esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Military Software Defined Networks
## 4 Identified Significant Research Contributions
### Architectures
Overall, the main contribution of this article, is to present a summary of related challenges identified within the proceedings of a TTCP-C3I (The Technical Cooperation Program--Command, Control, Communications and Information Systems) group. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Application Domain** & **Contribution** & **Scope** & **Layer** & **Security** \\ \hline
[22] & Heterogeneous networks & Architectures & Design and management & Control & No \\
[19] & Aerial networks & Architectures & Monitoring and control & Control & Mentioned \\
[23] & Aerial networks & Control systems & Monitoring and control & Control & No \\
[24] & Military networks & Control systems & Policy based management & Control & Yes \\
[25] & Tactical networks & Services & Design and management & Control & Yes \\
[26] & Military networks & Control systems & Design and management & Application & Mentioned \\
[27] & Military networks & Services & Design and management & Application & Mentioned \\
[18] & Tactical networks & Architectures & Monitoring and control & Control & Mentioned \\
[16] & Tactical networks & Architectures & Monitoring and control & Control & Mentioned \\
[28] & Coalition networks & Control systems & Resource sharing & All & Mentioned \\
[29] & Military networks & Control systems & Policy based management & Application & Yes \\
[30] & Military networks & Control systems & Policy based management and Resource sharing & Application & Yes \\
[31] & Tactical networks & Architectures & Monitoring and control & Control & Mentioned \\
[31] & Coalition networks & Control systems & Resource sharing & Control & Yes \\
[32] & Mobile ad hoc networks & Control systems & Monitoring and control & Control & Yes \\
[33] & Mission critical networks & Control systems & Monitoring and control & Control & Mentioned \\
[34] & Distributed networks & Tools & Security & Application & Yes \\
[17] & Military networks & Architectures & Monitoring and control & Control & No \\
[35] & Naval networks & Basic evaluation & Basic evaluation & Control & Mentioned \\
[36] & Tactical networks & Basic evaluation & Basic evaluation & Control & No \\
[37] & Tactical networks & Basic evaluation & Basic evaluation & Control & Yes \\
[38] & Military networks & Tools & Simulations & Control & Mentioned \\
[39] & Military networks & Control systems & Policy based management and Resource sharing & Control & Yes \\
[40] & Coalition networks & Control systems & Security & Control & Yes \\
[41] & Tactical networks & Control systems & Design and management & Control & No \\
[42] & Coalition networks & Control systems & Monitoring and control & Control & Yes \\
[43] & Naval networks & Control systems & Monitoring and control & Control & No \\
[21] & Coalition networks & Architectures & Basic evaluation & Control & Yes \\
[44] & Military networks & Tools & Security & Physical & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identified significant research contributions.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 5 Proposed Algorithm
### Proposed DL LSTM Algorithm
#### 5.2.2 Handover Procedure  
The handover procedure proposed in this section implements a faster variation to the currently used procedure. The proposal is to make the admission control happen at the same time instant that the UE begins to process the handover command. This can be made possible because of the aggregated edge architecture, where, due to its centralized nature, the MEC can orchestrate both events to execute simultaneously. Hence, a reduction of 3 ms could be made to the handover procedure latency discussed in Section 5.1.2.  
