esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pattern Recognition and Quantitative Diagnosis
### Pattern Recognition of RC Valve Leakage
#### 5.1.1 Contrast Experiment I: Fault Pattern Recognition Based on Excitation CIs  
The K, RMS, and CC of the excitation signals of each group were pairwise combined as the two-dimensional feature indicators of the training SVM. The purpose of the training is to classify different RC failure modes based on the excitation CIs, and compare them with those that are based on the system CIs.  
Firstly, the corresponding CIs of normal valve, 2 mm leakage valve, 6 mm leakage valve, and 8 mm leakage valve were respectively numbered as 1, 2, 3, and 4. Secondly, each group of CIs and their class labels were used for SVM training to generate an SVM trainer. Afterwards, various CIs with not assigned the class label were put into the SVM trainer to classify, and the classification effect is shown in Figure 12.  
None of the three excitation CIs can completely classify the valve features of the four statuses, as shown in Figure 12. In Figure 12a, the normal valve is not separated from the 2 mm leakage valve. There are some feature points crossing each other between 6 mm and 8 mm leakage valve, and the distribution of feature points is relatively scattered, which is not conducive to feature classification, as in Figure 12b. Although, the four valve faults are not completely separated, and there are individual points that cross together in Figure 12c. However, the distribution of characteristic points is relatively concentrated, which is beneficial to classification.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 6 Discussion  
Based on the previous extensive set of experiments, it has been shown that TBIDS and MBIDS are overperforming the ABC, S-LEACH, and MS-LEACH in almost every measured criterion. This is due to the design nature of TBIDS and MBIDS. In TBIDS, the detection is fully occurred at the sink node as well as in MBIDS. Besides, TBIDS is a threshold-based algorithm where the required computation is very light, and it could be done in a small number of processing cycles. It also checks only the CHs on the received message route; so, it does not have to go through all of the routing table records. On the other hand, MBIDS has to receive the Ctrl message from two different paths; therefore, there is a delay in receiving them. At the same time, the Ctrl message might be lost, and the sink node has to wait for the timeout period. This explains the high percentage of packet loss in MBIDS. ABS, unfortunately, depends on artificial Bees, where it takes some time to stabilize. This also interprets the lower performance of the ABC algorithm. Other algorithms, such as S-LEACH and MS-LEACH, work perfectly against sinkhole attack as long as the used secure keys are not compromised. Once a node is compromised, the sinkhole attack could go without detection. In addition, they require extra overhead in terms of messages and computation due to the pair secure keys and their authentication process. It has been noticed that the number of sink nodes has a great effect on network performance. The results of 8000 sensor nodes and 30 sink nodes show that it is recommended to reduce the number of nodes to be served by the sink node. However, that puts a burden on the accurate threshold values required by the algorithms.  
Figure 19: Packet loss for large-scale networks, 8000 sensor nodes, with 10, 20, and 30 sink nodes.  
Figure 18: Packet loss (**a**) for small-scale networks, (**b**) for large-scale networks.  
The weakness of the TBIDS algorithm is that it bases its decision on certain thresholds. The thresholds could differ from one network to another according to its size and network configurations. This threshold has to be learned at the setup phase of the network. In this paper, the trial and error approach is followed to find the best threshold values, which is a very exhaustive and time-consuming process. It could be more efficient to have an artificial intelligence training method to identify the best threshold values for different types of networks. In fact, this is scheduled as part of my future work.  
MBIDS also suffers from two overheads--a control message that is sent along with the data message and the same control message that is sent in a separate path. This causes a slight problem in the detection time as well as in packet loss performance. In addition, the sink node has to investigate the whole route to check on the sinkhole attack.
