process more robust and economical. In fact, such technology can be utilized to assess water quality based on the combined impact of different parameters that can be a complex task otherwise. Traditionally, a water quality index (WQI) is a weighted average of selected ambient concentrations of pollutants providing a single number that represents the overall water quality at a certain location and time. The most frequently used WQIs include the National Sanitation Foundation Water Quality Index (NSFWQI) [6], the Canadian Council of Ministers of the Environment Water Quality Index (CCME) [7], the Oregon Water Quality Index (OWQI) [8], etc. However, the application of the WQIs on water samples is a biased approach as each index is built specific to certain locations or water types or is sensitive to specific parameter concentrations or dependency on the weights assigned [9]. Such limitations make the traditional WQIs unsuitable for application on any general water body. To overcome these challenges, an enhanced water quality index (EWQI) is proposed in this research that uses machine learning and data mining methods to analyse the combined impact of different factors, including hydrological, topographical, meteorological, air and physico-chemical parameters and assigns appropriate weights using the tree-based, i.e., CatBoost and LightGBM, methods. Thus, a machine learning approach is proposed as a replacement WQI that can remove the bias and can be applied to any water body regardless of the selected parameters.
## 4 Conclusions  
The results proved that the temperature associated with mild frost, \(-3\)\({}^{\circ}\)C, over 12 h, produced a clear effect on the composition of phenols, volatile compounds, and quality parameters. Thus, this temperature may permit the production of virgin olive oils with no defects, although some quality decay is expected, which is reflected in the lower concentration of volatile compounds.  
PCA and PLS-DA analysis clearly differentiated between VOOs from fresh olives and VOOs from frozen olives, showing that mild frost also promotes a series of biochemical reactions in the fruit that affects the final VOO composition. In addition, these statistical analyses carried out on the physical chemical and chemical data of the samples also showed a separation of oils according to their harvest time. Thus, this result points out  
Figure 5: Results discrimination of partial least square (PLS-DA) analysis carried out on the selected variables to classify virgin olive oils from fresh (FrO) and frozen (FzO) olives (**A**) and the corresponding loadings plot (**B**).  
that fruit ripening is another important variable to consider at the time of olive harvesting, particularly when VOOs with high levels of phenolic compounds are desired. The frost condition makes the harvesting time more relevant, especially in relation to \(\alpha\)-tocopherol, which mainly declined in oils from frozen olives. Although the total concentration of volatile compounds was not affected by harvest time, there was less development in oils from frozen olives, particularly aldehydes and C6 alcohols related to the lipoxygenase cascade (hexanal, (_E_)-2-hexenal, hexan-1-ol, (_E_)-2-hexen-1-ol).  
Oils from olives subjected to mild frost preserved the basic characteristics of virgin olive oil, with minor changes in their chemical quality parameters and with a greater impact on volatile compounds, while maintaining the phenolic compounds responsible for the healthy properties of the oil.  
As a general conclusion, the effect of freezing at a mild temperature (\(-3\)\({}^{\circ}\)C) was influenced by ripening, although no general conclusion can be achieved for all the chemical and physical-chemical parameters. This information complements previous studies and highlights the importance of considering freezing phenomena to have efficient harvesting optimization and control quality, particularly in areas with a high risk of low temperatures.  
