esa-sceva/satcom-chunk-collection huggingface dataset:
# Fibers Effects on Contract Turbulence Using a Coupling Euler Model
## 2 Coupling Models and Methods
### Main Numerical Step for Coupling Simulation  
The fluid-solid coupling Euler model with fiber additional stress terms can achieve the numerical solutions of the whole computational domain by using the turbulence solver of Fluent. The main calculation steps are as follows:  
1. Equations (15), (17), (20) and (23)with \(Q_{i}^{V}=Q_{ij}^{R}=S_{ij}^{R}=Q^{k}=S^{k}=Q^{S}=S^{S}=0\), are solved to get \(\overline{V_{i}}\), \(\overline{V^{\prime}_{i}V^{\prime}}\), \(k\) and \(\varepsilon\) of Newtonian fluid.
2. Equation (1) is solved by Equations (6)-(13) to get \(f(x,\varphi)\).
3. Substitute \(f(x,\varphi)\) into \(a_{ijmn}=\int p_{i}p_{j}p_{m}p_{nf}(x,\varphi)d\varphi\), \(a_{ij}=\int p_{i}p_{j}f(x,\varphi)d\varphi\) to get \(a_{ijmn}\) and \(a_{ij}\).
4. Calculate \(\mu_{f}\) with the fiber geometry and concentration parameters.
5. Substitute \(\mu_{f}\), \(a_{ijmn}\), \(a_{ij}\), \(\overline{V^{\prime}_{i}V^{\prime}_{j}}\), \(k\)\(\varepsilon\) etc. to Equations (15), (17), (20) and (23) with Equations (16), (18), (19), (21), (22), (24) and (25) and update \(\overline{V_{i}}\), \(\overline{V^{\prime}_{i}V^{\prime}}\); \(k\) and \(\varepsilon\) of suspension flow after iterative calculation.
6. Iteration ends if convergence, otherwise return to step (2) through (5).
###### Abstract  
Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used in brain-computer interface (BCI) systems to identify a participant intent in controlling external devices. However, due to a series of reasons, including low signal-to-noise ratios, there are great challenges for efficient motor imagery classification. The recognition of left and right hand MI-EEG signals is vital for the application of BCI systems. Recently, the method of deep learning has been successfully applied in pattern recognition and other fields. However, there are few effective deep learning algorithms applied to BCI systems, particularly for MI based BCI. In this paper, we propose an algorithm that combines continuous wavelet transform (CWT) and a simplified convolutional neural network (SCNN) to improve the recognition rate of MI-EEG signals. Using the CWT, the MI-EEG signals are mapped to time-frequency image signals. Then the image signals are input into the SCNN to extract the features and classify them. Tested by the BCI Competition IV Dataset 2b, the experimental results show that the average classification accuracy of the nine subjects is 83.2%, and the mean kappa value is 0.651, which is 11.9% higher than that of the champion in the BCI Competition IV. Compared with other algorithms, the proposed CWT-SCNN algorithm has a better classification performance and a shorter training time. Therefore, this algorithm could enhance the classification performance of MI based BCI and be applied in real-time BCI systems for use by disabled people.  
motor imagery (MI); electroencephalogram (EEG); simplified convolutional neural network (SCNN); continuous wavelet transform (CWT) +
Footnote †: journal: Applied Sciences
## 4 SMART DShot: ML-Enhanced Architecture
### System Architecture Overview  
The adaptive feedback mechanism in Figure 4 provides three critical benefits that distinguish our approach from static systems:  
Real-time Algorithm Optimization: Performance metrics from the Enhanced ESC Output continuously update algorithm weights in the HATC system. This enables dynamic selection of the most appropriate algorithm (KFTC, RLSTC, or FLTC) based on current error characteristics, achieving the superior 31.01% success rate of HATC compared to individual algorithms (24.51-29.34%).  
