esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 3 Results and Discussion  
Qualnet network simulator setup has the following steps:  
* Design a mobile network topology that reflects the real-world characteristics you want to simulate.
* Determine the number of mobile nodes, their initial positions, mobility models, communication ranges, and movement patterns.
* Integrate content sources and destinations based on your simulation objectives  
Simulations were performed using Qualnet version 5.0.1. This simulator was chosen because it supports large-scale networks with hundreds of nodes and has a library with several protocols for MANETs already implemented, thus enabling an easy and reliable comparison. For all tests, the test conditions were as follows:  
* Map size--1200 m by 1200 m;
* Maximum communication distance--150 m;
* Propagation model--two-ray;
* Simulated time--110 s;
* Knot speed--15 m/s;
* Node direction--random.  
The parameters configured for the HRAN were as follows: an interval between HELLO messages of 7 s, use of 4 levels in the heat gradients, BFs size of 256 bits, a TAB filters update time of 21 s and a path improvement interval of 20 s.  
Figure 7: Route repair: (**a**) node failure (intermediate nodes); (**b**) route repair with i = 2.
## 3 Mathematical and Problem Formulations  
In this section, the text addresses the mathematical problem formulation of a proposed method for achieving optimal coordinated control of UPFCs and PSSs in response to cyber-physical attacks based on the process stages illustrated in Figure 2. The discussion considers several factors, including the generation oscillation index, voltage deviations, and optimal generation cost. To improve the optimization of the multi-objective mathematical function, which is introduced later, a developed multi-objective optimization algorithm is utilized, and its intricacies are explained within this section. Consequently, the modeling of cost functions for the purpose of damping oscillations and addressing other techno-economic indices is thoroughly examined in the subsequent subsections.  
As illustrated in Figures 1 and 2, the proposed approach is used in two states of the system, namely (1) normal operation and (2) post cyber-physical attack. According to the proposed algorithm shown in Figure 2, the central processing control of the system receives input signal data at each time step of the simulation or in real-world scenarios. The central processing unit then analyzes the technical and economic objectives of the system and attempts to optimally coordinate the controllers of the power system stabilizers (PSSs) and unified power flow controller (UPFC) to move the power system to a stable and economically optimal operation point, whether in normal operation or during the post-disturbance period following a cyber-physical attack. For instance, as depicted in Figure 1, when a cyber-physical attack such as a line outage or generator outage occurs in any area of the power system, such as area 2, it can disrupt the power and energy flow paths in other transmission lines. This disruption can push the power system into a non-optimal state in terms of both technical and economic operations. In some cases, it might even drive the system to an unstable point. To address this, the proposed approach utilizes real-time and online data (including frequency, voltage, and power) from data measurement units (DMUs) installed at each bus within the system. Operating within a hierarchical control framework, this approach sends appropriate and practical control signals to the unified power flow controller (UPFC), as shown in Figure 3, and the power system stabilizers (PSSs).  
The goal is to protect and stabilize the system against cyber-physical attacks, considering both the technical aspects (such as voltage and frequency deviations) and economic aspects (such as generation costs).
# Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission
## 2 Materials and Methods
### AE Source Characterization
Dependence of cumulative AE hits vs. time was approximated using the Palmer-Heald equation for the data obtained after testing of each 10 specimens in the series. An example of such an approximation is given in Figure 4b. For each dependence, the values of the parameter \(D\), the error \(\delta D\), and the coefficient of determination \(R^{2}\) were determined and presented in the Table 2. The parameter \(D\) was calculated based on the values presented in Table 2 and constituted 151.1 \(\pm\) 8.4 (1/mm). Figure 4: Cumulative acoustic emission (AE) hits \(N_{\Sigma}\) relations: (**a**) cumulative AE hits \(N_{\Sigma}\) vs. current stress \(\sigma/\sigma_{y}\) and its Palmer–Heald approximation; (**b**) cumulative AE hits \(N_{\Sigma}\) and loading force \(P\) vs. time \(t\). Since the maximum load in the operation cycle of the dragline is approximately \(0.6\sigma_{y}\), the approximately number of AE hits related to crack length is \(N_{\Sigma}/a=D\left(\sec(0.3\pi)-1\right)\approx 106\). Figure 5 shows the dependence of the amplitudes of AE impulses emitted by the crack depending on time (Figure 5a) and the AE impulse amplitudes probability distribution in double logarithmic axes (Figure 5b). The amplitudes of the AE impulses emitted by the crack do not exceed 72 dB, and the amplitude distribution corresponds to the Weibull law with the parameters \(k=1.17\), \(\lambda=6.13\). Assessment of the AE emissivity and determination of the range of AE impulse amplitudes emitted by the crack are used to determine the parameters of the AE structural health monitoring system and estimate the reliability of AE impulse detection against background noise.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
#### 3.1.2 The Trajectory of the Mobile Anchor Node  
As shown in Figure 2, the mobile anchor node starts at point \(x_{1}\), moves along a linear trajectory, and ends at point \(x_{m}\). In the whole process, the mobile anchor node moves at a uniform speed and periodically broadcasts its real-time location message at a fixed transmission power. The length of the trajectory is \(L\), the interval between adjacent broadcast points is \(\Delta L\), and the number of broadcast points on the trajectory is  
\[m=L/\Delta L+1 \tag{1}\]
## 3 Results and Discussion  
### Chemical-Physical Parameters  
#### 3.1.1 Density and Porosity  
On the basis of the EN 13055:2016 rule [23], a material can be defined as lightweight aggregates (LWAs) if the density is lower than 2.0 g.cm\({}^{-3}\). The LWAs obtained with the formulations reported in Table 1 fall into this category. The experimental data showed density values between 1-1.5 g.cm\({}^{-3}\). Comparing the compositions, those containing pumice in partial substitution of clay were always lighter (lower density and higher porosity). The trend indicates an increase in density when nutrients are present and again when they are vitrified. This is explained by the fact that the ashes and the fertilizer glass present in the mixture have a fluxing role during the sintering and contribute to the final densification.  
In Figure 2 the correlation between the apparent density and the total porosity for the LWAs analyzed is plotted. Correlation may be described as the degree of association between the two variables considered. It is possible to note an inverse proportionality: increasing apparent density decreases the porosity. It is possible to verify the goodness of the experimental data by the correlation coefficient: R\({}^{2}\) = 0.9378. Besides, the regression highlights a linear relationship. The porosity values ranged between 40%-60%, and, as referred above, the lighter materials were more porous.
## 2 Background and Related Work
### B-Tree on the Flash Storage Device  
A B-tree index structure is widely used to quickly access the stored data in file systems and database management systems. However, it may be inefficiently built on the flash storage device due to its frequent updates for the same node.  
Figure 2 shows the B-tree index structure that consists of root node A and three-leaf nodes (B, C, and D). Assume that every node is mapped on a flash page. If a record with key 7 is inserted, leaf node B will be updated with the new record. In the sector mapping, as shown in Figure 1(a), updated leaf node B' is simply written into an empty page on the flash block and the old page storing leaf node B is invalidated. Since the amount of invalid page increases as the number of insert operations increases, the flash storage device should perform garbage collection to reclaim the invalid pages. This garbage collection invokes many read/write operations in addition to erase operations.  
In the block mapping, as shown in Figure 1(b), the block including leaf node B is removed in advance for updating leaf node B. After erasing the block, updated leaf node B' is stored and valid nodes (A, C, and D) are rewritten into the erased block. In this example, inserting a record with key 7 invokes many flash operations (e.g., one erase operation and four read/write operations). Compared to the sector mapping, an insert operation requires more read, write, and erase operations on the flash memory to update the leaf node.  
Through these two examples, it is obvious that the performance degradation occurs in the flash storage device regardless of FTL mapping algorithms when constructing the B-tree index structure. To improve the performance, various B-tree variants have been proposed for flash memory. In general, they can be classified into two categories, buffer-based B-trees and structure-modified B-trees. In the following subsection, we review the features and problems of the previous B-tree index structures in more detail.
