esa-sceva/satcom-chunk-collection huggingface dataset:
# Ischemic Preconditioning (IPC) Enhances the Accuracy and Stability of Proprioception
## 3 Results
TPDR: two-point discrimination threshold on right lower limb; TPDL: two-point discrimination threshold on left lower limb; D40R: difference in position sense test of right lower limb; D40L: difference in position sense test of left lower limb. Based on estimated marginal means, the significance level (\"\"\") for mean differences was set at 0.05, with multiplicity adjustment via the Bonferroni method. After ischemic preconditioning, the accuracy and stability of two-point discrimination thresholds were improved (Figures 3 and 4). The greatest improvement was observed at 90 min after ischemic preconditioning. On the following day (24 h after ischemic preconditioning), the two-point discrimination thresholds remained close to the levels immediately after ischemic preconditioning. There were no significant differences observed between the left and right sides. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multirow{2}{*}{**(I) Time**} & \multirow{2}{*}{**(J) Time**} & \multirow{2}{*}{**Error of the Mean (I–J)**} & \multirow{2}{*}{**SD**} & \multirow{2}{*}{**\(p\)**} & \multirow{2}{*}{**CI, 95\%**} \\ \hline \multirow{8}{*}{D40L} & \multirow{8}{*}{Pre} & Post & 3.742 & 2.519 & 0.928 & \(-\)3.722 & 11.206 \\  & & 90 min & 8.384 * & 1.749 & 0.001 & 3.203 & 13.565 \\  & & 24 h & 1.121 & 2.541 & 1.000 & \(-\)6.408 & 8.650 \\  & & Pre & \(-\)3.742 & 2.519 & 0.928 & \(-\)11.206 & 3.722 \\  & & 90 min & 4.642 & 1.597 & 0.056 & \(-\)0.088 & 9.373 \\  & & 24 h & \(-\)2.621 & 1.530 & 0.624 & \(-\)7.155 & 1.913 \\  & & Pre & \(-\)8.384 * & 1.749 & 0.001 & \(-\)13.565 & \(-\)3.203 \\  & 90 min & Post & \(-\)4.642 & 1.597 & 0.056 & \(-\)9.373 & 0.088 \\  & & 24 h & \(-\)7.263 * & 1.688 & 0.003 & \(-\)12.263 & \(-\)2.263 \\  & & Pre & \(-\)1.121 & 2.541 & 1.000 & \(-\)8.650 & 6.408 \\  & 24 h & Post & 2.621 & 1.530 & 0.624 & \(-\)1.913 & 7.155 \\  & & 90 min & 7.263 * & 1.688 & 0.003 & 2.263 & 12.263 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 3: Accuracy\({}_{\rm TPD}\) estimate marginal mean values. The upper and lower boundaries of the box denote the 75 th and 25 th percentiles, respectively. Whiskers span 1.5 \(\times\) IQR, with observations beyond this range designated as outliers. A solid horizontal line within the box indicates the median value. Asterisks (*) denote statistically significant differences between competitive level groups: *, \(p<0.05\); ***, \(p<0.0001\). Following ischemic preconditioning, there was a notable enhancement in both the accuracy and stability of knee joint proprioception (Figures 5 and 6). The optimal improvement was attained at the 90-min mark after ischemic preconditioning. Interestingly, even on the subsequent day (24 h after ischemic preconditioning), knee joint proprioception was able to maintain a level similar to that immediately following ischemic preconditioning, with no significant disparities observed between the left and right sides. These findings align with the outcomes observed for two-point discrimination thresholds.