Attack Pattern Learning: The feedback loop enables the system to adapt to attack signatures identified by the Anomaly Detector. This information updates the algorithm selection criteria, providing the adaptive capability that static systems lack. The continuous learning process ensures the system evolves faster than attack patterns.  
Environmental Adaptation: Operating conditions are continuously monitored through the feedback path, allowing the system to maintain optimal performance across the environmental variations tested in our 16 operational scenarios. This adaptation is crucial for maintaining performance across temperature variations (\(-40\)\({}^{\circ}\)C to \(+85\)\({}^{\circ}\)C), voltage fluctuations, and EMI conditions.  
This closed-loop architecture fundamentally differentiates SMART DShot from open-loop security systems, enabling proactive rather than reactive protection.
## 4 Vertical Spring Design for Vertical Seismic Isolation Device
### Helical Coil Spring Design  
In this study, a helical coil spring, which is widely used in industry, was selected as another spring type to provide vertical stiffness. The main design variables that determine the stiffness characteristics of a helical coil spring are the coil diameter, section diameter, and number of turns, as shown in Figure 9.  
In above figure, \(L_{\text{f}}\) and \(L_{\text{a}}\) indicate the free length and assembled length with compression force, respectively.  
The relationship between the applied force (\(F\)) and the total stretch (\(y\)) of a helical coil spring in Figure 9 can be approximately described as follows [24]:  
\[y=\frac{F\big{(}8D^{3}N_{a}\big{)}}{d^{4}G} \tag{4}\]  
where \(D\), \(d\), \(N_{a}\), and \(G\) represent the helical coil diameter measured from spring axis to center of section, diameter of circular section, number of active turns, and shear modulus of the material, respectively.  
As the helical coil spring has relatively less rigidity than the disc spring, it is recommended to install the springs in parallel. In this study, four helical coil springs, which have a spring diameter \(D\) = 88 mm and coil section diameter \(d\) = 12 mm, are considered for a vertical stiffness design corresponding to vertical design load of 10 kN.  
Table 7 presents the results of design summary for the set of helical coil springs that satisfies the design VIF.  
As shown in the above table, as the design VIF decreases, the number of coil turns required for the spring stiffness increases rapidly, which leads to a great increase in the total height of the spring. In this study, the design VIF of 2.5 Hz-3.0 Hz was selected to be an appropriate total height of the helical coil spring for substanti  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design** & **Required** & **Required** & **Total Height 1** & **Total Shut** & **Static Disp.** \\
**VIF** & **Stiffness** & **Number of** & **(mm)** & **Disp.** & **for 10 kN** \\
**(Hz)** & **(kN/m)** & **Disc Spring 1** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1.0 & 39.5 & 352 & 2182 & 1408 & 253.3 \\
2.0 & 157.9 & 88 & 546 & 352 & 63.3 \\
2.5 & 246.7 & 58 & 360 & 232 & 40.5 \\
3.0 & 355.3 & 40 & 248 & 160 & 28.1 \\
3.5 & 483.6 & 30 & 186 & 120 & 20.7 \\
4.0 & 631.7 & 22 & 136 & 88 & 15.8 \\
5.0 & 987.0 & 16 & 99 & 64 & 10.1 \\ \hline \hline \end{tabular}  
* In case of a serial stack.  
\end{table}
Table 6: Design summary of stacked disc springs for various design VIF.  
Figure 9: Configuration and dimensional parameters for helical coil spring.
# Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns
## 3 Results and Discussion of Key Issues in Application Scenarios
### Ground-Based Observation in Single-Antenna Pattern
In addition, different soil types also lead to variations in the signal's penetration distance. Therefore, the model described in Section 2.3 can be used to determine the relative depth of signal detection.
