esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Current Consumption with Encryption  
Security is often neglected, even in commercial systems [122; 123], mostly because resource-constrained device are usually not powerful enough to handle secure communications protocols [124]. In the experiments performed with ZiWi's nodes, the effect on the current consumption was analyzed when making use of encryption while communicating a node with a local gateway. Specifically, in the case of the NodeMCU-based modules, TLSv1/SSLv3 was used with ECDHE-RSA-AES128-GCM-SHA256 as cipher suite, while in ZigBee-based nodes, Application Support Sublayer (APS) encryption was enabled. The code differs slightly for the NodeMCU nodes that use SSL, so it has been also uploaded to a different folder in Ziwi's GitHub repository [120].  
Current measurements were obtained by using an Arduino and an Adafruit INA219, which allows for measuring up to 26 V and that provides enough precision (it can work in high-precision mode to measure 0.1 mA steps with a maximum of \(\pm\)400 mA or, in low-precision mode, can make use of 0.8 mA steps with a maximum of up to \(\pm\)3.2 A).  
Table 10 shows the average current consumption obtained with and without making use of encryption in sensor and actuator nodes. In the case of sensor nodes, it can be observed that the use of Secure Sockets Layer (SSL) generates an additional computational and communications load that increases the average consumption through time, deriving into the clear differences shown in Table 10. However, the use of a lighter (and less robust) encryption mechanism like ZigBee's APS barely increases the average consumption, but increases privacy and security with respect to not using any encryption at the nodes. Therefore, developers have no excuse for not securing ZigBee nodes  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Node** & **WiFi** & **Channel** & **Average Local** & **Average Remote** & **Packets** & **Packets** & **TX** & **Packets** & **Packet Delivery** \\
**Distance** & **Enabled** & **Overlap** & **RSSI (dBm)** & **RSSI (dBm)** & **Sent** & **Received** & **Errors** & **Loss** & **Success Rate (\%)** \\ \hline Short & No & No & \(-36\) & \(-36\) & \(100\) & \(100\) & \(0\) & \(0\) & \(100\%\) \\ \hline Short & Yes & No & \(-42\) & \(-44\) & \(100\) & \(96\) & \(3\) & \(1\) & \(96\%\) \\ \hline Short & Yes & Yes & \(-49\) & \(-44\) & \(100\) & \(86\) & \(14\) & \(0\) & \(86\%\) \\ \hline Medium & No & No & \(-59\) & \(-54\) & \(100\) & \(100\) & \(0\) & \(0\) & \(100\%\) \\ Medium & Yes & No & \(-55\) & \(-50\) & \(100\) & \(96\) & \(4\) & \(0\) & \(96\%\) \\ Medium & Yes & Yes & \(-56\) & \(-51\) & \(101\) & \(78\) & \(20\) & \(3\) & \(77.23\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: ZigBee success delivery rate in the presence of WiFi cross-interference.  
using APS, but alternative energy efficient security mechanisms have to be further studied in order to improve or replace traditional cipher suites for SSL communications.
# Four-Channel Buck-Type LED Driver with Automatic Current Sharing and Soft Switching
## 3 Specification Considerations  
Figure 23 shows the LED driver with automatic current sharing proposed in this paper. The system includes the main circuit and the feedback circuit. The main circuit is derived from an interleaved buck converter with low voltage stress and high step-down ratio [23]. As for the feedback control circuit, it adopts a current-detecting integral circuit (IC) named ACS712 to obtain the analog signal of the total output current. After sampling without analog-to-digital converter (ADC), this signal is sent to the FPGA, named EP3C5E144C8N, as a circuit control kernel, for calculation, to obtain suitable duty cycles to drive the four switches after individual isolated gate drivers, named TLP250H. Table 1 shows the specifications of the overall LED driver system. The specifications of LED and the specifications of components are shown in Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Specifications** \\ \hline  & Rated Load: CCM \\ System Operating Mode & Half Load: BCM \\  & Light Load: DCM \\ \hline Rated Input Voltage (\(V_{in}\)) & 400 V \\ \hline Rated Output Current per LED String (\(I_{o,ntel}\)) & 0.35 A \\ \hline Minimum Output Current per LED String in & 0.175 A \\ CCM (\(I_{o,min,BCM}\)) & 0.0875 A \\ \hline Minimum Output Current per LED String in & \\ DCM (\(I_{o,min,DCM}\)) & 0.0875 A \\ \hline Rated Output Power (\(P_{o,ntel}\)) & 38.64 W (4 Strings with 8 LEDs per String) \\ \hline Minimum Output Power in CCM (\(P_{o,min,BCM}\)) & 17.36 W \\ \hline Minimum Output Power in DCM (\(P_{o,min,DCM}\)) & 8.12 W \\ \hline Switching Frequency (\(f_{s}\))/Period (\(T_{s}\)) & 100 kHz/10 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Specifications** \\ \hline Forward Voltage (\(V_{F}\)) & 2.95–3.85 V \\ \hline DC Operating Current (\(I_{E,max}\)) & 400 mA \\ \hline Pulse Forward Current & 500 mA \\ \hline Junction Temperature & 125 \({}^{\circ}\)C \\ \hline Operating Temperature & \(-\)40\(\upmu\)+85 \({}^{\circ}\)C \\ \hline Typical Light Flux Output & 100 lm@350 mA \\ \hline \hline \end{tabular}
\end{table}
Table 2: LED Specifications.  
