esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Infrasonic Time Domain Characteristics
As shown in Figure 9, the rock specimen underwent five cycles of cyclic loading and unloading, with progressive stress increments during the loading phases and decrements during the unloading phases, with each cycle forming a closed hysteresis loop. The peak stress of the rock specimen gradually increased with each loading, the peak strength of the rock specimen under cyclic loading and unloading was 98.127 MPa, and the internal structure of the rock specimen gradually accumulated damage. During the low-stress phase of the first and second cycles, the infrasonic amplitude exhibited relatively stable variations with minimal fluctuations, consistently remaining below 0.004 V. During the initial loading cycle, the relatively low infrasonic amplitude indicates fewer internal fractures and higher structural integrity of the rock specimen, which is attributed to the compaction characteristics inherent in the initial stage of rock deformation. With the increasing stress, the amplitude of infrasonic waves changes significantly, and the amplitude of infrasonic waves increases rapidly when the peak stress is reached in each loading, such as the infrasonic amplitudes of 0.00672 V and 0.01037 V at point A (691 s) and point B (886 s), respectively, of the fourth cycle, and the amplitude change is significantly enhanced, indicating that the internal fracture in the rock specimen is initiated and starts to expand. During the unloading phase, the amplitude of infrasonic waves may decrease, but they do not fully return to the initial level, indicating the accumulation of damage within the rock specimen. In the high-stress stage, the amplitude of infrasonic waves changes most significantly: the peak of infrasonic amplitude gradually increases, the fracture inside the rock specimen  
Figure 8: Infrasonic time-domain characteristics of rock specimens under uniaxial compression. Figure 7: Unloading deformation modulus–number of cycles. gradually propagates and accumulates, and the damage of the rock specimens is further aggravated by each loading cycle. For example, at point C of the upper stress of the fifth cycle (1231 s), the amplitude of the infrasonic waves changes significantly, with an amplitude of 0.008604 V, indicating that the internal fracture of the rock crack propagates and coalesces rapidly. At points D and E of the sixth cyclic loading stage, the amplitudes of infrasonic waves were 0.00358V and 0.00604V, respectively, and while the changes in amplitude were more pronounced, the number of infrasonic signals that showed significant changes was significantly reduced. After point E and before peak strength, the rock specimen approaches critical failure state while exhibiting infrasonic amplitude stabilization with low-magnitude fluctuations.
## 5 System-Level Performance Evaluation
### Performance Evaluation Setup  
In order for the analysis approach presented in the previous section to lead to realistic and useful results, it is important to present the main hardware components (including the different configuration options considered) utilized as well as the actual data, comprised by samples taken from real EEG/ECG signals, which are two of the most demanding modalities in epilepsy monitoring (which represents our main application scenario).  
The most important component influencing our evaluation is the implemented compression hardware module. As analyzed in Section 4, the hardware module throughput (thus the delay required to compress a specific amount of data) depends on the clock frequency based on Equation (1).  
The power consumption depends also on the frequency clock. Therefore, we considered four different clock frequencies; then, we measured the power consumption and based on Equation (5) the following table has been extracted. Table 5 presents the two main performance metrics of the compression hardware implementation proposed.  
Table 5 depicts also a tradeoff that, as will be proven, comprises a critical factor with respect to the overall performance evaluation. As depicted, as the clock frequency increases, both power consumption and throughput also increase. Consequently, when compressing a specific amount of data increasing the clock frequency implies that, on one hand, the component needs more energy while active but, on the other hand, it performs its operation much faster, and consequently the time period required to be active is drastically reduced. A secondary observation concerns the fact that the main factor of power consumption increase is the dynamic power and not the static power (its influence is rather negligible).
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 4 Comparison between Calculation and Test Results
### Dnv-Gl (2016)  
The shear strength of specimens calculated by the DNV-GL code were compared with the test results, as shown in Figure 17. It can be seen that the DNV-GL code had better applicability when the shear strength in the test was below 3 MPa; only two specimens have a calculated value 10% higher, which indicates the reliability of this code for predicting the axial strength of grouted joints. However, as the shear strength of the specimen increased, the calculation result became smaller; 36 out of the 51 specimens have test results that are more than 10% above the calculation result. It can be considered that the DNV-GL code has excellent applicability when the shear strength of the grouted joint is relatively low, and it also can guarantee safety when the shear strength is high.  
