esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Route Discovery Time
#### 4.3.2 Under NDMR  
The two-way handshake process of discovering two node-disjoint paths under NDMR is shown in Figure 4a.  
The latency of delivering the PREQ from the source to the destination at which the second path has been found, denoted by \(t_{PREQ}^{NDMR\cdot 2 HS}\), can be calculated as follows:  
\[t_{PREQ}^{NDMR\cdot 2 HS}=n_{2}^{NDMR}\times t_{prop.}+(n_{2}^{NDMR}+1)\times t _{node}. \tag{21}\]The latency of delivering two PREPs from the destination back to the source, denoted by \(t_{PREP1}^{NDMR\text{-}2 HS}\) and \(t_{PREP2}^{NDMR\text{-}2 HS}\), can be determined thusly:  
\[t_{PREP1}^{NDMR\text{-}2 HS}=n_{1}^{NDMR}\times t_{prop.}+(n_{1}^{NDMR}+1)\times t _{node}, \tag{22}\]  
\[t_{PREP2}^{NDMR\text{-}2 HS}=n_{2}^{NDMR}\times t_{prop.}+(n_{2}^{NDMR}+1)\times t _{node}, \tag{23}\]  
where \(n_{1}^{NDMR}\) and \(n_{2}^{NDMR}\) are the hop counts of the first and second paths, respectively.  
Since \(n_{1}^{NDMR}\leq n_{2}^{NDMR}\), therefore \(t_{PREP1}^{NDMR\text{-}2 HS}\leq t_{PREP2}^{NDMR\text{-}2 HS}\).  
The total time used for route discovery under NDMR, denoted by \(t^{NDMR\text{-}2 HS}\), is as follows:  
\[t^{NDMR\text{-}2 HS}=t_{PREQ}^{NDMR\text{-}2 HS}+max(t_{PREP1}^{NDMR\text{-}2 HS },t_{PREP2}^{NDMR\text{-}2 HS}). \tag{24}\]  
Therefore:  
\[t^{NDMR\text{-}2 HS}=2n_{2}^{NDMR}\big{(}t_{prop.}+t_{node}\big{)}+2t_{node}. \tag{25}\]  
The three-way handshake process of discovering two node-disjoint paths under NDMR is shown in Figure 4b.  
The latency of delivering the first PREQ from the source to the destination, denoted by \(t_{PREQ1}^{NDMR\text{-}3 HS}\), can be calculated as follows:  
\[t_{PREQ1}^{NDMR\text{-}3 HS}=n_{1}^{NDMR}\times t_{prop.}+(n_{1}^{NDMR}+1)\times t _{node}. \tag{26}\]  
The latency of delivering the second PREQ from the destination to the source, denoted by \(t_{PREQ2}^{NDMR\text{-}3 HS}\), can be calculated thusly:  
\[t_{PREQ2}^{NDMR\text{-}3 HS}=n_{2}^{NDMR}\times t_{prop.}+(n_{2}^{NDMR}+1)\times t _{node}. \tag{27}\]  
The latency of delivering the second PREP from the source back to the destination, denoted by \(t_{PREP2}^{NDMR\text{-}3 HS}\), can be determined as follows:  
\[t_{PREP2}^{NDMR\text{-}3 HS}=n_{2}^{NDMR}\times t_{prop.}+(n_{2}^{NDMR}+1)\times t _{node}. \tag{28}\]  
The route discovery time can be determined as follows:  
\[t^{NDMR\text{-}3 HS}=(n_{1}^{NDMR}+2n_{2}^{NDMR})\big{(}t_{prop.}+t_{node}\big{)} +3t_{node}. \tag{29}\]  
From Equations (20), (25) and (29), it is clear that the route discovery time of NDMR is smaller than that of MP-AODV.
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 3 Signal Processing  
The signal analysis is currently focused mainly on signals related to the operation or the deformation of the structure, with the remaining sensors providing valuable information when necessary. As shown in Figure 3, the processing method includes triggering methods, event identification, signal alignment, and average curve calculations.
## 3 Results, Analysis, and Discussions
### Experimental Dam-Break Test of the IBS Frame  
After fabricating the IBS model components in the laboratory, the components were assembled in the testing dam-break at the deck's specific position, which is 1370 mm away from the pond area inside the dam-break tank. The ponded area was measured at 2440 mm \(\times\) 2000 mm \(\times\) 300 mm. A pressure meter is an instrument that was used to detect external load or pressure. Eight waterproof pressure meters were installed at specified locations in front of the IBS frame model for measuring hydrodynamic impact pressure. One load cell was installed at the top of the frame by tightening it with the bolt to record the vibration of the column bolt. Three accelerometers were used on the frame to record accelerations and displacements in the direction of the flow, respectively. Figure 13a-c display the exact positions of the instruments.  