###### Abstract  
The photochemical reflectance index (PRI) and red-edge region of the spectrum are known to be sensitive to plant physiological processes, and through measurement of these optical signals it is possible to use non-invasive remote sensing to monitor the plant photosynthetic status in response to environmental stresses such as drought. We conducted a greenhouse experiment using _Quercus__i__lex_, a Mediterranean evergreen oak species, to investigate the links between leaf-level PRI and the red-edge based reflectance ratio (R690/R630) with CO\({}_{2}\) assimilation rates (\(A\)), and photochemical efficiency (F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{M}}\) and Yield) in response to a gradient of mild to extreme drought treatments (nine progressively enhanced drought levels) and corresponding recovery. PRI and R690/R630 both decreased under enhanced drought stress, and had significant correlations with \(A\), F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{M}}\) and Yield. The differential values between recovery and drought treatments of PRI (\(\Delta\)PRIrecovery) and R690/R630 (\(\Delta\)R690/R630 recovery) increased with the enhanced drought levels, and significantly correlated with the increases of \(\Delta A_{\mathrm{recovery}}\), \(\Delta\)F\({}_{\mathrm{V}}\)/F\({}_{\mathrm{Mrecovery}}\) and \(\Delta\)Yieldrecovery. We concluded that both PRI and R690/R630 were not only sensitive to enhanced drought stresses, but also highly sensitive to photosynthetic recovery. Our study makes important progress for remotely monitoring the effect of drought and recovery on photosynthetic regulation using the simple physiological indices of PRI and R690/R630.  
chlorophyll fluorescence (ChIF); drought; Mediterranean; photochemical reflectance index (PRI); photosynthesis; R690/R630; recovery +
Footnote †: journal: JOTA
## 3 Assignment and Results  
The analysis of the pure rotational spectra in the microwave region was carried out using Watson S-reduced effective Hamiltonian [20]. Entire observed transitions were fitted and assigned using the \(I^{\mathrm{r}}\) representation in the Pickett SPFIT/SPCAT (spectral fitting analysis software) [21]. The final fitting results were reformatted to convert SPFIT errors to standard errors [22]. All of the computational and  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **MP2/cc-pVDZ** & **B3 LYP/cc-pVDZ** \\ \hline A(MHz) & 1666.94 & 1716.36 \\ B(MHz) & 410.30 & 405.16 \\ C(MHz) & 349.82 & 345.92 \\ I. a(amu-Å\({}^{2}\)) & 303.18 & 294.45 \\ I. b(amu-Å\({}^{2}\)) & 1231.72 & 1247.36 \\ I. c(amu-Å\({}^{2}\)) & 1444.68 & 1460.98 \\ P. a(amu-Å\({}^{2}\)) & 1186.61 & 1206.95 \\ P. b(amu-Å\({}^{2}\)) & 258.07 & 254.03 \\ P. c(amu-Å\({}^{2}\)) & 45.11 & 40.42 \\ \(\mu_{\mathrm{b}}\)(Debye) & 3.20 & 2.82 \\ \(\Delta\)(amu-Å\({}^{2}\)) a & \(-90.22\) & \(-80.83\) \\ kappa & \(-0.91\) & \(-0.91\) \\ \hline \end{tabular} \({}^{\mathrm{a}}\Delta=l_{\mathrm{c}}-l_{\mathrm{a}}-l_{\mathrm{b}}\).  
\end{table}
Table 1: The computational rotational constant, moment of inertia, planar moment of inertia, dipole moment and asymmetric parameter of benzophenone.  
Figure 5: Optimized global minimum geometries for benzophenone on the MP2/cc-pVDZ theory level.  
