esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method to Reduce Non-Nominal Troposphere Error
## 5 Method to Reduce the Non-Nominal Troposphere Error
### Simulation Analysis of the Proposed Method
Because the satellites in the elevation range of (5\({}^{\circ}\), 30\({}^{\circ}\)) can help improve the satellite geometry, we must find an appropriate method for selecting the optimal satellite subset to achieve a balance between satellite geometry and the non-nominal troposphere error. Based on the above analysis, this paper proposes the following process:  
1. Obtain the optimal satellite subsets using the brute method and the greedy method. 2. Analyze the distribution characteristics of the optimal satellite subsets. 3. Determine the number \(M\) of satellites in the optimal satellite subset based on the greedy method and then compare the results with those obtained by the brute method to verify the accuracy of the results. 4. Assess the satellite distribution according to the optimal satellite subsets obtained by the brute and greedy methods. Next, divide the elevation ranges into (5\({}^{\circ}\), 30\({}^{\circ}\)), (30\({}^{\circ}\), 60\({}^{\circ}\)) and (60\({}^{\circ}\), 90\({}^{\circ}\)). The distribution in the three elevation ranges is k1:k2:k3. 5. Select the optimal satellite subset to minimize \(VPL_{\text{C}}\). For all visible satellites, assuming that there are M1, M2, and M3 satellites in each elevation range, k1 \(\times\) M1, k2 \(\times\) M2 and k3 \(\times\) M3 satellites should be selected, respectively. The selection criterion is the GDOP contribution of each satellite. The M and k1 : k2 : k3 values in the above process are determined by the following simulation analysis. The GDOP is related to the distribution and number of visible satellites. The GDOP will vary when different satellite subsets are involved in the positioning solution. For convenience, the GDOP contribution of the \(i\)th satellite is [20]  
Figure 16: Processing flow of the proposed new method. \[\Delta GDOP_{i} =trace(\frac{G_{m}h_{i}^{T}h_{i}G_{m}}{S_{ii}}) \tag{18}\] \[S_{ii} =1-h_{i}G_{m}h_{i}^{T}\] (19) \[h_{i} =[ \begin{array}{cc}e_{i1}&e_{i2}&e_{i3}&1\end{array} ] \tag{20}\]  
where \(h_{i}\) is the observation vector of the \(i\)th satellite. A higher value of \(\Delta GDOP_{i}\) indicates that the satellite geometry is improved after the addition of the \(i\)th satellite. Thus, the satellites with higher \(\Delta GDOP_{i}\) should be prioritized. Figure 16 presents the processing flow corresponding to the proposed method above.
## 3 Results and Discussion
### Optimization of Extraction Conditions Using Response Surface Methodology  
Based on the results obtained from the single-factor experiments, the optimal ranges for the extraction time (X\({}_{1}\)) from 60 to 180 min, the extraction temperature (X\({}_{2}\)) from 40 to 60 \({}^{\circ}\)C and the solvent-to-material ratio (X\({}_{3}\)) from 40/1 to 80/1 (mL/g) were selected and applied for the optimization process using the response surface methodology (RSM) combined with the Box-Behnken model. 3.1 Fitting Models for Prediction of TPC Extraction Yield (Y\({}_{1}\)), TSC Extraction Yield (Y\({}_{2}\)) and DPPH Antioxidant Activity (Y\({}_{3}\))  
The extraction yields of the TPC (Y\({}_{1}\)), TSC (Y\({}_{2}\)) and DPPH antioxidant activity (Y\({}_{3}\)) of the experimental runs are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{N_{o}}\) & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{Y_{1}}\) & \(\mathbf{Y_{2}}\) & \(\mathbf{Y_{3}}\) \\  & & & & **(mg GAE/g DW)** & **(mg AE/g DW)** & **(\(\mu\)M TE/g DW)** \\ \hline \(1\) & \(-1\) & \(-1\) & \(0\) & \(15.98\) & \(8.05\) & \(90.14\) \\ \(2\) & \(-1\) & \(0\) & \(-1\) & \(15.26\) & \(7.04\) & \(69.58\) \\ \(3\) & \(-1\) & \(0\) & \(+1\) & \(14.62\) & \(10.87\) & \(90.65\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental