esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Figure 4a presents the time-dependent photo responses of the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD at a 5 V bias under a 254 nm light from both the sapphire side and the CsPbBr\({}_{3}\) side. Using a timer switch every ten seconds, and under a 254 nm UV light with a light density of 500 uW/cm\({}^{2}\) and a 405 nm laser with a light density of 1 uW/cm\({}^{2}\), the on/off switching behavior also presents a smooth curve after manifold cycles. Figure 4: Time-dependent photo response characteristics of the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD under 254 nm (**a**) and 405 nm (**b**) lights. The light–current density values versus light–intensity plots, along with the fitting results using the power law of J\(-\)P\({}^{\alpha}\), for the PD under 254 nm (**c**) and 532 nm (**d**) lights. The time-dependent photo response of the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD from the sapphire side is higher than it is from the CsPbBr\({}_{3}\) side. As a PD, it is important for the device to obtain data quickly. The response times of devices have been calculated using the following formula [29]:  
\[\rm I=I_{0}+Ae^{(-t/\tau 1)}+Be^{(-t/\tau 2)}\]  
where I\({}_{0}\) is the steady-state photocurrent, A and B are the fitting constants, and \(\tau_{1}\) and \(\tau_{2}\) are the relaxation time constants for the fast and slow components, respectively. Under a 254 nm light, this PD achieved increased relaxation times of 2.22 s (slow) and 0.31 s (fast) when illuminated from the sapphire side. When illuminated from the CsPbBr\({}_{3}\) side, the PD reached 2.36 s (slow) and 0.55 s (fast). Under 405 nm illumination, the PD reached 0.05 s (slow) and 0.05 s (fast). These results show that the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD's performance is superior. Moreover, the relationship between current density (J) and light intensity is shown in Figure 4c on the test results of the device under 254 nm of illumination with a light intensity ranging from 100 uW/cm\({}^{2}\) to 700 uW/cm\({}^{2}\) at a 5 V bias. The light-current density values versus the light-intensity plots, along with the fitting results obtained using the power law of J\(-\)P\({}^{\alpha}\), are shown in Figure 4c. As fitted with the power law of J\(-\)P\({}^{\alpha}\), the device received an \(\alpha\) value of about 0.99. Figure 4d shows the power law results of the Ga\({}_{2}\)O\({}_{3}\)/CsPbBr\({}_{3}\) heterojunction PD under a 532 nm light with a density ranging from 200 uW/cm\({}^{2}\) to 2.5 uW/cm\({}^{2}\) at a 5 V bias, where this device received an \(\alpha\) value of about 0.86. When the value of \(\alpha\) approaches 1, it suggests that the photocurrent has linear dependence on incident light-power density. It also indicates that photogenerated carriers can separate efficiently [16]. Therefore, these data prove that the introduction of Ga\({}_{2}\)O\({}_{3}\) into an inorganic perovskite device can enhance the performance of perovskite UV PDs.
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 2 Satellite Systems in Remote Sensing
### Spectral Bands and Spectral Indices  
A **spectral band** is a defined portion of a spectral range that is generally used to attribute data collected from a sensor. Different satellite instruments measure the central wavelength (CW) for each band differently. For reference, NASA and ESA used different algorithms to measure CW for Landsat-8 and Sentinel-2. For Landsat-8, the center wavelength is calculated using the Full Width at Half Maximum (FWHM) method, which essentially uses the average from a large percentage of the centered distribution. The \"lower and upper\" values are the FWHM boundaries. On the contrary, center wavelength values, in the case of S-2 values, are calculated using the average derived from the metadata files for each satellite. These metadata indicate the minimum, maximum, and central values for each band. It seems that ESA uses a weighted average.  
The **spectral index** is a quantity that depends on the various spectral bands of an image per pixel and is calculated using the band values. The selected bands differ depending on the index we would like to calculate; however, the majority of them are computed using the normalized difference formula \((Bx-By)/(Bx+By)\): the difference between two selected bands normalized by their sum. This method minimizes the effects of illumination from shadows and clouds while also enhancing the spectral features that are not initially visible. There is a great variety of spectral indices used for different tasks. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are some common spectral indices.
