esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model and Assumptions
### Network Model
#### 3.1.1 Group Deployment of Sensor Nodes  
In this paper, we consider a sensor network deployment model where nodes are deployed in groups. The underlying expectation is that the nodes in the same group are geographically close to each other after deployment. This group deployment model is not new and has been used for various applications in sensor networks. Some examples include group-based key pre-distribution [19], public key authentication using group knowledge [20], localized anomaly detection [21], key management schemes in group deployed wireless sensor networks [22] and detecting duplicates in wireless sensor networks using group deployment knowledge [18].  
The typical deployment procedure that is followed in such group-deployed applications begins with the sensor network architect partitioning the sensor field into territories by choosing appropriate deployment points in the field. Having chosen the deployment points, which amounts to having decided on the number of territories to partition the sensor field into, the architectet gathers the sensor nodes into groups and assigns them to their intended territories. This assignment is done by programming the nodes in each group \(G\) with a group ID \(G_{id}\), a unique id within the group \(U_{id}\) and their pre-assigned intended group deployment two-dimensional \((x_{G},y_{G})\) coordinates. Keying materials are also pre-loaded onto each node using any of the techniques outlined in the literature [1, 23, 24, 25] for pairwise key establishment. As is common in sensor networks, every message exchanged between sensor nodes is encrypted with this pairwise key and includes the ID of the sender in plaintext to thwart simple spoofing attacks. The nodes are then deployed, as groups of nodes, via an airplane to their pre-assigned group deployment points as the plane flies over these locations. While not all sensor networks are deployed exactly as described above, deployment models such as this are quite similar in spirit. In our work, we exploit this spatial relationship among sensors in a group to detect duplicates in the network.
# Rosehip Seed Oil-Incorporated Chitosan Films for Potential Fruit Packaging Applications
## 3 Results and Discussion
### Total Phenolics Content and Antioxidant Properties of Chitosan/RSO Edible Films  
Food oxidation is a deteriorating process that leads to changes in its chemical, sensory, and nutritional attributes over time. The antioxidant potential of edible films is typically determined by their composition. By incorporating natural antioxidants, such as essential oils (EOs), the oxidation of sensitive components like proteins and lipids can be slowed down, thereby significantly improving the quality, stability, and shelf life of food products. Additionally, the inclusion of natural antioxidants can help preserve or enhance the sensory characteristics of the food. Utilizing essential oils, particularly when loaded into edible films, has shown promising results in extending shelf life, improving stability, and enhancing sensory properties of packaged foods [73]. Essential oil components act on the  
Figure 6: DSC thermograms of RSO and chitosan/RSO edible films.  
food's surface, contributing to its shelf life and imparting a characteristic odor and flavor. Furthermore, the incorporation of essential oils can confer antioxidant and antimicrobial effects to the films.  
Phenolic compounds containing polyhydroxyl groups are often responsible for the overall antioxidant activity. These components possess significant abilities to scavenge and inhibit lipid peroxidation.  
Table 5 shows that films containing only chitosan exhibit minimal antioxidant effects due to synergetic action between the amino group and hydroxyl groups, which produce stable macromolecules. Similar results were reported by [PERSON] et al. [47] and [PERSON] et al. [74]. The addition of RSO improved the antioxidant capacity of the films significantly like the effect of other essential oils [13]. For example, the antioxidant activity obtained in the present study of chitosan with added 0.5% rosehip oil (5% emulsion) was comparable to that of a chitosan film containing the same amount of cinnamon oil [75]. Moreover, a linear dependence is observed between the antioxidant potential and the total polyphenol content as the concentration of RSO emulsion in the films increases.  
It is clear that the presence of polyphenolic compounds in the films is due to the added RSO, which predominantly contains catechin, epicatechin and rutin [76]. They far exceed the polyphenol values found in chitosan films, which contain the same amount of canola, clove, basil, and lemongrass oils [77]. The obtained values for the polyphenol content correspond to the content of polyphenols in pure oil [78]. Therefore, it can be concluded that polyphenols do not interact with the chitosan matrix and retain their activity.  
The antioxidant activity of the films is again directly related to the antioxidant activity of the RSO. Considering the amount of oil incorporated in the films and the values quoted in the literature for non-incorporated oil, it can be concluded that the incorporation of oil into the chitosan matrix does not alter its antioxidant activity [79].
