esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structural Principle, Detection Mechanism, and Performance Indicators
### Refractive Index Sensing Mechanism
#### 2.2.3 MMI Sensing Detection Mechanism  
Based on different incident conditions, MMIs can be classified into three types: (1) general interference, (2) paired interference, and (3) symmetric interference. The optical field simulation shown in Figure 6 is the simplest structure among the three types.  
When:  
\[exp\bigg{(}i\frac{m(m+2)\pi}{3L_{\pi}}z\bigg{)}=1\;or\;(-1)^{m}, \tag{14}\]  
we can derive that when the length \(L\) of the multimode waveguide region satisfies the condition:  
\[L=a\times 3L_{\pi}(a=0,\;1,\;2\dots\dots). \tag{15}\]  
Images corresponding to the input field profile are formed at \(x\) and \(-x\). Table 1 is the comparison of imaging characteristics. According to the above analysis, the detection mechanism of MMI is based on the fact that changes in the refractive index of the MMI medium affect the speed of light transmission and the phase shift in the waveguide. This, in turn, affects the interference phenomenon and causes changes in the intensity or wavelength of the output light. By measuring these changes, the variations in the refractive index of the surrounding medium can be determined.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interference Mechanism** & **General** & **Paired** & **Symmetric** \\ \hline Inputs \(\times\) Outputs & N \(\times\) N & \(2\times\) N & \(1\times\) N \\ First single image distance & \(3L_{\pi}\) & \(L_{\pi}\) & \(3L_{\pi}/4\) \\ First N-fold image distance & \(3L_{\pi}/\)N & \(L_{\pi}/\)N & \(3L_{\pi}/4\)N \\ Excitation condition & none & \(c_{m}=0\) & \(c_{m}=0\) \\ Inputs location & any & \(y=\pm W/6\) & y = 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of imaging characteristics for the three types of MMI.  
Figure 6: Three types of MMI structures.
## 2 Theoretical Background
### Similar Scales and Features Investigated  
While vastly predominant in the common use, the mel-scale is not the sole nonlinear scale viable. Other options proposed in the literature include the Bark Scale [50] and the Equivalent rectangular bandwidth (ERB)-rate scale [51], to cite the two main competitors. In both cases, several Hertz-Bark/Hertz-ERB relationships were proposed in an analytical form, similarly to the Mel-Scale,  
Figure 1: The Mel-Frequency Cepstral Coefficients (MFCCs) extraction procedure; frame #5 is used as an example. From left to right: the acceleration time history and its corresponding power spectrum, the power cepstrum of the same, and the resulting MFCCs in the quefrency domain.  
as defined in [52] and in [53], respectively. Some of these alternatives, such as the Bark-Frequency Cepstral Coefficients (BFCCs) [54], have also been recently tested by the Authors for SHM purposes. In this context, three novel scales, namely, the Energy COntent-based MMFCCs (ECO-MMFCCs), the MOdal Frequency-centred MMFCCs (MO-MMFCCs), and the LOw-Frequency Restricted MMFCCs (LO- MMFCCs) have been recently proposed [22].  
Apart from different psychoacoustical, Mel-like scales as the ones just mentioned, non-triangular weighting functions can also be used, such as gammatone filters [55]. Additionally, the acceleration THs can be pre-processed before applying the extraction procedure. An option is the mentioned Teager-Kaiser Energy (TKE) Operator. This, combined with the Gammatone filters, is the core of the method proposed in this work and will be better detailed in the two next subsections.  
To sum up and for comparison with the previous studies, Figure 2 reports a pictorial representation of the tested approaches.
## 2 Materials and Methods
### Gap Filling and Statistical Analysis  
To calculate the daily and seasonal variations of _Ne_, it was necessary to interpolate missing values due to problems such as power outages and equipment failure. Linear interpolation was used to fill gaps that were shorter than 3 h. For longer gaps, _Ne_ was filled based on the light-response curve to daytime data (_PAR_\(\geq\) 5 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) with 10-days windows, and the night-time exchange (_PAR_ < 5 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) was filled based on measured \(T_{air}\) with Equation (2) in whole measurement duration. For simple statistical analysis, Microsoft Excel functions were used. Correlation analysis was applied to check if environmental variables had an influence on light response parameters and analysis of variance was used to analyze the differences among nitrogen addition treatments in _GPP_, \(R\) and _Ne_, using SPSS 23.0 (SPSS Inc., Chicago, IL, USA).
