esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion and Scaling in Geothermal Heat Exchangers
## 6 Degradation Mitigation for Heat Exchangers
### Protective Coating Systems
Ceramic coatings offer significant promise for safeguarding geothermal heat exchangers, particularly due to their effective corrosion protection in highly corrosive environments. For instance, a Pd-Ni-TiO\({}_{2}\) coating has been utilized on 316L stainless steel to mitigate corrosion damage in hot dilute sulfuric acid (20 wt.% H\({}_{2}\)SO4 solution at 60 \({}^{\circ}\)C) [102]. Ceramic coating systems have been explored in the GeoHex project for geothermal applications [84; 103; 104]. TiO\({}_{2}\) and TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) coatings were deposited on CS using suspension plasma spray (SPS) and solution precursor plasma spray (SPPS). SPPS was favored for better stoichiometric control as it employs precursor particles, and introduces micro- and nano-features, like columns and lamellae, improving hydrophobicity. The results indicated that SPPS-deposited TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) coatings displayed hydrophobic properties (water contact angle 126\({}^{\circ}\)), making them more promising for geothermal heat exchanger protection compared to hydrophilic TiO\({}_{2}\) coatings (water contact angle 18\({}^{\circ}\)). In a study conducted by [PERSON] et al. [68], various ceramic coatings, including TiO\({}_{2}\), SiO\({}_{2}\), and SiO\({}_{2}\)-EPS (a heptadecafluorodecyltri-isopropylcase compound), were applied to AISI 304 stainless steel using sol-gel and liquid phase deposition (LPD) methods. These coatings were assessed for their effectiveness in mitigating scaling and corrosion in heat exchangers exposed to hot-dry-rock (HDR) geothermal water. The experiments were conducted in two types of simulated geothermal fluid from the Yingshen area of China. One fluid was rich in calcium carbonate for scaling tests, while the other closely resembled real geothermal water, containing HCO\({}_{3}\), Cl\({}^{-}\), and Na\({}^{+}\) ions. The fluid temperature at the heat exchanger inlet was maintained at 150 \({}^{\circ}\)C during exposure experiments, which lasted up to 21 days. The results indicated that when exposed to the scaling-specific fluid, LPD TiO\({}_{2}\) and sol-gel TiO\({}_{2}\) coatings reduced fouling by 48% compared to the uncoated 304 alloys. Furthermore, when tested with the simulated real geothermal fluid, sol-gel SiO\({}_{2}\) and SiO\({}_{2}\)-FPS coatings demonstrated a reduction in fouling of over 30% compared to the bare substrate. Electrochemical tests conducted on these coatings also revealed a substantial reduction in corrosion rates, approximately 60%, in comparison to the Type 304 stainless steel substrate. These promising results suggest practical applications for these coatings for in-service protection of geothermal heat exchangers, as concluded by the authors.
## 5 Conclusions  
In this paper, an experimental testing campaign aimed at quantifying frequency effects on the fatigue responses of adhesively bonded cylindrical butt joints was performed. Fatigue tests were executed using the ultrasonic loading frequency (i.e., 20 kHz) and commonly adopted low frequencies (i.e., 5, 25, 50 Hz). A combined analytical and numerical methodology was applied to design and verify the specimens under investigation.  
The main findings of this study are:  
* At a fixed applied load, specimens tested at an ultrasonic frequency are prone to fail in the very high cycle fatigue range (N > 10\({}^{7}\)), whereas specimens subjected to common frequencies fail in the high cycle fatigue range, thus confirming the presence of a frequency effect.
* Specimens tested at an ultrasonic frequency express run-out data below 15 MPa compared to 10 MPa in the case of 25 and 50 Hz frequencies. Specimens at 5 Hz do not present run-outs.
* There is an increase in fatigue performance among specimens tested in the low-frequency range, and this is visible through the S-N curve at the 50% probability level.
* Data scattering was investigated through standard deviations, and the experimental results demonstrated a minor impact on fatigue properties (i.e., 0.3038 at 5 Hz, 0.5763 at 25 Hz, 0.4687 at 50 Hz, and 0.7691 at 20 kHz);
* Interpolating data at N = 2 \(\times\) 10\({}^{6}\) for three probability levels (P = 10, 50, 90%) showed the possibility of strength prediction.  
