esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Variance
#### 3.1.1 Analysis of Micro-Hardness  
The ANOVA analysis on the S/N of micro-hardness is shown in Table 5 below. The \(p\)-value for SiC powder ratio was 0.001, which was far less than the significance level 0.05. Therefore, SiC powder ratio had significant influence on the micro-hardness during laser cladding. The contribution of SiC powder ratio was 92.08%, which also indicated the micro-hardness was primarily affected by the SiC powder ratio, while the other processing parameters had negligible impact on the micro-hardness.  
Table 6 was obtained by averaging the S/N of micro-hardness in different levels for each factor. Delta represented the difference between the maximum and minimum value for each factor. The rank in the last row was the order by sorting the categorical delta value from largest to smallest. The main effects plot for S/N of micro-hardness was generated by plotting the mean values in Table 6 for each factor, shown in Figure 3. The grey dash line was plotted as a reference line standing for the overall mean value for all the 16 samples. It was evident that the SiC powder ratio had the most significant impact on the micro-hardness, which was consistent with the ANOVA result in Table 5. The increase of SiC powder ratio brought a significant increase of micro-hardness. Since a larger SiC powder ratio indicated increased more SiC particles in the composite powder mixture were delivered for laser cladding, the amount of Si and C element decomposed from SiC in the molten pool was thus increased. Compounds were formed between the decomposed Si and C elements with the Fe and Cr elements from 316L stainless steel powder, which refined the grain and increased the micro-hardness [16; 37; 38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **Laser Power** & **Scanning Speed** & **Gas Flow** & **Powder Ratio** \\ \hline
1 & 29.60 & 31.63 & 29.69 & 25.17 \\
2 & 31.01 & 30.24 & 30.62 & 29.11 \\
3 & 30.95 & 29.96 & 30.58 & 32.87 \\
4 & 30.21 & 29.94 & 30.87 & 34.62 \\ \(\Delta\) & 1.41 & 1.69 & 1.18 & 9.46 \\ Rank & 3 & 2 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average S/N of micro-hardness for each factor in different levels.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source** & **DF** & **Seq SS** & **Adj SS** & **Adj MS** & **F Value** & **Contribute** & _p Value_ \\ \hline LP & 3 & 5.307 & 5.379 & 1.7690 & 2.82 & 2.31\% & 0.209 \\ SS & 3 & 7.796 & 7.787 & 2.5988 & 4.14 & 3.39\% & 0.137 \\ GF & 3 & 3.216 & 3.210 & 1.0719 & 1.71 & 1.40\% & 0.335 \\ PR & 3 & 211.550 & 212.006 & 70.5167 & 112.36 & 92.08\% & 0.001 \\ Error & 3 & 1.883 & 1.873 & 0.6276 & & 0.82\% & \\ Total & 15 & 229.752 & & & & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA analysis on S/N of micro-hardness.  
Figure 2: Test for normality regarding (**a**) S/N of micro-hardness; (**b**) S/N of wear volume.
# Dynamic-Breakdown of the Ball-Screw Inerter in ISD System
## 2 Modeling and Analysis of the DBoI in ISD System
### Modeling of the DBoI in the ISD System  
A typical ISD system comprises a spring, an inerter, and a damper. In general, an inerter is connected with a protective or tuning spring in parallel. Ignoring the influence of the damper, the dynamic-breakdown issue of an inerter connected with a spring in series can be seen as the issue of DBoI. This research focuses on the DBoI in the ISD system, especially when the inerter is connected with a spring in parallel. The diagram of a typical ISD system is shown in Figure 1.  
As shown in Figure 1, forces acted on shown two terminals are denoted as \(F_{1}\) and \(F_{2}\). The displacements of the two terminals are denoted as \(z_{1}\) and \(z_{2}\). The inerter's inertance is denoted as \(b\), the spring's stiffness is denoted as \(k\), and the damper's damping coefficient is denoted as \(c\). \(F_{1}\) and \(F_{2}\) can be expressed by Equation (1).  
\[F_{1}=F_{2}=b(a_{1}-a_{2})+c(v_{1}-v_{2})+k(z_{1}-z_{2}) \tag{1}\]  
where \(v_{1}\) and \(v_{2}\) are the derivatives of \(z_{1}\) and \(z_{2}\), i.e., the velocities of the two terminals, respectively. \(a_{1}\) and \(a_{2}\) are the second derivatives of \(z_{1}\) and \(z_{2}\), i.e., the accelerations of the two terminals, respectively.  