Figure 23: System configuration of the proposed LED driver circuit.
## 5 Discussion and Use Cases
### Blocking Chat Servers  
In this section, we verify the chat servers' IPs by blocking them using firewall rules on the pfSense software [34]. Blocking these servers would let us know the connectivity pattern of the client app. For example, once the chat servers are blocked, the chat messages should not be delivered to another end. For experiments, we blocked the traffic of IP addresses of chat servers (on Table 3) for LAN and WAN through firewall rules. As a result, the client app of Signal was not able to connect to these servers and the messages were not sending as depicted in Figure 19. Signal servers use port 443 to connect to the client which cannot be blocked as other apps used 443 for communication purposes, (443 is an SSL communication port and highly resistant to eavesdropping and interception). Remote servers providing services using this port are trustworthy and verified without any doubt. Similarly, the web servers listen on this port that accepts and establishes secure connections for web browsers that desire strong communication security. Hence, blocking these servers would help to stop the Signal app services.  
Figure 17: A sample screenshot of identified server using DNS query.  
Figure 18: Type A Server Addresses.  
If we look at the Signal app chat behavior on a mobile device in Figure 19, it tried almost 16 min to deliver the text message but failed. Meanwhile, the app tried to connect to all the above-mentioned chat servers. The Wireshark-based analysis shows the details of these servers in Figure 20. When the client app tried to connect to these servers to deliver a message to another user, the mentioned servers did not respond or a 0 was sent in response to the client requests. It was noted that all services were working correctly without the restriction of IP addresses on the firewall. However, firewall rules used to block/delay the chat activities are mentioned in Table 6. With the help of the rules and list of servers, the chat service was disrupted. The device of target User A tried to send messages to other servers but failed to do so. Similarly, Figure 21 shows that the client application tried to connect the main servers to send messages. However, the connection was denied with each of them as highlighted in black.  
Figure 19: Screenshot of message sending failed.
# Make predictions from new data. y_pred = clf.predict(X_test),
## 7 Results
### Increase in Error with Distance to Perforations  
Normally, only a few penetrometers are carried out and, subsequently, there will not be too many columns near such tests. This greatly limits the relevant data, resulting in a handicap when using certain algorithms.  
The calculations performed up to this point have only considered data that was within 2 m or less of its associated penetrometer.  
Through an iterative calculation, the increase in error has been verified, based on the F-score and the correlation coefficient as the distance to the perforation increases. It must be borne in mind that this error depends on the type of soil. With a homogeneous layer distribution (with horizontal continuity) in the soil, the error should not be large, but it can be important in highly variable terrain.  
The results obtained for the different sites (for fully developed DTs and taking depth into account) are shown in Figure 19.  
Figure 17: Real and calculated values for site 3 considering depth.  
Figure 18: Real and calculated values for site 4 considering depth.  
As can be seen, the precision is maximum when distances between columns and the penetrometer are small (in the order of 2 m). From that point, there is a rapid decline in the first few meters which later slows down.  
In sites 3 and 4, the point of maximum precision does not occur with the minimum distance but instead, a little further. This may be due to the bias that would occur when considering very small samples.  
