esa-sceva/satcom-chunk-collection huggingface dataset:
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results  
### Simulation Setup  
In the simulation, data collection is performed by a single sink node positioned on the water surface, while all the nodes are randomly dispersed within a three-dimensional area measuring 10,000 \(\times\) 10,000 \(\times\) 10,000 m\({}^{3}\). These sensor nodes have an initial energy of 1000 J, a transmit power of 100 W, a receiving power of 8 W, and a power consumption of 2 mW for an idle state. During the simulation process, the simulation is conducted using a loop structure where the number of nodes increases from 300 to 600. Nodes located within 3000 m from the bottom are referred to as source nodes, and for each round of the network, a source node is randomly chosen to generate 100 packets for transmission. Each data packet has a size of 64 bytes, with an additional 8 bytes for the header. The primary simulation parameters are provided in Table 1. The above are the settings of the common parameters of the three protocols. In addition to this, regarding the VBF [10] and HH-VBF [11] protocols, the radius of the \"routing pipe\" is set to 2000, and the Tdelay is set to 0.1 s.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Node numbers & 300–600 \\ Network dimensions (m\({}^{3}\)) & 10,000 \(\times\) 10,000 \(\times\) 10,000 \\ Data packet size (bytes) & 64 \\ Header size of data packet (bytes) & 8 \\ Size of other packets (bytes) & 8 \\ Initial energy (J) & 1000 \\ Maximum transmission range (m) & 2000 \\ Transmission power (W) & 100 \\ Receiving power (W) & 8 \\ Transmission rate (kbps) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
Figure 6: The data forwarding phase.
## Concept and Kinematic Model Formulation
### Working Principle  
In order to extrapolate deviations and calibrate the MT, a set of Fiber-optic Bragg Gratings strain sensors (FBG) are embedded in the vertical axis [28]. FBG sensors may offer many advantages since they ensure a dynamic performance up to 260 Hz [28], permitting the exposure and measurement of distortions acting during working operations. The determination of the tool tip displacement is a complex process if it is directly extrapolated from the strains. For this reason, it is more convenient to employ FBG as a displacement sensor and to measure the overall elongation (integral effect) of some critical point-to-point dimensions of the MT geometry.  
The proposed adaptive structure consists of an innovative machine tool vertical axis made of CFRP material, which integrates a set of the fiber Bragg grating (FBG) displacement sensors (S1-S5), as shown in Figure 1. The FBG sensors have been placed in the vertical-axis structure in order to measure the total axial elongation of each side of the structure in the direction of the fibers' orientation, combining static loads and thermal gradients. These on-line measures are used as input in a structural mathematical model that predicts the deviation of the tool tip in three spatial directions. The outcome is an algorithm, represented by linear equations, that is integrated into the computer numerical control (CNC) to compensate the position of the tool tip in real-time during the working operations. The coefficients of the prediction models were calculated using a Multiple Regression Analysis (MRA). The model was built by simulation of the Finite Element Analysis (FEA) applied to the ram that was subjected to different static loads and thermal conditions.  
The working conditions have been simulated through a set of loads representing the undesired factors during the machining. Finally, the model has been validated through a set of experimental tests on a prototype in order to prove the effectiveness of the FBG sensors.
## 2 System Description  
### Microgrid System  
Figure 1 shows the microgrid test model considered in this study. The MG is composed of loads, four controllable generators (CGs), renewable energy sources, and battery energy  
storage systems (BESSs). CG refers to dispatchable gas and diesel generators whose output can be adjusted according to instructions from a centralized MG operator. The loads are categorized into classes A (critical load) and B (non-critical load) based on their importance. Renewable energy sources such as wind turbines and PV systems are considered to be non-controllable in this study. Therefore, the flexibility of CG and BESS is essential for maintaining the balance between the supply and demand of MGs.
