esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Response of Bridge Piers to Structure Separation under Near-Fault Vertical Earthquake
## 2 Theoretical Model and Vertical Displacement Calculation
### Theoretical Solution of Displacement Response of Bridge in Vertical Contact Stage
\begin{array}{l}\varphi_{nb1}(x)=A_{1}\sin k_{bn}x+B_{1}\cos k_{bn}x+C_{1} \sinh k_{bn}x+D_{1}\cosh k_{bn}x\\ \varphi_{nb2}(x)=A_{2}\sin k_{bn}x+B_{2}\cos k_{bn}x+C_{2}\sinh k_{bn}x+D_{2} \cosh k_{bn}x\\ \varphi_{nr}=E_{1}\sin k_{rn}\xi+F_{1}\cos k_{rn}\xi\end{array}  \tag{11}\]  
where \(k_{bn}\) and \(k_{rn}\) are the wave numbers of flexural wave and longitudinal wave respectively. \(A_{1},B_{1},C_{1},D_{1},A_{2},B_{2},C_{2},D_{2},\ E_{1},F_{1}\) are the coefficients. The boundary conditions of the characteristic equation are as follows:  
\[ \begin{array}{l}\varphi_{nb1}(-L)=0,\ \varphi_{nb2}(L)=0,\ \varphi_{nr}(0)=0\\ \varphi^{\prime\prime}{}_{nb1}(-L)=0,\ \varphi^{\prime\prime}{}_{nb2}(L)=0\end{array}  \tag{12}\]  
The continuity conditions of feature direction are as follows:  
\[ \begin{array}{l}\varphi_{nb1}(0)=\varphi_{nb2}(0),\ \varphi^{\prime}{}_{nb1}(0)= \varphi^{\prime}{}_{nb2}(0),\ \varphi^{\prime\prime}{}_{nb1}(0)=\varphi^{\prime\prime}{}_{nb2}(0)\\ \varphi_{nb1}(0)=\varphi_{nr}(H)+\frac{E_{r}A_{r}\varphi^{\prime}{}_{nr}(H)}{ K_{c}},\ \ E_{b}I_{b}(\varphi^{\prime\prime}{}_{nb1}(0)-\varphi^{\prime \prime}{}_{nb2}(0))=E_{r}A_{r}\varphi^{\prime}{}_{nr}(H)\end{array}  \tag{13}\]  
The orthogonal consistency of bending wave and longitudinal wave can be obtained by Equation (10):  
\[\int_{-L}^{0}\rho A\varphi_{mb1}q_{nb1}dx+\int_{0}^{L}\rho A\varphi_{mb2}q_{nb2 }dx+\int_{0}^{H}\rho A_{r}\varphi_{mr}\varphi_{nr}d\xi=\delta_{mn} \tag{14}\]By introducing Equations (12) and (13) into Equation (11), the wave functions of the bridge structure can be obtained as follows:  
\[ \begin{array}{l}\varphi_{nb1}(x)=M_{n}A_{n}(-\frac{\sin k_{n}(x+L)}{\cos k_{n} }+\frac{\sinh k_{n}(x+L)}{\cosh k_{n}L})\\ \varphi_{nb2}(x)=M_{n}A_{n}(\frac{\sin k_{n}(x-L)}{\cos k_{n}L}-\frac{\sinh k_ {n}(x-L)}{\cosh k_{n}L})\\ \varphi_{nr}(\xi)=A_{n}\sin k_{m}\xi\end{array}  \tag{15}\]  
By introducing Equation (15) into Equation (13) can solve for \(M_{n}\) and \(A_{n}\). Through the orthogonality condition, the time function \(q_{n}(t)\) differential equation of the bridge can be obtained:  
\[\omega_{n}^{2}q_{n}(t)+2\xi\omega_{n}\dot{q}_{n}(t)+\ddot{q}_{n}(t)=\ddot{Q} _{n}(t) \tag{16}\]  
By Laplace transformation, \(q_{n}(t)\) can be obtained:  
\[ \begin{array}{l}q_{n}(t)=e^{-\zeta_{2}\omega_{n}t}(q_{n}(0)\cos\omega_{d}t+ \frac{\dot{q}_{n}(0)+\zeta_{2}\omega_{n}q_{n}(0)}{\omega_{d}}\sin\omega_{d}t) \\ \qquad+\frac{1}{\omega_{d}}\int_{0}^{t}\epsilon^{-\zeta_{2}\omega_{n}\tau} \ddot{Q}_{n}(\tau)\sin(\omega_{d}(t-\tau))d\tau\end{array}  \tag{17}\]  
In Equation (7) \(\omega_{d}=\sqrt{1-\zeta_{2}^{2}}\omega_{n}\).