###### Abstract  
In the space environment, cosmic rays and high-energy particles may cause a single-event upset (SEU) during program execution, and further cause silent data corruption (SDC) errors in program outputs. After extensive research on SEU and SDC errors, it has been found that SDC errors in the routing program in satellite networks may lead to the emergence of Sinkhole (SH) and Grayhole (GH) nodes in the network, which may cause damage to satellite networks. To find and solve the problems in time, a digital-twin-based detection and protection framework for SDC-induced SH and GH nodes in satellite networks is proposed. First, the satellite network fault model under SEU and the generation mechanism of SH and GH nodes induced by SDC errors are described. Then, the data structure based on digital twins required by the proposed detection and protection framework is designed, and the detection methods of SH and GH nodes induced by SDC errors are proposed. SKT and LLFI simulation tools are used to build a simulated Iridium satellite network and carry out fault injection experiments. Experiment results show that the accuracy of the proposed detection method is 98-100%, and the additional time cost of routing convergence caused by the proposed framework is 3.1-28.2%. Compared with existing SH and GH detection methods, the proposed methods can timely and accurately detect faults during the routing update stage.  
satellite network; routing mechanism; SDC error; digital twin; sinkhole; grayhole; detection and protection
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.4 Pin Shorting  
Introducing a shorting pin between the patch planes and the ground increases the effective size of the antenna, resulting in a reduction of the essential physical dimensions of it for an explicit frequency of operation [2; 3]. This is a similar technique, where the ground plane doubles the height of a monopole antenna. Therefore, it generally produces a planar inverted-F antenna (PIFA) with identical resonance performance, similar to a double-sized antenna deprived of the shorting pin [34]. However, it can cause a reduction in antenna aperture, resulting in a significant decrease in antenna directivity, which can affect the effective gain of the antenna directly [32].
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 2 Materials and Methods
### Gridding  
To allow a comparison of the results from FireBIRD as well as the MODIS and VIIRS reference data considering their differing spatial resolutions, each identified active fire location is assigned to a grid cell in a homogenous, hexagonal grid. The H3 Hexagonal Hierarchical Spatial Index [29] is used to obtain a mesh of grid cells, in which each cell covers an equally sized area of the Earth's surface. This geospatial indexing further allows the efficient aggregating and querying of the study site data. An approximate cell size of 1 km\({}^{2}\) is used. This size reflects the resolution of the MODIS thermal sensor at nadir. Due to geometric distortions towards the edge of the swath, a MODIS active fire pixel can actually cover an area of a size up to several square kilometers. A grid size thus coarse, however, would diminish the advantages of the HSRS sensor's higher resolution, which is one of the study points in this research.  
Since the FireBIRD as well as the reference sensors all feature differing spatial resolutions, the FRP values are normalized by area using the exact size of the respective pixel, yielding a measure of FRP per ha. In case of the MODIS and VIIRS reference data, the archived vector product from the FIRMS website is used, where each point represents merely the centroid of the original raster pixel. The size of the original pixel is therefore calculated using the respective attribute fields (scan and track, respectively). For each pixel centroid, the correlating grid cell is determined and the normalized FRP value for each fire pixel is stored for each cell. The average is determined consecutively. These steps are performed separately for each sensor, as well as for each overpass. As a reference, the entirety of vector points featuring the closest temporal and spatial vicinity to the respective FireBIRD overpass is selected from the original FIRMS dataset.
