esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Construction of Moisture Content Prediction Model
#### 2.3.3 Long Short-Term Memory Neural Network Model  
LSTM is an improved Recurrent Neural Network (RNN) [42], which was proposed by [PERSON] et al. [43] Based on the traditional RNN, the memory cell is added, which includes three modules, namely, forget gate, input gate, and output gate, to control the inflow and outflow of internal information.  
Forget Gate: Determines how much of the past should be discarded by the memory cell. The activation function \(\sigma\) is the _sigmoid_ function.  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{8}\]  
(\(x_{t}\): input for the current time step; \(h_{t-1}\): hidden state of the previous time step; \(W\): weighting of the model; \(b\): bias parameter).  
Input Gate: Determines how much of the currently entered information will be stored in the memory cell.  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{9}\]  
Figure 4: Flowchart of ELM model construction.  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{10}\]  
(_tanh_: hyperbolic tangent activation function).  
Cell State Update: Combining the results of the forget gate and the input gate updates the state \(C_{t}\) of the memory cell.  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{11}\]  
Output Gate: Decide which parts of the memory cell will be used as outputs \(h_{t}\), which affects the state of the next moment.  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{12}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{13}\]  
The input layer takes the full set matrix of temperature and humidity variables and the matrix of sensitive variables as inputs, respectively, and the output gate outputs the predicted value of outgoing corn machine moisture.
## 3 Method and Results
### Accuracy and Statistical Significance of Results
Table 2 was calculated with point-by-point predicted-versus-actual porosity pairs for about 200 ms of data per well, spanning a larger interval than the main reservoir. For reservoir volumetrics, the porosity variation with depth (or seismic record time) is less significant than the average porosity over the reservoir interval. Figure 14 shows actual vs. predicted average porosity plots over reservoir interval. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Validation** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\ \hline
**Correlation** & & & & \\
**Coefficient** & 0.64 & 0.7 & 0.76 & 0.79 \\ \hline
**Mean Absolute** & & & & \\
**Error** & 1.68 & 1.62 & 1.42 & 1.4 \\ \hline
**Standard Error** & 2.27 & 2.12 & 1.91 & 1.82 \\ \hline
**F** & 395 & 542 & 788 & 941 \\ \hline
**Significance F** & 5.57 \(\times\) 10\({}^{-67}\) & 2.36 \(\times\) 10\({}^{-84}\) & 8.90 \(\times\) 10\({}^{-109}\) & 7.31 \(\times\) 10\({}^{-122}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: R, mean absolute error (FU), standard error, F, and Significance F for actual versus predicted porosity logs for only the wells not used in the training process. For predicting the average porosity of the interval (Table 3), there is a more significant systematic improvement in statistics from Group 1 to Group 4, with correlation and statistical significance increasing and prediction error decreasing. The poor correlations for Group 1 and Group 2 have a significance of F greater than 0.05, which usually indicates that the correlation is not statistically significant. Such comparisons suggest that attribute Groups 3 and 4 are particularly important for accurately predicting average porosity at the reservoir level.
