esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket
#### 3.3.5 Groundwater Pressure  
Groundwater pressure determines the hydraulic gradient and further affects the leakage. Large groundwater pressure can overcome the self-sealing stress of smaller percolation channels or split the seal interface to form a larger percolation channel, which is the reason why large groundwater pressure is more likely to cause leakage. Increasing the contact stress can reduce the size of the critical percolation channel and further improve the sealing performance of joints. This section analyzed the relationship among the contact stress (in Figure 14, replaced by a joint gap), groundwater pressure, and leakage. As shown in Figure 14, the leak rate increases linearly with the groundwater pressure, and the slopes of their curves increase exponentially with the increasing joint gap. This is because the underwater pressure directly provides the power for leakage, and its influence on the unit time leakage is linear, while the increase of the joint gap further leads to the increase of the number and diameters of leakage channels, and the leakage increases exponentially with the joint gap.  
Figure 14: 3D diagram of the leakage, joint gap, and groundwater pressure. The leakage calculation takes the groundwater pressure from 0–2 MPa and joint gap from 0–12 mm.  
Figure 13: Influence of the contact surface roughness on leakage. Curves of different colors indicate the relationship of leakage with the root -mean -square roughness contact surface height profile in the case of different joint gaps.
# Soybean Germination Response to Algae Extract and a Static Magnetic Field Treatment
## 4 Discussion
In our previous study, we compared the effects of _Cladophora glomerata_ (10%) and two types of magnetic fields, static magnetic field (induction of 400 mT and exposure time 3, 6, and 12 min) and alternating magnetic field (frequency of 50 Hz, an induction of 30 mT and exposure time 1, 2.5, and 5 min), on the germination of soybean seeds and chlorophyll content in seedlings. Synergistic action of algal extract and pre-sowing stimulation of seeds with static magnetic field for 3 min and alternating magnetic field for 2.5 min provided the best results [7; 14]. The simultaneous use of 10% algal extract and 3, 6 and 12 min pre-treatment of seeds with 400 mT SMF improved germination percentage as compared to the control and SMF alone. For a 12 min stimulation time, it was also improved as compared to extract alone [14]. According to our research, only simultaneous use of extract and a 3 min SMF stimulation time resulted in higher germination percentage compared to extract alone, SMF, and the control group [7]. In the present study, we found an improvement in the number of germinated seeds for the combination of 20% extract and 3 min 250 mT SMF compared to all the other groups. In a previous study, we also checked chlorophyll content measured with SPAD chlorophyll meter. Treatment with the combination of 10% extract and 3 min 400 mT SMFimproved this parameter compared to all other groups [7]. In the present research, for the use of both stimulating factors together, we did not find any improvement in chlorophyll content measured using a SPAD. However, simultaneous use of 20% extract with SMF and 80% with SMF resulted in higher chlorophyll \(b\) and total chlorophyll. The first group also improved carotenoids level. No prior studies analyzed the influence of algal extract and SMF together on plant and root length, or also plant weight. According to the present research, simultaneous use of 20% extract and 3 min 250 mT SMF improved both root and plant length. In the case of hypocotyl length, the difference was statistically significant. For the 80% extract, there was no difference in root and hypocotyl length, but, for epicotyl length, there was a statistically significant decrease. The applied treatment did not cause any significant changes in plant weight for any of the extract concentrations.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 5 Evaluation
### Performance Evaluation
#### 5.2.5 Intrusion Detection under Different Moving Speeds  
As a special case, human detection under different moving speeds plays an important role in intrusion detection systems. The four volunteers are asked to walk in a regular pattern at 1.5 m/s, 0.7 m/s, and 0.2 m/s, respectively, in the meeting room. A 30 s data segment is randomly divided from the data of the first volunteer walking at the speed of 0.7 m/s as the training data, while the walking data of the other three volunteers under different speeds is used as the test data. The window size is 5 s, and the results are the mean value of the three volunteers. The human detection performance of RDFID is compared to PADS and FRID. The FN and FP rate of the three approaches under different moving speeds is shown in Table 6. As indicated in the table, the trends of the FN rate of the approaches are the same that they all increase as the moving speed becomes slower. The influence of human motion to the transmission of the wireless signal decreases when the moving speed becomes slower. Especially when the person moves far away from the first Fresnel zone, the reflected signal is submerged in the signal from the LOS path. As a result, it is of great difficulties to extract effect environmental change information from the received signal. On the other hand, it can be seen that the FN rate of RDFID is lower than the other approaches.  