## 4 Simulation Results
### The Percentage Distribution of the Localization Error  
Figure 11 shows the distribution histogram of the localization error, comparing the Anchor-based algorithm, the AUV-aid algorithm, the AFLA algorithm, the ACFL algorithm and the PCFL algorithm. We deploy 20 mobile nodes stochastically in the simulation region. All mobile nodes perform self-localization simultaneously by running the experiment 50 times independently. When the localization error of PCFL and the localization error of ACFL are both less than 2.5 m, the percentage distribution is 88% and 23%, respectively. However the distribution percentage of the localization error of AFLA is lower than that of PCFL. And the distribution percentage of the localization error of Anchor-based is also lower than that of PCFL when the range of the localization error is from 0 to 2.5. The percentage distribution of the localization error of AFLA is the highest compared with the other four algorithms when the localization error is varying from 2.5 to 5. The percentage distribution of PCFL is zero when the localization error is varying from 5 to 20. In a word, PCFL has better localization performance and smaller localization error.
## 2 Control Algorithm
### Parameter Tuning
#### 2.5.4 Effect of \(\alpha\)  
Similarly, the role and influence of \(\alpha\) on a higher order system are unclear. However, we may approach this problem through a second order system. When \(n=2\), our algorithm suggests \(s=2\dot{e}+(\lambda+2\alpha)e+\alpha^{2}\int ed\tau\). Defining \(\dot{z}=e\) yields \(s=2\ddot{z}+(\lambda+2\alpha)\dot{z}+\alpha^{2}z\). Moreover, \(e\to 0\) when \(z\to 0\).  
A similar statement can be made for \(\alpha\). The function of our algorithm is to force \(s=0\) first. The property of \(s=0\) will then force \(z=0\) when \(t\) approaches infinity. Since \(s=2\ddot{z}+(\lambda+2\alpha)\dot{z}+\alpha^{2}z\), \(z\) converges exponentially if the discriminant is greater than zero, and converges oscillatorily if the discriminant is less than zero, where the discriminant is defined as  
\[D = (\lambda+2\alpha)^{2}-8\alpha^{2} \tag{36}\]  
Defining \(\alpha=\rho\lambda\) and \(\rho>0\) yields  
\[D = \lambda^{2}\left[(1+2\rho)^{2}-8\rho^{2}\right] \tag{37}\] \[= \lambda^{2}(1+4\rho-4\rho^{2})\]  
The condition for \(z\) to converge exponentially is \(0<\rho\leq 1.2\). This approximation is not accurate enough because the actual behavior of \(z\) is also influenced by other parameters. We may conclude \(z\) converges oscillatorily if \(\rho\geq 1\).  
Given \(\rho\geq 1\), the overshoot of \(z\) is influenced by damping ratio of the system. The damping ratio \(\zeta\) can be approximated by  
\[\zeta = \frac{\lambda+2\alpha}{\sqrt{2}\alpha} \tag{38}\] \[= \frac{1+2\rho}{\sqrt{2}\rho}\]  
when \(\rho=1\), \(\zeta=2.213\). When \(\rho\rightarrow\infty\), \(\zeta=\sqrt{2}\).  
Since \(e=z\), the oscillation of \(\zeta\) does not directly equal the oscillation of \(e\). Moreover, the nonlinearity of the system also makes the quantitative prediction inaccurate. However, the trend of the oscillation must be similar. The larger the value \(\alpha\) is, the smaller the overshoot \(e\) has.  
Figure 5 demonstrates the comparison. \(\alpha=10\) in Figure 5a, \(\alpha=1\) in Figure 5b, and \(\alpha=0.1\) in Figure 5c. Other parameters are set identically. It is obvious that a very small \(\rho=\alpha/\lambda\) causes no overshooting in the system. This is reasonable. When \(\alpha=0\), the algorithm degenerates to a conventional SMC. Although large \(\rho\) causes overshooting, the magnitude is small.  
The time constant of convergence for an oscillatory \(z\) can be approximated by  
\[\tau_{\alpha} = \frac{1}{(\lambda+2\alpha)/2} \tag{39}\] \[= \frac{2}{\lambda(1+2\rho)}\]  
when \(\rho\to 0\), \(\tau_{\alpha}=2/\lambda\). When \(\rho\rightarrow\infty\), \(\tau_{\alpha}\to 0\).  
Although large \(\rho\) suppresses overshooting and speeds up the convergence of outputs, \(\alpha=\rho\lambda\) grows exponentially compared to the contribution to control signals. Hence, the trade-off of a fast response is a very large control. A very large control is usually energy consuming or impossible in implementation.
