esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware-Based Architecture for DNN Wireless Communication Models
## 4 Proposed DNN-Based MIMO OFDM Models
### Model Type III  
The main task of the previous DNN models is to improve the receiver performance. However, in these designs, our DNN models are added as add-ons and still almost look like a conventional MIMO OFDM receiver. In model type III, the conventional DSP signal demapping and detection modules at the receiver are replaced by the DNN model. It makes the MIMO OFDM receiver simpler. In addition, model type III can help improve performance compared to conventional receivers. The architecture is shown in detail in Figure 5.  
Figure 4: Proposed Model Type II.  
In this model, we have two DNN models for the 2 \(\times\) 2 and 4 \(\times\) 4 MIMO OFDM systems. The numbers of neurons for the 2 \(\times\) 2 model in each layer are 512, 512, 256, 128, and 16, respectively. The numbers of neurons for the 4 \(\times\) 4 model in each layer are 1024, 512, 256, 128, and 8, respectively. The input number corresponds to the entire pilot's real and imaginary part numbers and the received signal. Note that the pilot symbols are necessary for model type III to avoid detection ambiguity since there is no explicit channel equalization. Every 4-bit (2 \(\times\) 2 model) or 8-bit (4 \(\times\) 4 model) of the transmitted data is grouped and predicted based on a single model trained independently, which is then concatenated for the final output, like model type I. The ReLU function is used as the activation function in hidden layers for both 2 \(\times\) 2 and 4 \(\times\) 4 MIMO OFDM systems. In the output layer, the Sigmoid function is applied the same as model type I for the 4 \(\times\) 4 MIMO OFDM model, and the Softmax function is applied to predict the output for the 2 \(\times\) 2 MIMO OFDM model.  
\[F_{Softmax}(x_{i})=\frac{e^{x_{i}}}{\sum e^{x_{i}}} \tag{16}\]  
In particular, we use Softmax for the output layer of the 2 \(\times\) 2 model, so we need to build a one-hot encoder table for the 2 \(\times\) 2 MIMO OFDM model with QPSK modulation. Because the output layer of the 2 \(\times\) 2 model has 16 nodes at the output, it generates 4 bits of output in one execution. Table 1 shows an example of one-hot encoding for the 2 \(\times\) 2 MIMO OFDM model with QPSK modulation, which is used in this model type III.  
Figure 5: Proposed Model Type III.
## 4 Discussion  
From the performance comparison of different aspects several main findings were obtained by comparing the performance of the SVR/LSTM/Deep-Neuro-Fuzzy/TDFWNN/ResNet-BiLSTM based on attention.  
Firstly, the deep learning network (ResNet-BiLSTM) achieves the best performance. Our framework can capture the local dependence between the temporal dimension and spatial location. It is more suitable for extracting and estimating time-series data. Secondly, the attention mechanism enabled ResNet-BiLSTM to obtain better generalization ability, which could automatically learn feature weights and extract important features of the sEMG signal.  
In addition, since the duration of muscle contraction is longer than the size of the sliding window, the results proved that our model can still achieve good performance.  
Figure 12: Accuracy comparison of different algorithms (NRMSE and Coefficient of determination).  
This may be because the long-term shrinkage activity can be regarded as a composition of several short-term shrinkage patterns. The model can find and extract motion features even if the window size cannot contain a complete shrinkage pattern.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Phase and Communication Procedure
#### 3.1.2 \"Hand Shake\" Phase  
The \"Handshake\" phase begins when the client successfully establishes a TLS connection with the server. The following steps outline the information exchange process:  
1. **Step 1**: The client generates public and private keys based on the Kypber algorithm. Then, the public key is packed and sent to the server.
2. **Step 2**: The server unpacks the public key from the client. Then, the server encapsulates a shared key with the client's public key and sends it back to the client as a cipher.
3. **Step 3**: The client decapsulates the cipher with its private key and extracts the shared key.
4. **Step 4**: The client and server use the shared key with AES-GCM encryption for all future communications.  