It can be seen that the FP rate of the three approaches under different moving speeds is relatively stable and keeps at a low level. It indicates that the probability of false alarm of the three approaches is low when detecting human motion.  
As can be seen from Figure 12, the PD of the three approaches all suffer a decrease when the moving speed becomes slower. The performance of human detection can be affected by different moving speeds, but the overall detection performance can meet the requirement of security in a regular smart home environment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **FN** & & & **FP** & \\ \hline
**Moving Speed (m/s)** & **1.5** & **0.7** & **0.2** & **1.5** & **0.7** & **0.2** \\ \hline RDFID & 1.2 & 1.8 & 3.4 & 1.2 & 1.2 & 0.9 \\ \hline FRID & 2.1 & 3 & 4.2 & 2.1 & 2.3 & 1.8 \\ \hline PADS & 2.2 & 3.3 & 5.7 & 3.3 & 2.3 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: FN/FP of human detection under different moving speeds (%).  
Figure 12: PD of human detection under different moving speeds.
## 6 Results
### Comparison of Complexities and Convergence Rate  
Table 4 shows the time and memory complexity comparison of our proposed GCNN with other state-of-the-art methods such as GCNN, OpGCN, DeepWalk [49], and version of Knowledge Graph Convolutional Networks known as KGCN-neighbor [50]. Here, \(L\) represents the number of layers, \(n\) is the total number of nodes in the graph, \(e\) is the total number of edges, and \(b\) is the batch size. Additionally, \(r\) represents the number of neighbors clustered for each node, and \(h\) is a constant that refers to the feature dimension of hidden nodes. The implementation depicts that our proposed GCNN realizes the lowest time and memory complexity and converges at a faster rate than the other methods.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 4 Shallow Underwater Acoustic Channel Model  
The acoustic signals in an underwater channel undergo both frequency and time selectivity, making the channel double selective [43]. The bandwidth of the channel is limited, on the order of a few kilohertz over longer distances, due to the absorption losses and multipath experienced by the acoustic waved underwater [44]. The limited bandwidth availability severely restricts and reduces the inter carrier spacing in multicarrier communication systems [45]. This makes the system highly sensitive to even tiny values of Doppler shifts and it undergoes ICI. The speed of sound is much lower compared to that of radio waves and thus motion induced attenuation is of concern [46]. For a channel with \(L\) multipath components that are time shifted, the response [43] can be expressed mathematically as:  
\[H(t,\tau)=\sum_{p=1}^{L}A_{p}(t)\delta\big{(}\tau-\tau_{p}(t)\big{)} \tag{8}\]  
where \(A_{p}\) denotes the amplitude of the \(pth\) component, \(\tau_{p}\) represents it delay coefficient, and \(\delta\) is the Dirac delta function. The envelope response of the shallow underwater channel was divided into two parts: the deterministic response and the random multipath fading.  
Considering \(s(t)\) to be the CPM modulated time domain digital signal from Equation (1), Figure 5 shows it passing through the channel blocks. This allows for controlling the SNR, modifying channel taps and the acoustic absorption pathloss parameters from [PERSON]'s formula [47]. The typical delay spread for a shallow underwater acoustic channel is between 10 to 20 ms and can be as long as 100 ms [48].
## 3 Numerical Results
### Visibility Inside the Building  
Visibility is a critical indicator for assessing safety conditions during evacuation in the event of a fire. The comparative analysis of the three scenarios (Figure 13) reveals significant differences in maintaining a breathable and safe environment within the stairwell. In scenario S0, the progressive and severe contamination of the stairwell by combustion products is observed. The visibility values remain consistently below 3 m throughout the entire height at both T = 240 s and T = 360 s, indicating a complete inability to maintain a usable evacuation route. Smoke freely penetrates from the fire compartment due to the thermal stack effect and distributes vertically in an uncontrolled manner.  
Regarding the dissipation of combustion gases inside the building (Figure 14), the visibility distribution confirms the previous conclusions about the vertical airflow behavior within the stairwell. Scenario S1 stands out as the most effective, maintaining an overall visibility level adequate for evacuation. Although a localized decrease in visibility down to approximately 2-3 m is observed in the lower part of the stairwell, it remains consistently above the minimum functional threshold. This area does not disrupt the continuity of evacuation, being controlled by the downward jet induced by pressurization, which ensures that the evacuation route remains operational throughout the duration of the fire.  
