esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Confusion Matrix for the LBP-FFT-CNNs Classification Model  
We adopt a confusion matrix to evaluate classification performance. Figure 9 shows a confusion matrix for the proposed classification model. The horizontal line represents prediction results of the model, and the vertical line represents the actual category. The four regions represent true positive (\(TP\)), false negative (\(FN\)), false positive (\(FP\)), and true negative (\(TN\)). The \(F1\)_Score_ is a value between 0 and 1. When the _precision_ and _recall_  
Figure 8: Determination coefficient based on LRA, LBP_CNNs and LBP-FFT-CNNs for regression.  
are both high, the \(F1\)_score_ will also be high. \(F1\)_score_ is an indicator that comprehensively considers _precision_ and _recall_ and is used to evaluate the overall performance of a binary classification model. The \(F1\)_score_ of our proposed classification model reaches 0.971, _accuracy_ is 0.963, _Precision_ is 1, and _Recall_ is 0.944.  
\[Precision=\frac{TP}{TP+FP} \tag{31}\]  
\[Recall=\frac{TP}{TP+FN} \tag{32}\]  
\[F1\ Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{33}\]
## 5 Discussion
### Key Features and Predictions of Turn-Taking Coordination Failures
This result may be due to the spatial constraints of the VR environment used in the study, which simulated the size of a meeting room. The relatively limited range of movement might not have been sufficient to observe the effects of spatial distance on non-verbal signal transmission. Future studies could investigate this factor in larger VR environments, such as plazas or halls, where increased activity ranges may amplify the impact of distance on turn-taking coordination. We validated the predictive performance of the logistic regression model using the significant features for turn-taking failure prediction. The model demonstrated high accuracy on the test set, supporting the critical role of these features in turn-taking coordination and confirming their robust predictive capability in new data. From an application perspective, these significant features can guide the development of VR systems equipped with failure prediction and real-time feedback capabilities. By integrating a prediction model for turn-taking difficulties, systems can provide assistive notifications when participants are at risk of failing to acquire a turn. This would not only enhance the natural flow of turn transitions but also minimizes unnecessary disruptions introduced by the system during interactions.
## 3 The Implementation of the Novel ARPD-RTK/INS Algorithm
### State Initialization  
The first step of the ARPD-RTK/INS algorithm is to calculate the initial state vectors, including position vector \(\mathbf{x}_{0}\), inertial parameter vector \(\mathbf{v}_{0}\), and ambiguity vector \(\mathbf{N}_{0}\). We utilize the double-differenced pseudorange and carrier-phase as well as Doppler measurements to derive the initial position, velocity, and float ambiguity vectors by the weighted least squares estimation. Subsequently, the integer ambiguity solution is obtained by LAMBDA. The detailed implementation is shown in Figure 4. In the meantime, initial roll and pitch are obtained from the accelerometer measurements, while yaw is obtained from the gyroscope measurements by integrating the changes in velocity as well as orientation. The initial biases of the inertial sensors are set to zero.  
Figure 4: The implementation of single epoch RTK.  
Figure 3: Overall flow chart of the ARPD-RTK/INS algorithm.
## 5 Conclusions  
Based on the ANSYS software, an unanchored steel storage tank was established as the evaluation object, and three earthquake records were selected to investigate the influence of the multi-directional components of ground motion. The numerical simulation results show that the seismic response of the tank may be underestimated if the tri-directional components are ignored. It is necessary to consider the influence of the multi-directional component in the seismic performance evaluation of a liquid-storage tank.  
1. The dynamic behavior of the tank structure is highly sensitive to seismic excitation direction. The axial compressive stress of the tank wall reached 34.11 MPa under the application of the tri-directional component of the El-Centro ground motion, and the value exceeds the allowable value in the specification. The results show that there is obvious \"elephant-foot\" deformation near the bottom of the tank, indicating that buckling failure of the tank wall has occurred.
