esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 2 Method
### Loss Function  
The loss function proposed in this chapter consists of three components: adversarial loss, perceptual loss, and gradient loss [35]. Adversarial loss is the inherent loss function in generative adversarial networks. By incorporating the perceptual loss function, the detailed features of the denoised time-frequency spectrum can be enhanced. The gradient loss function serves to prevent the generator from distorting the edges of the time-frequency signal [36; 37]. The overall loss function is formulated as follows:  
\[\ell=\ell_{\text{adv}}+\alpha\cdot\ell_{per}+\beta\cdot\ell_{grad} \tag{4}\]  
In Equation (4), \(\alpha\) and \(\beta\) represents the coefficients of perceptual loss and gradient loss, respectively. In this paper, we simply set both of them to 1.  
The adversarial loss enables the generator to produce more realistic samples, allowing the discriminator to more accurately distinguish between real and generated samples. For the discriminator, when the input is the time-frequency spectrum of a real signal, the discriminator output should be close to 1, whereas when the input is a time-frequency spectrum generated by the generator, the discriminator output should be close to 0. The standard GAN utilizes the negative log-likelihood loss as the adversarial loss, but this can lead to vanishing gradients. In this work, we follow the principle of the Least Squares GAN (LSGAN) and adopt the least squares loss function for the adversarial loss:  
\[\ell_{adv}=\mathbb{E}_{\text{s}\sim P_{data}(\text{s})}\Big{[}(D(s)-1)^{2} \Big{]}+\mathbb{E}_{\text{y}\sim P_{data}(\text{y})}\Big{[}(D(G(y)))^{2}\Big{]} \tag{5}\]  
For perceptual loss \(\ell_{per}\), the feature extraction module of the discriminator adaptively extracts perceptual features from the time-frequency spectrum:  
\[\ell_{per}=\mathbb{E}_{\text{s}\sim P_{data}(\text{s})}\Big{[}\|\phi_{D}(G( \text{s}_{i}))-\phi_{D}(y_{i})\|_{2}^{2}\Big{]} \tag{6}\]  
In Equation (6), \(\phi_{D}\) represents the feature extraction module.  
While the perceptual loss can effectively enhance the output details of the generator, it can also lead to distortions and artifacts in the signal, causing deformations in the edge regions of the time-frequency spectrum. By computing gradients and comparing the rate of change of elements along the x and y axes, the outlines of signal edges can be reinforced, improving the stability of output details. In this paper, we employ the Sobel operator, using the convolution kernels \(G_{x}\) and \(G_{y}\) defined in Equation (7) to calculate the gradients along the x and y directions:  
Figure 5: Structure of discrimination module.  
\[\begin{split} G_{x}=\begin{pmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{pmatrix}\\ G_{y}=\begin{pmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{pmatrix}\\ grad(s)=\left|Conv(s,G_{x})\right|+\left|Conv(s,G_{y})\right|\end{split} \tag{7}\]  
And the gradient loss \(\ell_{grad}\) is  
\[\ell_{grad}=\left\|grad(s)-grad(\hat{s})\right\|^{2} \tag{8}\]
## 4 Conclusions  
In this study, friction stir welding was performed on high-strength steel sheets with tensile strengths ranging from 590 MPa and 1500 MPa, and their microstructures and strengths were assessed. When the strength of the BM was 980 MPa or higher, the RZ was fully martensitic, whereas evident softening could be observed in the PRZ and TZ. The softening of the PRZ and TZ could be attributed to the formation of tempered martensite,  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Hardness (Hv)**} & \multirow{2}{*}{\
\begin{tabular}{c} **Average Hardness** \\ **in Recrystallized Zone** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Minimum Hardness in** \\ **Partial Recrystallized Zone** \\ **and Tempered Zone** \\ \end{tabular} } \\ \cline{2-5}
**Base Materials** & **FSW** & **Laser** & **FSW** & **Laser** \\ \hline DP590 & 310 & 374 & 230 & 225 \\ \hline DP780 & 332 & 389 & 265 & 252 \\ \hline DP980 & 494 & 480 & 257 & 287 \\ \hline TRIP1180 & 475 & 497 & 295 & 348 \\ \hline MS1300 & 430 & 417 & 247 & 270 \\ \hline HPF1500 & 500 & 493 & 284 & 316 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the microhardness of materials subjected to laser welding [25; 26].  
