esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Analysis of Acoustic Emissions in the Asinelli Tower during Local Seismic Activity
## 2 Materials and Methods  
An array of six broadband AE transducers (with a flat frequency response nominally in the range of 50-500 kHz, as declared by the manufacturer, i.e., well beyond the frequency range of signals propagating in the masonry) was fixed to the north-east angle of the Asinelli Tower at an average height of 9.00 m above ground level. The frequencies of interest in the old masonry, around 30 kHz, were still detected by the adopted transducers as already reported in previous papers for similar surveys [37]. Figure 1a-c show the tower and the transducers applied to a masonry wall portion.  
The transducers were connected to a six-channel acquisition system able to store AE signal parameters such as arrival time \(t\) (determined with accuracy of 0.2 \(\upmu\)s), duration, peak amplitude, and ring-down count (number of times the AE signal exceeds a preset threshold). A time accuracy of 0.2 \(\upmu\)s, equivalent to a sampling rate of five mega-samples per second, is adequate to measure frequency components up to 500 kHz (only frequencies up to 10 times smaller than the sampling rate are usually considered), covering satisfactorily the AE frequency range.  
Prior to starting the monitoring, were preliminarily performed for a representative period of time, i.e., 8 h, in order to determine the level of spurious signals. Thus, the signal acquisition threshold was set to 100 \(\upmu\)V in order to filter out the electrical noise (see a typical signal waveform in Figure 1d). Keeping fixed the threshold made it possible to capture possible variations in the noise level due to changeable environmental conditions like traffic, weekdays, weekends, etc.  
Figure 1: (**a**) The Asinelli Tower (with Garisenda Tower on the left) in the city centre of Bologna; (**b**) Monitored portion of the masonry wall with the applied AE transducers, printed from [30]; (**c**) AE transducer adopted for the monitoring; (**d**) Typical AE signal formed by the sequence of P-, S- and surface waves.  
AE monitoring began on 23 September 2010 at 5:40 p.m. and ended on 28 January 2011 at 1:00 p.m. for a 2915-h period [30]. All the AE signals were found to fall in the amplitude range of 100 to 12,800 \(\upmu\)V or, equivalently, of 40 to 82 dB, if the signal peak amplitudes \(A_{\text{max}}\) are expressed in decibels (dB), \(A_{\text{dB}}=20\log_{10}(A_{\text{max}}/1\ \upmu\)V) [38]. Because of heterogeneity of masonry, the point location method of AE sources exploiting signals recorded by multiple transducers could hardly be applied.
# Investigation on Characterization of Typical Characteristic in Compressor Based on Flat Plate Model
## 2 Flow-Induced Noise Theory
\(\delta_{ij}\) is the Kronecker symbol (\(\delta_{ij}=1\), \(i=j\); \(\delta_{ij}=0\), \(i\
eq j\)). Taking the first formula of Equation (6) for partial derivatives with respect to time and the second formula with respect to space, and subtracting the results:  
\[\frac{\partial^{2}\rho}{\partial t^{2}}=\frac{\partial}{\partial x_{i}}\left( \frac{\partial\rho v_{i}v_{j}}{\partial x_{j}}+\delta_{ij}\frac{\partial p}{ \partial x_{i}}+\frac{\partial\tau_{ij}}{\partial x_{j}}\right) \tag{8}\]  
Subtracting \(c_{0}^{2}\frac{\partial^{2}\rho}{\partial x_{i}^{2}}\) from both sides of Equation (8):  
\[\frac{\partial^{2}\rho}{\partial t^{2}}-{c_{0}}^{2}\frac{\partial^{2}\rho}{ \partial x_{i}^{2}}=-\frac{\partial^{2}T_{ij}}{\partial x_{i}\partial x_{j}} \tag{9}\]  
The relationship of the parameters in Equation (9) gives:  
\[\left\{ \begin{array}{l}T_{ij}=\rho v_{i}v_{j}-(p-c_{0}\rho)\delta_{ij}+\tau_ {ij}\\ p=p_{0}+p_{\rm a}\\ \rho=\rho_{0}+\rho_{a}\end{array} \right. \tag{10}\]  
