esa-sceva/satcom-chunk-collection huggingface dataset:
# CVE2 ATT&CK: BERT-Based Mapping of CVEs to MITRE ATT&CK Techniques
## 1 Introduction
### Unsupervised Labeling Technique of CVEs  
The unsupervised labeling technique introduced by [PERSON] et al. [15] considers a multi-head deep embedding neural network model that learns the association between CVEs and MITRE ATT&CK techniques. The proposed representation identifies specific regular expressions from the existing threat reports and then uses the cosine distance to measure the similarity between ATT&CK technique vectors and the text description provided in the CVE metadata. This technique manages to map only 17 techniques out of the existing 192. As such, multiple techniques are not covered by the proposed model. Thus, a supervised approach for technique labeling might improve the recognition rate among techniques.
## 3 Results and Discussion
### Number of Replications  
To make decisions once the simulation model has been validated, it is necessary to know the number of replications (independent simulations). For this purpose, the procedure for estimating the mean with a specific error was used, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Replications or Runs** & **Average Manufacturing Time (min)** \\ \hline
1 & 3469 \\
2 & 3467.97 \\
3 & 3439.94 \\
4 & 3448.79 \\
5 & 3434.99 \\
6 & 3450.19 \\
7 & 3476.31 \\
8 & 3470.78 \\
9 & 3461.51 \\
10 & 3448.08 \\
11 & 3456.14 \\
12 & 3444.58 \\
13 & 3444.25 \\
14 & 3465.9 \\
15 & 3455.42 \\ Average & 3455.59 \\ Standard Deviation & 12.4949 \\ \hline \end{tabular}
\end{table}
Table 4: Replications carried out and their average manufacturing time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Runs** & **Xj** & **Yj** & **Zj** = **Xj** - **Yj** & \(\begin{pmatrix}-\\ Zj-Z_{15}\end{pmatrix}^{2}\) \\ \hline
1 & 3465.66 & 3469.00 & \(-\)3.34 & 76.38 \\
2 & 3490.00 & 3467.97 & 22.03 & 276.55 \\
3 & 3485.22 & 3439.94 & 45.28 & 1590.41 \\
4 & 3460.81 & 3448.79 & 12.02 & 43.82 \\
5 & 3440.12 & 3434.99 & 5.13 & 0.07 \\
6 & 3450.23 & 3450.19 & 0.04 & 28.72 \\
7 & 3445.30 & 3476.31 & \(-\)31.01 & 1325.68 \\
8 & 3463.12 & 3470.78 & \(-\)7.66 & 170.56 \\
9 & 3459.15 & 3461.51 & \(-\)2.36 & 60.21 \\
10 & 3449.51 & 3448.08 & 1.43 & 15.76 \\
11 & 3455.28 & 3456.14 & \(-\)0.86 & 39.18 \\
12 & 3482.27 & 3444.58 & 37.69 & 1042.64 \\
13 & 3443.08 & 3444.25 & \(-\)1.17 & 43.16 \\
14 & 3462.00 & 3465.90 & \(-\)3.9 & 86.49 \\
15 & 3463.23 & 3455.42 & 7.81 & 5.8 \\ \hline \end{tabular}
\end{table}
Table 3: Two-week simulation runs.  
For the calculation of the number of replications, a sample of 15 data points of the average time between each production process was used, with an absolute error of 5.4 s and a confidence level of 95%. Substituting the obtained values into the equation, the input data are as follows:  
\[\overline{X}=3455.59,\ S=156,\ \beta=5.4,\ \alpha=0.1\]  
\[\min\{i\geq 15:t_{0.95}\sqrt{\frac{156}{i}}\leq\ 5.4\}\]  
For 15 replications, the equation is as follows: \(i=15\)  
\(1.660\sqrt{\frac{156}{15}}=5.3\leq 5.4\) The condition is met.  
Therefore, it is determined that the simulation model is statistically valid with an optimal number of replications of 15 runs.
## 2 Methodology
### Power Generation Forecasting Architecture  
The overall architecture of the forecasting method for the daily power generation of the group of small hydropower stations proposed in this paper is divided into three parts: a multimodal data set, a multimodal deep learning network, and a late fusion network, as shown in Figure 2.  
1. Multimodal data set. It is a heterogeneous mixed-dimensional data set composed of precipitation data with two-dimensional spatial distribution characteristics and daily power generation data with one-dimensional temporal series characteristics.
