esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Expressions
### Low-Frequency Wake-Up Chip  
The low-frequency wake-up chip is used to generate an interrupt to the MCU with minimal influences of interferences. Typically, in real applications, the WuRx are used in environments, where interferences are expected due to the presence of other electromagnetic waves. This aspect influences the working behavior of the WuRx and leads to false wake-up events. To avoid false positives, one method based on the pattern correlation can be used to activate and to match incoming signal with valid pattern stored in the wake-up chip. The LF WuRx chip AS3933 is a low-power, three-channel ASK receiver that can generate a wake-up call to the MCU. The AS3933 is designed for carrier frequencies of 15 kHz to 150 kHz using OOK. This is not sufficient for radio transmission in the sub-gigahertz band. Therefore, the OOK signal is digitally modulated to a sub-carrier of 18.7 kHz and up-converted to 868 MHz. When the signal is detected at the receiver part, the passive envelope detector converts it back to the kHz band. It is then captured by the AS3933. For the use of the internal crystal oscillator as reference clock, the signal frequency can be chosen with Equation (13).  
\[f_{\text{carr}}=f_{\text{RC}}\cdot\frac{8}{14} \tag{13}\]  
Based on the specification of AS3933, incoming wake-up packets must be Manchester-coded [44]. If the wake-up protocol is valid, the AS3933 will generate an interrupt to the MCU.  
Figure 13 shows the demodulated WuPt used throughout this work. The packet with enabled pattern detection is displayed in Figure 13a and consists of a carrier burst, preamble, and 16-bit address, whereas Figure 13b exposes the amplitude modulation of the signal.  
Figure 11: Schematic of the BJT amplifier with emitter architecture.  
Figure 12: Small-signal circuit of the BJT amplifier according to [45].  
For each active channel, the AS3933 provides a RSSI value. The RSSI indicates how strong the input signal is and the inverse representation of the gain of the variable gain amplifier (VGA). The gain of all channels is set to maximum in listening mode. As soon as a signal is detected, the automatic gain control (AGC) is activated and the gain of the VGA is set to the correct value and is between 0 and 31 [44]. The relationship between gain and RSSI can be shown with Equation (14) [46].  
\[\mathrm{G}\;[\mathrm{db}]=G_{\mathrm{offset}}-20\;\mathrm{dB}\cdot\log_{10} \left(\frac{V_{\mathrm{in}}}{V_{\mathrm{ref}}}\right) \tag{14}\]  
The offset gain of the AS3933 \(G_{\mathrm{offset}}\) corresponds to 62 dB, and \(V_{\mathrm{ref}}\) represents the minimum detectable power of the input signal 80 \(\upmu\)VRMS.
# Color Texture Image Complexity--EEG-Sensed Human Brain Perception vs. Computed Measures
## 4 EEG Experiment and Analysis
### EEG Analysis  
The signals were low- and high-pass filtered in the range 1-49 Hz, using the Chebyshev type II filter and FIR filter, and artifactual epochs were rejected based on variance and max-min criterion, when 20% of the channels have excessive variance or the difference between the maximum and minimum peak exceeded 150 \(\upmu\)V, respectively. Further, segmented epochs were baseline corrected when considering 100 ms from the fixation period. The signals were investigated in time (ERPs) and frequency (Power spectral density). The power spectrum was computed from 3 to 40 Hz, on the trial interval (0-1350 ms), based on the Fourier transform with Kaiser window and the logarithmic spectral power is presented as \(10\cdot log_{10}(power)\). For further details on signal processing, please see [48].  
Figure 1: Stimuli images examples: one color image (_Uni_), natural color texture (_Nat_) image (VisTex) and synthetic color fractal texture image (_Frac_).