\(p_{0}\) and \(\rho_{0}\) are the pressure and density of the flow without acoustic disturbance. \(p_{a}\) and \(\rho_{a}\) are the pressure and density variation caused by the sound pressure pulsation. \(c_{0}\) is the sound velocity outside the sound source and the mean flow region, and \(T_{ij}\) is the Lighthill stress tensor. Combining \(\frac{\partial^{2}\rho_{0}}{\partial t^{2}}-{c_{0}}^{2}\frac{\partial^{2}\rho _{a}}{\partial x_{i}\partial x_{i}}=0\) gives:  
\[\frac{\partial^{2}\rho_{a}}{\partial t^{2}}-{c_{0}}^{2}\frac{\partial^{2}\rho _{a}}{\partial x_{i}\partial x_{i}}=-\frac{\partial^{2}T_{ij}}{\partial x_{i} \partial x_{j}} \tag{11}\]  
For the flow in the low Mach number, on the premise of the small amplitude and ignoring high-order terms above the second order:  
\[\rho v_{i}v_{j}=(\rho_{0}+\rho_{a})v_{i}v_{j}\approx\rho_{0}v_{i}v_{j} \tag{12}\]  
Without considering the entropy source term and the viscous stress:  
\[T_{ij}=\rho_{0}v_{i}v_{j} \tag{13}\]  
Finally, the Lighthill acoustic analogy equation can be presented as:  
\[\left\{ \begin{array}{l}\frac{\partial^{2}\rho_{a}}{\partial x^{2}}-{c_{0}}^ {2}\frac{\partial^{2}\rho_{a}}{\partial x_{i}\partial x_{j}}=-\frac{\partial^ {2}T_{ij}}{\partial x_{i}\partial x_{j}}\\ \frac{\partial^{2}\rho}{\partial x_{i}\partial x_{j}}=\frac{1}{\partial^{2} \rho}\frac{\partial^{2}\rho}{\partial x^{2}}\end{array} \right. \tag{14}\]The acoustic analogy equation is consistent with the continuity equation and the momentum conservation equation. \(T_{ij}\) is an unknown quantity that can be derived from the complete N-S equation solved with the flow field calculation. The Lighthill acoustic analogy separates the flow field and the sound field artificially. The right side term of the first equation above can be regarded as the source term, which can be obtained from the flow field calculation, and the left side is the typical sound wave equation. The sound source information can be derived from the flow field calculation result by the analogy between the two equations above.
## 3 Results and Discussion
### Sulfur Analysis
#### 3.1.2 Partial Least Square Regression (PLSR)
A zero RMSE value means that the predicted value with spectral variables obtained by LIBS exactly matches the measured value by other conventional analysis methods. Therefore, the prediction ability of LIBS can be verified when the RMSE value is close to zero. The \(R^{2}\) values for calibration and cross-validation were improved from 0.9265 to 0.9408 and from 0.8981 to 0.9146, respectively. The RMSEC and RMSECV values were decreased from 0.0746 to 0.0670 and 0.0893 to 0.0818, respectively. Figure 4: PLSR model results (**a**) original data (**b**) the third-order polynomial with five points (**c**) the third-order polynomial with seven points (**d**) the third-order polynomial with nine points. To compare the degree of improvement, the predicted error of sulfur concentration between the PLSR for original data and SG smoothing-processed data is shown in Figures 6 and 7, respectively. The relative error was calculated individually from the  
Figure 5: PLSR model results (**a**) the fifth-order polynomial with seven points (**b**) the fifth-order polynomial with nine points (**c**) the fifth-order polynomial with eleven points (**d**) the fifth-order polynomial with thirteen points. reference value and predicted value. As shown in Figure 6, it was found that the maximum relative error value for PLSR of the original data was 24.61%. The error is quite high even though the absolute error was quite low; with a value of 0.1604%. As a result of the sulfur concentration in the coal being so small, even a small change can cause large variation. Therefore, a slight decrease in the error causes a significant improvement in the precision. Compared with the original data, the largest relative error value in processed data with the SG smoothing was 20.44%, as shown in Figure 7. When the data processing method is applied, the largest relative error value could be reduced by 4.174%. Therefore, the accuracy can be improved by processing data in a real-time sulfur concentration measurement system.