In conclusion, scenario S1 provides an optimal balance between the dissipation of toxic gases and the maintenance of functional visibility within the stairwell, ensuring  
Figure 13: Visibility distribution in the cross-sectional plane Y = 5.75 m, corresponding to the times T = 240 s and T = 360 s.  
essential conditions for a safe and uninterrupted evacuation. This configuration enables the simultaneous control of air contamination and visual clarity along evacuation routes, thus offering a clear operational advantage over the other scenarios analyzed.
# Analysis of Non-Minimum Phase System for AC/DC Battery
## 4 Boost/Buck Converter Extension with Snubber RC Circuit
### State-Space Averaging and Small-Signal Model Analysis in Boost Mode with RC Snubber  
State-space average equations of the converter shown in Figure 10 for the boost mode will be discussed. The state-space average equations will have another extra equation due to the RC damping components. The voltage \(v_{cd}\), which is the voltage across the damping capacitor \(C_{d}\), is changing regarding the switching cycle and should be expressed.  
Figure 8: Inductor current \(i_{L1}(t)\) in boost mode with minimum phase condition and its normalized FFT: (**a**) Inductor current waveform \(i_{L1}(t)\). (**b**) Normalized FFT of inductor current waveform \(i_{L1}(t)\).  
Figure 9: Inductor current \(i_{L1}(t)\) in buck mode: (**a**) Without minimum phase condition. (**b**) With minimum phase condition.  
The state-space average equations for the boost mode with RC damping circuit can be expressed as follows:  
\[\frac{di_{L1}(t)}{dt} = \frac{v_{g}(t)\;-\;v_{o}(t)\;+\;d_{1}(t)\;v_{c}(t)}{L_{1}},\] \[\frac{di_{L2}(t)}{dt} = \frac{-v_{c}(t)\;+\;v_{o}(t)}{L_{2}},\] \[\frac{dv_{c}(t)}{dt} = \frac{i_{L2}(t)\;-\;d_{1}(t)\;i_{L1}(t)}{C}-\frac{v_{c}(t)\;-\;v_ {cd}(t)}{R_{d}C}, \tag{11}\] \[\frac{dv_{cd}(t)}{dt} = \frac{v_{c}(t)-v_{cd}(t)}{C_{d}R_{d}},\] \[\frac{dv_{o}(t)}{dt} = \frac{i_{L1}(t)\;-\;i_{L2}(t)}{C_{o}}-\frac{v_{o}(t)}{R_{o}C_{o}}.\]  
The DC equilibrium equations are similar to equations that were obtained in (2), with extra an equation for \(v_{cd}\) that can be expressed as follows:  
\[V_{cd} = V_{c} = \frac{V_{g}}{1-D_{1}}. \tag{12}\]  
The converter variables can be decomposed the same as equations obtained in (3), adding an extra equation due to the RC snubber components as follows:  
\[v_{cd}(t) = V_{cd}+\;\Delta v_{cd}(t), \tag{13}\]  
where \(V_{cd}\) represents the dc equilibrium point and \(\Delta v_{cd}(t)\) represents small variation around it. Two new transfer functions must be calculated with the RC snubber circuit: the duty cycle to inductor current \(Gi_{L1}d\_u\_RC(s)\) and the duty cycle to output voltage \(Gvd\_u\_RC(s)\) represents as follows:  
\[Gi_{L1}d\_u\_RC(s) = \frac{A_{v}{}^{3}\,V_{g}\,(C_{o}\,L_{2}\,R_{d}\,R_{o}\,(F_{1}s^{ 2}+F_{2}s^{3})+F_{3}s+2)}{Den\_u\_RC(s)},\] \[Gvd\_u\_RC(s) = \frac{A_{v}{}^{2}\,V_{g}\,(H_{1}s^{3}+H_{2}s^{2}+H_{3}s+R_{o})}{ Den\_u\_RC(s)},\]  
Figure 10: IBuck boost converter with RC snubber damping.  
Figure 11: TF boost mode for: (**a**) Bode plot \(G_{IL1}d\_u\_RC(s)\). (**b**) Bode plot \(Gvd\_u\_RC(s)\). (**c**) Pole and zero map for \(G_{IL1}d\_u\_RC(s)\). (**d**) Pole and zero map for \(Gvd\_u\_RC(s)\).
