esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 6 Training and Detection Time Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Reference** & **Year** & **Method** & **Accuracy (\%)** &
\begin{tabular}{c} **Computation Time** \\ **(in Seconds)** \\ \end{tabular} \\ \hline \multirow{4}{*}{ClaMP} & [PERSON] et al. [2] & 2022 & QNN & 52.1 & 2698 \\  & [PERSON] et al. [40] & 2020 & QSVM & 73.5 & 10000 \\  & Proposed & 2020 & RF & 92 & \\ \cline{1-1}  & [PERSON] et al. [39] & 2020 & BO-SVM & 95.3 & **34.9** \\  & [PERSON] et al. [39] & 2023 & ACO, DT & 97.69 & 4365 \\  & Proposed & 2024 & LSTM-BO-SVM & **98.2** & 138.17 \\ \hline \multirow{4}{*}{CICMalDroid-2020} & [PERSON] et al. [35] & \multirow{4}{*}{2021} & KNN & 85 & \multirow{4}{*}{\(\backslash\)} \\  & [PERSON] et al. [35] & 2021 & SVM & 86 & \\ \cline{1-1}  & & DT & 88 & \\ \cline{1-1}  & [PERSON] et al. [37] & 2022 & DNN & 93.5 & 305.15 \\ \cline{1-1}  & [PERSON] et al. [36] & 2022 & Hist GB, SMOTE & 94.09 & \\ \cline{1-1}  & [PERSON] et al. [38] & 2022 & Hist GB & 95.25 & \\ \cline{1-1}  & [PERSON] et al. [38] & 2022 & XGBoost & 95.3 & \\ \cline{1-1}  & & BO-SVM & 96.4 & **45.88** \\ \cline{1-1}  & Proposed & 2024 & LSTM-BO-SVM & **98.6** & 498.21 \\ \hline \hline \end{tabular} Bold represents the optimal result.  
\end{table} 
Table 6: Comparison with other methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Model** & \
\begin{tabular}{c} **Training Time** \\ **(in Seconds)** \\ \end{tabular} &
\begin{tabular}{c} **Detection Time** \\ **(in Seconds)** \\ \end{tabular} \\ \hline \multirow{4}{*}{ClaMP} & LSTM-BO-SVM & 136.94 & 1.23 \\  & BO-SVM & 34.68 & 0.22 \\  & LSTM & 110.49 & 0.96 \\  & LSTM-SVM & 113.09 & 1.27 \\  & RNN-BO-SVM & 124.63 & 1.11 \\  & BiLSTM-BO-SVM & 160.58 & 2.89 \\ \hline \multirow{4}{*}{CICMalDroid-2020} & LSTM-BO-SVM & 496.63 & 1.58 \\  & BO-SVM & 45.56 & 0.32 \\ \cline{1-1}  & LSTM & 450.4 & 1.12 \\ \cline{1-1}  & LSTM-SVM & 456.58 & 1.35 \\ \cline{1-1}  & RNN-BO-SVM & 412.12 & 1.04 \\ \cline{1-1}  & BiLSTM-BO-SVM & 560.47 & 3.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training and detection time of six models. According to Table 6, the LSTM-BO-SVM model proposed in this paper achieved the highest accuracy on both datasets. Although the BO-SVM model is slightly less accurate, it offers a better computation time compared to other research methods listed in Table 6. Some methods, while having higher accuracy, require a longer computation time. For example, the method proposed by [PERSON] et al. [39] achieved an accuracy of 97.69% on the ClaMP dataset, with a computation time of 4365 s.