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 1 Introduction
Though it is encouraging to use CNNs to detect structural damages in the above research, however, it is not clear whether a CNN trained by numerical simulations is applicable to detect damages in a real structure from vibration measurements. As for real-world structures, it is difficult to obtain a large number of network training samples from actual experimental measurements, which can be effectively compensated by the finite element analyses (FEA). It is expected that the combination of experimental measurements and FEA can significantly improve the practical applications of CNNs in SDD. In this paper, a SDD method combining numerical simulations with experimental measurements is proposed. A three-dimensional steel frame is taken as the structural model, the CNN architecture is designed and the training samples are obtained by the FEA of the steel frame. Finally, the experimental data is inputted into the FEA-trained CNN to validate its detection effectiveness.
## 3 Results and Discussion
### Sulfonation
Finally, the SO\({}_{3}\)- groups have been confirmed to have been introduced into the polystyrene/PVDF alloy particles by the FTIR spectra. Figure 2: The effect of the swelling agent on sulfonation. One hundred grams of polystyrene/PVDF alloy particles (\(R_{\text{St-DVB/PVDF}}\) = 0.8) were reacted with 600 mL of concentrated sulfuric acid at 80 \({}^{\circ}\)C. The upper bar indicates the addition of 100 mL of 1,2-dichloroethane (DCE) and the lower bar indicates the absence of DCE. Figure 4 describes the effect of the reaction temperature on the sulfonation. As a result, the maximum sulfonation degree existed at about 80 \({}^{\circ}\)C, whether the PVDF content (\(R_{\text{st-DVB/PVDF}}\)) was high or not. On the one hand, raising the reaction temperature, e.g., from 60 to 80 \({}^{\circ}\)C, had promoted the rate and degree of sulfonation. However, on the other hand, too high of a sulfonation temperature, which was above the DCE's boiling point (83.5 \({}^{\circ}\)C), e.g., 85 and 90 \({}^{\circ}\)C, would weaken the penetration of the concentrated sulfuric into the polystyrene/PVDF alloy particles. In addition, it was found that the sulfonated products at 85 and 90 \({}^{\circ}\)C had become brownish-black and lost their thermoplasticity for the alloy membrane formation. Therefore, we can propose that DCE acted as not only a swelling agent, but also a protectant for the sulfonation process of the polystyrene/PVDF alloy particles. Figures 5 and 6 demonstrate the effects of PVDF content (represented by \(R_{\text{st-DVB/PVDF}}\)) and DVB content in the polystyrene/PVDF alloy particles on the sulfonation degree, respectively. As shown in Figure 5, both the sulfonation degree and the experimental ion exchange capacity (\(IEC_{\text{Experiment}}\))  
Figure 4: The effect of the reaction temperature on sulfonation. One hundred grams of polystyrene/PVDF alloy particles (DVB content = 5%) were reacted with 100 mL of DCE and 600 mL of concentrated sulfuric acid for 8 h at serial temperatures. Figure 3: FTIR spectra of **(a)** polystyrene-DVB particles, **(b)** PVDF powder, **(c)** polystyrene/PVDF alloy particles and **(d)** polystyrene/PVDF-based cation exchange alloy resin. gradually increased with increasing \(R_{\text{st-DVB-PVDF}}\). Especially from the changing trend of the sulfonation degree, we do have reason to believe that the interpenetration of the hydrophobic and inert PVDF had remarkably decreased the sulfonation accessibility of polystyrene-DVB component (from 85.5% to 69.7%). At the same time, the decrease of the sulfonation degree attributed to the increase of the DVB content was no more than 6% (from 83.1% to 77.3%), as shown in Figure 6. Taken as a whole, a favorable sulfonation degree (above 80%) and a suitable ion exchange capacity (1.5-2.4 mmol/g) can be achieved by simply adjusting the amount of styrene and/or DVB.
## 2 Methodology, System Description, and Problem Formulation
### Problem Formulation  
To maximize the utilization of bandwidth resources and ensure the quality of service (QoS) for all users, we aim to design an optimal multiuser access control method for the above system by exploiting the real-time and dynamic network resources. We consider a scenario in which there are \(l\) users requesting to access the system to watch the same \(360^{\circ}\) VR video. We need to find the best user access decision from the \(2^{l}\) access strategies. To maximize the number of users served by the system while ensuring the QoS for each accessed user, the following probability-constrained integer programming problem can be used to decide the user access control method:  
\[\underset{\mathbf{x}}{\mathrm{max}}\underset{i=1}{\overset{l}{\mathbf{1}}}( P_{\mathrm{out}}^{(i)}(\mathbf{x})\leq\delta) \tag{3}\]  
where \(\mathbf{1}(\cdot)\) is the indicator function, satisfying that \(\mathbf{1}(Event)=1\) if \"_Event_\" is true, and \(\mathbf{1}(Event)=0\) otherwise; \(P_{\mathrm{out}}^{(i)}(\mathbf{x})\) represents the delay violation probability of user \(i\) when the given user access indicator vector is \(\mathbf{x}\); and \(\delta\) is the threshold for the delay violation probability that must be guaranteed to ensure the QoS for all admitted \(360^{\circ}\) VR video users.  