D.P. ([PERSON]): Conceptualization, Methodology, Formal analysis, Investigation, Software, Writing--Original Draft, Visualization; L.G.: Supervision--Review and Editing, Project administration, Funding acquisition; D.P. ([PERSON]): Supervision, Writing--Review and Editing, Project administration, Funding acquisition; M.R.: Supervision, Writing--Review and Editing; A.T.: Formal analysis, Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
## 2 Related Work and Problems
### Related Work  
Previous studies have investigated the mapping between cybersecurity databases [8; 9; 12; 13]. The aims of [8; 9] are similar to this research. CVE is mapped to CAPEC using TF-IDF [8] and Doc2 Vec [9]. However, [8] focuses on limited vulnerabilities. The previous studies did not provide a valid evaluation or a comparison of different algorithms. On the other hand, this study uses the dataset defined by MITRE to conduct experiments comparing multiple algorithms.  
Recently, research on mapping of ATT&CK [14] and CVE [15; 16; 17] has intensified. The approach in this study should be applicable to map CVE and ATT&CK. In the future, the tracking will be expanded to include ATT&CK.  
The authors of [18; 19] propose a method to combine similarity algorithms. In [18], a hybrid method, combining Doc2 Vec weighted by TF-IDF and a vector space model weighted by TF-IDF, is proposed. Here, hybrid metaheuristic and machine learning methods are considered as a growing research domain [20; 21; 22; 23] extract hidden topics from the textual description of each attack pattern. Although the approach in this study uses a simple similarity algorithm, this work builds upon previous studies.  
Some studies have used cybersecurity databases to create vulnerability ontology models [24; 25; 26] and to analyze and assess risk and security [27; 28; 29; 30; 31; 32]. However, they do not describe the information retrieval process from cybersecurity databases. Others have focused on mining methods and information retrieval from each cybersecurity database [33; 34; 35; 36; 37]. In particular, [33; 35] introduce information retrieval processes by following the relationships among cybersecurity databases. Unlike previous studies, this study explicitly evaluates the accuracy of the links between databases.
## 2 Materials and Methods
### Optical Transmittance Characterization  
The optical transmittance of a bare glass slide was first characterized through the optic transmittance meter (WTM-1100) at 550 nm as baseline. Monolayer and bilayer graphene and the Gr/AgNW/Gr with different AgNW concentrations were transferred on the slides independently and characterized by the meter. The measured values were divided by the baseline value to get the characterized transmittance accordingly.  
As shown in Figure 3, a three-layer thin film model is introduced to characterize the optical transmittance of the composite. The wavelength of the light source is 550 nm, which is far greater than the AgNW diameter (\(\sim\)30 nm). Therefore, the diffraction effect on AgNW and interference between two graphene layers are neglected. The total optical transmittance \(T\) can be expressed as follows:  
\[T=\frac{I}{I_{0}}=T_{G}\cdot T_{A}\cdot T_{G} \tag{1}\]  
where \(T_{G}\) is the transmittance of graphene layer. For monolayer graphene, \(T_{G}\) is 97%, while for two-layer graphene, the value drops to 93%. \(T_{A}\) is the transmittance of the AgNW network, which varies with concentrations. \(T_{A}\) values with five concentrations were measured first and then the total optical transmittance could be calculated using Equation (1).
## 3 Dynamic Model and Integrated Controller of FWIDSV  
To verify the practical performance of FWIDSV with the reference vector field as the following vehicle, the 3-DOF FWIDSV dynamics model and integrated controller are used in this section to describe the motion of FWIDSV. The control structure of FWIDSV is as shown in Figure 7. The desired lateral and longitudinal total force of the vehicle is obtained from the following control layer, and the desired yaw moment is obtained by the SMC. The tire force allocator layer uses LQP to decouple the total force and moment, and the tire force of each wheel are obtained. The desired tire force is achieved by the actuator control layer by controlling the tire steering angles and the driving torques.  
Figure 7: Structure of four-wheel independent driving and steering (FWIDSV) platoon following control.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator
### Regulation of the LV-VSC
#### 3.1.2 Active Damping Function  
Although the PCC harmonic voltage, \(u_{\text{pcc,h}}\), has been feedforwarded to the modulator of the LV-VSC to generate the required voltage, \(u_{\text{o,B}}\), the stability of the LV-VSC still faces the challenge of filter resonance caused by the LCL filter. Thus, the active damping function is integrated into the LV-VSC controller and suppresses the filter resonance. Among different active damping schemes, the capacitor current-based active damping is preferred to provide a virtual resistor in series at the filter capacitor branch. As shown in Figure 8, \(R_{\text{v}}\) is the active damping gain or the virtual resistance in series at the filter capacitor branch. According to the design criterion in reference [4], the optimal value of \(R_{\text{v}}\) is designed based on the filter resonant frequency. In our case study, it can be designed at the value of 30 \(\Omega\).
