esa-sceva/satcom-chunk-collection huggingface dataset:
# Function-Oriented Applicability Evaluation of Technical Folding Based on Expert Knowledge
## 4 Developing the Central Elements of the EvalTech Method
### Definition of Equations for Similarity Analysis
#### 4.2.1 Calculating the Similarity Between Functions  
For determining the similarity between functions of the new technical problem and characteristic functions from the solution database, the similarity equation \(\text{sim}_{\text{case1}}\) is defined. This equation basically determines whether a characteristic function i is used in the new, technical problem or not. The weighting w\({}_{\text{i}}\) also considers the importance of the respective function in the context of technical folding. The weighting w\({}_{\text{i}}\) corresponds to the absolute frequency of a used function in relation to the absolute frequency of all other used characteristic functions. For example, one function of the use case used here is \"shield material flow\". According to Case Base 1, this function is a characteristic function of technical folding. It occurs a total of 9 times in 21 cases, cf. Figure 3. Within these cases, the total number of different functions is 110. For example, in the comparison of the functions of the use case with the characteristic function \"shield material flow\", the parameter o\({}_{\text{i}}\) is equal to 1 and the weighting w\({}_{\text{i}}\) is equal to 9/110. To calculate, the parameters o\({}_{\text{i}}\) and w\({}_{\text{i}}\) must then be determined for the comparison with each of the other nine characteristic functions, and the multiplication of the respective parameters is added.  
The similarity equation, \(\text{sim}_{\text{case1}}\), is calculated by the equation below, where SuD stands for System under Development:  
\[\text{sim}_{\text{case 1}}=\sum_{\text{i=1}}^{\text{10}}\text{o}_{\text{i}} \times\text{ }\text{w}_{\text{i}}\begin{cases}\text{o}_{\text{i}}=1,\text{ if the function occurs in the SuD}\\ \text{o}_{\text{i}}=0,\text{ if the function does not occur SuD}\end{cases}, \tag{2}\]  
The equation \(\text{sim}_{\text{case1}}\) always calculates the parameters o\({}_{\text{i}}\) and w\({}_{\text{i}}\) on the basis of the current case 1 and determines the characteristic functions using the latest data. This makes the equation flexible with regard to extensions of case 1.
## 3 Numerical Method and Verification
### Verification of the Numerical Model  
In order to verify the reliability of the above numerical method, a calculation model by the numerical method is firstly used to simulate the shield tunnel shaking table test [22]. The scale model experiment was conducted in the Structural Engineering Laboratory of Shandong Jianzhu University, as shown in Figure 5a. In this experiment, a multi-channel hydraulic servo drive is used to simulate control and the model box is a layered shear box. In the model experiment, gypsum and admixture were selected as the structural materials of the tunnel model and the soil layer was original silty clay. The hard rock model was prepared with quartz sand as the skeleton material, Portland cement and gypsum as the cementing material and was made by uniformly mixing and vibrating according to the mass ratio. The physical and mechanical parameters of the materials are obtained through the mechanical and geotechnical tests.  
Figure 4: The acceleration time history and Fourier spectra of bedrock ground motion: (**a**) Time history curve of ground motion; (**b**) Fourier spectrum.  
Figure 5: Schematic Diagram of Shaking Table Test and Numerical Simulation: (**a**) Shaking table test; (**b**) Finite element model.  
The finite element model of the shaking table test was established by using the numerical method adopted in this paper, as shown in Figure 5b. In order to make the seismic waves inputted by the numerical model consistent with the seismic waves inputted by the test, the acceleration time history record of the shaking table is used as the seismic excitation of the numerical model. Considering that there are many experimental conditions, some experimental conditions are selected for numerical simulation to compare experimental results and numerical calculation results. Due to the peak bending moment diagram of the tunnel structure obtained from the shaking table test (Figure 6a), we selected the peak bending moment diagram of the tunnel structure to compare the results. It is concluded that the change trend of the structural bending moment obtained from the numerical simulation and the model test is basically consistent, which proves that the calculation method and boundary conditions adopted by the numerical method are reliable and can be used for subsequent numerical research.