Figure 2a shows the status before the DBoI in a typical ISD system. Figure 2b shows the status during the DBoI. When DBoI in the ISD system occurs, the inerter's two terminals begin to contact instantly, the inerter's kinetic energy is released and results in impulse. The impulse caused by DBoI in the ISD system is denoted as \(P_{\rm t}\). The impact force is denoted as \(F_{\rm p}\).  
Actually, the DBoI in the ISD system is a short process. The start time of dynamic-breakdown is denoted as \(t_{\rm c}\), when the two terminals begin to contact. The end time of dynamic-breakdown is denoted as \(t_{\rm t}\), when the two terminals disconnect. When DBoI in the ISD system occurs, the dynamic relation can be expressed by Equation (2).  
\[b(a_{1}-a_{2})+k(z_{1}-z_{2})+c(v_{1}-v_{2})=-F_{\rm p} \tag{2}\]  
During the dynamic-breakdown, the integral form of Equation (2) can be expressed as Equation (3).  
\[\int_{t_{\rm c}}^{t_{\rm t}}b(a_{1}-a_{2}){\rm d}t+\int_{t_{\rm c}}^{t_{\rm t} }k(z_{1}-z_{2}){\rm d}t+\int_{t_{\rm c}}^{t_{\rm t}}c(v_{1}-v_{2}){\rm d}t=- \int_{t_{\rm c}}^{t_{\rm t}}F_{\rm p}{\rm d}t \tag{3}\]  
The impulse caused by DBoI in ISD system, \(P_{\rm t}\) can be expressed as Equation (4).  
\[P_{\rm t}=\int_{t_{\rm c}}^{t_{\rm t}}F_{\rm p}{\rm d}t \tag{4}\]  
Figure 1: Diagram of a typical ISD system.  
Figure 2: Dynamic model of the DBoI in a typical ISD system. (**a**) Before dynamic-breakdown; (**b**) During dynamic-breakdown.
## 2 Materials and Methods
### Calculation of Vegetation Indices  
The normalized vegetation index (NDVI), ratio vegetation index (RVI) and difference vegetation index (DVI) have been widely used in different crops' growth monitoring [39] and yield estimation [40]. In the study, NDVI, DVI and RVI were calculated using the following equations.  
\[NDVI_{i,j}\ =\ \frac{NIR_{i}+RED_{j}}{NIR_{i}-RED_{j}} \tag{2}\]  
\[RVI_{i,j}\ =\ \frac{NIR_{i}}{RED_{j}} \tag{3}\]  
\[DVI_{i,j}\ =\ NIR_{i}-RED_{j} \tag{4}\]  
where \(NIR_{i}\) is the reflectance of band \(i\) in the \(NIR\) spectral region (760 nm-900 nm), and \(RED_{j}\) is the reflectance of band \(j\) in the red spectral region (620 nm-760 nm).  
Since the Rikola hyperspectral camera had 62 bands, it was necessary to determine which band combination was best suitable for yield estimation at different growth stages. Therefore, in this study, all possible two-band combinations between red and near-infrared in types of NDVI, RVI, DVI at four growth stages were calculated. The correlation between these VIs and grain yield were analyzed, and VIs at each growth stages with the highest Pearson correlation coefficient were selected as the optimal vegetation index for rice yield estimation.
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
## 3 Results
### Operative Parameters Analysis
The mean value of height in the function of trampling level and position revealed a homogeneous trend (LSD: 0.15), with higher values of height in the corners and no significant differences in the central area after 60 min (1.32 cm) and 90 min (1.37 cm) of moving. The mean value of height in the function of AM model and trampling level revealed how the height decreased when the trampling level increased (LSD: 0.14). The height values averaged over the days showed significant differences between the AM models at each trampling level, except for the trampling level of 60 min (on average 1.7 cm). The results of the height analyses are visually presented in Figure 5.  