## 3 Experimental Results and Discussion
### Security Analysis
#### 3.2.4 Information Entropy Analysis  
Information entropy is the most important measure of randomness. Let \(m\) be the source of information; the formula for calculating information entropy can be expressed as follows:  
\[H(m)=\sum_{i=0}^{2^{n}-1}p(m_{i})\log_{2}\frac{1}{p(m_{i})}, \tag{14}\]  
where \(n\) is the number of bits that are required to represent symbol mi and \(p(m_{i})\) is the probability of \(m\). The maximum entropy of an 8-bit gray image is equal to 8 when all pixels are equally distributed, which shows that the information is random. If the value is close to 8, it is unlikely for an encrypted image to be decrypted by attackers [11]. Table 6 shows the comparative entropy between the plain and cipher image.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**[PERSON] Image (\(849\times 600\))**} & \multicolumn{2}{c}{**[PERSON] Image (\(512\times 512\))**} \\ \cline{2-5}  & **Plain Image** & **Ciphered Image** & **Plain Image** & **Ciphered Image** \\ \hline
**Horizontal** & 0.990870 & \(-0.012164\) & 0.967504 & 0.005335 \\
**Vertical** & 0.990991 & \(-0.011759\) & 0.973323 & \(-0.016496\) \\
**Diagonal** & 0.987203 & 0.017524 & 0.958654 & 0.052681 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation coefficient values.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**Correlation Coefficient Values**} \\ \hline \multirow{2}{*}{**Wonder Woman (2880 × 1800)**} & **Horizontal** & 0.025846 \\ \cline{2-3}  & **Vertical** & 0.0166156 \\ \cline{2-3}  & **Diagonal** & 0.001811 \\ \hline \multirow{2}{*}{**Lena image (\(256\times 256\))**} & **Horizontal** & 0.033227 \\ \cline{2-3}  & **Vertical** & 0.022104 \\ \cline{2-3}  & **Diagonal** & \(-0.032824\) \\ \hline \multirow{2}{*}{[41]} & **Horizontal** & \(-0.0796392\) \\ \cline{2-3}  & **Vertical** & 0.0166156 \\ \cline{2-3}  & **Diagonal** & 0.0032779 \\ \hline \multirow{2}{*}{[42]} & **Horizontal** & 0.0014 \\ \cline{2-3}  & **Vertical** & 0.0171 \\ \cline{2-3}  & **Diagonal** & 0.0054 \\ \hline \multirow{2}{*}{[43]} & **Horizontal** & \(-0.0015\) \\ \cline{2-3}  & **Vertical** & \(-0.0032\) \\ \cline{2-3}  & **Diagonal** & 0.0008 \\ \hline \multirow{2}{*}{[44]} & **Horizontal** & 0.0019064 \\ \cline{2-3}  & **Vertical** & 0.0038175 \\ \cline{2-3}  & **Diagonal** & \(-0.0019482\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of the correlation coefficient test for the Lena image (\(256\times 256\)).  
Furthermore, comparison results between the proposed method and other algorithms for the Lena image (\(256\times 256\)) are illustrated in Table 7. The analysis in Table 7 demonstrates that our algorithm was better than all those presented in the literature since the information entropy analysis results for our method were closest to 8. Large images were also included to demonstrate that the presented method can not only be used with small images but also with large.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Image Reconstruction  
Image reconstruction was performed using open-source Matlab software that was developed by [PERSON], [PERSON], and [PERSON]. The software is called \"The Microwave Radar-based Imaging Toolbox (MERIT) [34]. It aims to produce a robust, consistent framework for microwave image signal processing and reconstruction.  
In the imaging software, collected signals are preprocessed to remove artefacts from system components such as skin and tissues, as well as antenna coupling signals. The software uses the Rotational Subtraction method to perform artefact removal. The imaging setup shown in Figure 19 is used to perform the first radar measurement set. After that, the antenna array is physically rotated around its center by 36\({}^{\circ}\), and a second radar measurement set is performed. Undesired signals such as those from skin, tissues, and antenna coupling are almost identical and appear at the same time position. In contrast, tumor response appears at a different time position in the two data sets. Thus, the imaged object (tumor) response is isolated, and clutter is eliminated by subtracting the rotated scan from the original scan. Applying this technique depends on the homogeneity of the breast within the rotation angle. Therefore, during antenna array rotation, the distance between antennas and skin remains unchanged, while skin and tissue properties and thickness are the same.  