2. Multimodal network. The multimodal network is composed of a CNN and an MLP. The branch of the CNN has six layers (\(L_{1,1}\) to \(L_{1,6}\)), and the input layer \(L_{1,1}\) receives the data set of the spatial distribution of precipitation. The deep convolution layer \(L_{1,2}\) to \(L_{1,4}\) extracts the deep features of the input variables, and the ReLU function and the BatchNormalization method are used in each layer to activate and adjust the distribution of the extracted feature data. The flattening layer \(L_{1,5}\) and the fully connected layer \(L_{1,6}\) integrate the highly abstracted feature data after multiple convolutions to facilitate subsequent feature fusion. The MLP branch has three layers (\(L_{2,1}\) to \(L_{2,3}\)) which receive the historical power generation data of the several previous periods to extract the variation characteristics of the power generation capacity of the entire group of small hydropower stations in the short term during the present period.
3. Late fusion. The late fusion network consists of four layers (\(L_{1}\) to \(L_{4}\)), and the number of nodes in each layer is \(16\), \(8\), \(4\), and \(1\). The joint layer \(L_{1}\) receives and dimensionally connects the temporal and spatial feature information of precipitation and the feature information of power generation capacity changes extracted from the multimodal network. The fully connected layers \(L_{2}\) to \(L_{4}\) form a simple neural network, which carries out regression fusion and analysis on the extracted feature information and utilizes a linear function to activate the output layer to obtain the final forecasting value.  
The detailed parameters of each layer of network in the proposed model are shown in Table 1.
## 2 Materials and Methods
### Construction of the Remote Sensing Vegetation Index (RSVI)  
The indices we selected for the RSVI primarily reflect vegetation characteristics such as greenness (EVI), coverage (FVC), productivity (GPP), and canopy structure (LAI). These components were chosen because they are directly relevant to monitoring vegetation health and dynamics. Greenness indices, for instance, are well-established indicators of photosynthetic activity and vegetation vigor ([PERSON] et al., 2017). Coverage indices help quantify the extent of vegetation, which is crucial for understanding spatial patterns ([PERSON] et al., 2018). Productivity and canopy structure indices provide insights into the biomass and physical characteristics of vegetation vegetation.  
Figure 4: The verification of the correlation between TVDI and soil moisture during the drought period with black crosses indicating the pixels that passed the significance test (\(p<0.05\)).  
Figure 3: Conceptual diagram of TVDI, adapted from [PERSON] et al. (2018).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Drought Types** & **Values** \\ \hline
1 & Moist & 0 \(<\) TVDI \(\leq\) 0.2 \\
2 & Normal & 0.2 \(<\) TVDI \(<\) 0.4 \\
3 & Mild drought & 0.4 \(<\) TVDI \(<\) 0.6 \\
4 & Moderate drought & 0.6 \(<\) TVDI \(<\) 0.8 \\
5 & Severe drought & 0.8 \(<\) TVDI \(<\) 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Climatic classification based on the TVDI.  
vegetation, which are critical for assessing ecosystem function [27; 28; 29; 30]. The selection of these specific indices was guided by their established relevance in vegetation monitoring and by their ability to capture key aspects of vegetation condition across different climatic regions. However, there is a strong correlation among these four indices, making principal component analysis (PCA) an appropriate choice for construction. To prevent the impact of scale differences on the weights, each index must be standardized prior to performing PCA [61]. This is shown in Equations (5)-(7).  
Due to the nonuniform dimensions of the EVI, FVC, GPP, and LAI, it is imperative to normalize each index before formulating the RSVI, guaranteeing that their values fall within the 0 to 1 range, as illustrated in Equation (5):  
\[N=\left(V-V_{min}\right)/\left(V_{max}-V_{min}\right) \tag{5}\]  
In this context, \(N\) represents the normalized value, \(V\) is the value of a specific grid cell, \(V_{min}\) is the minimum value of the grid cells, and \(V_{max}\) is the maximum value of the grid cells.  