Conceptualization, N.R. and C.P.; methodology, N.R. and F.T.; software, J.S.; formal analysis, C.P.; investigation, N.R., C.P. and B.S.; resources, N.R. and F.T.; writing--original draft preparation, N.R., F.T., D.L.G.-G. and C.P.; writing--review and editing, D.L.G.-G. and J.S.; visualization, B.S. and D.L.G.-G.; project administration, N.R.; funding acquisition, N.R. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ANID (Fondecyt Project 1191682), Chile.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 6 Results and Discussion
### Performance Evaluation by Computer Simulation
That is, there is no large difference between Schemes 1 and 2 with respect to the second hop. Figure 10: Average path loss in IEEE model CM3. Figure 11: Packet delivery failure ratio with constant \(d_{2 nd}\). Figure 12: Energy efficiency with constant \(d_{2 nd}\). Figure 13: Average number of transmissions with constant \(d_{2 nd}\). Figures 14-16 show the performance results for fixed communication distance in two hops \(d_{2 hops}=d_{1 st}+d_{2 nd}\) (1.5 m) and varying the \(d_{1 st}\) and \(d_{2 nd}\) values. For \(d_{1 st}=1.5\) m, data are transmitted using only a single hop. Thus, the proposed scheme satisfies the QoS requirements for Data A and B, while both standard schemes approach do not, like in the first scenario. Also, when comparing the standard schemes and the proposed scheme, the performances of both standards are worse than the proposed one. For example, Data A of the proposed scheme satisfies PDFR \(<10^{-2}\), while that of both standards do not satisfy PDFR \(<10^{-1}\). This is because the correcting capability of error correcting codes used in those standards is lower than that of the proposed scheme. In other words, the standard
# Design of Time-Synchronized Switch Based on Zynq
## 3 System Hardware Design
### SFP Interface Circuit  
In order to enhance transmission speed while ensuring long-distance data transmission, optical fiber is selected for data transfer. The SFP interface module is connected to the GTH transceiver of the Zynq, and the block diagram of the SFP interface design is shown in Figure 8, with a data transmission rate of up to 12.5 Gb/s.  
Figure 8: SFP design block diagram.  
Figure 6: DDMTD clock design.  
Figure 7: GTH reference clock design.  
For the selection of SFP types, they can be classified based on transmission distance, mainly into three categories--short-range, medium-range, and long-range--in order to meet diverse clock synchronization requirements. The optical module chosen for this design has a transmission wavelength of 1310 nm and a receiving wavelength of 1490 nm. The GTH interface supports multiple network transmission protocols. If it is necessary to change the physical layer network interface of the GTH, this can be achieved by modifying the GTH IP core or its source code, thereby allowing for the extraction of timestamps.
# Laser Cavitation Peening: A Review
## 4 Key Factors of Laser Cavitation Peening
### Bubble Size  
As a larger bubble produces a larger impact during cavitation bubble collapse [52; 53; 54; 55], the most important factor of laser cavitation peening is the bubble size. In order to demonstrate the monitoring of bubble size, Figure 10 shows the relationship between the developing time, \(t_{D}\), and the diameter of laser cavitation, \(d_{max}\)[16]. In Figure 10, the red circles show the data with respect to the hemispherical bubble and the other circles indicate the spherical bubble. \(t_{D}\) is defined as the time from laser ablation to the first collapse of the  
Figure 9: Schematic diagram of mechanisms and main factors of improvements in fatigue strength via laser cavitation peening.  
laser cavitation. For example, this was approximately 1 ms in the case of Figure 6. The \(d_{max}\) was evaluated using a high-speed video camera (see Figure 6a). Figure 10 was obtained by changing the laser pulse energy. As shown in Figure 10, \(d_{max}\) [mm] is proportional to \(t_{D}\) [\(\mu\)s] and the relation is described in Equation (1), including both spherical and hemispherical bubbles [16].  
\[d_{max}[\text{mm}]=0.0103\;t_{D}\;[\mu\text{s}] \tag{1}\]  
Regarding Rayleigh [56], the collapse time, \(t_{c}\), for a bubble from radius, \(R_{0}\), to collapse is given by Equation (2).  
\[t_{c}=0.91468\;R_{0}\;\sqrt{\frac{\rho}{p}} \tag{2}\]  
When the values \(p=0.1013\) MPa and \(\rho=998\) kg/m\({}^{3}\) are substituted into Equation (2), Equation (3) is obtained.  
\[R_{0}\;[\text{mm}]\;=\;0.0111\;t_{c}\;[\mu\text{s}] \tag{3}\]  
By definition, \(R_{0}\) is half of \(d_{max}\) and \(t_{c}\) is half of \(t_{D}\), so the proportional constants in Equations (1) and (3) are almost equivalent. Thus, it was concluded that the bubble size can be evaluated by measuring the developing time of the bubble.  
In order to reveal the relation between bubble size and peening intensity, Figure 11 shows the relation between the developing time, \(t_{D}\), and the arc height, \(h\)[16]. As shown in Equation (1), the bubble size can be estimated using \(t_{D}\). As mentioned in the explanation of Figure 8, the peening intensity can be evaluated using the arc height. In Figure 11, duralium, stainless steel, and N-strip were used; duralium is a soft metal and N-strip is a hard material compared with stainless steel. Thus, the threshold impact, which corresponds to the material hardness, was assumed. When the threshold impact was assumed, the arc height was proportional to the maximum volume of the laser cavitation [16]. This is reasonable, as the cavitation intensity is proportional to the volume [52; 53; 54; 55].