Figure 16: Comparison of shear strength calculated using DNV and tested results.  
Figure 17: Comparison of shear strength calculated using DNV-GL and tested results.
## 6 Results
### Energy Mode Consumption  
The results of the energy measurements are provided in Table 3, along with the advertised consumption of three BLE SoCs. As each manufacturer uses a different terminology  
Figure 11: RSSI measurements. for its EMs, the comparison was conducted based on the available functionalities. When no explicit data were given for a mode, a calculation using the consumption per megahertz and the base frequency of the SoC was performed to obtain a value. Despite this, some solutions simply lacked an equivalent comparison for each mode and are therefore not present in each row. The comparison did not account for frequencies or peripheral optimizations, as too many possible combinations exist, and most of them are application-specific. Overall, we observed that the current consumption values advertised by Silicon Labs are lower than the measured values during the experimentation. The most notable difference was found during reception mode. According to the manufacturer's value, EFR32 BG22 should be the most energy-efficient SoC, but the measured value ranked it second. The difference was mainly due to the current consumption of the additional components (e.g., extra sensors, debugging IC, etc.) of the development board, which cannot be isolated. Another variation occurred because some manufacturers advertised the average value obtained through a 50% RX duty cycle. However, although the EFR32 BG22, CC2640, and NRF52840 were similar, ESP32 exhibited substantially higher energy consumption while in receive mode, making it the most energy-intensive of the four. When in active mode (EM0), EFR32 BG22 seemed to be the most efficient solution, consuming only half as much power as CC2640 and the NRF52840. Notably, this performance may vary based on the specific application and computing needs of the application. Of the four available SoCs, ESP32 was the most powerful; however, its power consumption was ten times that of the others. When in their lowest energy state, both EFR32 BG22 and NRF52840 could receive a WuS using 10,000 times less current than when in their main radio receiving state. This striking contrast in consumption highlights the benefits of this technology's WuR capabilities. The ESP32 system-on-a-chip (SoC) consumed the most power overall due to its superior computing capabilities. In comparison with the other solutions, ESP32 is less suitable for long-term devices in terms of energy efficiency. Nonetheless, it is still relevant in certain use cases where more intensive data processing is required, at the cost of the energy budget. Overall, WuR implementations provide energy consumption results that are very close to that of the lowest non-WuR system (CC2640) while maintaining the ability to receive data. Of all the SoCs assessed in this study, EFR32 BG22 appears to be the most
## 4 Analysis and Experimental Verification
### Experimental Results Analysis  
The measured load-deflection curves of US1, LGTS1-1, US2, and LGTS2-1 are presented in Figure 16. The thickness parameters \(\tau^{\prime}\) of the specimens US1 and LGTS1-1 are different, and the slot shapes and other geometric parameters are same. The specimens US2 and LGTS2-1 also have consistent features. Compared with US1, the load capacity of LGTS1-1 is improved significantly, which indicates that a decrease in \(\tau^{\prime}\) dramatically enhances the load capacity of slotted disk springs, as shown in Figure 16a. In Figure 16b, a similar result is also shown when comparing the load-deflection data of US2 and LGTS2-1. Consequently, it is concluded that a decreased \(\tau^{\prime}\) increases the load capacity. This result is in good agreement with the theoretical analysis results in Section 3. It is found from the experimental results shown in Figure 16 that the load-deflection curve of spring LGTS2-1 has a more significant increase. The specimens LGTS1-1 and LGTS2-1 have different magnitudes of \(C\) and \(C_{1}\), which is a possible reason for this phenomenon. To explore the above conjecture, the load variation rates (\(\psi_{C}=P(f,\tau^{\prime}=-0.5)/P(f,\tau^{\prime}=0)-1\)) with different \(C\) and \(C_{1}\) values are calculated, as shown in Figure 17, with corresponding geometric parameters of \(a=150\) mm, \(h_{0}=5\) mm, and \(t^{\prime}_{m}=6.5\) mm. It is demonstrated that a smaller \(C\) and greater difference between \(C\) and \(C_{1}\) enhance the influences of \(\tau^{\prime}\) on the load capacity. The values of \(C\) and \(C_{1}\) for LGTS1-1 and LGTS2-1 are 1.5 and 2.083 and 1.293 and 2.5, respectively, which confirms the above conjecture.  