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 2 Antenna Design and Analysis
### Antenna Design for 5.8 GHz
\[\left(\mathrm{AF_{12}}\right)_{\mathrm{n}}=\cos\!\left[\frac{1}{2}\!\left( \mathrm{kdcos}\theta+\beta_{\mathrm{d}}\right)\right]=\cos\!\left\{\frac{1}{2} \!\left[\frac{2\pi}{\lambda_{0\;5\;\mathrm{sG}}}\!\left(\mathrm{L_{i}}+\left( \mathrm{L_{1}}/2\right)\times 2\right)\!\cos\!\theta+\beta_{\mathrm{d}} \right]\right\}=\cos\!\left[\frac{1}{2}\!\left(100.92^{\circ}\times\cos\! \theta+\beta_{\mathrm{d}}\right)\right]\!, \tag{2}\]  
At 5.8 GHz, the difference in phase excitation, \(\beta_{\mathrm{d}}=\beta_{\mathrm{A}}-\beta_{\mathrm{B}}=\beta_{\mathrm{C}}- \beta_{\mathrm{D}}\), can be derived by (3). Where \(\beta_{\mathrm{A}}\), \(\beta_{\mathrm{B}}\), \(\beta_{\mathrm{C}}\), and \(\beta_{\mathrm{D}}\) are the phase excitations of Elements A, B, C, and D, respectively. In addition, \(\beta_{\mathrm{A}}\) equals \(\beta_{\mathrm{C}}\), and the negative sign is the phase lag. \[\beta_{\mathrm{d}}=-\frac{2\pi}{\lambda_{0\;5\;\mathrm{sG}}}\times\left[ \mathrm{L_{i}}+\left(\mathrm{L_{1}}/2\right)\times 2-\mathrm{T_{w}}\right]=-90.48^{ \circ}, \tag{3}\]  
In (2), replacing \(\beta_{\mathrm{d}}\) with \(-90.48^{\circ}\), the normalized array factor of Elements 1 and 2 can be written as (4). It has two maximums at \(\theta=+26^{\circ}\) and \(\theta=-26^{\circ}\), only one minimum at \(\theta=+180^{\circ}\), and forms an end-fire array toward \(0^{\circ}\). \[\left(\mathrm{AF_{12}}\right)_{\mathrm{n}}=\cos\!\left[\frac{1}{2}\!\left(100. 92^{\circ}\times\cos\!\theta-90.48^{\circ}\right)\right]\!, \tag{4}\]  
The normalized total field of the two-element array antenna, \(\left(\mathrm{E_{total}}\right)_{\mathrm{n}}\), is (5). It has two maximums at \(\theta=+60^{\circ}\) and \(\theta=-60^{\circ}\), only one minimum at \(\theta=+180^{\circ}\), and forms an end-fire radiation toward \(0^{\circ}\). \[\left(\mathrm{E_{total}}\right)_{\mathrm{n}}=\left\{\sin^{2}\!\theta\times \left(\mathrm{AF_{12}}\right)_{\mathrm{n}}\right\}_{\mathrm{n}}\!, \tag{5}\]  
Figure 6 shows the relative power patterns of the vertical infinitesimal dipole, the array factor of Elements 1 and 2, and the total field of the two-element array antenna.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
###### Abstract  
The limitation of indoor visible light coverage and the attenuation of its signal when propagating in line-of-sight has seriously affected the stable communication of receiving devices when users move randomly and also aggravated the power consumption of visible light networking systems. According to the above situation, on the basis of the heterogeneous networking of visible light communication (VLC) and RF communication integration, this article proposes a horizontal-vertical collaborative handover strategy based on the communication blind area dwell time (CBD-HVHO). Combining asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) technology with networking handover technology, ACO-OFDM is used to determine the indoor communication blind area by calculating the bit error rate (BER) value at the signal receiver while reducing the multipath interference generated by visible light signals during channel transmission. To achieve this, set the communication blind channel interruption time as the threshold time, compare the communication blind area dwell time with the threshold time, and finally combine the horizontal and vertical collaborative handover strategies based on the communication blind area dwell time. The simulation results show that the handover probability is 0.009, the average number of handovers is 1.006, and the average network throughput is 195.2826 Mbps. Compared with the previously proposed immediate vertical handover (I-VHO) scheme and the dwell vertical handover (D-VHO) scheme, the communication stability is significantly improved, and the power consumption of the network system is reduced to a certain extent.  