experimental studies have proven that benzophenone in the ground state is of C\({}_{2}\) symmetry, but with the two phenyl groups in a paddle-wheel-like orientation [8; 9; 23; 24]. In that case, the permanent electric dipole moment of benzophenone lies along the \(b\)-principle axis allowing transitions obeying \(b\)-type selection rules (\(\omega\leftrightarrow\omega e\) and \(\omega e\leftrightarrow\omega\), \(e\) for even \(o\) for odd values of the quantum numbers \(K_{a}\) and \(K_{c}\)) [25]. The assignment of the frequency transition lines in the 2-8 GHz range was assisted by the rotational constants fitted from the previous microwave and mm-wave experimental data, as shown in Figure 3. The top spectrum with 1 GHz bandwidth showed a strong leaking LO signal at the center, 6.6 GHz, with a portion displaying assigned rotational transitions in Figure 4. The bottom trace in Figure 3 simulated by using the parameters from the [PERSON] group [8] agreed well with our experimental data, especially in frequency. A total of 138 new \(b\)-type transitions were assigned in the 2-8 GHz range with \(J_{max}=23\) and \(K_{a\ max}=7\). The newly assigned transitions, along with the microwave data measured by the [PERSON] group between 8 and 14 GHz [8], were fitted and the obtained spectroscopic constants are given in Table 2. The root-mean-square (RMS) deviation \(\sigma\) of the combined fit is 2.6 kHz, with the typical linewidths of about 150 kHz. All the newly assigned rotational transitions of benzophenone in 2-8 GHz are provided in Supplementary Materials.
## 2 Materials and Methods  
### Study Design and Population  
The study group consisted of 40 patients diagnosed with myocardial infarction hospitalized at the Department of Cardiology and Internal Medicine at University Hospital No. 1 in Bydgoszcz. Upon admission, all patients underwent a comprehensive series of basic laboratory tests to assess the progression of inflammation, cardiac function, lipid profiles, and protein levels, following the hospital's established standard procedures. To ensure the accuracy and relevance of our findings, blood samples were collected explicitly within the first 24 h post-admission, a critical period for assessing the acute phase of MI and its impact on biochemical markers. This timing was meticulously chosen to capture the immediate physiological responses and biochemical changes triggered by the event. During this pivotal phase, a portion of the blood samples were designated for the analysis of AOPP and uric acid, allowing for a comprehensive evaluation of oxidative stress and its correlation with the pathophysiological processes of MI. Patients with any stage of chronic kidney disease (CKD) were excluded from the study to mitigate potential confounding effects on serum uric acid and AOPP levels, as CKD significantly influences the body's ability to process and eliminate these compounds. Similarly, individuals presenting with abnormal creatinine levels were also excluded, given that creatinine is a crucial marker of renal function, and its abnormal levels could indicate underlying renal impairment not previously classified as CKD. This exclusion criterion was essential for ensuring the homogeneity of the study population and the validity of the association between uric acid,AOPP levels, and myocardial infarction outcomes. The complete list of inclusion/exclusion criteria is presented in Table 1.  
The control group consisted of 30 healthy volunteers who routinely donated blood at the local blood donation center (Bydgoszcz, Poland). A licensed physician rigorously assessed the determination of a participant's health status. Based on these comprehensive health evaluations, the decision to forward a patient's blood sample for inclusion in our study was made solely by the assessing physician. We established a control group without coexisting diseases to examine the relationship between uric acid and AOPP. This decision aimed to minimize confounding variables, such as age-related changes and unrelated health conditions that could influence uric acid and AOPP levels, potentially masking the specific interaction we sought to examine. The protocol of the study was approved by the Ethics Committee of the Nicolaus Copernicus University in Torun, Collegium Medicum in Bydgoszcz (approval number KB 406/2010). Each patient provided written informed consent to participate in the study before recruitment.
## 3 FCI-VMD Method Based on BAS
### Proposed Method  
The FCI-VMD method proposed in this paper takes the weighted frequency index as the objective function. The signal-noise separation point is determined by the fuzzy entropy. Since BAS is an algorithm for finding the minimum value, according to Equation (24), the parameter optimization process can be described as follows:  
\[\left\{\begin{matrix}\Phi(X_{r}),\Phi(X_{l})=\min\{FCI\}\\ k=3,\;4,\ldots,\;10\end{matrix}\right.. \tag{25}\]  
where \(FCI\) is the objective function to be optimized and \(k\) is the parameter to be optimized for VMD. Generally speaking, \(k\) is an integer between 3 and 10. The specific steps are as follows:  
1. Set the VMD initial parameters of the original signal, \(f(t)\), and set the initial parameters of BAS, such as the range of parameter \(k\), number of iterations, and iteration step size.
