esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Experiments
### Boverbream
#### 4.2.1 Processing--Boverbream Glacier  
A DEM, shown in Figure 13, was generated to represent the glacier's condition at the end of the glaciological year in 2021. This DEM was derived from PlanetScope imagery captured between August and September of that year (see Table 3), which exhibited minimal snow cover. The selected dataset includes ten PlanetScope scenes, illustrated in Figure 14. In particular, no images were available from an ascending orbit during the study period. It should be noted that a period of one month can already mean one meter of melting in glaciated regions, causing inconsistencies between the images, but this is far below the level of detection using PlanetScope-derived DEMs, as determined by [19], and is therefore accepted due to the low availability of usable images. The GLO-30 DEM served as approximation DEM, following a methodology consistent with the processing of the Shisper glacier.  
Details of the processing parameters and statistics of the calculated DEM are provided in Table 3. It is important to note that the DEM extends beyond the Boverbream region itself. The numbers thus encompass the entire overlapping image area, meeting minimum overlap and convergence criteria, rather than solely focusing on the densest overlap area specific to Boverbream. Despite reporting a mean intersection error of 3.1 m (0.8 px), it should be noted that this average pertains to the entire processed area. Specifically around Boverbream (outlined in Figure 14), the mean and maximum errors are lower, at 2.8 m (0.7 px) and 10.2 m (2.6 px), respectively.  
Figure 13: Calculated DEM of the glacier area of interest from PSS images in 2021.  
Figure 14: Boverbren glacier study area situated in the Smorstabb Massif, Jotunheimer, Norway, analyzed using PSS images for DEM calculation, representing conditions in August–September 2021.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**PlanetScope DEM 2021**} \\ \hline \multicolumn{2}{l}{**Configuration**} \\ \hline Number of images & 10 (NIR) \\  & 20210828\_095603\_87\_2455, \\ Scene IDs & 20210828\_100017\_04\_2459, \\  & 20210829\_102532\_1001, \\  & 20210829\_102533\_1001, \\  & 20210913\_100744\_79\_2251, \\  & 20210913\_105036\_37\_2413, \\  & 20210914\_102653\_1040, \\  & 20210914\_104522\_58\_240f, \\  & 20210914\_104524\_89\_240f, \\  & 20210928\_100200\_0e20 \\ Minimum overlap (\%) & 10 \\ Minimum convergence angle (\({}^{\circ}\)) & 4 \\ Ortho workflow & yes (GLO-30 DEM) \\ \hline \multicolumn{2}{l}{**Bundle-block adjustment**} \\ \hline Number of triangulated points (sparse) & 3386 \\ Mean residuals of images (px) & 0.6 \\ Mean residuals of triangulated points (px) & 0.6 \\ \hline \multicolumn{2}{l}{**Stereo reconstruction**} \\ \hline Mean intersection error (m) & 3.1 (max: 27.2) \\ \hline \multicolumn{2}{l}{**DEM sampling**} \\ \hline DEM coverage (km\({}^{2}\)) & 752.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Settings of Boverbren glacier DEM processing.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 4 Simulation Results
### Road Holding
Both of the implemented variants of the Skyhook algorithm are intended not necessarily to suppress fluctuations of wheel normal forces, but rather to mitigate the body vibration. However, as can be seen in Figure 18, the road holding is improved by both of them. For the front wheel, the Skyhook-1 DOF offers a slightly better result with a 13% improvement for the best case over zero current, while the Skyhook-2 DOF gives an 11% improvement. For the rear wheel, a 5% improvement can be obtained for the Skyhook-1 DOF, whereas, for the Skyhook-2 DOF, it is 4% improvement. Similar results can be observed for vehicle longitudinal velocity equal to 10 ms\({}^{-1}\), see Figure 19, where both variants of the Skyhook algorithm offer better results when compared to those that were obtained for zero current. In the case of the front wheel and the braking manoeuvre, Skyhook-1 DOF and Skyhook-2 DOF gave comparable improvements of 19% and 18%, respectively. For the rear wheel during the accelerating manoeuvre, the Skyhook-1 DOF provides 17% improvement in road holding, while the Skyhook-2 DOF offers 20% improvement. Even though the Skyhook algorithm has been used to mitigate vehicle body vibration, it also improves road holding. This can be explained by the fact that, for higher amplitudes of road-induced excitations, dominant body resonances are induced. Body vibrations significantly affect forces generated in the front and rear suspension and, thus, the normal forces acting on the front and rear wheels. This effect is more clearly visible for the body having significant mass when compared to other vehicle components. Therefore, suppression of the body resonance lead to a considerable suppression of the wheel normal force and it improves the road holding.