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## Abbreviations  
\begin{tabular}{l l} UAV & unmanned aerial vehicle \\ DDQN & deep double Q-learning network \\ GPSR & greedy perimeter stateless routing \\ DDQN-MTGPSR & multi-objective optimized GPSR routing protocol \\ DSDV & destination-sequenced distance-vector \\ OLSR & optimized link state routing \\ DSR & dynamic source routing \\ AODV & distance vector routing \\ HRP & hybrid routing protocol \\ RL & reinforcement learning \\ DL & deep learning \\ DRL & deep reinforcement learning-based \\ MDP & Markov decision process \\ QL & Q-learning \\ QGeo & geographic routing protocol \\ RFLQEGO & reward function learning for QL-based geographic routing protocol \\ GLAN & geolocation ad hoc network \\ AGLAN & adaptive GLAN \\ DNN & deep neural networks \\ QoS & quality of service \\ DDQN & deep double Q-learning network \\ SA & annealing \\ GA & genetic algorithm \\ PSO & particle swarm optimization \\ ReLU & rectified linear unit \\ RWP & randomized waypoint model \\ PDR & packet delivery rate \\ Average E2E delay & average end-to-end delay \\ \(\boldsymbol{Var_{E}}\) & node average residual energy variance \\ PRE & percentage of node average residual energy \\ \end{tabular}
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 6 Directions for Future Research
### Deliberate Interference  
One peculiarity of the forest environment is the potential to fly over areas that are being used for the illegal cultivation of crops, particularly those associated with producing illicit drugs. Considering this, there is a risk of deliberate interference with the unmanned aircraft, such as having it shot down by a criminal entity. This topic appears to be absent from theliterature. Given that practitioners are already managing this issue in a few different ways, there is an opportunity for future research to study the prevalence of deliberate interference in forest environments and the sorts of procedures that practitioners are already using to manage this threat.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 3 MATE Algorithm
### Initialization of MATE Parameters  
The initialization method of MATE algorithm parameters refers to the signal capture algorithm of the navigation receiver and is combined with the generalized mutual correlation time-delay estimation method to increase the useful components of the signal by weighting the mutual correlation function, and the structure diagram of it is shown in Figure 3:  
Figure 3: The structure of the MATE initialization algorithm.  
Figure 2: (**a**) Diagram of 8 PSK modulated signal timing; (**b**) diagram of 8 PSK modulated signal autocorrelation.  
\(\Phi(\omega)\) in the figure denotes the weighting function, and here the Hassab-Boucher (HB) weighting function is used, which has better noise immunity [17], with the following expression:  
\[\Phi(\omega)=\frac{\left|G_{xy}(\omega)\right|}{G_{xx}(\omega)G_{xy}(\omega)} \tag{8}\]  
where \(G_{xy}(\omega)\) is the input signal and local signal cross-correlation function, and \(G_{xx}(\omega)\) is the input signal autocorrelation function.  
The algorithm systematically scans predefined frequency search intervals according to established frequency steps. Through exploiting the intrinsic relationship between Fast Fourier Transform (FFT) operations and circular convolution, it computes correlation values for each frequency bin. Subsequent analysis of these correlation metrics enables identification of the maximum autocorrelation peak, which facilitates the initial estimation of both carrier frequency offset and modulation phase discrepancy between transmitted and received signals [18].