Figure 15: Experimental setup for testing the responses of the slotted disk springs.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 5 Discussion
The first one is to take into account the unique band characteristic of Sentinel-2 and, then, analyze the spectral response mechanism to determine the sensitive characteristic bands to highlight the water body from the background. The second one is to employ the Otsu algorithm to automatically identify the segmentation threshold and, following that, the water body and nonwater body can be separated clearly. The determination of the sensitive characteristic band is critical to achieve better water body extraction performance and the new water index, namely SWI, has been confirmed to enhance various water body types. Therefore, this method has application potential for larger-scale surface water mapping. The limitation of this method is the relatively low spatial resolution. Currently, the highest spatial resolution of Sentinel-2 is 10 m, while the sensitive characteristic bands selected by this method is 20 m. Therefore, some narrow rivers and small lakes cannot be accurately extracted in urban areas. Although there are some documents that possess adopted image fusion methods to improve spatial resolution [32], they also face two challenges: (1) The complexity of fusion methods restricts the efficiency of large-scale surface water extraction; (2) changes in the spectral response mechanism after fusion will affect the performance of water body enhancement. Therefore, efficient and reliable  
Figure 7: The performance of water body enhancement and extraction. ((**a**) original image, (**b**) water body enhancement using NDWI, (**c**) water body enhancement using SWI, (**d**) water body extraction using NDWI, (**e**) water body extraction using SWI, and (**f**) extraction performance comparison). image fusion methods for Sentinel-2 requires further development in order to improve the accuracy of water body extraction in large scales.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 2 Magnetic-Balanced Inductive Link System
### Magnetically-Balanced Structure  
The positions of the three coils and their notations are demonstrated in Figure 3. The coil separation (\(d\)) denotes the distance between the external power coil and the implant coil. The distance (\(d_{13}\)) denotes the center distance between the partially-overlapped external data coil and the external power coil. The skew distance (\(d_{12}\)) denotes a small offset of the implant coil towards the external data coil along the positive Y axis. Associating Figure 3 with Figure 2, it can be seen that the coupling (\(k_{12}\)) between \(L_{1}\) and \(L_{2}\) is used for the WPT. The coupling (\(k_{23}\)) between \(L_{2}\) and \(L_{3}\) is used for the uplink data transmission. However, the coupling (\(k_{13}\)) between \(L_{1}\) and \(L_{3}\) can transfer the power carrier to \(L_{3}\), which induces substantial interference on the uplink signal receiving. To reduce the strong power carrier interference, the two external coils are designed to be partially overlapped and magnetically balanced to minimize their coupling coefficient (\(k_{13}\)), while \(k_{12}\) and \(k_{23}\) should be kept at a fairly high level to provide sufficient coupling for the power and uplink data transmissions. The relative distances \(d_{13}\) and \(d_{12}\) are two significant coil position parameters that need to be optimized and selected in the proposed inductive link, and the optimization procedure is presented in Section 4.  
Figure 3: 3D model of the magnetic-balanced inductive link.  
Figure 2: Block diagram of the magnetic-balanced inductive link system.
# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 1 Introduction
To demonstrate the proposed approach, a software-defined radio microwave module (SDR-MM) is used as a case study. The SDR-MM was selected due to its multi-disciplinary engineering nature, versatility in various operational concepts, and modularity that allows for different system configurations to be evaluated [24]. (2) Analytical models (described in Section 3). Combining system and analytical models enhances the engineering design process by comprehensively understanding the system and informed decision making based on functional requirements and technical performance. (3) Architecture definition (described in Section 4). The detailed architecture and description of the three architectural alternatives are described in this section. (4) The MCDM model (described in Section 5). The MCDM processes are used to evaluate and prioritize alternative architectures based on assessment criteria. Integrating system models, analytical models, and MCDM processes forms the basis of MBE, a holistic approach to enhance efficiency, reduce errors, and facilitate early resolution of architecture issues. MBE, along with multi-domain inputs and modeling tools, enables the selection of the optimal architecture alternative and supports the optimization of the system architecture. Finally, a brief conclusion is presented in Section 6 along with suggestions for future work. Figure 1: Integrated methodology and process for system architecting and trade-off analysis.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 3 Data Collection
### Criteria for Alternative Route Selection  
In this study, alternative route selection criteria were defined based on the Federal Highway Administration (FHWA) handbook. These criteria can be defined as truck restrictions used for assessing alternative route candidates. They serve as measures of how effectively a route is being utilized for diversion.  
Data for selection criteria were collected as variable indicators, processed to create input layers for network analysis, and finally applied to evaluate potential alternative routes to select the optimal route for truck traffic. Generally, the alternative route is the shortest route, but some increase in distance can be allowed to avoid specific road characteristics that are not suitable for trucks.  
Multiple sets of data were collected for each selection criterion. Data were separated into several feature classes and projected into the same coordinate system for the analysis.  
To select an alternative route that can accommodate heavy vehicle characteristics and restrictions, selection criteria were defined.  
The main characteristics for assessing whether a candidate route is feasible for truck traffic were specified as follows:  
1. Roadway geometry, including lane width and number of lanes;
2. Roadway conditions, including level of service and speed limit;
3. Heavy vehicle restrictions, including vertical clearance restrictions and insufficient bridge design loads;
4. Neighborhood impacts, including proximity to schools and hospitals.  
After identifying the alternative route criteria, relevant data were collected from various sources. Data were extracted in shapefile format, and additional data processing was done utilizing data management tools in the ArcGIS platform.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves
### Hydrodynamic Theory in Ship Hydroelasticity
#### 2.3.2. Incident Wave Force  
For ships sailing in regular waves, the nonlinear incident wave force acting on the instantaneous wetted elastic hull is expressed as:  
\[F_{I}(t)=-\rho\zeta_{A}\iint\limits_{S(t)}\mathbf{n}\cdot\mathbf{u}_{r}( \mathrm{i}\omega_{e}-U\frac{\partial}{\partial x})\phi_{0i}ds(r=1,\;2,\cdots,m) \tag{8}\]  
The irregular waves can be theoretically generated by the superposition of an infinite number of component regular waves. Based on Equation (8), the nonlinear incident wave forces acting on ships in irregular waves can be expressed as follows:  
\[F_{I}(t)=-\rho\sum_{i=1}^{\infty}\zeta_{Ai}\iint\limits_{S(t)}\mathbf{n}\cdot \mathbf{u}_{r}(\mathrm{i}\omega_{ei}-U\frac{\partial}{\partial x})\phi_{0i}ds (r=1,\;2,\cdots,m) \tag{9}\]  
where \(\zeta_{Ai}\), \(\omega_{ei}\) and \(\varphi_{0i}\) are respectively the incident wave amplitude, wave encounter frequency and wave potential of the \(i\)th component regular wave.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 3 Review on Optimization Methods under Continuous Phase Shift
### Manifold Method  
Recognizing that the constraint set \(\mathcal{F}\) forms a complex circle manifold in model C1, another low-complexity method is based on manifold optimization. A representative algorithm in this category is the Riemannian conjugate gradient (CG) method [14], which solves problem (5) on an oblique manifold through alternatively computing the Riemannian gradient, finding the conjugate direction, and performing retraction mapping. A graphical representation of various steps of the Riemannian CG method is illustrated in Figure 5.  