## 2 Related Works  
The unrestricted and widespread usage of the unlicensed 2.4-GHz ISM bands, coupled with the asymmetric transmit power and medium access rules, results in harmful mutual interference among coexisting wireless systems. The most affected are the low-power systems, such as IEEE 802.15.4-based WSNs. Various experimental and theoretical studies have highlighted WSNs' susceptibility to the external interference, especially from high transmit power IEEE 802.11-based WLANs. Many experimental studies (e.g., [10; 23; 24]) show that an IEEE 802.15.4 link operating on a channel overlapped by an IEEE 802.11 network can experience packet losses of up to 50-70%. In light of these performance studies, it is evident that without an interference detection and avoidance mechanism, WSNs cannot satisfy any reliability or dependability conditions required by the aforementioned industrial applications. The most common interference detection technique, also recommended by the ZigBee standard [25], is to utilize energy detection-based spectrum sensing and avoid the channels with an energy level above a certain threshold. However, in order to design an intelligent interference avoidance technique, the type of interference and its behavior in the time and frequency domains need to be identified first. As interference scenarios may evolve in time, adaptive mitigation approaches with an individual strategy are efficient and are recommended [11]. There exist two main approaches to interference classification, where the distinction is made based on the information source used to extract the features to analyze: (i) raw channel energy measurements (i.e., RSSI samples), and (ii) bit error patterns in a corrupted packet. The existing methods for interference classification available in the literature are shown in Figure 2 and further discussed in the following sections.
## 3 Results
### Influence of the Atmospheric Stability on Wind Speed Gradients
#### 3.7.2 Interaction of Coastal Wind Speed Gradients with OWF Wakes
As a consequence of the wake superposition that reduces upstream wind speed, the horizontal wind speed gradient downstream has a stronger slope in the first 30 km than the reference horizontal wind speed gradient. This increases the horizontal wind speed gradient and therefore the adjustment distance towards the equilibrium. The wakes indeed subdivide the horizontal wind speed transect of \(u_{10}\) into two horizontal wind speed gradients. The first horizontal wind speed gradient is the upstream wind starting from the shore to the cluster N-4 and is a result of the step in the surface roughness between the coa  
Figure 14: **(a,b)**: Average wind speed gradients for the “INCS” class from unstable cases for easterly (**a**) and southerly (**b**) wind directions. (**c,d**)**: The same as (**a,b**) from stable conditions. These curves refer to the horizontally and vertically oriented rectangles shown in Figure 2b passing through cluster N-4 (black line, “N4”) and void of windfarms (red line, “Ref”) for, respectively, easterly and southerly winds. Green curves correspond to thermal stability for the “N4” transect (solid line) and the “Ref” transect (dashed line). The blue and orange dashed lines refer to the fitted empirical model. horizontal wind speed gradient downstream the cluster N-4, starting from the highest reduced wind speed increases gradually towards a certain \"normal\" conditions, where the wake effects are attenuated. In this sense, the wind farms can also be considered as a surface rougher than the sea. Obviously, the locations of the cluster N-4 on the curve of \(u_{10}\) derived from easterly and southerly winds (Figure 14) are quite different. The cluster N-4 is about 35-50 km and 65 km far offshore with respect to eastern and southern coasts, respectively. The cluster N-4 is located where the wind speed is more or less stabilised for southerly wind direction. However, this is the opposite for easterly wind direction. Hence, this cluster undergoes stronger horizontal wind speed variations in easterly than southerly wind directions. This could lead to weaker (or no impact) on the power production for southerly than easterly winds.
