esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Proposed Solution
### Signal Features  
We select four signal features, belonging to two main classes: time domain and amplitude domain. The logic behind the selection is related to the properties of the interfering signals, such as the transmission airtime of IEEE 802.11 transmission and the periodicity of time domain pattern of the MWO RF leakage, as discussed in Section 3. To simplify the feasibility of the whole spectrum sampling and feature extraction processes on COTS WSN devices, our approach is to minimize the size of the RSSI trace, as well as the complexity of feature calculation methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(D_{F}\) & \(D_{W}\) & \(D_{M}\) & **Classification Outcome** \\ \hline
1 & 0 & 0 & The channel is free from the interference sources in the analysis. \\
0 & 0 & 1 & A MWO was active during the sensing period. \\
0 & 1 & 0 & An IEEE 802.11 network was overlapping the channel in the analysis. \\
0 & 0 & 0 & The source of interference is unknown. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The four interference classes in the analysis.  
Figure 4: Details of the decay for the proposed detection algorithm. The logic input signals are generated by the four SVMs in Figure 3.
## Symbols and Acronyms  
\begin{tabular}{l l} \(\mu\) & Mean \\ \(\sigma\) & Standard deviation \\ \(F_{O}(x)\) & Normal cumulative distribution function \\ \(\hat{F}_{n}(x)\) & Observed cumulative frequency \\ \(f_{o}(x)\) & Probability density function \\ \(D^{+}\) & Upper difference between the observed cumulative frequency and normal cumulative Distribution \\ \(D^{-}\) & Lower difference between the observed cumulative frequency and normal cumulative distribution \\ \(D\) & Maximum difference between the observed cumulative frequency and normal cumulative distribution \\ \(D_{\alpha}\) & Maximum tabulated difference between the observed cumulative frequency and normal cumulative distribution \\ \(c_{\alpha}\) & Coefficient of significance \\ \(k(n)\) & Tabulated expression which determines \(D_{\alpha}\) \\ \(E_{i}\) & Expected frequency \\ \(O_{i}\) & Observed frequency \\ \(\alpha\) & Level of significance \\ \(n\) & Number of samples \\ \(\lambda\) & Rate parameter \\ \(\beta\) & Scale factor \\ \(\alpha\) & Shape factor \\ \(\mu l\) & Location factor \\ \(\lambda_{\alpha}\) & Skewness shape factor \\ \(\alpha_{d}\) & Distribution shape factor \\ \(X^{2}\) & Chi square \\ \(X^{2}\alpha\)(k-r-1) & Tabulated chi square \\ DR & Demand Response \\ HVAC & Heating, ventilating and air condition \\ SVM & Support-vector machines \\ POE & Post Occupancy Evaluation \\ \end{tabular}
## 4 Performance Evaluation  
To measure the performance of the proposed DYMO-Auction approach, we tested it in different scenarios. We also compared our proposed method with the SSI auction task allocation, in which it is assumed that all information about the tasks is known, that all tasks are announced in each auction sequentially, and one task will be assigned in each auction.  
The following performance metrics have been used to evaluate the proposed methods:  
1. Quality satisfaction, which measures the quality satisfaction of the tasks. If the tasks have been assigned to robots with similar qualities, the average quality level for all assigned tasks for each individual robot will be nearly equal to the robot's quality level, and it is computed as follows: \[R_{q}^{i}=\frac{\sum_{j=1}^{l}q_{j}}{l},\] where \(l\) is the number of tasks assigned to robot \(R_{i}\), and \(q_{j}\) is a task quality requirement.
2. Load balance, which indicates whether the total load (the number of tasks) has been divided equally among the available robots or not. The optimal load balancing is when all robots execute the same number of tasks. \[R_{L}^{i}=\left\lceil\frac{n}{m}\right\rceil\] where \(n\) is a number of tasks, and \(m\) is the number of robots.
3. Sum of the total traveled distance (TTD), which is the sum of the distance traveled by a robot to accomplish all of its assigned tasks. The distance traveled by a robot \(R_{i}\) that is assigned \(k\) number of tasks is computed as follows: \[TTD=\sum_{i=1}^{n}\sum_{j=1}^{k}d_{ij},\] where \(d_{ij}\) is the distance between the robot \(R_{i}\)'s location and the nearest task \(T_{j}\), computed using Equation (4).