To ensure the comparability of the RSVI for the same study area across different years, it is necessary to establish consistent maximum and minimum values. PCA was subsequently performed on the normalized indices of greenness, coverage, productivity, and canopy structure. This can be represented by Equation (6):  
\[RSVI_{0}=PCA_{1}[f(EVI,FVC,GPP,LAI)] \tag{6}\]  
The RSVI is standardized to values between 0 and 1 to facilitate measurement and differentiation of the indices. This is demonstrated in Equation (7):  
\[RSVI=\left(RSVI_{0}-RSVI_{0-min}\right)/\left(RSVI_{0-max}-RSVI_{0-min}\right) \tag{7}\]
## 3 Methodology
### Space Syntax Monitoring Scene Analysis  
To implement the space syntax analysis, a blueprint of the surveillance scene is generalized to reduce the computational complexity. Three types of analysis are carried out and integrated to generate the reward map of video coverage: convex space, visibility graph, and axial space. These three types of features are then weighted together for a reward raster map, which indicates the space syntax importance of each pixel. We assume that the video cameras covering each pixel will obtain the reward points of that pixel. Then, the camera placement optimization process will be converted into a game in which each camera will be moved to different points, and the most visible area overall will receive the highest number of reward points. To play the game automatically, a DQN-based algorithm is designed to obtain more points by moving the location and orientation of the cameras.
## 5 Results and Discussion
The relays with buffers enable opportunistic use of the spectrum, but its full benefits can only be accrued if we are able to transmit the packets residing in the buffers with the minimum delays. Therefore, one of the key feature of a buffer-aided scheme is the average delay, which is measured by counting the number of time slots a transmitted packet stays in a relay buffer. The average delay as a function of SNR for the BAMR, max-link, and max-max schemes is depicted in Figure 17. For this comparative analysis, the buffer size was kept intentionally low in order to keep the delay low for each buffer-aided scheme. It can be noted from the results that BAMR exhibited significantly better  
Figure 16: Average throughput vs. SNR. Figure 15: Outage probability vs. SNR. performance compared to that of both the max-max and the max-link schemes. The performance gain of BAMR was due to the fact that the virtual full-duplex operation was employed more frequently and packets were not trapped in the buffers for several time slots, whereas the performance degradation observed in the other two schemes resulted from the use of one single-link activation due to which packets tended to remain for several time-slots in the buffers.
## 2 Estimation of Interruption Costs Arising from Transmission System Originated Failures
### Sector Periodic Demand Model
The next step of the proposed methodology is therefore to subdivide each of the N periods into peak and off-peak sub-periods using a cut-off factor (\(CF_{p}\)) as shown in Figure 3b and calculated in (4) for each period \(p\). \[CF_{p}=0.9\times max(\{H_{h}\})\big{|}H_{h}\in RD_{p} \tag{4}\]  
The peak load, \(L_{p}\), is next determined as the representative load for the peak sub-period p as shown in (5). The probability of occurrence of the representative load \(L_{p}\) is given by (6). \[L_{p}=max\big{(}\{H_{i,h}\}\big{)}|H_{i,h}\in RD_{p} \tag{5}\]  
\[Prob_{p}=\frac{\big{|}\left\{H_{i,h}\ |\ H_{i,h}\in\big{\{}D_{i,p}\big{\}},H_{i,h }\geq CF_{p}\right\}\big{|}}{d\times 24} \tag{6}\]  
The load profiles \(D_{iN+1}\) in the (N + 1)th demand period are next obtained by aggregating load data from the N off-peak sub-periods as given by (7). The outages during off-peak sub-periods are due to random failures of bulk system components. The average load given by (8) is therefore taken as the representative load, \(L_{N+1}\) in the (N + 1)th period. The probability of occurrence of the representative load \(L_{N+1}\) is given by (9). \[D_{i,p=N+1}=\bigcup_{p=1}^{N}\big{\{}H_{i,h}\big{|}H_{i,h}\in\big{\{}D_{i,p=1,2,\ldots N}\big{\}},H_{i,h}<CF_{p}\big{\}} \tag{7}\]  
\[L_{p=N+1}=\frac{1}{\big{|}\left\{H_{i,h}\ |\ H_{i,h}\in\big{\{}D_{i,p=N+1}\} \right\}\big{|}}\sum_{H_{i,h}\in\big{\{}D_{i,p=N+1}\big{\}}}H_{i,h} \tag{8}\]  
\[Prob_{p=N+1}=\frac{\big{|}\left\{H_{i,h}\ |\ H_{i,h}\in\big{\{}D_{i,p=N+1} \big{\}}\right\}\big{|}}{d\times 24} \tag{9}\]  
Finally, the 'N + 1' step discrete probability distribution is obtained from the representative load level and its associated probability derived from (5), (6), (8), and (9) from the SPD model as shown in (10). \[\left\{D_{i,p=1,2,\ldots N}\right\}\in\left\{D_{i}\right\}\left|\mu\left(D_{i =j}\right)\approx\mu\left(D_{i\
eq j}\right)\text{ and }med\left(D_{i=j}\right)\approx med\left(D_{i\
eq j}\right) \text{ for j}=1,2,\ldots,d \tag{2}\]0  
The proposed methodology is developed with the objective of its application in emerging power systems transitioning to meet low-emission environmental targets. The intermittent renewable generation and electric vehicles are incorporated in the load profile of the customer sector that owns them. Since the proposed methodology incorporates an SPD model that includes clusters of identical diurnal load profiles distributed across the annual evaluation period, it can be applied to any type of power system. The number of sub-periods would differ based on the power system net load variability in low-carbon power systems, whereas the SPD model of a conventional power system can be divided into seasonal sub-periods, as the load profiles in each season are more or less similar.