## 3 Dynamic Analysis of PMSM Based on Rotor Eccentricity
### No-Load Analysis  
The back EMF is directly affected by the rotor eccentricity and depends on the pole/slot combination. In this analysis, an eight-pole, nine-slot PMSM was selected to confirm the effect of rotor eccentricity. Figure 3 shows the magnetic flux density of the analyzed model. When comparing the case with no eccentricity to cases with 1 and 1.5 mm eccentricities, it was confirmed that the magnetic flux density formed more unevenly when eccentricity occurred. A no-load analysis was performed to analyze the back EMF of the analysis model based on the rotor eccentricity at 0, 1, and 1.5 mm, in which it was confirmed that additional harmonics were generated from the unbalanced back EMF. The harmonics of the unbalanced back EMF affected the control quality consequently. Figure 4 shows the back EMF results based on the rotor eccentricity, where \"ec\" denotes the eccentricity ratio. It represents the value of how eccentricity occurred based on the length of the nominal air gap. Figure 5 shows the results of fast Fourier transform (FFT) analysis of the back EMF. Figure 5a-c indicates ec = 0, 1, and 1.5 mm, respectively. Harmonics, except for the fundamental wave, are shown to compare the harmonics in  
Figure 2: Vector control diagram of PMSM for dynamic analysis.
# Planar UWB Monopole Antenna with Tri-Band Rejection Characteristics at 3.5/5.5/8 GHz
## 3 Parametric Study
### Variation of Ground Parameters  
The effect of changing parameter W3 on the simulated return loss is shown in Figure 6. The middle notched-band shifted towards lower frequency with the increasing of W3 and at the value of 7.3 mm, the desired middle stopband was obtained. Figure 7 shows how the parameter of the slot on the ground plane affected the performance of antenna. It can be found that the performance of the third notched-band mainly depended on the parameter L5, and L5 = 0.7 mm was chosen as an optimum value.  
Figure 5: Effect of parameter \(R1\) on the simulated return loss.  
Figure 6: Effect of parameter \(W3\) on the simulated return loss.  
Figure 7: Effect of parameter \(L5\) on the simulated return loss.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### k-NN Classifier Results  
The results of the k-NN are presented in this subsection, applied for 5 and 10 days, respectively, using the Euclidian distance kernel for k = 9 nearest neighbors (see Appendix A, calibration of the k-NN parameters and Figure 10). The confusion matrix in Table 8 is the testing result of the k-NN, five days before the swarming event, with the calibrated hyperparameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **True** & **False** \\ \hline Positive & 306 & 44 \\ Negative & 82 & 884 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of SVM classifier applied ten days before the swarming event.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **True** & **False** \\ \hline Positive & 354 & 12 \\ Negative & 16 & 1008 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of SVM classifier applied five days before the swarming event.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
0.0 (Non-Swarming) & 96\% & 95\% & 96\% & - \\
1.0 (Swarming) & 98\% & 98\% & 98\% & - \\ Accuracy & - & - & - & 97\% \\ Macro avg & 97\% & 97\% & 97\% & - \\ Weighted avg & 97\% & 97\% & 97\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: SVM Classifier results applied five days before the swarming event.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline
0.0 (Non-Swarming) & 79\% & 87\% & 83\% & - \\
1.0 (Swarming) & 95\% & 92\% & 93\% & - \\ Accuracy & - & - & - & 90\% \\ Macro avg & 87\% & 89\% & 88\% & - \\ Weighted avg & 91\% & 90\% & 91\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: SVM Classifier results applied ten days before the swarming event.  
As shown in Table 9, k-NN provides 98% accuracy; thus, k-NN into the most accurate classifier for detecting swarming five days before the event. The results are presented in Table 9 and were similarly taken in proportion to the SVM classifier from 249 samples and 837 samples of Non-swarming raw sound data recordings.  
The confusion matrix in Table 10 is the k-NN testing results ten days before the swarming event with the calibrated hyperparameters  
Despite the close-to-the-event high detection accuracy that the k-NN classifier can provide, as shown in Table 11, in the range of 10 days, k-NN scores a low 85% accuracy compared to the SVM classifier. The results of 10 days were similarly taken in proportion to the SVM classifier from 350 samples of swarming and 966 samples of Non-swarming raw sound data recordings.
