esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Different Digestion Methods of USEPA 3050, 3051, and 3051A on Analytical Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multicolumn{2}{c}{**Method 3050**} & \multicolumn{2}{c}{**Method 3051**} & \multicolumn{2}{c}{**Method 3051A**} & \multicolumn{2}{c}{**SM-Savage Sludge**} \\  & **Concentration,** & **Accuracy,** & **Precision,** & **Concentation,** & **Accuracy,** & **Precision,** & **Size** & **Concentation,** & **Accuracy,** & **Precision,** & **Size** & **Consensus** & **Confidence** \\  & **(mg/kg)** & **\% RSD** & **(mg/kg)** & **\%** & **\% RSD** & **1** & **(mg/kg)** & **\% RSD** & **\% RSD** & **2** & **Value, (mg/kg)** & **Interval** \\ \hline B & \(89\pm 0.5\) & \(89.9\) & 2.8 & \(84\pm 0.6\) & \(94.8\) & \(2.9\) & \(-5.6\) & \(92.4\pm 0.4\) & \(92.9\) & \(1.3\) & \(3.4\) & (9.9) & \\ P & \(27105\pm 0.45\) & \(90.9\) & \(3.4\) & \(2750\pm 0.65\) & \(92.2\) & \(2.1\) & \(1.5\) & \(27496.0\pm 70\) & \(93.7\) & \(0.8\) & \(3.1\) & \(28.6\) & \(27,906-31,746\) \\ Mo & \(5.9\pm 0.2\) & \(92.2\) & \(6.2\) & \(5.6\pm 0.4\) & \(87.5\) & \(2.6\) & \(-5.1\) & \(5.8\pm 0.2\) & \(90.6\) & \(1.8\) & \(-1.7\) & 6.4 & 5.9-6.9 \\ \hline \hline \end{tabular}  
* Note: Values in bracket are not certified. They are listed for information only: – No available; Bias: 1: Bias of method 3051 for method 3050 = (elemental concentration 3051 – elemental concentration 3050)/ elemental concentration 3050 \(\times\) 100; Bias: 2 Bias of method 3051A for method 3050 = (elemental concentration 3051A – elemental concentration 3050)/ elemental concentration 3050 \(\times\) 100.  
\end{table} 
Table 5: Results (in mg/kg) of the analysis of Standard Reference Material (SRM)-swavage sludge EnviroMAT (BE-1) using different United States Environmental Protection Agency (USPEA) digestion methods.
## 2 Problem Definition  
For time stamp \(t\), a data stream consists of a continuous sequence of instances: \(\{x_{1},x_{2},\cdots,x_{t-1},x_{t},x_{t+1},\cdots\}\). Each instance \(\mathbf{x}_{t}=(s_{1}^{t},s_{2}^{t},\cdots,s_{m}^{t},y^{t})\) is a feature vector of \((m+1)\)-dimensional attributes, which consists of \(\mathbf{s}_{t}=(s_{1}^{t},s_{2}^{t},\cdots,s_{m}^{t})\) and a class label \(y\). The data stream also can be seen as a sequence of sets \(\mathbf{D}=\{\cdots,d_{t-1},d_{t},d_{t+1},\cdots\}\) where each element \(d_{t}\) is a set of instances in a sliding window at time \(t\).  
In a data stream, the data distributions and definitions of target classes change over time. These changes are categorized into gradual or sudden concept drift depending on the appearance of novel classes in a stream and the rate of changing definitions of classes [15].  
The aim of this paper is to present a high-performance ensemble classification algorithm of noisy data streams to improve the classification precision and sensitivity to concept drift with low computation complexity. At each time stamp \(t\), the classification algorithm outputs a class prediction \(y\) for each incoming instance.