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 2 Configuration of the Motor  
The proposed multi-DOF cylindrical ultrasonic motor consists of a cylindrical stator and a spherical rotor placed on the stator. A rod can be extended from the rotor to drive the load. Figure 1 shows the structure of the designed stator. The overall size of the stator is 20 \(\times\) 20 \(\times\) 29 mm, and the rotor diameter is 20 mm. The main structural parameters are H\({}_{1}\) = 10 mm, H\({}_{2}\) = 5.1 mm, and H\({}_{3}\) = 4 mm. The stator consists mainly of a support body, a clamping body, PZT sets, a matching block, and a fixing screw. The above components are threaded and locked between the stator and the fixing screws, making installation simple and easy. The PZT sets are divided into a longitudinal vibration ceramic set and bending vibration ceramic set, with the longitudinal vibration set in the upper part.  
The upper part of the support body is recessed and has a built-in magnet to hold the rotor in place while applying pre-pressure. The magnet embedded in the stator is made into a curved surface to ensure a uniform air gap between it and the rotor. The air gap is set at 0.3 mm to obtain the maximum suction force. The support body and the matching block have adjustment slots for decreasing the bending stiffness so that the bending vibration frequency approaches the longitudinal vibration frequency. The adjustment slots in the support body are also used to amplify the bending vibration amplitude at the end to increase the output efficiency.  
The clamping body Is in the middle of the stator, between the PZT set (3) and (5) in Figure 1. The energy level of longitudinal and bending vibrations is weakest at this position, and the clamping body can effectively avoid energy loss. The clamping body is shown in Figure 2. The four middle sections of the clamping body are hollowed out to form flexible chains, which reduce vibration transmission and avoid excess vibration. Fastening screws are used to secure the stator components and ensure the stable operation of the stator.  
Figure 1: Schematic diagram of the stator: (1) support body, (2) clamping body, (3) bending vibration ceramic set, (4) spherical magnetic, (5) adjustment slot, (6) longitudinal vibration ceramic set, (7) matching block, (8) screw, and (9) bottom press ring.
## 4 Results
### Slope Stability Analysis in Evolving Saturation Scenarios
At 20 cm b.g.l., the calculated SFs are quite high over the whole area, but not sufficient to justify slope instability. Furthermore, the minimum soil thickness does not contribute much to the decrease in the SF. Combined with a minimum stabilizing contribution given by the residual suction in areas that do not reach a complete saturation, these conditions lead to general slope stability over the whole area. At 40 cm b.g.l., the SFs are, however, still quite high but the contribution of the greater soil cover thickness leads to a decrease  
Figure 8: Maps showing the depth of the infiltration water table (**upper row**) and the values of the static SF (**lower row**) at a maximum depth of soil cover (**cover bottom**) at time stages from T1 to T4. of the slope stability conditions. The suction is, in any case, present and stabilizing since the capillary fringes are effective. At 60 cm b.g.l., maximum modelled depth of rainfall infiltration there is little presence of infiltration water and the SF decreases. At 80 cm b.g.l., the soil covers are always unsaturated since infiltration never reaches these depths. At greater depth, the SF noticeably decreases, remaining above the unit and reaching minimum values assuming the mobilization of the entire thickness of the soil covers (Figures 7 and 8). Comparing the SF maps in dry and rainy conditions (Figure 9), obtained assuming the mobilization of the entire thickness of the soil covers, it is possible to see how the infiltration tends to lower the value of the SF, bringing some areas close to the unit value. The presence of infiltration water would have weighed approximately uniformly over the entire study area, reducing the SF by decimals up to units depending on the wetting front depth reached (Figure 9). However, the areas of maximum rainfall and greater infiltration depth are found in the zones with a lower PGA (peripheral areas), confirming that the rainfall has played mostly an aggravating role in the reduction of the stability conditions but did not act as a trigger. Figure 9: Maps showing: static SF at the maximum depth of soil covers considering absence of rainfall (**top-left**); static SF at the maximum depth of soil covers considering partially saturated conditions due to rainfall (**top-right**); difference between SF obtained by the two conditions (**bottom-left**); depth of infiltration level (**bottom-right**).