## 5 Discussion of the Results  
The results from the thematic analysis identified themes that aligned with responses from the survey. Consequently, the analysis confirms that HF were prominent in both qualitative and quantitative data.  
During the [PERSON] kappa test, inter-rater agreement was high for go-around scenarios, but this was substantially lower for approach and landing, indicating variations in interpretation agreement. With many pilots reacting differently to the same scenario, it demonstrates a considerable ambiguity in pilot response strategies. Hence, it would be important to review training practices for the approach and landing phases.  
Most ordinal regression results lacked statistical significance. Prior to this research, the required sample size was not determined a priori, so this can be improved in future research. Also, it was dependent on pilot involvement, and as only 43% completed the survey fully, pilot retention could be improved through the use of participation incentives. Therefore, for future research, stratified sampling based on experience, age, and gender may improve validity.  
Research Question One: Which Human Factors Are Prevalent During the Approach, Landing, and Go-Around Phases and Procedures?  
The literature review identified prevalent HF, with this study subsequently focusing on situational awareness, decision-making, and complexity. The analysis then further explored GA culture, pressure, communication, and power dynamics. These factors were noted across pilot interactions, training, and ATC communication. Human factors were selected based on the prior literature, although additional areas such as fatigue and automation emerged and can be expanded on in any future studies.  
Furthermore, given that aviation is a complex socio-technical system, these findings emphasise the importance of analysing organisational and systemic human factors. Thesecould include implications for instructors, flight schools, and ATC to better understand the impact on pilots.  
Additionally, there were also factors not typically captured by standard HF taxonomies, such as the impact of instructor experience and the effect of ab initio training. Hence, further analysis should be conducted to understand these themes and determine their influence on pilot behaviour during high-risk phases.
## 2 Materials and Methods
### Sample Preparation for Determining Figures of Merit  
Stock solutions of unlabeled standards and deuterated internal standards were prepared in methanol such that spiking 150 \(\upmu\)L of stock solution into 15 mL of juice or grape macerate resulted in the following calibration concentrations: 400, 200, 100, 50, 25, 12.5, 6.25 ng/L IBMP with 100 ng/L \(d_{3}\)-IBMP; 400, 200, 100, 50, 25 \(\upmu\)g/L linalool with 100 \(\upmu\)L \(d_{3}\)-linalool. Calibration standards were prepared by spiking into Chardonnay juice (for linalool) or Pinotoir homogenate (for IBMP) that contained no detectable IBMP or linalool by SPME-GC-MS. The juice and homogenate used for calibration standards was prepared as described in \"Evaluation of SPMESH sheet-DART-MS in comparison to SPME-GC-MS\". Each calibration level was analyzed in triplicate. Quantification was performed by matrix-matched calibration curves, in which the standards were spiked into grape juice or homogenate with undetectable concentrations of the target analyte (Chardonnay or Pinot nor). Because IBMP and linalool have low volatility at room temperature as defined by Henry's Law coefficients, losses due to volatilization during sample transfer were expected to be limited. Limits of detection were calculated from calibration curves based on 3 \(\times\) signal-to-noise (S:N) ratio; limits of quantification were based on 10 \(\times\) S:N.