# An Internet of Things-Based Low-Power Integrated Beekeeping Safety and Conditions Monitoring System
## 2 Related Work on Internet of Things (IoT) Beehive Incident Response and Management Systems
### IoT Beehive Conditions Monitoring Systems
The proposed IBSMC system architecture described in the following section is a novel IoT incident response system that includes the most critical temperature and humidity condition IoT sensors as well as active hive management mechanisms (IoT actuators and automated processes) for the process of humidity and temperature regulation in critical condition events. Furthermore, the proposed IBSMC system makes use of the Low Power Wide Area (LPWA) networking protocol LoRaWAN [30]. This protocol is capable of wide range coverage of up to 12 km away from the hives, for the process of data transmission in the free ISM RF band of 868 MHz. The IBSMC low energy data transmissions and low-cost system sensors and actuators enables the ability of automated extreme conditions interventions coupled with a robust safety and incident reporting mechanism for all types of safety incidents. The proposed capability of detecting beehive condition changes and reaction is performed with the use of low-cost and low energy consumption actuators deployed in all beehive cells. Furthermore, data transmissions are performed in the ISM band without unnecessary LTE provider costs for the beekeeper. Table 1 shows the existing systems' capabilities regarding safety and conditions monitoring as well as the advantages of the proposed IBSMC system with respect to existing ones.
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 2 Background  
### Signal Model  
Figure 3 shows a uniform circle array of radius \(r\) consisting of \(M\) omnidirectional elements. Assuming that \(p\) far-field signals \(s_{i}(i=1,2,\ldots,p)\) are incident on a uniform circular array, the received signal of the \(l\)-th element at moment \(t\) is:  
\[x_{l}(t)=\sum_{i=1}^{p}s_{i}(t-\tau_{li})+n_{l}(t)l=1,2,\ldots,M \tag{1}\]  
where \(n_{l}(t)\) is the receiver noise of the \(l\)-th antenna since the signals incident on the antenna array elements is assumed to have some noise associated with them. \(\tau_{li}\) is the propagation time delay, calculated as follows:  
Figure 2: Error calibration method based on transfer learning.  
\[\tau_{li}=r\cos(\theta_{i}-\gamma_{l}) \tag{2}\]  
where \(\gamma\) are the angular positions of the antenna array elements which can be calculated as \(\gamma_{l}=2\pi l/M(l=0,1,\dots,M-1)\). Equation (1) can be written in a matrix form:  
\[X(t)=AS(t)+N(t) \tag{3}\]  
where \(X(t)\), \(S(t)\), \(N(t)\) are given by:  
\[X(t)=(x_{1}(t),x_{2}(t),\dots,x_{M}(t))^{T} \tag{4}\]  
\[S(t)=(s_{1}(t),s_{2}(t),\dots,s_{P}(t))^{T} \tag{5}\]  
\[N(t)=(n_{1}(t),n_{2}(t),\dots,n_{M}(t))^{T} \tag{6}\]  
where \(X(t)\) is an \(M\times 1\) dimension array snapshot vector, \(N(t)\) is \(M\times 1\) dimension array noise vector, \(S(t)\) is \(P\times 1\) dimension space signal vector, and \(A\) is \(M\times P\) dimension array steering vector matrix.  
\[A=\begin{bmatrix}a_{1}(\omega_{0}),&a_{2}(\omega_{0}),\dots,&a_{N}(\omega_{0} )\end{bmatrix} \tag{7}\]  
Among them, the steering vector \(a_{i}(\omega_{0})\) is:  
\[a_{i}(\omega_{0})=\begin{bmatrix}\exp(-j\omega_{0}\tau_{1i})\\ \exp(-j\omega_{0}\tau_{2i})\\ \vdots\\ \exp(-j\omega_{0}\tau_{Mi})\end{bmatrix}i=1,2,\dots,N \tag{8}\]  
The fast Fourier transform algorithm is used to extract the phase difference of the input array signal as an input to the intelligent DOA estimation model. The phase difference between the \(i\)-th element and the \(j\)-th element of a uniform circular array is represented by \(\theta_{ij}\). The phase difference of the signal of array elements can be expressed as the phase difference square array, as shown in Equation (9).  