## 2 Experimental
### Synthesis of Tert-butyl-3-(3-(methoxycarbonyl)phenyl)-1H-indazole-1-carboxylate (Compound **11**)  
As shown in Scheme 1, compound **11** was obtained through Pd-catalyzed Suzuki-Miyaura coupling of compounds **3** with **10**, which is comprehensively described in Section 2.11. Yellow oils (yields of 6-96%, Table 3 and Figure 4). \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta_{\rm H}\), ppm: 8.66 (1H, t, \(J\) = 1.8 Hz, ArH), 8.20 (2H, m, ArH), 8.13 (1H, d, \(J\) = 7.9 Hz, ArH), 7.98 (1H, d, \(J\) = 8.2 Hz, ArH), 7.57 (2H, m, ArH), 7.38 (1H, t, \(J\) = 7.6 Hz, ArH), 3.95 (3H, s, methyl), 1.74 (9H, s, _tert_-butyl) (Figure S20, Section S20, Supplementary Materials). \({}^{13}\)C NMR (101 MHz, CDCl\({}_{3}\)) \(\delta_{\rm C}\), ppm: 166.83, 149.40, 148.87, 141.12, 132.74, 132.50, 130.87, 130.39, 129.39, 129.09, 129.02, 124.19, 124.10, 121.33, 115.07, 85.18, 52.38, 28.28 (Figure S21, Section S21, Supplementary Materials). ESI-HRMS (positive, m/_z_): 353.1518 (Calcd. for [M+H]\({}^{+}\) 353.3909) (Figure S22, Section S22, Supplementary Materials). Anal. Calcd. for C\({}_{20}\)H\({}_{20}\)N\({}_{2}\)O\({}_{4}\): C, 68.18; H, 5.68; N, 7.95. Found: C, 68.75.01; H, 5.38; N, 6.61. FT-IR (KBr) \(\sigma\), cm\({}^{-1}\): 2983 (m, methyl on _tert_-butyl), 1729 (vs, C=O), 1649 (m, C=N), 1380 (m, C-N), 1246 (m, C-O) (Figure S23, Section S23, Supplementary Materials).  
Figure 4: Recycling experiments on Suzuki–Miyaura cross-coupling of compounds **3** with **10**, catalyzed by Pd catalyst and immobilized over imidazolium ionic liquid: (**a**) Pd(OAc)\({}_{2}\) over ILs; (**b**) PdCl\({}_{2}\) over ILs; (**c**) PdCl\({}_{2}\)(dppf) over ILs; (**d**) PdCl\({}_{2}\)(dbbpf) over ILs.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 4 User Finite State Machine for Proposed Scheme
### Scenarios  
In this section, we explain seven scenarios in the user FSM that can occur when a user is activated under various mobile communication environments. It can be determined by the final states \(\mathcal{F}\) in Figure 3. We first explain the RLF condition in Figure 3. If a user notices the channel state is not good enough to continue data transmission (i.e., c1. \(\gamma_{i}<Q_{\text{out}k_{s}}\)), the user waits until the channel state is recovered (i.e., c3. \(\gamma_{i}>Q_{\text{in}k_{s}}\)) during the \(T310\) period. If the channel state is not recovered after c4 (i.e., \(T310\) is expired) or there is no remaining MR that the user can send (i.e., c8. \(N_{r}(i)=N_{\text{set}}(l_{s})\)), it declares RLF. Furthermore, during mobility, if the SINR is lower than the SINR threshold, i.e., c12. \(\gamma_{i}<\gamma_{\text{th}}\), it also declares RLF. The description of each scenario is provided below:  
**F1**: \"RLF\"--the case where RLF occurs before the serving beam triggers beam switching or handover. Then, the user has to recover the connection with the serving beam.  