The delay violation probability of user \(i\) can be defined as the average probability that the E2E delay \(D_{\mathrm{M},l}^{(i)}\) in each video playback period \(l\), from the time when user \(i\) issues a real FoV request to the time when the user can watch a new video block, exceeds the threshold \(D_{\text{th}}\) during the entire video playback process:  
\[P_{\text{out}}^{(i)}(\textbf{x})=\frac{1}{L}\sum_{l=1}^{L}P(D_{\text{M},l}^{(i) }>D_{\text{th}}) \tag{4}\]  
where \(L\) is the total number of video playback periods. We will derive the expression of the delay violation probability in the next section.
# Durability of High Volume Glass Powder Self-Compacting Concrete
## 1 Introduction
[PERSON] et al. (2008) [8] compared the moisture transport properties of concrete containing fly ash and glass powder at 10% cement replacement level. The glass powder modified concrete performed better than fly ash and control mix at both early and later ages. Although a number of researchers have studied the durability of glass powder concrete for a wide spectrum of issues including ASR, abrasion and sulfate attack, limited information is available on the effect of different fineness and replacement levels of glass powder on the transport properties in concrete. In this study, the major durability indicators: porosity, permeability, resistivity, shrinkage and chloride migration for glass powder incorporated concrete are investigated from 3 to 545 days of curing. The durability indicators provide a practical way to identify the likely performance of glass powder containing SCC [22; 23].
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 4 Discussion
### LoS Communication over Larger Distances  
Communication over larger distances is certainly possible in LoS conditions. During the measurement campaign, signals were successfully received at distances up to almost 30 km from the base station. However, due to varying terrain elevation along the trajectory, optical NLoS conditions sometimes occur, resulting in weaker signals and causing some packet loss. Radio waves tend to follow the curvature of the terrain, increasing the range slightly beyond the optical horizon, resulting in continued successful communication, albeit with some packet loss.  
At 30 km, the connection was sometimes lost, depending on the position. Driving back and forth between two locations at a 30 km distance, the connection was often restored, indicating the continued influence of terrain elevation. Significantly larger communication ranges should be possible with higher antenna elevations.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 4 Performance Analysis of SSPSWs
### Monotonic Loading Mechanical Performance  
The bilateral-joined SSPSWs exhibited consistent stress distribution patterns, with pronounced stress concentrations at the slot ends, initiating yielding and plastic hinge formation (Figure 10a,b). This energy dissipation mechanism was facilitated by the sequential stress propagation from the slot termini toward the inter-slot column midspans. In contrast, the quadrilateral-joined configurations demonstrated holistic inter-slot column yielding, with bidirectional stress gradients from the edge to center regions (Figure 10c-f). Notably, models SPSW-FT8 and SPSW-FB36 revealed underutilized slit-adjacent columns exhibiting minimal stress concentrations, while the non-slit zones developed full sectional stresses. The absence of localized plastic hinges at the slot ends in quadrilateral systems fundamentally limits their ability to exploit the slotted-plate load-bearing mechanisms, thereby validating bilateral connection configurations as the optimal design strategy for SSPSWs.
## 3 Position Control Policy Traning of the PCM Based on MAML Meta-Reinforcement Learning
### Meta-Reinforcement Learning Modeling and Policy Network Structure Design  
State space (\(s\)). State information is crucial for making action decisions. The state of the continuum manipulator is determined by the current pressure values of each pneumatic muscle. Therefore, the state space is composed of the current pressure values of each pneumatic muscle, denoted as \(s=[u_{11},u_{12},u_{13},u_{21},u_{22},u_{23}]\) with a dimension of 6.  