# Measurement of Ultra-High Speed by Optical Multistage Cascade Frequency Reduction Technology
## 2 Principle
### Light Path Construction
When the frequency reduction is made by N stages, the signal strength of each stage is only \(1/\mathrm{N}\) of the original signal strength, so it is necessary to amplify the signal  
Figure 2: Schematic diagram of the corresponding relationship between (**a**) velocity curve and (**b**) two-stage cascade frequency signal. first. At the same time, in order to improve the contrast and signal-to-noise ratio of the interference signal, it is necessary to attenuate the power and adjust the balance of signal light and reference light, respectively. If the signal-to-noise ratio decreases due to increased noise, the contrast between the frequency curve and the background in the spectrogram decreases. This will lead to an increase in the error of the proposed speed signal frequency and an increase in the error when the two signals are spliced.
## 5 Simulation Results  
### Simulation Setting  
To evaluate our proposed methods, we generate the four different non-uniform UEs distribution datasets obtained by [41]. The four different user distributions are Tokyo, Osaka, Sendai, and Nagoya, as shown in Figure 8.  
We assume that the HAPS works at a height of 20 km. Each HAPS can cover an area within a 20 km radius. The transmit power and bandwidth of each antenna array are 43 dBm and 20 Mhz, respectively. We consider that the transmission frequency is 2 GHz. Considering the interference between HAPSs, we set 18 H We set the updated value of horizontal tilt, horizontal HPBW, vertical tilt, and vertical HPBW to 20 deg, 4 deg, 10 deg, and 4 deg, respectively. We use python to implement all simulation programs.  
We set 100 epochs and 100 steps in each epoch, in the three RL-based approaches (\(\mathcal{Q}\)-learning, DQN, and DRLEA). In the DQN and DRLEA, we set the learning rate of the neural network as 0.0001. The discount factor \(\alpha\) is 0.65 and the learning rate \(\beta\) for \(\mathcal{Q}\) value calculation is 0.75 in the three RL-based approaches. In the PSO approach, the number of iterations is 100, the number of particles is 100, and the \(\omega_{1}=0.5\) and \(\omega_{2}=0.5\).
## 4 Measurement/Calibration of \(R_{s}\)
### Laboratory Calibration
#### 4.1.2 Static Renewal Design  
In this calibration mode, the samplers were deployed in a relatively small volume of exposed water, which was refreshed batchwise. To minimize the reduction of analyte concentration due to uptake into samplers during each batch exposure, the frequency for refreshing test water should be high and the solution concentration should be measured at the beginning and the end of each renewal period [29; 87]. When the average aqueous concentrations did not change greatly during renewals, the uptake curve was generated, allowing for a more straightforward modelling of the \(R_{\mathrm{s}}\) by Equation (4) [71].  
There are some strengths in this calibration method, such as simple operation, great feasibility to test many types of waters, and easy performance to alter environmental variable. However, this method has some disadvantages, for example, intensive exposure in water sampling and analysis, and the inability to systematically test the effects of water turbulence [112].
## 1 Introduction
#### Paper Structure  
The article consists of seven sections. The introduction provides a comprehensive overview of the research and points out the gaps that led to this research article. Section 2 points out related work in the literature. Section 3 then describes the research methods and introduces the concepts of effective risk and performance variables; Section 4 explains the application of the developed model to a case study. Section 5 describes the results of the case study; Section 6 analyzes the results; and finally, Section 7 draws conclusions from the work conducted.