\[\begin{pmatrix}\theta_{11}&\theta_{12}&\cdots&\theta_{1M}\\ \theta_{21}&\theta_{22}&\cdots&\theta_{2M}\\ \vdots&\vdots&\ddots&\vdots\\ \theta_{M1}&\theta_{M2}&\cdots&\theta_{MM}\end{pmatrix} \tag{9}\]
## 3 Methods
### Geolocation Accuracy Evaluation  
To evaluate the geolocation accuracy of the BNU-1 L1A/L1B product, we re-extracted the CPs from the BNU-1 L1A/L1B product by the SIFT algorithm as the verification points and calculated the root mean squared error (RMSE) of the verification points:  
\[RMSE\ =\ \sqrt{\frac{\sum_{i=\ 1}^{n}[{R_{xi}}^{2}+{R_{yi}}^{2}]}{n}} \tag{3}\]where \(n\) is the number of points participating in the accuracy evaluation, \(R_{xi}\) and \(R_{yi}\) represent the residuals of the \(i\)-th extracted points from the BNU-1 and the reference MODIS image in the X and Y coordinates, respectively.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### Statistical Analysis  
We assessed the MOS results data of the HD video from three conducted test cases using a hypothesis analysis to determine if there was any significant difference in the case of using the proposed VQoSRR versus not using it. The statistical analysis was performed using one-factor analysis of variance (ANOVA), and its results were calculated in Microsoft Excel. This test evaluates whether there are significant differences in the means of three or more independent groups. The Hypotheses for all test cases were defined as follows: hypothesis H\({}_{0}\): means MOS values are equal from all 3 test cases where \(\mu\)1 = \(\mu\)2 = \(\mu\)3; hypothesis H\({}_{1}\): MOS means are not all equal, and the significance level \(p\)-value was set to \(\alpha\) = 0.05. Table 5 presents the ANOVA results. According to the \(p\)-values in Table 5, it can be seen that there was a significant difference when comparing the proposed VQoSRR to N_VQoSRR because the \(p\)-value from the hypothesis test H was less than 0.0001, which is lower than 0.05.  
Figure 14: Comparison between subjective and objective metrics QoE. (**a**) For BE class service; (**b**) for SD class service; (**c**) for HD class of service.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source of Variation**} & **Sums of Squares** & **Degrees of Freedom (df)** & **Mean Squares (MS)** & **F** & \(p\)**-Value** & **Remark** \\ \hline
**Between Treatments** & 60.6 & 2 & 30.3 & 75.8 & \textless{}0.0001 & Significant \\ \hline
**Error (or Residual)** & 18.6 & 42 & 0.44 & & & \\ \hline
**Total** & 79.2 & 44 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical Analysis Results for MOS Metric of HD Video.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods
### Modelling Using the CNN-RNN Hybrid Network
A dropout layer, parameterised with a probability of 0.2, is inserted between the two layers to mitigate overfitting. The architecture terminates with a fully connected layer and a regression output layer, enabling sequential prediction tasks. RNN-based models, such as BILSTM, are adept at capturing both short- and long-term temporal dependencies by learning hierarchical relationships within sequential data. To train the hybrid CNN-RNN framework, an initial 80% of the dataset is allocated to model training, while the remaining 20% is reserved for testing, facilitating the evaluation of predictive accuracy on unseen temporal sequences. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**CNN Design**} \\ \hline
**Layers Configuration** & **Parameters** \\ \hline Sequence input layer & Feedback of three delays of the output [3 1 1] \\ Sequence folding layer & Sequence input layer \(\rightarrow\) 2D format \\ Convolution2d layer & \multicolumn{2}{c}{Dilation factor = [1 1]} \\ Batch normalisation layer & \(data^{normalized}=\frac{data-\mu}{\sigma}\) where \(\mu\) is mean \\ ELU layer & \(f(x)=\left\{\begin{array}{ll}x,&x\geq 0\\ \exp(x)-1,&x>0\end{array}\right.\) \\ Convolution2d layer & Dilation factor = [2 2] \\ ELU layer & \(f(x)\) \\ Convolution2d layer & Dilation factor = [4 4] \\ ELU layer & \(f(x)\) \\ Convolution2d layer & Dilation factor = [8 8] \\ ELU layer & \(f(x)\) \\ Convolution2d layer & Dilation factor = [16 16] \\ ELU layer & \(f(x)\) \\ Average pooling2d layer & Pool size = 1, stride = [5 5] \\ Sequence unfolding layer & \multicolumn{2}{c}{–} \\ Flatten layer & It converts \(H\)-by-\(W\)-by-C-by-\(N\)-by-S to (\(H^{*}W^{*}\)C)-by-\(N\)-by-\(S\) \\ \hline \multicolumn{2}{c}{**RNN design**} \\ \hline BILSTM layer & Hidden unit = 64 \\ Dropout layer & Probability = 0.2 \\ BILSTM layer & Hidden unit = 16 \\ Output the last time step of the sequence \\ Fully connected layer & Number of responses = 1 (configuration failure) \\ Regression layer & Loss function = \(MSE\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed CNN-RNN model and layer configuration.