# Integrating Cork Biochar as a Green Filler via Photopolymerization
## 3 Results and Discussion
### Thermal Analysis of Cork Biochar-Based Composites
#### 3.2.3 Thermal Conductivity (TC)  
TCs are essential in polymer composites for heat dissipation applications. This depends on factors such as filler type, concentration, polymer matrix, composite morphology, and processing techniques. High-TC fillers such as carbon nanotubes, graphene, and boron nitride improve thermal performance. Proper dispersion and interaction between the fillers and polymer matrix are key to optimizing TC [47; 48]. As discussed in Section 3.1, the cork biochar retains its three-dimensional honeycomb-like structure after the pyrolytic process and integrates into the photocuring resin during printing. The cork biochar forms a network structure inside the polymeric resin. These network structures enable thermal energy transfer with minimal phonon scattering [49].  
The conductivities of the CBPCs and the photocurable resin were then measured. The graph is shown in Figure 5.  
As can be observed in Figure 5, the addition of cork biochar resulted in an increase in thermal conductivity for all CBPCs, compared with resin alone, even with a small amount of cork biochar, with CBPC 0.35\(\%w/w\) showing the highest increase. The graph shows that as the \(\%w/w\) of cork biochar increased, the conductivity value increased until reaching a maximum at CBPC 0.35\(\%w/w\). At this point, no increase in thermal conductivity was achieved by boosting the biochar content. As concluded in the previous Section 3.2.2 of the  
Figure 5: Thermal conductivities of cork biochar photopolymer composites and neat photocurable resin.  
DSC analysis, CPBCs with higher biochar contents exhibited higher heterogeneity, which caused phonons to scatter through the amorphous regions and possible crystalline regions created by the biochar, thereby reducing the thermal conduction efficiency.  
Considering the above, proper dispersion and strong filler-matrix interaction of cork biochar can lead to improved heat transfer within the composite.
###### Abstract  
Combating the OS-level malware is a very challenging problem as this type of malware can compromise the operating system, obtaining the kernel privilege and subverting almost all the existing anti-malware tools. This work aims to address this problem in the context of mobile devices. As real-world malware is very heterogeneous, we narrow down the scope of our work by especially focusing on a special type of OS-level malware that always corrupts user data. We have designed mobiDOM, the first framework that can combat the OS-level data corruption malware for mobile computing devices. Our mobiDOM contains two components, a malware detector and a data repairer. The malware detector can securely and timely detect the presence of OS-level malware by fully utilizing the existing hardware features of a mobile device, namely, flash memory and Arm TrustZone. Specifically, we integrate the malware detection into the flash translation layer (FTL), a firmware layer embedded into the flash storage hardware, which is inaccessible to the OS; in addition, we run a trusted application in the Arm TrustZone secure world, which acts as a user-level manager of the malware detector. The FTL-based malware detection and the TrustZone-based manager can communicate with each other stealthily via steganography. The data repairer can allow restoring the external storage to a healthy historical state by taking advantage of the out-of-place-update feature of flash memory and our malware-aware garbage collection in the FTL. Security analysis and experimental evaluation on a real-world testbed confirm the effectiveness of mobiDOM.  
OS-level malware; data corruptions; detection; recovery; mobile devices; TrustZone; flash translation layer +
Footnote †: This manuscript is an Extended Version of our Conference Paper Published in Applied Cryptography and Network Security Workshops, Kamakura, Japan, 21–24 June 2021.
## 3 Parallel ARIA Implementation
### Parallel ARIA Implementation on GPU
The number of blocks is fixed (1024 \(\times\) 32) and performance is measured by increasing the number of threads. We compare the performance of global and shared memory implementations, such as comparing performance by number of blocks. The performance results are shown in Table 6.