## 6 Results and Discussion
#### Comparison with Previous Works  
Many studies have been conducted to detect and classify road damage using machine learning (ML) and deep learning (DL), but because most of these methods used different datasets and evaluation measurements, comparing the results objectively is difficult. The results of the previous work are shown in Table 8. In [PERSON] et al. (2018), they presented a Deep Convolutional Neural Network (ConvNet) to detect cracks in the road. The ConvNet consists of two convolutional layers and two fully connected layers. The dataset consists of 500 pavement images with and without cracks at a resolution of 3264 \(\times\) 2448. The images were collected using a smartphone at the USA's Temple University campus. The results achieved a precision of 0.86%, a recall of 0.92%, and an F1 score of 0.89%. Our approach achieved a better accuracy of 0.99%, a recall of 0.99%, and an F1 score of 0.99% with two convolutional layers followed by two fully connected layers, with six different damage classes classified. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) applied a crack detection model dependent on a convolutional neural network (CNN). The model was trained using the VGG16 framework and a dataset collected from the Danish Technological Institute Dataset. It achieved an accuracy of 92.27%; as for our VGG-16 model, it achieved a better accuracy of 95.1%. [PERSON] et al. [PERSON] et al. (2017) presented several convolutional neural network (CNN) classification models to detect the crack image. The dataset consists of 2173 pavement images with and without cracks at a resolution of 256 \(\times\) 256. The result of the AlexNet model achieved 94% accuracy and 88% precision. Our model AlexNet achieved a better accuracy of 98.4% and a precision of 0.99%.
## 3 The Zero-Server Markov Chain  
Markov chains, which use the concept of arrival and service rates, can be applied to yield steady-state system probabilities. However, though an ordinary Markov chain can be used to analyse when the client can communicate with the server, in mobile environments, a new type of Markov must be used to analyse the system as the server will not always be available. Thus, a zero-server Markov chain (ZSMC), in which only arrivals occur, as shown in Figure 4, must be employed to deal with these situations.  
A zero-server Markov chain is inherently unstable because after a long time, the length of the queue will go to infinity. In order for the system to become stable, the ZSMC must be coupled with another service-based Markov chain (SBMC) that serves customers/packets, allowing them to leave the system. Thus, the system must be represented by two chains, the ZSMC and the SBMC. The state of the system is given by N,M where N is the number of requests and M is the number of servers. For the ZSMC, M is zero, so the state is given by N, 0 with probability \(P_{N,0}\). An example of an SBMC is an exhaustive service model in  
Figure 4: A zero-server Markov chain [38].  
which when the server arrives at the queue, it serves exhaustively until there is no one left in the queue before it moves to the other queues. This is explored fully in [29].
# Modeling of Positive Switching Impulse Discharge of UHV Transmission Line Air Gaps
## 3 Discharge Characteristics
### The Influence of Tower Width  
According to [25], the relationship between the breakdown voltage and the width of the tower is the following:  
\[U_{50(w)}=U_{50(1)}(1.03-0.03w) \tag{16}\]  
where \(U_{50(w)}\) is the breakdown voltage with the tower width of \(w\), and \(U_{50(1)}\) is the breakdown voltage with the tower width of 1 m.  
However, in previous tests and studies, there was a certain discrepancy between the results and the equation. According to [15], as the width of the tower increases, the breakdown voltage decreases.  
Figure 3: Research on line height.  
As the gap length increases, the influence of the tower width on breakdown voltage will decrease. Therefore, the influence of gap length cannot be ignored. In order to determine the influence of tower widths and gap lengths on the breakdown of UHV transmission lines, single conductor-tower leg gaps were studied. The gap lengths of gaps were 4 m, 5 m, 6 m, 7 m, and 8 m, and the tower widths were 1 m, 2 m, 3 m, 4 m, 5 m, and 6 m. The breakdown voltage of the above gaps was calculated by the breakdown voltage calculation method and the results are shown in Figure 5.  
The results show that when the gap length was 8 m, the breakdown voltage with a tower width of 6 m was 3.6% lower than that with a tower width of 1 m. When the gap length was 4 m, the breakdown voltage with a tower width of 6 m was 6.4% lower than that with a tower width of 1 m. These results are consistent with the conclusions of [15]. Fitting the results, the tower-width discharge model of transmission lines with a tower width in the range of 2-6 m is obtained:  
\[U_{50(w)}=U_{50(1)}(1-0.014w+\frac{wd}{1000}) \tag{17}\]  
The model is verified by the test results of [26]. The breakdown voltage of the gap with a tower width of 5 m was calculated by the tower-width discharge model and a previous formula based on the breakdown voltage of the gap with a tower width of 2 m. As shown in Figure 6, the calculated results of the tower width discharge model were closer to the experimental data than those of previous formulas, with a maximum error of 3%, which proves the correctness of the tower-width discharge model.  
