esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dataset and Data Processing
#### Climate Data and Phenology Extraction  
Monthly meteorological data from 2001-2014, including temperature, precipitation, and solar radiation, were acquired from the China Meteorological Data Sharing Service System (downloaded from [39]) All meteorological data used in this study were verified by China's Meteorological Information Center (located in Beijing, China) [40], thus false or missing data from some of the stations were eliminated [11]. There are 653 meteorological stations recording temperature and precipitation data (301 in Northern China, 258 in Southern China, and 94 in the Tibetan Plateau), and 99 stations recording solar radiation (50 in Northern China, 38 in Southern China, and 11 in the Tibetan Plateau; Figure S2). The Kriging method was used for the spatial interpolation of climate data across the study area [41].  
Vegetation phenology metrics (Start of Season (SOS) and End of Season (EOS) for each year) were extracted using TIMESAT software (which is widely used for simulating vegetation phenology [42; 43]) by applying the Savitzky-Golay (S-G) method to generate smooth time-series MODIS EVI data. We adopted an adaptation strength of 2.0, no spike filtering, seasonal parameter of 0.5, S-G window size of 2, and amplitude season start and end of 20% to calculate the phenology parameters. Length of Season (LOS) was calculated as the difference between the SOS and EOS values.
# An Efficient Opportunistic Routing Protocol with Low Latency for Farm Wireless Sensor Networks
## 5 Experiment and Analysis
### Analysis of End-to-End Delay  
Figure 6 shows the difference between the four comparison algorithms minus the end-to-end average delay of our algorithm. As the number of dead nodes increases, the communication will be severely affected, and the delay will increase. However, the difference shows that the studied algorithm has obvious advantages over the other algorithms, which can reduce the delay while maintaining a large throughput. In combination with the throughput, the number of packets received by the sink in other algorithms decreases rapidly in late simulation, so the four algorithms have a shorter delay. When the number of simulation rounds is greater than 300 rounds, the nodes of the comparison algorithm almost all die and no longer perform data transmission, so the delay difference does not exist.  
From the Figure 7, we can see that the box shape of our algorithm is the flattest, and Algorithm 2 has the longest box shape. This means that the average delay obtained by this algorithm is stable and can maintain a good average delay for a long period. Since Algorithm 4 considers the remaining opportunistic path transmission cost when constructing candidate sets, it also has a stable delay performance. However, the mean and median of the delay in this algorithm are smaller than that of the other algorithms.  
Figure 5: Network throughput under a different number of nodes.  
Figure 6: End-to-End delay difference.  
Figure 8 shows the average delay with a different number of nodes. The changing trend of the average delay under the different number of nodes is also consistent with the changing trend of that in a simulation round, which shows our algorithm also maintains a relatively stable delay with different network structures.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 4 Modeling Channel Occupation Using SCHC
### Theoretical Model
#### 4.3.1 Block 1  
This block is made up of the complete sequence of the Regular SCHC Fragment messages that make up the first transmission window. The window ends when the SCHC ACK message has been processed on the sender side. The SCHC ACK message was sent by the receiver, indicating which tiles were received and which were not in the last window. This block has a duration time \(T_{B1}\) given by Equation (5).  
\[T_{B1}=n\cdot(T_{F}+RD_{2})+(T_{AR}+RD_{1})+TP_{ACK} \tag{5}\]  
where  
* \(T_{F}\) is the time it takes to transmit a Regular SCHC Fragment message over the wireless medium. Its calculation is explained in Section 4.2;
* \(n\) is the amount of Regular SCHC Fragments that must be sent to transmit all tiles within a transmission window. Remember that the model considers one transmission window and several retransmission windows;
* \(RD1\) is the time that elapses between the end of a transmission on the uplink and the end of the \(RECEIVE\_DELAY\_1\) timer defined in [12];
* \(RD2\) is the time that elapses between the end of a transmission on the uplink and the end of the \(RX2\) timer defined in [12]. It corresponds to the sum between \(RECEIVE\_DELAY\_2\) and \(RX2\);
* \(T_{AR}\) is the time to transmit a SCHC ACKREQ. Its calculation is explained in Section 4.2;
* \(TP_{ACK}\) is the processing time of the SCHC ACK Packet.