## 5 Results
### Results from the Simulation Dataset
#### 5.2.3 Analysis for Different SNR Conditions
Continuing to observe Table 4, it can be found that the relative value difference \(\delta\) of LBI-BPSO is the lowest, and the one of IOIF is the highest. This shows that for the data used in this paper, the wavelength selected only by the amount of information has higher requirements on the SNR. Compared with Partitioned Relief-F, the relative numerical differences \(\delta\) of OA, AA and Kappa coefficients of LBI-BPSO decreased by 4.95%, 5.64% and 8.16, respectively. Compared with IOIF, the \(\delta\) of OA, AA and Kappa coefficients of LBI-BPSO decreased by 33.29%, 35.97% and 57.11, respectively. Compared with GA-BPSO, LBI-BPSO reduces the \(\delta\) of OA, AA and Kappa coefficients by 21.93%, 25.65% and 40.01, respectively. A comparison of the above classification results under different SNR conditions shows that the LBI-BPSO method combining the two wavelength selection criteria of information amount and inter  
Figure 13: (**a**) Comparison of OA under different SNRs; (**b**) comparison of AA under different SNRs (**c**) Comparison of Kappa coefficients under different SNRs. class separability can still maintain good classification accuracy under the condition of low SNR. Furthermore, LBI-BPSO has stronger tolerance to noise compared with IOIF and Partitioned Relief-F, which perform wavelength selection according to the amount of information and GA-BPSO whose objective function is the inter-class distance. In sum, the comparison and analysis of the classification results for LBI-BPSO, Partitioned Relief-F, IOIF and GA-BPSO were completed for SNR conditions of 1000, different abundance conditions and different SNR conditions.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 3 Results and Discussion
### Comparing Windows and Frequency-Based Reconstruction  
One of the question that could arise now is why not to apply the Fourier transform to DTOFs signals and perform the frequency based reconstruction described at Equation (5). To answer this question, reconstructions were also performed using Fourier transform datatypes for an inclusion 3.5 cm deep with the same optical values in the previous section.  
In Figure 18a, the reconstruction obtained by using Fourier transform coefficients from 0 MHz up to 1.5 GHz in steps of 100 MHz is shown. The magnitude of the signal Fourier coefficients are shown at Figure 18b. The results show that using frequencies up to 1.5 GHz yields a similar reconstruction to proposed windows. However, if only frequencies lower than 1.5 GHz are used the reconstructions suffers from instabilities. This agrees with the results of [45] that suggest that useful information can be found at GHz order.  
The relative equivalence of frequency and windows based results can be understood by realizing that in both cases, the complex numbers associated to each frequency are fitted. In the case of Fourier transform datatype, this is done directly, but for temporal windows, this is done by windowing the frequency domain. Nevertheless, there are some important differences. First, as was shown in Section 2.4, by applying the Fourier transform to a noisy signal the noise becomes correlated. This does not occur when not overlapping time windows such as Tukey windows are used. Second, when reconstruction is done directly in frequency domain, the linear system to solve is complex while in the temporal windows case it is real. Third, temporal window-based reconstruction is more intuitive to understand, since it is associated to photon arrival times. Therefore, in many cases, a weight matrix could be built in order to weight some windows over other ones, depending on how noisy they are. This fact makes the problem much more easy to stabilize compared to frequency based reconstructions. Last, in the same fashion as the paper published by [PERSON] et al. [36], a system that directly measures a gated DTOF (e.g., using rectangular gates) instead of the full curve could be faster than nowadays  
Figure 17: (**a**) Reconstructions for Mellin–Laplace and Tukey windows from same simulated data as Figure 13; cross–Born approach was used to deal with instrumental factors such as IRF or photon noise. The red circle indicates the correct location of the inclusion. (**b–d**) Quantitative metrics for cross-Born based reconstructions.  
time-resolved systems. Moreover, those gated DTOFs could be used in the proposed reconstruction process since they are equivalent to Tukey windows. Such a system could be potentially faster than state-of-the-art time-resolved systems without compromising reconstruction quality.
