esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to explore the sound absorption and noise reduction performance of closed-cell aluminum foam in the tunnel, the field test of the sound absorption performance of aluminum foam board was carried out based on the installation of aluminum foam board in the whole line of Haoshanyu Tunnel on Qinglan Expressway. Combined with the existing loudspeaker test and typical tunnel measurements, a new field test method for the noise reduction performance of closed-cell aluminum foam board was proposed for two different working conditions including fixed-point pure tone sound source condition and mobile vehicle sound source condition. The testing results of the new methods were analyzed, and it showed that the closed-cell aluminum foam has good sound absorption property at the frequency spectra between 250 Hz and 1000 Hz, and the farther away from the sound source, the better the sound absorption effect. In the research on the noise reduction effect of actual vehicle, it was found that the insertion loss of the closed-cell foam aluminum board is about 4 dB(A), which indicated that the closed-cell aluminum foam can play a certain noise reduction effect in the tunnel.  
highway tunnel; noise reduction; closed-cell aluminum foam; field test; sound absorption; insertion loss +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
### Feature Fusion of Multiple Sensors  
For a rotating machine, several vibration signals can be collected by sensors installed at different locations on the machinery, and many parameter values can also be calculated as the features from the vibration signal collected by each sensor. Using the proposed sensitive IMF selection method, the useful information from each sensor for fault diagnosis is obtained, and traditional features are extracted. If all features are employed to diagnose bearing faults, the fault diagnosis model will be complicated and time-consuming. Furthermore, each feature can contain more or less failure information to facilitate the fault diagnosis of the bearings. Principal component analysis (PCA) can play a role in projecting high-dimensional data to low-dimensional data to reduce the redundancy of the dataset [42]. Meanwhile, the low-dimensional data is able to reflect the main information of the original high-dimensional data. However, one drawback of PCA is that its threshold is hard to adaptively select. Another frequently used feature fusion method is the DS evidence theory [43], which has become a more widely used approach in recent years. However, it also has problems. For example, the exponential complexity of the computations and fusing of conflicting data can produce counterintuitive results [44]. In this paper, we use correlation analysis to fuse these obtained features from different sensors. The procedure is as follows.  
(1) The reconstructed signals with selected sensitive IMFs of the used sensors are obtained using the sensitive IMF selection method described in Section 3.1; these are marked as \(x_{s1}(t)\), \(x_{s2}(t)\dots x_{sn}(t)\), where sn is the serial number of the sensor.  
(2) The characteristic parameter values are calculated from the reconstructed signals of the sensors with the definitions and calculated formulas of the features.  
(3) The correlation coefficients of any two reconstructed signals are calculated and the two reconstructed signals, \(x_{new1}(t)\) and \(x_{new2}(t)\), with the greatest correlation coefficients are determined. For normal conditions, either of these two reconstructed signals, \(x_{new1}(t)\) or \(x_{new2}(t)\), can be selected as the base signal. For the investigated vibration signal, this reconstructed signal, which has the lowest correlation with the normal signal, is regarded as the so-called base signal. Then, the correlation coefficients of the base signal and all reconstructed signals are calculated to define a weight, \(w^{\prime}_{k}\), by  
\[w^{\prime}_{k}=\frac{r_{x_{b},x_{k}}}{\sum_{k=1}^{K}r_{x_{b},x_{k}}}, \tag{15}\]  
where \(r_{x_{b},x_{k}}\) is the correlation coefficient of the base signal and reconstructed signals; \(x_{b}\) is the base signal; and \(x_{k}\) is the reconstructed signal and can be equal to \(x_{b}\).  
(4) After obtaining these weights, the fusion features are calculated by \[F_{l}^{\prime}=\sum_{l=1}^{L}w_{l}^{\prime}F_{l}, \tag{16}\]  
where \(F_{l}\) is the original feature and \(F_{l}^{\prime}\) is the fusion feature.
## 4 Application in Chenxi Bridge
### Data Correction  
The moving average filter method was used to make the data smoother. This filtering process allows for a detailed examination of the most significant fluctuations within a given time frame. It should be noted that the proposed method does not need the smoothing process to calculate the identification results. After the process of filtering, the smooth data can be obtained, as shown in Figure 17. It can be known from the figure that the total time taken for the truck to pass the bridge was about 26 s.  