# Measurement of Lower Extremity Alignment Using a Smartphone Application
## 3 Results
### Validity of the Smartphone Application as a Diagnostic Tool  
To determine the validity of the angular deformity-leg angle measurement obtained via the smartphone application, the correlation between PLEAA and HKAA measured twice by evaluators A and B was analyzed using Pearson's correlation coefficient. HKAA and PLEAA measured by evaluator A [HKAA (A) and PLEAA (A)] showed a positive correlation with a value of 0.608 (\(p\) < 0.01). On the other hand, HKAA and PLEAA measured by evaluator B [HKAA (A) and PLEAA (B)] showed a positive correlation with a value of 0.627 (\(p\) < 0.01) (Table 5) [25]. It can be seen that HKAA and PLEAA measured by evaluators A and B showed a positive correlation in the scatter plot (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Evaluator A**} & \multicolumn{2}{c}{**Evaluator B**} & \multirow{2}{*}{**ICC**} & \multicolumn{2}{c}{**95\% CI**} \\  & **(Mean \(\pm\) SD)** & & & **Min** & **Max** \\ \hline HKAA & 3.47 (2.84) & 3.82 (2.96) & 0.984 & 0.964 & 0.992 \\ PLEAA & 3.38 (3.31) & 3.44 (3.23) & 0.969 & 0.953 & 0.979 \\ \hline \hline \end{tabular}
\begin{tabular}{c} HKAA: hip-knee-ankle angle; PLEAA: predicted lower extremity alignment angle; CI: confidence interval; SD: standard deviation. \\ \end{tabular}
\end{table}
Table 4: Intraclass coefficient for interrater reliability (evaluator A versus evaluator B).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Evaluators A** and **B (\(\mu\) = 90 *)**} & \multirow{2}{*}{**ICC**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{2-2} \cline{4-5}  & **Measure 1 (Mean \(\pm\) SD)** & & **Measure 2 (Mean \(\pm\) SD)** & & **Min** & **Max** \\ \hline HKAA (A) & 3.43 (2.81) & 3.52 (2.91) & 0.985 & 0.978 & 0.990 \\ PLEAA (A) & 3.35 (3.27) & 3.41 (3.46) & 0.970 & 0.954 & 0.980 \\ HKAA (B) & 3.82 (2.96) & 3.82 (2.97) & 0.995 & 0.992 & 0.997 \\ PLEAA (B) & 3.44 (3.37) & 3.44 (3.19) & 0.968 & 0.951 & 0.979 \\ \hline \hline \end{tabular}
\begin{tabular}{c} HKAA: hip-knee-ankle angle; PLEAA: predicted lower extremity alignment angle; SD: standard deviation; CI: confidence interval; ICC: intra-class correlation coefficient. * The total number of lower extremities was 90.  
\end{table}
Table 2: Intraclass coefficients for intrarater reliability (95% CI).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Evaluator**} & \multirow{2}{*}{**ICC**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{2-2} \cline{4-5}  & **HKAA (Mean \(\pm\) SD)** & & **PLEAA (Mean \(\pm\) SD)** & & **Min** & **Max** \\ \hline HKAA-PLEAA (A) & 3.47 (2.84) & 3.38 (3.31) & 0.752 & 0.623 & 0.837 \\ HKAA-PLEAA (B) & 3.82 (2.96) & 3.44 (3.24) & 0.767 & 0.647 & 0.847 \\ \hline \hline \end{tabular}
\begin{tabular}{c} HKAA: hip-knee-ankle angle; PLEAA: predicted lower extremity alignment angle; CI: confidence interval; SD: standard deviation. \\ \end{tabular}
\end{table}
Table 3: Intra-class coefficients for intra-rater reliability (HKAA versus PLEAA).
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 3 Results
### Evaluation of Hysteresis
#### 3.1.3 Evaluation of Hysteresis for the Case of a 4.55 kg Load Applied  
For the situation of a 4.55 kg load applied to the free end of the muscle, the measurement data are presented in Table 5 and Figure 9.  
In this case, the values measured at the deflation of the muscle are higher than the values measured at inflation, the maximum distance between those being 2.4 mm at a pressure of 3.5 bar.  
At inflation, with a 4.55 kg mass attached to the free end of the pneumatic muscle, the polynomial regression that best fits the measured data is presented in Equations (5) and (6). The evolution of these values and the 95% range of confidence can be seen in Figure 10.  
\[\Delta l_{inflation} =0.071-0.421\cdot p+0.786\cdot p^{2}-0.055\cdot p^{3} \tag{5}\] \[\Delta l_{deflation} =0.270+0.005\cdot p+1.016\cdot p^{2}-0.106\cdot p^{3} \tag{6}\]  
The functions are validated by the correlation coefficient (\(r=0.999\)), and by the coefficient of determination (\(r^{2}=0.999\)).  
Figure 8: Polynomial regression. (**a**) Inflation; (**b**) deflation.  