No further statistics analysis was conducted on the operative performance of the autonomous mowers, but the data about the trampling level allow us to calculate the electric energy consumption of the autonomous mowers; it was calculated considering the lower value of the range reported in the manual (because of the minimum height of the turfgrass) (Table 5). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Source** & **Passages** & **Height** \\ \hline AM model &. & NS \\ Trampling level (TL) & *** & *** \\ Position & *** & *** \\ Trampling level \(\times\) Position & ** & *** \\ AM model \(\times\) Trampling level & *** & *** \\ AM model \(\times\) Position & NS & NS \\ AM model \(\times\) Position \(\times\) TL & NS & ** \\ \hline \(p\) \(<\) 0.001 \(\times\)***\({}^{*}\); 0.001 \(<\)\(p\) \(<\) 0.01 \(\times\)***\({}^{*}\); 0.05 \(<\)\(p\) \(<\) 0.1 \(\cdot\)\({}^{*}\); NS: Not Significant. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of three-way ANOVA testing the effects of AM model, trampling level, position, and their interaction on the number of passages and turf height. Figure 3: Mean value of Passages in function of autonomous mower model (Robot) and trampling level (Level) and trampling level (Level) and position. Error bars represent standard errors.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 4 New Microspeaker Design  
The size of a speaker affects its performance, i.e., the SPL. A large speaker typically has better performance owing to the larger magnets, top plates, yoke, coil, and other components. Large magnets can generate a greater Lorentz force. In contrast, small speakers limit the dimensions of the magnets, yoke, top plates, and coil, resulting in a lower Lorentz force. To increase the low-frequency SPL while reducing the speaker dimensions, we needed to consider the structures as well as other design factors. The proposed microsppeaker, 1410_SPK, was designed with a length of 14 mm, width of 10 mm, and height of less than 2.1 mm. For this design of 1410_SPK, we considered the magnetic circuit structure, air gap, and coil. The flux density and the force were calculated at \(z=0\) mm (initial position) with an input voltage of 3.1 Vrms.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Depth-Based Layering MAC (DL MAC)
#### 4.2.1. _Hight-Based Protocols_  
Hybrid protocols are another type of MAC classification that takes advantage of some of the frequency domain and bandwidth features [92]. Frequency domain protocols are more typically utilized for time-sensitive applications since they are more vulnerable to multipath hidden node issues and have larger and longer propagation delays [93; 94].
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis
### Reynolds Equation and Its Dimensionlessification
#### 2.2.2 Texture Distribution and Oil Film Thickness  
Figure 3 has shown the schematic diagram of homogeneous hexahedral texture, \(\alpha\) is center angle of oil film, the distribution of textures has \(N\) columns along the radial direction, each column is \(M\), the texture number is \(N\times M\), the central angle of texture is \(\alpha_{1}\), the texture depth is \(h_{t}\).  
Then, the right boundary \(\Phi_{r}\) and left boundary \(\Phi_{l}\) of the texture structure can be expressed, respectively, as:  
\[\left\{\begin{array}{c}\Phi_{r}=\frac{\alpha}{2\pi(N+1)}-\frac{\alpha_{1}}{ 4\pi}\\ \Phi_{l}=\Phi_{r}+\frac{\alpha_{1}}{2\pi}\end{array}\right. \tag{11}\]  
Based on texture distribution mentioned above, if we set the radial length of the textures as \(L\), and the distance between adjacent textures in the radial direction as \(\Delta m\), then \(\Delta m=(r_{2}-r_{1})/r_{2}/(M+1)\). If we set the circumferential angle between every two adjacent textures as \(\Delta\alpha\), then, \(\Delta\alpha=(\alpha/2\pi)/(N+1)\). The radial lower boundary of the texture is a circular arc C\({}_{R1}\) with radius \(R_{1}=r_{1}/r_{2}+n\Delta m+(n-1)\)L/\(r_{2}\), and the upper boundary is a circular arc C\({}_{R2}\) with radius \(R_{2}=r_{1}/r_{2}+n\Delta m+n\)L/\(r_{2}\), where \(n\) denotes the \(n^{th}\) texture along the inner radius toward outer radius.  
Figure 3: Uniform distribution of hexahedron textures (\(r_{1}\) is inner radius; \(r_{2}\) is outer radius; \(\alpha_{1}\) is angle of a texture structure; \(\theta\) is center angle; The dark area is textured area; The light-colored area is the non-textured area).  
After defining the circumferential left and right boundaries \(\Phi_{l}\) and \(\Phi_{r}\) and the radial upper and lower boundaries \(C_{R1}\) and \(C_{R2}\) of the texture dimple, the oil film can be divided into two parts: the texture area and the non-texture area. When \(R_{1}\leq R\leq R_{2}\), and \(\Phi_{r}\) +\(k\Delta\alpha\)\(\leq\Phi\leq\Phi_{l}+k\Delta\alpha\), the thickness of oil film is \(h=h_{t}+h_{0}\). When \(R\leq R_{1}\) or \(R_{2}\leq R\) and \(k\Delta\alpha\leq\Phi\leq\Phi_{gb}+k\Delta\alpha\) or \(\Phi_{gt}+k\Delta\alpha\leq\Phi\leq(k+1)\Delta\alpha\), the thickness is \(h_{0}\), \(n=1\), \(2\), \(3\dots\)\(M\), \(K=1\), \(2\), \(3\dots\)\(N-1\).  
