esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Bayesian Decision Making  
We want to use the information gained about the uncertainty of the TSC to decide about whether or not consider the classification as a leak or no-leak situation. Bayesian decision theory ([PERSON], 1992) can be used to make an optimal decision based on cost related to the different choices one could make. We describe a Bayesian decision rule with loss functions and present an algorithm for probabilistic decision support in Section 2.7. This algorithm is applied and showcased through an example in Section 3.7.  
A loss function in terms of Bayesian decision-making states how costly an action or decision is. In a binary classification setting we have two classes \(c:\{c_{1},c_{2}\}\) and \(a\) possible actions \(\{\alpha_{1},\ldots,\alpha_{a}\}\). The loss function \(\lambda(\alpha_{i}|c_{j})\) is the cost associated with taking action \(\alpha_{i}\) if the class is \(c_{j}\). By considering the loss associated with a decision we can define the expected loss or conditional risk  
\[R(\alpha_{i}|\mathbf{x})=\sum_{j=1}^{2}\lambda(\alpha_{i}|c_{j})P(c_{j}| \mathbf{x}). \tag{4}\]  
Here, \(P(c_{j}|\mathbf{x})\) is the posterior predictive probability for a given class \(c_{j}\), given a time series \(\mathbf{x}\). Remember, the predictive posterior distribution is estimated through MC estimation from the BCNN for each time series. Given a time series \(\mathbf{x}\) it is possible to minimize the expected loss by choosing the action that minimizes the conditional risk. A decision rule \(R\) is a function that takes the input time series to a space of possible actions \(R\colon\mathbb{R}^{d}\to\{\alpha_{1},\ldots,\alpha_{a}\}\). The expected loss of a decision rule can be stated as  
\[L=\int R(\alpha(\mathbf{x})|\mathbf{x})p(\mathbf{x})dy.\]  
The aim is to use the rule \(\alpha(\cdot)\) that minimizes \(R(\alpha(\mathbf{y})|\mathbf{y})\) for all \(\mathbf{y}\). Minimize the conditional risk based on the actions is in fact the optimal decision given our information  
\[\alpha^{*} =\arg\min_{\alpha_{i}}R(\alpha_{i}|\mathbf{x})\] \[=\arg\min_{\alpha_{i}}\sum_{j=1}^{c}\lambda(\alpha_{i}|c_{j})P(c_ {j}|\mathbf{x}).\]In the case of only two possible actions and classes, the conditional risk can be expressed as  
\[R(\alpha_{1}|\mathbf{x}) =\lambda_{11}P(c_{1}|\mathbf{x})+\lambda_{12}P(c_{2}|\mathbf{x}),\] \[R(\alpha_{2}|\mathbf{x}) =\lambda_{21}P(c_{1}|\mathbf{x})+\lambda_{22}P(c_{2}|\mathbf{x}).\]  
The decision rule then becomes to choose the actions which give the smallest overall risk. We choose action \(\alpha_{1}\) if  
\[R(\alpha_{1}|\mathbf{x})<R(\alpha_{2}|\mathbf{x}).\]  
From Equation (4) we have that the risk can be expressed in terms of the loss function and the posterior distribution. In the two class and action case, we decide action \(\alpha_{1}\) if  
\[(\lambda_{21}-\lambda_{11})P(c_{1}|\mathbf{x})>(\lambda_{12}-\lambda_{22})P(c _{2}|\mathbf{x}).\]  
We want to use the concepts outlined above for the classified time series and their posterior predictive distribution. This results in a novel approach for decision support in environmental monitoring under uncertainty.
## 2 Electromagnetic Interference Characteristics of Converter Station to the Surrounding Environment
### Electromagnetic Interference in Converter Station  
The converter station is the main part of the High Voltage Direct Current (HVDC) transmission system. Its function is to complete the interface between the AC and DC power systems. Its core structure is the power electronic converter, constructed by high-voltage (HV) semiconductor switches connected in HV electronic valves. The electromagnetic emission of the HVDC converter station is mainly caused by the switching of the valves of the converter station. The converter valves will alternately open and break off when working, and that leads to rapid transients of the switching voltage and internal current of the valves. This type of electromagnetic emission is characterized by the transmission of energy, which spreads a wide and continuous spectrum of noise. In addition, the commutation of the semiconductor devices (inside the valves) may produce corona discharge, sparks, etc. This may cause additional electromagnetic interference, and the switching operation transient processes will result in high frequency and larger spectrum electromagnetic noise.  
