esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Malware Analysis Based on API Sequence Semantic Fusion
## 3 Methodology
### Detailed Archetecture
#### 3.2.2 Vectorized Representation Based on API2 Vec  
To express the relationship between functions in API sequences and reduce the amount of computation, we map the API call sequences from strings into vectors.  
One-hot encoding is well known as a traditional method for vectorization. However, this method is associated with several problems. Firstly, the relationship between APIs is independent through one-hot encoding, which means that the lack of contextual connections is evident. Additionally, the feature vectors will be quite sparse and bring challenges to the subsequent process. To address this problem, we use an API2 Vec vectorization method based on Word2 Vec to perform word embedding operations on APIs.  
In the API2 Vec vectorization method, we vectorize string data through a shallow neural network, and the semantic information of the words is represented in the form of word vectors. Through this approach, we reduce the dimensionality of word vectors, and the word vectors corresponding to APIs with similar semantics are also similar. For example, the system functions NtOpenFile and NtReadFile both belong to file operations. NtOpenFile is used to open a file, and NtReadFile is used to read a file, which means the two functions are semantically similar. Then, in the new multi-dimensional space, the expressed word vector should also be relatively close. On the contrary, the RegOpenKeyEx function is used to open a specified registry key, which is different from the former file operation functions, and the distance of the word vectors is relatively far.  
The training process of API2 Vec is shown in Figure 2. The detailed illustrations are as follows:  
* We first encode the API information through one-hot encoding to obtain a high-dimensional sparse feature vector.
* We then train a shallow neural network to obtain the hidden layer weight of each API. The input layer and the hidden layer weights are jointly calculated to obtain the output vector, which is also the word vector form of the API and can uniquely identify each API. We use the Skip-Gram model for weight training due to the relatively small association between APIs. Assuming that an API is represented by a 32-dimensional vector feature, for 295 kinds of APIs, compared to the 295-dimensional one-hot encoding form, the API2 Vec method can be used to reduce the dimension of the word vector to 32 dimensions.
* The word vector representation of each API name with a fixed dimension is finally calculated and stored in the form of a dictionary.  
Through the vectorization of the name of each function in API sequences, the distance relationship between functions can be better expressed, and the amount of computation is reduced through moderate dimensionality reduction, which facilitates the model to efficiently learn features in a specific functional sequence.
## 2 Materials and Methods
### Optimal Coverage Sampling Density  
After the basic unit decomposition, the study space is divided evenly into sub-regions of the same size. One basic unit is a sub-region and the sampling position is its centroid. At this time, the distance between any two adjacent sub-regions of interest is equal, and the spatial sampling coverage density can be represented by this distance. Given the space and \(L\), the spatial sampling coverage density \(d=\sqrt{3}L\) and the length \(|P|=d*(N|M|-1)\) of the generated path \(P\) can be preliminarily determined. Since the average power consumption of the UAV is almost constant during flight, we assume that the power consumption of the UAV follows the linear model \(f(P)=u_{e}*|P|\), where \(u_{e}\) is a constant control parameter, representing the energy consumption per unit length of the UAV, and \(f(P)\) is the energy consumption in the path \(P\). By adjusting the size of \(L\), the number \(\mid M\mid\) of sub-regions of interest in the study area can be intuitively increased or decreased, and the size of \(\mid P\mid\) can be controlled. Combined with the energy budget \(E_{dbt}\), the optimal parameter \(L^{*}\) and the largest \(\mid M\mid\) can be found, such that the resulting coverage path has the densest coverage sampling. The optimal spatial coverage density \(d^{*}\) is determined by \(L^{*}\), as shown in Formula (2):  
\[\begin{split}\mathrm{d}^{*}=\sqrt{3}L^{*}&=\underset {L}{\operatorname{arg\,max}}|M|\\ s.t.& f(p)=u_{e}\cdot|P|<E_{dbt}\end{split} \tag{2}\]
# Structural Damage Identification Using a Modified Directional Bat Algorithm
## 5 Conclusions  
In this paper, a modified directional bat algorithm (MDBA) has been proposed for structural damage identification. By combining the individual optimal solution and elimination strategy, the optimization ability of the directional bat algorithm was strengthened. In this method, the problem of damage identification was transformed into a mathematical constrained optimization problem, and the objective function was established by frequency and mode shape. The MDBA was used to solve the constrained optimization problem. The performance of the MDBA was evaluated through the numerical simulation of a beam-type bridge and a truss-type bridge, and it was compared with other algorithms (PSO, DE, SFLA) and other variations of BA (DBA, LBA, TMBA). The following conclusions could be drawn:  
1. The MDBA has the advantages of less parameters, fast convergence speed and strong global search ability, which provides a more effective and potential tool for solving structural damage identification problems.
