esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Dynamic Equation of Deployment Mechanism  
The dynamic equation of the mechanical system in Figure 11a during the first deployment stage:  
\[\begin{split}\left[I_{\text{p}}+I_{\text{o}}+\frac{I_{\text{i}} \cos^{2}\gamma\sec^{4}\theta_{2}}{\left(\cos^{2}\gamma\tan^{2}\theta_{2}+1 \right)^{2}}\right]\theta_{2}+\frac{2I_{\text{i}}\sin^{2}\gamma\cos^{2}\gamma \tan\theta_{2}\sec^{4}\theta_{2}}{\left(\cos^{2}\gamma\tan^{2}\theta_{2}+1 \right)^{3}}\theta_{2}^{2}+\frac{c\cos^{2}\gamma\sec^{4}\theta_{2}}{\left(\cos ^{2}\gamma\tan^{2}\theta_{2}+1\right)^{2}}\theta_{2}+\\ \frac{k\cos\gamma\sec^{2}\theta_{2}[\arctan(\cos\gamma\tan\theta_ {2})-\theta_{0}^{*}]}{\cos^{2}\gamma\tan^{2}\theta_{2}+1}=0,\end{split} \tag{12}\]  
where \(\theta_{0}^{*}=\arctan(\cos\gamma\tan\theta_{0})\).  
The exact solution of Equation (25):  
\[\begin{split}\theta_{2}(t)&=\theta_{0}\!\left[- \frac{\zeta\text{e}^{-\zeta\omega_{\text{n}}t}\sinh\!\left(\sqrt{\zeta^{2}-1} \omega_{\text{n}}t\right)}{\sqrt{\zeta^{2}-1}}-\text{e}^{-\zeta\omega_{\text{ n}}t}\cosh\!\left(\sqrt{\zeta^{2}-1}\omega_{\text{n}}t\right)+1\right],\\ \text{where }\omega_{\text{n}}=\sqrt{k^{*}/I_{\text{p}}},\,\zeta= \frac{c^{*}}{2\sqrt{k^{*}/I_{\text{p}}}},\,k^{*}=k\cos^{2}\gamma\text{ and }c^{*}=c\cos^{2}\gamma.\end{split} \tag{13}\]
## 4 Empirical Results
### \"Porter Effect\" of ETS Pilot Policy  
Based on the DID model in and Equations (1) and (2), we evaluate the effects of the ETS pilot policy on carbon emissions and GDP. In Table 5, m1 indicates adding the provincial and time-fixed effect model without control variables. The results show that the interaction term is negative, but the coefficient is not significant. After adding the control variables, the m2 interaction term's coefficient is significant, and the R squared is significantly increased from 0.235 to 0.946, which indicate that the initial carbon trading pilot policy mechanism is not sufficiently perfect and that it is vulnerable to other policies. Therefore, it is necessary to introduce important control variables to make the results more robust. m2 shows that the policy can reduce carbon emissions by 10.0%. The analysis of the control variables shows that the coefficients of some of the control variables are significant. Population, carbon intensity, and per capita GDP have significant positive effects on carbon emissions; and the environmental pollution control intensity, research structure, and research intensity have negative effects.  
The following analyzes the economic effects of the policy. m3 shows that the interaction coefficient without the control variables is not significant, and m4 shows that the overall result is significantly positive after adding the control variables. The R squared increases from 0.532 to 0.957, indicating that the ETS pilot policy can achieve economic effects. Among the variables, the capital stock, employment, and energy consumption factors all have significant positive effects. This study concludes that the ETS pilot policy can achieve the \"Porter effect,\" which is basically consistent with [14; 40; 41; 42]. [PERSON] et al. argued that the policy cannot achieve the \"Porter effect\" in the short term because the economic effect is not significant [16], which is possibly due to starting the experiment in different years and the different calculation methods of the control variables.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees  
Dependability uses notions like availability and reliability to assess the system's operability based on whether a failure has occurred or not [54]. Performance determines a system's capacity to function without faults. The idea of performability was proposed in an effort to close the performance gap with reliability [20]. Performability is a concept that evaluates the probability that a system will operate at a certain level of performance even in the event of a system failure. Performability is the likelihood of exhibiting the system's measurable performance level \(B\) with respect to the achievement level (\(A\), (\(B\in A\))) of system \(S\). Performability, \(Pr(B)\) is stated as follows in Equation (1).  
\[Pr(B)=P(\omega|Y_{S}(\omega)\in B) \tag{1}\]  
where \(Y_{S}\) is the performance of system \(S\). In this paper, the following specifications are introduced and used to define the system's accomplishment level.  