Action space (\(a\)). The action space encompasses the entire set of actions available to the agent. Each pneumatic muscle of the PCM can be independently pressurized and depressurized. So, the pressure increments of each pneumatic muscle \(\Delta u\) can be used as actions, that is \(a=[\Delta u_{11},\Delta u_{12},\Delta u_{13},\Delta u_{21},\Delta u_{22}, \Delta u_{23}]\), with a dimension of 6.  
Reward function (\(r\)). The reward function plays a critical role in reinforcement learning training. It is primarily used to guide the agent's behavior. For actions that align with the desired behavior, the reward function provides a positive reward, while for actions that do not, it provides a negative reward. Thus, the design of the reward function directly impacts the learning speed, operational efficiency, and the final control performance of the trained  
Figure 1: The structure of the PCM. (**a**) The PCM; (**b**) the cross-section of the PCM.  
model. As the objective of this paper is to control the end-effector of the PCM to reach a specified position, the negative of the difference between the current end position and the target position can be used as the reward function to encourage the policy to control the end position of the PCM as closely as possible to the target position. So, the reward function can be expressed as  
\[r=-\left[\left(x-x_{target}\right)^{2}+\left(y-y_{target}\right)^{2}+\left(y-y_ {target}\right)^{2}\right] \tag{18}\]  
where \((x,y,z)\) is end position of the PCM and \(\left(x_{target},y_{target},z_{target}\right)\) is the target position.  
Structure of policy network. For the task of the end position control of the PCM, the design of the policy network structure is based on the action space and state space. The policy network learns the mapping from the current state to the optimal action. In the REINFORCE algorithm, the input to the policy network is the state of the agent at the current time, and the output is the mean and variance of the normal distribution of the action taken by the agent. The policy is represented by a four-layer fully connected network, with 256 neurons in the middle two layers, and each layer uses the tanh activation function. The Adam optimizer is employed to compute the policy gradient and update the network weights.  
The policy network outputs a Gaussian distribution of the actions taken by the agent. Sampling from this Gaussian distribution yields the specific action taken by the agent in the current state, along with the probability of taking that action.
## 2 Simulation Details
### Interatomic Potential
#### 2.1.2 Morse Potential  
To reproduce the graphene/Cu and Cu/Cu interaction, the Morse potential with parameters \(D_{e}\) = 0.3429 eV, \(R_{e}\) = 2.8660 A and \(\beta\) = 1.3588 1/A for Cu-Cu [58]; and \(D_{e}\) = \(-\)0.100 eV, \(R_{e}\) = 2.220 A, \(\beta\) = 1.700 1/A for Cu-C is chosen. For graphene/Cu, the cutoff distance is 6.5 A. As graphene shows weak interaction with Cu [47; 59; 60; 61; 62], this enables use of simple pair interatomic potentials, such as Lennard-Jones or Morse [30; 63], for such interactions.  
\[E_{Cu-C}(r)=D_{e}[(1-e^{-\beta(r-R_{e})})^{2}-1], \tag{3}\]  
where \(D_{e}\) is the binding energy, \(R_{e}\) is the equilibrium distance, and \(\beta\) is the potential parameter characterizing the bond strength.  
The Morse potential has been used successfully to describe the interaction of metals with graphene and silicene [64], the interaction of atoms of the light elements C, N, O with atoms of the fcc metals Al, Ag and Ni [53], and the catalysis of nanotubes [65]. It was shown in [66] that the Morse potential provides a more precise and generalized description even for the simulation of covalent materials and surface interactions. Graphene/Me systems were also simulated with this potential [27; 31; 32; 67; 68].