It can be seen that the lower the number of threads (32), the better the performance is to use global memory. This is because the process of copying from global to shared memory is higher than the improvements achieved using shared memory. The greater the number of threads, the greater the performance difference that can be achieved using shared memory. In the performance of the shared memory implementation, it can be seen that as the number of threads increases, the performance becomes better than that of global memory, but the computational throughput decreases. This is because as the number of threads increases, more bank conflicts occur. Tables 7-9 show the overall performance of global, shared memory, and extended Sbox using shared memory implementations. Overall, depending on the implementation, it is more efficient to use global memory in implementations with fewer than 32 threads, and more efficient to use shared memory when using more than 32 threads. Increasing the number of blocks can improve memory throughput, but does not significantly improve performance as compute throughput cannot support it. Therefore, a large number of blocks is not always efficient, so it is recommended to use an appropriate number of blocks depending on the size of the data. Memory throughput has always been higher than computational throughput. Therefore, for the number of threads, it is recommended to use the number of threads 64, 128 because the compute throughput is highest when using shared memory. \begin{table}
\begin{tabular}{c c c c c} \hline
**Memory Type** & **Thread** & **Duration (ms)** & **C. Throughput (\%)** & **D. Throughput (\%)** \\ \hline \multirow{8}{*}{M Memory} & 32 & 0.97 & 56.21 & 94.82 \\  & 64 & 1.94 & 56.41 & 95.16 \\ \cline{1-1}  & 128 & 3.87 & 56.53 & 95.39 \\ \cline{1-1}  & 256 & 7.95 & 55.29 & 93.94 \\ \cline{1-1}  & 512 & 17.94 & 48.55 & 83.42 \\ \cline{1-1}  & 1024 & 47.65 & 36.69 & 62.24 \\ \hline \multirow{8}{*}{Shared Memory} & 32 & 1.01 & 69.46 & 97.85 \\ \cline{1-1}  & 64 & 1.91 & 65.27 & 98.99 \\ \cline{1-1}  & 128 & 3.70 & 63.25 & 99.56 \\ \cline{1-1}  & 256 & 7.32 & 61.81 & 99.77 \\ \cline{1-1}  & 512 & 15.64 & 56.97 & 93.15 \\ \cline{1-1}  & 1024 & 33.09 & 53.37 & 87.52 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance as the number of threads increases (C.: compute, M.: memory, blocks: 1024 \(\times\) 32).
## 5 Selection of Attributes, Modeling, and Results
error & \(0.995576\) & \(1.000356\) & \(0.992946\) & \(1.015140\) & \(1.007062\) \\ \hline \multicolumn{5}{c}{Models using attributes SHMU, Satellites (Forward selection 10 attr), Day of Year} \\ \hline Correlation coefficient & \(0.2398\) & \(0.3101\) & \(0.3535\) & \(0.1445\) & \(0.2839\) \\ Relative absolute error & \(1.022736\) & \(0.948921\) & \(0.815373\) & \(1.054828\) & \(0.870082\) \\ Root relative sq. error & \(0.995421\) & \(0.951980\) & \(0.966335\) & \(1.135082\) & \(0.961922\) \\ \hline \multicolumn{5}{c}{Models using attributes SHMU, Satellites (Forward selection 15 attr), Day of Year} \\ \hline Correlation coefficient & \(0.2611\) & \(0.3029\) & \(0.3283\) & \(0.2056\) & \(0.2714\) \\ Relative absolute error & \(1.007581\) & \(0.959974\) & \(0.805193\) & \(0.998895\) & \(0.878738\) \\ Root relative sq. error & \(0.982944\) & \(0.955482\) & \(0.972581\) & \(1.021172\) & \(0.962601\) \\ \hline \multicolumn{5}{c}{Models using attributes SHMU, Satellites (FSRF-TEST 10 attr), Day of Year} \\ \hline Correlation coefficient & \(0.2888\) & \(0.323\) & \(0.3305\) & \(0.3005\) & \(0.3698\) \\ Relative absolute error & \(0.947149\) & \(0.919711\) & \(0.804691\) & \(0.937511\) & \(0.866946\) \\ Root relative sq. error & \(0.964184\) & \(0.945773\) & \(0.969808\) & \(0.960605\) & \(0.928711\) \\ \hline \multicolumn{5}{c}{Models using attributes SHMU, Satellites (FSRF-TEST 5 attr), Day of Year} \\ \hline Correlation coefficient & \(0.3299\) & \(0.3438\) & \(0.36\) & \(0.3284\) & \(0.3999\) \\ Relative absolute error & \(0.936913\) & \(0.913589\) & \(0.793238\) & \(0.938661\) & \(0.866407\) \\ Root relative sq. error & \(0.945296\) & \(0.937339\) & \(0.965524\) & \(0.945354\) & \(0.921331\) \\ \hline \multicolumn{5}{c}{Models using attributes SHMU, Satellites (Refier 10 attr), Day of Year} \\ \hline Correlation coefficient & \(0.2194\) & \(0.2357\) & \(0.2848\) & \(0.2272\)We compared several different model types and noted that the most encouraging results were typically achieved by linear regression, gaussian processes, SMO regression (support vector machine with improved sequential minimal optimization), MSP regression tree (regression version of C4.5 with pruning and smoothing), and random forest (consisting of 100 regression trees): see the Tables 2-4 above. In most cases, the top performer was random forest. We attribute this to the fact that it internally employs ensemble learning and that particular trees in it are trained independent of each other, which effectively prevents overfitting.