Figure 11: SEM morphology of the fracture surfaces of (**a**) DP590; (**b**) DP780; (**c**) DP980.  
and it occurred more severely in the FSW process than in the laser welding process, wherein the cooling rate is higher.  
As the strength of the material increased, the joint efficiency and elongation of the weldments decreased. The strain map results showed that for DP590 steel, the stress is concentrated in the BM, and for the other steels, the stress is concentrated in the PRZ and TZ. Because the softening of the PRZ and TZ due to the thermal history during welding deteriorated the mechanical properties, the heat input during welding and the consequent decrease in the hardness in the PRZ and TZ should be carefully managed to suppress the deterioration in the weld strength.  
Investigation, H.Y. and M.K.; Methodology, H.Y. and M.K.; supervision, S.Y., S.H. and C.K.; writing--original draft, H.Y. and M.K.; writing--review and editing, C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008750) and Industrial Strategic Technology Development Program (20002809).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### The Background to Deblurring Algorithms
#### 2.1.3 The Lp_LOGS Model  
Blurred images are generally considered images corrupted by blur kernels and additive noise. They can be represented by the following linear mathematical model:  
\[\mathbf{G}=\mathbf{H}*\mathbf{F}+\mathbf{\eta} \tag{4}\]  
Figure 1: Contour maps of the different norms. (**a**) the L1 norm, (**b**) the L2 norm, and (**c**) the Lp-pseudo-norm.  
where \(G\) represents the degraded image to be deblurred, \(H\) is the blur kernel, and \(\eta\) is the additive noise, specifically the salt-and-pepper noise in this study.  
The application of the Lp_LOGS model to solve \(F\) is an ill-posed inverse problem, which is expressed as:  
\[F=\underset{F}{\text{argmin}}\|H*F-G\|_{p}^{p}+\mu[R_{OGSTV}(F)], \tag{5}\]  
where \(\|H*F-G\|_{p}^{p}\) is the fidelity term, \(R_{OGSTV}(F)\) is the prior term, and \(\mu\) is the balance coefficient used to balance the prior and the fidelity terms.  
The Lp_LOGS model leverages the Lp-pseudo-norm and overlapping group sparse TV regularization term, vastly outperforming the traditional ATV model in terms of deblurring. However, the Lp_LOGS model only considers the overlapping group sparse constraints of the low-order gradient information of the image.  
To mine the prior information of the high-order gradient of an image, the second-order overlapping group sparse TV regularization term was introduced into the Lp_LOGS model. The proposed novel deblurring model is expressed as:  
\[\begin{array}{l}F=\underset{F}{\text{argmin}}\|H*F-G\|_{p}^{p}+\mu_{1}[ \varphi(K_{h}*F)]+\mu_{2}[\varphi(K_{v}*F)]\\ +\mu_{3}[\varphi(K_{h}*K_{h}*F)]+\mu_{4}[\varphi(K_{v}*K_{v}*F)]+\mu_{5}[ \varphi(K_{v}*K_{h}*F)],\end{array} \tag{6}\]  
where \(\mu_{i}(i=1,2,3,4,5)\) represents the balance coefficient, \(K_{h}*K_{h}\) is the second-order horizontal difference convolution kernel, \(K_{v}*K_{v}\) is the second-order vertical difference convolution kernel, and \(K_{v}*K_{h}\) is the second-order mixed difference convolution kernel.  
This model was named Lp_HOGS to represent a deblurring model based on the Lp-pseudo-norm with high-order overlapping group sparsity regularization.