2. Decompose the AE signal using VMD, and calculate the \(\Phi(X_{r})\) and \(\Phi(X_{l})\) of all modes.
3. Determine if an iteration termination condition is reached: If \(\Phi(X_{r}),\Phi(X_{l})>FCI\), then \(k=k+1\), and continue the iteration; else, \(k=k-1\), and stop the iteration.
4. Decompose the signal again with the optimized parameters.
5. Determine the antennae for the decomposed signal by calculating the fuzzy entropy; then, reconstruct the effective signal.
# Laser-Ultrasound-Based Grain Size Gauge for the Hot Strip Mill
## 4 Conclusions  
A new online grain size gauge based on laser ultrasonics has been installed in a hot strip mill and presented. It is a unique solution for austenite grain-size measurement during hot rolling with:  
* Grain-size calculation performed with a new reference object-free model/algorithm utilizing the generation event as a reference spectrum.
* The model used for grain-size calculation is based on 52 samples that are evaluated with EBSD/MPGR and is correlated with more than 23,000 LUS measurements. The correlation coefficient R\({}^{2}\) of this model is 0.8.
* Calculated grain sizes from online LUS measurements provide the ability to detect small variations in austenite grain size along the strip in situ.  
This gauge creates new possibilities for the process control in the mill by enabling new information for feedback and feedforward control by optimizing the grain size. The gauge is also expected to accelerate the material development of new alloying concepts by rapid prototyping facilitated by the new grain-size data.  
Conceptualization, M.M., B.H., L.B. and H.S.; Data curation, M.M., A.J., J.L. and S.H.; Formal analysis, M.M.; Funding acquisition, M.M. and H.S.; Investigation, M.M., A.J., J.L. and S.H.; Methodology, M.M., A.J., B.H. and L.B.; Project administration, M.M., A.J., L.G., L.B., H.S. and M.B.; Resources, L.B., H.S. and M.B.; Software, M.M. and S.H.; Supervision, M.M., L.G., H.S. and P.L.; Validation, M.M. and L.B.; Visualization, M.M.; Writing--original draft, M.M.; Writing--review & editing, M.M., A.J., B.H., L.B. and M.B. All authors have read and agreed to the published version of the manuscript.  
This work was partially financed by the strategic innovation program Metallic Materials, by Vinnova, the Swedish Energy Agency, and Formas grant no. 2018-03916 and 2015-03474, as well as the European Union's Research Fund for Coal and Steel (RFCS) research program under grant agreement no. RFCS-2018-847296.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Visually presenting incorrect or unexpected decisions of the model with Grad-CAM provides the opportunity to evaluate the validity of the model's accuracy and make corrections when necessary. At the same time, by seeing which visual features the model focuses on, it can be understood how decisive these features are. Grad-CAM visualizes the decisions of a pre-trained transfer model,  
Figure 1: X-ray images of the non-defective tires. allowing interpretation of how well the model can generalize across different tasks. In the context of tire defect detection, transparency is of utmost importance in establishing trust among quality control operators, regulatory bodies, and other stakeholders. Understanding how and why an AI system arrives at a particular defect classification is essential for the acceptance and adoption of such systems in the tire manufacturing industry [8]. XAI serves as a valuable tool for tire quality control professionals by providing them with insights into the rationale behind the AI system's defect determinations [13]. By offering interpretable explanations through Grad-CAM, XAI empowers operators to validate and verify the decisions made by AI models, thereby aiding them in making well-informed judgments about the tire's quality. This support not only improves the overall efficiency of defect detection but also provides operators with a deeper understanding of the factors that influence the AI system's output. * This paper introduces a novel approach to detecting foreign objects in tire radiographic X-ray images using an Xception-based deep learning network. * The proposed method demonstrates high efficiency in identifying foreign objects in defective tire images, with a primary focus on building a comprehensive tire foreign object dataset. * Furthermore, the proposed detection model is investigated using the Grad-CAM method to provide a comprehensive interpretation of the decisions made. * By analyzing the interaction between AI models and a wide range of tire defect images, we seek to provide a more comprehensive understanding of both the capabilities and limitations of AI-powered tire defect detection systems in industrial settings. * For this study, an original dataset is obtained from a global tire manufacturer located in Kocaeli, Turkiye. * This research contributes to the tire manufacturing industry's efforts to leverage cutting-edge technology for improving quality control processes and ensuring the production of safer and more reliable tires. The rest of this paper is designed as follows. Section 2 briefly presents the related works. Section 3 presents the methodology and dataset of this study. Section 4 shows and discusses the experimental results. Section 5 presents the conclusions.