2. Under seismic multi-directional components action, the vertical uplift and horizontal slip behavior between the tank bottom and foundation occur. When the X + Y + Z excitation of the El Centro ground motion is carried on, the uplift and the slip response reaches the maximum, which are 1.91 and 3.39 times higher than those of the X excitation. The base shear of tank will increase after considering the vertical component. However, the overturning moments decrease in different degrees when the tri-directional excitation cases are considered.
3. Discussion on the limitation of this work: The case study investigated in this paper is still limited. For example, only an unanchored tank structure is analyzed using three earthquake ground motions, ignoring the soil-structure interaction effect. The seismic performance of the storage tank is closely related to the input seismic wave excitation. The conclusions based on the analysis of just one sample is limited and may lack universal significance. The preliminary conclusion needs to be verified by a sufficient number of earthquake records with a wider spectral range.  
Conceptualization, R.Z. and H.W.; methodology, R.Z.; software, S.C. and K.S.; validation, R.Z.; formal analysis, R.Z.; investigation, Z.Z.; resources, R.Z.; data curation, S.C.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z.; supervision, R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (Grant No. 51608462), Shandong Provincial Natural Science Foundation (Grant No. ZR2019 MEE099), and the Fundamental Research Funds for the Central Universities (Grant No. 18 CX02081A).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 2 Sources of Magnetic Forces in PMSMs
### Magnetic Pressures on the Rotor
#### 2.2.2 Oscillating Magnetic Pressure Created by the Stator Winding
Once the harmonics of the stator winding flux density referenced to the rotor frame are identified (see Tables 4 and 5), the harmonics of the magnetic pressure on the rotor surface can be computed by applying [PERSON]'s tensor law (2) (see the results in Tables 6 and 7). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Fixed Reference (Stator)**} & \multicolumn{2}{c}{**Moving Reference (Rotor)**} \\ \hline
**Order** & **Sign** & **Order** \\ \hline \(n=1\) & \(s=1\) & \((\text{s}-n)=0\) \\ \(n=5\) & \(s=-1\) & \((\text{s}-n)=-6\) \\ \(n=7\) & \(s=1\) & \((\text{s}-n)=-6\) \\ \(n=11\) & \(s=-1\) & \((\text{s}-n)=-12\) \\ \(n=13\) & \(s=1\) & \((\text{s}-n)=-12\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Flux density harmonics created by the armature referenced to the stator and to the rotor in the Qs48p8 machine. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Fixed Reference (Stator)**} & \multicolumn{2}{c}{**Moving Reference (Rotor)**} \\ \hline
**Order** & **Sign** & **Order** \\ \hline \(n=1\) & \(s=-1\) & \((\text{5s}-n)/5=-1.2\) \\ \(n=5\) & \(s=1\) & \((\text{5s}-n)/5=0\) \\ \(n=7\) & \(s=-1\) & \((\text{5s}-n)/5=-2.4\) \\ \(n=11\) & \(s=1\) & \((\text{5s}-n)/5=-1.2\) \\ \(n=13\) & \(s=-1\) & \((\text{5s}-n)/5=-3.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Flux density harmonics created by the armature referenced to the stator and to the rotor in the Qs36p15 machine. In accordance with the obtained results, it needs to be remarked that in fractional machines (for instance the Qs36p15 machine), fractional pressure harmonics appear on the surface of the rotor. On the other hand, in integer machines (for instance the Qs48p8 machine), only integer and even harmonics appear. Furthermore, in the case of fractional machines, the spectrum of the pressure on the rotor surface presents more harmonics than in the case of integer machines.