## 2 Methods
### Cycle Slips Completeness Test  
The mathematical rationale for the detection and repair of cycle slips is based on statistical hypothesis testing. Cycle slip detection and repair consists of two distinct tasks. The first is to identify whether a cycle slip occurs in the observed value, and the second is to calculate the cycle slips' value and repair the cycle slips [34].  
A system modeling method based on hypothesis testing is used to construct cycle slips test statistics. If there are no cycle slips, the probability density function follows a Gaussian distribution with a mean of 0. However, if there are cycle slips in the observed value, the probability density function follows a Gaussian distribution with a mean of \(b\), and the standard deviation is both \(\sigma\), which is expressed by:  
\[H_{1}:DV \sim N(b,\sigma) \tag{16}\] \[H_{1}:DV \sim N(b,\sigma) \tag{17}\]  
where \(b\) is the size of the cycle slips.  
Then, the false alarm rate and the missed alarm rate of the cycle slips' detection can be established as [35]:  
\[P_{\text{F}}=P(|DV|\geq T_{\text{D}}|H_{0})=erfc\bigg{(}\frac{T_{\text{D}}}{ \sqrt{2}\sigma}\bigg{)} \tag{18}\]  
where \(T_{\text{D}}\) is the set cycle slips' detection threshold.  
Corresponding to the false alarm rate, the missed alarm rate represents the probability that the cycle slips does exist, but the threshold is not exceeded. The missed alarm rate can be expressed as [35]:  
\[P_{\text{M}}=P(|DV|<T_{\text{D}}|H_{1})=erfc\bigg{(}\frac{b-T_{\text{D}}}{ \sqrt{2}\sigma}\bigg{)}-erfc\bigg{(}\frac{b+T_{\text{D}}}{\sqrt{2}\sigma} \bigg{)} \tag{19}\]where \(erfc(x)=\frac{2}{\sqrt{\pi}}\int\limits_{x}^{\infty}e^{-t^{2}}dt\), the detection threshold is \(T_{\text{D}}=m\sigma_{\Delta N_{MW}}\). The maximum missed detection rate is obtained when \(b=1\)[35]. When m = 3 (99.73% confidence level), an optimal combination is obtained [35].  
Due to the multipath effect, the accuracy of the pseudorange is seriously affected by the traditional method, which leads to the failure of the rounding repair. When the model detects cycle slips, the approximate number of cycle slips \(\Delta N_{MW}\) is repaired by (similarly to \(\Delta N_{GF}\)):  
\[N_{1}=\text{round}(\Delta N_{MW}) \tag{20}\]  
where round means to find the integer value closest to \(\Delta N_{MW}\).  
The success rate of the cycle slips' repair can be expressed as [36; 37]  
\[P_{0}=1-\sum_{i=1}^{\infty}[erfc(\frac{i-|\Delta N_{1}-round(\Delta N_{1})|}{ \sqrt{2}\sigma})-erfc(\frac{i+|\Delta N_{1}-round(\Delta N_{1})|}{\sqrt{2} \sigma})] \tag{21}\]  
Given a confidence level \(\alpha\), if \(P_{0}\) is greater than \(\alpha\), the cycle slips' repair can be considered successful. The flowchart of the proposed algorithm is shown in Figure 1.  
Figure 1: Flow chart of AMG cycle slip detection and repair method.