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 3 Dataset  
The synthetic seismic dataset used in this paper was generated through numerical simulations of the acoustic wave equation with variable density via a finite difference code in 2D. The dataset consists of a series of seismograms that represent the wavefield recorded by receivers at specific locations along the 2D model. These receivers capture the wavefield over time, forming individual seismic traces. The collection of these traces constitutes a seismogram.  
In brief, the dataset is structured as two-dimensional matrices with real values. Each column in the matrix corresponds to a seismic trace, and each row is associated with propagation time. In this work, these data are visualized as images, where each pixel represents a measurement for a specific trace at a particular propagation time.  
This dataset contains 931,200 traces. In the context of the generated seismograms, 1200 shots were carried out, incorporating 776 receivers per shot, which ensures wide and detailed coverage of the area of interest. A maximum offset of 8 km was registered, providing an extensive spectrum of subsurface data. Additionally, a sampling interval of 2 ms was adopted, which allows for a temporal resolution adequate for capturing fine details in the seismic reflections.  
Gaussian noise--known for its normal probability distribution--was added to the synthetic data to simulate seismic data with distinct signal-to-noise ratios. The focus was on producing data representations with explicit divisions of signal by noise, particularly highlighting scenarios where the divisions were set at 1, 2, and 5. This method is instrumental in mimicking the inherently unpredictable nature of actual seismic data while providing a framework for evaluating different analysis methodologies under various levels of noise.
# A Microscopic Traffic Model Considering Driver Reaction and Sensitivity
## 3 Stability Analysis
### Numerical Stability
When \(h=0.5\), the velocity of the 1 st vehicle decreases to 3.2 m/s and then increases to 4.3 m/s at 37.5 s. At 47.5 s, the velocity oscillates between 3.4 and 4.4 m/s as shown in Figure 2h. When \(h=1.0\), the velocity of the 1 st vehicle decreases to 3.1 m/s and then increases to 4.2 m/s at 38.0 s. At 55.0 s, the velocity oscillates between 3.8 and 4.2 m/s, and at 179.0 s, it fluctuates between 3.2 and 4.5 m/s as shown in Figure 2i. With \(\frac{\tau}{\tau_{k}}=1\) (typical driver), at \(h=0.3\) the velocity of the 1 st vehicle decreases to 3.1 m/s and then increases to 4.2 m/s at 38.0 s. At 47.5 s, the velocity oscillates between 3.7 and 4.2 m/s, and at 179.5 s, it fluctuates between 2.7 and 4.9 m/s as shown in Figure 2j. When \(h=0.5\), the velocity of the 1 st vehicle decreases to 3.2 m/s and then increases to 4.3 m/s and then increases to 4.3 m/s at 37.0 s. At 53.5 s, the velocity fluctuates between 3.8 and 4.3 m/s, and at 179.0 s, it oscillates between 1.4 and 6.0 m/s as shown in Figure 2k. When \(h=1.0\), the velocity of the 1 st vehicle decreases to 3.1 m/s and then increases to 4.3 m/s at 37.0 s. At 52.0 s, the velocity fluctuates between 3.7 and 4.3 m/s, and at 179.5 s, it oscillates between 0.04 and 7.3 m/s as shown in Figure 2l. Figure 3 presents the time and space evolution of the vehicles over a circular road of length 1000 m for the ID and proposed models. This shows the instability that results with both models in the form of stop and go waves. The thick red line represents the 1 st vehicle, while the black lines correspond to the other 20 vehicles. For the ID model, as \(\delta\) increases, the stop and go waves propagate over time and space as shown in Figure 3a,c. For the proposed model, with \(\frac{\tau}{\tau_{s}}<1\) (aggressive driver), the stop and go waves are larger, and with \(\frac{\tau}{\tau_{s}}>1\) (sluggish driver), they are smaller than with \(\frac{\tau}{\tau_{s}}=1\) (typical driver) as shown in Figure 3d,l. The results in Figures 2 and 3 show that with the proposed model, the velocity oscillations are larger for an aggressive driver, and hence, more stop and go waves are produced. This is expected since there are significant interactions between vehicles, which causes congestion [9]. With a sluggish driver, the oscillations are smaller, and hence, fewer stop and go waves are produced. This is because there are fewer interactions between vehicles so the traffic is smooth [9]. The ID model [17] employs a constant exponent \(\delta\), which is not based on driver response but rather is a compromise used to characterize all situations. Hence, with the ID model, the velocity oscillations and stop and go waves produced are based on this value, which is inadequate and unrealistic.