## 1 Introduction
[17] addressed that to get the data in workable order for calculation, analysis, and benchmarking, the missing or invalid (mainly negative) data should be replaced with data during time periods or days that were similar to the invalid points, taking weather condition into account as well. For example, a few missing data would be replaced by the previous or following few proper data, or their average. Several hours' missing data would be replaced by data of the same time periods on the previous or following day, considering weekdays and weekends. The same goes for missing or invalid data of an even longer period. [PERSON] et al. [18] used data anterior and/or posterior to the sensor failure in order to rebuild the missing information. Two different approaches were presented: an interpolation technique used for the estimation of missing solar radiation data and an extrapolation technique based on artificial intelligence deals with indoor temperature estimation. The papers in the last paragraph addressed the selection among only a few (two or three) imputation methods according to the length of missing data gaps, sensor types, and the nature of missing data. Too much domain knowledge is set to select among the sensors, making the method less automated. Ensemble method, which is a pool-based method that can better address the variation problem is rarely applied for building sensors. Additionally, few studies are found to apply the ensemble method to customize the imputation for different sensors with missing data. One more key issue for data imputation techniques lies in the difficulty of validation. Since the information of missing data are missed already and the truth data is lost forever, it is hard to create some testing or validation scenarios to quantify the performance of data imputation methods. In this paper, a framework is proposed to deal with the above two problems. First, a validation module is developed based on pattern recognition. This module identifies good data points that have similar characteristics with the missing data. The selected good data can mimic missing data but with truth value, which create testing scenarios to validate the effectiveness of the single data imputation method. Second, with the validation data decided, a pool of data imputation methods is tested under the validation dataset, to find an optimal imputation method for each sensor, which is basically an ensemble method. The selected imputation method is expected to be different from sensor to sensor, which can optimize the accuracy of imputation under different sensor types and different mechanism and randomness of data missing for each sensor. The paper is organized as follows. The data imputation framework is developed in Section 2. The developed framework is applied to a real-building case study to demonstrate its effectiveness in Section 3. Results and discussion are introduced in Section 4. Conclusions are drawn in Section 5.
## 3 Results
### Psychomotor and Neuromuscular Performance
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & **Group** & **Base** & **2 h** & **48 h** & **96 h** & **G** & **T** & **G\(\times\)T** \\ \hline \multirow{4}{*}{\begin{tabular}{c} PVT \\ Correctness \\ (score) \\ \end{tabular} } & Placebo & 36.7 & 37.7 & 37.7 & 37.3 & & & \\  & (SD) & \(-\)3.91 & \(-\)2.16 & \(-\)2.21 & \(-\)5.17 & & & \\  & 95\% CI & 33.9-39.5 & 36.2-39.2 & 36.1-39.3 & 33.6-41.0 & 0.568 & 0.498 & 0.969 \\ \cline{2-10}  & WGE & 37.2 & 37.9 & 38.1 & 37.5 & & & \\  & (SD) & \(-\)3.77 & \(-\)3.21 & \(-\)2.77 & \(-\)4.17 & & & \\  & 95\% CI & 34.5-39.9 & 35.6-40.2 & 36.1-40.1 & 34.5-40.5 & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} PVT \\ reaction time \\ (msec) \\ \end{tabular} } & Placebo & 335.3 & 338.8 & 340 & 346.1 & & & \\  & (SD) & \(-\)34.07 & \(-\)28.4 & \(-\)26.07 & \(-\)32 & & \\  & 95\% CI & 311.0-359.7 & 318.5-359.1 & 321.4-358.7 & 323.2-369.0 & 0.073 & 0.355 & 0.626 \\ \cline{2-10}  & WGE & 345.2 & 350.3 & 345.8 & 349.2 & & & \\  & (SD) & \(-\)32.12 & \(-\)34.29 & \(-\)33.24 & \(-\)39.13 & & & \\  & 95\% CI & 322.3-368.2 & 325.7-374.8 & 322.0-369.6 & 321.2-377.2 & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} DMS \\ correctness \\ (score) \\ \end{tabular} } & Placebo & 26.5 & 25.2 * & 26.7 & 26.7 & & & \\  & (SD) & \(-\)2.68 & \(-\)3.68 & \(-\)3.68 & \(-\)3.56 & & & \\  & 95\% CI & 24.6-28.4 & 22.6-27.8 & 24.1-29.3 & 24.2-29.2 & 0.136 & 0.019 & 0.768 \\ \cline{2-10}  & WGE & 27.3 & 26.0 * & 27.2 & 26.6 & & & \\  & (SD) & \(-\)3.27 & \(-\)4 & \(-\)3.19 & \(-\)4.67 & & & \\  & 95\% CI & 25.0-29.6 & 23.1-28.9 & 24.9-29.5 & 23.3-29.9 & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} DMS \\ time \\ (msec) \\ \end{tabular} } & Placebo & 2942.1 & 2533.5 * & 2598.9 * & 2283.9 * & & & \\  & (SD) & \(-\)1948.13 & \(-\)1747.26 & \(-\)1495.75 & \(-\)1524.06 & & & \\  & 95\% CI & 1548.5-4335.7 & 1283.5-3783.4 & 1528.9-3668.9 & 1193.7-3374.2 & 0.272 & 0.025 & 0.416 \\ \cline{2-10}  & WGE & 3335.8 & 2507.6 * & 2806.3 * & 2908.4 * & & \\  & (SD) & \(-\)1948.13 & \(-\)1390.8 & \(-\)1849.86 & \(-\)1535.01 & & & \\  & 95\% CI & 1846.8-4824.7 & 1512.7-3502.5 & 1483.0-4129.6 & 1810.3-4006.5 & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} DMS \\ reaction time \\ (msec) \\ \end{tabular} } & Placebo & 1784.9 & 1740.9 & 1711.7 & 1533.2 & & & \\  & (SD) & \(-\)715.58 & \(-\)790.94 & \(-\)516.6 & \(-\)518.58 & & \\  & 95\% CI & 1273.0-2296.8 & 1175.1-2306.7 & 1342.1-2081.2 & 1162.3-1904.2 & 0.139 & 0.061 & 0.412 \\ \cline{1-1} \cline{2-10}  & WGE & 1905.1 & 1693.3 & 1881.8 & 1823.8 & & \\  & (SD) & \(-\)351.92 & \(-\)366.75 & \(-\)759.4 & \(-\)629.23 & & \\  & 95\% CI & 1653.3-2156.8 & 1431.6-1954.9 & 1338.5-2425.0 & 1373.7-2274.0 & & & \\ \hline \hline \end{tabular} Note. * p \(<\) 0.05, indicate a significant different compared to baseline. DMS, delayed-match-to-sample task; G, group; G \(\times\) T, group \(\times\) time; msec, millisecond; PVT, psychomotor vigilance task; T, time; WGE, wild ginseng extract.  
\end{table} 
Table 3: The results of psychomotor performance following acute eccentric exercise.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Preprocessing
#### 3.2.3 Dataset Shape Conversion  
The original dataset, which includes simulated attacks, is structured in a three-dimensional (3D) format, as illustrated in Figure 1(a). In this format, the thirteen features are distributed across the eight channels of the GPS receiver. In our study, we convert the datasets from the three missions from 3D to a two-dimensional (2D) format. This transformation involves converting the channel-based structure into individual samples. Each channel is treated as an independent row. As a result, the number of samples increases, while the number of features (thirteen) remains unchanged, as illustrated in Figure 1(b). This approach preserves the chronological order of the RX timestamps and maintains the temporal integrity of the dataset.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 2 Methodology
### Measurement Distortions Analysis  
After interference suppression, the main goal is to characterize distortions in terms of mean and standard deviations in the code and carrier phase measurements from the beamforming process in  
Figure 3: Multi-antenna processing approach using MPDR beamforming.  