## 1 Introduction  
Recently, various remote sensing sensors have been used and their performance has developed rapidly [1]. Therefore, the range of remote sensing image users is expanding, and user requirements are also being advanced. In order to meet user needs, research is being actively conducted to simulate and generate remote sensing images that are limitedly acquired by various weather, environmental, and satellite operating conditions. In this issue, we deal with the research results regarding the generation of more diverse images for various environments, climates, and weather conditions, and we use them to increase the number of learning images, simulate military operations, and simulate seasonal images.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 5 Results and Discussion
### Predictive Performance
#### 5.3.1 Experimental Setup  
To evaluate our proposed approach, we divided the capture files in nine datasets, which are composed of three hours of legitimate traffic and one hour of malicious traffic each. Each dataset refers to a different profile and infection type combination, as presented in Table 1. We organised the evaluation into three phases: finding an optimal time-window size, considering that smaller windows will result in faster identification of botnets; discovering the least number of instances needed for training; and optimising the classifier's hyperparameters. Table 2 presents the features and their respective value ranges in the datasets.  
All tests were performed using the holdout method with 50 repetitions to minimise variance in the results. The metrics used to evaluate the proposed approach were [37]:  
* **Precision**: \(\frac{TP}{TP+FP}\) the percentage of classified botnets instances that are truly botnets;
* **Recall**: \(\frac{TP}{TP+FN}\) effectiveness of the approach in identifying botnets;
* **Specificity**: \(\frac{TN}{FP+TN}\) how effective is the approach in identifying instances that are legitimate;
* **Accuracy**: \(\frac{TP+TN}{TP+FN+FP+TN}\) the overall effectiveness of the approach;
* **AUC**: \(\frac{1}{2}(\frac{TP}{TP+FN}+\frac{TN}{TN+FP})\) ability of the approach to avoid false classification;
* **F1-score**: \(\frac{2}{recall^{{}^{\prime}}+precision}\) relation between recall and precision.  
TP, TN, FP and FN stand for true positives, true negatives, false positives and false negatives, respectively.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.6 Ground Subsidence Risk  
The ground subsidence risk was calculated by summing the occurrences of ground subsidence within the grid. The ground subsidence risk was categorized into three levels (1, 2, 3) and was further divided into three conditions based on the dataset conditions. Areas with no ground subsidence inside the grid were assigned level 1, which is relatively safe. Areas with 1, 1-2, or 1-3 instances of ground subsidence were assigned level 2, which necessitates caution. Areas with 2, 3, 4, or higher number of ground subsidence instances were assigned the more hazardous level 3 based on the level 2 evaluation. The occurrences  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Factors**} & \multicolumn{1}{c}{**Category**} \\ \hline \multirow{4}{*}{Diameter (mm)} & \multirow{2}{*}{50} & 1–50, 51–100, 101–150, 151–200, 201–250, 251–300, 301–350, \\  & & 351–400, 401–450, 451–500, 501–550, 551–600, 650–700, \\  & & 701–750, 751–800, 801–850, 851–900, 901–1000 \\  & 1–100, 101–200, 201–300, 301–400, 401–500, 501–600, 601–700, \\  & & 701–800, 801–900, 901–1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._of ground subsidence within the grid are shown in Table 1 and were used to evaluate the ground subsidence risk based on specific conditions. The ground subsidence risk exhibited imbalanced data characteristics, which were addressed by applying the SMOTE sampling technique [11].
# A Novel Optimization Algorithm Combing
## 4 Experiments
### The Gradient Effect on ABC/Best/1 and ABC/Best/2  
In addition to the GABC algorithm, with the help of the best solutions in the current population, [PERSON] et al. [18] also proposed the ABC/best/1 and ABC/best/2 strategies. The best solutions are used to direct the movement of the exploration. Their search equations are devised as follows:  
\[v_{ij}=y_{j}+\phi_{ij}\Big{(}x_{ij}-x_{kj}\Big{)} \tag{8}\]  
\[v_{ij}=y_{j}+\phi_{ij}\Big{(}x_{ij}-x_{kj}\Big{)}+\phi_{ij}\Big{(}x_{lj}-x_{mj }\Big{)} \tag{9}\]  
where the indices \(k\), \(l\) and \(m\) are different from \(i\), \(C\) is a non-negative constant parameter, \(\Psi_{ij}\) is a random number in [0, 1], and \(y_{j}\) is the \(j\)th element of the global best solution.  