The peak data indicate that a heavy load crossed the bridge, typically corresponding to a heavy truck, as illustrated in Figure 14b. From Figure 15, the data within the red rectangular region were selected for analysis, with the results presented in Figures 16 and 17. It can be seen that the time taken for the heavy truck to pass the bridge was 26 s and the length of the bridge is 540 m. Thus, it can be deduced that the speed of the truck was 76 km/h, which can be the approximate initial parameter for the identification process.
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
###### Abstract  
Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light paired images and amplifying noise while enhancing brightness. Based on existing experimental observation that most luminance information concentrates on amplitudes while noise is closely related to phases, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. In our method, the low-light image is firstly transformed into the amplitude component and phase component via Fourier transform. The luminance of low-light image is enhanced by CycleGAN in the amplitude domain, and the phase component is denoising. The cycle consistency losses both in the Fourier transform domain and spatial domain are used in training. The proposed method has been validated on publicly available test sets and shows that our method achieves superior results than other approaches in low-light image enhancement and noise suppression.  
low-light image enhancement; fourier transform; noise suppression; deep learning; noise suppression; CycleGAN +
Footnote †: journal: Applied
## 3 Results and Discussions
### Torrefaction Index  
A study by [PERSON] et al. [39] introduced the torrefaction index (TI) based on the ratio between the energy density enhancement factors at a given condition to the reference condition and showed a linear dependence of energy density on the torrefaction temperature. Additionally, the authors also suggested 300 \({}^{\circ}\)C and 60 min as the reference torrefaction temperature and residence time, respectively. However, the study conducted comprised only of lignocellulosic biomasses. The torrefaction temperature and residence time for non-lignocellulosic biomass, such as sewage sludge, are comparatively milder owing to its higher organic fraction in contrast to lignocellulosic biomass.  
Table 2 shows a comparison between the TI obtained in this study to that of [PERSON] et al. [39]. From the table, it is clear that the value obtained in this study coordinates very well to the predicted value range. From this it can be seen that the TI increases with increase in torrefaction temperature until of 300 \({}^{\circ}\)C, after which the value starts declining. From this it can be suggested that torrefaction above 300 \({}^{\circ}\)C would be undesirable for sewage sludge.  
[PERSON] et al. [40] suggested that torrefaction of 300 \({}^{\circ}\)C was more efficient than 260 \({}^{\circ}\)C. In addition, [PERSON] et al. [41] performed torrefaction of wheat straw and concluded that the upper limit of temperature, i.e., 300 \({}^{\circ}\)C, is the desired temperature for torrefaction. Contrarily, [PERSON] et al. [42], in their study, concluded that the optimum temperature range for torrefaction of sewage sludge was  
Figure 4: Degree of torrefaction of torrefied samples as a function of the torrefaction residence time.  
300-350 \({}^{\circ}\)C, although the torrefaction of sewage sludge was performed in a fixed bed. However, the present study suggests an optimum temperature of 250-300 \({}^{\circ}\)C. The difference in the temperature range in the literature and this study may refer to the fact that the chemical and structural composition of lignocellulosic and non-lignocellulosic biomass is dissimilar and is also based on the composition and the origin of the biomass. Therefore, the use of a torrefaction index might be beneficial as a pre-assessment tool to generate ideas about the quality of the torrefied product before conducting the experiment itself, as highlighted by [PERSON] et al. [39] in their studies.
# Wireless-Channel Key Distribution Based on Laser Synchronization
## 6 Conclusions  
In this paper, we propose a high-speed wireless key distribution scheme based on the chaos synchronization of nonlinear oscillators and experimentally demonstrate it using DFB lasers. Our experiments demonstrate laser synchronization induced by a common wireless signal with a cross-correlation coefficient of 0.95. Based on the broadband synchronization responses of the lasers, a secure key generation rate of up to 150 Mbit/s with a BER below \(3.8\times 10^{-3}\) is accomplished. Our numerical results show that the distribution distance of the scheme can reach several hundred meters in a free space. The findings of our study indicate that the utilization of common-signal-induced laser synchronization holds promise as a viable approach for attaining high-speed wireless key distribution.  
