esa-sceva/satcom-chunk-collection huggingface dataset:
# Germanium Single Crystals for Photonics
## 11 Study of Laser Etching of Semiconductors Using Germanium as an Example  
Chemical etching with aggressive acids and alkalis is commonly used in the electronics industry to reveal the structure of a material [100]. Today, this is the standard method for studying the structure of semiconductors. It is known that the dislocation density on germanium is revealed by chemical etching of the [111] plane.  
In the process of studying the preparation of material samples for subsequent diffusion welding, in works [101; 102], an alternative, environmentally friendly, safe method of promptly identifying the structure of semiconductors by means of the effect of intense  
Figure 11: Time series of microwave signals measured at the detector receiver with the laser switched off (1) and laser irradiation (2) of the surface of a single-crystal germanium plate with an incident radiation power of the laser diode P\({}_{\mathrm{las}}\) = 2.64 W. Vertical scale: 2 mV/div; time scale: 500 \(\mathrm{\SIUnitSymbolMicro s}\)/div. Reprinted with permission from Ref. [99]. 2019, Pleiades Publishing, Ltd.  
frequency-pulse laser radiation on the material was proposed. Single-crystal germanium was used as a model material.  
In work [101], studies were carried out on both dislocation-based, industrial germanium, GMO brand, n-type (specific electrical resistance \(\sim\)5 Ohm\(\cdot\)cm, doped with antimony at a concentration of \(\sim\)3\(\cdot\)10\({}^{14}\) cm\({}^{-3}\)), and dislocation-free samples, similar in impurity composition.  
The samples were cut from one single crystal, respectively. Samples of the main crystallographic faces were studied. After polishing using conventional optical technology [103], the deviation of the sample surface from the orientation of the planes [111], [110], and [100] was less than 10 arc min. The initial surface roughness (before exposure to laser radiation) was 0.5-0.6 nm.  
The samples were exposed to scanning-focused pulse-periodic intense radiation of a nanosecond laser (wavelength 355 nm, duration \(\sim\)10 ns, energy density \(\sim\)0.5-1.3 J/cm\({}^{2}\), pulse repetition frequency 100 Hz). At a fixed energy density, an area of \(\sim\)1 \(\times\) 4 mm\({}^{2}\) was processed. After changing the energy density, another area was processed. About 30 laser pulses fell on the same area of the surface during such processing.  
Before and after such treatment, the samples were examined using a Zygo NewView 7300 optical profilometer and a JEOL JSM 6610 LV scanning electron microscope (SEM).  
Figure 12 shows the {111} surface of dislocation Ge after exposure to scanning nanosecond radiation of a UV laser with an energy density of E = 1.14 J/cm\({}^{2}\)[101]. Traces of \"dry\" etching, which arose as a result of the radiation, are clearly visible. They are similar to dislocation pits obtained by traditional chemical etching. A similar picture was not observed on other planes of the dislocation crystal, as well as on all three planes of the dislocation-free crystal.
# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 5 Application Validation and User Testing of the VR-Tool Application
In addition, it shows where to look for the evidence of unbalance or misalignment failures in an induction motor, as shown in Figure 13B;
* _Gear wear_: MCSA has also been applied as a part of the condition assessment approaches to identify faulty rotating mechanical elements such as gearboxes. Gradual wear on gear teeth was simulated with a 0% wear condition and 3 conditions of uniform wear on gear teeth of 25%, 50% and 75%. Variations of simulated wear under  
Figure 12: (**A**) Detail of the UI interface and disassembled induction motor. (**B**) User training in induction motor fault detection. Figure 13: (**A**) Top view showing the example of displayed misalignment to the user. (**B**) User training in induction motor fault detection. different wear conditions can be seen in detail in Figure 14. The identification of gear-box faults can be carried out by analyzing specific fault-related frequency components using Equation (7).