Therefore, the main source of interference in the converter station is the converter valve, and the secondary source of interference is the other equipment [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The following analysis is studying those sources of interference.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 4 Proposed Methodology
### Features Extraction
#### 4.2.1 Mfcc  
The MFCC feature is applied to sub-class A as input to the classifier in the proposed SED model. Because humans can hear sounds at frequencies below 1 kHz, this measure is used as the foundation for MFCC feature extraction. Additionally, two feature types, namely, a pre-emphasis filter and triangular-filter, are used by the MFCC; these are spaced logarithmically above 1000 Hz or linearly below 1000 Hz. On the mel-frequency scale, a subjective pitch is provided to capture key phonetic features of speech (i.e., audio signals). The following steps clearly describe the MFCC used in the proposed SED model:  
1. The first step in the MFCC is to apply a pre-emphasis filter to increase the strength of the signal at higher frequencies. To decrease noise during audio capture, the following filtering procedure is carried out. Equation (1) presents the input-output connection in the time domain which provides the framework for this filter: \[y(n)=x(n)-ax(n-1)\] (1) where \(x\) is the input audio signal, \(\alpha\) is a constant filter coefficient with a value equal to \(0.9\leq\alpha\leq 1\), \(y\) is the output audio signal, and \(n\) is the time domain.
2. The audio signal is next split into frames within the range of 20 to 40 ms.  
Figure 2: Proposed stacked multi-model for SED.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **MFCC** & **RASTA-PLP** \\ \hline Sampling frequency & 44.1 kHz & 44.1 kHz \\ \hline Hop length & 20 ms & 20 ms \\ \hline Mel-filter length & 40 & Nil \\ \hline Model order & Nil & 12 \\ \hline Window & Hamming & Hamming \\ \hline Window size & 1024 & 1024 \\ \hline Do Rasta & Nil & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in feature extraction.  
3. A Hamming window is applied to each frame after the audio signal is segmented into frames, as modelled in Equation (2): \[y(n)=x(n)\times w(n)\] (2) where \(y(n)\) is the output audio signal and \(x(n)\) is the input audio signal convolved with \(w(n)\), that is, the hamming window calculated in Equation (3): \[w(n)=0.54-0.46 cos\bigg{(}\frac{2\pi n}{N-1}\bigg{)}\ 0\leq n\leq N-1\] (3) where \(N\) represents total number of frames and \(n\) is the time domain.
4. Using STFT, the time domain data are converted to frequency domain data for each frame \(N\), which procedure is formulated in Equation (4): \[P=\frac{|STFT(x_{i})|^{2}}{N}\] (4) where \(x_{i}\) denotes the \(i\)th frame of signal \(x\) such that \(i\) = 0, 1, 2,..., \(N-1\) and \(P\) is the power spectrum.
5. A triangular filter is used to calculate the filter bank using Equation (5): \[m=2595 log_{10}\bigg{(}1+\frac{f}{700}\bigg{)}\] (5) where \(m\) is the mel scale used to convert an audio frequency into a frequency range that people can hear and \(f\) represents the frequency, which can be computed using Equation (6): \[f=700\bigg{(}10^{m/2595}-1\bigg{)}\] (6)