Figure 12: Illustrations of the CFD results for 2D frame: (a) simulated fluid flow; (b) maximum velocity; (c) maximum pressure.  
All pressure meters and load cell sensor measurements were connected to a data logger to record a continuous time history response during the test. The rotating tank was locked while the opening gate was at the top position to fill in the tank with water to a target height. The sport light for video camera visibility was set at a position above the tank to record the model's movement due to water splash. Then, the motorized torsional device was switched on to allow the water tank to rotate clockwise; the water flowed downstream as a hydraulic bore pressure against the structure.  
Figure 13: IBS frame ready for testing: (**a**) schematic diagram; (**b**) front of the experimental test; (**c**) back of the frame.
## EUV and SXR Full Field Microscopes Based on a Double Stream Gas-Puff Target Source
### Full Field EUV Microscope
#### 3.1.4 EUV Full Field Imaging of Test Objects and Spatial Resolution Estimation  
As the first test sample, a 10 \(\upmu\)m TEM copper mesh (SPI supplies, West Chester, PA, USA), a period of 12.5 \(\upmu\)m, square holes with sizes of 7.5 \(\times\) 7.5 \(\upmu\)m\({}^{2}\) and 5 mm width bars, was imaged (Figure 6a,b). Such a well-defined structure provided a total photons absorption in the surface and the total transmission in the mesh holes, resulting in good optical contrast. The image, shown in Figure 6a, was acquired by a scanning electron microscope (SEM, FEI, 3D FEG, Hillsboro, OR, USA), while Figure 6b was obtained with a time integration of 200 EUV pulses at a 10 Hz repetition rate, corresponding to an exposure time of 20 s, and acquired using an i-Kon M CCD camera (from Andor,  
Figure 5: (a) Plasma profile in false colors, showing the most intense region of plasma emitting EUV radiation (red). The inset shows the Ar plasma image obtained with a pinhole camera. (b) Shows the EUV emission spectrum (H \(\times\) V) from Ar plasma generated by focusing Nd:YAG laser pulses irradiating a double stream gas-puff target. Figure modified, based on [58].  
Belfast, UK), cooled to \(-20^{\circ}\)C, with a readout set to 50 kHz, in order to minimize the read noise. The contrasts of Figure 6a,b, are reversed because the bright areas in the SEM image were obtained from backscattered electrons from the test mesh, while the light areas in the EUV microscope image (holes) are formed by photons transmitted through the holes in the mesh at the wavelength of 13.84 nm. The spatial resolution of the microscope was assessed by applying the knife-edge (KE) test [1], in the dashed line depicted in Figure 6b. The KE test was applied in a 2 um line, corresponding to \(\sim\)61 pixels in the EUV image. It results in a single-pixel size of 33 nm while the field of view (FOV) of the EUV image was \(\sim\)34 \(\times\) 34 um\({}^{2}\) in size. The KE was calculated in a 10-90% intensity change over a distance 3 pixels, corresponding to 95 nm, i.e., to a half-pitch spatial resolution of 48 nm (Figure 6c), which is almost one order of magnitude better than the obtainable resolution using a full field visible light microscopes, which is limited by a wavelength to 300 nm [58].  