## 2 Materials and Methods
### Full-Scale SVE System  
The soil remediation plan (SRP) considered for this full-scale case study was based on an SVE treatment. Due to the type of pollutants present in the vadose zone, the main SVE goals were (a) to intercept any produced vapor preventing it from rising up to the ground level and (b) to treat the contaminants in air collected via SVE. For the SVE design, a stepped-rate test was conducted with the pilot plant first to determine the actual capacity of the vent and then to select a flow rate for conducting constant-rate tests. The latter were conducted to evaluate the actual area of influence and efficiency of a given well or combination of wells [29]. Figure 4b,c show the site area with the core points and the position of the SVE air extraction wells with their radius of influence of 13 m, as it is known from the previous pilot field study. The total surface of the area was about 13.000 m\({}^{2}\). The project installations for this area included two main system lines with:  
* 12 extraction wells of SVE to a maximum depth of 3 m from the ground level with a slotted section between 1.3 and 3 m;
* 4 activated carbon vessels to treat the VOCs pumped out. In Figure 4d, all the mechanical parts of the SVE plant (overview in Figure 4e) are shown, in particular: 1. well intake regulation valve; 2. vacuum gauge; 3. predisposition for flowmeter; 4. bypass valve; 5. inlet valve for moisture separation; 6. valve on moisture separator for purging cylinder; 7. external connection for purging cylinder; 8. level switch for moisture separation; 9. dilution valve; 10. dust filter; 11. PSV vacuum-breaking valve; 12. temperature and moisture gauge; 13. manometer; 14. socket for Rilsan(r) tube. The remediation technological plants were installed in compliance with the authorized SRP. Completed in August 2013 and working from October 2013, they were subjected to a phase of functional tests until 2014 with the subsequent stabilization of the operating conditions. In the period 2014-2018, the air quality was analyzed with a site-specific VOC investigation campaign, based on IN-OUT VOC measurements. The trend value of VOCs extracted from the two
## 3 Results and Discussion
### Results for Time-Frequency-Domain
#### 3.1.1 Water-Filled Pipe in Infinite Porous Medium
The longitudinal guided wave velocities in a water-filled pipe embedded in infinite porous medium decrease as a function of the frequency, and slope downward, approaching the longitudinal wave velocity of the liquid in the pipe [42]. The phase velocity and trend of this model are compared to those of the water-filled pipe in the elastic medium model. Figure 2 shows that the phase velocity of the water-filled pipe embedded in elastic medium is slightly lower than the phase velocity of the water-filled pipe embedded in a porous medium, and the trend of changes in the water-filled pipe is consistent in both media. One may notice a small drop in the phase velocity curve for the L(0,1) wave between 0 and 0.04 MHz. When the pipe is embedded in a solid, leakage by  
Figure 2: Dispersion curves of guided waves in water-filled pipe embedded in different media. The internal and external radii of the pipe are \(a=14\) mm and \(b=16\) mm, respectively. The vertical and horizontal axes depict the phase velocity and frequency. Blue and red lines represent propagating modes in the water-filled pipe embedded in infinite elastic and porous media, respectively. both longitudinal and shear waves can occur, which leads to considerably high attenuation rates, especially when the acoustic impedances of the pipe material and the surrounding solid are similar [43]. However, because the fast and slow longitudinal waves, as well as the shear waves, exist in porous medium, there is a need to consider how to accurately capture all the guided wave modes of a water-pipe embedded in a porous medium. This change in complexity is best illustrated in the phase velocity diagram of the following example.
## 5 Numerical Evaluation
### Results of the Base Case  
The optimization problem of the service provider's revenue is solved, and the optimal control policy is shown in Table 5. In addition, we compare the optimal revenue under two methods. One method is the dynamic plan control (DPC) proposed in our study, and the other method is to keep all plans always open (APAO). The results of this comparison are listed in Table 5.  