## 2 Natural Polyphenols Directly Inhibiting Transmembrane Glucose Transport
### Green Tea Extracts with Inhibitory Effects on Glucose Uptake and Output
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compounds** & **Targets** & **Biological Models** & **Solvent** & **Dosage** & **References** \\ \hline \multirow{4}{*}{Alpha-mangostin} & \multirow{4}{*}{FASN} & Breast cancer MCF-7 cells & \multirow{4}{*}{-} & IC\({}_{50}\):3.57 & [19] \\  & & Breast cancer & & & \\  & & MDA-MB-231 cells & & & IC\({}_{50}\):3.35 & \\ \hline \multirow{4}{*}{Betulinic acid} & Stearoyl-CoA & HeLa cells & DMSO & 5–10 \(\mu\)g/mL & [20] \\  & desaturase 1 & Colon cancer stem cells & - & - & [21] \\ \cline{2-5}  & AMP-activated kinase pathway & WS-1, A549, MCF-7, H1299, H460 and MDA-MB-231 cells & DMSO & 0–50 \(\mu\)g/mL & [22] \\ \hline \multirow{4}{*}{Caffeic acid} & Glucose-6-phosphate dehydrogenase dehydrogenase & \multirow{4}{*}{In vitro} & \multirow{4}{*}{Tris-HCl buffer} & IC\({}_{50}\):0.481, 0.486 mM & [23] \\  & 6-phosphogluconate dehydrogenase & & & & \\ \cline{2-5}  & Glucose-6-phosphate dehydrogenase & Cultured rainbow trout gill cells & - & 0–0.1 mM & [24] \\ \hline \multirow{4}{*}{Certulenin} & FASN (cysteine in \(\beta\)-ketoacyl synthase domain) & In vitro & Potassium phosphate buffer & 0–80 \(\mu\)M & [25] \\ \hline \multirow{4}{*}{Curcumin} & AMP-activated kinase pathway & Ovarian cancer CaOV3 cells & - & 10–50 \(\mu\)M & [26] \\ \hline \multirow{4}{*}{Ellagic acid} & Glucose-6-phosphate dehydrogenase dehydrogenase & \multirow{4}{*}{In vitro} & \multirow{4}{*}{Tris/HCl buffer} & IC\({}_{50}\):0.072, 0.188 mM & [23] \\  & 6-phosphogluconate dehydrogenase & & & & \\ \hline \multirow{4}{*}{Emodin} & FASN & Colon cancer HCT116 and SW480 cells & - & Emodin (10–50 \(\mu\)M) and/or & [27] \\  & FASN & and SW480 cells & - & curulenin (100 \(\mu\)M) & [27] \\ \cline{2-5}  & Glucose transporter 1 & Pancreatic cancer & & & \\ \cline{2-5}  & Hexokinase II & MiaPaCa2 cells, & & & \\ \cline{2-5}  & Phosphofructokinase 1 & Athymic mice carrying pancreatic cancer cells & - & 0–200 \(\mu\)M & [28] \\ \hline \multirow{4}{*}{Epigallocatechin-3-gallate} & \multirow{4}{*}{FASN} & In vitro & - & 0.1–0.35 mM & [29] \\ \cline{2-5}  & & Hepatocellular & & \\ \cline{1-1}  & carcinoma HepG2 and HepG3B cells & DMSO & 0–160 \(\mu\)M & [30] \\ \cline{1-1}  & Acetyl-CoA carboxylase & Hepatocellular carcinoma HepG2 and HepG3B cells & DMSO & 0–160 \(\mu\)M & [30] \\ \cline{1-1}  & & Breast cancer MCF-7 and MDA-MB-231 cells & DMSO & 0–100 \(\mu\)M & [17] \\ \cline{1-1}  & Glucose transporter family & Human intestinal Caco-2/TC7 cells & DMSO & IC\({}_{50}\):0.091 mg/mL & [18] \\ \cline{1-1} \cline{2-5}  & Human erythrocytes & - & Ki\({}_{\text{EECCG}}\): 0.977 \(\mu\)M & [31] \\ \cline{1-1} \cline{2-5}  & Chorioocarcinoma & - & 0–100 \(\mu\)M & [32] \\ \cline{1-1}  & BeWo cells & - & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Naturally occurring compounds reported as metabolic antagonists in cancer glucose utilization and lipogenesis. Discussed in this review.
# A Research of Pulse Source for EFT Test of Electrical Vehicle Piles
## 2 Pulse Source Analysis  
The expected output pulse waveform of the source accorded with the double-exponential waveform and the single generating circuit, which could be equivalent to the RCL circuit, as illustrated in Figure 1. In the figure, K is the switch, C is the charging capacitor, L is the equivalent distributed inductance, R is the equivalent resistant, and E is the high-voltage charging source. Charged by the source to voltage of E, C supplied the load after K was triggered. Then, a double-exponential waveform electromagnetic pulse could be obtained under a certain physical condition.  