In order to clarify the effect of gradient information on these two algorithms, ABC/best/1G and ABC/best/1G are brought out by using the improvement strategy of Section 3. In Table 5, the ABC/best/1 is compared with ABC/best/1G, and ABC/best/2 is compared with ABC/best/2G on benchmark functions respectively. As seen from the left side of Table 5, the ABC/best/1G algorithm shows better performance than ABC/best/1 algorithm. Except for Rosenbrock and Griewank functions, the optimization results of the two algorithms for other functions are not significantly different. Therefore, the gradient-based measures have a small impact on the performance of the ABC/best/1 algorithm. From the right side of Table 5, the ABC/best/2G algorithm does not get smaller optimization results than ABC/best/2 algorithm on all functions. This means the improvement does not work for ABC/best/2 algorithm.  
The search strategies between the GABC algorithm and the ABC/best algorithms are different. The GABC algorithm searches for the new candidate solution toward the global best solution, but the ABC/best algorithms search for the new candidate solution around the global best solution. Actually, the original search equations of ABC/best algorithms always look for better results near the global best solution. Therefore, the advantages of this study's Measure 1 are not fully applicable to them. In addition, it can be clearly seen from the calculation results that the improvement on ABC/best/1 is significantly better than that on ABC/best/2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{\(D\)} & \multicolumn{2}{c}{**ABC/best/1**} & \multicolumn{2}{c}{**ABC/best/1G**} & \multicolumn{2}{c}{**ABC/best/2**} & \multicolumn{2}{c}{**ABC/best/2G**} \\ \cline{2-9
## 2 Materials and Methods
### Accuracy Assessment  
In both approaches, the dataset was divided into a training set and a test set, comprising 80% and 20% of the data, respectively. The training set was utilized for model training purposes, while the test set was employed to assess the model's performance on completely unseen data, after it had been fully trained.  
All performance metrics were calculated based on real soil tillage operations, providing an accurate assessment of the model's effectiveness in detecting actual tillage events. Several evaluation metrics were employed, including precision, recall, the F1 score, the F2 score, and the area under the receiver operating characteristic (AUC-ROC) curve. The AUC-ROC quantifies the performance of a binary classifier across various classification thresholds, providing insights into the trade-off between true positive and false positive rates.  
We utilized both the F1 score and the F2 score because the F1 score measures the balance between precision (the model's ability to correctly classify positive instances) and recall (the model's ability to correctly identify all positive instances). On the other hand, the F2 score places greater emphasis on recall, considering it twice as important as precision. This characteristic makes it particularly suitable when recall is a crucial factor in the classification problem, which is often the case in scenarios where false negatives carry significant consequences, such as the detection of soil tillage operations.  
For XGB, to find the best hyper-parameters, we used a random cross-validation (CV) method, considering 5 folds, in order to have an appropriate measure of the estimation error. To ensure the balanced representation of tillage operations, the folds were divided using a stratified approach, allowing each fold to contain a similar number of tillage operations. This stratification minimized the data imbalance within each fold and enhanced the robustness of the cross-validation process.  
The packages used were Numpy 1.18.5 to handle all the data processing, scikit-learn 1.0.24 for data partitioning, scipy 1.7.3 to perform the threshold optimization in the rules-based approach, imsklearn 0.9.1 to perform the SMOTE, and scikit-optimizer 0.8.1 for the Bayesian optimization. Python 3.8.4 was used.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation
### Parameter Analysis
#### 4.6.3 Structural Features  
* **Depth** Table 7 (_Depth_) and Figure 9(c) show the influence of different neural network depths \(d\) in the model. By setting the number of fully connected layers \(d\) (\(d\) = 0\(\sim\)5), we observe that the recall reaches its maximum value when \(d\) is 2. We believe that the reason is that an excessively deep network structure will result in feature dispersion, leading to worse results. Therefore, we choose 2 as the final depth of the network in the GNN.
