esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Convolutional Cross-Substitution Transformer Module
#### 3.2.2 Convolutional Feedforward Layer  
In general, the feedforward layer typically employs two fully connected layers to delve deeper into feature extraction. Nonetheless, convolutional operations are more adept at capturing spatial features and extracting nuanced local details. Therefore, in this study, convolutional operations are utilized as feedforward layers. The feed-forward layer structure, as applied in this paper, is delineated in the Figure 5. In the spatial branch, a convolution kernel of size \(3\times 3\) is harnessed to extract spatial information from the ascending number of channels. Subsequently, a convolution of size \(1\times 1\) is employed to adjust the channel number and fortify the network's spatial expression capability. Analogously, within the spectral branch, a convolution kernel of size \(1\times 1\times 7\) is deployed to further extract spectral features, while a convolution of size \(1\times 1\times 1\) is utilized to regulate the channel number and enhance the network's spatial expression capacity.  
```
1:\(x\): Input tensor of shape (Batch_Size, Channels, Width, Height, Depth).
2:\(dim\): Number of input channels (default = 24).
3:proj_kernel: Size of the convolutional kernel (default = (1, 1, 7)).
4:heads: Number of attention heads (default = 4).
5:dim_head: Dimension of each attention head (default = 16).
6:Initialize:
7:inner_dim \(\leftarrow\) dim_head \(*\) heads.
8:padding \(\leftarrow\) proj_kernel //2.
9:scale \(\leftarrow\) dim_head\({}^{\wedge}(-0.5)\).
10:to_q \(\leftarrow\) DepthWiseConv3D(dim,inner_dim,proj_kernel,padding,stride_1=1,bias=False).
11:to_k \(\leftarrow\) DepthWiseConv3D(dim,inner_dim,proj_kernel,padding,stride_1=1,bias=False).
12:pool_q \(\leftarrow\) MaxPool3d\(((1,1,7),stride=(1,1,7))\).
13:pool_k \(\leftarrow\) MaxPool3d\(((1,1,7),stride=(1,1,7))\).
14:to_out \(\leftarrow\) Sequential\((Conv3d(inner\_dim,dim,kernel\_size=1),Dropout(dropout))\). Function Forward(x):
15:shape \(\leftarrow\) x.shape.
16:\(h,n,ww,hh,c,h\) \(\leftarrow\) shape, heads.
17:// Linear projections
18:\(q\gets\) to_q_x // Query.
19:\(k\gets\) to_k(\(x\)) // Key.
20:// Pooling of queries and keys
21:pool_q \(\leftarrow\) pool_q(\(q\)).
22:pool_k \(\leftarrow\) pool_k(\(k\)).
23:// Compute attention for first stage
24:\(attn1\gets attention(pool\_q,k,q)\).
25:// Compute output values based on first attention
26:\(out\_v\gets attn1\).
27:// Compute attention for second stage
28:\(attn2\gets attention(q,pool\_k,out\_v)\).
29:// Compute final output
30:\(out\gets attn2\).
31:// Reshape to output format
32:\(out\gets rearrange(out,^{\prime}(bh)c(xyd)->b(hd)xyc^{\prime},h=h,y=hh,x=ww)\).
33:// Apply final linear layer
34:return\(to\_out(out)\).
```  
**Algorithm 2** Convolutional cross-substitution Attention used for spectral feature extraction  
Figure 5: Convolutional feedforward layer.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## 2 Synthetic Data Generation
### Synthetic Data Generation with XGBoost
#### Advantages of XGBoost for Synthetic Data Generation  
* Ensemble methods such as XGBoost are widely used in the data science community due to their solid out-of-the-box performance [23] allowing, e.g., also non-linear relationships. This solid out-of-the-box performance makes the XGBoost algorithm an optimal candidate for synthetic data generation, as the underlying distributions are often unknown.
* The key improvements to previous ensemble methods are subsampling the data set with replacement and choosing a random feature set. These improvements lead to highly independent base classifiers, and thus the XGBoost algorithm is robust to overfitting.
* The XGBoost algorithm is scalable and generally faster than the classical gradient boost machine (GBM), which makes it an excellent choice for generating large synthetic data sets.