2. For a variety of structural damage cases given different degrees of environmental noise, the MDBA could accurately locate the structural damage and estimate the damage degree, which shows its good robustness.
3. Compared with PSO, DE, SFLA and several BA variant algorithms, the MDBA has better accuracy and convergence rate in the case of the same small population and few iterations.  
Under a small population and few iterations, however, the MDBA showed the fast convergence and accurate identification in the two above numerical simulations, and the robustness performance and the sensitivity to small damage were verified. It should be noted that the proposed method needs to be further verified by complex large structures and more realistic 3D models, as well as through laboratory verification or actual structure verification.  
Conceptualization, Y.S., L.L. and Y.L.; methodology, Y.S. and Y.L.; validation, Y.S. and L.L.; investigation, Y.S.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China via Grant No. 2017 YFC1500603.  
Not applicable.  
The authors declare no conflict of interest.
# A Human-Inspired Control Strategy for Improving Seamless Robot-To-Human Handovers
## 2 Design of the Robotic Human-Inspired Control Strategy for HRH
### Dynamic Mechanical Model of the Physical Human-Robot Handover  
This section summarized a mathematical dynamic model of the HRH system. Presumably, the overall interaction can be represented as sub-equivalent-lumped-mass systems as depicted in Figure 6. The HSR can be modelled by the rigid body model (\(m_{R}\)) associated with effective viscous damping (\(b_{R}\)) to ground and driven by force \(F_{R}\) created by the hybrid control algorithm. The robot hand (gripper), which holds the rigid object mass \(m_{O}\), has the impedance parameters as \(m_{h}\), \(k_{h}\) and \(b_{h}\), respectively. The force sensor attached to the hand wrist can be mechanically presented as a spring (\(k_{s}\)) and dashpot (\(b_{s}\)) system. The impedance parameter notation of the human hand comprises the hand mass (\(m_{H}^{*}\)), the viscous damping (\(b_{H}\)) and stiffness (\(k_{H}\)), respectively. In addition, the displacements of the masses (\(m_{R}\), \(m_{h}\) and \(m_{H}\)) are, respectively, defined as \(x_{R}\), \(x_{h}\) and \(x_{H}\), in which it can be assumed as: \(m_{H}\)=\(m_{O}\)+\(m_{H}^{*}\). The state-space equations based modelling representing the overall HRH system can be formulated as:  
\[\begin{array}{c}\frac{d}{dt}\left[ \begin{array}{c}x_{R}\\ \dot{x}_{R}\\ x_{h}\\ \dot{x}_{h}\\ x_{H}\\ \end{array} \right]=\left[ \begin{array}{cccccc}0&1&0&0&0&0\\ -\frac{k_{s}}{m_{R}}&-\frac{(b_{R}+b_{s})}{m_{R}}&\frac{k_{s}}{m_{R}}&\frac{b _{s}}{m_{R}}&0&0\\ 0&0&0&1&0&0\\ \frac{k_{s}}{m_{R}}&\frac{b_{s}}{m_{h}}&-\frac{(b_{s}+b_{s})}{m_{h}}&-\frac{( b_{h}+b_{s})}{m_{h}}&\frac{k_{h}}{m_{h}}&\frac{b_{h}}{m_{h}}\\ 0&0&0&0&0&1\\ 0&0&\frac{k_{h}}{m_{H}}&\frac{b_{h}}{m_{H}}&-\frac{(b_{h}+b_{H})}{m_{H}}&- \frac{(b_{h}+b_{H})}{m_{H}}\\ \end{array} \right]\left[ \begin{array}{c}x_{R}\\ x_{R}\\ x_{h}\\ x_{H}\\ x_{H}\\ \end{array} \right]\\ +\left[ \begin{array}{cccccc}0&0&0&0&0&0\\ \frac{1}{m_{R}}&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0\\ \end{array} \right]\left[ \begin{array}{c}F\\ 0\\ 0\\ 0\\ 0\\ 0\\ \end{array} \right]\text{ and }y=\left[ \begin{array}{cccccc}1&0&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&0&1&0\\ \end{array} \right]\left[ \begin{array}{c}x_{R}\\ x_{R}\\ x_{h}\\ x_{h}\\ x_{H}\\ \dot{x}_{H}\\ \end{array} \right]+\left[0\right]F\]  