## 4 Stimulation Process of UV-C
### Moderate and High UV-C Dose Stimulation
This is due to the lower production of defense response compounds. The change is induced not only by the effect of UV-C operational factors, but the inherent characteristic of the fruit is also important. In general, high-dose UV-C conditions offer unfavorable stress resulting from low quality and defense mechanisms. Some investigations concluded that low dose irradiation led to hormonal effects improvement and microbial load reduction. On the other hand, high doses or continuous exposure could lead to a significant quality loss, decrease in shelf life, and lower antimicrobial activity [137; 138]. The disadvantages of inducing high UV-C dose were reported by previous research stating that accelerated ripening, senescence, lower stress response for bacterial wound, lower shelf life, and economical loss were associated with high dose exposure in postharvest fresh cuts [139]. Itis highly important to ensure the optimal UV-C dose in order to avoid compromising the textural quality, shelf-life elongation, and feasibility. The linked relationship between the UV-C dose with cellular level modification and the final quality attribute of the vegetable crop is depicted diagrammatically in Figure 2[127; 128; 129; 137; 138]. Table 2 shows miscellaneous horticulture products exposed at different UV-C doses and their resulting changes in quality texture, antioxidant, enzymes, and phenolic compounds. \begin{table}
\begin{tabular}{c c c c} \hline
**Commodity** & **Operating Condition** & **Key Finding** & **Reference** \\ \hline
**Tomato** & UV-C dose of 4 kJ\(\cdot\)m\({}^{-2}\), exposure & Increase in phenolic compounds content. & [133] \\ (_Solanum Lycopersicum_) & time of 6 min. & Higher antioxidant capacity in fruit & \\ \hline
**Blueberries** & UV-C dose of 0.43, 2.15, 4.30 and & treated with 2.15, 4.30, and compared to the control fruit. Increased & [140] \\  & 6.45 kJ\(\cdot\)m\({}^{-2}\). & phenolic components in a lesser amount & \\  & & at 0.43 kJ\(\cdot\)m\({}^{-2}\). & \\ \hline
**Fragrant pear (_Korla_)** & UV-C irradiation of 0.12, 0.24, 0.36, 0.48, 0.72 and 1.08 kJ\(\cdot\)m\({}^{-2}\). & low-dose UV-C irradiation (0.36 kJ\(\cdot\)m\({}^{-2}\)) & \\  & 0.48, 0.72 and 1.08 kJ\(\cdot\)m\({}^{-2}\). & enhanced the phenolic compound. & [141] \\ \hline \end{tabular}
\end{table} 
Table 2: Different horticultural product’s metabolic activities when subjected to a varied UV-C dose and other operational conditions. Figure 2: Diagrammatic representation for stress response as a result of low and high UV-C thermosis effect. The physiological modification encountered gives rise to changes in mechanical and other attributes of the vegetable.
## 3 Results
### Average Suppression Temperature (T)  
For a detailed comparison of the impact shown by DES on the CO\({}_{2}\) hydrate equilibrium temperature, the authors calculated the average depression temperature (\(\Delta\)T) demonstrated by the chemicals. Table 6 tabulates the values of average suppression temperature (\(\bar{\text{T}}\)) of the DESs, while Figure 10 displays the results graphically.  
The results proved that the TEAB:Glycerol-based DES showed thermodynamic gas hydrate inhibition property that was superior to the other conventional and ILs-based THIs selected for this study. When it involves QAS, the suppression behavior is achieved due to the hydrogen bonding of cations and anions of QASs with water molecules [14; 15]. The same reasoning is given for the successful hydrate suppression shown by DES. DESs show an affinity towards water molecules to form strong hydrogen bonds with them. The naturally available high number of hydroxyl groups in DESs gives it the enormous potential to perform as THIs.  