* Another important advance that makes this algorithm convenient in practice is its sparsity awareness. With the sparsity-aware split finding, it works with missing values by default. The split-finding algorithm learns the default direction at each branch directly from the non-missing data.
* Cache awareness and out-of-core computing are improvements that allow XGBoost for parallel computing.
* For both regression and classification, both types are implemented in various software products, such as the R package XGBoost (see [20]).  
However, if existing XGBoost implementations are used without adaptations to simulate synthetic data, complex structures, such as people in households and complex sample designs, cannot be taken into account. In this contribution and the software provided simPop, the application of XGBoost is adapted in such a way (see Section 2.3.3) to allow synthesizing complex data and procedures made available for hyperparameter tuning. In this contribution, we have adapted the application of XGBoost (and provided the new tools in the software simPop) to synthesize complex data. We have also made available procedures for hyperparameter tuning. See Section 2.3.3 for more details.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 5 Simulation Results
### The Simulation Results of TDMP Normalization Factor  
In this section, we calculated the normalization factor when the SNR was 1.7, 1.8, and 1.9 dB based on density evolution. The simulation results show that the normalization factor was stable at around 0.5, as shown in Figure 14, which can be used in the following simulations. Based on the density evolution process, the relationship between BER and the number of iterations of the BP algorithm, TDMP algorithm, and normalization factor simplification algorithm (TDMP-N) was simulated, the results are shown in Figure 15. Under the AWGN channel, the BER performance of LDPC codes with a code length of 2304 at different SNRs was simulated, as shown in Figure 16. The average number of iterations at different SNRs was simulated as shown in Figure 17. The convergence speed was analyzed from the perspective of density evolution. The simulation results in Figure 15 show that TDMP-N converged slightly faster than TDMP. The simulation results show that the TDMP-N convergence speed was slightly faster than TDMP, and the average iteration number was similar to TDMP. It indicates that the calculation process of TDMP-N was simpler and more convenient for hardware implementation in the case of less loss of BER performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Iteration SNR (dB)** & **0 dB** & **1 dB** & **2 dB** & **3 dB** & **4 dB** \\ \hline \multirow{2}{*}{DE} & BP & 20 & 20 & 13 & 5 & 3 \\  & TDMP & 10 & 10 & 7 & 3 & 1 \\ \multirow{2}{*}{GA} & BP & 20 & 20 & 9 & 4 & 2 \\  & TDMP & 10 & 10 & 7 & 2 & 1 \\ \multirow{2}{*}{Actual} & BP & 20 & 20 & 18 & 6 & 4 \\  & TDMP & 10 & 10 & 8 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Iteration times of BP and TDMP algorithms under different SNRs in DE, GA and actual simulation.  
Figure 13: Iterations needed for decoding correctly under different SNRs.  
Figure 16: The relationship between BER and SNR.  
Figure 14: The normalized factor calculated under different iteration times.  
Figure 15: The relationship between different iteration times and BER.
## 6 Conclusions and Discussion  
1. This paper combines an improved AHP method with an improved entropy weight method and uses game theory to ensure the reliability of the index weighting. On this basis, a cloud model is introduced, the actual situation of each index is reflected by the cloud parameters, and the evaluation results regarding the EPS performance are visually displayed by cloud images. The cloud model, which is based on the combination weighting method, not only gives full play to the advantages of the subjective and objective weighting method but can also directly reflect the evaluation grade of the EPS's performance and the fuzziness of the results; this improves the scientific evaluation of the multi-level system.
2. Through the comprehensive evaluation of the EPS system, it is concluded that the parameters of the comprehensive cloud model of the EPS are \(U\) (74.31, 6.08, 0.96). The results of the cloud model show that the EPS is in good working condition and that the main parts are less defective. However, the maintenance and repair of the parts that are easily damaged should be paid special attention in order to ensure the handling, stability, and safety of the vehicle.
3. This paper combines the subjective and objective weights of multiple factors using combination weighting in game theory. When this method is applied in a multi-level EPS evaluation system, the scientific and practical capacity of quantitative weights can be guaranteed at the same time. However, when cloud theory is applied to evaluate a multi-level system, both the weighted cloud model and the subordinated cloud model in this paper are developed based on the normal cloud model, but the normal cloud model cannot cover all the characteristics of the system. In the subsequent evaluation, different cloud model types should be adopted to produce an overall evaluation of the working environment of the EPS. Thus, more accurate and reasonable results can be obtained.  