visible light communication; heterogeneous networking; ACO-OFDM; communication blind area; horizontal-vertical collaborative handover
# ICN-Based Enhanced Content Delivery for CDN
## 2 Background and Related Work  
### ICN Cache in a Nutshell  
There have been early attempts [17; 18; 19] to develop a ubiquitous caching system within the IP networks to avoid repeated transmission. Nevertheless, the emergence of ICN has raised both the intensity and breadth of research on in-network caching to a new level. There is a vast literature addressing the challenges of content caching in ICN, most of which focus on the cache placement strategy [20; 21; 22], cache replacement policy [23] and cache capacity allocation [24]. Three major features differentiate the ICN cache from the existing cache system. First, ICN caching is transparent to upper-level applications and exists as an underlying service, making it more efficient and available to different types of traffic. Second, in ICN, the cache can be equipped on any network node. This ubiquity allows for content to be cached closer to the end users, reducing latency and improving content delivery. Third, the objects cached in most traditional cache systems are generally based on files. However, to meet the line-speed constraints of ICN routers [25], ICN employs the smaller unit of chunk as its basic caching unit. As a side effect of this, many analysis reference models of traditional cache become outdated to ICN.
## 1 Introduction
Consequently, a blind identification criterion for nonlinearity mitigation with best delay searching is designed. To eliminate the disadvantage of filter imperfections and be able to deal with multicarrier situations without too many extra operations added, strong signals and nonlinearities are extracted based on the threshold power in the frequency domain. Afterwards, their time domain waveforms are obtained by the inverse fast Fourier transform (IFFT) technique. Compared to the signal extraction realized by pass/stop-band filters, the proposed method can save not only the amplitudes of the original signals, but also their phase spectrum. This is a significant preparation step for making a reliable and precise best delay search. After that, an iterative formula based on the weighed iterative improvement (WII) [35] method is applied to minimize the ill-conditioned equation problem. The outline of the remainder of this paper is as follows: Section 2 explains the joint effect of group delay distortion and nonlinear distortion through a simplified model from the broadband RF front-end and, based on it, the blind identification criterion is designed. Then the mitigation architecture and optimization solution are suggested in Section 3. In Section 4, simulation experiments and RF measurement results are provided to verify the performance of the proposed method. Finally, conclusions are drawn in Section 5.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 3 System Architecture
### Linear Equalization
#### 3.2.1 Least Squares (LS)  
This is an LSE-based estimator. It uses one-fourth of the number of subcarriers in the OFDM system. If \(\hat{\mathcal{C}}_{k}\) represents the vector of the estimated sequence and \(H_{k}\) represents the estimated channel transfer function in the frequency domain, then the equalized sequence \(\widetilde{C}_{k}\) is given by:  
\[\widetilde{C}_{k}=\frac{\hat{\mathcal{C}}_{k}}{|H_{k}|}. \tag{10}\]
## 5 Case Study
### Terrorist Attack Damage Estimation  
#### 5.2.1 Attacking Weapon Used  
Historically, weapons used in terrorist attacks on the substation case have been TNT explosives weighing not more than 20 kg. For the purposes of this analysis, we assumed that each new attack would utilize the same type and maximum historical size of projectile. Uncertainty in the explosive weight affects the model results by the scaled distance expressed by [43]:  
\[Z=R/W^{1/3} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component Type** & **Number of Functioning Units** \\ \hline A & 10 \\ B & 17 \\ C & 4 \\ D & 4 \\ E & 4 \\ F & 8 \\ G & 3 \\ H & 2 \\ I & 1 \\ \hline \hline \multicolumn{2}{c}{Connecting Edge} & Number of Transmission Lines \\ \hline AE & 2 \\ AG & 1 \\ BC & 4 \\ BE & 2 \\ BG & 2 \\ BI & 1 \\ CD & 2 \\ DF & 1 \\ GH & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electric Substation Functioning Unit Layout (according to site layout map).  
Figure 2: Network Model of Electric Substation.  
where Z is the scaled distance, R is the stand-off distance [m], and W is the weight of the charge [kg].  
Thus, the threshold distance at which the structures will suffer severe damage varies linearly with the scaled distance Z.