More specifically, the Riemannian gradient of \(f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)}\) at the \(l^{th}\) iteration solution \(\mathbf{e}^{(l)}\) is obtained by projecting the Euclidean gradient of \(f\) at \(\mathbf{e}^{(l)}\) onto the tangent space (blue color step in Figure 5). After obtaining the Riemannian gradient \(\text{grad}_{\mathbf{e}^{(l)}}f\), the CG descent direction at point \(\mathbf{e}^{(l)}\) can be obtained as \(\mathbf{c}^{(l)}\), and \(\mathbf{e}^{(l)}\) is updated as \(\mathbf{e}^{(l)}+a^{(l)}\mathbf{c}^{(l)}\) on the tangent space, where \(a^{(l)}\) is an Armijo backtracking step size (red color step in Figure 5).  
Figure 4: (a) Linear upperbound \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\) for a quadratic function \(f(\mathbf{x}^{(n)},\mathbf{e})\) at a point \(\mathbf{e}^{(r)}\) on the unit circle. (b) Graphical representation of the GD method for updating \(\mathbf{\Theta}\), where \(b^{(r)}\) is the update step size and \(\
abla_{\mathbf{\Theta}}f\Big{(}\mathbf{x}^{(n)},e^{i\mathbf{\Theta}^{(r)}} \Big{)}\) is the gradient of \(f\) at the last iteration solution \(\mathbf{\Theta}^{(r)}\). Similar figures can be found in optimization literature, e.g., [13, 46].  
Since the updated \(\mathbf{e}^{(l)}+a^{(l)}\mathbf{c}^{(l)}\) may not be in the oblique manifold, the final point should be projected onto the oblique manifold by employing a retraction mapping (black color step in Figure 5). This method extends the GD method in the Euclidean space to the Riemannian manifold. Compared to the GD method in the previous subsection, the manifold method does not re-formulate the objective function in terms of \(\mathbf{\Theta}\) and thus avoids the many local minima as shown in Figure 4b. By guaranteeing that the complex circle constraint is satisfied in every iteration, the Riemannian CG method converges to a stationary solution [14]. The computational complexity of the Riemannian CG update is dominated by the gradient step, which only involves element-wise operations. This gives a linear complexity order with respect to \(M\).
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 2 Virtual Track Inspection Technology
### Vertical Curve  
The vertical curve is a critical component in the longitudinal profile of railways, connecting segments with varying gradients to enhance operational safety and passenger comfort when trains pass through gradient transition points. In Simpack, the configuration of vertical curves is accomplished by inputting key parameters, including length and gradient. When the gradient difference between adjacent segments exceeds 1%, a circular vertical curve is mandated to ensure smooth transitions, with the minimum radius governed by comfort criteria [21], The formula is as follows:  
\[R_{\text{sh}}\geq v^{2}/(3.6^{2}a_{\text{sh}}) \tag{1}\]  
where \(R_{\text{sh}}\) is the radius of the vertical curve, \(v\) is the train speed, and \(a_{\text{sh}}\) is the allowable vertical centripetal acceleration based on passenger comfort criteria.  
As metro systems and rapid transit lines with speeds exceeding 100 km/h continue to develop, various design codes and standards for track alignment have been established. However, discrepancies exist among technical standards and operational practises regarding values for vertical centripetal acceleration. For instance, the Metro Design Code (GB50157-2013) [22] stipulates two values--0.16 m/s\({}^{2}\) for standard conditions and 0.3 m/s\({}^{2}\) for challenging conditions--for lines operating at speeds below 100 km/h. In contrast, the Intercity Railway Design Code (TB 10624-2020) [23] and the Technical Specifications for Rapid Transit (T/CAMET 01001-2019) [24] define acceleration values for lines operating between 120 and 160 km/h, specifying limits of 0.15 m/s\({}^{2}\) and 0.26 m/s\({}^{2}\) for the former, and 0.25 m/s\({}^{2}\) and 0.34 m/s\({}^{2}\) for the latter, under standard and challenging conditions, respectively. Considering the additional margin introduced by rounding the vertical curve radius, this study standardizes the values of 0.15 m/s\({}^{2}\) for standard conditions and 0.3 m/s\({}^{2}\) for challenging conditions as the basis for further analysis and calculations.