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 3 Results  
### Sample Extraction Results and Model Parameter Settings  
The quality of the sample set has a significant impact on the results of the model training. Based on the geometric variables of the hull bulbous bow determined in Table 2, this study employed the Latin hypercube sampling (LHS) [28] method to uniformly generate 300 orthogonally distributed sample points in the design space, as shown in Figure 7.  
To verify the reliability of the LHS scheme, a nearest-neighbor analysis was conducted for all points. Specifically, the distance between each point and its nearest neighbor was calculated, and the mean and variance of all values were determined. These results were then compared with those of a random sampling scheme. The results are shown in Table 4.  
Resistance values for the 300 sample ship types were acquired using a CFD solver. The basis prediction model, the BP neural network, utilized logsig and purelin as activation functions, with a hidden layer of 10 neurons. Training was concluded upon achieving a target minimum error of \(10^{-6}\). The RBF neural network established a maximum of 20 neurons, with training concluding once the mean square error objective was achieved at \(10^{-6}\). In the CNN classification model, given that the input samples in this study consisted of one-dimensional sequence data, the convolution kernel size was configured to \(2\times 1\), the pooling layer size was set to \(2\times 1\), and the stride was set to 2. The ReLU function was selected as the activation function, the Adam gradient descent technique was used for optimization, and the learning rate was set to 0.001.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 4 Comparison of the Routing Protocols
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Protocol** & **Type of Game** & **Topology** & \begin{tabular}{c} **Data** \\ **Transmission** \\ \end{tabular} & **Scalability** & **Fault Tolerance** & \begin{tabular}{c} **Communication** \\ **Reliability** \\ \end{tabular} & \
\begin{tabular}{c} **Protocol** \\ **Complexity** \\ \end{tabular} &
\begin{tabular}{c} **Location** \\ **Awareness** \\ \end{tabular} \\ \hline CH-C-TERM1 [31] & Cooperative & Hierarchical & Multihop & No & No & No & Low & No \\ \hline RCR2 [53] & Cooperative & Flat & Multihop & No & Yes & Yes & Moderate & No \\ \hline Assignment game & & & & & & & & \\ approach [34] & Cooperative & Flat & & Yes & No & Yes & High & Yes \\ \hline PRGT3 [44] & Cooperative & Flat & Multihop & No & Yes & Yes & High & No \\ \hline Resource allocation strategy for & & & & & & & & \\ clustering-based WSNs [36] & & & & & & & & \\ \hline Intercluster routing & & & & & & & & \\ algorithm for WSNs [39] & & & & & & & & \\ \hline Routing framework based on energy and delay & & & & & & & & \\ conversion [37] & & & & & & & & \\ \hline COMO4 [38] & Noncooperative & Grid & Multihop & No & No & Yes & High & Yes \\ \hline Energy-aware trust & & & & & & & & \\ deviation scheme [32] & & & & & & & & \\ \hline GERA5 [13] & Noncooperative & Hierarchical & Multihop & No & No & No & Moderate & Yes \\ \hline GTER6 [10] & Evolutionary & Hierarchical & Multihop & No & No & No & High & Yes \\ \hline ERERO7 [41] & Evolutionary & Hierarchical & Multihop & Yes & Yes & Yes & High & Yes \\ \hline GEC5 [42] & Evolutionary & Hierarchical & Multihop & Yes & Yes & Yes & High & Yes \\ \hline ERG9 [45] & Evolutionary & Flat & Multihop & No & No & Yes & High & Yes \\ \hline GBRA [10] & Reputation & Flat & Multihop & No & Yes & Yes & Moderate & No \\ \hline \hline \end{tabular}  
* Cluster Head Cooperative Trustworthy Energy-Efficient multiinput multioutput 2 Reliable Coalition Formation Routing.3 Probabilistic Routing Based on Game Theory.4 Cooperation Optimal Protocol for Multirate Opportunistic Routing 3 Game-Theory-Based Energy-Efficient Routing Algorithm.4 Game Theoretical Energy Balanced Routing.7 Energy-Efficient Routing Protocol based on Evolutionary Game.5 Game-Theory-Based Energy-Efficient Clustering.8 Evolutionary Routing Game.9 Game-Theory-Based Routing Algorithm.  