## 5 Experiment Result
### Iteration Drive and Road Drive Spectrum  
In order to facilitate the observation of the change process of the drive signals in the iteration under different control strategies, the spectrum diagrams of the driving signals in the iteration are listed in Figure 12:  
As shown in Figure, as the iteration progresses, the drive signal gets closer to the target signal (the road spectrum). The large deviation in the lower frequency band under 1 Hz is because the response of the whole system, especially the acceleration sensor, is poor in the lower frequency band. In the intermediate frequency part from 1 Hz to 10 Hz which is the most sensitive frequency range for human body vibrations [24; 25], the drivers obtained by iteration are very close to the road spectrum. In the high frequency band above 10 Hz, because the amplitudes of driving signal and system response characteristics are very small, there are many burrs.  
From the results obtained with different control strategies, the drive signal of method 1 and 2 is very close to the road spectrum only after 2 to 3 times iteration, and in the subsequent iterations, the drive signal does not deviate significantly from the road spectrum.  
Figure 11: Normalized spectrum of response error with method 1–5; (the x axis of each graph represents the frequency, y axis represents the iteration times and the color varying from white to black represents the magnitude of the normalized spectrum).  
Figure 12: _Cont._  
From the drive signal of method 3, it can be clearly seen that the driving signal needs at least 4 to 5 times of iteration before it could be closer to the road spectrum. Therefore, the convergence speed of method 3 is much slower than that of method 1 and 2. Although the drive signal of method 4 is very close to the road spectrum after 3 iterations, it is not stable around the road spectrum, but appears obvious overshoot phenomenon, so its stability is poor. The result of method 5 is similar to method 3, with slow convergence speed.  
Although in Section 5.2, NSRE is always larger around 10 Hz, because the system has certain nonlinear characteristics around here. It can also be seen from the system TF estimation in Section 5.5. However, as a precondition, the road spectrum itself has a relatively small amplitude around 10 Hz, as described in Section 4.5. The drive spectrum in this section is still approaching the road spectrum gradually, without divergence or deviation at the natural frequency point of the system. Thus, the nonlinearity of the system at the natural frequency point does not cause the drive spectrum to diverge at a particular frequency point.  
Therefore, it is proved that methods 1 and 2 can make the convergence process of iteration faster, more accurate and more stable. It means that the RMS of NSRE of iteration could reach the minimum quicker, the minimum RMS could be smaller, and the average increase rate of RMS after the minimum point could be smaller.
## 4 Image Fusion Applications in NDT Inspection
### Fourier Transform-Based Approaches  
Fourier analysis is a well-known approach in mathematics, covering a large spectrum of applications (approximating a general function with the sums of more straightforward trigonometric functions). Fourier analysis decomposes an image into its sine and cosine components, with the transformation output representing the image in the frequency domain (the input image is the spatial domain). For completeness, general forms of Fourier synthesis and analysis equations, respectively, for a periodic sequence of \(x(n)\)[71] are shown below (\(c_{k}\) and \(N\) refer to Fourier coefficients and period, respectively):  
\[Synthesis:x(n)=\sum_{k=0}^{N-1}c_{k}e^{j2\Pi\hbar n/N} \tag{1}\]  
\[Analysis:c_{k}=1/N\sum_{n=0}^{N-1}x(n)e^{-j2\pi kn/N} \tag{2}\]  
Working in the frequency domain enables easier processing for image fusion. The work in [72] presents a useful example of Fourier-based NDT image fusion. Using a Gaussian function, the authors presented a cone-shaped filter in the Fourier domain of X-ray computed tomography (CT) and ultrasonic testing (UT). The fusion aimed to combine the advantages of the inspection methods, hence minimising issues such as noise, artefacts and low resolution. A test sample containing the specific dimensions of the internal channels was used to perform CT and UT volume measurements. The fused image provided reduced artefacts and improved the edge contrast compared with using the inspection technologies independently. It is noted that, as seen in many works in the literature, the Fourier transform has often been used as a tool to preprocess data prior to feeding them to the fusion rule.