**F2**: \"Too late beam switching\"--the case where RLF occurs with the serving beam and recovers the connection with the target beam.  
**F3**: \"Too late handover failure\"--the case where RLF occurs with the serving beam and recovers the connection with the target cell.  
**F4**: \"Handover success\"--the case where handover to the target cell is a success, with no RLF.  
**F5**: \"Too early handover failure\"--the case where RLF occurs immediately after the user is connected to a target cell and recovers the connection with the source beam.  
**F6**: \"Beam switching success\"--the case where beam switching with the target beam is a success, with no RLF.  
**F7**: \"Early beam switching\"--the case where RLF or data pending occurs immediately after the user is connected to a target beam, and the user recovers the data channel to the source beam.
## 4 Discussion
### Developing Predictive Models to Maintain Optimal LINAC Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Purpose** & **Key** & **Data, Methods** & **Results** & **Downtime Prediction** & **Year** & **References** \\ \hline \multirow{8}{*}{**[PERSON] et al.**} & To demonstrate the & \multirow{4}{*}{QA} & \multirow{4}{*}{490 data, ARIMA} & \multirow{4}{*}{The predictive model} & \multirow{4}{*}{NA} & \multirow{4}{*}{2020} & \multirow{4}{*}{[23]} \\  & feasibility of & & & & & & \\ predictive quality & & & & & & & \\ assurance using MPC & & & & & & & \\ tests that allow & & & & & & & \\ preventive & & & & & & & \\ maintenance & & & & & & & \\ procedures & & & & & & & \\ \hline \multirow{8}{*}{**[PERSON] et al.**} & A multivariate & \multirow{4}{*}{MLC} & \multirow{4}{*}{4280 log files, PCA using PI} & \multirow{4}{*}{NA} & \multirow{4}{*}{2020} & \multirow{4}{*}{[24]} \\  & approach to the & & & & & & \\ analysis of trajectory & & & & & & & \\ log data and used to & & & & & & & \\ predict upcoming & & & & & & & \\ MLC replacement & & & & & & & \\ needs & & & & & & & \\ \hline \multirow{8}{*}{**[PERSON] et al.**} & To increase the & \multirow{4}{*}{RF power} & 9 million & \multirow{4}{*}{Anomaly detection} & \multirow{4}{*}{NA} & \multirow{4}{*}{2020} & \multirow{4}{*}{[25]} \\  & LINAC’s reliability & & & & & & \\ by early detection & & & & & & & \\ and prediction of & & & & & & & \\ sources, ARI, & & & & & & \\  & operations, down to & & & & & & \\ the component level & & & & & & \\ \hline \multirow{8}{*}{**[PERSON] et al.**} & To predict & \multirow{4}{*}{QA} & \multirow{4}{*}{Machine status} & A customized & \multirow{4}{*}{NA} & \multirow{4}{*}{2016} & \multirow{4}{*}{[26]} \\  & component failure or & & & & & & \\ system dysfunction & & & & & & & \\  & & & & & & \\  & of the LINAC & & & & & & \\ \hline \multirow{8}{*}{**This study**} & Descriptive time & \multirow{4}{*}{Cause of } & \multirow{4}{*}{20 years (359 & maximum of 2820 h. & Downtime & \multirow{4}{*}{-} \\ series analysis for & & & & & & \\ LINAC & & dosimetric & seasonal & (\(p<0.001\), SD \(\approx 0.0\), & using a time & \\  & maintenance history & parts, etc.) & ARIMA & \(\sigma^{2}\) = 328.33 \(\pm\) 9.4, CC & series analysis & \\  & with downtime & & & & = 1.00, and & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular} Abbreviations: MPC, machine framework check; QA, quality assurance; RMSE, root mean square error; PCA, principal component analysis; PI, performance index; ARI, adjusted Rand index; NA, not applicable.  
\end{table} 
Table 3: Comparison with various prediction studies for maintaining LINACs.