When \(h_{0}=0.1\) mm, \(\alpha=12^{\circ}\), \(h_{t}=0.05\) mm, the oil film thickness is shown in Figure 4.
## 6 System Evaluation
### Xbee 868 MHz Radio Module
#### 6.2.1 Range Test  
As done in Section 6.1.2 the maximum range of the Xbee 868 radio module was tested. The tests were conducted with two different antennas: a 0 dBi (referred to as small) and a 4.5 dBi (referred to as big). Different combinations of large and small antenna for the receiver and the transmitter were tested (Table 4).  
The communication distance of 868 modules is not as high as expected. It is about 600 m in the best case (using big antennas), while in the specification sheet the antenna is said to have a range of tens of kilometers. However, this range is only achievable when using big directional antennas and only if the Fresnel zone (see Figure 14) is respected. Due to the project's requirements (space reasons), we are restricted to using small antennas and the transmitting antenna must be installed at ground level, the reason why a high percentage of the Fresnel zone is obstructed by the ground.  
Figure 13: Results of the transmission and mobility tests (2.4 GHz and 2 dBi antennas).
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 3 Method
### Overview  
As mentioned above, our initial research attempted to identify and eliminate the occluded region of the face, thereby addressing the OFER issue. This was achieved by removing the occluded block, allowing the network to focus solely on the unoccluded region of the face and recognize facial expressions. With this in mind, the MAFE system was designed to extract a range of features. This was achieved by implementing a region delineation strategy that is based on facial feature points and an attention weight selection mechanism. The latter allows the model to focus on crucial regions of the face, thereby reducing or eliminating the influence of occluded and irrelevant regions of the face on the network. The MAFE proposed in this paper is capable of obtaining fine-grained, global, and important region information from the image, which can then be used for expression recognition in occluded images. The flow of MAFE is illustrated in Figure 2 and consists of three main modules: multi-feature extraction, region detail feature fusion, and consistent feature recognition.  
A face image is first input to PTIR-50 and SWIN-E, which extract the global and fine-grained features, respectively. At the same time, Dlib is used to detect the face and cut the image into several regions. These regions are then fed into PTIR-50 for feature extraction, resulting in the acquisition of regional features. Subsequently, crucial regional features are identified, based on the self-attention of each region, and then integrated with fine-grained features to enhance the extracted feature data. Finally, a con-feature loss function is employed to facilitate mutual guidance between the fused and global features, with the global features subsequently being fed into an MLP for expression recognition.
# Framework for Malware Triggering Using Steganography
## 4 Evaluation
### Results and Analysis
#### 4.2.2 The Stego-Image Analysis
We noted the value of the SSIM is closer to 1, indicating that the generated image is nearly identical to the original image despite having an attacking data embedded. Moreover, the MSE value is closer to 0, which indicates a lower error rate, as the closer to zero is the better. Furthermore, the PSNR is greater than 50 dB, so it is complicated for human eyes to recognize the difference in the images. The MSE was observed to increase as the image size increased. SSIM, on the other hand, decreased with increased image size. As a result, the more characters encoded, the more aberration can be seen. To demonstrate that image-based attacks cannot be detected as malicious by antivirus engines, we used VirusTotal [39] to examine malicious code in a JavaScript file. This file was identified as malignant by 35 of 58 antivirus programs. However, when we embedded the malicious code into the image and tested it, we surprisingly found that none of the antivirus programs could detect the malicious code in the image or identify the file as containing hidden, malicious content. After all, neither the reputation nor the signature is available, as shown in Figures 13 and 14. Evasion is possible because antiviruses consider it an image devoid of any visible malicious code. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Color** & **Size Encoded Image** & **Size Polyglot Image** & **MSE** & **SSIM** & **PSNR** \\ \hline Grayscale with IP & 212 KB & 213 KB & 0.01099 & 0.99989 & 67.78051 \\ \hline Grayscale with W3C & 211 KB & 212 KB & 0.00859 & 0.99992 & 68.77387 \\ \hline Color with IP & 12.2 KB & 13.0 KB & 0.05264 & 0.99967 & 60.76431 \\ \hline Color with W3C & 11.8 KB & 12.6 KB & 0.04148 & 0.99972 & 61.88862 \\ \hline \end{tabular}
\end{table} 
Table 15: MSE, SSIM, and PSNR were calculated between the original and encoded images.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 2 Design Principles and Requirements for Upcoming MM Techniques