## 2 Short-Circuit Current Calculation in Distribution Network with Renewable DGs
### Short-Circuit Current Calculation for Distribution Networks with Renewable DGs  
There are three general methods to calculate the short-circuit current of distribution networks containing renewable DGs.  
In the first-type methods, the output current of all renewable DGs is regarded as zero in the short-circuit current calculation. In these methods, the influence of DGs on the short-circuit is ignored in the calculation. Since the output current of all DGs equals zero, the voltage of each node can be obtained via a power flow calculation, and then, the short-circuit current can be calculated based on Equation (7).  
In the second-type methods, the output current of all renewable DGs is regarded as 1.2 times the rated current in the short-circuit current calculation. The voltage at each node during normal operation of the distribution network is calculated under the condition of a rated output current of all renewable DGs, and then, the short-circuit voltage at each node and short-circuit current in each branch circuit is calculated with 1.2 times the rated output current of all DGs after a fault occurs at the fault point.  
In the third-type methods, the output current of renewable DGs is calculated according to the real value under LVRT strategies. The detailed processes are given as the following steps:  
Step 1: according to the real-time output of the renewable DGs, the voltage of all nodes is obtained via a traditional power flow calculation before the fault occurred. And then, the voltage at each node after the fault occurred is obtained using a simplified circuit, which corresponds to Equation (7). And then, the following equations can be obtained as  
\[I_{f}=\frac{1}{Z_{ff}}+\sum_{j\in H}\frac{Z_{fj}}{Z_{ff}}I_{j} \tag{8}\]  
\[U_{i}=1-Z_{if}I_{f}+\sum_{j\in H}Z_{ij}I_{j} \tag{9}\]  
where \(I_{f}\) represents the fault current at fault point \(f\), \(Z_{ff}\) represents the self-impedance at fault point \(f\), \(Z_{ij}\) represents the mutual impedance between node \(i\) and PCC \(j\), \(Z_{ij}\) represents the mutual impedance between node \(i\) and fault point \(f\), \(H\) represents the set of all PCCs, \(I_{j}\) represents the equivalent output current of DGs connected to node \(j\) under LVRT strategies, and \(U_{i}\) represents the voltage of node \(i\);  
Step 2: the voltage drop amplitude at the terminal of each DG is substituted into the equivalent functions of LVRT strategies to update the output current after the fault occurred;  
Step 3: the voltage at each node is updated based on the impedance matrix according to the difference in the output current of DGs before and after the fault under LVRT strategies;  
Step 4: steps 2 and 3 are carried out iteratively until the difference value of the voltage at each node in two consecutive calculation is less than a threshold value. And then, the output current of DGs and the voltage at each node can be obtained, as well as the short-circuit current of each branch circuit.
## 4 Performance Analysis of the BOC Signal and BPSK Signal
### The Hardware Application and Test  
The hardware structure uses AD9361 and XC7Z020 as the transmitter and receiver, as shown in Figure 17a. The RF frequency point of the system can be any frequency point in the range of 200 MHz to \(\sim\)6 GHz. In addition, we add the Doppler frequency, signal attenuation, and signal delay to the signal through the channel simulator, which is shown in Figure 17b, so as to better simulate the real environment. Some hardware test results are shown in Table 2.  
Figure 16: The switching time when \(\alpha:\beta=0.1:0.9\) and \(C/N_{0}=45\) dB-Hz. (**a**) the Doppler frequency error, and (**b**) the Doppler frequency measurement.  