# IPv6 Convergence for IoT Cyber-Physical Systems
## 3 IP-Convergence Model
### IPv6 over LoRaWAN  
We now consider how IPv6 over LoRaWAN can be supported. The LoRaWAN specification details the range of maximum payload sizes supported for both the US and European variants. In this study, we primarily examine the European 863-870 MHz and 915 MHz US cases initially. Our starting point is identifying how the reported worst-case 6 LoWPAN PDU size can be supported by LoRaWAN when considering EU863-870 MHz and US915 MHz cases for both no-repeater-compatible and repeater-compatible modes of operation. We detail the maximum payload sizes for the data-rate range supported for EU863-870 MHz for both no-repeater and repeater-compatible cases in Table A4 and Table A3, respectively. We present the same metrics for US915 MHz for both no-repeater and repeater-compatible use cases in Tables A1 and A2, respectively. For the reported worst-case PDU size, we find that eight of the eleven defined data rates for both of the repeater-compatible and no-repeater configurations could support the worst-case PDU size. We outline these in Table A6 (EU863-870 MHz no-repeater), Table A5 (EU863-870 MHz repeater compatible), Table A8 (US915 MHz no repeater), and Table A7 (US915 MHz repeater compatible).  
Recalling the initial challenge of supporting the worst-case PDU size of 81 octets for 6 LoWPAN, we note that LoWPAN_IPHC can compress the IPv6 header down to 7 octets, and therefore, we can potentially support smaller PDU sizes than the worst-case PDU length of 81 bytes. We propose an ultracompressed method to achieve this for all LoRaWAN data-rate options.
## 5 Characterization Using the Time Domain Measurement Data  
### Measurement Results  
#### 5.1.1 APD Results  
APD values of the measured noise are shown in Figure 6. For comparison, we also measured APD values of traditional additive white Gaussian noise, as shown in the solid line in Figure 6. Figure 6 shows that the APD values measured by the horizontal and vertical polarization modes of the antenna were approximately the same. The value of APD can be used as a measure of electromagnetic energy. The tail of the APD curve represents the maximum value of measured noise power. From the figure, we can see that the measured noise power values near the manual spot welder and the welder robot were similar, while the value measured in the office area was far less than that in the welding workshop, since the office area was less affected by mechanical equipment. The power of noise measured at the 916 MHz frequency point was the smallest, which was because the propagation loss increased with the increase of noise frequency.
## 5 Security Analysis and Comparison
### Differential Attack  
A differential attack is an attack on an encryption system that involves comparing and analyzing individual differences in plaintext concerning changes sent during encryption. The capacity to survive differential attacks is closely related to the sensitivity of the plaintext image. The number of changing pixel rates (\(NPCR\)) and unified average change intensity (\(UACI\)) of the encoded image can also measure the method's ability to survive differential assaults. The most common approaches for determining plaintext sensitivity are \(NPCR\) and \(UACI\) analyses [30]. Table 4 displays the simulation results for the \(NPCR\) and \(UACI\) mathematical structures. Mathematically, \(NPCR\) and \(UACI\) are expressed as:  
\[NPCR=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}K(i,j)\times 100\% \tag{23}\]  
\[UACI=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\frac{|a_{1}(i,j)-a_{2}(i, j)|}{255}\times 100\% \tag{24}\]
## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud  
As we mentioned in the previous section, SDNs are one of the primary enabling technologies for edge cloud networks. Their network programming capability make them indispensable for controlling, monitoring, and orchestrating the network. In this section, we discuss several proposed solutions for edge cloud using an SDN controller.
