esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
###### Abstract  
Railway communications are closely impacted by the evolution and availability of new wireless communication technologies. Traditionally, the critical nature of railway services, the long lifecycle of rolling stock, and their certification processes challenge the adoption of the latest communication technologies. A current railway telecom trend to solve this problem is to design a flexible and adaptable communication architecture that enables the detachment of the railway services--at the application layer--and the access technologies underneath, such as 5G and beyond. One of the enablers of this detachment approach is software-defined networking (SDN)--included in 5G architecture--due to its ability to programmatically and dynamically control the network behavior via open interfaces and abstract lower-level functionalities. In this paper, we design a novel railway train-to-ground (T2G) communication architecture based on the 5G technological enabler SDN and on the transport-level redundancy technique multipath TCP (MPTCP). The goal is to provide an adaptable and multitechnology communication service while enhancing the network performance of current systems. MPTCP offers end-to-end (E2E) redundancy by the aggregation of multiple access technologies, and SDN introduces path diversity to offer a resilient and reliable communication. We carry out simulation studies to compare the performance of the legacy communication architecture with our novel approach. The results demonstrate a clear improvement in the failover response time while maintaining and even improving the uplink and downlink overall data rates.  
5G; train-to-ground; software-defined networking; multipath TCP; adaptable; reliability; resiliency; path diversity +
Footnote †: journal: Electronics Electronics
## 3 Methodology and Mathematical Formulations
### Airflow Physics and Turbulence Modeling
#### 3.3.6 Solution Initialization for Flow Analysis  
The solution initialization process began with applying the standard k-\(\epsilon\) (STD) turbulence model using first-order numerical discretization. As discussed in the previous section, turbulence boundary conditions were applied to establish the initial simulation environment. The first simulation was run for 2000 iterations or until the solution residuals converged to less than \(10^{-3}\). Following this initial phase, the turbulence model was switched to the SST model, utilizing second-order numerical discretization to ensure higher accuracy. This transition ensured that the solutions maintained consistency and achieved the desired level of accuracy for detailed analysis. The complete solution initialization workflow is illustrated in Figure 8, while Table 2 summarizes the grid number and Table 3 summarizes the numerical settings applied during each simulation stage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & **Grid 1** & **Grid 2** & **Grid 3** & **Grid 4** & **Grid 5** \\ \hline Total & 5,736,520 & 9,934,053 & 10,935,499 & 16,365,087 & 21,505,898 \\ \hline Cartesian hex element & 1,408,485 & 726,235 & 838,697 & 5,507,841 & 5,442,178 \\  & 24.55\% & 7.31\% & 7.67\% & 33.66\% & 25.31\% \\ \hline Polyhedral transition element & 1,164,269 & 2,569,734 & 2,831,350 & 4,714,679 & 3,409,531 \\  & 20.30\% & 25.87\% & 25.89\% & 28.81\% & 15.85\% \\ \hline Poly prism element & 3,163,766 & 6,638,084 & 7,265,452 & 6,142,567 & 12,654,189 \\  & 55.15\% & 66.82\% & 66.44\% & 37.53\% & 58.84\% \\ \hline Number of boundary layers & 5 & 10 & 10 & 10 & 15 \\ \hline Average Y\({}^{+}\) & 12 & \textless{}1 & \textless{}1 & \textless{}1 & \textless{}1 \\ \hline Peak memory (Gb) & 24.92 & 39.32 & 42.57 & 59.84 & 84.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid independence analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solution Control** & **Method** \\ \hline Spatial discretization & \\ Pressure & Standard \\ Momentum & 2 nd Order upwind \\ Turbulence kinetic energy & 2 nd Order upwind \\ Specific dissipation rate & 2 nd Order upwind \\ All species & 2 nd Order upwind \\ Energy & 2 nd Order upwind \\ \hline Multigrid & \\ Flow & F-cycle \\ Turbulence kinetic energy & Flexible \\ Specific dissipation rate & Flexible \\ All species & Flexible \\ Energy & F-Cycle \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical settings.