Figure 4: \(U_{50}\) of conductor-tower gaps with different heights.  
Figure 5: \(U_{50}\) of conductor-tower gaps with different widths.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 3 Pixel-Style Avatar Generation Method
### Human Video Stream Processing  
We use the type of monocular RGB webcam that is generally used in online meetings as an input device and applied the RVM to each frame to obtain human matting results. According to the RVM [26], a frame \(I\) can be considered a linear combination of the foreground \(F\) and background \(B\) based on a transparency coefficient \(\alpha\) as follows:  
\[I=\alpha F+(1-\alpha)B \tag{1}\]  
Because only the human region is required for our virtual environment, we only retain \(\alpha F\) for the foreground while omitting the background part \((1-\alpha)B\) to obtain a final human-only segmented video frame \(I\) in four RGBA channels. Based on frame \(I\), we parse  
Figure 1: Our framework consists of two components, namely video stream processing and visualization. The video stream processing component generates a high-resolution segmented head and low-resolution body video, and extracts 3D pose data. The visualization component uses the results of the video stream processing to visualize a pixel-style avatar.  
Figure 2: Results of RVM and MediaPipe Pose. (**a**) Original input frame of the upper body in \(640\times 480\) resolution. (**b**) RVM output without background. (**c**) Upper-body joints from MediaPipe Pose.  
the human video frame into high-and low-resolution regions. Because high-resolution video can express facial details to enhance the communication experience during a video conference, we define the head region of the user as having high resolution. Because body features such as cloth color with large regions can be represented roughly, for efficient computation, we define the body region of the user as having low resolution.  
To partition the head region in a human video efficiently, we generate an adaptive head range based on extracted 3D pose landmarks. Figure 3 presents the head region and relative pose landmarks. We specify that the range of the head is a square with the nose (blue point in Figure 3) as the center point and twice the 2D relative distance from the neck joint to the nose joint in the frame as the side length. Because the neck joint is not included in the 33 joints of MediaPipe Pose, we calculate the midpoint between the left and right shoulder joint 2D coordinates as the position of the neck joint. We calculate the square side length of the head based on the position of the nose and neck, and then crop the head region from the high-resolution human-only segmentation results. The final head region corresponds to the red ox in Figure 3.  
For the low-resolution body region, to reduce the computational complexity and realize real-time performance, the body parts in the high-resolution human body-only video stream are downsampled by a factor of \(\frac{1}{2}\)\(\frac{1}{4}\)\(\frac{1}{8}\) or \(\frac{1}{16}\) of the original video resolution by applying the Gaussian pyramid approach. The results are then represented in the form of a point cloud in the visualization process.
## 2 Background and Related Work
### Cache Attacks
#### 2.2.3 Prime+Probe  
Shared memory is not always available through deduplication in virtual environments as most cloud providers turned off this feature after several attack demonstrations. However, _Prime+Probe_[9; 41] still works, and, by targeting the L3 cache, an attacker can still extract sensitive information. Since _Prime+Probe_ is agnostic to special OS features in the system, it can be applied in virtually every system.  
_Prime+Probe_ consist of three steps: First, the attacker fills a cache set with known data. Second, the attacker triggers (or waits for) the victim to perform an encryption. Third, after the encryption has finished, the attacker tries to access the known data place during the first step and measures the time it takes to determine if the previously loaded data have been evicted by the victim process. If so, the attacker discovers which lines the victim process has used.  
This attack was first proposed for the L1 data cache by [1] and later expanded to the L1 instruction cache [42]. These approaches require both victim and attacker to share the same core, which diminishes practicality. However, it has been recently shown to be applicable to LLC. Researchers have bypassed several difficulties to target the LLC, such as retrieving its complex address mapping [22; 23; 43], and recovered cryptographic keys or keyboard typed keystrokes [11; 41; 44]. Furthermore, the _Prime+Probe_ attack was used to retrieve a RSA key in the Amazon EC2 cloud [45].  
These attacks highly rely on precise timers to retrieve the desired information about the victim memory access. If a defense system tries to either restrict access to the timers or to generate noise that could hide timing information, the attack is less likely to succeed. Once again, attackers have been able to overcome this difficulty. The _Prime+Abort_ attack [4] exploits Intel's implementation of Hardware Transactional Memory (TSX) so it does not require timers to retrieve the information. It first starts a transaction to prime the targeted set, waits and finally it may or may not receive and abort depending on whether the victim has or has not accessed this set. That is, no need for timers.  