values of TPC, TSC and DPPH antioxidant activity of the extracts obtained from 15 combinations of the input variables. Figure 4: Influence of solvent-to-solid ratio on the extraction efficiency of TPC (**A**), TSC (**B**) and antioxidant activity (**C**). Different characters indicate statistically significant differences among the values (\(p<0.05\)). The results in Table 2 show a significant variation in the extraction yields of the TPC, TSC and DPPH antioxidant activity from the different experimental runs. Figure 5 illustrates the correlations between the actual values obtained from the experiments and the predicted values generated from the models. The points lying within the two boundary curves and being close to the bisector line in Figure 5 indicate strong consistency between the actual and predicted values for most of the experimental runs. The statistical analysis also shows very high correlation coefficients (R\({}^{2}\)) between the actual and predicted values of the TPC, TSC and DPPH antioxidant activity (0.95, 0.92 and 0.91, respectively). Further statistical analysis of the model's fit indicates that the \(p\)-values for the lack of fit of all prediction models were greater than 0.05 (P\({}_{\text{Lack of Fit}}\) = 0.322, 0.599 and 0.151 for the models of TPC, TSC and antioxidant activity, respectively); hence, the established models were confirmed to be suitable to predict the output responses. The predicted extraction yields of TPC, TSC and DPPH antioxidant activity from COG rhizomes as determined by the optimization models can be calculated according to the following regression equations:
# Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework
## 2 Background  
MITE ATT&CK Framework  
CKC follows a clearly defined linear step sequence, which is useful for understanding the cyberattack process. However, this model is not effective for analyzing the individual behaviors of an adversary, determining correlations between the detailed actions and tactical objectives of an adversary, or identifying the associations between data sources for attack and countermeasures for defense [13]. MITRE ATT&CK, unlike the Cyber Kill Chain, maps and indexes virtually everything regarding an intrusion from both the attack and defense sides. Therefore, MITRE ATT&CK is suitable for modeling various cyberattacks.  
[PERSON] et al. [14] compare the attack on U.S. broadcasters in Iran and the cyberattack on U.S. lawmakers by Russia using the CKC model and MITRE ATT&CK and found the following.  
Clearly classify recent cyberattacks into seven phases (1) Reconnaissance; 2) Weaponization; 3) Delivery; 4) Exploitation; 5) Installation; 6) Command and Control; 7) Action on Objectives;  
* Express cyberattack technology correctly; and
* Suggest specific security measures.  
The MITRE ATT&CK framework documents and categorizes cyber adversary behavior into tactics, techniques, and procedures (TTPs) [15], which are not restricted by an order. Figure 1 compares the intrusion steps of CKC and MITRE ATT&CK.  
MITE ATT&CK is a security threat knowledge database that analyzes how an adversary invades and spreads through computer systems, focusing on data identified in actual breaches. Data are described by the three elements of TTP, which enables the construction of defense in depth (DiD).  
* Tactics: the methods and objectives used in the attack;
* Techniques: 185 techniques and 367 sub-techniques have been identified as technical cyberattack methods for achieving tactical objectives;
* Procedures: the sequential processes of an attack.  
MITRE updated the MITRE ATT&CK Enterprise framework in October 2020 by integrating _Reconnaissance_ and _Resource Development_ into two tactics for MITRE PRE-ATT&CK, which describes the preparation phase of an attack [16]. Table 1 describes each tactic in MITRE ATT&CK.  