## 5 Performance Evaluation and Discussion  
### Simulation Environment  
We use the MATLAB simulator to evaluate the performance of our proposed MSE algorithm. Simulation parameters are listed in Table 1, where 100 sensor nodes are distributed randomly in a rectangular region of \(200\times 200\) m\({}^{2}\). There are 20% of advanced nodes which are equipped with 400%  
Figure 6: Maintain the routing path.  
more energy than normal nodes (which means m = 0.2 and \(\alpha=4\)). Obviously, the network with high density of advanced nodes will have a relatively long lifetime.  
\begin{table}
\begin{tabular}{c c c} \hline
**Simulation Parameters** & **Representation** & **Unit** \\ \hline N & Number of sensor nodes & 100 \\ \(E_{0}\) & Initial energy of sensor nodes & 0.2 J \\ \(E_{DA}\) & Data aggregation & 5 nJ/bit/signal \\ \(E_{elc}\) & Energy dissipation to run the radio device & 50 nJ/bit \\ \(\varepsilon_{fs}\) & Free space model of transmitter amplifier & 10 pJ/bit/m\({}^{2}\) \\ \(\varepsilon_{mp}\) & Multi-path model of transmitter amplifier & 0.0013 pJ/bit/m\({}^{2}\) \\ \(l\) & Packet length & 4,000 bits \\ \(d_{0}\) & Distance threshold & \(\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\) m \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 7: Flowchart for our maintenance method.
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 1 Introduction
[26] conducted acidic salt spray tests and immersed corroded wires in sodium chloride solutions for fatigue loading, showing a significant reduction in the fatigue lifespan of corroded wires. The concept of equivalent initial flaw size (EIFS) was established to evaluate the crack initiation lifespan [27]. It is defined as the predicted long crack propagation model starting from EIFS, which is consistent with actual fatigue crack propagation [28]. The following two aspects should be noted: (1) EIFS is not a physical quantity associated with the component; (2) EIFS is a hypothetical flaw size that can be used as the initial crack size in the long crack propagation model [29]. Based on the EIFS theory, this study presents a novel lifespan model to quantitatively analyze the various stages of lifespan and the impact of corrosion and corrosion-fatigue coupling for steel wires. In Section 2, \"Corrosion Fatigue Lifespan Model based on EIFS Theory\", the EIFS theory is expounded and the corrosion fatigue lifespan model of the steel wire based on the EIFS theory has derived and illustrated. In Section 3, \"Corrosion Fatigue Performance of Steel Wire\", fatigue lifespan evaluation of the steel wire and quantitative analysis of corrosion fatigue coupling effect are conducted. In Section 4, \"Corrosion Fatigue Lifespan Assessment of Parallel Wire Cable\", a comprehensive corrosion-fatigue lifespan calculation method for parallel steel wire cables is established based on the series-parallel fiber bundle model, and a case study of Runyang Suspension Bridge is conducted. The novelties of the approach are as follows: the life model based on the EIFS can consider the various stages (short crack propagation and long crack propagation) of lifespan and quantitatively analyze the impact of corrosion and corrosion-fatigue coupling for steel wires. This research may contribute to the study of corrosion-fatigue damage in bridge cables, involving assessment, maintenance, and replacement for the bridge cables.
## 5 Performance Analysis
### Lifetime of WSNs  
Figures 13-15 show that D-LEACH was superior to the other protocols in terms of network lifetime. When the network size was 100 \(\times\) 100 m\({}^{2}\), the network lifetime of D-LEACH was 1.5% and 0.6% greater, respectively, relative to LEACH and MH-LEACH, as shown in Figure 13. When the network size was 200 \(\times\) 200 m\({}^{2}\), the network lifetime of the proposed algorithm increased by 452.2% and 15.6%, respectively, relative to LEACH and MH-LEACH, as shown in Figure 14. When the network size was 300 \(\times\) 300 m\({}^{2}\), an increase of 1168.3% and 111.9%, respectively, for D-LEACH compared with the other two algorithms, as shown in Figure 15. The main reason for these results is that by determining the direction of data transmission for the sensor nodes, the energy retention rate of the sensor nodes increases and prolongs the lifetime of the network because it reduces unnecessary energy consumption caused by reverse transmission.  
Figure 14: The number of dead sensor nodes in WSNs (200 \(\times\) 200 m\({}^{2}\)).  
Figure 13: The number of dead sensor nodes in wireless sensor networks (WSNs) (100 \(\times\)100 m\({}^{2}\)).  