# Efficient Data Exchange between WebAssembly Modules
## 5 Results and Discussion
### Generated Code
#### 5.2.3 Integrity  
Since Karmem generates code for multiple programming languages and facilitates cross-language communication through WASM via the Karmem runtime, tests are conducted using Karmem itself. The primary objective of these tests is to ensure compatibility and data integrity across languages. While this testing approach introduces potential challenges related to bootstrapping and bug propagation, it provides the flexibility to dynamically modify and compile new versions of the same schema.  
Figure 35 illustrates the interface used for communication between modules, whose host is responsible to call the producer and then call all consumers. In this setup, a producer generates an arbitrary message, serializes it using Karmem, and the corresponding consumers parse and process the message. The processing involves reading all fields, converting each value to a string, and then adding the converted value to a hash digest, which is SHA-2. The final hash result is then compared with the expected checksum provided by the producer. The rationale behind converting to strings is to ensure accurate value interpretation, instead of relying on just the binary representation.  
The integrated testing framework currently supports the creation and modification of simple schemas, iterating and testing all fields. It also accommodates manually written tests and allows for the reuse of pre-compiled WASM modules, which is crucial for ensuring backward compatibility between different versions of Karmem. Additionally, the framework can incorporate malicious producers to generate specially crafted messages, helping identify potential vulnerabilities.  
Due to the deterministic behavior of WASM and the lack of access to OSes features, such as date-time and random number generators, tests are always deterministic, and random data can only be derived from the entropy of \"producer_input\". In case of a bug, it is possible to regenerate the exact output. Tests must be conducted on each Karmem update, but not exclusively. It also should be executed when new compilers and language versions are available, ensuring forward compatibility with upstream changes.
###### Abstract  
Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information.  
Keywords:1D-CNN; hyperspectral image; atrous-convolution; classification; random spatial hyperspectral image +
Footnote †: journal: Medical Image and Image Classification  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 4 Proposed Trust-Based Lightweight Authentication Strategy  
In vanilla NDN, trusted self-signed authorities are always reachable. Hence, a decision about a data packet authenticity can always be made. However, such energy and time consuming strategy may not be feasible in FANETs for several-fold reasons such as mobility, network fragmentation, and battery restriction, to name a few. Furthermore, this strategy adversely affects the functioning of real-time and delay sensitive FANET applications such as disaster management. To address these problems, we propose a mechanism where we use the established trust among nodes to check the data authenticity. The established trust helps in deciding whether to bypass the authenticity check and accept the data without check.  
For the data coming from an untrusted UAV, the traditional vanilla NDN authentication process is maintained in FNDN in order to ensure maximum confidence in the received data. However, for trusted UAVs, content authenticity is periodically checked offline, and the higher trust of the producer, the longer is the period of forwarding without check. Finally, for data coming from the ground stations, the normal NDN data authentication is automatically used. Therefore, checking the data authenticity of the already authenticated data through the wired part of the link, is not needed.  
Figure 7 gives an overview of our proposed adaptive authentication process. In the figure, UAV \(B\) has a lower trust value (not trusted), therefore data received from \(B\) must be authenticated every time it is received by node \(A\). Finally, the sequence diagram showing a step-by-step inter-UAV communication is presented is Figure 8.  
Figure 7: Overview of the proposed Trust-based data authentication mechanism.
## 2 Virtual Power Plant (VPP) Communication Architecture
### Service Types and Traffic Requirements of VPP  
The communication service between the VPP control platforms and the end devices through the aggregation control gateways has various QoS requirements in terms of bandwidth, delay, and reliability. Table 1 shows the typical service types and traffic requirements of the VPP systems. We chose the frequency regulation, demand response, and power trade market as the typical service examples of VPP systems for further discussion.  