## 3 Optimization Algorithms
### Coverage  
One goal of this optimization problem is to maximize the coverage of the sensor network. After the target space is discretized sampled, binary variable \(c(i_{1},j_{1},\varphi_{1},i_{2},j_{2})\) is defined using the discretized sensor pose parameters and measuring point coordinates:  
\[c(i_{1},j_{1},\varphi_{1},i_{2},j_{2})=\begin{cases}&\text{If a camera at the grid point}\\ 1&(i_{1},j_{1})\text{ with orientation }\varphi_{1}\\ &\text{covers grid point }(i_{2},j_{2})\\ 0&\text{Otherwise}\end{cases} \tag{13}\]  
where \(i_{1}\in[0,s_{xb}-1],j_{1}\in[0,s_{yb}-1],i_{2}\in[0,s_{xm}-1],j_{2}\in[0,s_{ ym}-1],\varphi_{1}\in[0,s_{\varphi}-1]\). \(s_{xm}\) and \(s_{ym}\) represent the number of sampling points along the x-axis and the y-axis, respectively. After converting the above discrete values into coordinates and angles in the continuous target space, the value of \(c(i_{1},j_{1},\varphi_{1},i_{2},j_{2})\) can still be calculated by Inequalities (2). During the simulation process, all values \(c(i_{1},j_{1},\varphi_{1},i_{2},j_{2})\) are calculated in advance and stored in a matrix.  
After determining the parameter combination \(\big{(}\Delta_{b},\Delta_{m},s_{\varphi}\big{)}\) and sensor layout scheme, we define matrix \(\mathrm{V}=\big{(}v_{ij}\big{)}\), where the elements are  
\[v_{ij}=\begin{cases}1&\text{If the $i$th camera covers the grid point $j$}\\ 0&\text{Otherwise}\end{cases} \tag{14}\]  
where \(v_{ij}\) can be calculated using Formula (13). After that, the quantity of sensors covering the jth point can be expressed as  
\[n_{j}=\sum_{i}v_{ij}. \tag{15}\]  
Additionally, we let a binary variable represent whether or not grid point \(j\) complies with the \(k\)-coverage criterion, which is described as  
\[b_{j}=\begin{cases}1&n_{j}\geq k\\ 0&n_{j}<k.\end{cases} \tag{16}\]  
Consequently, the total number of sampling points in the measurement region that are covered by at least \(k\) sensors is  
\[N_{c}=\sum_{j}b_{j}. \tag{17}\]  
Lastly, the ratio of the number of sampling points covered to the total number of sampling points defines the coverage \(r\) of the target space,  
\[r=\frac{N_{c}}{s_{xm}s_{ym}}. \tag{18}\]  
This results in the establishment of a quantitative criterion for evaluating the target space's coverage. Maximizing \(r\) (i.e., maximizing \(N_{c}\) when \(s_{xm}\) and \(s_{ym}\) already determined) in Equation (18) is necessary to guarantee that the target space is covered to the greatest extent possible.