The \"handshake\" phase implements the Kyber-KEM described in [4]. Compared to DH-KEA, where the cryptography is based on a discrete logarithm problem that can be solved efficiently by [PERSON]'s algorithm [24], the cryptography in Kyber-KEM is based on the Learning With Errors (LWE) problem. The LWE problem involves finding a secret vector given a series of linear equations with noise, which is hard even for quantum computers due to the difficulty of lattice problems [25].  
AES-GCM is considered relatively quantum-safe compared to the encryption scheme DH-KEA due to its basis in symmetric key cryptography, which quantum computers cannot break as easily. While [PERSON]'s algorithm provides a quadratic speedup, reducing the effective key length, a 256-bit AES key would still require 218 operations to break, which remains computationally infeasible. Additionally, AES-GCM provides authentication alongside encryption, enhancing security.  
Testing and evaluations of post-quantum cryptographic algorithms, including AES-GCM and Kypber KEM, have been integrated into various cryptographic libraries, such as the wolfFSSL library [2], in collaboration with the Open Quantum Safe project. These evaluations are part of ongoing efforts by organizations like the National Institute of Standards and Technology (NIST) [26] to ensure the robustness of cryptographic methods against quantum threats. For example, performance and security evaluations on FPGA platforms have demonstrated the feasibility and strength of AES-GCM in post-quantum scenarios [27].
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Field-Effect Transistors (FET)  
Field-effect transistors are voltage-controlled [6] devices consisting of two electrodes (the source and the drain electrode), a semiconducting channel, an insulating gate, and a conducting gate electrode [13]. The current flows between the source terminal and the drain terminal through the semiconducting channel by applying a source-drain potential. Voltage applied between the source terminal and the gate terminal controls the current flow between source and drain as well as the conductivity of the conduction channel. For a constant source-gate voltage, exposure to gaseous analytes can affect the conductivity of the conduction channel [6; 13]. Notably, the main difference between chemiresistors and FET sensors is the ability of the latter to provide not only current variations but also threshold voltage changes upon analyte exposure [142].  
Depending on the gaseous analyte-type and the carrier type of the channel material (holes/electrons), the charge carrier concentration of the semiconducting channel material can be changed upon device exposure. In the case of an n-channel FET, oxidizing gas exposure reduces the majority carriers of the channel region thus decreasing the current flow. In the case of exposure to a reducing gas or of a FET with a p-type channel exposed to an oxidizing gas, the current flowing through the channel increases. Measurement of current variations permits the detection as well as the quantification of analytes after appropriate calibration [13]. The applied gate voltage permits control of sensitivity, providing it is set so as to permit the maximum conductance variation [142].  
A chemical FET can also possess a gas-selective coating/a series of coatings, between the transistor gate and the analyte. Different chemical modification of the gate allows reaction with different chemical species hence permitting their differentiation [43; 143]. For ion sensitive FETs charged species at the sensing interface of the gate, change the polarization of the underlying semiconductor/dielectric interface. Electron conductance through the semiconducting channel is sensitive to gate polarization and the chemical modification of the gate can either attract or repel the semiconductor-charge carriers. Thus, by measuring the source-drain current, the polarization of the sensitive interface is determined [51].  
Channel conductivity is also affected by the gas-analyte polarity. Adsorption (molecular gating) of polar molecules on the outer surface of the conducting channel is considered to provoke changes in the electric field. Molecular binding of non-polar molecules can potentially change the density of charged surface states of the functionalized semiconductor surface, due to analyte induced conformational alternations, or can affect the dielectric medium close to the semiconductor surface [6; 144].
## 3 Experimental Results
### Characteristics of Fracture Propagation
#### 3.1.4 Morphologies of the Main Fracture Surfaces  
Since tortuosity is difficult to evaluate transverse and oblique fractures, a three-dimensional scanner is used to get the morphologies of the main fracture surfaces and evaluate their roughness. Raw coordinate data points of the main fracture surface can be obtained by three-dimensional scanning and a digital surface is reconstructed by the Geomagic Studio software. The morphologies of typical fracture surfaces are shown in Figure 10.  