To summarize, the presented attacks target the cache, selecting one memory location that is expected to be accessed by the victim process. They consist of three stages: initialization (the attacker prepares the cache somehow), waiting (the attacker waits while the victim executes) and recovering (the attacker checks the state of the cache to retrieve information about the victim). They differ in the implementation, requirements and achievable resolution.
## 3 Our Method
### Color-Correction Branch
#### 3.3.1 Advantages of Lab Color Space  
The RGB color space is the most widely used color space in color image processing. However, the red, green, and blue (R, G, B) components are highly correlated, and the space does not effectively separate brightness information from color information. As a result, it is easily affected by changes in brightness, occlusion, shadows, and other factors [42]. Thus, in cases where lighting conditions are inconsistent in an image, it can lead to uneven color representations, which may affect processing performance.  
The Lab color space is a color model designed based on the opponent color theory of human visual perception. It represents colors using three independent components: L, a and b. The L component indicates lightness, with a range from 0 to 100, representing brightness from complete black to complete white [43; 44]. The a component represents the color range between red and green, while the b component represents the color range between yellow and blue, both with a range of \(-\)128 to +127.  
As shown in Figure 5, the RGB color space is represented by a cubic structure and consists of red (R), green (G) and blue (B) channels, whose colors are formed by the mixture of the three, but the coupling of luminance and color information is not conducive to color adjustment and image enhancement. Meanwhile, Lab color space adopts independent luminance (L), green-red (a), and blue-yellow (b) channels, which decouples the color information from the luminance information and is more in line with the human-eye perception characteristics. Therefore, Lab color space is more advantageous in tasks such as color correction, white balance adjustment, etc. It also improves the stability of color representation, reduces color shifts due to equipment differences, and performs better in computer-vision tasks such as image dehazing, image restoration, and super-resolution reconstruction.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method
### Pole Segmentation Based on Improved Mask R-CNN
#### 2.2.2 Improvement of Mask R-CNN
Given an intermediate feature map \(F\in\mathbb{R}^{C\times H\times W}\), a 1D channel attention map \(M_{\text{C}}\in\mathbb{R}^{C\times 1\times 1}\) is inferred through Channel Attention Model. After element-wise multiplication of \(M_{\text{C}}\) and F, the adjustment result \(F^{\prime}\) of the Channel Attention Model is obtained, which is also used as the input of the Spatial Attention Model. Then, the 2D feature attention map \(M_{\text{S}}\in\mathbb{R}^{1\times H\times W}\) is inferred through the Spatial Attention Model, which is multiplied by \(F^{\prime}\) to get the final output feature map \(F^{\prime\prime}\in\mathbb{R}^{C\times H\times W}\). \[ \begin{split}& F^{\prime}=M_{\text{c}}(F)\otimes F\\ & F^{\prime\prime}=M_{\text{s}}(F^{\prime})\otimes F^{\prime} \end{split}  \tag{3}\]  
Figure 4: Comparison of original bounding box and expanded bounding box. Figure 5: CBAM attention mechanism. The CBAM attention mechanism is integrated into the convolution (Conv) and deconvolution (Deconv) parts of the Mask R-CNN head network, which are the Conv_cbam block and the Decon_cbam block, respectively. The head network structure followed by the introduction of the CBAM attention mechanism is shown in Figure 6.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 3 Results
### Classification in One Dimension  
The results below summarize the performance of classifiers with the two types of features, both for one dimension (valence or arousal) with two classes (positive and negative for valence, high and low for arousal) and for two dimensions (valence and arousal) with four classes I-IV (see Figure 1).  
A first screening of classifiers was performed across each dimension using default parameters, considering time and frequency features and wavelet scattering features with scattered windows separately. Table 1 summarizes the overall performance (accuracy and F1-score) of different classifiers. From the results, it can be observed that the mean performance of the classifiers using wavelet scattering features is higher than using time and frequency features for both valence and arousal. The classifiers that perform best with wavelet scattering are Ensemble (accuracy: 89.3% valence and 89.1% arousal) and KNN (82.7% valence and 81.2% arousal), followed by SVM, Discriminant Analysis, Decision Tree, and Naive Bayes.  