Such estimation shows that the obtained resolution is 60% larger than the expected diffraction limit, which, according to the Rayleigh resolution, is 1.22\(\Delta\)r\(\sim\)60 nm. This discrepancy depends on the outer zones of the zone plate, fabricated with a zone width-to-depth ratio equal to 4, in a 200 nm thick PMMA layer. Moreover, it has to be taken into account that the zone plate is a dispersive diffractive element and that the considered EUV radiation is not strictly monochromatic and the presence of other spectral components, although weak comparing to the dominating 3s-5d Ar\({}^{7+}\) line, affects the quality of the acquired image.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## 3 Results and Discussions
### Two-Dimensional Examples
#### 3.2.1 2D Flow in a Channel  
This example is characterized by the stability and accuracy criteria. Air flows in a channel with 8 mm/s of an average velocity (\(V\)) at the inlet. The channel has 1.5 m of a length (\(L\)) and 20 cm of gap (\(H\)). The pressure drop (\(\partial P/\partial x\)) inside the channel is \(-4.54\times 10^{-5}\) Pa/m. The density (\(\rho\)) and dynamic viscosity (\(u\)) of air are 1.2 kg/m\({}^{3}\) and 1.89 \(\times\)\(10^{-5}\) kg/m.s, respectively. The analytical solution of the velocity distribution at a full development regime is [27; 28]:  
\[u=\frac{H^{2}}{2\mu}\bigg{(}\frac{\partial P}{\partial x}\bigg{)}\bigg{[} \bigg{(}\frac{y}{H}\bigg{)}^{2}-\bigg{(}\frac{y}{H}\bigg{)}\bigg{]}. \tag{92}\]  
The following data are used in the LBM simulation:  
\[\tau_{f}=0.7, \tag{93}\]  
\[\rho\mathbf{u}^{\rm eq}=\sum_{f}\mathbf{e}_{i}\mathbf{e}_{i}, \tag{94}\]  
and  
\[S_{i}=0. \tag{95}\]  
The LBM simulation of flow in a channel is performed using the tabled data, Table 7. Figure 9 demonstrates the fully developed velocity profile. As shown, good agreements between the LBM and analytical solutions are achieved. To show the effect of the lattice Mach number (\(Ma_{(LS)}\)) on the accuracy of the LBM, Figure 10 is plotted. As seen in Figure 10, as the \(Ma_{(LS)}\) increases, the error between the LBM and analytical resultsincreases too. The error is due to the pseudo-incompressibility of the LBM. Based on the numerical experimentations, the stability limit of the LBM is at \(Ma_{(LS)}\) equals 0.49, where the scheme becomes unstable beyond this number.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### System-Level Techniques  
The system level policy covers the hardware level devices where the power can be changed by either varying some parameters or by varying the operating mode. A compromise between energy reduction and performance is achieved by using several operating modes ranging from high performance, high power to low power, low performance. CPU settings include CPU voltage, frequency and power mode.
## 1 Introduction
The proposed method has several key advantages:  
* The proposed model introduces a novel spatial attention mechanism that enhances the model's focus on critical deformation areas, improving risk detection sensitivity. * It demonstrates a robust and adaptable framework that is suitable for various railway conditions and spatial environments, providing high efficiency across multiple scenarios. * The experimental results show that InSAR-RiskLSTM significantly outperforms the baseline models in accuracy and response time, underscoring its effectiveness for real-time risk prediction. The remainder of this paper is organized as follows. Section 2 presents the methodological framework, including the problem formulation and detailed description of the InSAR-RiskLSTM model. Section 3 outlines the experimental setup, including datasets,evaluation metrics, and comparative analyses. Section 4 discusses the results, highlighting the key findings and implications for railway maintenance. Finally, Section 5 concludes the paper with a summary of its contributions and potential directions for future research.
## 3 Mathematical Model
### Driving Force  
According to the joint action of laser and arc, the distribution of driving forces inside the hybrid molten pool is exceptionally complex. During the laser-arc welding process, the melt pool is mainly affected by droplet impact, buoyancy, surface tension, recoil pressure, electromagnetic force, etc. Due to the existence of temperature intervals from the liquid phase line to the gas phase line of the metal material, the temperature of the liquid metal at different locations within the molten pool during the welding process varies, forming a temperature gradient. The density of the molten metal at different temperatures differs, resulting in the formation of thermal buoyancy:  
\[F_{a}{}^{1}=\rho_{0}\beta\beta\Delta T \tag{8}\]  
where \(F_{a}{}^{1}\) is thermal buoyancy, \(\Delta T\) is the temperature difference, \(\beta\) is the coefficient of thermal expansion of the material, and \(\rho_{0}\) indicates the density at the measured point.  
Surface tension acts at the gas-liquid interface and is mainly influenced by temperature. The surface tension formula is:  
\[\gamma^{1}=\gamma_{1}+k_{1}(T-T_{m}) \tag{9}\]  
where \(\gamma^{1}\) is surface tension, \(\gamma_{1}\) is the surface tension of the solid metal, \(\kappa_{1}\) is the surface tension coefficient, and \(T_{m}\) is the melting point.  