# On the Detection and Removal of Oxides from Laser-Treated Surfaces
## 3 Results
### Oxide Analysis
While EDS provides compositional information based on individual elements, XRD provides information based on crystallographic phases. In this case, the oxide phase was identified as a corundum phase (Al\({}_{2}\)O\({}_{3}\)). In the literature, the oxidation products of the Ti6 Al4V alloy were typically Ti-based [28; 29; 30] or a mixture of Ti- and Al-based oxides [31; 32; 33]. However, these cases mostly concerned isothermal oxidation, while the process during laser ablation is highly dynamic. Moreover, a fraction of the oxide content present before the laser treatment may have been retained due to the different ablation thresholds of the oxide and the base metal. Also, XRD is not particularly sensitive to small variations in elemental compositions, so the possibility of the corundum phase containing Ti, Al, and V elements cannot be ruled out. A short note on the experimental scatter concerning the values presented in Tables 1-6: For the EDS values, obtained with the current setup, typical standard deviations were in the range of 10-15% of the presented value; for the XRD values, they were around 1% (absolute value). These numbers refer to a single measurement; on top of this, spatial variation from place to place may be considered. For cases where such variation was observed to a non-negligible extent, ranges from several measurements are shown. In this regard, it should be noted that the XRD data were taken from a much larger area---3 cm\({}^{2}\), compared \begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **O (wt\%)** \\ \hline Steel (ground) & 1.7–2.1 \\ Steel (grid) & 2.9 \\ Steel (lines) & 3.9 \\ Steel (dimples) & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: EDS results on steel samples. ‘Ground’ represents a clean sample; ‘grid’, ‘lines’, and ‘dimples’ represent samples oxidized by variations of laser texturing. ‘Grids’ and ‘lines’ are shown in Figure 1b,a, respectively, while ‘dimples’ are similar to those shown in Figures 3 and 4 on other substrates. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **O (wt\%)** & **Oxide (wt\%)** \\ \hline Ti (ground) & 0.6 & 0 \\ Ti (dimples) & 11–12 & 17.9 \\ Ti (dimples + N\({}_{2}\) shielding) & 13–15 & 18.1 \\ Ti (dimples + laser cleaning) & 11–12 & 2.4 \\ Ti (grit-blasted + storage) & 31 & 52.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EDS and XRD results on Ti alloy samples; ‘ground’ represents a clean sample, the three rows with ‘dimples’ represent samples oxidized by variations of laser texturing, as shown in Figure 4 in the same order; and ‘grit-blasted + storage’ represents an example of a significantly oxidized sample (initially treated by grit blasting then stored for a long time in open air). to 1.1 mm\({}^{2}\) for EDS. The general detection limits of these techniques are reported to be of the order of 0.1-1%, depending on the particular setup and material combination [34; 35; 36].
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
## 2 Methodology and Principle
### LSM in Imaging Domain  
#### 2.2.1 The Global Space-Varying Point Spread Function  
According to the acoustic-wave equation, Hessian can be expressed in the frequency domain as:  
\[H(x_{1},x_{2},\omega)=L(x_{1},\omega;x_{s},x_{r})^{T}L(x_{2},\omega;x_{s},x_{r }), \tag{6}\]  
where \(x_{s},x_{r}\) are the positions of shots and receivers, and \(L\) denotes the sensitivity kernel, satisfying  
\[L(x,\omega;x_{s},x_{r})=f(\omega)G(x,x_{s},\omega)G(x,x_{r},\omega), \tag{7}\]  
where, \(f(\omega)\) is a seismic wave, \(G(x_{1},x_{s},\omega)\) is the Green function excited from the shot position \(x_{s}\).  
The PSF is commonly used as the approximation for Hessian, including a row of elements in the Hessian matrix \(x_{0}\).  
\[K(x_{2},x_{0},\omega)=L(x_{2},\omega;x_{s},x_{r})^{T}[L(x_{0},\omega;x_{s},x_{ r})\delta(x-x_{0})]. \tag{8}\]  
Here, \(K(x_{2},x_{0},\omega)\) reflects the PSF of the perturbation at the point \(x_{0}\).  
Substitute Equation (8) into Equation (6), mark the point spread function at \(x_{i}\) as \(K_{i}\), and Hessian can be expressed as,  
\[\mathbf{H}=\big{[}\begin{array}{cccc}\mathbf{K}_{1}&\cdots&\mathbf{K}_{i}&\cdots&\mathbf{K} _{m}\end{array}\big{]}^{T}. \tag{9}\]  
Substitute the above equation into Equation (4) to obtain:  
\[\mathbf{I}_{i}=\mathbf{K}_{i}\mathbf{m}. \tag{10}\]  
The reflectivity model can be obtained by  
\[\mathbf{m}=\frac{\mathbf{I}_{i}}{(\mathbf{K}_{i}+\mathbf{\alpha})}, \tag{11}\]  
where \(\mathbf{\alpha}\) is the regularization parameter. The Equations (10) and (11) can be also expressed in the space domain as:  
\[\mathbf{I}_{i}=\mathbf{K}_{i}*\mathbf{m}, \tag{12}\]  
and:  
\[\mathbf{m}=\mathbf{I}_{i}\otimes\mathbf{K}_{i}, \tag{13}\]  
where, \(*\) and \(\otimes\) represent the operation of convolution and deconvolution, respectively. It reveals that, the migration image at \(x_{i}\) is the superposition of the convolution of the global reflectivity model and the point spread function at \(x_{i}\); on the contrary, the reflectivity model at \(x_{i}\) can be computed within a high-dimensional deconvolution of the corresponding point spread functions.