Figure 9: Evolution of the axial contraction versus pressure (\(M\) = 4.55 kg).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pressure** & \multicolumn{5}{c}{**Length [mm]**} & \multicolumn{5}{c}{**Arithmetic**} & **Standard** \\ \cline{2-9}
**[bar]** & **1** & **2** & **3** & **4** & **5** & **Mean** & **Deviation** \\ \hline \multirow{9}{*}{Inflation} & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 0.5 & 0.2 & 0.1 & 0 & 0.1 & 0.1 & 0.1 & 0.06325 \\  & 1 & 0.5 & 0.4 & 0.4 & 0.7 & 0.5 & 0.5 & 0.10954 \\  & 1.5 & 0.9 & 1 & 1.1 & 1 & 1 & 1 & 0.06325 \\  & 2 & 1.7 & 2 & 1.9 & 1.9 & 2 & 1.9 & 0.10954 \\  & 2.5 & 2.9 & 3 & 3.1 & 3 & 3 & 3 & 0.06325 \\  & 3 & 4.4 & 4 & 4.7 & 4.6 & 4.3 & 4.4 & 0.24495 \\  & 3.5 & 5.7 & 5.8 & 5.9 & 5.8 & 5.8 & 5.8 & 0.06325 \\  & 4 & 7.6 & 7.7 & 7.8 & 7.5 & 7.4 & 7.6 & 0.14142 \\  & 4.5 & 9.5 & 9.7 & 9.8 & 9.7 & 9.8 & 9.7 & 0.10954 \\  & 5 & 11.4 & 11.2 & 11.5 & 11.2 & 11.2 & 11.3 & 0.12649 \\  & 5.5 & 12.7 & 12.9 & 12.7 & 12.8 & 12.9 & 12.8 & 0.08944 \\ \hline \multirow{9}{*}{Deflation} & 6 & 14 & 14.1 & 13.9 & 14 & 14 & 14 & 0.06325 \\  & 5.5 & 13.4 & 13.4 & 13.2 & 13.2 & 13.3 & 13.3 & 0.08944 \\  & 5 & 12.5 & 12.3 & 12.3 & 12.4 & 12.5 & 12.4 & 0.08944 \\  & 4.5 & 11.4 & 11.3 & 11.5 & 11.3 & 11.5 & 11.4 & 0.08944 \\  & 4 & 9.9 & 9.8 & 9.8 & 9.7 & 9.8 & 9.8 & 0.06325 \\  & 3.5 & 8.1 & 8.3 & 8.2 & 8.1 & 8.3 & 8.2 & 0.08944 \\  & 3 & 6.4 & 6.6 & 6.4 & 6.6 & 6.5 & 6.5 & 0.08944 \\  & 2.5 & 4.6 & 4.7 & 5.1 & 4.9 & 4.7 & 4.8 & 0.17889 \\  & 2 & 3.5 & 3.6 & 3.3 & 3.7 & 3.4 & 3.5 & 0.14142 \\  & 1.5 & 2.3 & 2.4 & 1.9 & 2.3 & 2.1 & 2.2 & 0.17889 \\  & 1 & 1.2 & 1.6 & 1.4 & 1.7 & 1.1 & 1.4 & 0.22804 \\  & 0.5 & 0.5 & 0.4 & 0.5 & 0.7 & 0.4 & 0.5 & 0.10954 \\  & 0 & 0.2 & 0.1 & 0.3 & 0.2 & 0.2 & 0.2 & 0.06325 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Muscle axial contraction for a 4.55 kg external load.
## 2 Data Acquisition  
### IMU Sensor  
The sensors used in this study are called MTw Awinda (hereafter referred to as Awinda sensors), manufactured by Movella Inc., which is headquartered in Henderson, NV, USA. These sensors are wireless and easy to integrate small microelectromechanical system inertial sensors that are convenient for real-time human motion tracking. Awinda sensors ensure accurate and well-synchronized data among all connected sensors, which is vital in human pose estimation. The sensors are accompanied by a free software named MT Manager, which has the functionality of recording and exporting raw inertial data and orientation data of each sensor.  