## 4 Analysis On Asymptotic Outage Probability
### Asymptotic Outage Probability at D2D User 2  
Similar to the derivation of \(OP_{D1-asym}\), an asymptotic outage expression for \(OP_{D2-bi}\). It is noted that the related exact expression is presented in (25), now it can be derived as  
\[\begin{array}{l} OP_{D2-asym}=\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c }K\\ k\end{array}\right)(-1)^{k-1}\frac{\lambda_{2}}{\theta k\varpi\lambda_{f_{2}}+ \lambda_{2}}\left(1-\frac{\theta k}{\lambda_{2}}\right)\right)\\ \times\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{(a_{1  
and with regard to lower bound, it can be obtained lower bound of user \(D_{2}\) as  
\[\begin{array}{l} OP_{D2-floor}=\left(1-\sum\limits_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{\lambda_{2}}{\pi\alpha\rho\lambda_{f_{2}}+ \lambda_{2}}\right)\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{(\alpha_{1}-\epsilon_{2}\mu_{2})\lambda_{1} }{(\alpha_{1}-\epsilon_{2}\mu_{2})\lambda_{1}+\epsilon_{2}\alpha\lambda_{f_{1} }}\right)\\ +\left[\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{\mu_{2}\lambda_{2}}{\alpha_{2}\lambda_{2}+ \epsilon_{2}\alpha\lambda_{f_{2}}}\right)\right.\\ +\left(1-\frac{\lambda_{2}}{\lambda_{2}+\epsilon_{2}\alpha\lambda_{f_{2}}} \right)\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{(\alpha_{1}-\epsilon_{2}\mu_{2})\lambda_{2 }}{(\alpha_{1}-\epsilon_{1}\mu_{2})\lambda_{1}+\epsilon_{1}\alpha\lambda_{f_{ 2}}}\right)\\ -\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{\alpha_{2}\lambda_{2}}{\alpha_{2}\lambda_{ 2}+\epsilon_{2}\alpha\lambda_{f_{2}}}\right)\\ \times\left(1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{(\alpha_{1}-\epsilon_{2}\mu_{2})\lambda_{ 2}}{(\alpha_{1}-\epsilon_{1}\mu_{2})\lambda_{1}+\epsilon_{1}\alpha\lambda_{f_{ 1}}}\right)\left(1-\frac{\lambda_{2}}{\lambda_{2}+\epsilon_{1}\alpha\lambda_{f _{2}}}\right)\right]\\ \times\left(\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{(\alpha_{1}-\epsilon_{2}\mu_{2})\lambda_{ 1}}{(\alpha_{1}-\epsilon_{1}\mu_{2})\lambda_{1}+\epsilon_{1}\alpha\lambda_{f _{1}}}\right).\end{array} \tag{29}\]  
**Remark 1**.: _These approximate performances provide easy way to evaluate system performance rather than complex manner of derived expressions in term of outage probability. It is expected that these approximate expressions exhibits corresponding curves in simulation and they will match with exact curves achieved by analytical expressions presented in Section 3._
# Performances of the Synergy of Silica Fume and Waste Glass Powder in Ternary Blended Concrete
## 3 Discussion of Results
### Fourier Transform Spectroscopy Details on Compressive Strength  
From Figure 9, the combination of glass powder and silica fume affects the bond characteristics of the binder. The silicate organization in the OPC binder is different from the blended glass/silica-fume concrete. The Si-O-Si vibration at 966 cm\({}^{-1}\) is grossly affected due to the infusion of additional amorphous silica from two different sources. This causes the broadening of this band, which suggests more silicate concatenation in blended, glass-infused silica-fume concrete. The vibration of sulphate bonds in gypsum is equally affected at the wavenumbers of 1115, 1107 and 1130 cm\({}^{-1}\) for the OPC, blended silica-fume and ternary (glass and SF) blended binders, respectively. The presence of amorphous silica in glass or SF could cause changes in the chemical composition of the binder either by infusing Si in portlandite to form CSH or in the hydration of C\({}_{3}\)A or C\({}_{4}\)AF compounds to form hydrogarnet (CASH). The changes in the configuration of portlandite also caused the decrease in the carbonation of the ternary binder. The vibration of the O=C=O bond was more pronounced with a stronger peak at 1437 cm\({}^{-1}\), but appears to broaden and flatten out in the presence of amorphous silica in both WGP and SF. This suggests that amorphous silica in supplementary cementitious materials could cause a reduction in the carbonation process by pore blockage in the aftermath of secondary hydration. Similarly, the vibration of the O=C=O bonds from CO\({}_{2}\) from CO\({}_{3}\)\({}^{2-}\) (calcite) at the wavenumber of 2347 cm\({}^{-1}\) was sparingly observed in the blended silica/glass (ternary) binder. The reduction in hydroxyl-based compounds in glass/silica-fume concrete caused the OH\({}^{-}\) vibration peak at 3642 cm\({}^{-1}\) to become shallow with the reduced intensity and vibrational frequency at the wavenumbers of 3638 cm\({}^{-1}\) and 3642 cm\({}^{-1}\) in the binary silica-fume (C\({}_{90}\)G\({}_{0}\)S\({}_{10}\)) and ternary blended silica/glass (C\({}_{90}\)G\({}_{\rm x}\)S\({}_{10-\rm x}\)) concretes, respectively. In this regard, the incorporation of WGP and SF in blended concrete further enhanced its durability in terms of the reduction in carbonation due to the consumption of portlandite to form CSH.