Conceptualization, Y.W. and A.W.; methodology, J.X. and A.W.; validation, J.X. and Y.S.; investigation, J.X., X.Z., L.M. and Y.Z.; writing--original draft preparation, J.X.; writing--review and editing, A.W. and Y.W.; supervision, Y.W. and Y.Q.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 62035009); Program for Guangdong Introducing Innovative and Enterpreneurial Teams; and Guangdong Provincial Key Laboratory of Photonics Information Technology (Grant No. 2020B121201011).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results
### Regional Variability of Clustering Properties
In the NAS area, a moderate value is acquired (\(a=1.04\)), probably due to the co-existence of swarm activity and aftershock sequences. Another indicator for the existence of swarm activity in CG is the large value of the background seismicity (\(\mu=0.43\)) compared to NAS and CII. High values of the background rate can indicate the existence of aseismic loading transients [40]. [PERSON] et al. [70] observed increased values of the background component of the fitted ETAS model when it was applied to pre-swarm and swarm activity, respectively. For the comparison of the productivity among the three areas, since they have different completeness magnitudes, we use the following relation,  
\[N=k(M_{i})P(M\geq m_{c}^{*})=Ke^{a(M_{i}-m_{c})}e^{-\beta(m_{c}^{*}-m_{c})}, \tag{3}\]  
which yields the number of earthquakes above magnitude \(m_{c}^{*}\), generated by a main shock of magnitude \(M_{i}\). Figure 8 shows the number of direct triggered events, \(K(M_{i})\), from an earthquake of magnitude \(M_{i}\). We consider \(m_{c}^{*}=2.2\), which is the maximum completeness magnitude among the three datasets. The exponent of the exponential magnitude distribution is expressed by \(\beta\) and is defined as \(\beta=\sum_{i=1}^{N}\beta_{i}/N^{*}\), where \(N^{*}\) is the number of clusters for each dataset and \(\beta_{i}\) their corresponding exponent values. Concerning the distribution of aftershocks in time, the normalized Omori law distribution is used, given by \(g(t)\). In CII, the seismic sequences seem to be more productive, as shown in Figure 8, with NAS and CG to exhibit smaller values. Combined with the higher background rate for the area of CG (\(\mu=0.43\)), we may say that a significant part of Corinth Gulf's sequences cannot be contributed to the triggering effect of mainshocks but different underlying mechanisms seem to play an important role. Conversely, in CII area, mainshock-aftershock sequences seem to dominate, generating a rich number of aftershocks (very low background rate, \(\mu=0.15\), and high productivity of mother events). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Area** & \(\mathbf{p}\) & \(c\) & \(a\) & \(K\) & \(\mu\) & \(\beta\) & \(N^{*}\) \\ \hline CG & 1.23 & 0.0171 & 0.82 & 0.74 & 0.43 & 2.13 & 26 \\ CII & 1.31 & 0.11 & 1.29 & 0.44 & 0.15 & 2.21 & 9 \\ NAS & 1.26 & 0.0324 & 1.04 & 0.51 & 0.28 & 2.03 & 17 \\ \hline \end{tabular}
\end{table} 
Table 5: Generic ETAS parameter values for the three study areas. \(N^{*}\) denotes the number of sequences with \(N\geq 30\).
## 2 Materials and Methods
### Example Application Case  
The example case study represents a section of a distribution grid in which a technical VPP (TVPP) is located. The TVPP aggregates and optimizes the scheduling units with respect to services that can be offered to the grid operator. In the example at hand, the TVPP consisted of three wind turbines and two industrial loads that can be used for congestion management to prevent transformer overloads. In addition to these controllable and flexible units, some inflexible units like households and industrial loads were situated in the distribution grid.  
Figure 4 depicts the example case and the corresponding simulation setup. In the chosen scenario, grid congestions can result from overloading the transformer, which connects the distribution grid to the transmission grid. The threat of transformer overload exists due to excessive feed-in of wind turbines with high simultaneity. To avoid permanent damage by overloading grid equipment, a solution that mitigates grid congestions needs to exist before the equipment suffers irreparable harm.  
The example has been implemented as a MOSAIK scenario. Each simulation step includes the simulation of the system operation as well as the planning process of the TVPP.  
The planning process is as follows: During a simulation step, a forecast of the upcoming power consumption or production of each of the units is performed and forwarded to the power system analysis tool Pandapower [38]. In this tool, power flow calculations for the grid section are performed  
Figure 4: Scenario of exemplary application case.  
on the power forecasts. Based on its calculated results Pandapower passes technical restrictions to the controller agent of ISAAC. In case of an imminent transformer overload, the controller agent initiates a negotiation between the unit agents. The objective of this negotiation is to find an overall aggregated schedule, comprised of the individual schedules of each unit, which mitigates the transformer overload.  