Figure 3 shows the flow diagram of the modified handover preparation phase for the proposed algorithm and considers RAN architecture, where virtualized AMF and UPF instances are hosted on the aggregated edge at the CU of gNB. All other remaining parts of the handover procedure, i.e., the handover execution and completion phases, remain the same as described in Section 2.1.1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification for Potential BS**} & \multicolumn{4}{c}{**Parameters of Current BS**} \\ \cline{2-5}  & \multicolumn{3}{c}{**DR Met**} & \multicolumn{2}{c}{**DR Not Met**} \\ \cline{2-5}  & **CQI \textless{} 3** & **CQI \textgreater{} 3** & **CQI \textless{} 3** & **CQI \textgreater{} 3** \\ \hline GMCF & \(1\) & \(0\) & \(1\) & \(1\) \\ GMCL & \(1\) & \(0\) & \(1\) & \(1\) \\ GMAF & \(1\) & \(0\) & \(1\) & \(1\) \\ GMAL & \(1\) & \(0\) & \(1\) & \(1\) \\ GNCF & \(1\) & \(0\) & \(1\) & \(0\) \\ GNCL & \(1\) & \(0\) & \(1\) & \(0\) \\ GNAF & \(1\) & \(0\) & \(1\) & \(0\) \\ GNAL & \(1\) & \(0\) & \(1\) & \(0\) \\ OMCF & \(1\) & \(0\) & \(1\) & \(1\) \\ OMCL & \(1\) & \(0\) & \(1\) & \(1\) \\ OMAF & \(1\) & \(0\) & \(1\) & \(0\) \\ OMAL & \(1\) & \(0\) & \(1\) & \(1\) \\ ONCF & \(1\) & \(0\) & \(1\) & \(0\) \\ ONCL & \(1\) & \(0\) & \(1\) & \(0\) \\ ONAF & \(1\) & \(0\) & \(1\) & \(0\) \\ ONAL & \(1\) & \(0\) & \(1\) & \(0\) \\ PMCF & \(1^{*}\) & \(0\) & \(1^{*}\) & \(0\) \\ PMCL & \(1^{*}\) & \(0\) & \(1^{*}\) & \(0\) \\ PMAF & \(0\) & \(0\) & \(0\) & \(0\) \\ PMAL & \(0\) & \(0\) & \(0\) & \(0\) \\ PNCF & \(1^{*}\) & \(0\) & \(1^{*}\) & \(0\) \\ PNCL & \(1^{*}\) & \(0\) & \(1^{*}\) & \(0\) \\ PNAF & \(0\) & \(0\) & \(0\) & \(0\) \\ PNAL & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: LUT for handover decisions based on DL LSTM classifications.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks  
As we discussed in the introduction, the SDN technology is based on separating the control layer from the data layer (forwarding devices) to facilitate network scalability and management operations. However, the process of discovering the network topology relies on cooperation between the SDN-Controller and forwarding devices layers, and the responsibility lies primarily with the control layer. The control layer topology discovery task mainly consists of three main operations: SDN-Switch discovery, link discovery (i.e., links between SDN-Switches), and host discovery [33; 34] as illustrated in Figure 2.  
Since we need OpenFlow to act as a data pipeline between the control layer and the data layer, the loading of the topology discovery process is entirely on the controller layer. Therefore, the OpenState protocol has been proposed as a modification of OpenFlow that attempts to divide the load of the topology discovery process between SDN-Controller and SDN-Switches. As a result, OpenFlow SDN-Switches can be directly programmed, allowing them to implement redirection rules without relying solely on the remote controller. As a future improved release of OpenFlow, OpenState has not yet been deployed [57].  
The OpenFlow architecture combines two layers through the use of flow tables in the forwarding devices layer. Moreover, each flow table entry contains three features (rule, action, and statistics) [15] and each flow table contains action fields that are linked with each flow entry. These flow tables data and commands are transmitted between two layers by using a control channel. In detail, the use of flow table and control channel techniques will be discussed in Sections 2.3 and 2.4.  
However, the topology discovery services are discussed in detail as the follows.
## 4 Proposed System Models for Cardiovascular Disease Prediction
### Description of DB-Net and DVR-Net for Cardiovascular Disease Prediction
#### 4.4.2 Deep Vanilla Recurrent Network for Cardiovascular Disease Prediction  
DVR-Net is a parallel model in which data flows independently through two distinct processing paths: one with recurrent layers and another with fully connected layers, as shown in Algorithm 2. First, data pass through several dense layers. Hidden patterns in the data are captured by these layers as they gradually extract foundational feature representations using weighted connections and activation functions. Data are processed concurrently by recurrent layers. To construct meaningful feature representations, each layer uses connections that capture dependencies across time steps utilizing both past and present inputs. The final layer creates a complete feature set by concatenating the outputs from the dense and recurrent paths. The DVR-Nets prediction is then generated by passing this final combined representation through an output layer. To avoid the common vanishing gradient problems in DVR-Net, the parallel processing structure enables effective gradient flow in both pathways, which helps to ensure stable training. Richer and more varied representations are provided by DVR-Nets. Two separate pathways that capture different feature types make it more suitable for spotting hidden patterns.  