## 4 Experimental Vehicle
### Sensors and Controllers  
Figure 11 shows the installation layout of vehicle sensors and controllers in the actual vehicle test. An IMU and GPS fusion sensor, IFS2000, is equipped above the cab of the vehicle's front body with an RTK positioning accuracy of 10 mm. The abovementioned steering-by-wire system is mounted on the right frame of the front body. KTGJ-CAN-S, the angle sensor, is mounted on the articulated center and used to measure the angle between the front and rear car body at 1000 Hz. On the rear body frame are SC12-20K gear speed sensor and XW-IMU525 IF02 IMU, which measure the rear axle rotation speed and rear body acceleration, respectively. In addition, tire pressure sensors are installed on each wheel.  
For controllers, an automated driving control unit (ADCU), EAXVA04, serves as an intelligent computing platform to receive and compute-intensive sensor data and perform fusion work, in which the path planning controller and path tracking controller are also integrated. The vehicle control unit (VCU), model EV2274A, acts as a controller to receive control outputs from the ADCU and convert them into control signals for each actuator.  
The hardware architecture and communication between sensors, controllers, and actuators are shown in Figure 12. The GPS and IMU of front and rear vehicles input information to ADCU through the RS232 serial port. The ADCU receives information from  
Figure 11: The installation layout of sensors and controllers on the vehicle body.  
Figure 10: Driving-by-wire system.  
the angle sensor, odometer, and tire pressure sensor through the CAN bus. The ADCU and VCU communicate with each other through the CAN bus. The VCU receives analog signals from the hydraulic pressure sensors. In addition, the VCU sends PWM waves to the steering motor controller, throttle actuator, brake actuator, and other actuators (e.g., dumping system and parking system).
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 4 Engineering Applications
### Aerospace Engineering  
Strong fluid transients occur in the filling up process of propulsion feedlines of satellites and launchers. In the experimental works of [30; 61; 63; 176], different configurations of rocket fuel-filled pipe rigs were tested. An overview of the main concerns experienced in the aerospace community with respect to fluid-hammer was reported by [177]. The study mentions the need for detailed investigation of Fluid-Structure Interaction in combination with thermal heat transfer during fluid-hammer waves in satellites or launchers. In addition, the importance of FSI in the filling of pipelines during the start up of the propulsion systems of spacecrafts was highlighted by [178], claiming that more experimental research should be focused on this line.
## 2 Materials and Methods
### Selection of the SNVs  
Six single nucleotide variations (SNVs; formerly SNPs), _FOLR1_ rs2071010, rs630074, _FOLH1_ rs61886492, _GGH_ rs11545078, rs3758149 and _SLC19A1_ rs1051266 were selected for this study. The SNVs were collected from the National Centre for Biotechnology Information (NCBI) Single Nucleotide Polymorphism Database (dbSNP) [24; 25]. In the 1000 Genomes Project dataset, the minor allele frequencies (MAFs) for the analyzed single nucleotide variants (SNVs) are as follows: 6-8% for allele A (rs2071010), 46-64% for allele T (rs630074), 2-7% for allele A (rs61886492), 8-14% for allele A (rs115450780), 24-30% for allele A (rs3758149), and 49-60% for allele C (rs1051266). Given the limited availability of Central European samples in the 1000 Genomes Project dataset, we applied a selection criterion of MAF \(>\) 5% based on the European populations included in the dataset. A detailed summary of the selected variants is provided in Table 1.  
In silico analysis was performed to predict the impact of SNVs using PredictSNV.2 [26]. This tool identifies the functional impact of SNVs, based on the evaluation of six databases CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA to develop category-optimal decision thresholds. The output displays the results of the five best-performing tools in neutral or deleterious form [27]. For the six SNVs tested, we obtained four neutral (rs2071010--88% expected accuracy, rs630074--77% expected accuracy, rs61886492 89% expected accuracy rs1051266--89% expected accuracy) and two deleterious effects (rs11545078--87% expected accuracy and rs3758149 with 91% expected accuracy in _GGH_ gene) in the PredictSNV 2 output [26].