Since IMU sensors suffer from drifting errors and environmental magnetism, validating and evaluating their performance is a necessary step before their usage. A study by [27] compared the Awinda sensor system and an 8-camera Qualisys optical motion capture system for walking and static poses. The minimum and maximum average root mean square error (RMSE) results for 18 lower-limb joints were 3.2\({}^{\circ}\) and 10.1\({}^{\circ}\) for walking and 3.7\({}^{\circ}\) and 8.0\({}^{\circ}\) for the static pose, respectively. Additionally, the effectiveness of the Awinda sensor system was evaluated in a study by [28] in comparison to the Optotrak motion capture system using three activities namely walking, descending stairs, and ascending stairs. Resultantly, a mean estimation error of the joint angles ranged from a minimum of 1.38\({}^{\circ}\) to a maximum of 6.69\({}^{\circ}\). However, since experiment environments affect the performance of the Awinda inertial sensors, the sensors were tested in our optical motion capture indoor experiment. In particular, verifying the performance of the Awinda inertial sensors' orientation is the main goal as their orientation is used to compute the joint angles. To do so, five-minute data were collected using a rectangular rigid frame with markers and an Awinda sensor mounted on it. Resultantly, the orientation deviation of the Awinda sensor system from the Optotrack motion capture system was 1.45\({}^{\circ}\), 1.66\({}^{\circ}\), and 0.67\({}^{\circ}\) corresponding to the x, y, and z axes. On top of the lower results, our data-collection experiments were conducted for a shorter period, 10 min, to avoid any possible long-term error. However, more importantly, our actual data-collection experimentation was carried out in a barely magnetized outdoor space. The magnetization of the site was verified by the magnetic norm of the sensors as recommended by the manufacturer, which hardly varies. This is because there are no big man-made structures in the outdoor experimental site. Therefore, the Awinda sensor system data are sufficient to rely on for this study's experimental and analytical needs.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 2 Related Work  
Many routing protocols have been proposed in the last decade to provide reliable communication in an underwater and smart environments. As in [2], to address the issues of network recovering delay and design complexity a Self-Organized Underwater wireless sensor Network (SOUNET) is proposed. It uses tree topology for the deployment of nodes. Dynamic addresses are assigned to each node for their identification, hop count, or hierarchical level. For the addition of a new node at any level, only a parent address is required. It also solves the nodes isolation problem and closed loop, but SOUNET is not capable enough to provide reliable communication for a long routing path. In [3], a detailed description of localization based and localization free UWSNs routing protocols are discussed. Protocols that require prior network information before starting routing is called localization based, whereas localization free methodology is opposite to it. Every designed routing protocol for underwater communication has its limitations in terms of reliability, delay, data packets losses, and throughput issues. In [5], a comprehensive survey of existing UWSNs applications, their deployment challenges and opportunities in UWSNs environment are presented by the authors. They have gathered the data of existing applications and divided it into five major fields that are furthered divided into relevant subclasses.
# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 2 The Proposed Method
### Blending Ensemble Models
\[y_{i}=\omega_{0}+\omega_{1}x_{11}+\omega_{2}x_{12}+\ldots+\omega_{k}x_{ik}+b_ {i} \tag{2}\]  
where \(y_{i}\) is the dependent variable, \(x_{11}\) to \(x_{ik}\) denote the independent variables, \(\omega_{0}\) to \(\omega_{k}\) are the partial regression coefficients, and \(b_{i}\) is the error term. The parameters are adjusted to obtain the matrix Equations (3) and (4). \[ \begin{array}{c}Y=\begin{pmatrix}y_{1}\\ y_{2}\\ \vdots\end{pmatrix},X=\begin{pmatrix}1&x_{11}&x_{12}&\cdots&x_{1k}\\ 1&x_{21}&x_{22}&\cdots&x_{2k}\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ 1&x_{n1}&x_{n2}&\cdots&x_{nk}\end{pmatrix},\omega=\begin{pmatrix}\omega_{0}&b _{1}\\ \omega_{1}\\ \vdots\end{pmatrix},b=\begin{pmatrix}b_{2}\\ \vdots\end{pmatrix}.\\ y_{n}\end{array}  \tag{3}\]  
\[ \begin{array}{c}Y=\omega X+b\end{array}  \tag{4}\]  