```
0: Dataset \(X\), labels \(y\)
0: Predicted labels \(\hat{y}\) for CVD classification
1:Input: CVD data \(X\)
2:Initialize Dense and Recurrent Layers
3: Pass \(X\) through multiple dense layers to capture static feature representations:
4: Dense Output \(\leftarrow\) Dense Layers(\(X\))
5: Pass \(X\) through multiple recurrent layers to capture sequential dependencies:
6: Recurrent Output \(\leftarrow\) Recurrent Layers(\(X\))
7: Concatenate the outputs of dense and recurrent layers:
8: Concatenated Output \(\leftarrow\) Concatenate(Dense Output, Recurrent Output)
9: Pass the concatenated output through the final prediction layer for CVD classification:
10:\(\hat{y}\leftarrow\) Prediction Layer(Concatenated Output) return\(\hat{y}\)
```  
**Algorithm 2**Deep Vanilla Recurrent Network for Cardiovascular Disease Prediction
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 4 Proposed Approach
### Calculation of Optimal Cluster Head Number  
In the traditional K-means algorithm, the primary number of clusters is set randomly in the clustering process. If the number of clusters is not set properly, the performance of FANETs deteriorates. When the number of clusters is small, the energy consumption for communication will increase, but if the number of clusters is large, the advantages brought by UAV clustering will be weakened. In the clustering process, to reduce the clustering overhead and utilize the bandwidth efficiently, the primary number of clusters needs to be optimally determined for the initial clustering center selection. According to [22], the throughput of a single node in the FANET can be expressed as  
\[T=\Theta\bigg{(}\frac{B}{\sqrt{N}}\bigg{)}, \tag{3}\]  
where \(N\) is the number of network nodes, \(B\) is the communication bandwidth, and \(\Theta(\cdot)\) is the asymptotically tight bound. After clustering, we can obtain the throughput of each CM, as given by  
\[T_{\text{CM}}=\Theta\bigg{(}B_{1}/\sqrt{\frac{N}{k}}\bigg{)}, \tag{4}\]  
where \(B_{1}\) is the bandwidth within the cluster and \(k\) is the number of clusters. In addition, the throughput of each CH is  
\[T_{\text{CH}}=\Theta\bigg{(}\frac{B_{2}}{\sqrt{k}}\bigg{)}, \tag{5}\]where \(B_{2}\) is the bandwidth between clusters. It assumes that the network traffic is uniformly distributed. Because of the throughput balance between the inter-cluster and the intra-cluster communications the proportion of \(T_{\text{CM}}\) used for traffic in/out to other clusters is \(\frac{k-1}{k}\), and the portion should be smaller or equal to \(T_{\text{CH}}\)[23], i.e.,  
\[\frac{k-1}{k}T_{\text{CM}}\leq T_{\text{CH}}. \tag{6}\]  
When \(\frac{k-1}{k}T_{\text{CM}}\) reaches the maximum throughput, namely, when inequality (6) holds, the primary number of clusters \(n\) is obtained as  
\[n=\frac{B_{2}}{B_{1}}\sqrt{N}+1. \tag{7}\]  
If \(N\) is large enough, the optimal number of clusters \(n\) is approximated to  
\[n=\frac{B_{2}}{B_{1}}\sqrt{N}. \tag{8}\]  
Based on the optimal number \(n\), K-means clustering algorithm can be used to divide \(N\) UAVs into clusters. The pseudocode of the K-means clustering algorithm is listed in Algorithm 1.  
```
0: The optimal number of clusters \(n\), The UAV nodes are modeled as a point set \(S=\{s_{1},s_{2},...,s_{n}\}\)
0:/*Initialization*/  randomly select \(n\) initial clustering centers uniformly from \(S\)
1:Repeat
2: calculate the distance from each node to each cluster center, and the node is assigned to the nearest cluster
3: after all nodes are allocated, the centroids of \(n\) clusters are recalculated
4:until centroids remain unchanged
5:return clusters set \(CL=\{CL_{1},CL_{2},...,CL_{n}\}\)
```  
**Algorithm 1** K-means Clustering Algorithm
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 3 Neural Network Architectures
### Vision Transformer  
The transformer is an end-to-end natural language processing (NLP) model proposed in 2017 that employs a self-attention mechanism to capture global dependencies and facilitate parallel processing [38]. Traditionally, CNNs have been regarded as the fundamental architecture in vision applications; however, transformers have recently emerged as a promising alternative [39]. The vision transformer (ViT) was the first transformer architecture applied to computer vision tasks and has been used for image classification, object detection, and video processing [40].  