* Frequency regulation. The VPP provides the frequency regulation service through the control of DERs. Energy storage is one important unit for frequency regulation in VPPs. The participation of frequency regulation in VPPs presents great challenges to current communication networks. The delay requirement of fast frequency regulation traffic is less than 280 ms, with a communication delay requirement of less than 120 ms. The delay requirement of regular frequency regulation traffic is less than 1 min, with a minimum communication delay requirement of seconds. Its target delay is less than 50 ms and the required reliability is higher than 99.999%. The frequency regulation events trigger the time-critical communication in VPPs.
* Demand response. Demand response traffic is generated periodically. The normal demand response is time-triggered communication with cycles in minutes. The delay requirements of this type of traffic ranges from 500 to 1000 ms. The reliability requirement is as high as 99.999%. The emergency demand response is event-triggered communication. It has much lower delay requirements compared with the normal demand response.
* Power trade market. The transmission of power trade market information has a normal QoS requirement with delay of 10 s and reliability of 99.99%.
## 2 Material and Methods
### Landslide Prediction Models
#### 2.4.4 Particle Swarm Optimization (PSO)  
PSO as a Meta-heuristic algorithm was introduced by [PERSON] et al., 1995 [123; 124]. The ability to optimize nonlinear problems and fast convergence as well as a few computations are the most remarkable features of this algorithm. These features make PSO significantly different from other evolutionary algorithms such as genetic algorithms. This algorithm stems from being used by the swarm intelligence of birds and fishes, which they apply it to discover the best means to find food. In this algorithm, each bird is implemented as a particle which in fact represents a solution to the problem. These particles are searched in n-dimensional space, where n is the number of problem parameters, to find the optimum answer for the problem. To this aim, particles are randomly scattered in the search space. Then in each iteration, based on Equations (20) and (21) each particle adjusts its location by finding the best location that it has ever been in and the best one adjacent to its neighbor.  
Supposing \(x_{i}^{t}=\left(x_{i1}^{t},x_{i2}^{t},\ \ldots,\ x_{in}^{t}\right)\) and \(v_{i}^{t}=\left(v_{i1}^{t},v_{i2}^{t},\ \ldots,\ v_{in}^{t}\right)\) are respectively the location and rate of change in location of the \(i\)-th particle in \(t\)-th iteration, therefore, according to Equations (1) and (2), the location and rate of change in location of the \(i\)-th particle in \(t+1\)-th iteration can be determined as follows:  
\[v_{i}^{t+1}=\omega.v_{i}^{t}+c_{1}.r_{1}\left(p_{i}^{t}-x_{i}^{t}\right)+c_{2 }.r_{2}\left(g_{i}^{t}-x_{i}^{t}\right)with-v_{max}\leq v_{i}^{t+1}\leq v_{max} \tag{20}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{21}\]  
where, \(x_{i}^{t}\) is the previous location of i-th particle, \(p_{i}^{t}\) is the best location found by \(g_{i}^{t}\) the \(i\)-th particle, \(i\) is the best location found by other particles and \(r_{1},r_{2}\) are random numbers from 0 to 1. Moreover, the three parameters \(\omega\), \(c_{1}\) and \(C_{2}\) are the cognitive coefficient, social coefficient, and inertia weight, respectively. It should be noted that there are diverse articles in order to set these parameters. In this paper, they are determined based on the following equations:  
\[\omega=\frac{1}{2 ln2}\ and\ c_{1}=c_{2}=0.5+ln2 \tag{22}\]  
It is noteworthy that this algorithm continues until the best location found by each particle is the equal of the best position chosen by all the particles. In other words, all the particles are concentrated in one point of space, and in fact, the answer to the problem is optimized.