To achieve mitigation, all unit agents receive the flexibilities (e.g., a set of potential schedules) from their unit simulation models. The flexibilities only allow schedule changes that take local restrictions into account. In the example case, the congestion can be prevented either by capping of the feed-in of the wind turbines or by load shifting of the industrial consumers. After the negotiation, each agent sends the determined schedule to its corresponding unit simulation model.  
In order to simulate the operation of the system, the power values of the units are also transmitted to Pandapower in each simulation step. These inputs are used for a load flow calculation that reveals whether the planning process of the TVPP has been successful.  
Given the right parameterization, this example is able to show how highly simultaneous feed-ins of wind energy collectors can cause a grid congestion by provoking an overloading of a transformer and how a TVPP can use the flexibility of its units to mitigate said congestion.
## 3 Application of the ORW Optimization Model--Case Study
### Input Data for the Case Study
This combined approach generated a total of 100,000 samples, each containing random values for the variables defining the soil model. The input data for the reduction coefficients \(k_{a}\) and \(k_{p}\), which relate to the interaction between the embedded retaining structure and the soil on the active and passive sides, respectively, are mainly based on empirical estimates. Therefore, a triangular distribution was chosen to derive the cumulative function for these coefficients, as shown in Table 4. This distribution was chosen to reflect the practical uncertainties associated with these variables. From a technical point of view, it is recognized that these variables have defined upper and lower limits. Therefore, they are modeled as unequally distributed random variables with certain bounds, as described in Equations (7) and (8). For variables with known bounds, a triangular distribution was used to accurately represent their probabilistic properties [34]. \[k_{a}=\ CDF^{-1}(U_{i})= \begin{cases}0.5+\sqrt{\frac{U_{i}}{12}}\ U_{i}\leq \ \frac{1}{3}\\ 1-\sqrt{\frac{1-U_{i}}{6}}\ U_{i}>\frac{1}{3}\end{cases}  \tag{7}\]  
\[k_{p}=\ CDF^{-1}(U_{i})= \begin{cases}0.3+\sqrt{\frac{2U_{i}}{100}}\ U_{i}\leq \ \frac{1}{2}\\ 0.5-\sqrt{\frac{4-4U_{i}}{100}}\ U_{i}>\frac{1}{2}\end{cases}  \tag{8}\]  
where \(U_{i}\) denotes randomly distributed values for \(k_{a}\) within the interval [0.5-1.0] and \(k_{p}\) within the interval [0.3-0.5], which were generated using the rand() function in Excel. This process resulted in a total of 100,000 samples of these random variables. The statistical parameters derived from these samples are summarized in Table 4 and are used in the expanded deterministic model that contains the condition for the target failure probability. The probability density function (PDF) of each random variable is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Random Variable** & **Statistical Parameter** & **Value** & **Distribution** \\ \hline \(\varphi^{\prime}{}_{k}\) (\({}^{\circ}\)) & Mean value & 35.15 & Normal \\ \(c^{\prime}{}_{k}\) (kPa) & Standard deviation & 0.82 & Normal \\ \(c^{\prime}{}_{k}\) (kPa) & Mean value & 6.6 & Normal \\ \(\gamma_{k}\) (kN/m\({}^{3}\)) & Standard deviation & 1.05 & Normal \\ \(\gamma_{k}\) (kN/m\({}^{3}\)) & Mean value & 20.55 & Normal \\ \(\gamma_{k}\) (kN/m\({}^{3}\)) & Standard deviation & 0.24 & Normal \\ \(k_{a}\) & Maximal value & 0.5 & Normal \\ \(k_{a}\) & Mean value & 0.71 & Triangle \\ \(\gamma_{k}\) (kPa) & Standard deviation & 0.1 & Triangle \\ \(\gamma_{k}\) (kPa) & Minimal value & 0.3 & Triangle \\ \(k_{p}\) & Maximal value & 0.39 & Triangle \\ \(\gamma_{k}\) (kPa) & Standard deviation & 0.11 & Triangle \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values of random variables after MCMC sampling.