6. The inverse logarithm of this log spectrum should be determined after obtaining the relative auditory spectra.
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 4 Conclusions  
This experiment was mainly aimed at the preparation and photodegradation performance of the modified photocatalytic composite membranes. The PVDF microfiltration membrane was modified by the surface coating method with different photocatalysts: TiO\({}_{2}\)/PVDF, NT/PVDF, TG/PVDF, and NTG/PVDF, respectively. The properties of the modified membranes were characterized by SEM, XRD, FT-IR and a water contact angle meter, and the effects of the different photocatalysts on the photocatalytic activity of the modified membranes were investigated. The results showed that the surface loading of the NTG/PVDF membrane was more complete and uniform and that the doping of N and GO had no significant effect on the crystal structure of nano-titanium dioxide, mainly an anatase crystal structure. The hydrophilic functional groups on the surface of the modified membranes were increased and the hydrophilicity was remarkably improved. The pure water flux of the NTG/PVDF membrane was the best due to the increased hydrophilicity of NTG, the relative flux of the MB solution was also improved by photodegradation under different illumination conditions. The NTG/PVDF membrane showed the best photodegradation performance with degradation rates reaching 86.5% and 80.6% under ultraviolet and sunlight, respectively. Therefore, the NTG/PVDF membranes have great development prospects in sustainable water treatment.  
Conceptualization--[PERSON] and [PERSON]; Data curation--[PERSON]; Formal analysis--[PERSON]; Funding acquisition--[PERSON] and Y.G.; Investigation--[PERSON], [PERSON] and [PERSON]; Methodology--[PERSON]; Writing, original draft--[PERSON]; Writing, review & editing--[PERSON], [PERSON] and [PERSON].  
This research was funded by National Science Foundation Natural Fund, grant number 51508239.  
This work was supported by the National Science Foundation Natural Fund (NO.51508239), Jiangsu Province Industry-University Research Prospective Joint Research Project (NO. BY2016030-26), A Project Funded by Jiangsu Postgraduate Practice Innovation (NO. SJCX18-0989 and NO. SJCX18-1011).  
Figure 10: The MB photodegradation kinetics curves under (**a**) ultraviolet light and (**b**) sunlight.
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
## 3 Sensor Performance Experiment
### Analysis of Experimental Results  
Figure 3 shows the relation between the center wavelength changes and force variation for the two FBGs. The wavelengths of FBG1 present a red shift, which verifies that FBG1 is stretched and senses the positive strain. The wavelength of FBG2 show a blue shift, indicating that FBG2 is compressed and senses the negative strain. Both variation patterns are linear, and the linear fitting sensitivity is 19.48 pm/kN and \(-\)19.31 pm/kN, respectively.  
Figure 4 shows the average of wavelength shift differences between the two FBGs versus force. The fitting results are also given and the force sensitivity of the sensor is 38.79 pm/kN; in other words, 38.79 pm is approximately equivalent to 32.06 \(\mu\epsilon\) of optical fiber Bragg grating (the strain sensitive coefficient of FBG is 1.21 pm/\(\mu\epsilon\)), and the linear correlation coefficient is 0.9998. The measuring sensitivity is apparently improved, compared with the wavelength change of a single FBG as a measuring signal. The measured force sensitivity is basically consistent with the theoretically calculated value, and its influential factors are mainly the measurement process error and strain transfer efficiency.  
Figure 3: Wavelength changes of the two FBGs under different forces.  
Figure 2: Sensor performance test system.  
Figure 5 shows the curve of the pressure gauge value versus the applied force. It can be seen that the applied force is linear to the pressure gauge value. The sensitivity of the force and pressure gauge value conversion is 8.72 kN/MPa, and the correlation coefficient is 0.9988. Figure 6 shows the test comparison between the pressure gauge and the FBG sensor under different forces. It can be seen that the results of the pressure gauge value and the FBG sensor's calculated and theoretical values slightly differ from each other, and the largest difference values are 0.397 MPa and 0.3 MPa, respectively. Compared with the theoretical values, the pressure gauge values show a fluctuation in the measured data due to artificial reading error. The largest measured value of the FBG sensor is 16.94 MPa and is slightly smaller than the theoretical value of 17.24 MPa due to strain transfer and test errors. Therefore, the theory and experiment show that the designed FBG bolt force sensor can meet the measuring requirements, and this sensor can be used for the online monitoring and direct on-site reading of the force status of bolts in coal mine roadways.  
Figure 4: The average of wavelength shift differences between the two FBGs versus force.  
Figure 5: Change in sensor pressure gauge values under different forces.