Conceptualization, Z.D. and A.D.; methodology, Z.D. and A.D.; software, Z.D.; validation, A.D., formal analysis, C.Y.; investigation, J.T.; resources, C.Y. and J.T.; data curation, Z.D. and Y.C.; writing--original draft preparation, Z.D. and A.D.; writing--review and editing, Z.D. and Y.C.; visualization, Z.D.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key R&D projects in Shaanxi Province (2023-YBNY-241). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions on privacy. The authors declare no conflicts of interest.
## 2 Materials and Methods  
In LNMRS, resolution nodes at each level form a tree management structure according to service area inclusion relationship. The structure is defined and described in patent [30] and standards [20; 21]. Paper [19] clarifies the mathematical principles of this structure and proposes algorithms for the construction of this structure, and paper [31] deals with the server placement problem in this system.  
Figure 1 shows the service area inclusive relationship of resolution nodes in two levels, as well as the relationship with users in the physical network. The underlying network represents the physical network, the gray solid dots represent users, the two upper layers represents the distribution of resolution nodes at level \(i+1\) and level \(i\), black solid dots represent resolution nodes, dashed circles represent the service areas of the resolution nodes. Dash circles in the physical network correspond to service areas in the two upper layers. The service area inclusive relationship between nodes in neighbor levels reflects the parent-child relationship of these nodes in the tree structure.  
The following features of the LNMRS Structure will be used afterwards.  
* A resolution node provides a name resolution service with deterministic service latency only to users in its service area.  
Figure 1: Service area inclusive relationship among name resolution nodes in higher and lower levels of LNMRS. A resolution node in higher levels maintains a larger service area, which is consisted of the service areas of its children nodes.  
* A user in the service area of a child node must also be in the service area of its parent node, a user in the service area of the parent node must also be in the service area of a particular child node of this parent node.
* Deterministic service latency value of name resolution nodes in the same level is the same, while the deterministic service latency value of high-level name resolution nodes is larger than that of low-level name resolution nodes.
# Non-Crystallographic Symmetry in Packing Spaces
## 3 Examples of Permutation Groups on Packing Spaces
### The Pauli Matrices Group  
We apply the technique for imaging the symmetry group with the help of packing spaces to Pauli matrices, which are well known in physics. For this purpose, we will expand their number to the total subgroup, using the operation of matrices multiplication. The operation of the Pauli matrices multiplication in quantum mechanics is defined and leads to the commutation relations.  
The Pauli matrices group is a subgroup of the order 16:  
\(e=\begin{pmatrix}1&0\\ 0&1\end{pmatrix}\) (0); \(-e=\begin{pmatrix}-1&0\\ 0&-1\end{pmatrix}\) (1); \(\sigma_{x}=x=\begin{pmatrix}0&1\\ 1&0\end{pmatrix}\) (2);  
\(\sigma_{y}=y=\begin{pmatrix}0&-i\\ i&0\end{pmatrix}\) (3); \(\sigma_{z}=z=\begin{pmatrix}1&0\\ 0&-1\end{pmatrix}\) (4); \(\sigma_{-x}=-x=\begin{pmatrix}0&-1\\ -1&0\end{pmatrix}\) (5);  
\(\sigma_{-y}=-y=\begin{pmatrix}0&i\\ -i&0\end{pmatrix}\) (6); \(\sigma_{-z}=-z=\begin{pmatrix}-1&0\\ 0&1\end{pmatrix}\) (7); \(\sigma_{xy}=xy=\begin{pmatrix}i&0\\ 0&-i\end{pmatrix}\) (8);  
\(\sigma_{yx}=yx=\begin{pmatrix}-i&0\\ 0&i\end{pmatrix}\) (9); \(\sigma_{yx}=yz=\begin{pmatrix}0&i\\ i&0\end{pmatrix}\) (10); \(\sigma_{xy}=zy=\begin{pmatrix}0&-i\\ -i&0\end{pmatrix}\) (11);  
\(\sigma_{xz}=xz=\begin{pmatrix}0&-1\\ 1&0\end{pmatrix}\) (12); \(\sigma_{zx}=zx=\begin{pmatrix}0&1\\ -1&0\end{pmatrix}\) (13); \(\sigma_{xyz}=xyz=\begin{pmatrix}-i&0\\ 0&-i\end{pmatrix}\) (14);  
As a result, we obtain the following Cayley table for the Pauli matrices group (Table 11).  