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 4 Dynamic Model of Balloon-Borne UAVs
### Modal and Flutter Analysis  
In order to calculate the mode of the aircraft, it is assumed that the aircraft is in a free-flight state. Using MSC NASTRAN software for model analyses, natural frequency results are shown in Figure 6. Here, mode 2 represents the wing's vertical first-order bending, and mode 5 represents the load compartment's first-order bending. Mode 10 represents the horizontal stabilizer's first-order reversal, and mode 12 represents first-order bending in the vertical fin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic Roots** & \(\mathbf{t_{0.5}}\) **(m/s)** & \(\boldsymbol{T}\) **(s)** & \(\mathbf{N_{0.5}}\) **(s)** \\ \hline \(-3.6256\pm 2.7985i\) & 0.1912 & 2.2452 & 0.0852 \\ \hline \(-0.0405\pm 0.3807i\) & 17.1147 & 16.5043 & 1.037 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculated longitudinal characteristic roots in Equation (1).  
Figure 5: The longitudinal static stability of the launched aircraft.  
In addition, unsteady aerodynamic calculations also need to be considered. To this end, the doublet-lattice method is adopted. The atmospheric density is 1.225 kg/m\({}^{3}\) at sea-level, and the reference Mach number is 0.1. The unmatched flutter is used, and the results are shown in Figure 7. The results show that the flutter velocity is 35.2 m/s, and the flutter frequency is 6.69 Hz at sea level. The cruise velocity is much lower than the flutter velocity, and the control frequency is much lower than the flutter frequency. The flutter analysis of 4 km and 10 km is similar to the sea level. This shows that the influence of flutter cannot be considered in the aircraft's design.
## 2 Overview of Measurement Setup and Used Data-Extraction Method
### Data Extraction and Calibration  
This section explains the methods used to extract the measurement data samples from the graphs published in [4; 6; 7]. The results of AoA distributions are provided in the form of histogram and scatter plots in [4; 7]. The measurement data-sets are extracted by using the \"GetData Graph Digitizer\" software. The obtained samples in discrete form for angular distribution of multipath power (ADP) with respect to azimuth AoA are then converted from logarithmic to linear scale. After the scale transformation, the discrete data samples are divided with their respective maximum values to set its peak at value 1. This operation is performed to bring uniformity across the different measurement data-sets, for better presentation of the results. The ADP with respect to azimuth AoA for the selected three scenarios of [4] is plotted in Figure 1. The ADP with respect to azimuth AoA for the selected three scenarios for 28 GHz as well as 38 GHz of [7] is plotted in Figure 2. For the results presented in [6] in the form of color-coded contour-maps, we extracted the data samples through image processing tools by using color decoding. Then ADP of azimuth AoA is obtained by numerically integrating the extracted joint data samples of AoA and ToA. All the extracted empirical data-sets are converted from logarithmic to linear scale and then scaled along the vertical axis to fix their peak(s) at 1, which is performed by dividing all the values of a dataset by its peak value. After performing scaling (to set peak at value 1), the ADP of azimuth AoA for the selected three scenarios of [6] is plotted in Figure 3.  
Figure 1: Angular distribution of multipath power with respect to azimuth angle of arrival (AoA) for different outdoor mmWave (38 GHz) radio propagation environments; obtained after data calibrations, and normalization operations performed on the measurement results provided in [4]. (**a**) Tx-Rx separation < 150 m; (**b**) Tx-Rx separation > 150 and < 250 m; (**c**) Tx-Rx separation > 250 m Figure 7 of [4].  
Figure 2: Angular distribution of multipath power with respect to azimuth AoA for different mmWave (38 GHz and 28 GHz) indoor corridor environments; obtained after data calibrations, and normalization operations performed on the measurement results provided in [7]. (**a**) Tx-Rx separation = 3 m; (**b**) Tx-Rx separation = 5 m; (**c**) Tx-Rx separation = 6 m Figure 13 of [7].
## 4 UAN Classes
### WLAN UANs  
A WLAN UAN reuses existing IEEE 802.11 infrastructure already deployed in the city as the backhaul. In fact, many cities provide IEEE 802.11-based networks which offer several services to both the citizen and the municipality, such as Internet access, connectivity for surveillance systems, _etc._ Therefore, this UAN class requires the deployment of sensor nodes that must use a radio interface of the IEEE 802.11 family for compatibility with the backhaul.  