Figure 5 shows the trends of market share across periods under two methods. As illustrated in Figure 5, if the SP uses APAO, network congestion occurs in Period 4. When network congestion occurs, many customers leave the network, and the total market share of the five data plans drops dramatically. With DPC, the service provider opens data plans in a more reasonable way. Network congestion is avoided, and the total market share never decreases. A detailed analysis shows that before Period 4, the SP's revenue is 1.02% less if using DPC rather than APAO. However, the revenue of SP using DPC is 31.44% higher than APAO after a total of seven periods. Note again that DPC enables the SP to balance the benefit of earning more revenue in the short term and the cost of network congestion due to too much data traffic. Within the limited network capacity, DPC helps the SP reach a more reasonable allocation of resources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Plan 1** & **Plan 2** & **Plan 3** & **Plan 4** & **Plan 5** \\ \hline Initial percentage subscribed, \(\theta_{i,1}\) & 11.0\% & 12.1\% & 10.4\% & 6.3\% & 5.2\% \\ Churn rate \(q_{i}^{p}\) & 60\% & 60\% & 60\% & 70\% & 70\% \\ Churn rate \(q_{i}^{E}\) & 15\% & 18\% & 21\% & 24\% & 27\% \\ Willingness to buy SDP, \(w_{i}\) & 50\% & 50\% & 50\% & 60\% & 60\% \\ \hline \end{tabular}
\end{table}
Table 4: Base case: parameters related to the data plans and supplementary data packages.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **DP a 1** & **DP 2** & **DP 3** & **DP 4** & **DP 5** \\ \hline The allotted volume of full-speed data & 1 GB b & 3 GB & 6 GB & 8 GB & 10 GB \\ The price per period & € 34.45 & € 43.95 & € 67.75 & € 83.65 & € 98.15 \\ \hline SDP c 1 & SDP 2 & SDP 3 & SDP 4 & SDP 5 \\ \hline The additional volume of full-speed data & 250 MB & 250 MB & 250 MB & 250 MB & 250 MB \\ The price per purchase & € 4.95 & € 4.95 & € 4.95 & € 4.95 & € 4.95 & € 4.95 \\ \hline \end{tabular}  
* DP: data plan; b 1 GB \(\approx\) 1000 MB; c SDP: supplementary data package.  
\end{table}
Table 3: Overview of the five data plans and supplementary data packages.
## 4 Results and Discussion  
### Product Structure and Results  
The structure of the product intended for assembly is presented in the form of a modified directed graph of assembly states. Moreover, we assumed that parts in the directed graph (digraph) (or the assembled units) are marked as vertices, while the directed edges demonstrate the possible sequences (paths) for assembling them. It is further assumed that the assembly of further elements takes place by adding a part or subassembly consisting of more parts (treated as a single assembled part) to the nth stage assembly. The directed edges connecting the vertices contain information about the stability of the newly formed assembly state, the direction of attachment of the parts, and the tool applied. The described digraph can be generated automatically, based on the CAD assembly drawing. The basis for executing ASP according to the defined criteria for a specific assembly process is the determination of all assembly sequences that are feasible due to constraints of a constructional nature. The matrix record (e.g., in the form of an assembly states matrix or an assembly graph matrix) of assembly units enables us to determine all variants of assembly sequences using the appropriate algorithm (this procedure is not discussed here and it is reduced to finding all the paths in the digraph leading from the starting vertex \(x_{s}\), constituting the base part, to the final vertex \(x_{e}\), i.e., the last state of the assembled product--\(x_{s}\),..., \(x_{e}\)). The task of determining the sequence of assembly using artificial neural networks was performed for a sample product--a forklift door, consisting of eight main assembly units:  
* 1: door welded construction;
* 2: lock;
* 3: cassette lock;
* 4: door reinforcement bar with passenger's handle;
* 5: lock cover;
* 6: door seal;
* 7: lower glass;
* 8: upper glass. In the first stage, using the construction documentation, the base part was determined in the form of assembly unit no. 1. Then, a digraph of the structural limitations of the assembly states was constructed, shown in Figure 4.  