The characteristics exhibited by Choline Chloride, Propanediolic Acid, 3-Phenyl Propionic Acid, Itaconic Acid, and 3-Mercaptopropionic Acid-based DESs, as potential shale inhibitors in water-based drilling fluids, are additional evidence of the DESs to be efficient THIs [37]. In research on dehydration of natural gas by [PERSON] et al. [38], DESs based on Choline Chloride displayed significant absorption of H\({}_{2}\)O from natural gas down to the concentrations specified for pipelines to avoid hydrate formation. These works show the potential of DESs to be an ultimate alternative to the currently available conventional THIs, which have a lot of drawbacks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TEAAC:Glycerol 5 wt \%** & **TEAAC:MEG5 wt \%** & **TEAB:MEG5 wt \%** & **TEAB:Glycerol 5 wt \%** \\ \hline
**P/MPa** & \(\Delta\)**T/K** & \(\Delta\)**T/K** & \(\Delta\)**T/K** & \(\Delta\)**T/K** \\ \hline
2.0 & 2.00 & 1.35 & 0.70 & 1.80 \\
2.5 & 0.83 & 0.03 & 1.13 & 2.13 \\
3.0 & 1.60 & 0.10 & 2.30 & 2.55 \\
3.5 & 0.90 & 0.40 & 1.70 & 2.90 \\
4.0 & 0.60 & 0.10 & 1.80 & 2.60 \\ \hline
**T** (K) & 1.2 & 0.4 & 1.53 & 2.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average suppression temperature (\(\bar{\text{T}}\)) of DES systems at different pressure experimental conditions.  
Figure 10: The average suppression temperature of the studied DESs.
## 2 Dataset
### Analysis of Dataset
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Impact Factor** & **Descriptive** & **Unit** \\ \hline Target value & Horizontal displacement (HD) & Individual inclinometers and slope values at different depths. & mm \\ \hline  & Airline cumulative time & The exposure time of the foundation-pit floor & day \\  & (ACT) & slab, from excavation start to slab-pouring end. & \\  & & The overall length of the foundation-pit & \\  & Excavated Length (EL) & excavation at the time of the count was & \\  & & determined from the weekly & m \\  & & construction reports. & \\  & Whether the bottom plate is & Whether the silo housing the current tilt point & 0 for unpoured; \\  & poured (WBP) & has a poured footing. & 1 represents the \\  & & Application of the pour. \\ Dynamic & Axial force (AF) & Support axial force. & kN \\ characterization & Building settlement (BS) & Settlement of buildings around the main & mm \\  & & structure of the station. & mm \\  & Pipeline settlement (PS) & Pipe subsidence caused by excavation in the & mm \\  & & subway project. & \\  & Structural settlement (SS1) & Sinking of sections or structures during & mm \\  & & subway construction. & mm \\  & & Columns in buildings such as subway stations & \\  & & sank during the construction. & mm \\  & & Surface settlement (SS2) & Surface-settlement values obtained from the & mm \\  & & monitoring point. & mm \\  & & Water level (WL) & Presence of groundwater at different depths. & mm \\ \hline  & & & 1: Plain fill; \\  & & & & 2: Clay; \\ Static & & & & 3: Strongly weathered \\ characterization & Soil type (ST) & Identification of geotechnical types. & muddy sandstone, \\ factors & & & & 4: Moderately \\  & & & & weathered muddy \\  & & & & sandstone. \\  & & & & \\  & & Monitoring Depth & m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary table of characterization factor. and supporting frames, in the horizontal direction. This monitoring held great significance in ensuring the safety and stability of the foundation-pit construction. Any changes in the horizontal displacement of the enclosure structure indicated that the surrounding soil might have undergone displacement or deformation. The monitoring points for measuring the enclosure structure's horizontal displacement were mainly situated on the ground-connected wall. Monitoring data from specific dates for three monitoring points, namely CX-1, CX-2, and CX-58, were selected, and the line graphs were plotted, as shown in Figure 3. The enclosures show a consistent deformation pattern over time.