## 5 Reference Instrument Selection
* Severjamin has a spatial resolution from 400 m to 1 km depending on the operation mode can satisfy many minimal requirements for some measurements. * GLAS acquires the geophysical variables with a vertical spatial resolution of 10 cm, which does not satisfy the user requirement for sea ice thickness measurements. \begin{table}
\begin{tabular}{l|l l l} \hline \hline \multirow{2}{*}{**Instrument**} & \multirow{2}{*}{**Measurements**} & \multicolumn{2}{c}{**Requirements [109]**} \\ \cline{3-4}  & & **Accuracy** & **Spatial** \\ \cline{3-4}  & Soil Moisture at the surface & -100 m m m m & 100 m m \\ \cline{2-4}  & Sea ice thickness & 100 m & 100 m \\ \cline{2-4}  & Dominant wave direction & 10\({}^{\circ}\) & 1-5 km \\ \cline{2-4}  & Wind speed over the sea surface & 10.5 m m & 1-10 km \\ \cline{2-4}  & Significant wave height & 1.1 m & 1-25 km \\ \cline{2-4}  & Sea ice cover & 5 \% & 12 km-10 m \\ \cline{2-4}  & Ocean surface currents & 10.5 m m & 1-25 km \\ \cline{2-4}  & & 100 m & 1-25 km \\ \cline{2-4}  & EON- & & \\ Microwave [33] & Atmospheric pressure & 5 \% & 1-25 km \\ (Ka-, U-, D-bands) & over sea surface & 5 \% & 1-25 km \\ (22 channels) & Soil Moisture & & \\ \cline{2-4}  & & at the surface & \\ \cline{2-4}  & & Sea ice thickness & 1 cm \\ (L- band) & Crop grow \& condition & 2 km \\ \cline{2-4}  & Sea ice cover & 5 \% & 10 km-100 m \\ \cline{2-4}  & Wind speed & 1.5 m m & 1-10 km \\ \cline{2-4}  & over sea surface & & \\ SSM/1\({}^{a}\)[84] & Sea ice cover & 5 \% & 12 km-10 m \\ (K, Ka, W) & Sea ice type & 0.25/classes & 10 m \\ \cline{2-4}  & Sea ice drift & 10\({}^{\circ}\) & 10 m \\ \hline \multirow{2}{*}{**TMI \({}^{b}\)[1]**} & Wind speed & 0.5 m m & 1-10 km \\ \cline{2-4}  & over sea surface & 100 m & 12 km-10 m \\ \cline{2-4}  & Sea ice cover & 5 \% & 10 m \\ \cline{2-4}  & Sea ice type & 0.25/classes & 10 m \\ \cline{2-4}  & Sea ice drift & 0.5 m m & 10 m \\ \cline{2-4}  & Sea surface & 10.4 k & 1-10 km \\ \hline \multirow{2}{*}{**AVHRR/3**[61] (VIS, NIR, MWIR, TIR) (SST) & Ocean chlorophyll concentration & 0.05 mg/m \({}^{3}\) & 1 km \\ \cline{2-4}  & Ocean imagery and water leaving radiance & 5\% & 1 km \\ \cline{2-4}  & Color Dissolved Organic Mater (CDOM) & & 1 km \\ \cline{2-4}  & Sea Surface Temperature & & \\ \cline{2-4}  & & 0.3 K & 1-10 km \\ \cline{2-4}  & Detection of water & & \\ \cline{2-4}  & stress in crops & 5\% & 1-7 m \\ \cline{2-4}  & Estimation of crop & & \\ \cline{2-4}  & evapotranspiration & - & 1-10 m \\ \cline{2-4}  & Sea Ice Cover & 5 \% & 12 km-10 m \\ \hline COMIS [24] & CDOM & 5\% & 1 km \\ (VIS, NIR) & Sea Ice Cover & 5 \% & 12 km-10 m \\ \hline EON-IR & Sea Surface Temperature (SST) & 0.3 K & 1-10 km \\ \cline{2-4}  & Atmospheric pressure over sea surface & 5 \% & 1 km-25 km \\ \hline \multirow{2}{*}{**SCAT [1]**} & Wind speed over the sea surface & 10.5 m m & 1-10 km \\ \cline{2-4}  & Sea ice extent & 5\% & 12 km-10 m \\ \cline{2-4}  & Sea ice cover & 5\% & 12 km-10 m \\ \cline{2-4}  & Sea ice cover & 5\% & 12 km-10 m \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reference instruments selected to cover the measurements with gaps.