The metal is rapidly vaporized by the high energy density laser irradiation to form a metal vapor, which is rapidly detached from the metal surface. The reaction force of the metal vapor on the metal surface is the recoil pressure:  
\[P^{1}=0.54\delta_{1}\exp(\frac{(T-T_{g})H_{\rm a}}{TT_{g}R_{0}}) \tag{10}\]  
where \(P^{1}\) is the recoil pressure, \(\delta_{1}\) is the standard atmospheric pressure, \(H_{\rm a}\) and \(T_{g}\) are the latent heat of vaporization and gasification temperature of AH36 steel, respectively, and \(R_{0}\) is the ideal gas constant.
## 4 Modeling Process and Results
### Implementation of SI  
The SI model was constructed using the training dataset, and a total of 345 landslides were used to calculate the SI weights. By overlaying factor layers with the landslide inventory map, the relationships between factor classes and landslides were obtained (see Table 2). The definition implies that when the SI value is greater than 0, the factor class exerts a promoting effect on the occurrence of landslides. In contrast, when the SI value is less than 0, the factor class is not conducive to the occurrence of landslides [81]. As there are no landslides in certain factor classes (for example, the number of landslides is 0 when the land cover is water), for these classes, SI values cannot be calculated from the  
Figure 4: Methodological flowchart. formula (4). In this study, the minimum SI value (\(-3.352\)) was obtained when the altitude is 2200-2600 m, indicating that the probability of landslide occurrence is low in this class. Moreover, if there is no landslide in a factor class, the class is unfavorable for the occurrence of landslides. Therefore, the SI value of factor classes without landslides was set to a value less than the minimum value (namely \(-3.5\)) to indicate that these classes are extremely unfavorable for the occurrence of landslides.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Verification and Analysis  
Each image that becomes the test data will be marked with the edge of the iced transmission line by ground truth (expert labeling).  
System testing involves comparing the expert labeling results with our proposed scheme. By comparing the test image that an expert manually generated with the image that our proposed method detected, it is possible to determine the value of the confusion matrix component. The image will be converted into binary form. Then, the number of pixels detected by our proposed scheme will be known as the actual transmission line icing area by applying the AND operator. Transmission line icing will be marked with a binary value of 1, and non-line icing will be marked with a binary value of 0. Confusion matrix validation is used in the performance testing of our proposed scheme. Figure 27 is an example of comparing ground truth detection results to our proposed scheme detection results. The experiments in this paper used test images from 50 images taken randomly. The confusion matrix results from 50 PTLI images based on the experiment are shown in Table 4.  
Based on the result of the evaluation matrix for validation of our proposed scheme, the accuracy value is 97.71%, the precision value is 96.24%, the recall value is 86.22%, and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Recall** & **Specificity** & **Precision** \\ \hline
0.9771 & 0.9624 & 0.8622 & 0.9948 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix from 50 PTLI images.  
Figure 27: The evaluation image of the (**a**) ground truth image testing and the (**b**) proposed method.  
specificity value is 99.48%. Generally, our proposed scheme is reliable for PTLI images with complex backgrounds and images with poor lighting. Another advantage of this scheme is being able to distinguish between objects and backgrounds, making it easier to recognize objects in images with complex backgrounds. Identification of the top and bottom lines has also been successfully conducted by marking a different color between the top and bottom edges to make it easier to measure the thickness of the ice. Therefore, our proposed scheme effectively resolves iced transmission line identification issues and significantly increases automation.  
Our proposed scheme, the previous method, and a manual measurement obtained the ice thickness values at specified locations from the three image pairs. In addition, the manual measurement results are measured by a micrometer caliper with an accuracy of 0.05 mm. The previous method used Canny transform for edge detection and the Hough transform for upper and lower line detection [23; 24]. Table 5 shows the comparison results in detail. It shows that the mean absolute error is small based on all experiments, so this scheme is acceptable for ice thickness measurement. Assume the actual value of ice thickness is the manually measured value; the average accuracy of our method can reach 90%.
## 3 Data Used and Methodology
### Random Forests and Performance Analysis
\(R^{2}\) determines the fitness between the predicted and original values, whereas \(RMSE\) measures the variance of errors between the real and predicted values [55]. The formulae for both the statistical measures are:  
\[R^{2}\ =\ \frac{\sum_{i\ =\ 1}^{N}(\hat{y_{i}}-\ \overline{y_{i}}\ )}{\sum_{i\ =\ 1}^{N}(y_{i}-\ \overline{y_{i}})^{2}} \tag{2}\]  
\[\overline{y_{i}}=\frac{1}{N}\sum_{i=1}^{N}y_{i} \tag{3}\]  
where \(\overline{y_{i}}\) is the mean value; \(y_{i}\) and \(\hat{y_{i}}\) are observed and forecasted values and \(N\) is the number of data points. \[RMSE=\ \sqrt{\frac{SSE}{N}} \tag{4}\]  
\[SSE=\ \sum_{i=1}^{N}(\hat{y_{i}}-y_{i})^{2} \tag{5}\]  
where \(SSE\) is the sum of squared errors. The higher the value of \(R^{2}\), the better the predictive capability of the model is, with 1 depicting an exact relationship between observed and predicted values [8]. This provides a basis for whether the model is fit to be used for prediction.
