esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Buoyancy-Driven Square Cavity
### Case \(\text{Ra}=10^{6}\), \(\epsilon=0.6\), Sutherland Viscosity  
In this section, the present solutions are analyzed and compared with other works for \(\text{Ra}=10^{6}\) and \(\epsilon=0.6\). This benchmark is described in [47; 54], i.e., where the Sutherland viscosity law is taken from.  
\[\frac{\mu(T)}{\mu^{*}}=\bigg{(}\frac{T}{T^{*}}\bigg{)}^{\frac{3}{2}}\bigg{(} \frac{T^{*}+S}{T+S}\bigg{)}, \tag{18}\]  
with \(T^{*}=273\) K, \(S=110.5\) K, and \(\mu^{*}=1.65\times 10^{-5}\) kg /(m s).  
Figure 11 shows the convergence of the minimum, maximum, and average Nusselt numbers along the vertical hot wall for the full Navier-Stokes equations computed with two stabilization matrices. The black line is the reference solution taken from [53; 54], which solves the full Navier-Stokes equations on a \(1024\times 1024\) stretched grid with a maximum aspect ratio of 80. Table 3 further compares the Nusselt numbers with the benchmarks of [49; 50] that utilize low-Mach-number solvers based on stabilized FEM methods on very fine stretched meshes (with about 4.2 million and 0.86 million degrees of freedom, respectively).  
It can be seen that, while the method with the classic tau converges to the Nusselt numbers from above, the method with the incompressible HH  
Figure 10: Buoyancy-driven square cavity. Adapted solutions for \(\epsilon=0.01\) obtained with various methods and the HH tau.  
In general, for this test case, the Nusselt numbers computed with the present methods are a little higher than those reported in the literature.  
Figure12 compares the dimensionless Nusselt numbers obtained with the naive (nv) and standard (st) adaptivity approaches, showing that a higher accuracy is attained than for a uniform grid with the same number of degrees of freedom. In general, the Nusselt numbers computed with both adaptation procedures have better accuracy than those obtained for a uniform grid.  
Figure 11: Buoyancy-driven square cavity. Nusselt numbers along hot vertical wall for \(\epsilon=0.6\) and Sutherland viscosity.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Reference Solution** & **Nu** & **Nu\({}_{\text{min}}\)** & **Nu\({}_{\text{max}}\)** \\ \hline [PERSON] et al. [53; 54] & 8.6866 & 1.0667 & 20.2704 \\ [PERSON] [50; 54] & 8.6861 & 1.0674 & 20.3051 \\ [PERSON] [49] & 8.6866 & & \\ \hline
**Present Method** & **tau** & **Nu** & **Nu\({}_{\text{min}}\)** & **Nu\({}_{\text{max}}\)** \\ \hline Full NS & Classic & 8.6227 & 1.027 & 17.940 \\ Full NS & HH & 9.1134 & 1.093 & 21.629 \\ Full NS & Polner & 9.1142 & 1.093 & 21.632 \\ \hline \end{tabular}
\end{table}
Table 3: Nusselt numbers along hot vertical wall. \(\text{Ra}=10^{6}\), \(\epsilon=0.6\), Sutherland viscosity. Present methods on a \(140\times 140\) uniform mesh.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 2 Related Works
### SDN-Related Research
#### 2.1.1 SDN Non-Security Related  
A study on SDN emphasized the use of SDN programmability to achieve network traffic redundancy by connecting multiple data centers for high availability, rather than relying on numerous links from different internet service providers, and by establishing a centralized controller [44] (pp. 479-484). Another paper proposed utilizing a multipath routing technique within an SDN, which was simulated on a small network to demonstrate SDN's faster reaction time and programmability compared to static routes, dynamically responding to network topology changes during unexpected events or disasters [45] (pp. 478-483). Additionally, the study in [46] (pp. 1-6) focused on SDN-based communication in Smart Grids, highlighting features such as global/local traffic management and application-aware routing. This research introduced the concept of intelligent electronic devices to facilitate efficient data exchange in power line communication distribution networks.