It was assumed that the assembly of subsequent units takes place by adding another assembly unit to the assembly state of the nth stage. Based on the constructed digraph recorded in the form of the assembly state matrix, we determined, with the use of a selected graph search algorithm 252, those assembly sequences that were possible under the constraints of the structural nature (Table 1) [19,20], which constitute the basis for further analysis. Table 2 presents the most effective neural networks for predicting the assembly time of the discussed product. By assessing the values of the sum of squared differences error and
# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 2 Image Blur Detection and Restoration
### Image Blur Detection Based on FFT  
Discrete Fourier transform (DFT) is a discrete form of Fourier transform in the time and frequency domains, which converts a sample of the time domain signal into a sample of the discrete time Fourier transform (DTFT) frequency domain. In realistic applications, FFT is often used to efficiently compute the discrete Fourier transform. For an image of size N \(\times\) N, the two-dimensional discrete Fourier (DFT) formulation is shown in Equations (1) and (2).  
\[F(k,l)=\sum_{i=0}^{N-1}\sum_{j=0}^{N-1}f(i,j)e^{-i2\pi(\frac{ik}{N}+\frac{lj}{N})} \tag{1}\]  
\[e^{ix}=\cos x+i\sin x \tag{2}\]  
where \(f\) (\(i,j\)) represents the image in the spatial domain, \(F\) (\(k,l\)) represents the image in the frequency domain, and \(e^{ix}\) is the basis function corresponding to each point in the Fourier space.  
In the frequency domain, the high-frequency part represents the texture features of the image, while the low-frequency part represents the contour information of the image. Therefore, after the FFT processing of the image, the frequency domain amplitude of the image can be calculated via spectrum analysis, and the image can be judged as to whether it is blurred, as shown in Figure 1.  
In this paper, the average value of the spectral amplitude of an image was calculated to determine whether the image is blurred or not. When the average value of the spectral amplitude of an image is lower than a set threshold, it is determined to be a blurred image; otherwise, it is a normal image. The image blur detection process is shown in Figure 2.  
Figure 1: (**a**) The normal image. (**b**) The spectrogram of the normal image. (**c**) The blurred image. (**d**) The spectrum of the blurred image.
## 3 Methodology
### Theory for Computing Unavailability of the Investigated Infrastructure
Once all imperative parameters are ascertained, this culminating model can be harnessed to analyze the reliability of distinct components within the network. The Weibull distribution, prominently utilized in reliability engineering, is chosen to model lifetime \(X\). This distribution is discerned by two specific parameters [26]:  
* Scale parameter \(\theta\), which delineates the typical lifespan of a system or component. * Shape parameter \(\beta\), dictating the distribution's profile. When \(\beta\) is less than one, the distribution indicates a diminishing failure rate. A \(\beta\) of one signifies a consistent failure rate, typically illustrating random failures. Conversely, a \(\beta\) value greater than one connotes a surging failure rate, often corresponding to wear-out failures. Specifically, for \(\beta\) equal to two, there is a linear rise in failure rates as systems age, commonly seen in electrical components influenced by factors like intensive load or the surrounding environment. The Weibull distribution's PDF is defined as follows [26]:  
\[f(x;\theta,\beta)= \begin{cases}\frac{\beta}{\theta}(\frac{x}{\theta})^{\beta-1 }e^{-(x/\theta)^{\beta}}&x\geq 0,\\ 0&x<0.\end{cases}  \tag{8}\]  
Its Cumulative Distribution Function (CDF) is expressed as  
\[F(x;\theta,\beta)=1-e^{-(x/\theta)^{\beta}}. \tag{9}\]  
Within electrical engineering, recovery time \(Y\) is predominantly characterized by either a rectangular or exponential distribution. Notably, the exponential distribution, predicated upon MTTR specifications derived from authentic data, is favored for network components [26].