## 3 Dynamic Link Establishment of Mega-Constellations
### Simulation Analysis
From Figure 20, it is evident that, in most cases, the latency of the Manhattan configuration without dynamic links is higher than that of the '3 + 1' topology with dynamic links. Through the quantitative analysis of the simulation results, we found that, within a complete orbital cycle, the average minimum latency for the '3 + 1' topology with dynamic links is 49.8839 ms, with a standard deviation of 1.9694 ms. In contrast, the average latency for the Manhattan configuration without dynamic links is 52.2685 ms, with a standard deviation of 1.5634 ms. These results indicate that dynamic link topologies effectively reduce the information propagation delay. This is because dynamic links ensure that each satellite establishes connections with the nearest satellites, considering both cross-orbit and satellite number distances. As a result, the overall propagation delay in the network is reduced compared to the traditional Manhattan configuration. Regarding the standard deviation, the larger value observed after introducing dynamic links reflects their inherent instability, as they require real-time link switching based on current conditions. Furthermore, the introduction of dynamic links brings additional potential advantages. For instance, it enhances the network robustness by allowing the system to swiftly switch to alternative links even if certain links fail, thereby maintaining continuous communication. Additionally, dynamic links can adjust link configurations based on real-time traffic and network conditions, optimizing the resource utilization and overall efficiency. Therefore, from the perspective of configuration-based simulation results, the '3 + 1' topology with dynamic links significantly outperforms the Manhattan configuration without dynamic links in reducing communication latency. The introduction of dynamic links not only reduces information propagation delay but also enhances the network robustness and efficiency, particularly when considering cross-orbit and closely spaced satellites. In summary, the general graph maximum weight-matching algorithm with an elite retention strategy exhibits significant advantages when applied to dynamic link matching. It effectively enhances the performance and stability of satellite networks. Notably, this strategy not only demonstrates theoretical superiority but also provides more reliable and efficient communication services in practical applications. Future research could further explore the potential application of this strategy in different types and scales of satellite networks, as well as how to optimize the algorithm to adapt to increasingly complex network environments and communication requirements \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Longitude** & **Latitude** \\ \hline Los Angeles & \(-\)118.24 & 34.05 \\ Xiamen & 118.08 & 24.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Latitude and longitude of Los Angeles and Xiamen.
# Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms
## 3 Non-Linear Transformation-Based Algorithms
### Threshold Method
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cost Function** & \(\varphi\)**(_**\(n\)**)** & **Reference** \\ \hline \(J(n)=E\left[|e(n)|^{2}\right]\) & \(\begin{cases}\begin{array}{c}\epsilon(n)\times r(n)\\ \chi^{\prime}(n)=\begin{pmatrix}\chi^{\prime}(n)\\ 0\end{pmatrix}&\chi^{\prime}(n)\in[\varsigma_{1},\varsigma_{2}]\\ 0&\text{otherwise}\end{array}\\ J(n)=E\left[|e(n)|^{2}\right]\) & \(\begin{cases}\begin{array}{c}\epsilon(n)\times r(n)\\ \epsilon(n)=\begin{cases}\epsilon(n)\end{pmatrix}&\epsilon(n)\in[\varsigma_{1}, \varsigma_{2}]\\ 0&\text{otherwise}\end{array}\\ \chi^{\prime}(n)=\begin{cases}\chi^{\prime}(n)&\chi^{\prime}(n)\in[\varsigma_ {1},\varsigma_{2}]\\ 0&\text{otherwise}\end{array}\\ J(n)=E\left[|e(n)|^{2}\right]\) & \(\begin{cases}\begin{array}{c}\epsilon(n)\times r(n)\\ 0\end{array}&\text{otherwise}\end{array}\\ J(n)=\begin{cases}\begin{array}{c}\epsilon(n)=\begin{cases}\text{sign}( \epsilon(n))&(\lfloor\chi^{\prime}(n)\rfloor)\\ 0&\text{otherwise}\end{array}\\ 0&\text{otherwise}\end{array}\\ J(n)=\begin{cases}\begin{array}{c}\epsilon(n)\times r(n)\\ \chi^{\prime}(n)=\begin{cases}\chi^{\prime}(n)&\chi^{\prime}(n)\end{array}\\ 0&\text{otherwise}\end{array}\\ J(n)=E\left[|e(n)|^{2}\right]\) & \(\begin{cases}\begin{array}{c}\epsilon(n)=\begin{cases}\text{sign}( \epsilon(n))&(\lfloor\chi^{\prime}(n)\rfloor)\\ 0&\text{otherwise}\end{array}\\ \chi^{\prime}(n)=\begin{cases}\text{sign}(\chi^{\prime}(n))&(\lfloor\chi^{ \prime}(n)\rfloor-\lambda)&(\chi^{\prime}(n)\in[\gamma_{1},\gamma_{2}]\\ 0&\text{otherwise}\end{array}\\ \gamma=\text{argmin}(N+||\chi^{\prime}_{r}(n)||^{2}+\sum_{k}^{N-1}\frac{2\pi}{ \delta})\\ \end{cases}\end{cases}\end{table} 
Table 4: Summary of adaptive algorithms in Section 3.1.  
estimation methods for thresholds have a high computational complexity. Algorithms that implement hyperbolic tangent functions to the input and error signals still require offline estimation of threshold parameters. Robust M estimation algorithms essentially achieve resistance to impulsive noise interference by limiting the error signal.