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 4 Conclusions  
Three magnetic nanocomposite iron oxide-silica shell materials were synthesized containing a different mass percent of iron oxide nanoparticles of around 10 nm diameter. The morphology of the nanocomposites depended on the mass percentage of MNPs used in the synthesis. The sample with the highest surface and best magnetic properties was chosen to conduct Pb\({}^{2+}\) adsorption studies from aqueous media (MNPs35@SiO\({}_{2}\)).  
MNPs35@SiO\({}_{2}\) presented a relatively high value of specific surface area 271.0 m\({}^{2}\) g\({}^{-1}\), total pore volume 1.0 cm\({}^{3}\) g\({}^{-1}\) and pore diameter 24.9 nm. The saturation magnetization value obtained for the nanocomposite, 12.4 emu g\({}^{-1}\), indicated that the synthesized material has a good response to an external magnetic field, which is an important feature for the applicability as adsorbent for removing heavy metals from waste water. The maximum adsorption capacity, 14.9 mg\(\cdot\)g\({}^{-1}\), is comparable with results obtained for similar nanocomposites reported in literature.  
The adsorption mechanism of Pb\({}^{2+}\) ions was established by kinetic, thermodynamic and equilibrium studies. The results indicated that the adsorption takes place at the surface of the material, being a spontaneous, endothermic process and physical bonds may be involved (hydrogen bonding or electrostatic attraction), but the process is mainly governed by chemisorption. Finally, for better process efficiency and ensuing widespread use, being affordable and with good stability, Pb\({}^{2+}\) desorption studies were conducted, establishing that it may be reused with good efficiency.  
Conceptualization, A.-M.P. and E.I.S.; synthesis: R.N. and E.-M.P.; structural characterization: R.N.; nitrogen sorption: C.I.; SANS: A.L. and L.A.; XRD: R.L.; adsorption studies: M.C.; A.N.; writing--original draft preparation, A.-M.P. and E.I.S.; writing--review and editing, E.I.S. and O.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank to [PERSON] from West University of Timisoara, Romania for magnetic measurements and fruitful discussions. The authors thank to the Romanian Academy (Program 4) and the Inter-Academic Exchange Programs between Academy of Sciences of the Czech Republic and Romanian Academy and Hungarian Academy of Sciences and Romanian Academy. This work is dedicated to the memory of Assoc. [PERSON] (1963-2018) from Charles University of Prague and the Academy of Sciences of the Czech Republic.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning
## 5 Case Studies  
In this paper, two experimental models are used to verify the feasibility and efficiency of the proposed GCSA optimization approach, which was coded using MATLAB environment and executed on a 1.99 GHz Intel i7 processor and 8 GB RAM computer. The first case study, which represents a common test example in process planning optimization, considers the prismatic part that was initially proposed by [PERSON] et al. [25]. It contains 14 recognized machining features and 20 selected operations. The significant manufacturing information concerning features, operations, resources as well as precedence constraints is given in Table 2. The 3D solid model with the corresponding operation precedence graph (OPG) are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Definition** & **Operations** & **Machines()** & **Tools()** & **TADs(] & **Predecessors** \\ \hline F1—A planar surface & Milling (o1) & m2,m3 & t6,t7,t8 & +z & \\ \hline F2—A planar surface & Milling (o2) & m2,m3 & t6,t7,t8 & -z & o1 \\ \hline F3—Two pockets arranged as a replicated feature & Milling (o3) & m2,m3 & t6,t7,t8 & +x & o1 \\ \hline F4—Four holes arranged as a replicated feature & Drilling (o4) & m1,m2,m3 & t2 & +z,-z & o1,o5,o18 \\ \hline F5—A step & Milling (o5) & m2,m3 & t6,t7 & +x,-z & o1 \\ \hline F6—A rib & Milling (o6) & m2,m3 & t7,t8 & +y,-z & o1 \\ \hline F7—A