Figure 1: CKC vs. MITRE ATT&CK.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 4 Experimental Results
### Performance Analysis of the Multi-GPU Based Streaming BP-VISAR Architecture  
To evaluate the performance of the multi-GPU based BP-VISAR architecture, we implemented the streaming architecture of four GPUs in Figure 6(a). All the GPUs launched three streams of the BP-VISAR pipeline in Arch. S3-3 simultaneously. Only the size and the dimensions of the output frame generated were different from the single GPU based architecture Arch. S3-3 as in Figure 6(b).  
Table 7 and Figure 13 show the processing times of multi-GPU based streaming architectures for non-overlapped and overlapped modes. For lower resolution BP-VISAR frame processing, the processing times per frame are around 50 ms for lower resolutions of video SAR frames. Compared with the processing times in Arch. S3-3 with three streams running on a single GPU, there is no advantage to using the multi-GPU based architecture for resolutions less than 1024 \(\times\) 1024, but there is an increased cost for quad-GPU. However, the processing is accelerated by 3.2 times both for non-overlapped and overlapped modes in 4096 \(\times\) 4096 resolution. From the results, for lower resolution video SAR frame processing, the overhead for frequent control of the multi-GPU data transfer and kernel execution is too large to take advantage of multi-GPU based parallel processing. However, for higher resolution video SAR frame processing, the frequency for controlling the multi-GPU data transfer and kernel execution decreases due to the longer backprojection and data transfer times.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Resolution**} & \multicolumn{8}{c}{**Number of Streams**} \\ \cline{2-10}  & **1-Way** & **2-Way** & **3-Way** & **4-Way** & **5-Way** & **6-Way** & **7-Way** & **8-Way** \\ \hline
1024 \(\times\) 1024 & 6.1 & 3.2 & 3.2 & 3.0 & 3.0 & 3.0 & 3.1 & 3.1 \\
2048 \(\times\) 2048 & 14.4 & 10.3 & 10.2 & 10.2 & 10.2 & 10.2 & 10.2 \\
4096 \(\times\) 4096 & 49.0 & 45.1 & 41.3 & 41.0 & 41.0 & 41.1 & 41.2 & 41.2 \\ \hline \end{tabular}
\end{table}
Table 8: BP-VISAR frame generation time with the number of streams in the multi-stream based processing architecture, overlapped mode (\(\alpha=0.8\), unit: ms).  
Figure 12c shows the visual profiling timeline for executing the quad-GPU streaming BP-VISAR architecture. There are time lags between the starting times of the pipelines of the GPUs. However, except for the starting time lag, there is no dependency between the operation of the pipelines in the GPUs because the pipelines are not bandwidth limited but computation limited. Although the H2D and D2H data transfer in a GPU may conflict with the data transfer in the other GPUs, the bandwidth between the host and the GPUs is sufficient for fast data transfer. Thus, the latency caused by the bus contention is negligible.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 4 Discussion
### Parameter Sensitivity Analysis
#### 4.1.2 Strength Parameters of Ground  
Based on the bearing capacity Equation (8), the influence of cohesion and internal friction angle on the bearing capacity can be obtained, as shown in Figure 10. From Figure 10a, it is evident that the bearing capacity increases as the internal friction angle of the soil increases. The internal friction angle reflects the soil's ability to resist sliding, and higher friction means the soil can better resist the anchor's pull-out force. Cohesion represents the ability of soil particles to stick together. As cohesion increases, the soil's overall strength improves, leading to a greater resistance against the applied load. The increased cohesion strengthens the anchor's hold in the soil, thus enhancing the bearing capacity. These two factors together influence the anchor's performance, with higher cohesion and internal friction angle providing greater resistance to failure under load.
## 4 Numerical Analysis Software and Methods Used
### The Process of Establishing Numerical Models
#### 4.4.4 Generation of Mesh  
After the model was established and the structure was assigned material properties, the model was divided into finite elements to perform calculations. The accuracy of the model simulation is related to the mesh density. Increasing the density of the mesh increases the computation time of the model.