K is an idle switch, and turned on at \(t\) = 0; thus, the equivalent equation can be as follows:  
\[Ri+L\frac{di}{dt}+\frac{1}{C}\int idt=E, \tag{1}\]  
Differentiating Equation (1), a second differential equation can be obtained as follows:  
\[L\frac{d^{2}t}{dt^{2}}+R\frac{di}{dt}+\frac{1}{C}i=0, \tag{2}\]  
The square roots of Equation (2) are obtained as follows:  
\[\left\{\begin{array}{l}p_{1}=-\frac{R}{2L}+\sqrt{\left(\frac{R}{2L}\right)^{ 2}-\frac{1}{LC}}\\ p_{2}=-\frac{R}{2L}-\sqrt{\left(\frac{R}{2L}\right)^{2}-\frac{1}{LC}}\end{array} \right., \tag{3}\]  
Figure 1: Equivalent circuit of capacitor-discharge pulse power source.  
Suppose that the voltage on \(C\) is \(U_{c}(t)\) and the initial condition is \(i(0)=0,\ U_{c}(t)=E\), the discharge process of the circuit could be regarded as overdamped when \(R>2\sqrt{\frac{L}{C}}\). Then, the current equation is as follows:  
\[i(t)=-\frac{E}{L(p_{2}-p_{1})}\big{(}e^{p_{1}t}-e^{p_{2}t}\big{)}, \tag{4}\]  
Hence, the output voltage pulse of the load \(R\) is obtained as follows:  
\[U_{O}(t)=KE\big{(}e^{-\alpha t}-e^{-\beta t}\big{)}, \tag{5}\]  
In addition,  
\[\left\{\begin{array}{c}K=\frac{R\sqrt{C}}{\sqrt{R^{2}C-4L}}\\ \alpha=\frac{1}{2 LC}\Big{(}RC-\sqrt{R^{2}C^{2}-4 LC}\Big{)}\\ \beta=\frac{1}{2 LC}\Big{(}RC+\sqrt{R^{2}C^{2}-4 LC}\Big{)}\end{array}\right.. \tag{6}\]  
where \(\alpha\) is commonly far more than \(\beta\). The rise and fall time (\(t_{r}\) and \(t_{f}\)) of the pulse could be obtained using Equation (7) under the case of \(R>10\sqrt{\frac{L}{C}}\), as follows:  
\[t_{r}\approx 2.2\frac{L}{R},\ \ t_{f}\approx 2.2 RC. \tag{7}\]  
The simulated waveform of pulse output is demonstrated in Figure 2, where \(C=0.1\) uF, \(L=10\) uH, \(R=300\)\(\Omega\), and \(E=50\) kV.
## 3 Results and Discussion
### LR-DLSPPW Fabrication Process Flow and Key Issues
In order to etch through the simulated \(\mathrm{Al_{2}O_{3}}\)/Ag(Au)/\(\mathrm{Al_{2}O_{3}}\) (75-12-240 nm) stack, we developed the process of 500 nm thick (taking into account required over-etch) silicon oxide hard mask fabrication using standard plasma-enhanced chemical vapor deposition (PECVD), e-beam litho and ICP dry etch process flow. We observe that high-quality waveguide topologies could be nanofabricated using the proposed low-damage dry etching process. Unfortunately, the silicon oxide hard mask final removal process became the insurmountable problem. Dry etch removal leads to plasmonic metals damage, while both wet hydrogen fluoride (HF):DIW (1:100, 1:50) and vapor HF methods result in plasmonic metal adhesion failure to the bottom alumina layer (Figure 9b-c). Nevertheless, we consider that further silicon oxide hard mask removal recipe optimization (HF:DIW concentration and vapor HF etching recipe) may allow us to successfully finish the waveguide fabrication process. The experimentally measured etch selectivity of tungsten hard mask in \(\mathrm{BCl_{3}}\) gasses to alumina is relatively high, 3:1, which makes it possible to use a significantly smaller thickness of the tungsten hard mask as compared to silicon oxide. Therefore, thinner hard masks could be used, which results in better e-beam lithography resolution and reduced aspect ratio, which improved access of etching reagents and reaction products removal. As the result, a 140 nm thick tungsten hard mask is required to etch through the simulated \(\mathrm{Al_{2}O_{3}}\)/Ag (Au)/\(\mathrm{Al_{2}O_{3}}\) (75-12-240 nm) waveguide stack.
## 3 Results and Discussion
### Photocatalytic Study  
The evolution of the photocatalytic degradation of RhB at different concentrations of doping is presented in Figure 11. The photodegradation rate is determined from the expression:  
\[\text{Degradation rate }(\%)=\frac{\text{A}_{0}-\text{A}_{t}}{\text{A}_{0}}\times 100\]  
A\({}_{0}\) and A represent the absorbances of the RhB solution before and after irradiation during a time t, respectively.  