boss & Milling (o7) & m2,m3 & t7,t8 & -a & o1,o5 \\ \hline F8—A compound hole & Drilling (o8) & m1,m2,m3 & t2,t3,t4 & & o1,o7 \\  & Reaming (o9) & m1,m2,m3 & t9 & -a & o1,o7,o8 \\  & Boring (o10) & m2,m3 & t10 & & o1,o7,o8,o9 \\ \hline F9—A rib & Milling (o11) & m2,m3 & t7,t8 & -y,-z & o1 \\ \hline F10—A compound hole & Drilling (o12) & m1,m2,m3 & t2,t3,t4 & & o1,o2,t6,o11 \\  & Reaming (o13) & m1,m2,m3 & t9 & -z & o1,o2,t6,o11, o12 \\  & Boring (o14) & m3,m4 & t10 & & o1,o2,o6,o11, o12 \\ \hline F11—Nine holes arranged as a replicated feature & Drilling (o15)Tapping (o16) & m1,m2,m3 & t1 & -z & o1,o2,o12,o13, o14 \\  & (o15)Tapping (o16) & m1,m2,m3 & t5 & & o1,o2,o12,o13, o14 \\ \hline F12—A pocket & Milling (o17) & m2,m3 & t7,t8 & -x & o1,o18 \\ \hline F13—A step & Milling (o18) & m2,m3 & t6,t7 & -x,-z & o1 \\ \hline F14—A compound hole & Reaming (o19) & m1,m2,m3 & t9 & +z & o1,o12 \\  & Boring (o20) & m3,m4 & t10 & +z & o1,o12, o19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features, operations, resource alternatives and predecessors of Part 1. The second case study considers a complex part taken from [PERSON] et al. [8] that consists of 28 machining features and a total of 46 machining operations. Its main manufacturing information is given in details in Table 3. The solid model of this complex part with the corresponding OPG are represented in Figure 8. The cost indices for both Part 1 and Part 2 are given in Table 4.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Cameras for Visual Surveillance
Recent attempts of using thermal cameras to detect driver disengagement have shown promising results in poorly illuminated conditions [84; 85]. Despite the advances and the accuracy of some visual sensing methods, several drawbacks remain. Cameras are known to be prone to performance degradation due to occlusion and sensitivity to illumination conditions [86]. Occluded faces prevent any effective acquisition of FAUs [62]. Glare and darkness can severely affect pose estimation [87], lip reading, and eye state detection. Privacy is another major concern [88], given that humans are not comfortable being monitored with visual surveillance at workplace and at home.
## 5 Conclusions  
This research represents a significant leap forward in the field of agricultural automation, particularly in the context of tomato harvesting. Our study centered on the development and extensive testing of an innovative hybrid model that integrates CNNs with PAFs, which is a combination that has been proven to be highly effective in accurately determining the center of gravity of tomatoes for robotic harvesting.  
In this study, we placed a particular emphasis on the integration of multispectral analysis and data-enhancement techniques, which allowed the model to effectively adapt to a variety of environmental conditions and characteristics of various tomato varieties. As a result, the model achieved an impressive recognition accuracy, reaching 96.4% on an extensive dataset of 2260 images. Despite the successes of the study, we identified areas for improvement, particularly in enhancing the model's ability to handle challenging scenarios, such as small or clustered fruits. Future research could focus on refining key point detection and extending the model's applicability to other crops and agricultural processes.  
In conclusion, our study contributes significantly to the advancement of technologies in agricultural automation. The high accuracy, efficiency, and adaptability of the developed model not only makes more effective and sustainable agricultural practices possible, but also opens up new opportunities for research and development in this field. The implications of this study extend beyond tomato harvesting, which suggests a promising path for the broader application of such technologies in the agricultural industry, which could transform the way that we approach food production and resource management on a global scale.  