After taking the Laplace transforms of the state space equations above, the transfer matrices are then given by:  
\[G_{1}(s)=\frac{x_{R}(S)}{F_{R}(S)}=\frac{A}{D},\ G_{2}(s)=\frac{x_{h}(S)}{F_{R} (S)}=\frac{B}{D}\,\ G_{3}(s)=\frac{x_{H}(S)}{F_{R}(S)}=\frac{C}{D} \tag{24}\]  
where\[
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 4 Simulation Results and Performance Analysis
### The Peak to Average Power Ratio (PAPR) Reduction Capacity of the Composite Signal  
The PAPR is defined as the ratio of the peak power to the average power of the transmitted signal. It is statistically analyzed via calculation of the complementary cumulative distribution function (CCDF) by determining the probability of occurrence of a set of PAPR levels. Figure 15 shows the PAPR reduction capacity of the proposed OFDM+BOC system for different power ratios (\(\alpha\):\(\beta\) = 0.1:0.9, 0.2:0.8, 0.3:0.7, 0.4:0.6, 0.5:0.5) determined via the CCDF. Compared to the original OFDM system, the difference in the PAPR reduction capacity of our proposed OFDM+BOC system is not significant, with a maximum difference of 0.8 dB. Moreover, the PAPR reduction capacity of the composite signal with a power ratio of 0.1:0.9 is almost the same as that of the original OFDM system.
## 3 Proposed Methods
### Learn a Centralised but Decomposable Critic  
To address the issue of underutilized rewards and other features outlined in Section 3.1, this paper employed the following approach: following the utilization of state information closely linked to the local dynamic model for actions by individual policy networks, the individual critic networks were amalgamated into a global critic network through a mixing function \(\phi\) parameterized by \(\theta\). The global network assessed the strategies of all actor networks collectively and subsequently incorporated rewards and other task-related features. The detailed process is illustrated in Figure 1c. The joint action value function \(Q_{tot}\) of the centralized critics is expressed as follows:  
\[Q_{tot}(\tau,\pi_{\theta},s;\phi,\varphi)=h_{\varphi}\Big{(}s,\left\{Q_{i} \big{(}\tau_{i},\pi_{\theta_{i}}(\tau_{i});\phi_{i}\big{)}\right\}_{i=1}^{n} \Big{)}, \tag{6}\]  
Here, \(\phi\) and \(\phi_{i}\) represent the parameters of the joint action value function \(Q_{tot}\) and the individual action value function \(Q_{i}\) for each agent, while \(\tau\) represents the trajectory data, and \(h_{\varphi}\) is a nonlinear monotone function represented by a neural network with parameter \(\varphi\). The strategy of the centralized but decomposable critic for evaluating agents involves training by minimizing the following losses:  
\[L(\phi,\varphi)=E\Big{[}\big{(}y^{tot}-Q_{tot}(\tau,u,s;\phi,\varphi)\big{)}^ {2}\Big{]}, \tag{7}\]  
In the provided equation, the target function \(y^{tot}=r(s,a)+\gamma Q_{tot}(\tau^{\prime},s^{\prime},\pi_{\theta^{-}}(z^{ \prime});\phi^{-},\varphi^{-})\) involves parameters of target actor, critic, and mixing network \(\theta^{-},\phi^{-}\), and \(\varphi^{-}\).  