## 5 Conclusions and Future Work  
Given the results obtained in the case study, it is possible to ratify the efficiency of the proposed methodology, especially concerning the possibility of circumventing the lock screen mechanism, which constitutes one of the main obstacles to the execution of mobile devices forensics. The case study carried out to validate the proposed methodology proved that the combination of the listed techniques is feasible and presents satisfactory results. It was possible to bypass the lock screen, which is one of the main obstacles to mobile devices forensics. This characteristic allowed the device data acquisition.  
Concerning encryption, with the FRP removal previously stored in the _Persist_ partition, it was possible to change the partitions, _boot_, _recovery_, and _system_, and thus bypass the process of exchanging cryptographic keys, because the private key was removed, and the system understands that no blocking mechanism has been configured or was removed, so the data partition (_/data_) will be decrypted without any brute force attack or reverse engineering of the cryptographic algorithm, just bypassing the mechanism transparently.  
It is important to note that the methodology has already been applied to other devices manufactured by Samsung, with Qualcomm, Exynos and MediaTek SoCs, and Xiaomi devices with Qualcomm SoCs. According to the studies that have been carried out, it is possible to apply the methodology on any devices that have TAPs for ISP and whose Special Firmware is available. However, we have not yet carried out tests with all devices, considering that the objective of the study is to address devices recently launched and that have a high volume of sales, which represents a greater attack surface.  
It is crucial to confirm that the tool used for data extraction and analysis (AXIOM, XRY, among others) is at the analyst discretion or according to availability. Also, it is still possible to combine other tools to make additional acquisitions.  
In devices with a security _patch_ or latest binary firmware version, modifications in the architecture or system organization may occur. These modifications may also occur in the implementation of a new security mechanism that prevents the methodology from working precisely as detailed, requiring the analysis of specific cases that are beyond the control of the proposed actions.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 4 Simulation Results
The exponential correlation model is formulated in [27], and \(r\) represents the correlation coefficient. Figure 7 shows that the convergence performance of all algorithms degrades with the serious channel correlation. Furthermore, it is worth noting that the proposed algorithm can still converge to the MMSE algorithm with a small number of iterations (i.e., \(K=3\) when \(r=0.1\), \(K=4\), when \(r=0.4\)). When \(r=0.1\), to ensure the performance of the final approximation, the required complexity of the proposed Jacobi-based algorithm, GS algorithm and SSOR algorithm is \(O\left(8N_{t}^{2}\right)\) (i.e., \(K=3\)), \(O\left(9N_{t}^{2}\right)\) (i.e., \(K=4\)) and \(O\left(10N_{t}^{2}\right)\) (i.e., \(K=4\)), respectively. Similarly, when \(r=0.4\), the required complexity of the proposed Jacobi-based algorithm, GS algorithm and SSOR algorithm is \(O\left(10N_{t}^{2}\right)\) (i.e., \(K=4\)), \(O\left(11N_{t}^{2}\right)\) (i.e., \(K=6\)) and \(O\left(14N_{t}^{2}\right)\) (i.e., \(K=6\)), respectively. Consequently, the proposed algorithm can still enjoy lower complexity than the GS and SSOR algorithm to achieve the near exact performance of the MMSE algorithm with serious channel correlation. Figure 6: BER curves with the different number of users. Figure 7: BER curves with different values of correlated magnitude.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 6 Security Analysis
### Cryptographic Core  
The cryptographic core is the same for both applications and is implemented using the MCL library [25]. This library supports the bilinear pairing operations and shows the best performance results according to [26]. The library is available in a native form for the Android platform and provides support for BN254 and BL12_381 elliptic curves. Note that our implementation uses the BN254 elliptic curve and requires bilinear pairing operations in the verification phase. Since the library is available only in native C/C++ form, we use the Android Native Development Kit (NDK) and a wrapper enabling the use of the library functions on the Android platform. The cryptographic core is represented with the CryptoCore class and implements eight methods: see Figure 6 for more detail. The first four methods implement the cryptographic algorithms (i.e., Setup, Issue, Show, Verify), and the other four methods provide support functions.