## 5 Feature Parameter Selection  
Eleven feature parameters were extracted in Section 4. To improve the accuracy of the SVM algorithm and reduce the number of feature parameters, two standards to choose the feature parameters are analyzed in this section: the correlation coefficient between geometric information and a certain parameter and the correlation coefficient between feature parameters.
## 3 Results
### WUE Analysis of the Five Natural Terrain Areas
#### 3.2.2 Multiple Fractal Analysis of WUE in Different Natural Terrain Regions
Concave f(\(\alpha\)) curves indicate widespread heterogeneity within the studied regions; however, the variance in the range of \(\alpha\) and the maximum value of f(\(\alpha\)) among the regions indicate different degrees of spatial heterogeneity between the regions. The f(\(\alpha\)) curve of the Huaibei Plain is relatively flat and declines slowly at high \(\alpha\) values, suggesting low heterogeneity for WUE. The f(\(\alpha\)) curve of the hilly land of the Jianghuai River starts at a higher f(\(\alpha\)) value and then drops rapidly, indicating significant variability in the WUE. The f(\(\alpha\)) curve of the hilly and mountainous area in southern Anhui transitions smoothly from high to low \(\alpha\) values, indicating a certain degree of spatial heterogeneity but a relative balance. The f(\(\alpha\)) curve of the Dabie Mountains in west Anhui is wide, with high f(\(\alpha\)) values in the high \(\alpha\) region indicating significant WUE heterogeneity in this area. The f(\(\alpha\)) curve of the plain alongside the river drops rapidly from high f(\(\alpha\)) values, and the curve width is relatively narrow, indicating relatively uniform WUE in this area. Figure 13: The \(\tau\)(q)–q curves for different natural terrain subdivisions: **(a)** the Huaibei Plain, **(b)** Jianghuai hilly region, **(c)** hilly and mountainous area of southern Anhui, **(d)** Dabie Mountains, and **(e)** plain alongside the river.
## 3 What Is SDN?
### Working Principle
#### 3.1.2 P4 Language  
P4 was first introduced in a research paper presented at the 2014 Special Interest Group on Data Communication Computer Communication Review (SIGCOMM CCR) conference [66]. Subsequently, the inaugural P4 workshop was conducted in June 2015 at Stanford University. An updated version of P4, known as \(P4_{16}\), was introduced between 2016 and 2017, supplanting the previous specification, \(P4_{14}\). Figure 6 shows the different released versions of the P4 language.  
P4 Runtime and OpenFlow are used for network programming, but they serve different purposes and operate at different levels of abstraction. On the one hand, OpenFlow is a communications protocol that gives access to the forwarding plane of a network switch or router (or firewall) over the network. It enables network controllers to determine the path of network packets across a network of switches. While OpenFlow is a protocol that enables SDN by giving administrators software-based access to the flow control tasks provided by switches and routers, P4 is a high-level language for programming protocol-independent packet processors. P4 is a field-specific programming language developed for managing  
Figure 5: Specification of OpenFlow from 2009 to 2015. Some possible enhancements were carried out during these periods to make it more efficient and scalable [63].  
Figure 6: P4 from 2018 to 2023 [67].  
data packet forwarding planes in network devices in pretty much anything that P4 can build [68]. Unlike regular languages like C or Python, P4 is specifically made for handling network data efficiently. It does not support any specific protocols. On the contrary, it authorizes users to define the required protocols in the program [68]. P4 enables the flexibility to reconfigure the data plane. It has been gaining increasing attention due to its alignment with the next-generation SDN standards, characterized by Open Interfaces and complete data plane programmability. As a result, P4 has witnessed widespread adoption across academia and industry in recent years [69].