esa-sceva/satcom-chunk-collection huggingface dataset:
# A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment
## 2 Related Works
### Overview  
The proposed TCP collects the weather information based on a farm's location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and informs a farm manager of the diagnosed result. Figure 2 shows the structure of TCP. The proposed TCP is composed of \(1\) gateway and \(2\) modules as follows. First, the optimized farm sensor gateway (OFSG) collects soil data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol, reduces the number of sensor data fragmentation times through the compression of a fragment header and transfers the collected soil data to the DSM. Second, the data storage module (DSM) receives the collected soil data from the OFSG and stores it and the weather data from the weather center in a farm central server. The DSM transfers the stored data to the CSM. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops with the soil data and weather data transferred from the DSM. The CSM transfers the diagnosed result to the farm central server of the DSM.  
Figure 2: The structure of the total crop-diagnosis platform (TCP). CSM: the crop self-diagnosis module; DSM: the data storage module; OFSG: the optimized farm sensor gateway; IPS: an interconnection protocol sub-module; SDFS: a sensor data fragment sub-module.
# Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System
###### Abstract  
Recently, the reconfigurable intelligent surface (RIS)-aided communication system has emerged as a promising candidate for future millimeter-wave wireless communications. Due to the short wavelength of millimeter wave, the antennas on the base station (BS) and the elements on the RIS can be densely packed. It usually causes the BS and RIS to be blocked by rain, snow, or dust, which will change the channel's characteristics and decrease the performance of communication system. In order to solve this problem, we propose an iterative compressed sense based algorithm for joint estimating the blockage coefficients of RIS and BS. Then, for the complete blockage scenario, we propose a low complexity algorithm for estimating the blockage coefficients. Our simulation results demonstrate the superior performance of the proposed algorithm to existing ones.  
reconfigurable intelligent surface (RIS); diagnosis; blockage +
Footnote †: journal: 2021  
1
Footnote 1: email: {[[https://doi.org/10.3390/electronics1020256](https://doi.org/10.3390/electronics1020256)]([https://doi.org/10.3390/electronics1020256](https://doi.org/10.3390/electronics1020256))}
## Appendix A Path Planning Algorithms  
### Potential Field Method/A*  
This algorithm utilizes the benefits of vector fields to provide a hybrid global and local planning approach. The Potential Field Method (PFM), like the concept of electrical charges, relies on artificial attractive and repulsive forces. The goal location acts as an attractive force (\(\vec{F}_{att}(q)\)) while obstacles provide repelling forces (\(\vec{F}_{rep}(q)\)). The total force (\(\vec{F}(q)\)) at any position is then found by summing the attractive and repulsive forces.  
In this application, the chosen potential function is a parabolic well resulting in Equations (A1)-(A3). \(\alpha\) and \(\beta\) are gain terms. The attractive equation is dependent upon the distance, \(d_{g}(q)\), and angle to the goal location while the repulsive equation is dependent upon the distance, \(d_{0}(q)\), and angle between the robot and the obstacle. \(s\) refers to the range of influence the repulsive field has around the obstacle. Outside of this value, the repulsive value is equal to zero. In practical implementation, the range of influence is equal to the maximum reliable range of the obstacle detection sensors in use. Here, \(\vec{F}(q)\) is a vector representing a direction and magnitude of force felt by the robot. \(\hat{d}_{g}\) and \(\hat{d}_{o}\) are unit vectors pointing in the direction of the goal and obstacle, respectively.  
\[\vec{F}(q) = \vec{F}_{att}(q)+\vec{F}_{rep}(q)\] (A1) \[\vec{F}_{att}(q) = -\alpha d_{g}\hat{d}_{g}\] (A2) \[\vec{F}_{rep}(q) = \begin{cases}\beta(\frac{1}{d_{o}(q)}-\frac{1}{s})(\frac{1}{d_{o} ^{2}(q)})\hat{d}_{o}&\text{if }d_{o}\leq s\\ 0&\text{if }d_{o}>s\end{cases}\] (A3)  
\(A\)* algorithm is based on a cost function minimizing path distance and is typically used for global planning. The cost function is given in (A4) where \(F(n)\) represents the total cost, \(G(n)\) represents the cost from the start node to the current node, and \(H(n)\) is the estimated cost from the current node to the goal node. When an admissible heuristic is used (i.e., the function never overestimates the cost of reaching goal), \(A^{*}\) finds the optimal path.  