## 2 Results and Discussion  
### Volatile Compounds Identified in Doubanjiang  
Volatile compounds in doubanjiang were extracted using SAFE and HS-SPME and identified using GC-MS. A total of 168 volatile compounds belonging to 13 chemical classes were identified. As shown in Table 1, hydrocarbons and esters were the predominant groups, each containing 30 and 26 volatile compounds, respectively, followed by alcohols, ketones, and acids, with each group including 27, 19, and 13 volatile compounds, respectively. For the SF-A sample, the most abundant volatile group (\(>\)5% average relative areas) was alcohols (17.86%), followed by phenols (10.08%) and esters (10.02%), while for the XH-B and XH-C commercial samples, esters (9.41% and 15.04%, respectively) and alcohols (6.14% and 7.01%, respectively) were major components (Table 2). This difference might be due to the different manufacturing conditions, as the SF-A sample was produced by a different company than XH-B and XH-C. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**RI *} & \multirow{2}{*}{**Compounds b**} & \multirow{2}{*}{**Odor Description c**} & \multirow{2}{*}{**Identification d**} \\ \cline{2-2} \cline{5-6}  & **DB-Sms** & & & & & \\ \hline \hline \multicolumn{6}{c}{_Esters (26) e_} \\
1 & \(<\)800 & \(<\)1000 & ethyl acetate 3 & balsamic, pineapple, sweet & MS, RI \\
2 & 816 & 1351 & ethyl lactate 1 & butter, floral, sweet & MS, RI, Std, O \\
3 & 850 & 1075 & ethyl 3-methylbutanoate 3 & apple, pineapple, sweet & MS, RI, Std, O \\
4 & 872 & 1134 & isoamyl acetate 2 & apple, banana, sweet & MS, RI, Std, O \\
5 & 1099 & 1520 & ethyl sorate 3 & apple, honey & MS, RI \\
6 & 1176 & 1688 & ethyl benzoate 3 & camomic, flower, fruit & MS, RI, Std, O \\
7 & 1201 & 1804 & methyl salicylate 1 & caramel, peppermint & MS, RI, Std, O \\
8 & 1240 & - & ethyl phenylacetate 2 & floral, rose, sweet & MS, RI \\
9 & 1242 & - & linaly acetate 2 & fruit, resinous, sweet & MS, RI \\
10 & 1247 & - & ethyl phenylacetate 1 & floral, rose, sweet & MS, RI, Std, O \\
11 & 1252 & 1837 & phenethyl acetate 3 & floral, rose, tobacco & MS, RI \\
12 & 1349 & - & neryl acetate 2 & floral, fruit, sweet & MS, RI \\
13 & 1368 & - & geranyl acetate 2 & lavender, rose, sweet & MS, RI \\
14 & 1522 & - & methyl laurate 1 & coconut, fat & MS, RI \\
15 & 1591 & 1849 & ethyl laurate 1 & floral, fruit, leaf, nut & MS, RI \\
16 & 1651 & - & methyl hydrosimonate 1 & - & MS, RI \\
17 & 1720 & 2016 & methyl myistate 1 & fat, honey, oil, orris & MS, RI \\
18 & 1789 & - & ethyl myristate 1 & ether, pleasant, soap & MS, RI \\
19 & 1818 & 2038 & isopropyl myristate 1 & cherry, cinnamon, waxy & MS, RI \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile flavor compounds identified in doubanjiang.
# Application of Metal Magnetic Memory Testing Technology to the Detection of Stress Corrosion Defect
## 3 Results and Discussion
### Analysis and Discussion
Different from experiment 2, the specimen in experiment 3 was corroded after having been loaded, so a strong leakage magnetic field \(H_{\rm c}\) was generated at the defect after the specimen was corroded, which is stronger than the leakage magnetic field \(H_{\rm m}\) and \(H_{\rm i}\). Under such conditions, the MFL signals are the vector sum of the initial magnetic field \(H_{\rm e}\), leakage magnetic field \(H_{\rm L}\), \(H_{\rm c}\), \(H_{\rm m}\), and \(H_{\rm i}\). Thus, the normal component \(H_{\rm n}\) of MFL signals presents a \"trough-peak\" shape, and the tangential component \(H_{\rm t}\) presents a \"peak\" shape. With the increase of corrosion degree, the vector sum of leakage magnetic field \(H_{\rm c}\), \(H_{\rm m}\), and \(H_{\rm i}\) increases, causing the MFL signals at the defect gradually increasing (see Figure 7).