# Interferences of Electromagnetic Pulses on Microcontroller Units
## 1 Introduction  
In the realm of modern semiconductor devices, one notable form of damage resulting from external electrical stresses is that caused by electromagnetic pulses (EMP). Due to the difficulties in establishing an EMP failure model and understanding the failure mechanism, the EMP effect on electronic systems is typically described and evaluated using failure thresholds, which are then used to reinforce the devices or electronic systems accordingly [1; 2; 3]. Typically, EMP experiments are classified into two types [4; 5; 6; 7; 8]: radiative EMP experiments and direct injection EMP experiments. Since the coupling path on the circuit board in the radiation EMP test is random, it is difficult to ensure that all coupling paths are related to the component being tested. Thus, it is challenging to model circuit failures in the radiation EMP test [9]. In view of this problem, the International Electrotechnical Commission (IEC) has formulated a set of test standards to improve the feasibility and credibility of component-level tests [10; 11; 12]. On the module level, although the test has the same limitations, it still has a certain value because of its authenticity [13; 14]. In the direct injection experiment, it is possible to know all the electrical circuits and design the coupling paths of the component. Hence, it is easy to model circuit failures, and the failure threshold of the component can be easily obtained [15; 16; 17; 18]. Therefore, the failure threshold of the test component is often obtained through the direct injection EMP experiment, and the radiation EMP experiment is used to simulate the real environment. By evaluating the sensitivity of a specific pin or port under strong electromagnetic interference (EMI), the sensitivity of a component-level microcontroller unit (MCU) to different electromagnetic environments can be measured, thereby obtaining the electromagnetic environment for the normal operation of the module [19; 20; 21; 22; 23; 24; 25]. In this study, an EMP simulator was used tomeasure the interference voltages at MCU pins under different voltages and full width at half maximum (FWHM) conditions in a radiation EMP test.
## 3 Results and Discussion
### Trace Elements Concentrations
TE concentrations in Akonolinga were compared with those found in other regions of Cameroon and the world in Table 5. According to the results, the average concentrations of Cr, Cu, Ni, Pb, and Zn in the present study are significantly higher than those found in Burkina Faso [55] and Niger [56]. However, the concentrations of Ba, Br, Co, Fe, and Mn in Lomie [8] are higher than those found in this study. Furthermore, the average values of Pb and Co in Niger [56] are, respectively, higher and lower than those found in this study; meanwhile, the opposite is true for the town of Dschang [57]. The comparative results show that TE concentrations vary from place to place. These results are coherent with those of many authors who have shown that TE concentrations in soil vary according to each trace element, soil type, sampling site, and anthropogenic activities [58; 59]. As for Ni and Cu, the concentrations in Douala [7; 60] are insignificant compared to those found in our work. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**TE** & **Min.** & **Max.** & **Mean** & **SD** & \begin{tabular}{c} **Detection** \\ **Limit** \\ \end{tabular} & \
\begin{tabular}{c} **Global** \\ **Average +** \\ \end{tabular} &
\begin{tabular}{c} **Max. Permissible** \\ **Con. in Soil** \\ \end{tabular} \\ \hline Ba & 16 & 207.2 & 89 & 61 & 1.5 & 550 & NA \\ Br & 2 & 13 & 7 & 3 & 0.5 & 2.8 & NA \\ Co & 11 & 22 & 17 & 3 & 3 & 10 & 30a \\ Cr & 73 & 267.3 & 110 & 36 & 1 & 35 & 150a \\ Cu & 1 & 68 & 22 & 14 & 0.5 & 25 & 100a, 30b \\ Fe & 6500 & 84,700 & 42,006 & 20,227 & 1 & 35,000 & 50,000a \\ Ga & 11 & 40 & 23 & 7 & 0.5 & 17 & NA \\ Mn & 57.5 & 696 & 293 & 178 & 1 & 600 & 2000a \\ Nb & 33 & 98 & 57 & 15 & 0.5 & 25 & NA \\ Ni & 21 & 84 & 52 & 18 & 0.5 & 20 & 50a, 40b \\ Pb & 15 & 111 & 31 & 22 & 0.6 & 20 & 100b \\ Sn & 3 & 8 & 5 & 1 & 0.5 & 5.5 & NA \\ Ti & 8900 & 19,300 & 13,004 & 2537 & 2 & 3000 & NA \\ V & 12 & 142 & 96 & 27 & 1 & 60 & NA \\ Y & 10 & 23 & 17 & 4 & 0.5 & 22 & NA \\ Zn & 33 & 148 & 62 & 30 & 0.5 & 71 & 300a \\ Zr & 411 & 1080 & 652 & 176 & 0.5 & 190 & NA \\ \hline \hline \end{tabular}  
NA = not available, + global average of the higher continental crust, a and b [39; 40; 41].  