Moreover, advanced cell association and group handover-based MM mechanisms are discussed to cope with heterogeneity in profiles and mobility patterns in the 5G network. These mechanisms lack in terms of tackling the complexity, network burden, etc., that will be faced by 5G and future MM mechanisms. Consequently, we have reached a new dimension, wherein MM solutions are required to be distributed, flexible, and able to maintain various use cases concurrently and keep account of the numerous other significant changes in 5G networks with dependability. Consider that decentralization will allow MM mechanisms to facilitate increases in attached devices with different mobility profiles (e.g., stationary IoT devices and users in high-speed vehicles). On the other hand, flexibility will permit them to adapt to the user, environment, and network situation (e.g., network potential, QoS, user mobility profile, type of flow, etc.). In this context, we also need to consider the following design concerns regarding MM mechanisms for 5G and future networks [25; 26]. These are as follows: (i) signaling (control plane) concern [27], (ii) backhaul concerns [28], (iii) resources computation concern [15; 29], (iv) concerns over physical layer (v) provision of service granularity [16; 30] and (vi) perspectives of parameters [24]. A comprehensive refurbishment of MM mechanisms for upcoming wireless networks will require quite a long period and effort to attain the optimum solution. Therefore, in the subsequent sections, we carry out an innovative qualitative analysis of several legacy techniques, current mechanisms, and calibration efforts and assess their suitability as enablers for MM in 5G and future networks.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 2 Proposed System  
As is shown in Figure 1, a typical teleoperation assembly system is constituted of a local side and a remote side. The local side is composed of an operator, a visualized interface of the remote environment, a haptic feedback glove, and a position locator. In the master subsystem, all the devices are wireless, which enables the operator to control the objects naturally and flexibly. On the remote side, a cooperative robot is installed to perform in-orbit assembly tasks. At the end of the robot, a force sensor is equipped to measure the contact force. The video streams are acquired using RGB depth (RGB-D) cameras. The communication between the local side and the teleoperation robot is performed by protocol/internet protocol (TCP/IP).  
When an operator sends the command to the remote side through the position locator device, the virtual robot, as well as the teleoperation robot, follow the trajectory of the locator device. A wireless position locator device (VIVE Tracker, HTC Inc.) was adopted in this system to perform motion tracking. Following a communication delay, the teleoperation robot is controlled by the commands, and the visual, contact force information were fed back to the operator.  
During teleoperation assembly, the robot must keep avoiding collisions with surrounding assembly parts. Even a minor collision can cause serious damage to the robot or workpiece. To improve the safety and efficiency of teleoperation assembly, this paper proposes a teleoperation-assisted system based on collision risk assessment. Two main procedures were implemented in the entire framework: First, a collision risk assessment model based on fuzzy inference was proposed, and motion security in the VR environment was evaluated. Secondly, assistant strategies were defined based on the evaluated collision risk to improve the manipulation safety and efficiency.  
Figure 2 describes the entire framework of the teleoperation system. Based on the visual information of the depth cameras, the graphic model of the teleoperation robots and environment was built using the 3D reconstruction technique. The operator obtained the shortest distance relative to the obstacle, delay time, and operation speed in a graphical interface by controlling the virtual robot in real time in a virtual environment. In this system, the shortest distance was measured by the proposed discrete expandable bounding box method. Based on the aforementioned three elements, this paper designed a fuzzy-theory-based collision risk assessment model to evaluate the security of motion. Furthermore, three guidance strategies were defined in this paper according to the evaluated collision risk, including partial view highlighting, variation in motion mapping ratios, and haptic risk warnings. These assist strategies were implemented with the described physical interaction units.  
Figure 1: Schematic of the teleoperation assembly system.  
Figure 2: The framework of the teleoperation-assisted assembly system based on collision risk assessment.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 3 Controller Design
### PD Control Design  
The commonly used PD (Proportional-Derivative) controller has been selected as the initial controller design. PD control is widely recognized for improving system stability and responsiveness.  
The control signal (u) is defined as the output of the PD controller, expressed by the equation:  
\[U=K_{p}\left(y_{ref}-y\right)+K_{d}\left(\dot{y}_{ref}-\dot{y}\right) \tag{14}\]