## 1 Introduction
Prescribed performance control, which consists of prescribed performance function (PPF) and error transformation, ensures that the attitude tracking errors converge at a specified rate to a preset arbitrarily small set of residuals while guaranteeing that the maximum overshoot value is smaller than the predefined value. More recently, [PERSON] et al. [22] proposed a prescribed performance controller to obtain an expected transient performance for hypersonic vehicle. [PERSON] et al. [23] designed a controller with prescribed transient performance to solve attitude control problem. [PERSON] et al. [24] proposed a low-complexity prescribed performance method for vehicles under unknown dynamics errors and external disturbances. However, the above PPF cannot freely adjust its decreasing rate in the transient process without changing the time to reach the steady state. This makes it difficult for PPF to meet different task requirements in practical applications. To overcome the drawbacks mentioned above, a novel nonsingular predefined-time sliding mode control method for VTHL RLV is proposed. The principal innovations are summarized as follows:  
* A novel predefined-time PPF (PTPPF) is proposed. The convergence time in the transient phase of PTPPF to the steady state can be flexibly adjusted by changing one parameter. Moreover, the decreasing rate of PTPPF in transient phase can also be adjusted by changing one parameter on the premise of not changing the convergence time of PPF to reach steady state. * A novel predefined-time sliding mode surface (SMS) is proposed. When the attitude tracking errors moving on the SMS, they can converge to the origin within a predefined time, and the value of convergence time can be adjusted by one parameter. * Based on PTPPF and the novel SMS, the RLV control system with a novel nonsingular controller is proposed to prevent the attitude tracking errors from exceeding the prescribed performance bounds. Meanwhile, the singularity can be avoided directly without using piecewise continuous functions. The remainder of this paper is organized as follows. The dynamic model of RLV and preliminaries are introduced in Section 2. The PTPPF and the error transformation are proposed in Section 3. In Section 4, the nonsingular predefined-time SMS and controller are proposed. The simulation results are shown in Section 5, and Section 6 presents the conclusions of this study.
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
## 4 Single-Frequency PPP Processing Results and Analysis
### Processing Results and Analysis  
In order to evaluate the proposed single-frequency PPP approach, the datasets at FTNA and SEYG stations on 5 September 2016 are processed in five different scenarios i.e., GPS, GPS/GLONASS, GPS/GLONASS/BDS, GPS/GLONASS/BDS/Galileo plus GIM.  
Figure 4 shows the PPP positioning errors based on the five processing scenarios. It is obvious that the GPS-only PPP solutions need a longer time to converge to stable values than the other constellation combinations. By combination of GPS with GLONASS, the GPS/GLONASS PPP has achieved significantly better convergence performance. As the BeiDou and Galileo observations have been assigned smaller weights than the GPS and GLONASS observations, the triple-constellation and quad-constellation PPP results are quite similar to the GPS/GLONASS dual-constellation PPP solutions, as seen from the blue, pink, and brown curves. After the GIM data is introduced, the quad-constellation PPP convergence performance is considerably improved, especially in the horizontal directions. Figure 5 shows the number of satellites and PDOP (position dilution of precision) for each processing scenario. Similar to the positioning error curves, the GPS/GLONASS dual constellations significantly improve the PDOP over the GPS-only single constellation. With integration to BDS and Galileo, the further improvement of PDOP is very limited, which also explains why the convergence performance cannot be notably enhanced with inclusion of BDS and Galileo.  
Figure 3: Geographical distribution of 12 MGEX stations.  
Table 1 provides the RMS statistics in the east, north and up coordinate components, as well as RMS for the three-dimensional (3-D) position to demonstrate a positioning accuracy in five-hour sessions. The RMS computations are based on the position solution errors within the last 15 min. It can be seen that the final positioning accuracy reaches a few centimeters and is quite close for different scenarios.  
Figure 4: Single-frequency PPP errors for different processing scenarios using datasets at FTNA and SEYG stations on 5 September 2016.  
Figure 5: Number of satellites and PDOP for different PPP processing scenarios using datasets at FTNA and SEYG stations on 5 September 2016.