# Assessment and Categorization of Biological Effects and
## 3 RF-EMF Exposure
### Health Safety Standards  
The thermal customary biological effect induced by exposure to EMF can be quantified by the specific absorption rate (SAR) which evaluates the energy absorbed by an element of the tissue. The SAR if multiplied by the exposure interval signifies the EMF entire-body or part of the tissue-specific absorbed energy amount. This energy generates a rise in tissue temperature, which can increase health risks. The SAR and corresponding temperature rise for given frequency and exposure time can be represented by, see [20] for more details:  
\[\text{SAR}=\text{P}/\rho=\sigma\;\text{E}^{2}/(2\;\rho) \tag{1}\]  
\[\Delta\text{T}=\sigma\;\text{E}^{2}\;\Delta\text{t}/(2\;\epsilon\;\rho) \tag{2}\]In (1) and (2), SAR is the specific absorption rate in (W/kg). P is the power loss density in (W/m3), E is the electric field strength in (V/m), \(\Delta\)T is the temperature rise in (\({}^{\circ}\)C) and \(\Delta\)t is the exposure time in (s). The tissue properties parameters \(\rho\), \(\sigma\), \(c\) are the density in (kg/m\({}^{3}\)), the conductivity in (S/m) and the specific heat in J/(kg \({}^{\circ}\)C), respectively. Note that the conductivity \(\sigma\) of human tissues depends not only on matter but also on frequency. It is mostly constant with growing frequency up to megahertz and then rises, often nearly linearly.  
Expressions (1-2) indicate that the higher the SAR and the longer the exposure time, the greater and more dangerous the increase in temperature will be. This behavior will be affected in addition to the field (intensity and frequency) and the exposure time by the density of the tissue and its thermo-electric properties. Thus, the SAR limits imposed by health safety depend on the part of the body considered and the nature of the subject exposed as well as the exposure conditions. Exposed subjects can be adult humans, children or animals. The exposed parts of the body are the head, trunk and limbs. Exposure conditions include different categories of exposed subjects (allied to the liaison with the source of exposure): workers involved in manufacturing, testing, and installing devices, users of the device, and nearby subjects. For all these situations, health safety standards set thresholds relating to SAR, \(\Delta\)T and fields triggered in the human body: magnetic flux density B, electric field E and current density J. Figure 2 summarizes the different input parameters, nature and condition of exposure and there effects on tissue. These effects must comply with the corresponding thresholds.
# A New Control Scheme for the Buck Converter
## 3 Mathematical Modeling and Components Selection  
### Mathematical Modeling  
In order to guarantee the stability of the system, the derivation of the open-loop transfer function is necessary. The literature about the mathematical modeling is presented in [15]. Details of the design procedure can be found in [15]. Figure 7 shows the open-loop structure of Figure 4. Similarly to [20], Equation (1) can be used to represent the open-loop transfer function of Figure 7. The \(G_{p}\)(s) expression in Equation (2) represents the buck converter. The compensation network \(A\)(s) with the error amplifier (EA) is represented by Equation (3).  
\[\mathrm{T(s)}=\frac{V_{o}(s)}{V_{i}(s)}=G_{P}(s)\cdot A(s) \tag{1}\]  
\[G_{P}(s)=\frac{V_{FB}(s)}{V_{i}(s)}=\frac{1}{R_{i}}\cdot\frac{1}{1+\frac{s}{Q \cdot\omega}+\frac{\beta^{2}}{\omega^{2}}}\cdot\frac{R_{LOAD}(R_{ESR}C_{o}s+1)}{ (R_{LOAD}+R_{ESR})C_{o}s+1} \tag{2}\]  
\[A(s)=\frac{V_{o}(s)}{V_{FB}(s)}=g_{m}\cdot R_{o}\cdot\frac{\left(1+\frac{s}{w_ {z}}\right)}{\left(1+\frac{s}{w_{p}}\right)} \tag{3}\]  
\[w_{z}=\frac{1}{R_{3}\cdot C_{1}},w_{p}=\frac{1}{R_{o}\cdot C_{1}} \tag{4}\]  
In Equation (2), \(\omega=\frac{\pi}{1_{GW}}\) and \(Q=\frac{2}{\pi},\ R_{i}\) is the gain of the \(\mathrm{T_{OFF}}\) ramp generator.  
Figure 6: Key waveforms of Figure 5.