## 3 Evaluation of Process Precision and Accuracy
### Process Quality Monitoring  
According to the variable transformation formula, \(y_{j,i}=\left(x_{j,i}-2.8\right)/0.03\), the subsample data of \(y_{j,i}\), subsample mean, and subsample standard deviation are depicted below:  
\[y_{1,1},y_{1,2},\ldots,\ y_{1,i},\ldots,\ y_{1,11},\ \overline{y}_{1}=\frac{1}{11 }\underset{i=1}{\overset{25}{\sum}}y_{1,i}=0.415,\ s_{1}=\sqrt{\frac{1}{n} \underset{i=1}{\overset{11}{\sum}}\left(y_{1,i}-\overline{y}_{1}\right)^{2}}=0.172\]  
\[y_{j,1},\ y_{j,2},\ldots,\ y_{j,i},\ldots,\ y_{j,11},\ \overline{y}_{j}=\frac{1}{11 }\underset{i=1}{\overset{25}{\sum}}y_{j,i}=0.452,\ s_{j}=\sqrt{\frac{1}{11} \underset{i=1}{\overset{11}{\sum}}\left(y_{j,i}-\overline{y}_{j}\right)^{2}}=0.187\]  
\[y_{25,1},\ y_{25,2},\ldots,\ y_{25,i},\ldots,\ y_{25,11},\overline{y}_{25}= \frac{1}{11}\underset{i=1}{\overset{25}{\sum}}y_{25,i}=0.444,\ s_{25}=\sqrt{ \frac{1}{11}\underset{i=1}{\overset{11}{\sum}}\left(y_{25,i}-\overline{y}_{25} \right)^{2}}=0.181\]  
\[\text{where }j=1,2,\ldots,25\text{ and }i=1,2,\ldots,11\text{. According to Equations (\ref{eq:20}) and (\ref{eq:21}), then}\]  
\[\overline{\delta}=\overline{\overline{y}}=\frac{1}{25}\underset{j=1}{\overset{ 25}{\sum}}\overline{y}_{j}=0.443\]\[\overline{\gamma}=\frac{1}{25}\sum_{j=1}^{25}b_{11}^{-1}s_{j}=b_{11}^{-1}\overline{ s}= 1.075\times 0.182=0.196\]  
Based on Table 1, the values of \(b_{11}\) and \(A_{n}\) are received, and the limits of the \(\delta\) control chart can be calculated below:  
\[UCL_{\delta}=\overline{\delta}+A_{11}\overline{\gamma}= 0.443+0.973\times 0.196=0.643;\]  
\[CL_{\delta}=\overline{\delta}=0.443;\]  
\[LCL_{\delta}=\overline{\delta}-A_{11}\overline{\gamma}= 0.443-0.973\times 0.196=0.252.\]  
Similarly, the values of \(B_{11}\) and \(B_{11}^{\prime}\) obtained from Table 1, and the limits of the \(\gamma\) control chart, can be computed as follows:  
\[UCL_{\gamma}=B_{11}\overline{\gamma}= 1.679\times 0.196=0.329\]  
\[CL_{\gamma}=\overline{\gamma}=0.196\]  
\[LCL_{\gamma}=B_{11}^{\prime}\overline{\gamma}= 0.321\times 0.196=0.063\]  
The above control limits of accuracy and precision indices can be used to supervise the process accuracy and precision.
## 5 The Entropy of a Wavelet Transformed Signal  
The wavelet transform is a resolution-based decomposition-hence with an in-built spatial model: see e.g., [16, 17].  
A redundant wavelet transform is most appropriate, even if decimated alternatives can be considered straightforwardly too. This is because segmentation, taking information into account at all available resolution scales, simply needs all available information. A non-redundant (decimated, e.g., pyramidal) wavelet transform is most appropriate for compression objectives, but it can destroy through aliasing  
Figure 2: Upper left: long-tailed histogram of marginal density of product of wavelet scales 4 and 5 of a \(512\times 512\) Lena image. Upper right, lower left, and lower right: histograms of classes 1, 2 and 3. These panels exemplify a nested model.  
potentially important faint features.  