To quantitatively analyze the roughness of main fractures, Area Ratio (AR) is introduced which is defined as the ratio of actual area to projected area of the main fracture surface. In this work, the actual area can be calculated by the reconstructed surface, and the projected area is the fracture surface on its best fitting surface. A higher AR signifies that the main fracture surface is rougher. Table 5 shows the values of AR of the main fracture surfaces.  
Figure 8: A schematic of the fracture tortuosity calculation.  
Figure 9: The tortuosity of longitudinal fractures on the vertical surfaces of specimens in group A, B and D.  
Comparing the AR of longitudinal fractures in hard (group A), soft (group B) and soft-hard (group D) rocks (Figure 10(a)), the result is consistent with the tortuosity of longitudinal fractures in the three different types of rocks (Figure 9), which indicates that the longitudinal fracture in soft rocks is the roughest. The overall AR is the weighted average of each main fracture AR in the rock with actual area as the weight, shown in Table 5 and Figure 10(b). The injection pressure has no significant effect on the overall AR of specimens in the same group, and the overall AR of main fractures in soft rocks (group B) is obviously higher than that of other specimens, which means the main fractures in soft rocks are the roughest.  
Figure 10: The morphologies of typical fracture surfaces: (**a**) longitudinal fracture in specimen A1; (**b**) oblique fractures in specimen B1; (**c**) transverse fracture in specimen C3-1; (**d**) transverse fracture in specimen D2-1; (**e**) longitudinal fracture in D2-1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Specimen NO.** & **Main Fracture Type** & **AR _()_** & **Overall AR _()_** \\ \hline A & A1 & longitudinal fracture & 1.067 & 1.067 \\  & A3 & longitudinal fracture & 1.070 & 1.070 \\  & B1 & longitudinal fracture & 1.151 & 1.162 \\  & B2 & oblique fracture & 1.170 & 1.162 \\  & B2 & longitudinal fracture & 1.192 & 1.239 \\  & B3 & oblique fracture & 1.332 & 1.239 \\  & C2-1 & transverse fracture & 1.181 & 1.188 \\ C & C3-1 & transverse fracture & 1.073 & 1.073 \\  & C3-2 & transverse fracture & 1.082 & 1.082 \\  & D1-2 & transverse fracture & 1.071 & 1.087 \\  & D2-2 & longitudinal fracture & 1.132 & 1.089 \\  & D3-2 & transverse fracture & 1.054 & 1.089 \\  & D3-2 & longitudinal fracture & 1.142 & 1.096 \\  & D3-2 & transverse fracture & 1.066 & 1.096 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Area Ratio (AR) of the main fracture surfaces.
## 4 Results and Discussion
### Execution Time Analysis of SpMV  
We performed experiments on SpMV computations with multiple block sizes and calculated the minimum, maximum, and average execution times of SpMV, as shown in Equations (4)-(6), respectively.  
Figure 4 shows the execution time comparison of the SpMV computation with various random multiple block sizes. The minimum, maximum, and average execution times of each matrix from the different application domains are shown in Figure 4. The dataset was sorted by nnz (minimum to maximum). Max time is a worst-case scenario in which the random block size is chosen with the maximum execution time. The average time is calculated as the aggregated execution time of the SpMV computation with multiple block sizes. The minimum execution time is the least execution time recorded against each matrix, and the associated block sizes are chosen as the target variable. The X-axis shows the different matrices from various application domains, and the Y-axis shows the execution time of SpMV computations on a logarithmic scale showing clear execution time difference between optimal and random selection of block sizes.  
\begin{table}
\begin{tabular}{l l l} \hline
**Software** & **Tools/Library** & **Version** \\ \hline Operating System & Windows 10 pro & 18,363.904 \\ OpenMP & OpenMp & Openmp-5.1 \\ Compiler & Visual Studio professional 2019 & Visual studio 16.5 \\ Python & Google Colab & Py 3.6.7. \\ Scikit-Learn & Sklearn & 0.23.1 \\ \hline \end{tabular}
\end{table}
Table 4: Software specifications.  