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology
### Alignment  
The subsequent step involves measuring the similarity of candidate entity pair based on their embeddings. After several epochs of gradient backpropagation training, the optimal parameters \(\theta^{q}\) for the encoder are determined using a validation set, resulting in the final output of entity embeddings. Most contemporary EA studies directly employ traditional similarity functions (e.g., cosine, Manhattan, Euclidean, etc.) to compute the similarity between entity pairs. However, these functions primarily focus on differences between pairs and, consequently, lead to two significant issues: (1) they neglect the influence of correlations between an entity and all other entities; and (2) many 1-to-n pairs arise in the alignment results. To address these limitations, we utilize a _consistency-based similarity function_ proposed in our previous work [49] to calculate the final similarity score for candidate entity pair. The consistency-based similarity function is defined as follows:  
\[d^{\prime}(\mathbf{e}_{s},\mathbf{e}_{t})=\frac{\max_{i\in E^{2}}d(\mathbf{e} _{s},\mathbf{e}_{i})+\max_{j\in E^{2}}d(\mathbf{e}_{i},\mathbf{e}_{j})}{2}-d( \mathbf{e}_{s},\mathbf{e}_{t}). \tag{16}\]  
A smaller \(d^{\prime}(\mathbf{e}_{s},\mathbf{e}_{t})\) indicates a higher the alignment probability for the entity pair \((\mathbf{e}_{s},\mathbf{e}_{t})\). This function introduces two local maxima (i.e., the row maximum and column maximum of the similarity matrix) based on the general similarity function, effectively mitigating the impact of correlation differences on alignment results. Experimental findings demonstrate that this function can significantly eliminate these influences and provide a more accurate measurement of entity pair similarity.
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 4 Conclusions  
In this paper, a waterborne polyurethane coating with high thermal conductivity is proposed, and then, its corrosion protection performance is studied through salt spray experiments, EIS measurements, and SKP tests. The results show that the addition of mixture fillers significantly improves the thermal conductivity of the coating. Especially for the modified coating with 2 wt % composite fillers, the thermal conductivity increases to 1.222 \(W\cdot\mathrm{m}^{-1}\cdot\mathrm{K}^{-1}\). In terms of corrosion resistance property, the waterborne polyurethane coating undergoes apparent corrosion damage, and a great number of rust spots occur on its surface after 6 d of salt spray tests. EIS measurement results also depict that the polarization resistance sharply decreases as the salt spray time increases. The polarization resistance decays to 3.24 \(\times\) 103 \(\Omega\cdot\mathrm{cm}^{2}\) when the salt spray time reaches 10 d, implying that the coating loses the protective effects on the substrate metal. However, for the modified coating, during the salt spray test, there is an absence of obvious corrosion phenomenon, and the polarization resistance is still as high as 1.31 \(\times\) 107 \(\Omega\cdot\mathrm{cm}^{2}\) at the final stage. Overall, the incorporation of 2 wt % composite fillers markedly enhances the corrosion resistance property of waterborne polyurethane coating. This is mainly attributed to the more compact structure and more excellent shielding action to the electrolyte. The surface Kelvin potential results further confirm the advantageous effects of mixture fillers on the corrosion protective performance. The corresponding protective mechanism of the composite coating is also proposed in this work.  
[PERSON] and [PERSON] conceived and designed the study; [PERSON] performed the experiments and wrote the paper; [PERSON] and [PERSON] helped with drawing and analysis; [PERSON], [PERSON] and [PERSON] reviewed the edited manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology project of State Grid Corporation of China (Study on performance evaluation and testing technology of environmentally friendly anti-corrosive coatings for transmission and distribution equipment, 5200-202119092A-0-00).  
Not applicable.  
**Data Availability Statement:** The raw/processed data required to reproduce these findings cannot be shared at this time due to legal or ethical reasons.  