\[F(n)=G(n)+H(n) \tag{10}\]  
In this implementation, the \(A^{*}\) generated path is converted into a vector field. This vector field is created by first producing waypoints to the goal location based on \(A^{*}\) planning. A vector field is then calculated that is attracted towards the generated path:  
\[F_{A^{*}}=-k_{A}d_{wpt}\hat{d}_{wpt} \tag{11}\]  
Each grid location is attracted to the next closest waypoint. \(F_{A^{*}}\) is the force produced from the \(A^{*}\) layer, \(k_{A}\) is a gain term, \(d_{wpt}\) is the distance between the current grid location and the next closest waypoint, and \(\hat{d}_{wpt}\) is a unit vector pointing in the direction of the next closest waypoint. The PFM and \(A^{*}\) layers are then added together and the PFM layer is updated as needed for new obstacles. Expanded details are found in [36]. The specific gain values used in for the simulation in this paper are given in Table 11.
# The Housing System Contributes to Udder Health and Milk Composition
###### Abstract  
The aim of this study was to determine the effect of the housing system and somatic cell count (SCC) on the composition and fatty acid profile of milk. A total of 419 milk samples were collected from one herd of 210 cows; 90 cows were kept in the tie-stall system, and 120 cows were kept in the free-stall system. The cows received the same fodder. Samples were collected four times, in winter. Udder health was evaluated based on SCC. The tie-stall system, mainly due to individual care, was superior to the free-stall system. Milk from cows kept in the tie-stall system had lower (\(p<0.01\)) SCC by 72 ths mL\({}^{-1}\) cells than milk from cows kept in the free-stall system. An increase in SCC was accompanied by decreases in daily milk yield and lactose concentration. Milk from cows housed in the tie-stall barn had higher polyunsaturated fatty acid (PUFA) contents and a lower n-6/n-3 PUFA ratio. Milk with a higher SCC contained more PUFAs and fewer monounsaturated fatty acids (MUFAs). Tie-stall housing contributed to an increase in the proportions of acids beneficial to the health of consumers. A comparison of two housing systems for cows on one farm showed that the free-stall system was associated with a higher SCC and a less favorable milk composition and fatty acid profile.  
CLA; fatty acids; free-stall; somatic cells; subclinical mastitis; tie-stall +
Footnote †: journal: Applied
###### Abstract  
We report on the fabrication of TiO\({}_{2}\) nanosphere (NS) monolayer arrays for the enhancement of light extraction quantum efficiency of GaN-based light-emitting diodes (LEDs). The fabricated TiO\({}_{2}\) NSs monolayer arrays were composed of different phases of anatase (An-) and amorphous (Am-) TiO\({}_{2}\). The arrays were transferred onto the topmost layer of LED chips via the facile icing transfer method. The LED chips covered with Am-TiO\({}_{2}\) NS monolayer arrays showed 3.0- times enhanced light output power intensity compared with reference LED chips at a fixed injection current of 100 mA. The enhanced light extraction of LED chips by an Am-TiO\({}_{2}\) NS monolayer can be attributed to a high transmittance (91.1%) in visible and increased light extraction probability of photons generated in LEDs, resulting from the enhanced light coupling efficiency by reduced total internal reflection (TIR). Finite-difference time-domain (FDTD) simulation results also agreed well with the experimentally observed results. Based on the experimental and theoretical results, our suggested Am- and An-TiO\({}_{2}\) NS arrays can be considered a very facile and effective method to improve the device performance of various visible LED chips.  