## 3 Research Methods
### Construction of the PSO-BP Neural Network Model  
#### 3.2.1 Particle Swarm Optimization  
Particle swarm optimization (PSO) simulates birds' migratory and foraging behavior, initializing particles in the feasible solution space, to represent potential optimal solutions. Each particle, with an initial speed and direction, iteratively seeks the optimal solution by adjusting its motion based on individual and collective best outcomes. In a D-dimensional space with n particles, the position of the \(i\)-th particle is represented as \(X_{i}=(X_{i1},\,X_{i2},\,\ldots,\,X_{id})\), and its velocity as \(V_{i}=(V_{i1},\,V_{i2},\,\ldots,\,V_{id})\). The best-known individual position is \(P_{i}=(P_{i1},P_{i2},\,\ldots,\,P_{id})\), and the global optimum, shared by the swarm, is \(P_{g}=(P_{g1},\,P_{g2},\,\ldots,\,P_{gt})\). The velocity and position update formulas for a particle are delineated as follows:  
\[v_{ij}^{t+1}=wv_{ij}^{t}+c_{1}r_{1}(p_{ij}^{t}-x_{ij}^{t})+c_{2}r_{2}(p_{gj}^{t }-x_{ij}^{t}) \tag{1}\]  
\[x_{ij}^{t+1}=x_{ij}^{t}+v_{ij}^{t+1} \tag{2}\]  
\[w(t)=w_{\text{max}}-\frac{t(w_{\text{max}}-w_{\text{min}})}{t_{\text{max}}} \tag{3}\]  
In this context, \(\mathcal{H}^{\prime\prime}\) ranges from 1 to \(\mathcal{H}^{\prime\prime}\), each number representing a particle in the swarm; \(\mathcal{H}^{\prime\prime}\) spans from 1 to \(\mathcal{H}^{\prime\prime}\), denoting the dimensionality of each particle. The iteration count is represented by \(\mathcal{H}^{\prime\prime}\), with \(t_{\text{max}^{\prime}}\) signifying the maximum allowed iterations. The learning factors are given as \(c_{1}\)- and \(c_{2}\)-. Random factors \(r_{1}\)- and \(r_{2}\)- are distributed within the \([0,1]\) interval. The inertia weight \(\mathcal{H}^{\prime\prime}\) helps balance global and local search capabilities, \(w_{\text{max}^{\prime}}\) and \(w_{\text{min}^{\prime}}\) defining its upper and lower limits. These elements collectively dictate the velocity and position updates of the particles in the PSO process.
# Activation-Based Pruning of Neural Networks
## 5 Results and Discussion
### Comparative Analysis
#### 5.4.2 Activation-Based Pruning against Similar Models  
In Table 10, we present a comparative analysis of different pruning methods that were tested under similar settings. All reference results used magnitude-based pruning on the LeNet-300-100 model using the MNIST dataset. The compression rate was calculated using the formula \(W_{\text{total}}/W_{\text{rem}}\), where \(W_{\text{total}}\) is the total number of weights of the model before pruning and \(W_{\text{rem}}\) is the remaining number of weights after pruning. Sparsity % and Remaining Weights % are complements of each other. We present both of these data as some tests refer to Sparsity % and others to Remaining Weights %. Remaining Weights % and Required FLOPS % are quantitatively equal in value, but render different meaning to the numbers. We included both as different pruning methods presented either or both of these results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pruning Type** & **Pruning Iterations** & **Pruning \%** & **Top 5\% Test Accuracy** \\ \hline Global & 5 & 75.03 & 71.49 \\ Global & 10 & 78.50 & 75.94 \\ Local & 4 & 82.37 & 74.90 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Global vs. local pruning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Importance Type** & **Pruning Type** & **Pruning \%** & **Top 5\% Test Accuracy** \\ \hline Neurons & Global & 74.68 & 70.64 \\ Neurons \& Weights & Global & 78.82 & 76.79 \\ Neurons & Local & 82.37 & 69.38 \\ Neurons \& Weights & Local & 82.38 & 80.47 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Importance-based scoring.  