* A1: A state where the mission can be executed and executed continuously even in the event that a specific failure arises in the multiplexed part;
* A2: Normal mission performance with failures; a further failure necessitates mission abortion;
* A3: A state where executing missions is no longer possible.  
An AUV as a whole can be referred to as a multi-state system if each subsystem is thought of as a single state. Based on the status of each subsystem, which has a certain performance level and probability, the expected instantaneous performance (EIP) can be computed as follows in Equation (2).  
\[EIP(t)=\sum_{i}g_{i}\cdot P_{i}(t) \tag{2}\]  
where \(g_{i}\) and \(P_{i}\) are the performance and probability of the system in state \(i\), respectively. This can also be called the expected performance utility level [55] or expected reward rate at time \(t\)[56; 57; 58]. In order to assess the system's performability for different missions, a phased FT that takes failure and performance into consideration was designed in this paper. The performability of each sub-FT is evaluated based on the FT for the currently executed mission. The performability of each component is integrated through a UGF to calculate the performability of the entire system. Based on the FT structure, the ranking of each performance is defined and the weight is calculated. The weighted arithmetic mean of performance based on the weights is calculated. The EIP of the system is evaluated using performability. The overall process of the proposed technique is shown in Figure 2 below.  
Figure 2: Performability evaluation process of the proposed method.
## 1 Introduction
### Definition of Novelty and the Research Hypotheses  
From the previous short literature overview, complex ML models such as DNN have been used in CDMS detectors' investigation. Although the estimation results were high, the problem is that these algorithms require substantial computational resources. Another problem is that these types of ML methods cannot be easily expressed as an equation.  
The authors present a novel approach through the use of genetic programming-symbolic regression (GPSR), which was applied to determine equations that can accurately reconstruct the particle interaction locations in the SuperCDMS. A public dataset [12] provided by a team from the University of Minnesota was used in the research. In comparison to the reviewed research, the research questions can be posed as follows:  
* Can an SE be obtained using GPSR that can accurately reconstruct the locations of the interactions in SuperCDMS detectors?
* Is it possible to obtain a set of robust SEs using GPSR with randomly tested hyper-parameters and validated through k-fold cross-validation that can reconstruct the locations of the interactions in the SuperCDMS with high accuracy?
* Is it possible to achieve even higher estimation accuracy in the reconstruction of the locations' interactions by combining multiple SEs that were obtained from different GPSR executions?
* Are all input variables required as model inputs to accurately reconstruct the interaction locations?The presented paper consists of the following sections, i.e., the Materials and Methods, Results, Discussion, and Conclusions. In the Materials and Methods, the detailed dataset description is provided with statistical analysis following the description of the research methodology. In this section, the used method is described. In the Results Section, the best set of SEs obtained after five-fold cross-validation (5-CV) is shown with GPRS hyperparameters used to obtain them. Within the Discussion, the obtained models and results of the statistical analysis are further presented and discussed. Based on the hypotheses, presented results, and discussion, the Conclusions are given in the final section. Besides that, the Conclusions Section provides the pros and cons of the proposed method and possible directions for future work.
###### Abstract  
In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.  
EMD; EEMD; cyclostationary; gearbox; wind turbine; condition monitoring
# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
###### Abstract  
This study focuses on improving the objective alignment of image quality assessment (IQA) algorithms with human visual perception. Existing methodologies, predominantly those based on the Laplacian of Gaussian (LoG) filter, often neglect the impact of color channels on human visual perception. Consequently, we propose a full-reference IQA method that integrates multi-channel visual information in color images. The methodology begins with converting red, green, blue (RGB) images into the luminance (L), red-green opponent color channel (M), blue-yellow opponent color channel (N) or LMN color space. Subsequently, the LoG filter is separately applied to the L, M, and N channels. The convoluted components are then fused to generate a contrast similarity map using the root-mean-square method, while the chromaticity similarity map is derived from the color channels. Finally, multi-channel LoG filtering, contrast, and chromaticity image features are connected. The standard deviation method is then used for sum pooling to create a full-reference IQA computational method. To validate the proposed method, distorted images from four widely used image databases were tested. The evaluation, based on four criteria, focused on the method's prediction accuracy, computational complexity, and generalizability. The Pearson linear correlation coefficient (PLCC) values, recorded from the databases, ranged from 0.8822 (TID2013) to 0.9754 (LIVE). Similarly, the Spearman rank-order correlation coefficient (SROCC) values spanned from 0.8606 (TID2013) to 0.9798 (LIVE). In comparison to existing methods, the proposed IQA method exhibited superior visual correlation prediction accuracy, indicating its promising potential in the field of image quality assessment.  
image quality assessment; multi-channel Laplacian of Gaussian filtering; contrast similarity; chromaticity similarity; pooled standard deviation
## 4 Conclusions  
XRD analysis revealed that PS chains cannot intercalate the hydrophilic interlayer space of the NaMt, which was expected. The same happened with the partially exfoliated OO@NaMt nanocomposite structure. The modified PScoMa hydrophilic chains partially intercalated the NaMt and the OO@NaMt clays. In the case of OrgMt clays, the OO fully intercalated such clays and secondary intercalation took place when the PS or PScoMa matrixes intercalated the OO@OrgMt nanostructure.  
