esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study
### Effect of Superstructure Weight  
The effect of the weight of superstructure on dynamic pile response was analyzed by varying the weight of the superstructure at 0, 300, 600, and 900 kN. Figure 16 shows peak envelopes of the pile lateral displacement according to the weight of a superstructure when input frequency of 0.5 Hz was applied. Although the superstructure weight increased from 0 to 900 kN, the lateral pile displacements were very similar regardless of the magnitude of input acceleration. The maximum value of the pile displacement decreased only by 26% when the superstructure weight decreased  
Figure 14: Dynamic pile response envelopes according to the relative density in dry condition: (**a**) Peak bending moment along the depth, (**b**) Maximum lateral displacement along the depth.  
Figure 15: Effect of kinematic force in different soil condition under earthquake.  
from 900 kN to 0 kN under liquefiable conditions. Similar results were reported by [PERSON] (2006) [1]. [PERSON] (2006) [1] analyzed the dynamic earth pressure on piles in liquefiable soil by 1 g shaking table tests and found that the amplitude of the dynamic earth pressure acting on the pile and the pile responses are similar irrespective of the superstructure weight. Figure 17 shows maximum pile lateral displacement envelopes for four different weight of the superstructure embedded in dry sand ([PERSON] and [PERSON], 2019 [23]). It was noted that lateral pile displacements were remarkably varied for different weights of the superstructure in dry condition, in contrast to liquefiable condition. It was identified that the maximum pile displacement in dry condition decreased to 92% when the superstructure weight decreased form 900 kN to 0 kN, whereas those in liquefiable condition decreased only 26%. Figure 18 shows the effect of inertial force in different soil condition under earthquake by comparing peak values of lateral pile displacement. The gap in liquefied soil condition shows much smaller value than the gap in dry soil condition. The difference between results from different types of soil condition was strikingly distinctive. It can be demonstrated that inertial force is much more dominant in dry soil conditions than in liquefiable soil conditions. The similar observation was reported through field investigations by [PERSON] (1997) [53]. He concluded that the inertial force is the predominant force in dry condition and responsible for the maximum bending moment near the pile head. These results showed that the effect of inertia induced by the superstructure was not remarkable under liquefiable condition compared with that under dry conditions.  
Figure 16: Dynamic pile response envelopes according to the weight of superstructure in liquefiable condition: (**a**) Input acceleration: 0.13 g, (**b**) Input acceleration: 0.25 g.
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 3 Engineering Applications
### System Reliability Analysis
#### 3.3.2 Variation of System Failure Probability with Failure Mode Correlation Coefficient  
This section analyzes the relationship between the reliability index of the bridge structural system and the Kendall rank correlation coefficient of the failure mode combination. The reliability index value of the system was calculated when the Kendall rank correlation coefficient of each failure mode combination was taken as 19 key data points between 0-1. Based on these data, the curves of the system reliability indexes with the change of the Kendall rank correlation coefficient for each failure mode combination were fitted as shown in Figure 3. From Figure 3, it can be seen that the Kendall rank correlation coefficients of the 3 rd failure mode and the 6 th failure mode combination had a relatively great influence on the reliability indexes of the bridge structure system; the combination of failure mode 3 with failure mode 5 and the combination of failure mode 5 with failure mode 6 had a relatively greater influence on the reliability indexes of the bridge structure system; the combination of failure mode 4 with failure mode 4 with failure mode 6, and failure mode 3 with failure mode 4 had relatively less influence on the reliability index of the bridge structure system; all the combinations associated with failure mode 7 had little influence on the reliability index of the structure system. Reference [34] points out that the main failure mode of the maximum cantilever state during construction is the lower edge compressive stress at 1/4 of the bridge span. Failure mode 3 and failure mode 6 are located at 1/4 of the bridge span. Therefore, the combination of failure modes 3 and 6 had a great impact on the reliability of structural system. The influence of failure mode combination on the failure probability of structural system is mainly reflected in the level of single-failure probability. The higher the failure probability of the failure mode, the greater the influence of this failure mode combined with other failure modes on the reliability of the structural system. This is consistent with the meaning indicated by the curves in Figure 3.  