###### Abstract  
As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.  
autoencoders; CNN; fabric defect detection; synthetic defect generation +
Footnote †: journal: Applied Sciences
# New Evolutionary-Based Techniques for Image Registration
## 2 The Proposed Evolutionary Computing General Framework for Image Registration
Consequently, an EA should operate on chromosomes having their solution part represented by a five sized real-valued vector. Each candidate solution corresponds to a potential transformation parameter \(\left(\mathrm{a}_{\mathrm{sol}},\mathrm{b}_{\mathrm{sol}},\mathrm{s}_{\mathrm{ sol}},\mathrm{\theta}_{\mathrm{sol}},\mathrm{d}_{\mathrm{sol}}\right)\). In the case of self-adaptive procedures, the chromosome representation should include a parameter part, the additional alleles being related to the mutation operator. Various similarity/dissimilarity measures have been reported so far, each one having its own strengths and shortcomings. In our developments we use one of the most studied similarity measures for solving the image registration problem, namely the Shannon mutual information (MI) method [16; 17; 18]. The main drawback of any MI-based EA that processes arbitrary grey level images is its computational complexity. In order to solve the complexity problem involved by the MI quality evaluation, we use the mutual information computed between the binary variants of the reference \(\mathrm{I}_{1}\) and the transformed image \(\mathrm{I}_{2}\left(\mathrm{\tilde{T}}_{\mathrm{SP}}^{-1}\right)\) respectively. Let \(I_{1},\ I_{2}\) be M\(\times\)N binary images. The mutual information between \(I_{1}\) and \(I_{2}\) is defined by [19]:  
\[MI(I_{1},\ I_{2})=\ H(I_{1})+H(I_{2})-H(I_{1},\ I_{2}) \tag{9}\]  
where \(H(\cdot)\) is the Shannon entropy, and \(H(I_{1},\ I_{2})\) is the joint entropy  
\[H(I)=\ -\sum_{i=1}^{2}P_{i}\cdot\log(P_{i}) \tag{10}\]  
\[H(I_{1},\ I_{2})=\ -\sum_{1\leq i\leq 2}p(i,j)\cdot\log(p(i,j)) \tag{11}\]  
Since  
\[MI(I_{1},\ I_{2})\ \leq\ \min(H(I_{1}),H(I_{2})) \tag{12}\]  
we get  
\[\underset{j}{\max}MI(I,\ J)=\ MI(I,\ I)=\ H(I) \tag{13}\]  
We define the fitness function of each genotype \(g\) corresponding to the transformation \(\tilde{T}_{SP}\), by  
\[fitness(g)=\frac{MI(I_{1},I_{g})}{MI(I_{1},I_{1})}=\frac{MI(I_{1},I_{g})}{H(I_ {1})} \tag{14}\]  
where \(I_{g}\ =\ I_{2}\Big{(}\tilde{T}_{SP}^{-1}\Big{)}\). Note that \(fitness(g)\leq 1\) and the maximum value of the fitness function is obtained when \(I_{g}=\ I_{1}\), which is equivalent to \(I_{g}\big{(}\tilde{T}_{SP}\big{)}\ =\ I_{2}\).
## 2 Methodology  
### Correlation Analysis  
The variation in electricity load during actual operation is influenced by various external factors. However, not all influencing factors are significantly correlated with the load. Including weakly correlated variables may reduce the accuracy of the model. The Spearman correlation coefficient (SCC) was used to select the most relevant features as inputs, and its formula is as follows:  
\[\rho=1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)} \tag{1}\]  
where \(d_{i}\) represents the rank difference between the variables \(x_{i}\) and \(y_{i}\), \(n\) is the sample size, and \(\rho\) ranges between [\(-\)1,1], where its absolute value increases with the strength of the correlation.  