The photodegradation rate of RhB in solution without photocatalyst is only about 5% after 120 min of irradiation. However, in the presence of the undoped ZnO photocatalyst, the photodegradation rate reaches 40%, in the same conditions. Interestingly, with the presence of ZnO-Eu-Na photocatalyst, the photodegradation rate further increased and reached about 93% for the same time of solar irradiation (Table 4). In addition, the ZnEuNa4 sample exhibits the greatest efficiency of photocatalytic degradation. When the result of this degradation reaction as a function of irradiation time are plotted (\(\ln(\text{A}_{0}/\text{A})=\text{f}(\text{t})\)), a linear curve is obtained (Figure 12), indicating a reaction with first order kinetic [45]. From Table 4, there is an improvement in the rate of photocatalytic oxidation of RhB, especially for the ZnEuNa4 photocatalyst, which clearly proves the influence of doping and its importance in the photodegradation of pollutant.  
Figure 11: Relative photocatalytic activity of ZnO:Eu:Na NCs under sunlight irradiation.  
Figure 12: Photodegradation kinetics of RhB using the ZnO:Eu:Na photocatalysts.  
The dominant species contributing to the RhB photodegradation can be identified by proceeding to a study of scavenger addition. For this goal, we added Ethanol and Benzoquinone (BQ) to the mixture during the irradiation for the quenching of the hydroxyl [46; 47] and superoxide [48] radicals, respectively. As shown in Figure 13, the photodegradation efficiency reaches 90% in the absence of scavengers for the ZnEuNa4 photocatalyst. However, the efficiency is limited to 32% and 19% in the presence of ethanol and BQ, respectively. The less capability of RhB degradation in the presence quenching agents proves that the hydroxyl and superoxide radicals are the major species in the presence of the photocatalysts and react identically to insure the RhB photodeterioration under sunlight irradiation.  
To test the stability of the ZnEuNa4 photocatalyst, the powder was centrifuged and washed with absolute ethanol and DI water after each cycle of photodegradation. As shown from Figure 14, the recycled usage has no appreciable effect on the photocatalytic properties of ZnEuNa4 photocatalyst till four runs, showing its high efficiency and its good stability.
## 4 Results
#### 4.1.3 Influence of Substrate Materials on Antenna Behavior
Textile substrates, although exhibiting the largest average area (\(\approx\)500 mm\({}^{2}\)), the lowest efficiency (\(\approx\)40%) and gain (\(\approx\)2 dBi), and low operating frequencies, are ideal for wearable systems due to their flexibility. In contrast, Rogers substrates enable the most compact designs (\(\approx\)1000 mm\({}^{2}\)), operate consistently at higher frequencies (\(\approx\)3 GHz), and offer moderate efficiency and gain, making them suitable for miniaturized high-frequency applications. RT Duroid materials position themselves as a high-performance option, offering superior efficiency (\(\approx\)70%), and stable behavior at moderately high frequencies (\(\approx\)2.5 GHz), and gain (\(\approx\)4 dBi), while maintaining a moderate footprint. On the other hand, FR4, a cost-effective and widely used material, exhibits a moderate area (\(\approx\)2000 mm\({}^{2}\)), with average efficiency (\(\approx\)60%) and gain (\(\approx\)5 dBi), albeit with considerable variability, in addition to its capability to operate at higher frequencies. Finally, unconventional substrates offer high versatility, with area and frequency spanning a broad range, along with moderate efficiency and gain, making them attractive for exploratory or customized designs. RQ2. What Are the Most Commonly Used Frequency Bands for RF Energy Harvesting in Energy Harvesting Applications? To address the second research question, this section examines the frequency bands most frequently employed in RF energy harvesting applications. Operating frequency selection is a critical factor in rectenna system design, directly influencing antenna dimensions, propagation characteristics, available ambient power density, and overall system efficiency. By analyzing distribution of target frequencies reported in the reviewed literature, this  
Figure 13: Boxplot showing gain distribution of different substrates for energy harvesting,  
section identifies dominant spectral regions, highlights preferred frequency bands--such as ISM, UHF, and sub-GHz ranges--and explores technical and regulatory motivations behind their prevalence in energy harvesting scenarios.