Figure 5 shows the comparison of execution time of the SpMV computation on the row-sorted data set (minimum to maximum). The X-axis shows the different matrices from various application domains sorted by row (minimum to maximum), and the Y-axis shows the execution time of SpMV computations on a logarithmic scale.  
The pre-processing or format conversion cost is highlighted in Figure 6, which shows execution time comparison with and without including the conversion time of CSR to BCSR format. The X-axis shows matrices in our dataset sorted on number of rows from minimum to maximum. Depending on the structure, CSR performs well for some matrices but overall BSCR (including conversion cost) shows better performance against matrices, included in our dataset.  
Figure 4: Execution time comparison of SpMV computation (dataset sorted by nnz -min to max).  
Figure 5: Execution time comparison of SpMV computation (dataset sorted by row-min to max).
# Multiparameter Analysis of the Ultrasonic Transducer Transfer Function Using a Genetic Algorithm
## 2 Transfer Function of Piezoceramic Transducer
At certain impedances of the ceramic material and the medium, the transfer function can be shaped by using a system with multicomponent matching layers that have defined impedances and thicknesses. One of the methods for selecting matching layer impedances is to calculate them in accordance with a Chebyshev, DeSilets or Souquet criterion. Figure 2 shows the transfer characteristics for a transducer operated in air with one or two matching layers, with the layer impedances being calculated in accordance with the above criteria (the assumed thickness of layer \(l_{i}\) was equal to \(\lambda/4\)). These characteristics were compared with the characteristics of the transducer without a matching layer and standardized by the maximum value of this characteristic for the frequency value \(f_{0}\). Figure 1: Model of a piezoceramic transducer with multicomponent matching layers. Figure 2 confirms that a change in the number and impedance values of the matching layers allows a certain influence on the shape of the transducer transfer function. However, the impedance values calculated from the Chebyshev, DeSilets or Souquet criteria are theoretical values. Finding a material for the matching layer that has a certain impedance may be problematic.
## 1 Introduction
For the breast ultrasound CAD system, a Support Vector Machine (SVM) is extensively employed as a preferred feature-based classification algorithm due to its effectiveness in predicting and diagnosing breast cancer, achieving optimal performance by utilizing various performance metrics. Moreover, the SVM model is considered as a reliable classifier in a CAD system because they train effectively in less time and offer efficient computational performance [33]. Several studies also demonstrated the superiority of the SVM classifier over other machine learning classification algorithms such as Random Forest, Decision Trees, Neural Network, Logistic Regression, etc. [34; 35; 36; 33]. A recent study by [PERSON] et al. [35] found that SVM achieved a higher accuracy of 97.66%, along with better results in other performance metrics, in comparison to other classifiers. They also marked that SVM takes less time, and the area under curve is bigger compared to other classifiers. Furthermore, in a comparative analysis, SVM classifier outperformed other algorithms such as AdaBoost, Logistic Regression, Neural Networks, and Random Forest, achieving the highest accuracy of 98.2456%, marking it as the most effective algorithm for categorizing breast cancer cases [36]. However, a paramount challenge for a CAD based classification system lies in the discovery and extraction of highly efficient and sophisticated computerized features that can proficiently differentiate between benign and malignant tumors. Moreover, employing a CAD system based on shape analysis is challenging as it is often time-consuming due to the extensive computational effort required for extracting morphological features and training the classifier. In addition, the limitation of diagnostic accuracy was often more attributable to the algorithms' insufficient feature extraction and computational capabilities rather than the quality of the image data itself. To develop a quick and efficient CAD system, this study introduces a cutting-edge technique for extracting mass irregularity features from ultrasound images in the frequency domain, enhancing CAD-based classification by capturing subtle shape variances from a comprehensive perspective. This study explores a method for extracting multiple sonographic features and modeling border irregularities, distinguishing nuanced shape and characteristic differences between benign and malignant lesions to improve CAD classification accuracy.