###### Abstract
Phenological cameras have the advantages of low cost, small size, convenient installation, and easy handling, and they are widely used to monitor plant dynamics [22]. The phenological camera-based color index was also used to estimate canopy GPP in many studies [23; 24; 25]. The performance of GPP estimated by color indexes from phenological camera photos was of varying vegetation types, and the estimation effect of GPP on grassland was better than that of evergreen coniferous forests and deciduous broad-leaved forests [11]. Thus, the phenology camera can provide more reliable daily greenness dynamics and can be fused with satellite remote sensing data to improve daily GPP estimate for alpine meadow ecosystems. In recent years, data-based machine learning has been widely used in remote sensing data fusion and model integration. Many studies have indicated that machine learning works well in vegetation productivity estimation [26; 27; 28]. Machine learning models find rules by training sample data and use these rules to predict unknown results. GPP estimation based on machine learning algorithms can make full use of existing multisource remote sensing data, and they can provide a long time series and high-precision estimation of GPP. Therefore, machine learning has advantage of combining multisource data by machine learning to estimate GPP. The machine learning method predicted GPP with an RMSE of 1.87 g C-m-2-day-1 and an R2 of 0.71 using meteorological and flux data from 33 AmeriFlux sites between 2000 and 2003 in America [29]. The machine learning method was also used to estimate eight days GPP (at the site level) with superior performance on the European spatial scale [30]. [PERSON] successfully estimated the daily GPP of the forest ecosystem using the machine learning method with MODIS data at site scale, and the RMSE reached 1.03 g C-m-2-day-1 [31]. These studies mentioned above indicated that the machine learning methods is an effective and widely used method to correctly predict GPP. The rapid use of phenology cameras in the Tibetan Plateau provides high-quality data that reflect alpine seasonal changes. However, in what extent it can be used to improve GPP estimation for alpine meadows has not been studied. In view of the advantages and disadvantages of phenology camera digital photos and MODIS data, combing digital camera and satellite remote sensing data is an effective way to improve GPP prediction accuracy on the Tibetan Plateau because digital cameras and satellite remote sensing provide complementary information. Thus, the objective of this study are to: (1) compare the performance of color index from phenological photos and vegetation indices from satellite remote sensing data in GPP estimation for alpine meadows on the Tibetan Plateau; (2) compare the performance of the LUE model and a machine learning method in GPP estimation, and (3) explore to what extent the GPP estimation accuracy can be improved by combining multisource remote sensing data for alpine meadows on the Tibetan Plateau.
# Adaptive Optimization Method for Piled Raft Foundations Based on Variable Pile Spacing
## 4 Illustrative Example
### Load and Boundary Conditions  
In the model, the loads on the raft are all applied to the nodes on the top surface of the raft, and the loads are all divided into 20 steps to consider the non-linear characteristics of soil layers during the construction. The shape of the surface loads in different models are shown in Figure 8. The first load step (\(i=1\)) of the three cases are all the gravity of the soil layers, and the other 19 load steps have the same load shape and value (total load/19) in Case 1 and Case 2, while that in Case 3 are different; see Figure 8 and Table 3, where P  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Elastic Modulus (Pa)** & **Poisson’s Ratio** & **Density (kg/m\({}^{3}\))** \\ \hline Pile & \(3\times 10^{10}\) & 0.2 & \(2.4\times 10^{3}\) \\ Raft & \(2\times 10^{10}\) & 0.2 & \(2.4\times 10^{3}\) \\ Bedrock & \(1.5\times 10^{10}\) & 0.3 & \(2.5\times 10^{3}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of raft, pile, and bedrock.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Soil Type** & **High-Level (m)** & \(\mathbf{R_{\text{f}}}\) & \(\mathbf{k}\) & \(\mathbf{n}\) & \(\mathbf{G}\) & \(\mathbf{F}\) & \(\mathbf{D}\) & \(\mathbf{k_{\text{ur}}}\) & \(\mathbf{\varphi(^{\circ})}\) & \(\mathbf{c(\text{RPa})}\) & \(\mathbf{\rho_{s}(\text{g/cm}^{3})}\) \\ \hline Fine sand & 18.4–27.4 & 0.9 & 642 & 0.6 & 0.4 & 0 & 0 & 972 & 37.1 & 15 & 1.1 \\ Fine sand & 15.5–18.4 & 0.8 & 672 & 0.6 & 0.4 & 0 & 0 & 1009 & 38.2 & 16 & 1.1 \\ Medium-coarse gravel & 13.5–15.5 & 0.8 & 519 & 0.55 & 0.4 & 0 & 0 & 781 & 34.9 & 12 & 1.2 \\ Medium-coarse sand & 1.9–13.5 & 0.7 & 462 & 0.5 & 0.4 & 0 & 0 & 701 & 34.6 & 11 & 1.1 \\ Medium-coarse gravel & \(-\)3.9–1.9 & 0.8 & 502 & 0.6 & 0.4 & 0 & 0 & 762 & 38.1 & 16 & 1.2 \\ Fine sand & \(-\)16.9–3.9 & 0.9 & 431 & 0.6 & 0.4 & 0 & 0 & 663 & 34.6 & 16 & 1.1 \\ Medium-coarse gravel & \(-\)21–16.9 & 0.8 & 568 & 0.6 & 0.4 & 0 & 0 & 901 & 34.2 & 20 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: Soil parameters of the Duncan–Chang (DC) model used in this paper [29].  