Figure 1: Variation of system reliability indexes with individual failure mode reliability index.  
Figure 2: Variation diagram of system reliability index with single failure mode reliability index when the variation range of reliability index is 4–8.  
In order to more clearly describe the influence of Kendall rank correlation coefficient of each combination in the overlapping part of Figure 3 on the system reliability index, Figure 4 enlarges the influence of the change of the Kendall rank correlation coefficient of failure mode combination on the system reliability index when the change range of the Kendall rank correlation coefficient is 0.8-1.0.
## 4 System Model and Assumptions
### Node Characteristics and Resource Constraints  
Processing Power (\(P_{i}\)): Each node \(i\) has a processing capacity \(P_{i}\), measured in transactions per second (TPS) [27]. To simplify the model, we assume that all nodes have identical processing power, \(P_{i}=P\), which reflects homogeneous computational capabilities. This assumption aligns with our goal of modeling maximum decentralization, as all nodes have equal opportunities to participate in network processes.  
Blockchain Ledger Size: Nodes are required to store the entire blockchain ledger, and this ledger grows over time as new transactions are appended to the blockchain.  
Storage Constraint (\(S_{max}\)): Each node has a maximum storage capacity \(S_{max}\). As the size of the ledger increases, storage requirements may exceed \(S_{max}\), potentially affecting node participation.  
Unlimited Storage: For the sake of the proof, we assume that storage capacity is sufficient and does not limit node participation. This assumption allows us to focus primarily on processing and communication constraints in our analysis.
## 3 Results and Discussion
### Parameter Screening with Plackett-Burman (PBD) and RSM Modeling
#### 3.1.2 Validation and Selection of BBD-RSM Model  
Table 3 shows the Box-Behnken design response surface methodology (BBD-RSM) ANOVA results at a 95% confidence interval. The adjusted \(R^{2}\) values for rising water level (RWL) and falling water level (FWL) responses are 0.99(99) and 0.99(25). The predicted \(R^{2}\) values are 0.99(98) and 0.97(80), respectively. This analysis indicates that the two values closely agreed, suggesting the model's validity. The small difference between the adjusted and predicted values, both <0.2, confirms the model's excellent predictability.
## 3 Two-Dimensional Frequency Domain
### SAR Raw Signal Spectrum  
The Omega-k algorithm works in the two-dimensional frequency domain; therefore, Fourier transform is applied in the azimuth and range direction to the SAR raw signal. On the other hand, for SAR signal processing, Equation (17) will be used, and rewriting said equation results in Equation (22), similar to that in [16; 19; 23; 24].  
\[s_{a}(t)=rect\bigg{(}\frac{t-\Delta t}{t_{p}}\bigg{)}exp\Big{(}1j\Big{(}-2\pi f _{0}\Delta t+\pi k_{r}(t-\Delta t)^{2}\Big{)}\Big{)} \tag{22}\]  
In Equation (22), digital signal processing is mainly conducted in phase; therefore, the range Fourier transform is applied:  
\[S_{A}(f_{t})=\int_{-\infty}^{\infty}s_{a}(t)exp(-j2\pi f_{t}t)dt;\ \ \ \psi(t)=-2\pi f_{0}\Delta t+\pi k_{r}(t-\Delta t)^{2}-2\pi f_{t}t \tag{23}\]  
where \(\psi(t)\) is the integrand phase and \(f_{t}\) is the fast-time frequency. Then, the derivative is applied to \(\psi(t)\) and is set equal to zero because it is a stationary signal, then \(t\) is solved; mathematically speaking \(\frac{\mathrm{d}\psi(t)}{\mathrm{d}t}=0\), solving for \(t\), we obtain the following:  
\[t=\Delta t+\frac{f_{t}}{k_{r}} \tag{24}\]  
Replacing Equation (24) in Equation phase (23), [16; 20; 24] or \(\psi(t)\), obtains the following phase:  
Figure 7: SAR raw data [19].  
\[\psi(f_{t})=-2\pi(f_{0}+f_{t})\Delta t-\frac{\pi f_{t}^{2}}{k_{r}} \tag{25}\]  
The development of digital signal processing is conducted in the SAR raw signal phase, so for now, we only considered the phase. Then, using Equations (23) and (24), the following result was obtained.  