# Novel Methodology for Condition Monitoring of
## 3 Methodology
### Thermal Matrix  
As aforementioned, the consideration of infrared cameras allows the obtainment of either digitized intensity values or temperature values; in this sense, it should be mentioned that in this proposed work, gray-scale infrared images are used to estimate the thermal matrix due to its simplicity. Therefore, the procedure of calculation of the thermal matrices is based on the estimation of the true temperature (thermogram) from each pixel that composes the gray-scale infrared image (Figure 2).  
\[T_{true}(x,y)=Tc_{min}+\frac{T_{gray}(x,y)}{T_{mgv}}\star(Tc_{max}-Tc_{min}) \tag{6}\]  
where \(T_{true}(x,y)\) is the value of the true temperature derived from the pixel intensity, \(Tc_{max}\) and \(Tc_{min}\) are the maximum and the minimum temperature of the infrared image, respectively, \(T_{gray}(x,y)\) is the value of the pixel intensity in the gray-scale image, and \(T_{mgv}\) is the peak intensity value in the infrared image.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Distance Factor of Adjacent Buildings
#### 4.1.2 HS Model  
This section uses the HS model to analyze the impact of varying the distance between the adjacent building and the diaphragm wall, comparing the distances of 1 He, 2 He, and 3 He. The analysis was conducted with fixed loads of 5 F (91 kPa) and 12 F (195 kPa), and fixed basement depths of 0 He, 0.25 He, 0.5 He, 0.75 He, and 1.0 He. The results show that at a distance of 1 He and a depth of 0 He with a load of 5 F, the maximum wall deformation was 16.3 mm; with a load of 12 F, it was 17.4 mm. At a distance of 2 He and a depth of 0 He with a load of 5 F, the maximum wall deformation was 15.13 mm; with a load of 12 F, it was 15.99 mm. At a distance of 3 He and a depth of 0 He with a load of 5 F, the maximum wall deformation was 13.42 mm; with a load of 12 F, it was 14.2 mm. This indicates that when the distance between the adjacent building and the diaphragm wall is 1 He, it has a more significant impact on wall deformation. When the distance exceeds 2 He, the impact on the wall begins to converge. Figure 11 illustrates the influence of the distance factor on the diaphragm wall under fixed depth and load conditions.  
Figure 10: Comparison of maximum deformation under different loads (MC model): (**a**) 5F; (**b**) 12F.
## 3 Model and Methodology
### Solution Procedure
#### 3.2.4 Three-Stage Procedure Execution Time-Frame
Equation (9) shows the objective function solved in Stage 3:  
\[Max\ Z=\sum_{m=1}^{M_{1}}\sum_{i=1}^{I}\left(\min\left(s_{i\mu},\ \left(frac**d_{i\mu} \right)\right)*Nsucc_{i}*\sum_{k=1}^{R^{\rho}}r_{i\mu k}^{\rho}\right) \tag{9}\]  
where _frac_ is a threshold (%) of activity duration (0 < _frac_ < 1) set to 0.25 based on the authors' previous study [36], \(Nsucc_{i}\) denotes the number of immediate successors of activity \(i\) and \(s_{im}\) represents the slack of activity \(i\) if executed in mode \(\mu\). Equation (9) establishes that the objective is to maximize the robustness measure, which is based on the slack of each activity in each available mode. It is worth noting that the final makespan resulting from Stage 3 cannot be larger than the makespan resulting from Stage 2, but it could be smaller than that from Stage 1. However, since the main objective is to produce a robust and practical makespan, the value of the robustness measure (RM), and not the makespan, determines if this reduced makespan will be used or not. For example, suppose a Stage 3 schedule has a makespan = 60 and RM = 140 and a Stage 1 schedule has a makespan = 62, but RM = 154. For this case, even though there is a schedule with lower makespan, 60, the selected schedule will be the one with makespan 62 because it has a higher RM. At the end of Stage 3, the schedule reported will be one with maximized robustness and with a makespan at least as low as the lower bound generated in Stage 1 and at most as high as the entropy-containing schedule generated in Stage 2.