In the hardware system, the clock difference between multiple spacecraft will cause the ranging error. Therefore, we adopt dual one way ranging to measure the true ranging error. Therefore, we adopt dual one way ranging to measure the true ranging error.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Acquisition time & 0.034489 s \\ Acquisition frequency & 8950 Hz \\ Radio frequency of S band & Uplink 2.9 GHz/ \\ The SNR of communication signal at receiving end & Downlink 3.1 GHz \\ The C/\(N_{0}\) of navigation and measurement signal & 11 dB \\ at receiving end & 65 dB-Hz \\ The Doppler frequency & Sinusoidal variation with amplitude \\ Channel attenuation & Rice fading channel \\ Doppler measurement accuracy & \textless{}0.01 m/s \\ Ranging accuracy & \textless{}0.1 m \\ BER & \(8.964\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hardware test results.  
Figure 17: The hardware test environment. (**a**) The test environment, and (**b**) the hardware picture.  
results, as well as to synchronize the clock of the whole network. The multiple devices exchange the local pseudo range measurement results. The true range \(D\) and clock difference \(\Delta T_{\text{clock}}\) between device C and device A are shown in Equation (19) and Figure 18.  
\[\left\{\begin{array}{l}D=\frac{1}{2}[(\rho_{\text{A}}+\rho_{\text{C}})-(\tau_{ \text{Ar}}+\tau_{\text{At}}+\tau_{\text{Cr}}+\tau_{\text{Ct}})\cdot c]\\ \Delta t_{\text{clock}}=\frac{1}{2}\Big{[}\frac{(\rho_{\text{A}}-\rho_{\text{C} })}{c}+(\tau_{\text{Ar}}-\tau_{\text{At}}+\tau_{\text{Ct}}-\tau_{\text{Cr}}) \Big{]}\end{array}\right. \tag{19}\]  
Here, \(\rho_{\text{A}}\) and \(\rho_{\text{C}}\) are the local ranging results with clock difference of devices A and C. \(\tau_{\text{At}}\) and \(\tau_{\text{Ct}}\) are the transmitting channel delay of device A and C. \(\tau_{\text{Ar}}\) and \(\tau_{\text{Cr}}\) are the receiving channel delay of devices A and C. \(c\) is the velocity of light. After the clock difference between slave devices and master device is obtained, devices A and B adjust the local clock to the master device.
## 3 Results and Discussion  
### Surface Morphology, XRD and SERS Characteristic of ZnO NRs  
The main advantage of growing ZnO NRs through dip coating and decorating AuNP through PLIP is that unlike complex sputtering deposition in high vacuum, the two-step process is a simple technique that allows precise control over the growth of ZnO seed layers. However, the ZnO seed layer may have a random coplanar orientation and a c-axis (002) perpendicular to the substrate, resulting in an incomplete c-axis orientation after chemical bath immersion [26]. Figure 2a illustrates the SEM images of the surface morphology of ZnO NRs, which reveals the hexagonal morphology of ZnO NRs with a diameter and length of approximately 300 nm and 4 \(\upmu\)m, respectively and random orientation. During the growth of ZnO NRs, nucleation first occurs near the grain boundaries between two adjacent particles in the ZnO seed layer [27]. Not all nanowire arrays are c-axis orientated because of the polycrystalline nature of the seed layers [27; 28]. Figure 2b depicts the XRD pattern of ZnO NRs, indicating a well-defined crystalline vertical c-axis (002), horizontal polar-terminated (001) and horizontal nonpolar low-symmetry (101) faces. The sharp XRD peaks prove the growth of the ZnO wurtzite structure [29]. Figure 2c depicts the Raman spectra of the R6G solution at various concentrations detected using ZnO NRs as the SERS substrate. Bare glass was also used as the substrate for the control experiment, which exhibited no Raman signal for 10\({}^{-3}\) M R6G. Strong Raman signals  
Figure 1: Schematic of fabricating surface-enhanced Raman scattering (SERS) substrate using zinc oxide (ZnO) nanorods (NRs) decorated with Au nanoparticles (AuNPs) synthesized through pulsed-laser-induced photolysis (PLIP).  
were observed at 612, 772, 1184, 1314, 1366, 1511 and 1650 cm\({}^{-1}\) on the ZnO NR SERS substrate for 10\({}^{-3}\) M R6G, which were attributed to xanthene ring bending and deformations, C-H bond bending, N-H bond bending and CH\({}_{2}\) waggling [22]. However, when the concentration of R6G was reduced to 10\({}^{-5}\) M, the Raman signal was considerably weak.