Figure 2: Multi-antenna processing using PM beamforming.  
the presence of interference. A differential technique is used to evaluate the measurement distortions. Measurements from one of the array antennas with clean data without interference acts as a reference channel. Single differencing between reference antenna measurements (with clean data) and those from the beamformed data (in the presence of interference) acts as a zero-baseline test. The resulting single difference should ideally have a zero mean if there are no distortions in the measurements from the antenna array processing. Measurement distortions refer to the distortions caused by the multi-antenna GNSS receiver process in the presence of interference.
## 2 Materials and Methods
### Antibacterial Activity of Levan
#### 2.9.3. Minimum Inhibitory Concentration (MIC)  
The MIC (minimum inhibitory concentration) was estimated by the broth microdilution method. The bacterial cultures of both _S. aureus_ (ATCC 33592) and _E. coli_ (ATCC 25922) were diluted to 10\({}^{6}\) CFU mL\({}^{-1}\) in Muller-Hinton broth media (MHB). Serial dilution of levan solubilized in DMSO was prepared in a 96-well plate to reach final concentrations of (256, 128, 64, 32, 16, 8, 4, 2, 1, and 0.5 \(\mu\)g mL\({}^{-1}\)) in 100 \(\mu\)L of bacterial suspension in MHB (10\({}^{6}\) CFU mL\({}^{-1}\)). Then, the plates were incubated at 37 \({}^{\circ}\)C for 16-20 h, and the MIC was recorded as the lowest inhibitory concentration of the visible growth [21].  
AntibioticIM Mechanism of Levan against Pseudomonas aeruginosa Strain MW846272 Using Crystal Violet Quantitative Assay  
According to the method in [22], with some modifications, levan antib biofilm activity was determined as follows: 100 \(\mu\)L of _P. aeruginosa_ strain MW846272 suspension containing approximately 2 \(\times\) 10\({}^{6}\) CFU mL\({}^{-1}\) (OD-0.4) were added to different concentrations of levan polymer (0, 0.125, 0.25, 0.5, 1, and 2 mg mL\({}^{-1}\)) to each of the 96 wells of a polystyrene plate. Then, it was incubated statically at 37 \({}^{\circ}\)C for 48 h. After the incubation time, the wells were washed twice with sterile 1\(\times\) phosphate buffer solution (PBS, pH 7.3) to eliminate the planktonic cells of bacteria; after air drying to fix the biofilm, methanol was added for 15 min, and the fixed biofilms were dyed with a 1% crystal violet solution for 10 min. Finally, each well was filled with pure ethanol, and at 630 nm the absorbance was measured using a microplate reader (ChroMate 4300, USA). The results were represented as a percentage of biofilm formation inhibition [23]. Statistical significance (_p_ < 0.05) was estimated using ordinary one-way ANOVA using the GraphPad Prism software version 9.1.2 (226), Inc., San Diego, CA, USA.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 3 Preliminaries  
### Noninformative Prior  
Prior knowledge is usually acquired through historical laws or expert experience. In some probabilistic inference problems using Bayes methods, we may have some prior knowledge that can be easily expressed through prior distribution. In many practical cases, however, we may not know what form of prior should be adopted, or it is not easy to obtain prior distribution. For example, in the coin toss problem, we would generally judge that the probability of heads or tails is 50% before tossing. Nevertheless, when the coin material is not uniform, it is hard to obtain the prior probability distribution of heads.  
In this case, we can use the noninformative prior [20] for inference and analysis. Frankly, the so-called noninformative prior does not mean that there is no information at all, but the information currently contained in the prior distribution is negligible for the sample data. This form of the prior distribution is designed to minimize the impact of prior on the posterior distribution [26], that is, the mean and variance of the posterior distribution are almost all learned from the input data. We will discuss this effect in detail in Section 4.6.  
The Jeffrey's prior mentioned in [27] can be used to determine the noninformative prior distribution of variables. This prior can ensure that the form of the prior does not change with the change of the parameter form (i.e., invariance). Jeffrey's prior was generalized to the case of multidimensional parameters by using the square root of the Fisher information matrix determinant as the variables' noninformative prior. The following is to derive the noninformative prior distribution when the variables follow a normal distribution.  