Figure 15: Compliance of the quaternion symmetry group and dual quaternions in PS: (**a**) P81\({}_{1}\) (2 3 1 0 7 6 4 5); (**b**) PS P16 1\({}_{1}\) (4 5 6 7 2 3 0 1 14 15 12 13 89 10 11).  
We choose to visualize a 4-color transformation g(8) = (8 9 6 2 15 3 5 14 1 0 12 13 11 10 4 7) = (0819) (2653) (4 15 7 14) (10 12 11 13) in the packing space P82\({}_{2}\). The result is presented in Figure 16.  
Package code in the program partition into polyomino (at [7]): 3232132310322230.  
Layer growth leads to the growth of shape in the form of a parallelogram, which is also characterized by an element of the Pauli matrices group (Figure 17).  
The sequence of growth is demonstrated on the diagram in Figure 18. Numerical information is placed on the three linear functions, which correspond to the layer-growth studies of partition into polyomino and other pieces of plane periodic mosaics [16, 17].  
\begin{table}
\begin{tabular}{c|
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 2 Background
### Quantization Frameworks  
The two most common frameworks that already provide functionalities to quantize for quantizing DL models are Tensorflow and Pytorch. These frameworks aim to quantize DL models that can run on resource-constrained CPU- or GPU-based hardware. However, these frameworks do not support FPGA-type hardware accelerators. On the other hand, Brevitas is a new Pytorch library for designing QNN models with different extraction levels that can be deployed to Xilinx FPGA via the experimental framework FINN [14]. Table 1 shows the currently most widely used quantization frameworks and the hardware they support.  
Since the interest of our work is oriented to build DL models that can be run on devices with limited computing resources such as FPGA, we focus on the Brevitas framework as it is the only framework that allows the design, development, and execution of QNN in FPGA [14; 17; 18; 19; 20]. Figure 2 shows the workflow followed by Brevitas, where:  
1. The model is developed and trained using Brevitas since it allows designing QNNs with different quantization levels oriented to their deployment in FPGAs. One distinguishing factor of Brevitas is its ability to allow input features to be quantized. Quantizing the input features is an aspect to consider since it is a feature that is not implemented in other frameworks and provides one more source of additional reduction that counts a lot in very constraineddevices such as the FPGA.
2. Once the model is trained, it is exported using the ONNX standard. ONNX enables DL models to be transformed into a portable format. This preliminary step is carried out to bring the DL model to specific hardware.
3. Once the model is in a standard format, it can be translated into the FPGA logic by FINN, using the Finn-hlslib Vivado HLS FPGA library.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Frameworks** & **Technique** & **Supported Hardware** \\ \hline \multirow{3}{*}{**TensorFlow Quantization**} & Post-training float16 quantization & CPU, GPU \\  & Post-training dynamic range quantization & CPU, GPU (Android) \\  & Post-training integer quantization & CPU, GPU (Android), EdgeTPU, Hexagon DSP \\  & Quantization-aware training & CPU, GPU (Android), EdgeTPU, Hexagon DSP \\ \hline \multirow{3}{*}{**Pytorch Quantization**} & Dynamic/weight only quantization & x86 CPUs with AVX2 support or higher. \\  & Static quantization & ARM CPUs (mobile/embedded devices) \\  & Dynamic quantization & NVidia GPU \(\
eg\) TensorRT through fx2 tr. \\ \hline \multirow{3}{*}{**PyTorch Brevitas**} & Quantization-aware training & \\  & - Integer quantization & FINN \(\
eg\) Xilinx FPGA \\ \cline{1-1}  & - Binary quantization & \\ \cline{1-1}  & - Ternary quantization & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The table above gives us a general idea of the frameworks for quantized neural networks.