The filtered signals are used for image reconstruction using the Delay-and-Sum (DAS) method. The method is based on synthetically focusing signals on points within the imaging domain. Synthetically focused signals from each channel are summed. Then, the energy of the summed signals is used to reconstruct the energy profile of the imaging domain. Points of high contrast exhibit coherent addition from multiple channels resulting in high energy at that point; thus, the areas of high dielectric contrast are highlighted. Synthetic focusing is achieved by compensating attenuation and phase for a given channel. The procedure is summarized in the steps below:  
* Propagation path is estimated from the Euclidian distance between the transmitting antenna, the points of interest, and the receiving antenna;
* Distance traveled through different media along the path is calculated;
* Dielectric properties of the media are estimated based on published dielectric datasets. The distance travelled and the dielectric properties are used to synthetically focus the signals.
## 3 Results
### Inverse Relationship between Ozone and HONO  
During the study period, it was observed that ozone concentrations fell below 20 ppb and an inverse relationship between HONO and O\({}_{3}\) became evident. This phenomenon occurred three times, each lasting between 18 and 24 h, as denoted by the shading in Figure 2a and presented in Figure 4a. The most notable period spanned from 03:00 on 18 December to 21:00 on 19 December, with a correlation coefficient (r\({}^{2}\)) of 0.94, firmly establishing the inverse relationship between ozone and HONO. The other periods occurred from 12:00 on 23 December to 15:00 on 24 December (r\({}^{2}\): 0.81) and from 12:00 on 12 December to 06:00 on 13 December (r\({}^{2}\): 0.52). The reduction in ozone levels can be attributed to NO\({}_{2}\)'s titration effect, whereas the rise in HONO levels is linked to its formation from nitrate particles and NO\({}_{2}\)[60, 61, 62, 63, 64]. Throughout the study period, HONO demonstrated a correlation with NO\({}_{2}\) (r\({}^{2}\): 0.42), while NO showed a lower correlation, even with excluded periods featuring NO concentrations exceeding 0.8 ppb (Figure 4b). The correlation between NO\({}_{2}\) and HONO increased to 0.65 during nighttime (21:00-06:00), attributable to the heterogeneous conversion of NO\({}_{2}\) on wet surfaces, which enhances nocturnal HONO production. Moreover, the high nighttime RH (with mean of 83%) likely influenced the NO\({}_{2}\) to HONO conversion [65, 66, 67, 68]. Distinct diurnal variations in HONO were also observed, with peaks occurring in the early morning and decreases in the late afternoon. At night, the primary source of HONO was from emissions and RH-dependent NO\({}_{2}\) conversions, whereas during the day, HONO primarily stemmed from NO-OH reactions, influencing OH production and atmospheric oxidation processes [65]. During the increase in HONO on 23 December, the HONO/NOx ratio averaged 0.085, higher than in the other periods (0.068 and 0.060), suggesting that mechanisms beyond heterogeneous NOx reactions play a more significant role in HONO formation during this time [69, 70]. As a result, this study corroborated the effectiveness of the spray chamber and the atmospheric chemical reactions of HONO, which can be tracked through real-time measurement instruments (O\({}_{3}\), NO\({}_{x}\)).  
Figure 3: Diurnal patterns of (**a**) HONO, (**b**) HNO\({}_{3}\), (**c**) O\({}_{3}\), and (**d**) temperature and solar radiation.
## 8 Application and Discussion of the Results
### Application on SSI
#### 8.1.1 Human Layer  
Similarly to cryptocurrencies, the SSI stack includes a human layer, in which the end-user uses their wallet to access services. Additionally, humans administrate servers, interact within the service desk, and provide the governance layer, among others. The categories of social engineering, human errors, and wallet threats also apply to SSI. However, successful attacks have other consequences for the users since SSI handles identity-related data. Furthermore, the described issues are more general, especially concerning social engineering.
# Time Domain Damage Identification Approach of the Newly Hollow Floor Structure with TRMD
## 5 Conclusions  
This paper presents a time domain damage identification approach for a controlled structure with TRMD. The equation of motion of a controlled structure with TRMD is derived. Afterward, the dynamic response and dynamic response derivatives with respect to the elemental variations are calculated by the Newmark method. A sensitivity-based model updating approach is employed to identify local damage by minimizing the acceleration response difference between the intact state and the damaged state.  