Traditionally, 802.11 radios have been characterized by a relatively large power consumption. Nevertheless, in the last few years, so-called ultra-low-power WiFi modules have appeared in the market. In these modules, the power consumption in reception is comparable to that of LR-WPAN hardware, whereas the power consumption in transmission is greater than that of the latter (which is compensated by the fact that transmit times are shorter since greater data rates are used in the IEEE 802.11 family [24]).  
The backhaul in WLAN UANs comprises nodes which may perform access point and mesh router functions. In this UAN type, a sensor node is in fact a client connected to one of the backhaul nodes. The latter have been deployed a priori (and thus are not equipped with sensors), usually in streetlights or traffic lights, and are provided with mains power.  
In WLAN UANs, the sensor nodes implement a classic IP-based protocol stack over IEEE 802.11 (Figure 3c), which does not need the 6 LoWPAN adaptation layer (although they would benefit in terms of energy savings from 6 LoWPAN features such as header compression). Backhaul nodes may support mesh routing functionality at the network layer or at the link layer (e.g., by using IEEE 802.11s [25]). The gateway is an IP router, deployed to connect the existing WLAN infrastructure to the Internet.
# Motion Estimation of Non-Cooperative Space Objects Based on Monocular Sequence Images
## 2 Proposed Method
### The Relationship between Object Motion and Camera Equivalent Motion
The attitude rotation matrix and translation vector of the object changing from moment \(i\) to moment \(i+1\) are \(\Delta G_{i}\) and \(\Delta L_{i}\). Then, the coordinate transformation relationship can be obtained:  
\[ \begin{bmatrix}X_{c,i}\\ Y_{c,i}\\ Z_{c,i}\end{bmatrix} =R_{i}\cdot \begin{bmatrix}X_{b}\\ Y_{b}\\ Z_{b}\end{bmatrix} +T_{i} \tag{3}\]  
\[ \begin{bmatrix}X_{c,i+1}\\ Y_{c,i+1}\\ Z_{c,i+1}\end{bmatrix} =R_{i+1}\cdot \begin{bmatrix}X_{b}\\ Y_{b}\\ Z_{b}\end{bmatrix} +T_{i+1} \tag{4}\]  
\[R_{i}=R_{i+1}\cdot\Delta G_{i} \tag{5}\]  
\[T_{i+1}=T_{i}-\Delta L_{i} \tag{6}\]  
where \(T_{i}\), \(T_{i+1}\) and \(\Delta L_{i}\) are described in the camera-fixed frame \(\{O_{c}\)-\(X_{c}\)\(Y_{c}\)\(Z_{c}\)\(\}\). Then, combining Equations (3) and (4), the following expressions can be obtained:  
\[ \begin{bmatrix}X_{c,i+1}\\ Y_{c,i+1}\\ Z_{c,i+1}\end{bmatrix} =R_{i+1}\cdot R_{i}^{T}\cdot \begin{bmatrix}X_{c,i}\\ Y_{c,i}\\ Z_{c,i}\end{bmatrix} -R_{i+1}\cdot R_{i}^{T}\cdot T_{i}+T_{i+1} \tag{7}\]  
Equation (7) can be written as follows:  
\[ \begin{bmatrix}X_{c,i+1}\\ Y_{c,i+1}\\ Z_{c,i+1}\end{bmatrix} =\Delta R_{i}\cdot \begin{bmatrix}X_{c,i}\\ Y_{c,i}\\ Z_{c,i}\end{bmatrix} +\Delta T_{i} \tag{8}\]  
where it can be denoted that:  
\[\left\{ \begin{array}{l}\Delta R_{i}=R_{i+1}\cdot R_{i}^{T}\\ \Delta T_{i}=T_{i+1}-\Delta R_{i}\cdot T_{i}\end{array} \right. \tag{9}\]  
The above observation movement process is essentially the relative movement of the camera and the object. It can be understood in two equivalent ways. The first interpretation is the real physical state; that is, the camera does not move, and the object moves. The second equivalent interpretation is to take the object as a reference. Imagine that a person stands on a moving object and observes a fixed camera. This can be equivalent to the object not moving and the camera moving, as shown in Figure 3. Therefore, \(\Delta R_{i}\) and \(\Delta T_{i}\) represent the rotation attitude matrix and translation vector of the camera-fixed frame \(\{O_{c}\)-\(X_{c}\)\(Y_{c}\)\(Z_{c}\)\(\}\) from moment \(i\) to moment \(i+1\). Furthermore, combining Equations (5), (6) and (9), the following equations can be obtained:  