\end{table} 
Table 4: Descriptive statistics for TE concentrations (ppm) in soil.
## 3 Payload Design
### Suppayload 1: Data Acquisition System
#### 3.3.3 Communications  
In the first iteration of the mission, the payload's performance during the mission was assessed solely through current consumption during flight. Consequently, one of the significant upgrades to RSONAR II involved the inclusion of downlink functionality for subpayload 1. This new capability allowed for continuous real-time monitoring of subpayload 1's status throughout the mission. In order to adhere to the allocated rate of 350 kilobits per second, we downlinked four 512 \(\times\) 512 pixel images captured from the PCO camera at 500-millisecond exposure intervals along with a CSV file that included subpayload 1's overall system health status. The CSV file comprised onboard time, mode of acquisition, imaging parameters, latest image count, and the PCO camera temperature. Due to the relatively high exposure rate used for these images, any RSO present in the downlinked images would appear as a streak, providing visual confirmation of RSO  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **AURICAMTM** & **PCO** & **FAI** & **IDS** \\ \hline Number Of Pixels & 20482 & 20482 & 2562 & 20482 \\ Pixel size (\(\mu\)m) & 5.5 & 6.5 & 26 & 5.5 \\ Field-of-View (degrees) & 35 & 29.6 & 26 & 41 \\ Focal Length (mm) & 25 & 25 & 6.89 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key specifications of four imagers (AURICAMTM, PCO, FAI, and IDS) are compared to illustrate the similarities in FOV and pixel size.  
imaging during flight. To ensure the allocated capacity was never exceeded, images were transferred in small, manageable packets, with multiple validations of downlink performance conducted prior to launch. RF communications were handled from the CSA balloon. An Ethernet cable was used to connect the PYNQ Z1 board on subpayload 1 to the CSA's communications module.
## 4 Results and Analysis
### Field Measurement Results and Analysis
#### 4.2.2 Actual Data Velocity Modeling  
The focus of seismic data pre-stack depth migration processing is to accurately obtain a migration velocity model. Without an accurate velocity model, accurate migration imaging is impossible. Velocity-depth modeling contributes to the difficulty and core of pre-stack depth offset imaging, and the acquisition of the velocity model can be divided into two methods: the first is the velocity model correction by meshing; the second is the modeling of velocities along layers. The former does not require layer constraints and is greatly affected by the information signal-to-noise ratio, which makes it difficult for the speed iteration to converge for information with a poor signal-to-noise ratio, while the latter is constrained by the accuracy of the information interpretation. The solution idea of velocity modeling in this paper is based on 3D seismic high-quality channel set data and adopts the scheme of using the above two methods alternately. Firstly, the velocity model is established under the constraints of the geological tectonic model, and the model constraints are used to adjust the velocity model iteratively two to three times. Since the accuracy of the velocity model in the upper layer directly affects the imaging results in the lower layer, the velocity should be adjusted from shallow to deep, layer by layer. When the velocity model reaches a certain accuracy, the grid lamination is used for one or two iterations for global velocity optimization to accurately find the velocity in the domain of the pre-stack time offset and ultimately realize the fine adjustment of the inter-layer velocity and finally obtain the results of the pre-stack time offset (as shown in Figure 10(a-1,b-1)). In this way, this method not only solves the influence of the accuracy of seismic data interpretation but also solves the problem of the slow convergence of iteration speed using only grid tomography. A time domain velocity model is built based on the pre-stack time offset results (Figure 10(a-2)), and then the time domain velocity body is converted into a depth domain layer velocity body by the constrained velocity inversion (CVI) technique (Figure 10(b-2)). Finally, the time domain model is offset to the depth domain to model the depth domain velocity.  