## 4 Experimental Methodology
### Data Gathering  
Selected industrial condition monitoring sensors offer the possibility of sensing a number of various parameters that need to be configured via a web-based interface. In the presented research, we are dealing with an industrial robot, which belongs to the class of sequential machines, along with presses, pneumatic linear actuators, and linear drives. For sequential machines, three vibration monitoring profiles are available. These profiles vary according to the speed of the machine. In the case of our research, in this context, the profile was set to sense at very fast process speeds. The sensor calculates the RMS and peak-to-peak values of the vibration velocity for the three axes within the configured time window of the vibration module.  
Both methods have their advantages and disadvantages and specific areas of application. The RMS method has proven to be more effective in monitoring the long-term effects of vibration on various equipment and structures. This method provides a comprehensive overview of the total vibration intensity, which is crucial for failure prediction. For this research, we decided to use the peak-to-peak (P-P) method. This represents the measurement from the minimum peak to the maximum peak of the signal (Figure 4). An increase in this value may indicate the occurrence of spikes or faults. Measuring this metric is useful in applications where it is important to know the maximum vibration amplitude, which aids in real-time fault detection [42].  
Figure 3: Sensor clamping method (**a**), and placement of sensors on the clamping plate, including orientation of accelerometer axes (**b**).  
The vibration velocity data is collected by the IO-Link hub, which subsequently communicates with the PC using Matlab script. One BCM sensor cyclically transmits four parameters at the same time (20 bytes of real-time data) through the IO-Link interface. These are divided into five slots, each with 4 bytes. The first four slots contain float32 numbers, which contain information about the P-P values of the vibration velocity for a certain set time window (20 ms). The next process is to decode the values and store them in a.csv file for post-processing. The experimental data were collected from three BCM sensors. Each sensor captures peak-to-peak velocity values in three axes (X, Y, and Z axes).  
Abnormal robot operation data includes vibration patterns in cases where the robot attachment to the base is loosened due to multiple forces in the operating process. This condition was simulated by manually loosening all screws after 6 h of operation. Therefore, in the case of our research, all data collected before the loosening moment is considered normal data. Since the fact that the exact moment when the base started loosening is clearly defined, we can consider that all data after this event correspond to anomalous conditions.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 5 Discussion  
### Optimal Estimation Parameters  
There are two estimation parameters in the present method that potentially affect the estimation accuracy: the number of reference points and the radius of the circumference. The two parameters change the measurement array for the wave celerity and influence the estimated water depth in different ways. Here, we discuss how the results vary with the choice of these parameters.  
The estimated wave celerity tends to converge with an increase in the number of reference points. Hence, the number should be determined by balancing the computational cost and accuracy. We computed the wave celerity by varying the number of reference points at 50 random grid points in each case to investigate the general convergence behavior. We evaluated the wave-celerity discrepancy \(\Delta c\) from the one obtained with a large number of reference points (an averaged value of \(N=80\)-\(100\)) at each location. We then defined the standard discrepancy as the RMS value of \(\Delta c\) at the 50 points and plotted its variation with varying \(N\) in Figure 11. The standard discrepancy originates from non-swell signals and is relatively large in Case 2 with low signal coherence. The discrepancy of 0.1 m/s minimally affects the estimated water depth in the present range of water depth according to Equation (2). Considering the balance of the computational cost and accuracy, we chose \(N=8\) in the present study, beyond which the wave-celerity discrepancy \(\Delta c\) will not decrease rapidly.  
The radius significantly affects the estimated water depth in a diverse way. To explore the influences of the radius, we implemented the depth inversion with various \(r\) values along the four transects of each case in Figure 12. When \(r\) is small, the seabed profile exhibits bias and fluctuations. This is primarily because a small error in propagation time significantly affects the wave celerity estimation when \(r\) is not sufficiently large relative to \(c\Delta t\). Additionally, the small radius tends to overestimate the water depth because of underestimating propagation times over the circumference. The cross-correlation analysis of the intensity signals over a small distance is affected by locally synchronous noise signals,  
Figure 10: Comparisons of seabed profiles by the present method, cBathy and survey data (RC boat) along the transects: (a) Case 1, (b) Case 2, (c) Case 3 and (d) Case 4.  
especially when swell signals are relatively weak. The bias and fluctuations become small when the radius increases to 20 m. The error also appears for a large radius of 40 m, which exceeds the wavelength in relatively shallow areas. A large radius leads to a higher chance of signal mismatching due to low cross-correlation between intensity signals. For successful estimation of wave celerity and water depth, the radius must be greater than \(c\Delta t\) and smaller than the wavelength in the present method. A radius of 20 m worked well over the four cases under the different wave and bathymetric conditions.