\[\left\{ \begin{array}{l}\Delta R_{i}=\Delta G_{i}^{T}\\ \Delta T_{i}=\underbrace{T_{i}-\Delta R_{i}\cdot T_{i}}_{A}-\Delta L_{i}\end{array} \right. \tag{10}\]  
Equation (10) provides the relationship between object motion and camera equivalent motion. Note that \(\Delta T_{i}\) consists of two parts, and the part \(\underbrace{T_{i}-\Delta R_{i}\cdot T_{i}}_{A}\) represents the equivalent translation vector of the camera-fixed frame caused by the object rotation.
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 5 Evaluation
### Performance  
Precision, recall, and F1-score are used as performance metrics. Intuitively, the precision quantifies the ability of the classifier not to label a sample as positive that is actually negative. The recall represents the ability of the classifier to retrieve all positive samples. The F1-score can be interpreted as a weighted harmonic mean of the precision and recall, which is useful to balance the trade-off between the two quantities and tends to give more weight to lower values [46]. An F1-score reaches its best value at 1 and the worst score at 0. The calculation of macro and micro average can refer to [28]. The larger the metric values, the better the classification of the system.  
The averaged classification performance over the 5-fold cross-validation is presented in Table 4. The confusion matrix in Figure 10 is calculated and normalized according to the prediction for each test sample in the 5-fold cross-validation experiment. We observe from Table 4 that: (i) the averaged precision, recall, and F1-score for each of the 5 classes are over 0.95; (ii) the averaged macro and micro for the three metrics are over 0.98 for the 5-class classification task; (iii) except for the standard deviation of the precision for the 0 Person class, the standard deviation for all performance metrics are relatively low (less than 0.1). These observations suggest that our classifier presents outstanding performance for the prediction task. Moreover, the high values of macro and micro F1-score (over 0.98) indicate that the classifier shows excellent performance on all classes over the entire data set. Meanwhile, it can be observed from Figure 10 that most of the off-diagonal values in the confusion matrix are close to 0 and all the diagonal values are larger than 0.96, suggesting that our approach can predict the number of walking pedestrians with high accuracy. On the other hand, [PERSON] et al. [15; 16] performed a similar classification task, but only achieved an averaged accuracy of 0.6875 for a 4-class classification task. The averaged accuracy is obtained by calculating the arithmetic mean of the accuracy for the four classes from Table 1 in [15], i.e., (0.8333 + 0.6667 + 0.3333 + 0.9167)/4 = 0.6875. This suggests that our approach is significantly better than [PERSON] et al.'s method [15; 16].  
Figure 9: Diagram of K-fold cross-validation with K=5. We split the whole dataset into 5 non-overlapping subsets. For the \(i\)-th fold, the \(i\)-th subset is used as test set and the remaining subsets as training set.