A numerical example for a six-story frame structure with a TRMD on the top is employed to validate the accuracy of the proposed method. It is shown that each floor displacement of the controlled structure is reduced in the earthquake excitation because of the damping effect of the TRMD. The numerical results have shown that the damage location and extent of the controlled structure in a one-damage case or multi-damage case can be identified accurately, even with 5% measurement noise considered. In addition, not all floor responses are required for the sensitivity-based updating procedure, and only a few sensors are needed in this proposed method. The shaking table test of a single-story frame equipped with TRMD is carried out to validate the accuracy of the proposed structural damage identification method in the laboratory. The proposed method can be used to detect the damage degrees and locations for the newly hollow floor structure with TRMD only by the measured controlled displacement.  
Conceptualization, S.L. and Z.Z.; methodology, L.M.; software, L.M. and S.L.; validation, Y.Y. and Z.Z.; formal analysis, L.M.; investigation, Y.Y.; resources, Y.Y.; data curation, Z.Z.; writing--original draft preparation, L.M.; writing--review and editing, Y.Y. and S.L.; supervision, Z.Z.; project administration, L.M.; funding acquisition, S.L. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (Grant No. 51678464) and the Hubei Key Laboratory of Roadway Bridge and Structure Engineering (Wuhan University of Technology) (Grant No. DQJJ201901), for which the authors are grateful.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this paper.  
The authors declare that they have no conflict of interest.  
Figure 10: Structural displacement response under El-Centro earthquake (0.2 g).
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 5 Conclusions  
The robustness of an active headrest system with virtual sensing to the head rotation has been investigated. A theoretical model has been developed to analyze the variation of three acoustic transfer responses with the head rotation and their effect on noise reduction performance. The simulation results show that the performance deterioration with the head rotation is mainly related to the variation of the transfer responses for virtual plants and the transfer responses between the virtual microphone locations and the physical microphone locations in the primary field.  
A robust optimization method has been used to minimize the maximum value of the residual noise at error points for different angles of human head rotation by designing optimal virtual plant models, which improves the robustness of the system against head rotation. This performs similarly as when using multiple virtual microphones around the human head at most rotation angles, but it requires less computation and is more practical. Using the optimization method, an optimized horizontally arranged dual-channel system provides a significant improvement in noise reduction performance at different head rotation angles in the frequency band of interest, which cannot be achieved by a single-channel system.  
The parameters of the method have an impact on the performance of the optimization. Although optimization of the transfer response of \(G\) achieves similar results to optimization of the transfer response \(S_{\mathrm{v}}\), the latter is more general because \(S_{\mathrm{v}}\) is not sensitive to the primary sound field. Setting a different lower threshold of noise reduction at the initial head position has little change in noise reduction performance as it only slightly affects the performance at positions where the rotation angle is very small.  
Finally, off-line experiments were carried out in an ordinary room, using the actual measured sound field and acoustic transfer functions for system optimization and to calculate the performance of the system. The results validate the effectiveness of the proposed optimization method. In future work, the control performance of the proposed method for broadband noise will be investigated and real-time experiments will be carried out in practical applications, such as active control systems for road noise in vehicles.  
Conceptualization, J.L., H.Z. and H.C.; methodology, H.C. and H.Z.; software, H.C.; validation, H.C.; formal analysis, H.C. and H.Z.; investigation, H.C. and H.Z.; resources, H.Z. and X.H.; data curation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, H.C., H.Z., J.L. and X.H.; visualization, H.C. and X.H.; supervision, H.C. and H.Z.; project administration, H.Z.; funding acquisition, J.L. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China with the grant numbers 11874218, 11874219 and 12274221.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Simulation Results and Discussion
### Simulation Results
The average measurement error for the standard Kalman filter applied to the location measurements of vehicle \(v_{1}\) is 56 m and 29 m for \(x\) and \(y\), respectively, compared to 0.25 m produced by the Kalman filter that is integrated with the autoregression model of order 1. The Kalman filter that was integrated with the autoregression model of order 4 has an average measurement error of 0.24 m, which is very close to that produced by the autoregression model of order 1. This can be further illustrated in Figures 14 and 15, which provide a more detailed view of the behavior of the model for \(p=1,2,4\). The fluctuations in error measurements are due to the constant variability in the vehicles' position measurements, as was found upon further inspection of the original time-series for longitudinal and latitudinal location measurements of both vehicles. Figure 11: Latitudinal positioning error produced by the standard Kalman filter and the \(Autoregression\)-Kalman filter for vehicle \(\mathbf{v_{1}}\). Figure 10: Longitudinal positioning error produced by the standard Kalman filter and the \(Autoregression\)-Kalman filter for vehicle \(\mathbf{v_{1}}\).