\[S_{A}(f_{t})=exp(j\psi(f_{t})) \tag{26}\]  
Then, using the signal of Equation (26), which is in the range-Doppler domain, we transform the signal to the two-dimensional frequency domain by applying the azimuth Fourier transform.  
\[S_{A}(f_{t},f_{n})=\int_{-\infty}^{\infty}S_{A}(f_{t})exp(-j2\pi f_{n}n)dt;\ \psi(f_{t},n)=-2\pi(f_{0}+f_{t})\Delta t-\frac{\pi f_{t}^{2}}{k_{r}}-2\pi f_{n}n \tag{27}\]  
where \(\psi(f_{t},n)\) is the integrand phase of Equation (27), \(f_{n}\) is the slow time frequency, and \(\Delta t=2\alpha\left(\frac{R(n)}{c}+\left(\frac{V_{s}}{c}\right)^{2}n\right)\) is the delay time; then, as it is a stationary signal, \(\frac{\mathrm{d}\psi(f_{t},n)}{\mathrm{d}t}=0\). The value of \(n\) is shown in Equation (28).  
\[n=\frac{-R_{0}\left(\frac{f_{n}c}{2\alpha V_{s}(f_{0}+f_{t})}+\frac{V_{s}}{c} \right)}{V_{s}\sqrt{1-\left(\frac{f_{n}c}{2\alpha V_{s}(f_{0}+f_{t})}+\frac{V_ {s}}{c}\right)^{2}}} \tag{28}\]  
Then, we replace \(n\) in Equation (27), finally obtaining a phase in the two-dimensional frequency domain and, consequently, the raw signal in the two-dimensional domain, which is shown in Equation (29).  
\[S_{A}(f_{t},f_{n})=exp(j\psi(f_{t},f_{n})) \tag{29}\]  
where:  
\[\psi(f_{t},f_{n})=-\frac{4\pi\alpha R_{0}}{c}\left(\sqrt{(f_{0}+f_{t})^{2}- \left(\frac{f_{n}c}{2\alpha V_{s}}+\frac{V_{s}}{c}(f_{0}+f_{t})\right)^{2}} \right)-\frac{\pi f_{t}^{2}}{k_{r}} \tag{30}\]
## 4 Discussion  
### Moment-Rotation Hysteresis Curves  
The moment-rotation hysteresis curves of the test specimens are presented in Figure 13. The hysteresis curves of specimens SJ-1 through SJ-6 all exhibit a significant pinching effect, whereas those of specimens SJ-7 and SJ-8 exhibit a far smaller pinching effect. The pinching effect was caused by the bulging deformation of the steel tube face, the deformation of the end-plate, the slippage of the anchored bolts and the crushing of concrete. The moment-rotation curve of each specimen basically exhibited a linear relationship in the initial elastic stage. After entering the inelastic state, the hysteresis curve of the specimen gradually became plump. With increasing drift ratio, the pinching effect became more evident.  
Specimens SJ-1, SJ-2, SJ-3, SJ-5, and SJ-6 all have a relatively plump hysteresis loop, demonstrating relatively strong hysteresis performance and seismic energy absorption capacities. The energy dissipation in specimens SJ-1 and SJ-2 was mainly realized by the crushing of concrete in the columns and the deformation of the steel tube walls, which was not an expected failure mode in engineering practice. Moreover, specimens SJ-1 and SJ-2, which experience failure mode I, exhibited particularly remarkable stiffness and strength degradation compared with the other specimens, which is attributed to the same reasons mentioned above. However, for specimens SJ-3, SJ-5, and SJ-6, which exhibit failure mode II, the stiffness and strength degradation were not significant.  
Due to its failure mode with brittle bolt fracture, the hysteresis loop of specimen SJ-4 is flat, indicating the poor ductility and energy dissipation capacity of the joint. Due to the weak beam used in specimens SJ-7 and SJ-8, a plastic hinge was observed at the root of the beam end, and the hysteresis loop was very plump, reflecting the highest hysteresis performance of the overall joints and subsequently good seismic performance and energy dissipation capacity.  