# Effect of Substrates on the Dynamic Properties of Inkjet-Printed Ag Thin Films
## 2 Vibration Transfer of Ag Inkjet-Printed on Flexural Substrates  
### Flexural Wave Propagation of Inkjet-Printed Substrates  
The effects of shear deformation and rotary inertia are negligibly small compared to those of the bending deformation due to the small thickness and width of the substrates. The equation of motion for inkjet-printed substrates excited in the transverse direction is expressed as [26]  
\[D\frac{\partial^{4}w(x,t)}{\partial x^{4}}+M_{b}\frac{\partial^{2}w(x,t)}{ \partial t^{2}}=0 \tag{1}\]  
where \(D\) is the bending stiffness, \(w\) is the deflection, \(x\) is the lengthwise coordinate from the origin, and \(M_{b}\) is the mass per unit length of the beam. Assuming the harmonic vibration is given by \(w(x,t)=\text{Re}\big{\{}\hat{w}(x)e^{i\omega t}\big{\}}\), the vibration response of a substrate is obtained from the following equation:  
\[\hat{w}(x)=\hat{A}_{1}e^{-\hat{k}_{b}x}+\hat{A}_{2}e^{\hat{k}_{b}x}+\hat{A}_{ 3}e^{\hat{k}_{b}(x-L)}+\hat{A}_{4}e^{-\hat{k}_{b}x} \tag{2}\]  
where \(\hat{A}_{n}(n=1,2,3,4)\) are complex coefficients, \(L\) is the length of the beam, and \(\hat{k}_{b}\) is the complex wavenumber. To consider the vibration dissipation of wave propagation, the complex stiffness was used as \(\hat{D}=D(1+i\eta)\), where \(\eta\) is the loss factor. Substituting Equation (1) into (2), the complex wavenumber is derived as  
\[\hat{k}_{b}=\left(\frac{\omega^{2}M_{b}}{\hat{D}}\right)^{1/4} \tag{3}\]  
To solve the unknown coefficients, \(\hat{A}_{n}\), the boundary conditions are applied as  
\[\hat{w}(0)=w_{0},\frac{\partial\hat{w}(0)}{\partial x}=\frac{\partial^{2} \hat{w}(L)}{\partial x^{2}}=\frac{\partial^{3}\hat{w}(L)}{\partial x^{3}}=0 \tag{4}\]The transfer function between the vibration responses at \(x=x_{1}\) and the input excitation, \(w_{0}\), is calculated as follows:  
\[\Lambda e^{i\phi}=\hat{w}(x_{1})/w_{0} \tag{5}\]  
where \(\Lambda\) and \(\phi\) are the amplitude and phase of the transfer function, respectively.  
The complex wavenumbers, \(\hat{k}_{b}=k_{br}-ik_{bi}\), were calculated numerically using the measured transfer function [27]. The iteration matrix equation of the Newton-Rapson method is given as  
\[\left[\begin{array}{c}k_{bfr}\\ k_{bfi}\end{array}\right]_{j+1}=\left[\begin{array}{c}k_{br}\\ k_{bi}\end{array}\right]_{j}-\left[\begin{array}{c}\text{Re}\Big{\{}\frac{ \partial\psi(-x_{1})}{\partial k_{br}},\frac{\partial\psi(-x_{1})}{\partial k_{ bi}}\Big{\}}\\ \text{Im}\Big{\{}\frac{\partial\psi(-x_{1})}{\partial k_{br}},\frac{\partial \psi(-x_{1})}{\partial k_{bi}}\Big{\}}\end{array}\right]^{-1}\left[\begin{array} []{c}\text{Re}\big{\{}\hat{w}(-x_{1})-w_{0}\Lambda e^{i\phi}\big{\}}\\ \text{Im}\big{\{}\hat{w}(-x_{1})-w_{0}\Lambda e^{i\phi}\big{\}}\end{array}\right] \tag{6}\]  
The complex bending stiffness with frequency was obtained using the calculated wavenumber.