In speech research, ViT has been applied to tasks such as speech emotion recognition [41] and biomedical signal classification [42] because it can capture both local and global features from speech represented as images. The architecture of our implemented model, shown in Figure 8, begins with the division of input spectrograms into smaller patches. These patches undergo linear embedding and are augmented with learned positional encodings before being processed by the transformer encoder. The encoder consists of multiple transformer blocks, each containing layer normalization followed by multi-head self-attention mechanisms and MLP layers with skip connections. This structure enables the model to learn relationships between patches at multiple scales, capturing both fine-grained details and global patterns in the speech signal. The final classification is performed through an MLP head with softmax activation, which produces age group predictions based on the encoded features.  
Figure 6: Multi-kernel 2D-CNN architecture.  
Figure 7: Receptive field (dotted rectangle) from parallel convolution layers (blue, green, and yellow rectangles).
## 3 Lightweight Cryptography for Smart Objects
### Public-Key (Asymmetric) LWC Algorithms
#### 3.2.2 Elliptic Curve Cryptography (ECC)  
ECC is an approach to public-key cryptography based on the algebraic structure of elliptic curves over finite fields. While RSA is based on exponentiation on finite fields, ECC depends on point multiplication on elliptic curves. An elliptic curve \(E\) over the finite field \(K\) (whose characteristic is not equal to 2 and 3) is defined as:  
\[E(K):y^{2}=x^{3}+ax+b\text{ with }a,b\in K\]  
Points \(P=(x,y)\in E(K)\) form an Abelian group, thus point addition and scalar point multiplication can be performed.  
ECC provides higher security and a better performance than the first generation public-key techniques (RSA and Diffie-Hellman). Moreover, ECC is the most interesting public-key cryptographic family for embedded environments because it can reach the same security level as RSA with much shorter keys, as shown in Table 2 ([[http://www.nsa.gov/business/programs/elliptic_curve.shtml](http://www.nsa.gov/business/programs/elliptic_curve.shtml)]([http://www.nsa.gov/business/programs/elliptic_curve.shtml](http://www.nsa.gov/business/programs/elliptic_curve.shtml))), and with computationally lighter operations, like addition and multiplication, rather than exponentiation.  
ECC has been accepted commercially and has also been adopted by standardizing institutions such as the American National Standards Institute (ANSI), the Institute of Electrical and Electronics Engineers (IEEE), the International Organization for Standardization (ISO), the Standards for Efficient Cryptography Group (SECG), and the National Institute of Standards and Technology (NIST) [39; 40; 41; 42; 43].  
The implementation of a lightweight hardware ECC processor for constrained devices is attracting a growing interest. In [23], a possible hardware implementation of a low-area, standalone, public-key engine for Elliptic Curve Cryptography, with a 113-bit binary field for short-term security and a 193-bit binary field for medium-term security, is presented. The choice of use of a binary field, rather  
\begin{table}
\begin{tabular}{c r r r r r} \hline \hline
**Symmetric-key size (bits)** & 80 & 112 & 128 & 192 & 256 \\ \hline
**ECC Key size (bits)** & 160 & 224 & 256 & 384 & 512 \\
**RSA Key size (bits)** & 1024 & 2048 & 3072 & 7680 & 15360 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of security levels between symmetric ciphers, ECC, and RSA (Recommended NIST key sizes).  
than a prime field, is related to the corresponding carry-free arithmetic, which fits well in hardware implementations. With respect to other ECC hardware implementations, the one presented uses a smaller area (in terms of GEs) and faster execution.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results
### Case II: ACA for M-QAM Signals  
The classification accuracy for M-QAM signals is presented in Figure 4. For all the considered cases of QAM, the ACA is approximately 100% at an SNR of 5 dB. As seen in Figure 4, it is evident that as the SNR increases from \(-10\) to 5 dB, the classification accuracy approaches satisfactory results. Figure 4 also shows the classification comparison of NLOS, i.e., from the GEO satellite to the user, and LOS, which is the GEO satellite to the IRS and the IRS to the user, and it is found that the ACA is much better for the LOS case as compared with NLOS. Table 3 shows the ACA comparison of local search and global optimization methods. From Table 3, it is clear that the GA-based optimization of Gabor features provides better results than local search methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR = 5 dB** & **GA** & **GA-PS** & **GA-FM** & **GA-FS** & **GA-FU** \\ \hline
**BPSK** & 99.37 & 97.20 & 96.30 & 96.43 & 97.76 \\ \hline
**QPSK** & 99.98 & 98.36 & 98.70 & 97.93 & 98.36 \\ \hline
**16-PSK** & 99.68 & 99.12 & 98.34 & 98.60 & 99.21 \\ \hline
**64-PSK** & 97.98 & 96.31 & 95.44 & 96.72 & 97.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ACA for M-PSK signals with IRS.  