The network lifetime simulation results regarding the time to FND are shown in Figure 16 for various network sizes. The figure also shows that the FND time for D-LEACH was later than that of LEACH and MH-LEACH. For a network size of 300 \(\times\) 300 m\({}^{2}\), the network lifetime of D-LEACH was significantly extended compared with that of the LEACH and MH-LEACH protocols. Table 2 shows that the network lifetime of D-LEACH is longer than that of other protocols.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 3 Pixel-Style Avatar Generation Method
### Pixel Human Visualization
#### 3.3.2 Point Cloud Body Visualization  
Body part visualization considers each pixel in the low-resolution body images obtained by downsampling in Section 3.2 and represents it as a particle of in a point cloud. We realize that when directly visualizing the low-resolution human-body-only segmented result as a point cloud, the edge of the point cloud appears unnaturally gray, as shown in Figure 5a. This is because we only retain the coefficient \(\alpha\) of the human foreground \(F\) in Equation (1) and discard the coefficient \((1-\alpha)\) of the background \(B\) when combining components. Because the background of the virtual environment changes over time, it is difficult to use the real-time background image in the virtual space and combine it with human-only segmented results according to Equation (1) using the coefficient \((1-\alpha)\). Therefore, we directly add the virtual space background \(B_{v}\) to the human-only segment result \(\alpha F\), and the combined frame \(I_{v}\) becomes  
\[I_{v}=\alpha F+B_{v} \tag{2}\]  
A greater combination coefficient for the background \(B_{v}\) results in a larger combined alpha value along the edges, making the edge color appear darker. Gray edges are particularly noticeable when visualizing low-resolution images. Therefore, to eliminate the gray edges around the avatar after converting it into a point cloud, we propose a method to weaken and remove such edges.  
First, we process the low-resolution segment result using Laplacian edge detection and subtract the edge detection result from the alpha channel. Figure 5b presents the results after subtracting the detected edges, where most of the gray edges are removed. Subsequently, the histogram equalization method is applied to the alpha channel to soften the sense of the edge further. Figure 5c reveals that the pixel avatar blends smoothly with the virtual space after alpha-channel histogram equalization. Finally, the result of the edge-processing method is converted into a point cloud, and we can obtain an avatar body that does not have strong edges separating it from the surrounding virtual environment.  
Figure 4: (a) Low-resolution point cloud head. (b) High-resolution 2D plane head.
## 4 IDAIC Framework
### Conclusions on the IDAIC Framework  
The outer ring in Figure 8, or invisible problem solving, is exploratory in nature; therefore, backward arrows are included to denote the back-and-forth nature of this activity. While this is also the case for visible problem solving, the latter phases of the IDAIC framework are less prescriptive and more transitions between phases are necessary. Furthermore, not every possible transition is shown in Figure 8. Similarly to the DST map proposed by [PERSON] et al. (2019), not every phase must be completed for successful industrial data analysis. The IDAIC framework merely provides an integrated solution for early adopters of digitalization to manage the 3B data issues throughout a data-driven industrial analysis project. It is not designed to eradicate every data challenge faced in industrial data analysis through rigid implementation. For example, in systems that are well maintained, visible problems may not occur rendering the operation assessment phase redundant.  
The contributions of the novel CRISP-DM integration-type adaptation proposed in this paper are summarized in Table 4. The main barriers to data-driven analysis in the industrial sector are the occurrence of broken and bad quality datasets, along with the lack of understanding of the domain (background). The necessary activities to manage these issues has been outlined in Table 2 and an example of specific tasks in the framework that contribute to the fulfilment of the data analysis enabling activities is outlined in Table 4. While we interpret the data challenges landscape as an overlapping Venn diagram as in Figure 2, an example of managing each issue is outlined in Table 4. Firstly, the completeness of the dataset is assessed by performing a data gap analysis in the data contextualization phase. In collaboration with the domain expert, the possible faults and data required to identify their occurrence are listed. A high-level quantitative measure of the completeness of the dataset may then be obtained to understand the value of the current dataset and the opportunity that exists if the system is further digitalized. Secondly, the quality of the dataset is evaluated by measuring the deviation from expected values as defined through domain integration. In the semantic accuracy analysis task, traditional expert rule sets have been repurposed to measure the quality of the dataset rather than identify faults in the system. Thirdly, knowledge-based FDD has been integrated into the data analysis to combine the benefits of data-driven and knowledge-based FDD. The high interpretability of these techniques facilitates better understanding of the operation of the system, reducing the knowledge gap between engineering and data science type approaches.