After screening, the best performance classifiers (accuracy and F1-score over 0.80) with default parameters and wavelet scattering features were further analyzed. Figure 4a,b show the confusion matrix for the 10-NN classifier, for the valence and arousal dimensions, respectively. The overall accuracy is 82.7% in the valence dimension and 81.2% in the arousal dimension.  
Figure 5a,b show the confusion matrices for the Ensemble Bagged Tree classifier for the valence and arousal dimensions, respectively. In this case, the overall accuracy is 89.4% for the valence and 89.2% for the arousal dimension.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Valence**} & \multicolumn{4}{c}{**Arousal**} \\ \cline{2-7}
**Classifier** & \multicolumn{2}{c}{**Time and Freq.**} & \multicolumn{2}{c}{**Wavelet**} & \multicolumn{2}{c}{**Time and Freq.**} & \multicolumn{2}{c}{**Wavelet**} \\  & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Features**} \\ \cline{2-7}  & **Acc.** & **F1** & **Acc.** & **F1** & **Acc.** & **F1** & **Acc.** & **F1** \\ \hline Decision Tree & 0.592 & 0.560 & 0.631 & 0.654 & 0.606 & 0.595 & 0.642 & 0.627 \\ Discriminant Analysis & 0.590 & 0.575 & 0.622 & 0.610 & 0.593 & 0.558 & 0.661 & 0.652 \\ Naïve Bayes & 0.526 & 0.381 & 0.602 & 0.614 & 0.577 & 0.509 & 0.618 & 0.615 \\ KNN & 0.566 & 0.603 & 0.827 & 0.834 & 0.572 & 0.601 & 0.812 & 0.821 \\ SVM & 0.597 & 0.556 & 0.629 & 0.612 & 0.584 & 0.499 & 0.667 & 0.654 \\ Ensemble & 0.588 & 0.593 & 0.893 & 0.896 & 0.587 & 0.595 & 0.891 & 0.894 \\ Mean & 0.577 & 0.545 & 0.701 & 0.703 & 0.587 & 0.559 & 0.715 & 0.711 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classifier screening in one dimension using short and long experiment data.  
Figure 4: Confusion matrices for the 10-NN classifier in the valence (**a**) and arousal (**b**) dimensions.
## 2 VIIRS Satellite Data
### VIIRS_ML Data  
VIIRS_ML data are available from the NASA Atmosphere Science Investigator-led Processing System at the University of Wisconsin (A-SIPS; [[http://sips.ssec.wisc.edu/](http://sips.ssec.wisc.edu/)]([http://sips.ssec.wisc.edu/](http://sips.ssec.wisc.edu/))). Following the strategy of the Dark-Target retrieval, the VIIRS_ML follows an average, then retrieve once logic [8]. This means that the averaging is upon observations (spectral reflectance) within the box, and following the MODIS protocol, the aerosol retrieval is performed only once. Using 10 \(\times\) 10 pixel aggregations,the VIIRS_ML aerosol product is reported at 7.5 km (at nadir) resolution based on M-band pixel resolution. The VIIRS_ML algorithm does the cloud masking by applying the internal spatial variability and reflectance threshold tests (e.g., 3 \(\times\) 3 pixel spatial variability and visible/1024/1038 nm tests). The strategies for masking, selecting and aggregating pixels for VIIRS_ML are described in [8]. Similar to the VIIRS_EDR, the AOD at 550 nm, 488 nm and 672 nm over land and the Quality Flag (QF) of aerosol retrievals during 2 May 2012 to 31 March 2014 are used. In order to be consistent with the analysis of VIIRS_EDR, VIIRS_ML data during 15 October 2012 to 27 November 2012 were not used for evaluation. The values of this QF are similar to the VIIRS_EDR QF1: 0 = bad, 1 = marginal, 2= good, and 3 = very good.  
As for the aerosol model type, [PERSON] et al. note that the aerosol model in NCP region is assumed to be moderately absorbing fine model during Winter (DJF) and Spring (MAM), and weakly absorbing fine model during Summer (JJA) and Autumn (SON) [22]. Since the aerosol type may differ day-to-day, this assumption is meant to be climatologically representative and can lead to errors in instantaneous AOD retrieval. These same assumptions are used for the VIIRS_ML.