Figure 7: Different view of PRFs: (**a**) the model of Case 1 and Case 2; (**b**) the model of Case 3; (**c**) cross-section view of the models of three cases.  
represents the total load distribution of raft plane. A rigid boundary condition was used at the bottom of the model, i.e., all the nodes with a \(Z\) co-ordinate of \(-26\) m are constrained in three directions. The peripheral nodes around the model only have displacement in the \(Z\) direction, and the motion in the \(X\) and \(Y\) directions is constrained.
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 2 Literature Review
### Quay Crane Assignment Problem  
As described by [PERSON] et al. [4], one key factor in terminal operations is the QC assignment strategy, which can be either time-variant or time-invariant. [PERSON] [14] investigated the problem of time-variant QC assignment with time-window constraints and proposed a mathematical model and a heuristic method based on tree search. The performance of the method is evaluated through a computational survey of a large number of test instances, and the impact of different time window structures on vessel processing time is analyzed. [PERSON] et al. [15] proposed a hybrid evolutionary algorithm to solve the dynamic time-invariant QC assignment problem with the objective of minimizing the processing time, waiting time, and delay time for each vessel. Computational experiments show that the method is suitable for solving this complex but important problem of terminal operations. The time-invariant QC assignment and scheduling problem--considering worker performance and yard truck deployment--was studied by [PERSON] et al. [16]. By building a mixed-integer linear programming model, they proposed an integrated planning approach to minimize vessel departure times and used heuristic algorithms and variable neighborhood searches to generate efficient solutions. [PERSON] and [PERSON] [17] proposed a distribution robust optimization model for a time-invariant QC scheduling problem dealing with uncertainty and solved by an exact decomposition algorithm.
## 6 Performance Metrics  
In this section, performance metrics measured in protocols evaluations will be discussed. In NS3 simulator, several AWK programs are used to analyze and calculate these metrics from the trace files generated from simulation file as shown in [29].  
* **Average Energy consumption (AEC):** the relation of sum of entire energy consumed by each node divided by the whole number of nodes \[\text{AEC}=\frac{\sum\limits_{n}^{i}(\text{Initial energy }\ y_{i}\ -\text{Final energy }\ y_{i}\ )}{\text{Total number of nodes}}\] (1)  
In general, the inspected routing protocol is indicated to be more efficient when a lesser amount energy is consumed during the simulation.  
* **Average throughput**: the average number of packets arriving at the destination node per second. \[\text{Throughput}=\frac{(\text{Number of received packets }\times\text{ Packet size }\times 8)}{\text{Total simulation time}}\] (2) This metric is utilized as a indicator of the reliability of the routing protocol when measured in different simulation environments.  
* **Normalized routing load (NRL)**: the ratio between the number of routing packets transmitted and the number of data packets effectively received at the destination node. \[\text{NRL}=\frac{\text{Number of tranmitted routing packets}}{\text{Number of received data packets}}\] (3)  
Typically, routing protocol aims to achieve a better performance while maintaining a minimum number of routing packets, thus higher NRL metric indicates more routing packets in the network and consequently lowering the efficiency of the routing protocol.  
* **Total dropped packets**: the total number of dropped data packets that have not successfully been sent to the destination node. Received data packets are dropped in two cases, either the buffer is full, therefore no additional packets can be managed or the duration that the packet has been buffered exceeds the time limit.
* **Packet Delivery Fraction (PDF):** the ratio of whole number of packets successfully received by the destination nodes to the total number of packets sent by the source nodes all over the simulation. \[\text{PDF}=\frac{\text{Number of received packets}}{\text{Number of transmitted packets}}\] (4)  
This metric reflects the reliability of a routing protocol in carrying packets from source to destination successfully.