# Antiplatelet Activity of Phenolic Compounds-Fortified Merlot Wine and Pure Phenolic Compounds
## 3 Results
### Antiplatelet Activity of lyophilized Wines and Phenolic Compounds
The antiplatelet potential of these compounds was studied at different concentrations (0.1, 0.25, 0.50, 0.75, and 1 mg/mL) in PRP (Figure 2). The results revealed that all phenolic compounds, except gallic acid, had antiplatelet activity depending on the agonist used. Specifically, epicatechin and catechin, both isomers, inhibited platelet aggregation induced by TRAP-6 and collagen. Catechin specifically had a concentration-dependent antiplatelet potential when platelets were stimulated by collagen. For its part, gallic acid did not show antiplatelet activity, while caffeic acid inhibited platelet aggregation stimulated by ADP and collagen, being more effective against the latter agonist (IC\({}_{50}\) = 0.83 \(\pm\) 0.23 mg/mL). On the other hand, quercetin showed antiplatelet potential against the three agonists studied, exhibiting potent inhibition in  
Figure 2: Study of platelet aggregation of free pure phenolic compounds induced by collagen, TRAP-6, and ADP. PRP was incubated with vehicle or phenolic compounds (0.1, 0.25, 0.50, 0.75, and 1 mg/mL). After 4 min of incubation at 37 \({}^{\circ}\)C, PRP was stimulated with the agonist to initiate platelet aggregation for 6 min. The negative control is DMSO 0.2% (absence of the extracts). The bar graph indicates maximum aggregation expressed as a percentage (mean \(\pm\) SEM; \(n\) = 3 for duplicate). Differences between groups were analyzed by one-way ANOVA using [PERSON]’s post-hoc test. ***\(p\) < 0.001, **\(p\) < 0.01, and *\(p\) < 0.05 denote statistically significant differences compared to the vehicle; ns: non-statistical difference with respect to the vehicle. each case (IC\({}_{50}\) = 0.81 \(\pm\) 0.31 mg/mL, collagen; IC\({}_{50}\) = 0.92 \(\pm\) 0.11 mg/mL, TRAP-6; and IC\({}_{50}\) = 0.65 \(\pm\) 0.62 mg/mL, ADP). These findings suggest that the phenolic compounds present in the F8 wine sample could be responsible for its antiplatelet activity. The presence of multiple compounds with antiplatelet activity may contribute to the synergistic effect observed in the F8 wine sample, reinforcing its positive potential in the inhibition of platelet aggregation and its possible implications for cardiovascular health.
# Predicting Human Location Using Correlated Movements
## 3 Preliminaries
### Location Extraction  
In this subsection, the way to extract locations for people is described. In this work, location information based on time slots is used. Note that a mobile device may be connected to several base stations (e.g., cell towers or access points) during a time slot. Therefore, in such cases, the way to determine the locations of people in a time slot is needed.  