FTIR measurements show that the higher interaction between clay and PS or PScoMa matrix existed in the cases of OrgMt compared to the cases of NaMt.  
Tensile tests show that the mixing of OrgMt with the PS or PScoMa increased the Young Modulus (E) as well as the tensile stress (\(\sigma\)), while the addition of NaMt decreased these properties.  
The elongation at break values decreased in both cases of clay addition, i.e., NaMt or OrgMt. Nevertheless, this could be balanced with the addition of OO which increases this property.  
DMA tests show that the mixing of coMa, OO, NaMt, and OrgMt with PS matrixes increased the storage modulus in both temperatures of 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. In all cases, the glass transition temperature remained in the region of 96-116 \({}^{\circ}\)C.  
Oxygen and Water-Vapor barrier tests show that the samples which exhibited the lowest permeability were PS3 OO@OrgMt and the PSOO@OrgMt.  
Antioxidant activity tests show that the OrgMt based films exhibited the higher antioxidant activity because of the higher OO content. This activity increased with the increase of this content.  
Antimicrobial activity tests show that even though the pure OO exhibited strong antimicrobial activity, no significant antimicrobial effect was observed in all cases of PS/clay films.  
The conclusion of this work is that the PScoMAxOO@NaMt and PSxOO@OrgMt films are promising candidate materials for future packaging applications as aroma active and/or antioxidant active films which could protect food products from lipid oxidation and prevent undesirable food odours.  
The synthesis experiments design, A.E.G. and C.E.S.; Characterization measurements carrying out and interpretation, A.E.G. and C.E.S.; Paper writing, A.E.G. and C.E.S.; The overall evaluation of this work, A.E.G. and C.E.S.; Experimental data analysis and interpretation, A.E.G., C.E.S., N.E.Z., A.A. and C.P.; The XRD, FTIR, OTR, tensile measurements, antioxidant activity, and WVTR experimental measurements carrying out, A.E.G. and C.E.S.; DMA experiment carrying out D.M. and A.K.-M.; Antimicrobial activity tests carrying out E.K. and V.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the funding programme \"MEDICUS\", of the University of Patras.  
The authors declare no conflict of interest.
# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 2 Conventional Sound Event Detection
### Global Pooling  
To make predictions for audio tagging, the 2D segmentation map is compressed to a scalar value, which is generally interpreted as a probability value of the occurrence of a specific event in the input recording. The occurrences do not have to be mutually exclusive, so each segmentation map is handled independently. Global max pooling (GMP) [27] is defined as follows:  
\[\mathrm{GMP}(\mathbf{H}_{c})=\max_{t,f}\mathbf{H}_{c}(t,f)\,, \tag{1}\]  
where \(\mathbf{H}_{c}\) is the segmentation map for class \(c\) and \(t\) and \(f\) are the indices of the time and frequency axes, respectively. The drawback of GMP is that it is sensitive to outliers and more affected as the segmentation map size increases. To overcome this problem, global average pooling (GAP) [27; 28] provides much more stable prediction results, which is computed as  
\[\mathrm{GAP}(\mathbf{H}_{c})=\frac{1}{TF}\sum_{t=1}^{T}\sum_{f=1}^{F}\mathbf{H }_{c}(t,f)\,. \tag{2}\]  
The GAP is less sensitive to outliers, but when an event occurs sparsely, the activation of the event becomes too small to be detected as having occurred. One of the efficient pooling methods that balances outlier robustness and the sparsity problem is global weighted rank pooling (GWRP) [18]. This GWRP operation is defined as follows:  
Figure 4: Postprocessing procedures for audio tagging and sound event detection. The spectro-temporal 2D audio feature map, denoted by \(\mathbf{X}\), is converted to the 2D segmentation map of the same size (\(\mathbf{H}_{i}\)) and 1D class prediction vector of length \(C\) (the number of event classes) to compute the prediction loss for audio tagging and model training. The segmentation maps are converted to \(C\) detection maps of length \(T\) (time), and thresholding is performed to find the onset and offset of the sound events (\(\mathbf{y}_{i}\)).  