## 2 Materials and Experimental Procedure
### Raw Material Handling  
Calcined magnesium residue (CMR) with different MgO contents was obtained after calcining MR at different temperatures situation, and MgO in CMR had different active MgO contents (MgO\({}_{\text{a}}\)) at different calcination temperatures. It is observable from Figure 2 that when the calcination temperature increased from 400 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C, the activity of CMR first increased and then decreased and the peak temperature of MgO\({}_{\text{a}}\) reached 700 \({}^{\circ}\)C. For MPC materials, the activity of magnesium oxide is proportional to the reaction rate, and a fast reaction rate is not effective for sample forming and preparation [39; 40]; therefore, it was necessary to find an appropriate temperature condition to maximize the physical properties of SLMKPC.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Component** & **Mg\({}^{2+}\)** & **Ca\({}^{2+}\)** & **B\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **Al\({}^{3+}\)** & **Fe\({}^{2+}\)** & **K\({}^{+}\)** & **Li\({}^{+}\)** & **Na\({}^{+}\)** & **Cl\({}^{-}\)** & **SO\({}_{4}\)\({}^{2-}\)** & **Loss on** **Ignition** \\ \hline Content (\%) & 39.55 & 0.58 & 4.22 & 0.029 & 0.022 & 0.21 & 0.044 & 0.019 & 0.12 & 0.88 & 0.32 & 25.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the magnesium residue.  
Figure 1: XRD spectrum of MR.  
It is also noticeable from Figure 2 that when the calcination temperature was less than 800 \({}^{\circ}\)C, the setting time was less than 3 min and molded SLMKPC was not obtained when the reaction speed was too fast. However, when the calcination temperature was greater than 900 \({}^{\circ}\)C, high-strength SLMKPC was obtained. SLMKPC samples prepared by CMR at the calcination temperatures of 1000 \({}^{\circ}\)C and 1200 \({}^{\circ}\)C had the best mechanical properties (Figure 2). In previous studies [23; 24; 25], it was found that MKPC produced by calcined B-MgO (1000 \({}^{\circ}\)C) had good mechanical properties and a long setting time.  
According to the above analysis, MR was calcined in advance in this experiment. MR was calcined in a box resistance furnace at 1000 \({}^{\circ}\)C for 1 h to obtain CMR. After natural cooling, the CMR was crushed to 10 mm particles, ground in a fast grinding mill for 1.5 min, and then passed through a 120-mesh sieve. The specific surface area and the average particle size were 30.118 m\({}^{2}\)/g and 10.527 \(\upmu\)m, respectively.
## 3 Literature Review
### Gaps Analysis  
Table 1 summarizes the previous studies that utilized machine learning models and imbalance handling techniques in software defect prediction. In our literature review summary, we observed the following gaps that we aim to fulfill in our research:  
* Gap 1. We observed that generally, GAN methods achieved satisfactory results in handling the imbalance problem in software defect datasets. In addition, boosting learning ensembles was found to be effective in predicting software defects. However, none of the conducted studies investigated combining GAN and its variations (e.g., CTGAN) with boosting ensembles to improve the performance of software defect prediction.
* Gap 2. Hyperparameter optimization plays a crucial role in the performance of GAN methods. Our literature review reveals that hyperparameters of GAN methods were never optimized in the field of software defect prediction, as shown in Table 2.
* Gap 3. There was no comprehensive comparison between the performance of GAN-based methods and traditional methods when combined with boosting ensemble learning.
* Gap 4. Prediction performance of GAN-based methods was never investigated when combined with undersampling as shown in Table 2.  
In this paper, we aim to perform a comprehensive empirical evaluation to investigate the effectiveness of combining GAN-based methods with boosting ensemble learning in imbalanced software defect prediction. First, we propose a model combining GAN-based methods with boosting ensemble learning to produce effective prediction in imbalanced defect datasets. Second, we will explore the impact of hyperparameter optimization of GAN methods when used for imbalanced software defect prediction. Third, we will empirically compare the prediction performance of GAN-based and traditional methods when combined with boosting ensembles to deal with the imbalanced classification of software defect datasets. Lastly, we will investigate the applicability of combining GAN-based methods with RUS by empirically evaluating their prediction performance in overcoming the imbalance issue in software defect prediction.