Figure 2: Average classification accuracy for M-PSK signals (BPSK, QPSK, 16-PSK, and 64-PSK).  
Figure 4: Average classification accuracy for M-QAM signals (QAM, 16-QAM, 64-QAM, and 256-QAM).  
Figure 3: MSE for M-PSK signals. (**a**) BPSK; (**b**) QPSK; (**c**) 16-PSK; (**d**) 64-PSK.  
The mean square error for the M-QAM signals is shown in Figure 5. The four-class problem for the classification of M-QAM signals is represented by \(\Xi=[\mathbb{C}_{5},\mathbb{C}_{6},\mathbb{C}_{7},\mathbb{C}_{8}]\), where \(\mathbb{C}_{5},\mathbb{C}_{6},\mathbb{C}_{7}\), and \(\mathbb{C}_{8}\) represent the QAM-, 16-QAM-, 64-QAM-, and 256-QAM-modulated signals, respectively. As evident from Figure 5, the MSE for NLOS is more than the LOS's.
## 4 Discussion and Results  
### Optimization Measurement Technique of Rayleigh Angles  
The Rayleigh angle must be determined before evaluating the surface defects on the piston rods. Firstly, as shown in Figure 8, ultrasonic signals were transmitted based on the pulse-echo method, and two ultrasonic transducers were utilized to obtain the signals reflected from the crack to perform the Rayleigh wave test. The distance was set to 5 mm between the crack sample and the point where the ultrasonic waves propagated the sample surface.  
Although the Rayleigh angle is generally determined using [PERSON]'s law, the peak-to-peak amplitude of the ultrasonic waves received at the optimal incident angle (29.1\({}^{\circ}\)) was measured first since each material has different properties, [1; 13]. Secondly, the peak-to-peak amplitude at a random small angle \(\theta_{1}\) was measured. Finally, the peak-to-peak amplitude received at a random small angle \(\theta_{2}\), where random small angles \(\theta\) can be repeated multiple times, was measured. The largest peak-to-peak amplitude of the three values became the Rayleigh angle used to perform the Rayleigh wave test. Figure 9 shows the Rayleigh wave angles determined using the pulse-echo technique to obtain the maximum peak-to-peak value. The results in Figure 9 were obtained in tests where the pulse-echo technique was applied using the ultrasonic inspection system, and the transmitting and receiving transducers were fixed and scanned while maintaining the  
Figure 7: Typical measurements of peak-to-peak amplitude in 700 \(\upmu\)m cracks; (**a**) Non-crack, (**b**) Circum crack, (**c**) Axial crack, and (**d**) Shear crack.  
Rayleigh angle. The incident point of the incident beam was fixed to 5 mm before the defect was fabricated. Figure 9a shows the peak-to-peak amplitude received after adjusting the random small angle \(\theta_{1}\) to \(28.6^{\circ}\), and Figure 9b shows the peak-to-peak amplitude received after adjusting the angle to \(29.1^{\circ}\). Figure 9c shows the peak-to-peak amplitude received after adjusting the random small angle \(\theta_{2}\) to \(29.6^{\circ}\). Figure 9d compares the three peak-to-peak amplitudes according to the Rayleigh angle. Therefore, the Rayleigh angle was set to \(29.1^{\circ}\), which had the largest peak-to-peak amplitude.  
Figure 8: A method to determine the Rayleigh angle on a piston rod with a crack.  
Figure 9: A-scan images of incident angles for Rayleigh waves: (**a**) \(28.6^{\circ}\), (**b**) \(29.1^{\circ}\), (**c**) \(29.6^{\circ}\), and (**d**) peak-to-peak.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 5 Application of DIKWP-TRIZ
### DIKWP-TRIZ Versus TRIZ  
#### 5.3.1 Traditional Framework of TRIZ and Its Limitations  
In Table 3, the traditional TRIZ framework primarily focuses on physical contradictions, such as the trade-off between strength and weight, while it overlooks more abstract issues like cognition, semantics, and ethics. Moreover, TRIZ's approach is grounded in handling known, well-defined contradictions within technical systems. Its efficacy diminishes when confronted with incomplete, inconsistent, or imprecise Data. TRIZ assumes an abundance of technical Data to analyze contradictions and apply its principles, making it more suited to optimizing known parameters rather than addressing problems characterized by uncertainty.