According to the values obtained and considering the high degree of uncertainty commonly associated with geotechnical works, the increase in error with distances to penetrometers greater than 2 m can be considered acceptable when making predictions if there are not enough values in closer distances.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 5 Experiments and Results
### Watermarking Robustness against Attacks
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Attack/Parameter** & **Value Range (Step)** & **MSE** & **PSNR (dB)** & **NCC** & **SSIM** & **MSSIM** & **\(B_{error}\)** & **BER** \\ \hline Gaussian Filter/\((N\times N)\) & 2 to 11 (1) & 0.0398 & 59.8776 & 1 & 1 & 1 & 1 & 0.0061 & 0 \\ \hline Median Filter/\((N\times N)\) & 2 to 9 (1) & 1.8910 & 58.8276 & 0.9999 & 0.9991 & 0.9985 & 0.5536 & 0.0001 \\ \hline Gaussian Noise/\((\sigma^{2})\) & 0.05 to 0.5 (0.05) & 0 & \(\gg\)60 & 1 & 1 & 1 & 0 & 0 \\ \hline Salt and Pepper Noise/(density) & 0.1 to 1 (0.1) & 0 & \(\gg\)60 & 1 & 1 & 1 & 0 & 0 \\ \hline Scaling/(scale factor) & 0.25 to 4 (0.25) & 0 & \(\gg\)60 & 1 & 1 & 1 & 0 & 0 \\ \hline Translation/(pixels) & 10 to 100 (10) & 157.8250 & 54.3528 & 0.9934 & 0.9664 & 0.9589 & 16.2102 & 0.0040 \\ \hline Rotation/(angle \({}^{\circ}\)) & 1 to 180 (15) & 4760.5956 & 20.6053 & 0.8262 & 0.5455 & 0.5376 & 185.4662 & 0.0462 \\ \hline Cropping/\(p\)\%) & 0 to 95 (5) & 23,540.7884 & 8.4022 & 0.4894 & 0.2696 & 0.2636 & 916.1224 & 0.2280 \\ \hline Contrast Enhancement/\((f)\) & 0.01 to 0.45 (0.04) & 0 & \(\gg\)60 & 1 & 1 & 1 & 0 & 0 \\ \hline JPEG Compression/\((qualityfactor)\) & 100 to 0 (10) & 0.4584 & 59.6741 & 1.0000 & 0.9999 & 0.9998 & 19.3859 & 0.0048 \\ \hline Histogram Equalization/(discrete levels) & \(2^{n}\), \(n\) was from 2 to 8 & 615.0076 & 43.5160 & 0.9765 & 0.9019 & 0.8826 & 36.6210 & 0.0091 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics’ averages using 49 grayscale images for all attacks with parameter variation, showing the metrics of the LOGO (MSE, PSNR, NCC, SSIM, MSSIM) and MetaData (\(B_{error}\), BER) extracted. Figure 14: Median filter (9): (**a**) Filtered _[PERSON]_ image. (**b**) Recovered watermark (LOGO). (**c**) Recovered MetaData. SP noise (0.5): (**d**) Noisy _[PERSON]_ image. (**e**) Recovered watermark (LOGO). (**f**) Recovered MetaData. JPEG compression (0): (**g**) Compressed _[PERSON]_ image. (**h**) Recovered watermark (LOGO). (**i**) Recovered MetaData.
## 3 Circuit Implementation
### Rail-to-Rail Sub-Sampling Charge Pump  
According to the Monte-Carlo simulation results of the VCO buffer, it is noticed that the common-mode voltages have a large range changing from 0.15 V to 0.55 V which may let the input transistors of the traditional SSCP be switched off. In order to mitigate this serious problem, we propose a rail-to-rail SSCP as shown in Figure 9.  
The rail-to-rail input stage is adopted by the folded structure and the headrooms of the current mirror transistors are relieved, which is suitable for the low voltage design.  
Figure 8: Transient simulation results of the proposed VCO buffer. (**a**) output amplitude variation, (**b**) output common-mode voltage variation.  
Figure 7: Structure of the proposed VCO buffer.  
Figure 9: Structure of the proposed rail-to-rail SSCP.  
The transconductor \(g_{m}\) is simulated as the input common-mode voltage varies as shown in Figure 10. The complementary inputs of the SSCP ensure that at least one pair of transistors (NMOS or PMOS) are always be turned on. The value of \(g_{m}\) shows 635 \(\upmu\)S and 800 \(\upmu\)S as the output common-mode voltage of the VCO buffer appears at the worst cases of the variation. Although the variation of \(g_{m}\) is 38.5%, the noise contribution of the proposed SSCP is quite small, thus, this variation can be neglected (we will discuss this in the following section).