# Manufacturing of Micro-Lens Array Using Contactless Micro-Embossing with an EDM-Mold
## Materials and Methods
### The Manufacture of the Micro-EDM Mold Insert  
The copper micro-electrode, of which diameter was 250 \(\upmu\)m, was used in micro-EDM to process micro-hole arrays. Figure 2a shows the schematic of the micro-EDM. Figure 2b shows the image of the electrical discharge machine (Sodick, Japan). The processing parameters of micro-EDM consist of voltage, pulse frequency, pulse width, and pulse interval [37; 38]. Through adjusting the processing parameters, micro-EDM can effectively fabricate micro-hole arrays. In the micro-EDM process, the voltage was set as 100 V, applied to the material. The workpiece material was a SKD-11 mold tool. The pulse frequency was set to 0.2 MHz. The pulse width and pulse internal was set to 500 and 400 nanoseconds, respectively. Figure 2c,d show the experimental results. From the local SEM image of micro-hole array, the diameter of micro-holes is consistent. It is noteworthy that the side wall of micro-holes is not smooth. Therefore, the micro-hole array needs to be polished being applying in embossing. The micro-electrodes always have wear during micro-EDM process, which results in the inconsistent depth of micro-holes [37]. In the study, the fabrication micro-hole was blind. The wear of micro-electrodes affected the depth accuracy of micro-holes, which should be avoided. Thus, the depth of micro-holes was ensured by position compensation. The diameter and depth of each hole was around 320 and 300 \(\upmu\)m, respectively.  
Figure 1: **(a)** Dimension of the polymethyl methacrylate (PMMA) rings; **(b)** effect of friction magnitude with PMMA deformation during ring compression test; **(c)** Shenzhen University’s precision glass molding machine (SZU’s PGMM30); **(d)** mold assembly; **(e)** time history of the force, temperature, and position; **(f)** before and after pressing of the PMMA ring.  
Figure 2: **(a)** The schematic of the micro-electric discharge machining (EDM); **(b)** picture of the experiment apparatus (AP1L); **(c)** the SEM diagram of the micro-hole array of mold; **(d)** the dimensions and layout of the micro-holes array.
## 2 Theoretical Analysis and Improvement Research on Finite Element Models
### Element Geometric Modeling  
The electromagnetic wave transmission channel encompasses several components, including the drill string, drilling fluid, casing, and formation [17]. Assuming the formation is a homogeneous medium with isotropic parameters, the model can be simplified for analysis. The entire drill string is considered a uniform structure with consistent inner and outer radii, disregarding the structure of couplings but accounting for conductivity and permeability changes due to contact. The modeling of the metal casing is consistent with that of the drill string and is connected to the external formation. The drilling fluid between the metal casing and the drill string is also assumed to be isotropic. The transmitting antenna is a helical loop mounted on the drill string, and the receiving antenna is another helical loop spaced at a certain distance. The helical loop wireless transmission model is shown specifically in Figure 3.  
Considering the symmetrical nature of the model, we constructed a two-dimensional axisymmetric model [9]. The helical loop antenna is represented by a point magnetic dipole source at its position, and by measuring the magnetic field amplitude at varying intervals, the voltage signals at different locations can be calculated.