Figure 9: Comparison of single shot before denoising (**a**) and after denoising (**b**) in study area.
# Seismic Performance Evaluation of Steel Buildings with Oil Dampers Using Capacity Spectrum Method
## 4 Application of the Proposed CSM on Steel Frame Buildings
### Demand Spectrum of Earthquake Ground Motions  
The six earthquake ground motions listed in Table 8 were selected as the extreme rare earthquakes defined by the Building Standard Law of Japan [3]. The earthquakes were generated using the algorithm developed by one of the authors [32]. Three of them (No. 1, 2, and 3) were the observed earthquake records scaled to have the maximum velocity of 50 cm/s. Another three (No. 4, 5, and 6) were the artificially generated earthquake ground motions that had a response spectrum compatible with the design response spectrum of the Level-2 design earthquake and phase spectrum of the observed earthquake records. Figure 10 illustrates the acceleration response spectra, with a 5% damping factor, of artificially generated earthquakes and the spectrum of the design code (thick solid line).
## 3 Results
### Apical Plasma Membrane Integrity and Apical \(\cdot O_{2}\)- Fluorescence Intensity  
Evans blue staining can be used to determine plasma membrane integrity [28]. It can be seen from Figure 4 that the root tips of the amaranth had obvious Evans blue absorption under Cd/BDE-153 interaction, indicating that Cd and BDE-153 had certain destructive effects on the plasma membrane of the root tips. Under Cd or single BDE-153 pollution conditions alone, the absorption of Evans blue increased with the increase in the pollutant concentration. Under the Cd/BDE-153 interaction, the uptake of Evans blue by the amaranth root tips gradually increased with the increase in the Cd contaminant concentration, and the degree of damage to the plasma membrane in the amaranth root tips by Cd increased.  
Under the interaction of Cd/BDE-153, with an increase in BDE-153 concentration, the absorption of Evans blue increased only at a high concentration of Cd (5 mg/L). Hence, the toxicity to the plasma membrane of the amaranth root tips caused by the highest concentration of Cd when interacting with BDE-153 is greater than that of Cd alone, which is consistent with the results of the BDE-153 and Cd accumulation in the amaranth, amaranth biomass, and chlorophyll contents.  
Figure 4: Plasma membrane integrity of amaranth root tips under different treatments (**a**), \(\cdot\)O\({}_{2}\)– fluorescence localisation staining (**b**), Evans blue absorption (**c**), and fluorescence intensity (**d**). (Cd\({}_{0}\)–Cd\({}_{3}\) represent Cd concentrations of 0, 0.1, 1, and 5 mg/L, respectively. B\({}_{0}\)–B\({}_{2}\) represent BDE–153 concentrations of 0, 5, and 20 mg/L, respectively).  
Polybrominated diphenyl ethers have been shown to increase the production of ROS in animal cells [39] and plant cells [4], leading to oxidative stress. Therefore, the production of superoxide anions was detected using a laser confocal scanning microscope (Figure 4b).  
It can be seen from Figure 4 that under the treatment of Cd and BDE-153, the fluorescence intensity of - in the root tips of the amaranth gradually increased with the increase in the pollutant concentration, indicating that plants produced a large amount of - under Cd/BDE-153 stress. When the concentration of the pollutants is low, the - fluorescence is mainly distributed in the root epidermis, while when the concentration of the pollutants is high, - fluorescence is distributed in the root tip and meristem. It can be seen from Figure 4d that Cd pollution alone and BDE-153 pollution alone increased the level of hydrogen peroxide in the roots. The hydrogen peroxide levels were further elevated in the amaranth roots under composite pollution conditions compared to single pollution. For example, the fluorescence intensity of hydrogen peroxide in the roots increased by 66.1% under 5 mg/L Cd pollution. This is similar to the results of confocal laser scanning microscopy, indicating that Cd and BDE-153 indeed lead to an increase in ROS production and are synergistic in combined pollution.