The different structural parameters of an anchored blind-bolted joint lead to different failure modes, which are eventually reflected in their different hysteresis curves. In addition, the bulging deformation of the steel tube wall can produce a good energy dissipation capacity but will also cause significant stiffness and strength degradation; the use of a thin end-plate and weak beam resulted in better hysteretic performance, which amounts to excellent seismic performance and energy dissipation capacity.  
Figure 13: Moment (\(M\)) versus rotation (\(\theta\)) hysteresis curves of the specimens.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Signal Classification  
With the evolution of wireless communication technology, signal recognition and classification have become necessary. Defined signal intelligence is a field of study and application that relies on extracting signal characteristics such as protocols, bandwidth, center frequency, modulation, and emitter identity from unknown radio frequency signals [58]. This field of study is further divided into subcategories dependent on the task. Automatic modulation classification and specific emitter identification are the most well-researched studies.
## 4 Results
### Acquisition and Tracking of GNSS Signals in Real Time
#### 4.3.1 Tests Results Using GPS and Galileo Signals at Nominal Power Levels  
Table 15 shows the test results obtained using signals received at nominal power. The receiver successfully acquired and tracked Galileo E1b/c, Galileo E5a, GPS L1 C/A, and GPS L5 signals, achieving navigation solutions with TTFF values of about 1 minute.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Step** & **Description** \\ \hline Step 1 & \begin{tabular}{c} Use the receiver in normal-sensitivity mode to obtain the ephemeris data, \\ the ionospheric data, and the UTC model for the visible Galileo satellites. \\ \end{tabular} \\ \hline Step 2 & \begin{tabular}{c} Use the receiver in high-sensitivity mode with assistance data, including the data \\ obtained in step 1, to receive GNSS signals at nominal power and obtain \\ navigation solutions \\ \end{tabular} \\ \hline Step 3 & \
\begin{tabular}{c} Stop the receiver and increase the signal attenuation until the \(C/N_{0}\) of some Galileo \\ E1b/c signals is down to 20 dB-Hz \\ \end{tabular} \\ \hline Step 4 &
\begin{tabular}{c} Use the receiver in high-sensitivity mode, to acquire and track weak GNSS signals and \\ obtain navigation solutions. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 14: Test procedure.
## 5 Threat Analysis and Risk Assessment (TARA)
### Attack Path Analysis  
The attack path analysis was executed for each threat scenario previously discovered. It delineated the routes that potential adversaries could traverse to exploit vulnerabilities. For each threat scenario, one or more distinct attack paths were identified, each providing details on the sequence of actions an attacker could attempt to navigate through the network of assets. To give an example, an attack path could be accessed through the WiFi module to the TCU, then reaching the gateway ECU to finally spoof the MEMS.
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 2 Preliminaries and Definitions
The fault diagnosis observers for a network of agents can have three main architectures based on an analysis provided in [20]:  
1. A centralized architecture involves a single master observer or filter that gathers information and manages inputs throughout the entire network. This approach results in a significant communication load because the master filter/observer must provide state information to all agents in the team for state and fault estimation. 2. A distributed architecture utilizes a hierarchical system of filters/observers. Each spacecraft operates a local filter that offers estimates to the master filter. This design effectively distributes the computational load among the local agents, simplifying the process. 3. A decentralized architecture operates without a hierarchy, eliminating the need for central supervision. Within this structure, each spacecraft processes its measurement data simultaneously with other spacecraft in the network, enabling efficient state and fault estimation without a centralized control mechanism. Clear schematic diagrams of all three fault diagnosis architectures are presented in Figure 5.  
Figure 4: Fault Diagnosis and Prognosis approaches. Figure 5: Fault diagnosis architectures: (**a**) centralized; (**b**) decentralized; (**c**) distributed. The communication between agents can be either continuous or event-triggered. The event-triggered communication method allows communication when necessary to reduce the overall communication load. This flexibility ensures efficient utilization of resources and minimizes unnecessary data transmission. In Sections 4 and 5, we review the existing publications concerning the FDP in both single satellite/UAV systems and networks comprising satellites and UAVs.