\end{table} 
Table 10: Comparison of routing protocols with regard to network architecture.
# RIS-Assisted Cooperative Time-Division Multiple Access
## 3 Simulation Results
Figure 5 shows the sum capacity of UEs and numbers of cooperative and non-cooperative time slots according to the transmit SNR. As observed in Figure 4, in the moderate SNR region, there exists opportunities for RIS-assisted transmission in cooperative time slots. As shown in Figure 5a, the sum capacity of the RC-TDMA is higher than that of the conventional TDMA in the moderate SNR region. In this region, the number of cooperative times is higher than zero, as shown in Figure 5b. This confirms that the RC-TDMA performs the additional RIS-assisted transmission in the cooperative time slots. In the other SNR region, there are no cooperative time slots. This means that the RC-TDMA operates as the conventional TDMA. Figure 6 shows the effective capacity for comparing power efficiency according to the transmit SNR. Since the RC-TDMA performs the additional RIS-assisted transmission in the moderate SNR region and operates as the conventional TDMA in the other SNR region, the effective capacity shows the same trends as Figures 4 and 5a. In other words, the RC-TDMA shows a higher effective capacity in the moderate SNR when \(\tau=0.1\). However, it shows the same effective capacity as the conventional TDMA in the other regions. With increasing \(\tau\), the effective capacity of the RC-TDMA decre  
Figure 4: Remaining required capacity comparison. Figure 5: Sum capacity comparison at (**a**) and the numbers of cooperative and non-cooperative time slots at (**b**). region. When \(\tau\) is 0.4, the RC-TDMA shows worse effective capacity than the conventional TDMA. Since \(\tau\) is the power consumption ratio between BS and the RIS, an increase in \(\tau\) means that the power consumption at the RIS increases relatively compared with that at BS. Thus, increased power consumption at the RIS leads to inferior power efficiency in the RC-TDMA. However, the proposed scheme can still maintain high efficiency from the perspective of time resources compared with the conventional TDMA.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 2 Numerical Modelling
### Convergence and Validation
#### 2.4.1. SPH Particle Density  
[PERSON] et al. [9] published the results of a pressure analysis conducted on an AL2024 standard Whipple shield. These authors used pressure gauges applied to the front of the projectile to calculate the average impact pressure on the front plate on the first \(0.5\)\(\upmu\)s of the impact. A similar procedure is followed in the numerical model developed in this work. The simulation was run with varying SPH particle densities and the results obtained for \(t\in[0,0.5]\)\(\upmu\)s are shown in Figure 8. Datapoints were extracted from the work of [PERSON] et al. [9] and have also been included in Figure 8 to allow or a comparison and validation to be made, where the average impact pressure \(p\) was determined as the average of the pressure distribution for \(t\in[0,0.5]\)\(\upmu\)s, for a range of selected SPH particles on the front side (towards the direction of impact) of the projectile, to match the data recorded by [PERSON] et al. [9].  
The general trend of the impact pressure results is similar to the experimental observations of [PERSON] et al., albeit with a relatively high level of scatter--the largest relative difference in peak average impact pressure between the numerical model and the obser  
Figure 8: Average impact pressure \(p\) for varying SPH density and comparison with observations by [PERSON] et al. [9].  
Figure 7: Simulation of standard Whipple shield model shown at: (**a**) \(t=0\)\(\upmu\)s, (**b**) \(t=2\)\(\upmu\)s, (**c**) \(t=4\)\(\upmu\)s, and (**d**) \(t=6\)\(\upmu\)s.  
vations of [PERSON] et al. [9] is below 43%. The results in Figure 9, showing the average impact pressure \(p\) and computational runtime against the SPH particle density, allow for a converging solution to be more easily identified. A clear convergence can be observed from an SPH particle density of 5 particles/mm to 13 particles/mm. The computational runtime is shown to exponentially increase with an increase in the SPH particle density. This clearly suggests that using fewer SPH particles within the model is a more efficient approach. The intersection of best-fit curves in Figure 9 can be used to select the optimum modelling approach and SPH particle density, which, in this case, is below 10 particles/mm. Optimising the computational runtime for the available resources was necessary, leading to an optimal particle density of 5 particles/mm.  