# Cyber-Attack Scoring Model Based on the Offensive Cybersecurity Framework
## 3 Offensive Cybersecurity Framework
### Encryption  
Encryption is the crucial technique used in cyber-attacks for various purposes. Traditional encryption techniques are used to encrypt confidential information to protect adversary users. The first encryption method was a substitution encryption. Representative encryption methods are the Caesar Cipher (B.C. 500) and the Monoalphabetic Cipher. Then, Polygram substitution, such as the Vigenere cipher (1585\(\sim\)1863) and Enigma cipher (1930), were developed. The creation of modern computing systems was followed by the development of symmetric encryption methods such as the Data Encryption Standard (DES) and Advanced Encryption Standard (AES). These two standards use the same key for encryption and decryption. As a result, an asymmetric encryption method was developed to share the secret key securely. Representative techniques in an asymmetric encryption are the Diffie-Hellman Key Exchange, Rivest-Shamir-Adleman (RSA) and the elliptic-curve cryptography (ECC) method. In the case of developed websites, login credentials, such as passwords, need to be saved in secure databases. In view of this circumstance, hash algorithms (MD5, SHA1 and SHA2) are used to protect users' credentials in the database.  
Ransomware uses a symmetric encryption method to encrypt users' valuable files such as images and documents. Ransomware also uses an asymmetric encryption algorithm with the attacker's private key to protect the encryption key that is used in a symmetric encryption [25]. In the era in which state-sponsored hackers are becoming increasingly active, these more elegant attackers are using various encryption methods to hide traces  
Figure 2: Offensive Cybersecurity Taxonomy.  
of their activities. After gaining confidential information from a victim, they use various encryption methods to evade being monitored by the victim's system and network. APT28 (sponsored by Russia) uses encrypted POST data to send a command to command and control (C&C) server with an obfuscation base64 (block cipher). The encryption schemes used from an offensive cybersecurity perspective are listed in Table 2, and the cases in which these methods were used in cyber-attacks are compared.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 2 Materials and Methods
### Range Ambiguity Resolution Algorithm
#### 2.3.1 Range Ambiguity Theory  
In the HRWS 3D imaging process of distributed SAR, the radar echo from two successive pulses will overlap at the receiver due to the high PRF, resulting in range ambiguity. Meanwhile, the range ambiguity comes from the side lobe of the antenna pitch beam. When the side lobe is strong enough, if the difference between the two-way delay of the echo in the sidelobe irradiation area and the two-way delay of the echo in the observation band is equal to an integral multiple of the pulse repetition period, the echo signals will be mixed, as illustrated in Figure 5, where C is the speed of light, _Tr_ is the pulse length, and _CTr_/2 is the target range.  
Figure 4: The overall flowchart of the image registration and range ambiguity resolution algorithm.  
To avoid range ambiguity, scene echoes two pulses of antenna transmission need to be fully received by the system, and the PRF must be low enough so that all near-to-far echo signals within the range of the SAR antenna beam are in the receiving window. In this case, the PRF should satisfy the following condition:  
\[PRF\leq\frac{c}{2W_{g}\sin\theta_{f}} \tag{5}\]  
where \(\theta_{f}\) denotes the incidence angle at the far end of the scene, \(W_{g}\) denotes the mapping bandwidth, and \(c\) denotes light speed.
## 4 Discussion  
The combination of analytical and numerical calculations, coupled with the parameter analysis, offers valuable insights into the performance of the ERS-TQZS VI. Our observations reveal that the ERS-TQZS VI configuration surpasses existing alternatives, such as the conventional TL VI and the TQZS VI, particularly in terms of its superior capability for attenuating low-frequency vibration. The results indicate that the ERS-TQZS VI can considerably attenuate resonance peaks of low-frequency vibration. Furthermore, the parameter study revealed that decreasing the shunt resistors improves low-frequency vibration attenuation while also maintaining the maximum energy harvested for a given excitation force. Additionally, the maximum output power at the resonant frequency exhibits a notable increase with higher excitation amplitude. These findings underscore the importance of maintaining optimal values for the remaining parameters, as deviating from these values can negatively impact the overall performance of the ERS-TQZS VI system.