The main purpose of this procedure is to estimate the regression coefficient \(\omega\). Specifically, a set of observations \(\{x_{i1},x_{i2},\ldots,x_{ik};y_{i}\}\) is selected, and the observations are brought into the multiple regression equation to obtain \(\hat{y_{i}}=\hat{\omega_{0}}+\hat{\omega_{1}}x_{11}+\cdots+\hat{\omega_{k}}x_{ nk}\). The least squares method is then used to find the regression coefficient, which is the minimum of the sum of squares of the deviations in the estimated values from the sample target values. At the same time, an L1 regularization term is added to the loss function to constrain the  
Figure 4: LSTM-RF-XGBoost algorithm model architecture. regression coefficient, and by minimizing the loss function after the above regularization, we can obtain the optimized regression coefficient \(\omega\), so the above MLR model is completed. \[L=\frac{1}{n}{\sum\limits_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}+\lambda\sum\limits_{j =1}^{k}\abs{\omega_{j}} \tag{5}\]  
\[\omega^{*}=arg\underset{\omega}{\min}\left\{\frac{1}{n}{\sum\limits_{i=1}^{n}(y _{i}-\hat{y_{i}})^{2}}+\lambda\sum\limits_{j=1}^{k}\abs{\omega_{j}}\right\} \tag{6}\]
## 3 Application to Cultural Heritage Science
### Muon Imaging  
A new potential improvement to the technique is represented by muon imaging. By placing a detector behind the sample, information about the spatial distribution of the sample can be obtained, especially for light elements. In [PERSON] et al. [53] and [PERSON] et al. [54], feasibility studies of muon imaging are reported. In both works, a highly pixelated CdTe detector is used to collect the signal (cadmium telluride is a rather good material for detection, especially in the range of hard X-rays). In [53], a proximity image (Figure 12) was obtained by placing the detector behind the sample holder and measuring it at 40 MeV/c.  
In [54], instead, a pinhole setup was used. In this study, the sample was composed of four different layers, one of which was made of small squares of lithium fluoride, as shown in Figure 13a. From the result, the LiF squares are resolved by the detector, as shown in Figure 13b, which covers the range from 27 keV to 35 keV. These two preliminary studies show the possibility of direct imaging: by adding the information coming from depth profiling, one can develop a three-dimensional analysis.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms
### Algorithmic Implementation Details
#### 5.2.4 Determination of the Number of Paths at Source end and the Angle between Paths  
If different source nodes install different camera resolutions, so the amount of data to be transmitted per unit time is different. In order to balance the paths, the size of each path packet is limited to no more than \(pk_{size}\) at a time. This requires \(Path_{num}=F/pk_{size}\) routes to be sent, of which \(F\) represents traffic. \(AngleScope_{path}\) represents the angle of interval between each path. In the edge zone, \(AngleScope_{path}\) of 1/2 is set aside as the interval, and the formula for calculating the interval angle between paths is as follows:  
\[AngleScope_{path}=\big{(}Angle_{Source\_begin}-Angle_{source\_end}\big{)}/ Path_{num} \tag{34}\]  
Figure 7: Two allocation strategies.  
\[Angle_{pathi}=Angle_{Source\_begin}-i*Angle_{Sep}-1/2*Angle_{Sep} \textsubscript{path} \tag{35}\]  
For example, Src1 needs 600 KB of traffic and 200 KB of packet size in a certain period of time. Assuming that each path can fully meet the delay requirement, the required number of paths is 600k/200k = 3. \(AngleScope_{pathi}=(405-315)/3=30^{\circ}\). According to the formula, the angles of the three paths are 390 degrees, 360 degrees, and 330 degrees, respectively.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 2 Design Methodology
### Phase 2: Design of Single Notched Band SWB Antenna (Antenna 2)  
The most critical concern in the design of a band-notched SWB antenna is the deployment of its notch-forming configurations to prevent nearby existing bands. The notch-band centered at 2 GHz (AWS), covering the frequency range of 1.8-2.2 GHz was prevented by placing an E-shaped stub on the top of the radiator. The antenna with a single band notch feature is presented in Figure 3. It can be noted that the dimensions of the reference designed SWB antenna (Antenna 1) remained unaltered when the notch-creating configuration was included with the reference antenna. Therefore, no re-tuning was needed for the reference SWB antenna for introducing the notch characteristics. The simulated VSWR of single-notched band SWB antenna, as shown in Figure 3, demonstrated the rejection of the frequency band of 1.8-2.2 GHz from the entire band of 1.2-47.5 GHz.