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 3 Antenna Analysis
### Parameter Studies  
To obtain available guidelines for the practical design of the proposed antenna, various important parameters that can influence the return loss and axial ratio at the boresight direction are examined. As a key parameter is studied, the other parameters are kept constant. The width \(L4\) of the high impedance microstrip line shows the crucial influence on the reflection coefficient and AR, due to its effect on the current distributions on the patch of the proposed antenna. As exhibited in Figure 10, the return loss and axial ratio are sensitive to different \(L4\) values. A reasonable axial ratio at 2.45 GHz can be achieved when \(L4\) is set to be 0.4 mm. If \(L4\) becomes larger or smaller, there is a drastic influence on the AR. Figure 11 demonstrates the effect of tuning \(L5\) on the performance of the proposed antenna. With reference to the curves in the figure, a linear increase in \(L5\) (from 6.3 mm to 6.5 mm) will result in shifting the resonance down to the lower frequency and deviating the CP towards the lower band.
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 5 Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
### Magnetic Resonance Imaging Image
Experimental results show that the proposed IWOA-based parametric adaptive array model method has significant advantages in image enhancement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Array Unit N = 1** & **Array Unit N = 2** & **Array Unit N = 4** & **Array Unit N = 8** \\ \hline \multirow{2}{*}{Fixed parameters} & PSNR = 11.5700 & PSNR = 13.4826 & PSNR = 16.7897 & PSNR = 22.9187 \\  & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 & a = 0.5; b = 1.5; h = 0.12 \\ \hline \multirow{2}{*}{WOA optimization} & PSNR = 15.4988 & PSNR = 19.6323 & PSNR = 26.6149 & PSNR = 41.1891 \\  & a = 0.5; b = 0.99; h = 0.05 & a = 0.5; b = 2.47; h = 0.03 & a = 0.5; b = 0.31; h = 0.09 & a = 0.5; b = 0.69; h = 0.07 \\ \hline \multirow{2}{*}{IWOA optimization} & PSNR = 15.5083 & PSNR = 19.6695 & PSNR = 26.6260 & PSNR = 42.3748 \\  & a = 0.51; b = 0.60; h = 0.06 & a = 0.5; b = 1.59; h = 0.04 & a = 0.57; b = 0.49; h = 0.08 & a = 0.64; b = 3.32;h = 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MRI image PSNR under different array saturation model methods (dB). Figure 8: MRI image enhancement for four classical filtering methods.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach
### Pose Estimation  
The use of a Fiducial Marker System for the pose estimation of bearings is advantageous due to its simplicity and accuracy [35]. This system involves attaching specific markers to a structure and measuring displacement based on them. In this study, a hybrid marker combining chessboard and ArUco markers, called ChArUco, was employed. The markers captured by the camera can be detected using OpenCV to identify corner points [36]. To estimate the 3D pose of the marker, the rotation matrix R and translation vector T, which describe the marker's orientation and position, must be calculated. This problem is referred to as the Perspective-n-Point (PnP) problem [37,38]. To solve this, the geometric projection transformation from the 3D world coordinate system to the 2D image coordinate system is defined as  
\[s\cdot\mathbf{x}_{i}=\mathbf{K}\cdot\left[\mathbf{R}|\mathbf{T}\right]\cdot \mathbf{X}_{i} \tag{1}\]  
where \(s\) is a scaling factor, K is the intrinsic camera matrix, R is the \(3\times 3\) rotation matrix, T is the \(3\times 1\) translation vector, and \(\mathrm{x}_{i}\) and \(\mathrm{X}_{i}\) represent the image coordinates and world coordinates in the homogeneous form, respectively, of the \(i\)-th corner point.  
The reprojection error, which quantifies the deviation between the observed 2D coordinates and the projected 3D points in homogeneous form, is defined as  
\[e=\sum_{i=1}^{n}\lVert\mathbf{x}_{i}-\pi(\mathbf{K},\mathbf{R},\mathbf{T}, \mathbf{X}_{i})\rVert \tag{2}\]  
where \(e\) is the reprojection error, which serves as the cost function in the PnP problem, \(n\) is the number of point correspondences, and \(\pi\) represents the projection function that maps 3D world points to 2D image coordinates using the estimated parameters. The optimization process calculates R and T by minimizing the cost function \(e\), utilizing the Levenberg-Marquardt algorithm. The resulting R and T are then used to determine the 3D displacement of the bearing.  