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 3 Approximating Stationary Kernels
### Learning Locally Stationary Kernel, GaBaSR
#### 3.2.3 Sampling to approximate the kernel  
As we established earlier, the kernel can be represented by:  
\[\kappa(\mathbf{x}-\mathbf{x}^{\prime})\approx\frac{1}{M}\mathbf{\varphi}^{T}(\mathbf{x})\mathbf{ \varphi}(\mathbf{x}^{\prime}),\]  
where \(\mathbf{\varphi}(\mathbf{s})=[\cos(\mathbf{\omega}_{1}^{T}\mathbf{s}),\ldots,\cos(\mathbf{\omega }_{M}^{T}\mathbf{s}),\sin(\mathbf{\omega}_{1}^{T}\mathbf{s}),\ldots,\sin(\mathbf{\omega}_{M}^ {T}\mathbf{s})]\), and \(\mathbf{\omega}_{i},i=1,2,3,...,M\) is a sampled from the learned Gaussian Mixture. In order to approximate the kernel, for each random representation, we take a candidate frequency with probability \(r=min(1,\alpha)\), where  
\[\alpha=\frac{P(\mathbf{y}|X,W_{-j},\mathbf{\omega}_{j}^{*})}{P(\mathbf{y}|X,W)}\]  
Now, if the task is a regression, then Equation (10) is used. With classification, Equation (11) is used. Then, we take a random number \(u\sim U(0,1)\) and accept \(\mathbf{\omega}_{j}^{*}\) if \(u<r\); otherwise reject \(\mathbf{\omega}_{j}^{*}\). For this, it is clear that we need to sample \(\mathbf{\omega}_{j}^{*}\) from:  
\[\mathbf{\omega}_{j}^{*}\sim\mathcal{N}(\mathbf{\omega}_{j}^{*}|\mathbf{\mu}_{Z_{j}^{*}} \Sigma_{Z_{j}})\]  
In order to compute \(P(\mathbf{y}|X,\Omega)\), it is necessary to identify what type of task is being solved, regression or classification.  
1. In the case of a **regression**: \[P(\mathbf{y}|X,\Omega)\propto\sqrt{\frac{|V_{N}|}{|V_{0}|}}\frac{b_{0}^{\mathbf{\omega} _{0}}}{b_{N}^{\mathbf{\omega}_{N}}}\frac{\Gamma(a_{N})}{\Gamma(a_{0})},\] (10) where \[\mathbf{w}_{N} =V_{N}(V_{0}^{-1}\mathbf{w}_{0}+\Phi(X)^{T}\mathbf{y})\] \[V_{N} =(V_{0}+\Phi(X)^{T}\Phi(X))^{-1}\] \[a_{N} =a_{0}+\frac{N}{2}\] \[b_{N} =b_{0}+\frac{1}{2}(\mathbf{w}_{0}^{T}V_{0}^{-1}\mathbf{w}_{0}+\mathbf{y}^{T} \mathbf{y}-\mathbf{w}_{N}^{T}V_{N}^{-1}\mathbf{w}_{N})\] \[\Phi(X) =(\mathbf{\varphi}(\mathbf{x}_{1})^{T},...,\mathbf{\varphi}(\mathbf{x}_{N})^{T}) ^{T}\]2. In the **classification** task, an approximation of the logistic regression is used, \[p(y_{i}=C_{1}|\mathbf{x},X,W)\approx sign\Big{(}\mathbf{w}_{N}^{T}\mathbf{\varphi}(\mathbf{x}) \Big{)},\] where \(sign(a)=\frac{1}{1+\exp(-a)}\). Thus, the likelihood is approximated by: \[\begin{split} p(\mathbf{y}|X,W)&=\prod_{i=1}^{N}p(y_{i }=C_{1}|\mathbf{x},X,W)^{y_{i}}(1-p(y_{i}=C_{1}|\mathbf{x},X,W))^{1-y_{i}}\\ &\approx\prod_{i=1}^{N}\Big{(}sign\Big{(}\mathbf{w}_{N}^{T}\mathbf{ \varphi}(\mathbf{x}_{i})\Big{)}\Big{)}^{y_{i}}\Big{(}1-sign\Big{(}\mathbf{w}_{N}^{T} \mathbf{\varphi}(\mathbf{x}_{i})\Big{)}\Big{)}^{1-y_{i}}\end{split}\] (11) where, \[\begin{split}\mathbf{w}_{N}&=V_{N}(V_{0}^{-1}\mathbf{w}_{0 }+\Phi(X)^{T}\mathbf{y})\\ V_{N}&=(V_{0}+\Phi(X)^{T}\Phi(X))^{-1}\end{split}\]
3. **Computing**\(\alpha\): the following criteria is used to accept a sample \(\mathbf{\omega}_{j}^{*}\) with probability \(r\): \[r=\min(1,\alpha),\]