This study carried out a correlation analysis based on publicly accessible data obtained from the Singapore Energy Market Corporation (EMC) platform, and the findings are presented in Table 1. The analysis revealed that temperature had a high positive correlation (SCC = 0.6830) with electricity load, while relative humidity had a high negative correlation (SCC = \(-\)0.6440). Electricity price showed a medium positive correlation (SCC = 0.5131). Wind speed had a low positive correlation (SCC = 0.2080), suggesting a limited impact. Dew point (SCC = 0.0694), pressure (SCC = 0.0615), and wind direction (SCC = 0.0639) had very low correlations with electricity load, indicating that these factors have minimal impact on load variations. Therefore, temperature, relative humidity, and electricity price were selected as the inputs. To further validate the effectiveness of these three factors, holiday data were selected for analysis in Table 2. As a special time period, holidays may influence consumer electricity usage differently from regular days, such as increased family activities and greater use of leisure and entertainment devices. The analysis' results showed that, even during holidays, the correlation between these three variables and electricity load remained strong, confirming the applicability and reliability of these factors during holidays.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **SCC** \\ \hline Price & 0.5131 \\ Temperature & 0.6830 \\ Dew Point & 0.0694 \\ Relative Humidity & \(-\)0.6440 \\ Pressure & 0.0615 \\ Wind Direction & 0.0639 \\ Wind Speed & 0.2080 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spearman correlation coefficient between load and factors.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 4 Drive of the Rapid Prototyping Inverter with Voltage Support Function
### Drive of PM15 FM30C Triphase Module  
The P2P voltage control algorithm represents a high level control system to coordinate the inverters in a distributed way aiming at regulating the voltages within the accepted limits. For the inverter to be able to follow the regulation of the P2P control algorithm, an internal control system has to be implemented and integrated with the GVSF. We have implemented a state-of-the-art current control loop, Phase-Locked-Loop (PLL), and Kalman filter to drive the inverter. The inner control system of the inverter is depicted in Figure 5.  
The inverter uses current-mode control to control the active and reactive power. The line current is tightly regulated by the current control loop, through the inverter AC-side terminal voltage. Then, the active and reactive power are controlled by the phase angle and the amplitude of the inverter current with respect to a rotating frame that is synchronized with the point of common coupling (PCC) voltage using PLL. A Kalman filter is placed in front of the PLL in order to ensure that the PLL input at all times matches an ideal sinusoidal waveform as closely as possible, even when the voltage is highly distorted by the presence of harmonics. This ensures fast and low distortion operation of the PLL. Kalman filter is used in this work because it efficiently deals with the uncertainty of tuning its parameters.  
The reference set-points of active and reactive power are calculated based on the PV profiles and the decisions of the GVSF as shown in Figure 5; then the reference set-points of active and reactive power are converted into d-q (direct-quadrature) reference set-points of the three phase current and  
Figure 4: Actuator agent setup (labels same as in Figures 1 and 2).  
these d-q set-points are used by the current control loop as a reference to control the d-component and q-component of the three phase current in order to follow the reference set-points of active and reactive power.  
Figure 5: Inner control system of the inverter integrated with the GVSF (labels same as in Figures 1 and 2).  
The details of the design and tuning of Kalman filter are presented in [29]. Chapter 8 in [30] presents the details of the design and tuning of the proportional-integral(PI) controllers used by the current control loop and PLL. The d-q transformation (of the current, voltage and power) can be also found in the same chapter.  
It is worth mentioning that we have not used a voltage control loop, because the voltage of the DC bus is fixed by the DC source. For the inverter to be able to inject power from the DC side to the AC side, the DC bus has to be charged to a DC voltage higher than 650.6 V, which is the peak-to-peak voltage (phase to neutral) of the grid connection (\(V_{pp}=2\times 230\times\sqrt{2}=650.6\)).
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
###### Abstract  
In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0-6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%.  
digital forensics; audio copy-move forgery; forgery localization; deep learning Article