The utilization of value function decomposition in the multi-agent actor-critic framework enables scalable learning of centralized critics. This approach addresses the challenges and impracticalities associated with centralized and single critic expansion in the presence of global state and joint action within the context of continuous state-action spaces in high-dimensional environments [52].
# Generation and Inhibition of SO\({}_{3}\) in Lead Smelting Flue Gas
## 3 Results and Discussion
### Generation of SO\({}_{3}\) in Experimentally Simulated Lead Smelting Flue Gas
#### 3.2.3 The Effect of Initial SO2 Content on SO3 Generation in Experimental Simulations
The variable conditions included a fixed initial O\({}_{2}\) content of 4%, with the SO\({}_{2}\) content adjusted to 10%, 14%, 18%, and 22%, while the N\({}_{2}\) content decreased proportionally to maintain a constant total of 96% for SO\({}_{2}\) + N\({}_{2}\). The experimental results (Figure 7) demonstrate the amount of SO\({}_{3}\) formed after PbS addition under these conditions: at the initial SO\({}_{2}\) contents of 10%, 14%, 18%, and 22%, the corresponding SO\({}_{3}\) concentration is below 0.1% after PbS addition. These values are significantly lower than the theoretical SO\({}_{3}\) levels calculated for scenarios without PbS addition. The minimal variation in SO\({}_{3}\) generation across different initial SO\({}_{2}\) concentrations indicates that the final SO\({}_{3}\) content is largely independent of the initial SO\({}_{2}\) level under simulated flue conditions at 600 \({}^{\circ}\)C, consistent with the theoretical predictions. Figure 7: The amount of SO\({}_{3}\) formed after adding PbS at different initial contents of SO\({}_{2}\). Consistently, as the oxygen content increases from 2% to 8%, under varying oxygen concentrations, the gas-phase SO\({}_{3}\) concentration is reduced to below 1% after the addition of PbS. In contrast, without an inhibitor, the SO\({}_{3}\) content increases with the rising oxygen levels, consistently exceeding 2%. In summary, the formation behavior of SO\({}_{3}\) in the flue gas from lead smelting is regulated by both thermodynamic equilibrium and reaction kinetics. There are significant differences in its homogeneous and heterogeneous reaction mechanisms. In an industrial context, the strategies of thermodynamic inhibition, high-temperature decomposition, and kinetic regulation using catalyst passivation to minimize SO\({}_{3}\) emissions should be integrated into designs of flue gas purification systems. The inhibition of SO\({}_{3}\) formation by consuming oxygen using PbS is also a feasible and economical method. Utilizing the lead smelting by-product PbS to consume residual oxygen has inherent economic advantages and can realize the recycling of waste. Firstly, it is manifested in the fact that there is no need to purchase reagents externally (such as ammonia or activated carbon), which can save raw materials. Secondly, it can simplify the process, reduce the complexity of the gas treatment system, and lower the investment and operation costs.
## 5 Experiment  
### Experimental Data  
Validated using C-MAPS data developed by the NASA's Ames Research Center, we simulated the degradation of various components such as fan, turbine, and compressor in a turbofan engine under 9000 pounds of thrust. Parameters recorded include pressure, temperature, and rotational speed. This dataset is known as the C-MAPS data [29]. Based on different engine operational conditions, this dataset was divided into four subsets. Among them, FD001 and FD003 contain single operational condition data, while FD002 and FD004 contain multi-operational condition data. Table 2 shows the descriptive information of the data, where fault mode 1 denotes a high-pressure compressor fault, and fault mode 2 denotes both high-pressure compressor and fan faults. The training set comprises data covering the entire lifecycle of the engine. The test set consists of monitoring data from early normal operation to a specific point before failure occurs.  