## 2 Materials and Methods
### Feature Extraction and Selection
#### 2.5.5 Feature Selection  
In machine learning algorithms for hyperspectral image classification, as the number of bands increases, a sharp increase in the required number of training samples is necessary. When the number of training samples is limited, the classification accuracy will initially increase and then decrease with an increasing number of bands involved in the computation, which is known as the \"[PERSON] phenomenon\" [37]. Therefore, it is necessary to remove redundant information, reduce the number of feature dimensions, reduce the complexity of data processing, and enhance the generalization ability of the model. In this study, the recursive feature elimination (RFE) algorithm was chosen for feature selection. The RFE starts from the entire dataset and selects features in a backward sequence, removing one feature at a time based on the lowest score until the optimal set of features is selected. The method is described as follows:  
Step 1: Train the full set of features \(M\).  
Step 2: Calculate the overall accuracy \(OAM\)-\(i\) after removing \(i\) features from \(M\).  
Step 3: Obtain the set of \(m\) with \(i\) features removed from the set of features \(M\).  
Step 4: Iterate Step 1 to Step 3 until the optimal subset of features is obtained. The order in which features are removed during the iteration of the RFE algorithm depends on the importance of the features.  
The feature selection process based on RFE in this study was implemented using the \"feature_selection\" module in scikit-learn, a machine learning library in Python ([[https://scikit-learn.org](https://scikit-learn.org)]([https://scikit-learn.org](https://scikit-learn.org)) (accessed on 18 January 2022)).
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 3 Circuit Implementation  
The DTC shown in Figure 5 is implemented in the Xilinx Virtex-6 FPGA chip. Part of the place and route of the Vernier delay line constructed by PDLs is shown in Figure 6. In the figure, the delay line on the left is the Start delay line, and the delay line on the right is the Stop delay line. The cyan lines are the propagation paths between the PDLs. In the figure, 1.459 ns is the path delay between PDL A68 and PDL A69, and 1.446 ns is the path delay between PDL B68 and PDL B69, which corresponds with  
Figure 5: Modified block diagram of the proposed DTC.  
Figure 7. The control modules of the Start delay line are marked in red, and those of the Stop delay line are marked in blue. The green lines indicate the controlling boundary of a single control module.  
One of the most important delays that may affect the performance of the DTC is the delay of propagation paths between the PDLs. The time delay of the propagation paths (\(t_{a}\) and \(t_{b}\) in Figure 5) is shown in Figure 7a, and the data are obtained from the FPGA editor (a design tool of the Xilinx ISE design suite). In the figure, most of the propagation paths on the Start delay line and the Stop delay line are equal and can be offset. The largest delay difference is 31 ps as shown in Figure 7b. The integral delay difference between the Start delay line and the Stop delay line is 202 ps. The measurement error introduced by the propagation paths is a fixed error and can be compensated.  
Another delay is the intrinsic delay of PDL, which is about 400 ps [12; 13]. The intrinsic delay indicates the time delay of a PDL when the number of working delay taps is set to 0. For a delay line with 240 PDLs, the intrinsic delay is about 100 ns. However, the intrinsic delay is identical on both  
Figure 6: Place and route of the proposed DTC based on programmable delay lines (PDLs).  
Figure 7: Delay characteristic of the propagation paths. (**a**) Delay of propagation paths of the two lines; (**b**) Delay difference between propagation paths of the two lines.  
lines. In the design shown in Figure 5, the time interval signals (Start and Stop) is generated at the last PDL of the delay lines, which does not need multiplexers to collect and select the output signal. By doing this, the measurement error caused by the intrinsic delay of PDL can be offset completely.
## 3 Understanding Data Analysis for IoT
### Importance of Data Analysis in IoT Systems  
The unprecedented volume, velocity, and variety of data generated by IoT devices necessitate the application of diverse data analysis techniques to extract meaningful information. Here are some key techniques relevant to IoT:  
Data analysis plays a pivotal role in IoT systems due to the massive volumes of data generated. However, it is not the data itself that is valuable, but the actionable insights derived from it. Herein lies the significance of data analysis:  
* Inform Decision Making: By transforming raw data into meaningful information, data analysis supports informed decision-making. Whether it is optimizing shipping routes, scheduling preventative maintenance, or managing port logistics, decisions backed by data analysis can significantly enhance efficiency and outcomes [68].