## 2 Non-Parameterized Time-Frequency Analysis
### Wigner-Ville Distribution
#### 2.3.2 Application  
From traditional signal processing, to the enhancement of deep learning models, and further to integration with other advanced technologies such as dynamic mode decomposition (DMD), variational mode decomposition (VMD), and convolutional neural networks (CNNs), the applications of the WVD in the field of structural health continue to expand. These applications not only improve the accuracy and efficiency of signal processing but also provide more comprehensive and accurate data support for SHM. Ref. [22] employs the smoothed pseudo Wigner-Ville distribution (SPWVD) to enhance deep learning models. The DMD-WVD method [23] combines dynamic mode decomposition with the Wigner-Ville distribution to eliminate cross-terms.  
The introduction of the quadratic chirp-scaled Wigner distribution (QSWD) [24] further enhances the advantages of signal detection. In combination with other deep learning models, time-frequency analysis methods for partial discharge signals based on VMD and WVD [25] have emerged, and the WVD has been integrated with CNNs for classification [26], specifically applied to composite material damage classification methods [27]. Additionally, there are methods based on the polynomial Wigner-Ville distribution [28], hybrid Wigner-Ville distributions defined in the octonion domain [29], and other innovative technical approaches that provide new ideas and methods for SHM.  
The development of the WVD in the field of structural health is also reflected in the depth of theoretical research. Researchers have not only conducted in-depth studies on the WVD itself but have also combined it with other mathematical and physical theories to explore its application potential in SHM. For example, study of the uncertainty principle of the coupled fractional Wigner-Ville distribution (CFrWVD) [30] helps us to better understand the behavior of the WVD in complex signal analysis; combination of the WVD with theories such as the quaternion linear canonical transform (QOLCT) [31] also provides new perspectives and methods for signal processing. The emergence of the WVD in special mathematical domains, such as the study of the interaction between the two-dimensional hypercomplex WVD and the quadratic-phase Fourier transform [23], is also noteworthy.
## 3 Deliberate Attacks on IECPS
### Cyberattacks
#### 3.1.4 Time Synchronization Attacks  
With the promotion of integrated energy systems, a large number of distributed energy systems will appear in the future. Scheduling these distributed energy systems requiresensuring that the clock signals of the distributed systems remain synchronized, which relies on the accuracy of the time scale signals from GPS/Bedou satellites and the correctness of the system clock synchronization protocol. However, the time synchronization devices of some energy systems are synchronized using civilian GPS clock signals that lack encryption mechanisms, which gives attackers an opportunity to forge clock signals. As shown in Figure 6, an attacker transmits a forged GPS clock signal in the vicinity of an attack target, and the attack target's time synchronization device has its internal time changed after receiving the forged GPS signal. The measurement device controlled by this synchronization device will sample the data at the wrong time, resulting in time misalignment in the measurement data transmitted to the control center. Currently, most of the processing schemes only consider the measurement errors caused by noise and packet loss, allowing TSA to easily bypass the system's protection mechanisms [45]. Furthermore, the deliberate attacker does not intrude or make physical contact with the time synchronization device, so this type of attack is difficult to localize.  
Although time synchronization attacks are not easy to detect, this does not mean that the clock signals can be modified at will. Literature [46] states that if an attacker blindly attacks the PMU, it may be detected by bad data algorithms. Therefore, when designing the TSA, it is necessary to know how much measurement deviation is caused by modifications of the clock signal. For a signal with a frequency of \(f\) Hz, assuming that the time when it is not attacked is \(t\) and the time after the attack becomes \(\overline{t}\), the resulting phase angle deviation is  
\[\sigma=f\times(\overline{t}-t)\times 360^{\circ}, \tag{18}\]  
where \(\sigma\) is the phase angle estimation error.  
Once the phase angle deviation is determined, the induced measurement deviation can be expressed as  
\[\pi_{\theta}=z_{0}\Big{(}e^{i\sigma}-1\Big{)}, \tag{19}\]  
Figure 6: Schematic of the TSA.  
where \(\pi_{\theta}\) is the measurement deviation; \(z_{0}\) is the original measured phase.