To more robustly validate the proposed models, a thorough comparison of the debris spread angle was also done. [PERSON] et al. [9] reported that the half-angle obtained using the aforementioned parameters to be 19.8\({}^{\circ}\). The debris spread half-angle for the LS-DNYA model was measured on the Whipple shield model at multiple time intervals and averaged to obtain a half-angle of 21.5\({}^{\circ}\). This corresponds to a relative difference of 7.9%, and is thus considered accurate, further validating the use of the proposed SPH particle density in the model for further analysis.
# Spatial Connections between Microplastics and Heavy Metal Pollution within Floodplain Soils
## 3 Results
### Spatial Relationships between Plastic and Metal Concentrations
#### 3.2.3 Correlations between Plastics and Heavy Metals  
A general spatial connection based on the spatial distribution patterns of plastics and heavy metals was found in the studied floodplain soils. Both contaminants show a comparable distribution on the catchment level, with higher contents in the upper reaches (site ELM), decreasing contents in the middle reaches (site ROT and STD), and a renewed increase in the lower reaches (site LIM), especially in their maximum values (Figure 3). Spatial correlations also occur in the depth distribution. Both the levels of heavy metals, expressed by pollution indices, and the levels of total plastic concentrations differ in the upper and lower soil layers. An accumulation of both contaminants can, therefore, be observed in the upper 40-50 cm of the floodplain soil, for both the proximal and the distal floodplain sites (Figure 4).  
The spatial correlations found provide an opportunity to also examine the possible correlations statistically. Spearman rank correlation coefficients (R\({}_{\rm SP}\)) of Plot data with individual heavy metals show significant (\(p\leq 0.05\)) weak (r\({}_{\rm SP}<0.3\)) positive correlations with the concentrations of Cr, Co, Ni, Cu, Zn, Cd, and Pb for the entire soil profiles (Table 1). With regard to the examined division of the accumulation into upper 50 cm and lower soil areas, the corresponding division of the dataset shows more significant (added V and Fe) and stronger (weak to clear, R\({}_{\rm SP}>0.5\)) correlations in the upper soil layers and no significant correlations in the lower soil layers. The same can be observed for correlations with the pollution indices, where significant weak correlations exist in the upper soil layers. No significant correlations can be observed for the relationship between Plot and soil properties, except for organic matter content (R\({}_{\rm SP}\) 0.31-0.36) and root density (R\({}_{\rm SP}\) 0.30-0.33). Therefore, it can be assumed that in the upper soil layers, the heavy metal contents increase (positive correlations) with increasing plastic content and likewise with increasing content of organic material. This can be an indication of chemical relationships, such as comparable adsorption conditions (organic-bound) or an indication of identical deposition conditions (flood water delivery).  
Within the context of possible chemical relationships, weak-to-strong, but always positive, inter-element correlations were found for individual heavy metals (Figure 5a), indicating a combined metal pollution from similar long-term sources [71]. Correlations between heavy metal contamination (pollution indices) and environmental drivers of heavy metal behavior (fine soil fraction, sand fraction, organic matter content, and pH) could not be found. Only PLI correlates strongly positively with organic matter content and RI and Ef correlate very weakly negatively with pH (Figure 5b).
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 1 Introduction
### Wireless Monitoring Systems
To ensure long autonomy for sensor nodes, it is essential to use ultra-low-power components in the electronic board design and implement a smart acquisition strategy. This approach keeps the sensor in a low-power \"sleep\" mode when data collection is unnecessary. Simultaneously, a consistent energy inflow is required to recharge the sensor's battery, typically achieved through an energy harvester [21]. Various energy-harvesting methods are discussed in the literature, including piezoelectric harvesters and micro wind turbines. However, for structures in outdoor environments, photovoltaic (PV) panels are the most effective solution, as they harvest solar energy, which is a reliable source with high power density [22].