[PERSON] et al. [2] presented an unstructured, iterative pruning method that uses magnitude-based pruning to achieve a 12-fold reduction in model size. [PERSON] et al. [7] employed an iterative pruning technique, where pruning occurred after training and achieved a 40-fold reduction in model size. [PERSON] et al. [6] presented an iterative pruning method where pruning occurred before training and achieved a 56-fold reduction in model size. [PERSON] and [PERSON] [5] used the lottery ticket hypothesis in the context of pruning small fully connected nets on MNIST. It is a structured pruning technique with importance scoring based on the weight magnitude. It achieved a seven-fold reduction in model size. [PERSON] et al. [17] presented an unstructured pruning, where pruning occurred during training. It achieved a compression rate of seven-fold. Activation-based pruning is an unstructured, iterative-based pruning method. The results demonstrated that activation-based pruning achieved an impressive 94-fold reduction in the model size.
## 3 Results and Discussion
### Temperature Distribution Caused by the Subsurface Impurity Defects
#### 3.1.2 Effect of Impurity Defect Parameters on Temperature Distribution
For defect located more closely to the surface, the thermal energy is more prone to transfer among the ambient air. Under this circumstance, less energy will be lost by thermal diffusion. Hence, the temperature is higher when the defect resides closer to the silica surface. However, when the defect is located deeper than 0.3 \(\upmu\)m beneath the surface, the effect of ambient air on thermal diffusion becomes negligible. From the simulations of temperature distributions shown above, we can conclude that impurity defects (especially for CeO\({}_{2}\)) located closer than 0.3 \(\upmu\)m away from the surface with defect radius ranging from 0.1 \(\upmu\)m to 0.15 \(\upmu\)m are most dangerous defects for lowering the laser damage resistance of polished fused silica optics.
# Some Practical Acoustic Design and Typical Control Strategies for Multichannel Active Noise Control
## 3 Three Active Control Strategies
### Decentralized Control  
Due to the reduced complexity and better feasibility for hardware implementation of multi-channel control systems, the decentralized control technique has been widely used [37; 38; 39; 40; 41; 42; 43; 44; 45]. To compensate the performance loss, a recent research proposed a method that properly chooses a so-called eigenvalue matrix so that a two-channel decentralized ANC system achieves the same noise reduction performance as the centralized one with guaranteed convergence [44].  
A comparison of several conventional control strategies has been conducted by computer simulations (see Figure 7 cited from [PERSON]'s 2019 paper [44]). It seems that the proposed eigenvalue selection method is equivalent to constantly changing the step-sizes of the two channels since the multiplication of the step-size and the eigenvalue matrix, which is a diagonal matrix for two-channel systems, determines the total update of the ANC algorithm. The decentralized control strategy could be generalized to a system with more channels and different gradient searching methods [40].  
Experiments using a decentralized active control system to reduce the vibration of a plate have been carried out in [43]. To compensate the instability caused by decentralized structure, a digital phase lag compensator has been designed. In this way, the eigenvalue of the open loop transfer function matrix could be managed so as to guarantee the stability of the multichannel decentralized system. The control system is mounted on a panel-cavity system as shown in Figure 8. At the corner of the cavity, there is a loudspeaker to generate the primary disturbance. Based on this experimental facility, the control performance of the proposed decentralized control system with different numbers of control loops are investigated. The total kinetic energy of the plate between 1 and 1 kHz has been used as a measure of the vibration reduction. The results for, respectively, two and nine control loops are presented in Figure 9. Generally, greater reduction can be achieved by nine control loops. This is because the control units distributed at the edges of the plate can effectively reduce the vibration of the corresponding mode.  
Figure 8: The experimental facility of a panel–cavity system: aluminum panel with error sensors and nine collocated sensor–actuator pairs. Figure is cited from [PERSON]’s 2015 paper [43].  
Figure 7: Convergence curves of the three methods with weighting factors of zero at **(a)** 130 Hz and **(b)** 200 Hz, where the step-sizes \(\mu_{1}\) = 1 \(\times\) 10\({}^{-7}\) and \(\mu_{2}\) = 1 \(\times\) 10\({}^{-6}\). Figures are cited from [PERSON]’s 2019 paper [44].