# Feasibility of a Drug-Releasing Radiofrequency Ablation System in a Porcine Liver Model
## 4 Discussion
### Limitations and Future Direction  
Several technical considerations were noted in the application of this method for the direct treatment of large livers, despite RFA with DRCWE resulting in a good outcome. First, further investigation with more animals and liver cancer models is needed to exactly evaluate the efficacy. Second, the gross examination of specimens was performed for the size measurement of ablation zones instead of histological analysis. The grossly measured size of the ablation zones may not be perfectly equal to the size of the actual coagulated tissue although gross appearance correlates with histological findings. In fact, because our results were simply represented the pharmacological response to therapeutic intervention (drug + RFA) by measuring the zone of coagulative necrosis, it was difficult to accurately evaluate the drug released extent. Third, the varying dimensions were measured thrice for each specimen and represented the mean value \(\pm\) SD. However, the possibility of an assessment error exists due to manual measurements. Fourth, we did not evaluate RFA only or drug-releasing only treated the porcine liver as a control group because of a limited number of animals. A comparative study is required for accurate evaluation. Further improvement in reproducibility to increase the efficiency of anticancer drug delivery to the ablation zone should be warranted. Nevertheless, this study could provide a reliable basis for testing the DRCWE feasibility.
## 3 Description of the Approach
### Approximate String Matching  
Approximate string matching (ASM) involves matching two strings while allowing a level of inexactness, as opposed to exact string matching (ESM). Two strings are said to be matching if they represent the same word, and in ESM this is determined by the two words being spelled in the exact same way. For ASM, a chosen amount of inexactness is allowed to provide for spelling mistakes or variances in spelling of the same word. One of the algorithms used in ASM is measuring the Levenshtein distance between strings. Two strings can be said to match if their dissimilarity (or Levenshtein distance) is below a predetermined threshold [10]. The Levenshtein distance between two strings is the minimum number of character-level edits required to change one word into the other. Character-level edits can include deletions, substitutions, or insertions [21]. Using this less strict version of string matching allows the system to match strings even if they have arbitrary differences which in turn decreases the need for the OCR to be completely accurate when reading from the document. ASM allows for some OCR errors, while still giving us the control to decide how strict we make the match threshold.
## 3 Solution of the Optimization Problem  
It can be observed that the optimization problem (13) is a non-convex quadratically constrained programming that involves semi-infinite programming. Particularly, the non-convexity with respect to the information beamforming matrix \(\mathbf{W}\) and the power splitting ratio \(\rho\) is owing to constraints (C1) and (C3). In addition, both the objective function in (13) and constraint (C2) involve infinitely many possibilities due to the CSI uncertainties. In general, there are no standard methods for solving the non-convex optimization problem. In extreme cases, we require an exhaustive search method to get the optimal solution that is computationally intractable even for a moderate network size. To deduce an efficient beamforming design algorithm for the considered optimization problem, we first recast the optimization problem (13) as a semi-definite programming (SDP) problem to avoid the non-convexity associated with the objective function, and constraints (C1) and (C3). Next, the infinite number of constraints is converted into an equivalent finite number of constraints. The performance of the reformulated optimization problem is the lower bound for the original optimization problem. Finally, we apply semi-definite programming relaxation (SDR) to obtain the globally optimal beamforming for the reformulated optimization problem. In practice, the considered optimization problem may be infeasible when the channels are in unfavourable conditions. However, in the following, we suppose that the problem is always feasible for investigating the design of different beamforming schemes.