* **Iteration** In the GNN, we need to perform certain iterations to optimize the parameters. We set \(T\) to 1, 2, 3, 4, and 5 to observe the impact of iterations on the network. Table 7 (_Iteration_) and Figure 9(d) show the differences in the recall for different situations. The recall obtained in cases where \(T>=3\) are very similar, but the larger \(T\) is, the greater the time cost. We infer that the scale is not sufficiently large to require deep iterations; for our functions, three hops are sufficient for the basic blocks to collect the information of their neighbors. Therefore, we set \(T\) to 3 as the number of iterations of the GNN.
* **Self-Loop** To ensure the block embeddings always focus on themselves while collecting network information, we add a _self-loop_ to the ACFG before the GNN starts. Table 7 (_Self-Loop_) and Figure 9(c) show the effect of this step on the results. The experimental results show that, after adding the self-loop, the recall is increased by 3.7%. This shows that the addition of a _self-loop_ can improve the ability of the GNN to extract structural features well.  
In summary, after multiple experimental verifications, we set the word embedding vector size to 64 and merge the embeddings of \"\(\langle s\rangle\)\" and the opcode as the instruction embedding. We design a vector of 56 dimensions to represent statistical features. In the  
Figure 10: Results for different training settings.  
GNN, we set the network depth to 2 and the number of iterations to 3. In addition, in terms of implementation details, we set the batch size to 32, the initial learning rate to 0.01, and attenuation is performed every 50 epochs.
## 5 Parametric Analysis
### Section Height  
Figure 19 displays the evolution of the ultimate moment, deflection, and reinforcement strain. The detailed results of FE analysis are given in Table 5. As the \(H\) increased from 300 to 450 mm, the failure modes obtained by the FE method transformed from concrete crushing into balanced, which could be attributed to the reduction in the \(\rho_{f}\). Specimens with \(H\) values of 350 mm, 400 mm, and 450 mm were all damaged due to balanced failure, indicating that the reinforcement ratio corresponding to balanced failure was actually a range instead of an exact value for certain materials. Although the same failure mode was obtained, there was a slight difference in the range of the crushed concrete. As expected, the range of the crushed concrete of specimens with an \(H\) value of 450 mm was slightly smaller than that of specimens with an \(H\) value of 350 mm, as illustrated in Figure 20.  
The increase of the \(H\) value was beneficial for the capacity of SFLC beams reinforced with GFRP bars, which was similar to the traditional RC beam. However, a minimum \(\rho_{f}\) was especially required for this kind of beam to avoid the unexpected rupture of GFRP bars. In terms of deflection, no direct relationship between the \(H\) and the \(\Delta_{\mathrm{s}}\) values was observed, since increasing the \(H\) increased the \(M_{u}\) and the stiffness simultaneously.  
Figure 19: Influence of the section height.  
Figure 20: Failure modes obtained by FE method: (**a**) Concrete crushing of model with height of 300 mm; (**b**) Balanced failure of models with height of 350 mm; (**c**) Failure mode of model with height of 450 mm.
## 4 WSN Clustering
### Centralized Low-Energy Adaptive Clustering Structure  
The low energy adaptive clustering centralized hierarchy (LEACH-C) [15] is the most recent version of the LEACH algorithm. Although a base station is included in LEACH-C, each node in LEACH is responsible for configuring the cluster. All of the energy and position information of the entire member nodes is received by BS. Routing is centralized when BS estimates the overall network energy for several nodes with higher energy states. A CH is picked from among the list of nodes in order to ensure that the nodes chosen have sufficient energy to serve as CH. As a result, the nodes are evenly distributed across the network, allowing the strain to be transferred gradually. Re-selection choices must be made by BS under the centralized technique, which means that overhead communication will lead to an increase in the number of times CH is selected. In addition to sending requests, each cluster will use a large amount of energy.