## 3 Results and Discussion
### Effect of Installation Position of Two HDR Dampers (\(l_{1}\)/\(l\), \(l_{2}\)/\(L\))  
To figure out the impact of installation positions of two HDR dampers (\(l_{1}\)/\(L\), \(l_{2}\)/\(L\)) on cable damping ratio, the installation position (\(l_{1}\)/\(L\)) of dampers are varied from 0.01 to 0.2 for different values of \(K_{1}\) and \(K_{2}\). Figures 8 and 9 show the relationship of \(\xi_{1}\) and \(l_{1}\)/\(L\) for different values of \(K_{1}\) and \(K_{2}\). The damping ratio is directly proportional to the installation positions of HDR dampers. For any value of \(K_{1}\), the damping ratio increases with the increase of \(l_{1}\)/\(L\) and \(l_{2}\)/\(L\). Furthermore, the values of damping ratio decrease with the increase of spring stiffness factors for an installation position of HDR dampers. The highest value the damping ratio 0.05 is found when \(l_{1}\)/\(L\) equals to 0.2. However, if the installation position is too far from cable ends, it is hard to fabricate, install and maintain the dampers. Moreover, it affects the aesthetic aspect of the bridge as well. Usually, the HDR dampers are normally installed around a normalized value of 0.02 to 0.05 from the cable ends [13].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(K_{1}\) & **100** & **200** & **500** & **1000** & **1500** \\ \hline \(\xi_{1\text{max}}\) & 0.0284 & 0.0273 & 0.0248 & 0.0223 & 0.0207 \\ \hline \(\lambda_{1}\) & 12 & 6 & 3 & 2 & 1.5 \\ \hline \end{tabular}
\end{table}
Table 4: Damping ratio \(\xi_{1\text{max}}\) with \(\lambda_{1}\).  
Figure 6: Relationship of (\(\lambda_{1}\); \(\xi_{1}\)) and \(K_{1}\).  
Figure 7: Relationship between \(\lambda_{2}\) and \(\xi_{2}\) for different values of \(K_{2}\).
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 5 Numerical Simulations
### Effect of the Transmit Power of BSs
The maximal energy-efficiency proportional fairness increases firstly when the transmit power of BSs in tier 2 is low, more power of BSs in tier 2 is low. This is because when the transmit power of BSs in tier 2 is low, more nodes associate with the BSs in tier 1 than in tier 2. Hence, the fairness among different tiers will be small and the energy-efficiency proportional fairness of nodes will firstly increase with the transmit power of BSs in tier 2. When the transmit power of BSs in tier 2 is high enough, more nodes associate with the BSs in tier 2 than tier 1. In this case, the fairness among different tiers will also be small. The energy-efficiency proportional fairness of nodes decrease with the transmit power of BSs in tier 2. For comparison, Figure 4 also shows that when the density of BSs in tier 1 is \(4\times 10^{-5}/\text{m}^{2}\), the energy efficiency proportional fairness of network is less than that of the density of BSs in tier 1 with \(8\times 10^{-5}/\text{m}^{2}\). This is because nodes will associate with BSs in tier 1 when the density of BSs in tier 1 is  
Figure 4: The mean energy efficiency proportional fairness with respect to the association probability of tier 2 obtained from the proposed suboptimal scheme and optimal two-dimension search scheme for different values of the transmit power of base stations (BSs). high. However, the energy efficiency proportional fairness of tier 1 is less than that of tier 2 due to the transmit power of BSs in tier 1 being higher. Hence, the energy efficiency proportional fairness of the network decreases. Meanwhile, it can be seen that the gap of energy efficiency proportional fairness of the network between the proposed suboptimal scheme and the optimal brute-force two dimension search can be ignored.