* Predictive Capabilities: Predictive analytics, a subset of data analysis, uses historical data to predict future events. In the context of maritime IoT, this could mean forecasting machinery failures or predicting fuel consumption, allowing for proactive measures and strategic planning [69].
* Efficiency Gains: Data analysis can identify inefficiencies and bottlenecks in processes. In the maritime industry, this could translate into better fuel management, efficient cargo handling, and reduced port dwell time, leading to cost savings and improved operations [70].
* Enhanced Safety and Security: By detecting anomalies and potential threats, data analysis can improve maritime safety and security. For instance, analyzing ship  
Figure 4: Overview of Data Analysis Techniques Relevant to IoT.  
movement data could identify potential collisions, while analyzing communication data could detect security threats [71].
## 4 Discussion and Implication
### Comparison of Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **Section** & **Method** & \(RMSE\) & \(bias\) & \(SD\) & **ICC [95\% CI]** & \(R\) \\ \hline \multirow{4}{*}{A} & \multirow{2}{*}{Curve} & Method 1 & 0.461 & 0.080 & 0.457 & 0.458 [0.304, 0.612] & 0.710 ** \\  & & Method 2 & 0.257 & 0.016 & 0.258 & 0.599 [0.485, 0.713] & 0.648 ** \\ \cline{2-8}  & \multirow{2}{*}{Straight} & Method 1 & 0.301 & 0.040 & 0.300 & 0.665 [0.551, 0.779] & 0.817 ** \\  & & Method 2 & 0.167 & 0.015 & 0.168 & 0.824 [0.764, 0.884] & 0.856 ** \\ \hline \multirow{4}{*}{B} & \multirow{2}{*}{Curve} & Method 1 & 0.223 & 0.026 & 0.222 & 0.826 [0.782, 0.869] & 0.854 ** \\  & & Method 2 & 0.167 & 0.017 & 0.167 & 0.892 [0.865, 0.919] & 0.906 ** \\ \cline{2-8}  & \multirow{2}{*}{Straight} & Method 1 & 0.219 & 0.015 & 0.220 & 0.717 [0.628, 0.806] & 0.782 ** \\  & & Method 2 & 0.169 & 0.009 & 0.169 & 0.805 [0.744, 0.866] & 0.843 ** \\ \hline \multirow{4}{*}{All} & \multirow{2}{*}{Curve} & Method 1 & 0.348 & 0.050 & 0.345 & 0.692 [0.634, 0.750] & 0.782 ** \\  & & Method 2 & 0.211 & 0.017 & 0.211 & 0.835 [0.804, 0.866] & 0.852 ** \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{Straight} & Method 1 & 0.260 & 0.026 & 0.259 & 0.746 [0.687, 0.805] & 0.825 ** \\ \cline{1-1}  & & Method 2 & 0.168 & 0.012 & 0.168 & 0.858 [0.826, 0.891] & 0.881 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of \(SF\) estimates from GNSS-based methods with IMU data by subject and track section. Note: A double asterisk (**) denotes statistical significance at \(p<0.01\). force and altered stride patterns, which likely exacerbate the inaccuracies of Method 1. Method 2, however, maintained robustness under these conditions due to its focus on IC events rather than vertical position patterns, making it less prone to the effects of asymmetries. While Method 2 demonstrated superior accuracy for _SF_ and _SL_ estimations, both methods showed comparable performance in estimating the _running velocity_. This finding suggests that Method 1 may still be suitable for analyses where the _running velocity_ is the primary parameter of interest, although Method 2 provides a more detailed and accurate assessment overall.
## 3 Non-Orthogonal Multiple Access
What is noteworthy is that the NOMA receiver comprises the detection, regeneration, and cancellation of the users' signals by a descending order of powers (up to the power of the reference user) before the reference user is detected. Consequently, the detection of the reference receiver is clean of interferences, and the performance achieved with conventional NOMA is already good. In this scenario, since the reference user is the one closer to the base station and with a power lower than all others, such signal detection is not carried out in the SIC receiver of other users, and cooperative NOMA is not implemented as its performance would be the same as conventional NOMA. Future research on NOMA will include performance evaluation in different user scenarios (more users, power levels, etc. ).