## 3 Results
### Performance Analysis by Method
To target the main question addressed in this paper, we aggregated the results by model class and visualized them using violin plots in Figure 5. The violin plots show the kernel density estimates for the two classes: \"Classical Machine Learning Methods\" (containing MDI, MERLIN and RRCF) and \"Deep Learning Methods\" (containing AE, GANF and TranAD). All density curves have two peaks: one around 0.5 for AUC ROC and 0.0 for F1 and UCR scores, and a smaller one around 0.9 (F1 score) and 1 (AUC ROC and UCR score). The peaks around 0.5 and 0 represent those results where the methods failed to detect anomalies, while the peaks around 0.9 and 1 mark successful anomaly detection. For the F1- and UCR scores, the area under the peaks at 0.9 and 1 is larger for the classical ML methods than for the deep-learning methods, indicating more successful anomaly detection for the \"Classical ML\" class. Conversely, the area under the peaks at 0.5 and 0 is larger for the deep-learning methods.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model  
To address the hardware resource scheduling problem in satellite data transmission ground stations, it is first necessary to clarify the variables involved in this issue and then establish a ground station resource scheduling model. This model will facilitate a better analysis of the objective function and constraint conditions related to the problem.  
Currently, each antenna system in the ground station is equipped with an independent FDC, LNA, and optical transceiver, with essentially consistent parameters. Therefore, we only consider four types of equipment resources: antennas, SDCs, demodulators, and RDs.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 3 Results  
### Convergence Test and Validation  
Various mesh sizes were compared to analyse the scale effect of mesh size. Different numbers of subgrade elements were considered. The subgrade model showed convergence when the mesh seed size was 20 mm. Therefore, the meshing was performed with a 50 \(\times\) 25 \(\times\) 25 number of elements for a 1000 mm \(\times\) 500 mm \(\times\) 500 mm subgrade soil mass. The subgrade was then subjected to various axial loads to obtain deformation values at the centre-top of the subgrade. The strain-controlled mode causes deformations of fixed magnitude to be determined. The convergence test for stresses at a point, A, at the centre-top of the subgrade is shown in Table 4. The time taken for the simulation performed on a computer with 32 GB RAM and a 6 GB graphics card with Core i5 is also mentioned.  
The results obtained from the experimental analysis were used to validate the numerical model for the same test setup. The model was subjected to the same conditions, and a pullout force per unit length vs. deformation plot was obtained for the geostrap, which was compared with the experimental findings for validation. The validation results are shown in Figure 3.  
A couple of reasons can be given for the slight deviation (around 5%) of the numerical results from the experimental findings. These deviations were due to the fully controlled testing in numerical models and the calculations for each element/mesh, the homogeneity of the subgrade and the geosynthetic material, and the consistency of the density and other mechanical properties in the numerical model.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Material** & **Element Type** & **Thickness (m)** & **Placement Depth (m)** & **E (MPa)** & \(\
u\) \\ \hline Subgrade & Solid & 0.5 & - & \(E\) = 40 a  & 0.3 \\ Geostrap & Solid & 0.01 & 0.150 & \(E_{g}\) = 65 b  & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of materials used in FEM modelling.  
The Von Mises stresses in the subgrade and geostrap (enlarged) with the top half removed from the viewport for visualisation are shown in Figure 4. The strain-controlled model uses the following expression for pullout:  
\[t_{s}=K_{s}\Delta_{s} \tag{3}\]  
where  
\(t_{s}\) = the traction/pullout force;  
\(K_{s}\) = the stiffness in the shear plane;  
\(\Delta_{s}\) = the displacement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Size** & **Meshed Subgrade** & **Stress at Point A** & **Simulation Time** \\ \hline
0.015 & & 7.8119 & 3 h 40 m \\
0.020 & & 7.8117 & 2 h 38 m \\
0.025 & & 7.8105 & 1 h 20 m \\
0.030 &