# An Improved Product Code-Based Data Hiding Scheme
## Product Code-Based Data Hiding
### Trivial Solution
#### 3.1.2 Extracting  
In the receiving end, once the \(7\times 7\) slicing bits (\(\mathbf{Y}\)) of the stego image block is obtained, the secret message block \(\mathbf{M}\) can be easily extracted by calculating the syndrome of \(\mathbf{Y}\), that is S(\(\mathbf{Y}\)) = P(\(\mathbf{Y}_{\text{IB}}\)) \(\oplus\)\(\mathbf{Y}_{\text{P}}\) = \(\mathbf{M}\).  
**Theorem 1**.: _The syndrome of \(Y\) is exactly equal to the secret message block \(\mathbf{M}\)._  
**Proof.** Since S(\(\mathbf{Y}\)) = S(\(\mathbf{X}\)) \(\oplus\) S(\(\mathbf{T}\)) and S(\(\mathbf{T}\)) = P(\(\mathbf{T}_{\text{IB}}\)) \(\oplus\)\(\mathbf{T}_{\text{P}}\) = P(\(\mathbf{0}_{4\times 4}\)) \(\oplus\)\(\mathbf{T}_{\text{P}}\) = \(\mathbf{TS}\), hence S(\(\mathbf{X}\)) \(\oplus\) S(\(\mathbf{T}\)) = S(\(\mathbf{X}\)) \(\oplus\)\(\mathbf{TS}\) = \(\mathbf{M}\), where \(\mathbf{T}_{\text{IB}}\) and \(\mathbf{T}_{\text{P}}\) respectively denote the \(\mathbf{IB}\) and \(\mathbf{P}\) parts of \(\mathbf{T}\); and the code array of a zero information array is a zero code array, that is the parity check part is a zero block.\(\square\)
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 3 Discussions and Outlook
### Large-Scale and Low-Cost Fabrication of Graphene Electrodes  
The widely used commercial Ag/AgCl gel electrode has the characteristics of large-scale and low-cost manufacturing. Graphene ECG electrodes have achieved more excellent performance in monitoring ECG signals; the achievement of large-scale, high-quality preparation will profoundly impact the extent of their application. For this challenge, we tried to achieve large-scale preparation of graphene electrodes by a roll-to-roll method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
(R2R) [15]. Compared to the traditional CVD method, the open-R2R CVD method does not need surface cleaning, polishing, or heat treatment of the copper substrate. The large area of graphene film (several meters long and 15 cm wide) was laminated onto an ethylene vinylacetate/polyethylene terephthalate EVA/PET substrate by rollers (Figure 6c). The resulting graphene/EVA/PET electrode has a lower electrode-skin impedance (less than 1 kHz) than commercial gel electrodes with high durability (can sustain 10 wash cycles). This provides an idea for how to prepare high-quality graphene ECG electrodes at a large scale and low cost.  
In addition, to achieve low-cost and high-performance ECG electrodes, we also tried to produce ECG electrodes using waste materials. The COVID-19 pandemic has led to the mass discarding of medical masks, which are made of non-woven fabrics that do not degrade easily and may not be suitable for reuse due to contamination [90]. However, after disinfection, the non-woven fabric can be an excellent substrate for graphene electrodes since it has good flexibility, durability, comfort, and air-permeability. As shown in Figure 6d, after being dip-coated by graphene nanoflakes, a breathable, non-woven electrode can be made into the desired pattern. Holes are punched in it to ensure the sticky, non-woven fabric at the back increases adhesion between the electrode and skin, which decreases impedance. The recycled, non-woven fabric/graphene ECG electrode shows good ECG signal monitoring performance and is a low-cost and environmentally friendly way to produce graphene electrodes.  
Moreover, the laser-induced/scribed technology introduced above is also promising for low-cost, large-scale manufacturing of graphene electrodes [62; 64; 65; 67]. Compared with traditional electrode fabrication methods, it has the advantage of realizing graphene fabrication and electrode patterning simultaneously.