The dataset includes data from three operational condition settings and 21 sensor measurements. Specific descriptions are provided in Table 3. Among them, \({}^{\circ}R\) denotes Rankine temperature units; \(psia\) represents pressure units in p  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure** & **Specific Parameters** \\ \hline Input & \(W_{1}=40,W_{2}=40,W_{3}=50,W_{4}=50\) \\ SAM layer & \(in=40,out=40,stride=1,softmax\) \\ TCN layer cov11 & \(kernel\_size=3,stride=1,padding=2,ReLU,Dropout(p=0.2)\) \\ TCN layer cov21 & \(kernel\_size=3,stride=1,padding=2,ReLU,Dropout(p=0.2)\) \\ Adam layer & \(lr=0.01,betas=(0.9,0.999),eps=1e-8\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of Adam-SAM-TCN model structure parameters.  
Figure 4: Overall flowchart of turbofan engine RUL prediction.  
absolute (\(lbf-/in^{2}\)); \(rpm\) stands for rotational speed in revolutions per minute; \(lbm/s\) indicates flow rate in pounds per second.
## 2 Rock-Drilling Robot Arm Modeling and Error Calculation
### Error Calculation  
The DH parameter method is employed to calculate the theoretical position coordinates of the robotic arm's end joints. The actual position coordinates of the corresponding joints' ends are then measured and recorded using a total station. The difference between the theoretical position coordinates and the actual position coordinates is calculated along the three XYZ coordinate axes, yielding the positioning errors \(X_{error}\), \(Y_{error}\) and \(Z_{error}\) in each respective direction. The total positioning error is represented as \(\sqrt{X_{error}{}^{2}+Y_{error}{}^{2}+Z_{error}{}^{2}}\).  
In this study, a Leica TS16 total station is used to collect and record the actual position coordinates of the robotic arm's end. The TS16 offers an angular accuracy of 1 arcsecond and a ranging accuracy of 1 mm + 1.5 ppm, ensuring that the accuracy requirements are met.  
The principle and process of data collection using a total station are as follows:  
1. The vehicle coordinate system was established based on the mechanical structure of the rock-drilling robot. Two prisms were installed on the robot, and their coordinates were measured within the vehicle coordinate system.
2. The total station was set up using these two prisms with known coordinates.
3. The total station was used to measure the coordinates of a reflective sticker placed at the end of the robot arm, thereby collecting accurate positional information of the robot arm's endpoint.  
Research on Positioning Error Compensation for Rock-Drilling Robot Arms Based on ISBOA-BP Neural Network  
To address the issues of the BP neural network, specifically its tendency to fall into local minima and its slow convergence speed, this paper utilizes the ISBOA to optimize the initial weights and biases of the BP neural network. This optimization enhances both the convergence speed and the output accuracy of the BP neural network.
## 4 Performance Evaluation
### QoE Parameters
It shows that, over time, a decrease in the quality of frames received by the destination with an important frame loss in GPSR and GPSR-2P. Figure 11: SSIM of received video frames in one 40 vehicles scenario. Figure 10: PSNR of received video frames in one 40 vehicles scenario.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
###### Abstract  
Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution.  
synthetic aperture radar; arc array antenna; keystone transform; frequency-domain; the spatial resolution +
Footnote †: journal: Sensors
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results
### Feature Importances When the Transmitter is at 30 m  
Figure 4 depicts the importance of all of the features. The three most important features (which are denoted via different colors) have the same relative ranking regardless of the machine learning method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Transmitter Height (m)**} & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**Random Forest**} \\ \cline{2-5}  & **MAE (dB)** & **R2** & **MAE (dB)** & **R2** \\ \hline
30 & 3.54 & 0.89 & 3.97 & 0.87 \\
35 & 3.17 & 0.91 & 3.35 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error metrics according to both methods for two transmitter heights.  
Figure 4: Feature importances, as estimated when the transmitter is placed at 30 m, via two machine learning models: (**a**) XGBoost; (**b**) Random Forest. The feature called _Buildings_ is ranked as the most important.  
The three most important features are _Buildings_, _LOS_3b_, and _Distance_. _Buildings_ is the top-ranked feature, in accordance with the fact that the mechanism of multiple reflections is expected to be strong at the particular transmitter's height. It is also worth observing that the importance of _LOS_3b_ (which indicates the presence of the over-rooftop diffraction) is significant, particularly when XGBoost is applied.