This research received no external funding.
## 5 Experiments
### Ablation Studies
#### 5.4.2 The Effects of Different Communication Bandwidth Ratios (\(\upmu\))  
To better understand the impact of uniformity using different \(\upmu\) values, we conducted experiments using extreme values in our clustering algorithm. The results are depicted in Figure 10.  
By testing these extreme values, we observed that the uniformity performance improved when the computation capability and communication bandwidth were more balanced. Specifically, the extreme values represented by the bottom left and bottom right in Figure 10 demonstrated poorer uniformity performance than the ones on the top. The worst performance case occurred when there was an overindulgence in computation capability. In most federated learning scenarios, the straggler spent significant time in transit, transferring data rather than actively performing computations. This imbalance between computation and communication resulted in a decrease in uniformity performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **CIFAR-100 (40\%)** & **CIFAR-100 (60\%)** \\ \hline
**ResNet34** & 1,432,763 & — \\ \hline
**MB\_ResNet34** & 122,351 & 256,291 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training time versus a certain amount of accuracy for different models on CIFAR-100 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**CIFAR-10**} & \multicolumn{3}{c}{**FEMNIST**} \\ \cline{2-7}
**Accuracy** & **60\%** & **70\%** & **80\%** & **60\%** & **70\%** & **80\%** \\ \hline
**ResNet34** & 225,743 & 811,432 & 2,432,746 & 47,701 & 68,869 & 286,324 \\ \hline
**MB\_ResNet34** & 23,036 & 48,558 & 131,692 & 5555 & 10,113 & 36,854 \\ \hline
**ResNet34 + Prox** & 217,639 & 824,295 & 2,142,973 & 29,639 & 48,454 & 168,844 \\ \hline
**MB\_ResNet34+ Prox** & **20,365** & **43,812** & **124,677** & **4563** & **6224** & **25,192** \\ \hline
**ResNet34 + Yogi** & 223,420 & 803,822 & 2,342,924 & 44,845 & 74,458 & 253,798 \\ \hline
**MB\_ResNet34 + Yogi** & 55,222 & 98,168 & 200,200 & 4013 & 5942 & 16,530 \\ \hline
**MobileNet\_v2** & 56,950,432 & — & — & & & \\ \hline
**MB\_MobileNet\_v2** & 33,457 & 60,464 & 138,418 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training time versus a certain amount of accuracy for different models on CIFAR-10 and FEMNIST datasets.
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 3 Experimental Study
### Water Content Alteration
#### 3.2.1 Vibration Response Changes with Water Content  
Figure 10 illustrates the typical time histories of three test cases with different water contents. It can clearly be observed that the amplitude for case W1 was the largest, while the waveforms for cases W2 and W3 were similar. Figure 11 illustrates the threshold numbers for 10%, 1% and 0.1% of the peak value. Compared with case W1, cases W2 and W3 had a smaller number of threshold counts; however, the difference between cases W2 and W3 was not obvious. This can be understood by noting that if the water content was large, the impedance difference between the soil and concrete was small, and accordingly, the reflection energy was weak.  
Figure 9: Energy time–frequency distribution diagram for different density cases.  
Using a Fourier transform, the frequency spectra for three different water content cases were obtained, as plotted in Figure 12. Each curve was averaged using multiple signals. It can be observed that the dominant frequency appeared at between 1640 and 1690 Hz for all cases. With increasing water content, the dominant frequency amplitude decreased. For cases W2 and W3, peak values around 850 and 1100 Hz were also observed. With increasing water content, the vibration energy shifted to lower frequencies.  
Figure 11: Number of counts for thresholds of (**a**) 10%, (**b**) 1% and (**c**) 0.1% of peak value.  
Figure 10: Acceleration waveforms for cases W1, W2 and W3.