If \(f\) is the original signal, or images, then the following family of redundant wavelet transforms includes various discrete transforms such as the isotropic, B\({}_{3}\) spline, a trous transform, called the starlet wavelet transform in [17].  
\[f=\sum_{s=1}^{S}w_{s}+w_{S+1} \tag{1}\]  
where: \(w_{S+1}\) is the smooth continuum, not therefore wavelet coefficients; \(w_{s}\) are wavelet coefficients at scale \(s\). Dimensions of \(f,w_{s},w_{S+1}\) are all identical.  
Nothing prevents us having a redundant Haar or, mutatis mutandis, redundant biorthogonal 9/7 wavelet transform (used in the JPEG-2000 compression standard). As mentined above, our choice of starlet transform is due to no damage being done, through decimation, to faint features in the image. As a matched filter the starlet wavelet function is appropriate for many types of biological, astronomical and other images [17].  
Define the entropy, \(H\), of the wavelet transformed signal as the sum of the entropies \(H^{s}\) at the wavelet resolution levels, \(s\):  
\[H=\sum_{s=1}^{S}H^{s} \tag{2}\]  
Shannon and quadratic Renyi entropies are additive, as noted in section 4. For additivity, independence of the summed components is required. A redundant transform does not guarantee  
Figure 3: Overplotting of the histograms presented in Figure 2. This shows how the classes reconstitute the original data. The histogram of the latter is the upper left one in Figure 2.  
independence of resolution scales, \(s=1,2,\ldots,S\). However in practice we usually have approximate independence. Our argument in favor of bypassing indepence of resolution scales is based on the practical and interpretation-related benefits of doing so.  
Next we will review the Shannon entropy used in this context. Then we will introduce a new application of the Renyi quadratic entropy, again in this wavelet transform context.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 3 Vehicular Applications and Connectivity Options  
A plethora of heterogeneous applications will be supported in the vehicular landscape, targeting different use cases, ranging from autonomous driving to traffic efficiency and infotainment. Vehicular applications exhibit different delivery demands, e.g., in terms of latency, throughput and reliability [17] and typically exchange data with spatial and temporal relevance. For instance, road traffic information (e.g., mean speed in a given road segment) is locally relevant: data collected in one area will be requested in the same area. The time validity of such data spans a few seconds or minutes; whereas the time validity of other types of data, such as the fees of charging stations for electric vehicles and the flyers of points-of-interest in a road area, may span several hours [18].  
Vehicular applications rely on the exchange of data among vehicles, between vehicles and roadside infrastructure and nearby sensors, pedestrians and remote server facilities, enabled through V2X connectivity. The V2X term covers, among others, both V2V and V2I communications, as shown in Figure 2.  
Although more than 20 years passed since a dedicated spectrum at 5.9 GHz was allocated to vehicular communications, the decision about the V2X radio access technology is still under debate and revolving between two mainstream technologies, i.e., IEEE 802.11 and Cellular-V2X (C-V2X).  
IEEE 802.11 initially captured the interest of the research community, due to operation simplicity and native support for V2V communications in an ad hoc manner. The IEEE 802.11p amendment, now superseded and part of the IEEE 802.11 standard [19], was conceived as an enhancement of the IEEE 802.11a specification, with physical and medium access control (MAC) layers' settings and procedures properly adjusted to support outdoor communications under high speed mobility. At the MAC layer, 802.11p relies on the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A node wishing to transmit senses the medium to detect if it is busy. If it is the case, a mechanism based on random backoff is performed to reduce the probability of collisions, which, however, cannot be prevented. More recently, the interest for 802.11-based V2X connectivity revamped thanks to the creation of a new IEEE task group, now preparing the IEEE 802.11 bd amendment.  
Figure 1: Forwarding Process in NDN.  
The group aims to investigate evolved physical-layer technologies that enhance the.11p coverage and throughput [20].