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Hibiscus subdariffa  
The other name for the herb is roselle; it is a sub-shrub that can grow up to 8 ft in height. It is a native plant of India and Malaysia but is grown in other parts of the world, such as China, Thailand, Sudan, Nigeria, Jamaica, etc. [71; 72]. It is a vital source of vitamins and minerals such as calcium, iron, phosphorus, riboflavin, and vitamin C. _Hibiscus subdariffa_ helps to fight against many diseases related to kidney stones, gastrointestinal disorders, and liver damage. It has antioxidant, antimicrobial, anti-inflammatory, anti-carcinogenic, anti-obesity, and anti-diabetic properties [73]. The main bioactive compounds, such as anthocyanins, flavonoids, and organic acids, are responsible for the anti-obesity activity of the herb [74]. The extract of _Hibiscus subdariffa_ has the potential to enhance insulin sensitivity and resistance while lowering lipid accumulation and oxidative stress. An increase in lipoprotein lipase activity and a decrease in the expression of the adipogenesis genes are two ways that anthocyanin suppresses lipid accumulation and decreases fat mass in high-fat diet-induced obese rats. The loss of visceral adipose tissue reduces inflammatory cytokines and oxidative state, which reduces insulin resistance and enhances insulin sensitivity in obese rats [75].
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 4 Algorithms for Waveform Optimization
### Heuristic Solution of Transceiver Waveform  
Since the match filter bound gives a sub-optimal solution, we leverage by setting it as an initialized solution and propose minimization methods with and without constraints on the transmitted wave.  
Unconstrained Minimization Method (UMM)Unconstrained optimization is viable given that the transmit power protocols are not enforced. Hence, UMM is proposed for the minimization problem of outage probability, and formulates as follows:  
\[\underset{\mathbf{f}}{\min}\qquad\;\;Pout(\mathbf{f},\mathbf{h},\gamma) \tag{23}\]  
Constrained Minimization Method (CMM)This method extends the UMM by constraining the allocated power of the transmitted wave. Thus, we cast the CMM as follows:  
\[\begin{split}\underset{\mathbf{f}}{\min  
```
1:Input: \(N_{T}\), \(N_{R}\), \(L_{T}\), \(L_{R}\), \(\mathbf{\ell}^{init.}\), and \(\mathbf{h}^{init.}\).
2:Output: \(P_{out}(\zeta)\), \(\mathbf{\ell}^{opt.}\), and \(\mathbf{h}^{opt.}\).
3:Set maximum allowable iterations (\(i_{max}\)), and the precision upper bound (\(\epsilon\)).
4:\(i=1\)
5:Compute \(P_{out}^{(i)}(\zeta)\) in (21) using \(\mathbf{\ell}^{init.}\) and \(\mathbf{h}^{init.}\).
6:repeat
7:\(i=i+1\)
8: Compute \(P_{out}^{(i)}(\zeta)\) in (21) using (23) or (24).
9: Update \(\mathbf{\ell}^{opt.}\) using algorithm sub-routines.
10: Compute \(\mathbf{h}^{opt.}\gets v\mathbf{R}_{0}^{-1}\mathbf{T}^{opt.}\).
11:if\(\{|P_{out}^{(i)}(\zeta)-P_{out}^{(i-1)}(\zeta)|\leq\epsilon\}\) && \(\{i=i_{max}\}\)then
12:set Condition = true.
13:endif
14:until (Condition = true)
```  
**Algorithm 1** Build of Heuristic Search enabled Waveform Design