Figure 10: The protective mechanism of the composite coating on the substrate metal, (**a**) waterborne polyurethane coating; (**b**) modified composite coating.  
**Acknowledgments:** We thank the staff at the same laboratory in the China Electric Power Research Institute.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### OFDM Based UWAC Systems
#### 2.2.2 OFDM-Based IBFD Systems
We discovered that this exclusive research could be expanded in the future to increase the UWAC protocols throughput and spectral performance. * **A-SIC**. A novel A-SIC mechanism is proposed that uses the normalized least mean square (NLMS) and adaptive self-interference cancellation (ASIC) schemes to prevent SI cancellation [44]. The received signal from the distant node is remodeled using the detected signal, and then the remodeled signal is subtracted from the received signal after the ASIC to obtain the RSI signal, which is then processed using the NLMS algorithm. This entire process is used to increase the ASIC algorithm's ability to suppress SI to an estimated level of ambient noise. Furthermore, un-coded QPSK with OFDM modulation is used to send and receive data between the two IBFD nodes. The experimental and simulation results show that this advanced cancellation scheme effectively reduces SI to ambient noise levels [44].
## 4 Experiments and Results
### Imperceptibility Analysis
#### 4.1.2 Quantitative Analysis  
To quantitatively analyze the imperceptibility of the watermarked information, we selected the Hausdorff distance [32], signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR) as the evaluation metrics [33]. The Hausdorff distance is a method to calculate the degree of similarity between two sets. The smaller its value, the higher the degree of similarity before and after embedding watermarks into BIM data, indicating the better imperceptibility of the digital watermarking. SNR and PSNR are parameters to measure the degree of influence of watermarking on the original data, considering the original element position and the position after watermark embedding as the normal signal and noise. The higher the values of SNR and PSNR, the less the watermark information affects the original data, indicating better imperceptibility. In BIM data, SNR and PSNR are calculated as shown in Equations (10) and (11).  
\[SNR(A,B)=10\times\lg\frac{\sum\limits_{i=1}^{n}{{x_{Ai}}^{2}}}{\sum\limits_{i= 1}^{n}{{({x_{Ai}}-{x_{Bi}})}^{2}}} \tag{10}\]  
where \(SNR(A,B)\) represents the \(SNR\) value of the BIM data after the watermark embedding, \(A\) is the original BIM data, and \(B\) is the data embedded with the watermark. \({x_{Ai}}\) and \({x_{Bi}}\) are the value of the element coordinates of the original BIM data and watermarked BIM data, respectively. \(n\) is the number of BIM data elements.  
\[PSNR(A,B)=10\times\lg\frac{n\times\max({{x_{Ai}}})^{2}}{\sum\limits_{i=1}^{n}{{ ({x_{Ai}}-{x_{Bi}})}^{2}}} \tag{11}\]  
where \(PSNR(A,B)\) represents the PSNR value of the BIM data after the watermark embedding, \(A\) is the original BIM data, and \(B\) is the data embedded with the watermark. \(n\) is the number of BIM data elements and \(\max({{x_{Ai}}})\) is the maximum value of the x-coordinate of the elements.  
\begin{table}
\begin{tabular}{c c c} \hline
**BIM Data** & **Before Embedding** & **After Embedding** \\ \hline Office building & & \\ Gymnasium & & \\ Structural model & & \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of model details before and after watermark embedding.  
The results of the calculation of these three variables complement each other and can fully illustrate the imperceptibility of the algorithm. The experimental results of the above three indicators are shown in Table 3.  
Table 3 shows the experimental results of the above three indicators. In general, the maximum error of the electronic map feature point plane is \(\pm 1.2\) m. After embedding the watermark, the maximum Hausdorff distance in the three BIM datasets was 0.000224 m, which is much lower than the feature point plane position error. Hence, the perturbation range of this algorithm map element is small. The SNR and PSNR of the experimental data are higher than 100 dB, indicating that the embedding of the watermark causes very little noise in the BIM data. In summary, the watermark had minimal impact on the original BIM data, and the imperceptibility was excellent.