###### Abstract  
The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor \((\rho)\). This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor \(\rho\) (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.  
adaptive control; minimum-variance controller; control penalty factor; self-tuning strategy; induction generator
## 4 Simulation and Discussion  
In this section, the effectiveness and accuracy of the modified SBR-FBSA algorithm proposed in this article are verified by comparison with the multilevel fast multiple algorithm (MLFMA) and the SBR-TSM method proposed in [24] recently. Then, we investigate the impact of various bottom surfaces, slope angles, surface roughness variations, incident azimuth- and pitch-angle variations, as well as the target, on the composite scattering characteristics of the canyon/valley composite rough surfaces.  
All simulation results are statistically averaged across 30 samples in this section. Windows 7 with an Intel(R) Xeon(R) Silver 4110 CPU running at 2.10 GHz and 128 GB of RAM serves as the computing platform.  
Before proceeding with the algorithm verification in Section 4.1, we illustrate the identical simulation setups in Sections 4.2-4.8. The size of the composite rough surface is set to \(L_{x}\times L_{y}=20\) m \(\times\) 60 m, \(l_{x}\times l_{y}=2\) m \(\times\) 2 m, the boundary lines located at \(y=\pm 10\) m. The working frequency is set as 10 GHz; therefore, the wavelength \(\lambda=0.03\) m and the relative permittivity of the terrestrial surface is (3.0, 0.000036) [42], and the water surface'sis (55.96, 34.95) [24]. The missile-like target is 10 m above the bottom of the canyon/valley composite rough surface, with the size of 5.26 m \(\times\) 2.82 m \(\times\) 1.16 m, as shown in Figure 7.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 3 Methodology
### Performance Evaluation  
Datasets utilized in this study were often class unbalanced, with the number of samples representing the non-seismic class being much greater than the number of samples representing the other classes [36]. In this situation, a simple classifier that predicted each sample as the majority class could achieve a high level of accuracy; thus, the total classification accuracy was insufficient to assess performance. As a result, we used the F-measure (F1) to evaluate model performance, which considers the correct classification of each class to be equally important. The F1 score is a metric that considers precision and recall. This is often referred to as the harmonic mean of both. Consequently, class imbalance was addressed by weighting the various classes according to their sample proportions. The Matthews correlation coefficient (MCC) [37], which emphasizes positives in samples, was also employed in this study. The specific formulas for the performance metrics such as F1 score and MCC are defined in Text S3 (Supplementary Materials).  
Furthermore, in this study, receiver operating characteristic (ROC) curves, which are plots of the true positive rate versus the false positive rate, were employed to assess the output quality of the classifier's performance. The ROC curve is often used in binary classification settings to assess the output of a classifier. To extend the ROC curve and ROC area for multiclass classification, the output was binarized, and one ROC curve was drawn and used to evaluate classifier quality per class. In addition, we computed the area under the ROC curve, known as the AUC, which was used to compare various models. In thisstudy, higher AUC values were regarded as better methods for identifying ionospheric perturbations prior to earthquakes.  
Figure 4: The bottom-up network framework architecture of the SafeNet model. 1-D CNN: One-dimensional convolutional neural network; Dropout: drop-out layer; Bidirectional LSTM: bidirectional long short-term memory layer. “Flatten” and “Dense” are the names of the functional layers.  
Finally, to visually illustrate the classification performance of each class, ternary probability diagrams and confusion matrices were used to depict the probability distributions for each input class in the test data, as well as the distribution of predicted and actual values.  
Five state-of-the-art machine learning models were benchmarked for the study task: gradient boosting machine [38], deep neural network (DNN) [39], random forest [40], CNN [41], and LSTM [42] models. These methods were implemented in Python (v. 3.6) with scikit-learn (v. 0.20.0) and Keras (v. 2.3.0). Because the explored models are sensitive to parameter selection, we chose parameters that yielded the best performance using Bayesian hyperparameter tuning, as described above. After the optimal parameters were determined for each method, the performances of the different methods were compared.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 3. Materials and Methods
#### 3.1.1. Generation of Synthetic Odorous Air Mixture  
Nine odorous VOCs commonly cited as key compounds responsible for livestock odor were selected for preparing a synthetic air mixture [2; 3; 4; 5; 6]. These include reduced sulfur compounds (methyl mercaptan, ethyl mercaptan, dimethyl sulfide (DMS), butyl mercaptan), VFAs (acetic, propanoic, butyric and isovaleric acid), and \(p\)-cresol, all very offensive with odor detection thresholds (ODT) at the low ppb levels (Table 3).  