The relationship between the camera and the marker used in the system can be defined as follows using R and T:  
\[\mathbf{X}_{c}=\mathbf{R}\cdot\mathbf{X}_{w}+\mathbf{T} \tag{3}\]  
To simplify Equation (3) for calculating the displacement in the time sequence, the following equation is applied through a binary operation:  
\[\mathbf{X}_{w,n}=\mathbf{R}_{n}^{-1}\cdot(\mathbf{X}_{c,n}-\mathbf{T}_{n}) \tag{4}\]  
where n is the number of the \(n\)-th frame. The displacement of the marker in its local coordinate system is then calculated as the difference between its positions in the n-th frame and the first frame, as follows:  
\[\Delta\mathbf{X}_{w}=\mathbf{R}_{n}^{-1}\cdot(\mathbf{X}_{c,n}-\mathbf{T}_{n} )-\mathbf{R}_{1}^{-1}\cdot(\mathbf{X}_{c,1}-\mathbf{T}_{1}) \tag{5}\]  
Since the camera remains fixed and is defined at the origin (0, 0, 0), the displacement calculation in the marker's local coordinate system simplifies to  
\[\Delta\mathbf{X}_{w}=\mathbf{R}_{1}^{-1}\cdot\mathbf{T}_{1}-\mathbf{R}_{n}^{ -1}\cdot\mathbf{T}_{n} \tag{6}\]
## 3 Analysis of Results and Mechanisms
### Analysis of Very Low-Frequency Wideband Mechanisms
The displacement amplitude of the rotational motion on both sides is not negligible with respect to the displacement amplitude of the center of the structure. By observing the vibration modes at points A\({}_{2}\) and C\({}_{2}\), it can be found that the energy is localized at the position of less stiffness in the structure. The difference is that the energy localization at point A\({}_{2}\) is much higher than that at point C\({}_{2}\). This is due to the fact that the smaller stiffness of the AREM structure and the ABH effect in the ABH structure are coupled with each other, which greatly improves the resonance characteristics of the AREM. It makes more energy localized at the center of the AREM. Figure 6 shows the vibrational attenuation of each interface in the eight-cycle AREM and WREM structures when excited by constant flexural and longitudinal waves, respectively, at the right end of the finite period. Comparing Figure 6b,d, it can be observed that the AREM finite-period structure can form more obvious attenuation domains at each interface in the corresponding interface frequencies under the flexural excitation. In contrast, the WREM finite-period structure can only form a more obvious attenuation domain after three to four cells in the BG range. The combined effect of the ABH effect and BG mechanism is the main reason for this phenomenon. After the flexural wave enters the AREM structure, it is not only suppressed due to the existence of the BG, but also because of the aggregation of the flexural wave by the ABH, it is gathered in the center of the AREM, so that it cannot continue to propagate forward. By observing Figure 6c,e, it can be found that the longitudinal attenuation of the finite-stage structure of the AREM and WREM is in the same surface, and the difference between the attenuation values of the two is not large. This shows that the ABH effect has little effect on the longitudinal attenuation. Figure 5: Eigenmode and displacement vector fields at special points C\({}_{1}\) and C\({}_{2}\) are the lower and upper boundary modes of the second flexural BG of the WREM structure.
###### Abstract  
This study investigated how midsole hardness of basketball footwear affects lower extremity biomechanics and impacts perception in drop vertical jumps. Eighteen male basketball players performed drop vertical jumps from three heights (31 cm, 46 cm, 61 cm) in basketball shoes of different midsole hardness (50, 60 Asker C). Biomechanical variables of the lower extremity and subjective perception were measured. This study found a significant drop height effect on the lower extremity biomechanics (\(p<0.05\)), with greater ground reaction forces, joint kinetics, and prelanding muscle activation levels observed at higher drop heights. Basketball shoes with a softer midsole led to higher forefoot peak force (\(p=0.028\)) amid lower rearfoot peak force (\(p=0.046\)), lower peak flexion moments at the ankle (\(p=0.024\)) and hip joints (\(p=0.029\)), and greater prelanding muscle activation in the rectus femoris (\(p=0.042\)) and tibialis anterior (\(p=0.043\)). It is concluded that changing midsole hardness within a commercially relevant range triggered a different prelanding muscle activation strategy and hence altered the magnitudes of ground reaction forces and joint loadings during landing. Subjectively, participants perceived higher landing impacts with greater drop heights, though the strength of the associations were weak.  
range of motion; ground reaction force; electromyography; work; perception Article