Let \(\lambda\) denote a threshold for location extraction (\(0\leq\lambda\leq 1\)). By using \(\lambda\), the representative base station with which the phone is associated the most is determined, since a mobile device may connect to several base stations during a time slot. Specifically, if the ratio of the time a person spends at a base station to the time slot length exceeds \(\lambda\), this base station is regarded as the representative human location in the specific time slot. In this work, \(\lambda\) and time slot length are set to 0.5 and 30 min, respectively. In cases where no base station satisfies the conditions for location extraction, or where a mobile phone does not receive any signal during a time slot, the person's location in that time slot is marked as undefined.  
In case of the MIT dataset, positions at which people rarely stay should be pruned. The locations are first arranged in descending order of occurrence frequency in the dataset. Then, a location set that contributes 98% of the cellular traces is selected for use in this work. Let \(\mathbb{L}\) denote the set of all symbolic locations in the dataset after the pruning process. In the MIT dataset, the total number of positions remaining after the above pre-processing is 482, i.e., \(|\mathbb{L}|=482\). Meanwhile, in the Dartmouth dataset, there are total 399 APs. Table 1 summarizes the main characteristics of both datasets.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 5 System Testing
### Analysis of Test Results with Different Insulation Media Thickness  
In practical applications, the thickness of insulation layers under different transmission voltages is different in order to account for the safety of the cables. The insulation thickness of commonly used 6-10 kV cables is generally 3.5 mm, 4.5 mm, 5.5 mm, and 6.5 mm. For these four insulation thicknesses, the recognition accuracy results of the three models were obtained after 100 instances of actual testing with the input voltage fixed at 8 kV; the results are shown in Table 4.
## 3 Measurement Results
### Specific Absorption Rate (SAR)  
In WBAN, SAR is a key component for safety measurement, which can be measured according to the standard as mentioned by [PERSON] et al. in [30]. SAR shows how the electromagnetic waves are absorbed by the tissues that can cause a temperature rise.  
Figure 9: (a) Setup for the 3D far-field measurement for meander line antenna in the anechoic chamber, (b) human chest model, and (c) human arm model.  
Figure 10: Simulated and measured radiation patterns of E and H planes at both bands, left side: the lower frequency, right: the higher frequency, **(a)** free-space (2.2 GHz and 5.6 GHz), **(b)** on-chest (2.3 GHz and 5.7 GHz), **(c)** on-arm (2.2 GHz and 5.7 GHz).  
In order to control the radiation damage to the human body, the SAR value should be lower than 1.6 W/kg according to the 1 g (Federal Communication Commission (FCC, U.S. standard)) [31], or lower than 2 W/kg following the 10 g (ICNIRP, European standard) [32]. However, high SAR values are usually obtained if the antenna is located close to the skin surface. In measurements, the gap between the model and the antenna was kept at 10 mm to be similar to our assumption in the simulation, which reduced the SAR values as well as the loading effects of the body. Average SAR values for 1 g and 10 g tissue at both bands were calculated; they were lower than the standard limits for an input power of 20 dBm (0.1 W). Table 4 shows different SAR values calculated using CST MWS. Three scenarios were studied, i.e., antenna placed (1) in free-space, (2) on-chest, and (3) wrapped around the arm. The truncated ground with the ShieldIt layer was enough for protecting the body from unwanted radiation hazards. Maps of SAR values for these scenarios with the assumption of the maximum input power of 0.1 W in both bands are illustrated in Figure 11.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Frequency** & **1-g** & **10-g** & **1-g** & **10-g** \\ \cline{2-5}
**(GHz)** & \multicolumn{2}{c|}{**(On-Body)**} & \multicolumn{2}{c|}{**(On-Arm)**} & \\ \hline
2.2–3.0 & 1.22 & 0.63 & 1.38 & 0.883 \\ \hline
5.7–6.0 & 0.75 & 0.71 & 0.692 & 0.325 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of different SAR values.  
Figure 11: Simulated SAR (10-g average) results when the antenna is on the (**a**) upper-right arm of a male subject (at 2.2 GHz (left) and 5.7 GHz (right)), and (**b**) on the chest center (2.3 GHz (left) and 5.7 GHz (right)).