Concentrations of target VOCs were generated by a standard gas generation (SGG) system based on permeation technology, as described elsewhere [31; 53]. A special air dilution system was assembled and tested to generate a wide range of concentrations.
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 6 Conclusions  
This work provided an in depth analysis of the collocated MIMO radar system under the relaxed assumption of instantaneous target states of target and clutter. Specifically, a key performance metric, i.e., the outage probability, is derived for generic settings and then synthesized based on the eigenvalues of clutter and transmitting weight matrices under the ratio of indefinite quadratic forms and by considering the characteristics of the channel environment as circular convolution. The presented solution is for the central Gaussian random variable; investigating the non-central case shall be an interesting extension of this work. We optimized the transceiver waveform under CMM and UMM approaches, which resulted in marked decrease of the probability of outage. Of the sub-routines used,  
Figure 6: Time complexity analysis of sub-routines: (**a**) scatter plot of time in seconds versus \(\gamma\) on \(y\)- and \(x\)-axis, respectively; (**b**) box plot of time in seconds at \(y\)-axis for the three methods.  
the active-set method showed faster convergence and it can be used in the joint optimization of both \(\mathbf{f}\) and \(\mathbf{h}\) as an extension. Moreover, outage probability can be minimized by investigating the covariance shaping based receiver waveform design.  
Although, the work presented in this paper has been applied on a collocated MIMO radar system, it can also be applied on distributed MIMO Radar system with multi-target considerations.  
Conceptualization, A.K.H., M.M. and U.M.A.-S.; methodology, A.K.H., M.M., U.M.A.-S. and M.K.A.; software, A.K.H. and M.K.A.; validation, A.K.H., M.M. and M.K.A.; formal analysis, A.K.H., M.M., U.M.A.-S. and M.K.A.; investigation, A.K.H., M.M., U.M.A.-S. and M.K.A.; resources, A.K.H., M.M., U.M.A.-S. and M.K.A.; writing--original draft preparation, A.K.H. and M.K.A.; writing--review and editing, M.M., U.M.A.-S.; supervision, M.M. and U.M.A.-S.; project administration, M.M. and U.M.A.-S.; funding acquisition, U.M.A.-S. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by Institutional Fund Projects under grant no. (IFIP:625-135-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.  
Not available.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### System Performance Evaluation
#### 2.4.2 Stability Evaluation of Point Cloud Acquisition  
To verify the stability of the system in point cloud data acquisition, statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and structural characteristic indicators (Hausdorff distance) are used for quantitative analysis. Among them, the smaller the Charm distance, the more similar the distribution characteristics of the two point cloud files. The Hausdorff distance calculates the maximum distance from each point in point set \(P\) to the nearest point in point set \(Q\) and the maximum distance from each point in point set \(Q\) to the nearest point in point set \(P\), and finally takes the larger value of these two maximum distances. The smaller the value of the Hausdorff distance, the more similar the structural shapes of the two point cloud files, and vice versa.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 3 Data Analysis
### Components Performance
#### 3.3.1 Exergy Destruction Rates  
An exergy analysis is conducted to identify and quantify the irreversibility sources and the contribution of all components to the overall performance deterioration. The lost opportunities to produce useful work in the main energy-transfer processes occurring throughout the cycle are obtained from Equations (7) to (10), which define the exergy destruction rates, \(\dot{I}_{i}\), in the expander, evaporator, condenser and pump, respectively:  