###### Abstract  
The high-altitude Hindu Kush-Himalayan region (HKH, average \(\sim\)5 km from msl) and the adjacent Indo-Gangetic plains (IG plains, \(\sim\)0-250 m msl), due to their geographical location and complex topography, are reported to be highly sensitive to climatic changes. Recent studies show that the impacts of climate change and associated changes in water resources (glacial/snow melt water and rainfall) in this region are multifaceted, thereby affecting ecosystems, agriculture, industries, and inhabitants. In this study, 45 years of Microwave Sounding Unit/Advanced Microwave Sounding Unit (MSU/AMSU)-derived mid-tropospheric temperature (TMT, 3-7 km altitude) and lower tropospheric temperature (TLT, 0-3 km altitude) data from the Remote Sensing Systems (RSS Version 4.0) were utilized to analyze the overall changes in tropospheric temperature in terms of annual/monthly trends and anomalies. The current study shows that the mid-tropospheric temperature (0-3 km altitude over the HKH region) has already alarmingly increased (statistically significant) in Tibet, the western Himalayas, and the eastern Himalayas by 1.49 \({}^{\circ}\)K, 1.30 \({}^{\circ}\)K, and 1.35 \({}^{\circ}\)K, respectively, over the last 45 years (1978-2022). As compared to a previous report (TMT trend for 30 years, 1979-2008), the present study of TMT trends for 45 years (1978-2022) exhibits a rise in percent change in the trend component in the high-altitude regions of Tibet, the western Himalayas, and the eastern Himalayas temperature Trend (MSU/AMSU 1978-2022) over the Tibet and Eastern IG plains) shows a negligible or much lower percent change (0% and 40%, respectively) over the last 14 years. Similarly, dust source regions in Africa, Arabia, the Middle East, Iran, and Pakistan show only a 130% change in warming trends over the past 14 years. In the monthly breakup, the 'November to March' period usually shows a higher TMT trend (with peaks in December, February, and March) compared to the rest of the months, except in the western Himalayas, where the peak is observed in May, which can be attributed to the peak dust storm activity (March to May). Snow cover over the HKH region, where the growing season is known to be from September to February, is also reported to show the highest snow cover in February (with the peak in January, February, or March), which coincides with the warmest period in terms of anomaly and trend observed in the long-term mid-tropospheric temperature data (1978-2022). Thus, the current study highlights that the statistically significant and positive TMT warming trend (95% CI) and its observed acceleration over the high-altitude region (since 2008) can be attributed to being one of the major factors causing an acceleration in the rate of melting of snow cover and glaciers, particularly in Tibet and the Eastern Himalayas.  
tropospheric temperature; Himalayas; MSU/AMSU; HKH; snow and glacier
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### Channel Evaluations between the Flexible Antenna and UWB Capsule Endoscope Model  
Finally, the usability of the proposed flexible antenna for capsule endoscopy is studied by evaluating the channel characteristics between the flexible antenna and an UWB capsule endoscope model. The UWB capsule antenna is illustrated in Figure 13a and was originally presented in [25]. Figure 13b presents the dimensions of the capsule model used in the simulations.  
In the simulation, the layer model described in Section 2 is used. The capsule model is set inside the small intestine layer in the same line as the flexible antenna. The simulations were carried out only until 7 GHz, since the power loss was expected to be excessively high after 7 GHz based on power flow results.  
The frequency domain channel characteristics, i.e., the channel parameter S21, is presented in Figure 14. As expected, the S21 parameter decreases smoothly as the frequency increases. In this case, the signal from the capsule could be detected easily up to 5.4 GHz until which point the channel strength remains above \(-65\) dB. Evidently, the receivers with better sensitivity, e.g., \(-85\) dB as stated in [60], could detect the signal even at 7 GHz.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 4 L2-CalSat Description
### Overall Features and Implementation Options
Therefore, an X-band high gain antenna would be required in the micro-satellite implementation option. The micro-satellite mass is limited to 100 kg of which roughly 13 % would be allocated for propellant, according to the propulsion subsystem specifications. In any case, this last option would have a significantly higher cost than the CubeSat scenario and should be programmed to be launched in the same rocket (or at least in the same period of time) than the main CMB telescope. So, it would be important to take all these factors into account from the beginning of the particular scientific mission design.