Let \(\mathbf{x}=(x_{1},x_{2},...,x_{n})\) be a sample from the population \(X\) that obeys the normal distribution \(\mathcal{N}(\mu,\sigma)\), and its log likelihood function is:  
\[l(\mu,\sigma)=-\frac{1}{2}\ln(2\pi)-n\ln\sigma-\frac{1}{2\sigma^{2}}\sum_{i=1 }^{n}{(x_{i}-\mu)^{2}}. \tag{1}\]  
Its Fisher information matrix is:  
\[I(\mu,\sigma)=\left(\begin{array}{cc}E\!\left(-\frac{\hat{\sigma}_{1}^{2}l} {\hat{\sigma}\mu^{2}}\right)&E\!\left(-\frac{\hat{\sigma}_{1}^{2}l}{\hat{ \sigma}\mu\hat{\sigma}}\right)\\ E\!\left(-\frac{\hat{\sigma}_{1}^{2}l}{\hat{\sigma}\mu\hat{\sigma}}\right)&E \!\left(-\frac{\hat{\sigma}_{1}^{2}l}{\hat{\sigma}\mu^{2}}\right)\end{array} \right)=\left(\begin{array}{cc}n/\sigma^{2}&0\\ 0&2n/\sigma^{2}\end{array}\right). \tag{2}\]  
\[\det I(\mu,\sigma)=2n^{2}\sigma^{-4}. \tag{3}\]  
Hence, the noninformative prior of the normal distribution parameters \((\mu,\sigma)\) is \(h(\mu,\sigma)\propto 1/\sigma^{2}\).
## 1 Introduction
### Literature Survey  
Greedy algorithms and convex optimization approaches are most widely used reconstruction methods. Greedy algorithms are commonly applied for solving minimum \(l_{0}\) norm problem with advantages of fast speed and easy implement [14]. Convex optimization approaches are a class of global optimization methods, which address minimum \(l_{1}\) norm problems with high stability and strong applicability [15]. Basis pursuit, basis pursuit de-noising (BPDN) [16], the least absolute shrinkage and selection operator (LASSO) [17], and the least angle regression [18] are typical convex optimization approaches. These approaches can be converted to linear programming or quadratic programming form, and then the optimal solutions can be obtained [19]. Recently, the reconstruction methods have been applied for system identification. For example, the orthogonal matching pursuit algorithm was applied to obtain the joint estimation of parameters and time-delays of the MISO finite impulse response systems and MISO controlled autoregressive systems [20; 21]. The compressive sampling matching pursuit algorithm was modified by employing the instrumental variable method to identify a class of closed-loop systems [22]. The LASSO approach is investigated for computing efficient model structure of the overparameterized nonlinear autoregressive, moving average exogenous systems [23]. In this paper, we aim to identify the MISO-OE systems with time-delays based on a convex optimization approach.  
Compared with the systems studied in [20; 21], the structure of the MISO-OE systems is more general and complex. Considering that the system output is nonlinear in the polynomial parameters, the MISO-OE system model can be transformed into a linear regression form by introducing intermediate variables. However, the intermediate variables are unmeasurable and make the identification difficult. Fortunately, the auxiliary model identification idea can solve this problem by replacing unmeasured variables with outputs of the auxiliary model which is constructed of measurable variables [24]. Combining the auxiliary model idea with conventional methods, many new identification methods have been developed such as the auxiliary model based stochastic gradient algorithm [25], and the auxiliary model based least squares (AM-RLS) algorithm [26]. It is indicated that the AM-RLS algorithm can obtain unbiased and consistent estimation of parameters under a persistent excitation condition. Inspired by the algorithms in [25; 26], we attempt to combine the auxiliary model idea with the convex optimization approach to deal with the joint estimation of parameters and time-delays of MISO-OE systems.