\[\dot{I}_{\rm exp}\,=\,\dot{m}_{\rm wf}\left[\,h_{\rm exp,in}\,-\,h_{\rm exp, out}\,-\,T_{0}\,\left(s_{\rm exp,in}\,-\,s_{\rm exp,out}\right)\,\right]\,-\,\dot{W}_{ \rm exp}\quad, \tag{7}\]\[\dot{l}_{\rm evap}\,=\,T_{0}\,\left[\,\dot{m}_{\rm wf}\,\left(s_{\rm evap,out}\,- \,s_{\rm evap,in}\right)\,-\,\dot{m}_{\rm hs}\,\left(s_{\rm hs,out}\,-\,s_{\rm hs,in}\right)\,\right]\quad, \tag{8}\]  
\[\dot{l}_{\rm cond}\,=\,-\,T_{0}\,\left[\,\dot{m}_{\rm wf}\,\left(s_{\rm cond,out} \,-\,s_{\rm cond,in}\right)\,-\,\dot{m}_{\rm cw}\,\left(s_{\rm cw,out}\,-\,s_{ \rm cw,in}\right)\,\right]\quad, \tag{9}\]  
\[\dot{l}_{\rm p}\,=\,\dot{W}_{\rm p}\,-\,\dot{m}_{\rm wf}\,\big{[}\,h_{\rm p,out} \,-\,h_{\rm p,in}\,-\,T_{0}\,\left(s_{\rm p,out}\,-\,s_{\rm p,in}\right)\, \big{]}\quad, \tag{10}\]  
where the dead-state temperature, \(T_{0}\), is set equal to that of the inlet cooling water stream: \(T_{0}\,=\,T_{\rm cw,in}\).
# Advanced Hysteretic Model of a Prototype Seismic Isolation System Made of Polymeric Bearings
## 3 Dynamic Oscillatory Test  
The first step in the present research was to perform dynamic oscillatory tests in order to analyze the load-deformation behaviour of PBs under different horizontal excitation frequencies. In order to obtain hysteresis loops, four PBs supporting a concrete slab (dimensions of the concrete base slab are 50 cm \(\times\) 50 cm \(\times\) 7 cm), were mounted on the shaking table platform and subjected to harmonic excitations with gradually increasing amplitudes resulting in different shear strain levels. The experimental testing was carried out at the excitation frequencies of 1 Hz, 2 Hz, and 5 Hz, which correspond to the range of excitation frequencies typical for earthquakes and mining tremors. The response of the system to cyclic horizontal loading was measured using a laser displacement sensor and an uniaxial accelerometer, as indicated by Figure 2.  
Experimentally obtained hysteresis loops for three different excitation frequencies are presented in Figure 3 (it should be mentioned that due to technical restrictions of the shaking table, limited number of hysteresis loops at the excitation frequency of 1 Hz could be obtained). Based on the loops, the equivalent damping ratio, \(\xi\), of the base isolation system was determined using the following formula (see [26]):  
\[\xi=\frac{\Delta W}{2\pi\cdot W} \tag{1}\]  
Figure 2: Base-isolated concrete slab during dynamic oscillatory test.  
where \(\Delta\)W is the energy dissipated in one cycle of motion (the area enclosed by the hysteresis loop) and W is a strain energy induced in a seismic isolation bearing given by:  
\[\mathrm{W}=\mathrm{F}(\mathrm{u})\cdot\mathrm{u} \tag{2}\]  
where u is a maximum shear deflection and F(u) is a lateral force recorded at u. Average values of equivalent damping ratios for hysteresis loops, recorded at three excitation frequencies, are briefly summarized in Table 1.  
Figure 3: _Cont._  
The results obtained from the dynamic oscillatory tests exhibit pronounced hysteretic behaviour of PBs, which confirms high energy dissipation properties of this base isolation system. Moreover, experimentally obtained hysteresis loops indicate that PBs exhibit higher stiffness at lower shear strain levels. Close inspection of Figure 3 shows that with increasing shear deflection, the slope of the hysteresis loop decreases. The same effect, which is often referred to as the stiffness degradation, can also be observed for other seismic isolation devises, such as HDRBs [12]. Additionally, results obtained from this investigation clearly demonstrate that the higher the excitation frequency, the lower the value of equivalent damping ratio, \(\xi\), as indicated by Table 1. The cyclic loading tests indicate that the damping ratio provided by PBs is within the range of about 20-49%, while for the most commonly used base isolators (LRBs, HDRBs, FPBs), the damping ratio is approximately 10-20%.