## 4 Conclusions  
To conclude, the third-order nonlinear susceptibilities \(\chi^{(3)}(-2\omega;\omega,\omega,0)\) and \(\chi^{(3)}(-\omega;\omega,-\omega,\omega)\) of tellurite and chalcogenide glasses have been measured using the recently developed THISHG and classical z-scan techniques. It has been shown that for correct retrieval of \(\chi^{(3)}(-2\omega;\omega,\omega,0)\) from the THISHG measurements, the influence of the ambient gas on the generated SH signal must be taken into account. In some cases, the gas contribution to the total detected signal can dominate over that of the sample. Using helium instead of an air atmosphere significantly decreases the contribution from gas, but even in this case, this contribution cannot always be ignored, especially for materials with a low nonlinearity comparable to that of fused quartz. Using a series of measurements with a calibrated sample (in our experiments it was fused quartz), it was possible to estimate the contribution of gas and obtain the nonlinear susceptibilities of the samples. Note that the interfering (additional) SHG from the ambient gas can also be beneficial and provide information on the complexity (existence of imaginary components) of the nonlinear susceptibility in the investigated samples. With the help of this procedure, we measured \(\chi^{(3)}(-2\omega;\omega,\omega,0)\) for the tellurite and chalcogenide glasses and found that these susceptibilities are close to \(\chi^{(3)}(-\omega;\omega,-\omega,\omega)\) (measured by the z-scan technique), which is consistent with the dominant role of nonresonant electronic nonlinearity in the investigated samples. This result is significant from both fundamental and applied points of view. From the fundamental point of view, it proves the commonly used assumption of Kleiman symmetry for glasses. From the point of view of application, it confirm the applicability of \(\chi^{(3)}(-\omega;\omega,-\omega,\omega)\) for estimating the effective second-order nonlinear susceptibility \(\chi^{(2)}_{eff}=3\chi^{(3)}E_{froz}\) (see the Introduction) used in studies of glass poling. Conceptualization, A.S. and S.B. ; methodology, Y.S., S.B. and A.K. ; theoretical analysis, E.B. and S.B. ; measurement, A.K., A.M., Y.S. and A.R. ; data analysis, A.M., A.R. and S.B. ; writing--original draft preparation, S.B. ; writing--review and editing, S.B. and A.S.; supervision, A.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Center of Excellence \"Center of Photonics\" funded by The Ministry of Science and Higher Education of the Russian Federation, contract No. 075-15-2022-316.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 8 Challenges of In-Vehicle Measurements
### Variable Light Conditions  
Camera-based vital sign monitoring requires, with the expection of cameras operating in the far infrared spectrum, light that can be detected by the camera. For in-vehicle applications, sufficient light cannot be guaranteed, for example while driving at night. Artificial illumination is therefore required, which can blind the driver in case light in the visible range is used and is thus a traffic risk.  
A solution may be the use of near infrared light which cannot be detected by the human eye, but can be detected by camera systems. However, it is not yet decided whether NIR light is harmful to the human eye. NIR light can damage both the cornea by thermal injury and the lens by cataractogenesis. [PERSON] et al. (2016) argue that, under daylight conditions, NIR light is always accompanied by visible light. The eye will regulate light intake by closing the iris and protect itself. However, under night conditions, the iris will be widely open, allowing artifical NIR illumination to enter the eye unattenuated. Damage to the eye after long-term exposion is therefore conceivable.  
Apart from the absence of light and hence the requirement for artifical illumination, varying light conditions can also pose a challenge, e.g., driving through an avenue with large trees or shadows from other cars can rapidly change the signal recorded by the camera. Hence, a correction algorithm is required. For example, [PERSON] et al. (2017) applied spatio-temporal filtering and achieved a correlation with the reference of 0.99. [PERSON] et al. (2018) used knowledge about the external light source to correct the camera video.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 3 The Proposed Method
### AED Matrix  
A mapping matrix between the spherical harmonic function and Fourier series was found in our previous work [31]. Its dimension is \((2N+1)^{2}\times(2N+1)^{2}\). The mapping matrix has the function to transform the steering vector in (12) into the Kronecker product of two Fourier series vectors. Each vector has a Vandermonde structure only dependent on azimuths or elevations, respectively,  
\[\mathbf{y}(\theta,\phi)=\mathbf{E}[\mathbf{f}(\phi)\otimes\mathbf{f}(\theta)], \tag{15}\]  
where operator \(\otimes\) represents the Kronecker product. The \(\mathbf{f}(\phi)\) and \(\mathbf{f}(\theta)\) can be expressed as  
\[\mathbf{f}(\phi)=\left[e^{iN\phi},\cdots,1,\cdots,e^{-iN\phi}\right]^{T}, \tag{16}\]  
\[\mathbf{f}(\theta)=\left[e^{-iN\theta},\cdots,1,\cdots,e^{iN\theta}