\[\mathbf{h}_{c} = \underset{t,f}{\text{downsort}}(\mathbf{H}_{c})\] \[\text{GWRP}(\mathbf{H}_{c}) = \frac{1}{\sum_{i=1}^{TF}d^{i-1}}\sum_{i=1}^{TF}d^{i-1}\mathbf{h}_{c }(i)\;, \tag{3}\]  
where the function \"downsort\" sorts the 2D input in descending order to generate a 1D sorted list of the whole elements, so \(\mathbf{h}_{c}^{i}\) is the \(i\)th largest value in \(\mathbf{H}_{c}\) with 2D values flattened to a 1D vector \(\mathbf{h}_{c}\). A hyperparameter \(0\leq d\leq 1\) is a decaying weight enabling small activations to contributed less to the computation of the pooling output. This is a generalized weighted pooling, which is equal to GAP for \(d=1\) and GMP for \(d=0\). GWRP is good for a weakly supervised SED task because GAP overestimates and GMP underestimates the tagging result [16]. However, GWRP also has a drawback that the computation speed is very slow because the sorting must be preceded by the calculation of the weight, and it is hard to find the proper hyperparameter \(d\).
## 4 Simulation Results and Discussions
### Results and Discussions
#### 4.3.2 Evaluation Result of the Delay-Aware on the Adaptive Slicing System
When the current delay, \(D_{i}\), of the mMTC requests approaches its maximum delay, \(MTD_{i}\) the the value of \(MTD_{i}-D_{i}\) will become very small and will result in a very low ASP value, which will increase its service priority. Therefore, the DA-ASA achieves greater fairness between the slices by increasing the performance of the mMTC slice. In addition, it adds a slight enhancement to performance of the URLLC compared to ASA. Figure 17: Average waiting time of network slices vs. total offered traffic loads (Experiment 2). Figure 18: TBP of system vs. total offered traffic loads (Experiment 2). The results indicate that the addition of the \(MTD_{i}\) factor in the DA-ASA serves an additional role in enhancing QoS requirements for the URLLC and mMTC slice. Moreover, this enhancement comes at the cost of a slight degradation in eMBB performance compared to ASA. However, this improvement does not lower the performance measures of eMBB slice below the mMTC slice' performance. the performance measures of eMBB slice' traffic is still better than that of mMTC slice' traffic using both schemes. Additionally, the ASP value in both systems maintain slice isolation by preventing traffic from one slice from adversely affect the service of other slices, particularly in the case of URLLC slice.
## 3 Application of Drive-by Methodology on the Railway Track Subsystem
### Rail Condition-Based Assessment
Based on experimentally validated models, the authors performed simulations of the wheel-rail noise for different corrugation levels and applied the Wavelet Packet Transform (WPT) to decompose the acoustic signal. The energy levels of relevant parts of the decomposed signal regarding corrugation detection were used to assess the corrugation amplitude. The simulated operational conditions were then used for fitting a KSM model establishing a relationship between acoustic energy, corrugation amplitude, and corrugation wavelength. The model was validated based on field tests, and the predicted amplitudes were compared with measured ones, resulting in an amplitude relative error of 10.5%. [PERSON] et al. [56] demonstrated that it is also feasible to detect corrugations from noise measured inside the car body of a high-speed train. Based on experimental investigations, it was concluded that corrugations are usually correlated with noise frequencies between 400 and 700 Hz. A corrugation indicator was then proposed based on the scale-averaged wavelet power, computed for the aforementioned frequency range, and the wavelength was derived from the PSD of the noise measured at the track sections with corrugation. A comparison with traditional methods showed that the proposed methodology can accurately identify corrugation. The possibility of installing the microphone inside the car body is very attractive, since the sensor can be very well protected; however, its performance in more general conditions, such as a train full of passengers talking, still needs to be demonstrated. One aspect hardly addressed in most works involving damage to rails is the comparison of the performance of different damage features against various interferences. [PERSON] et al. [57] conducted parametric studies comparing the influence of position error from the GPS sensor, vehicle damping, and vehicle natural frequency on the identification accuracy achieved, using a data-driven methodology and four different damage features: (i) temporal-frequency, (ii) spatial-frequency (iii) spatial amplitude, and (iv) signal-energy (spatial squared amplitude). Some of the results of these studies are presented in Figure 9 for the identification of a broken rail. As can be seen, the signal energy was the most accurate feature among all the tested conditions. In addition, from this figure it is also possible to conclude that the position error is the parameter that most impacts the results and the signal-energy (spatial squared amplitude) feature is quite immune to this effect. This finding reaffirms the need to have reliable position systems for the success of data-driven approaches. [PERSON] et al. [58] demonstrated that the squared amplitude computed from the signal's envelope derived from the Hilbert transform is also a very powerful feature for rail damage identification.