TiO\({}_{2}\) nanosphere monolayer; transfer method; GaN LED; light extraction; compound semiconductor +
Footnote †: journal: Applied Sciences  
without limiting the substrate material, the reduction of post-thermal annealing damage is an essential issue. Among the possible methods, the icing transfer method may be a good candidate because during processing, it uses deionized (DI) water as a binder with a soft baking temperature and short time (60 \({}^{\circ}\)C/ 30 s), none of which causes severely harmful effects, such as the deformation or degradation of the device.  
In this study, we fabricated a uniformly arrayed TiO\({}_{2}\) NSs monolayer with different crystal structures of amorphous (Am-TiO\({}_{2}\)) and anatase (An-TiO\({}_{2}\)) and transferred them onto the LED chips to enhance the light extraction efficiency. The formation of the ordered TiO\({}_{2}\) NS monolayer array and its transfer were conducted by a simple unidirectional rubbing method and icing transfer method assisted by DI water. Our transferrable TiO\({}_{2}\) NS monolayer arrays can be an excellent way to improve the device performance of LED chips. The experimental and finite-difference time-domain (FDTD) simulation-based theoretical results confirmed that the Am-TiO\({}_{2}\) NSs monolayer enhanced the extraction efficiency of LED chips.
# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
### Algorithm Comparison  
The proposed method was compared with the traditional machine learning-based methods previously studied in order to verify the effectiveness of the proposed method [29]. The BP neural network and GA-BP neural network were used for fault identification, and 118 sets of data were selected for each fault, of which 94 sets were selected as the training set and 24 sets as the test set. The results of the two conventional machine learning algorithms are shown in Table 6.  
On the basis of the diagnosis results in Table 3, it can be concluded that the inner ring fault diagnosis of the bearing based on SDP images with the VGG16 network can successfully perform the diagnosis function, and the accuracy was higher than that of traditional machine learning diagnosis. Nonetheless, the feature processing of faults was time consuming.  
Since a single sensor is not able to fully extract the data from the bearing, DS fusion was used. The accuracies of the traditional machine learning algorithm and the deep learning algorithm of VGG16 before and after multi-sensor fusion are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Diagnostic Methods** & **Features** & **Accuracy \%** & **DS Theory** \\ \hline BP neural network & Traditional time and & 99.85\% & 100\% \\ GA-BP neural network & frequency-domain & 99.86\% & 100\% \\ VGG16 & SDP images & 99.99\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of VGG16 with the BP and GA-BP algorithms.  
Figure 7: Diagnostic accuracy of bearings before and after fusion. (**a**) DE_10_40\({}^{\circ}\) and FE_4_30\({}^{\circ}\) fusion. (**b**) DE_10_20\({}^{\circ}\) and FE_4_30\({}^{\circ}\) fusion. (**c**) DE_1_10\({}^{\circ}\) and FE_4_30\({}^{\circ}\) fusion.  
According to Figure 8, the accuracies of both machine learning algorithms and deep learning algorithms were significantly improved after fusion, and the method proposed in this present study is able to achieve accurate fault diagnosis, reaching accuracies even higher than those of traditional machine learning algorithms.
## 3 Spectrum Management Issues
### M-Milmo
#### 3.5.1 Minimizing BER  
As the future radio 6G demands an extensive array of antenna deployment due to numerous users and its data requirements, a technology that fulfills its desire is M-MIMO. M-MIMO has 20\(\times\) more antennas than a conventional MIMO system, and traditional methods are complex and inefficient to support the M-MIMO system. In [227], the authors have contributed to the new design of signal detection at BS by the antenna. For this reason, they have suggested a computationally less complex approximate message passing algorithm for the discovery of the M-MIMO system by using MATLAB simulations. Hence, the results are astoundingly amazing as a suggested technique provides a smart and efficient tradeoff between computational complexity and BER performance; moreover, it is less complex for the detection of M-MIMO systems. Furthermore, it can be stretched to large M-MIMO systems with many antennas and users. A study [228] has compared M-MIMO and conventional MIMO  
Figure 6: FDD (Frequency Division Duplex) vs. TDD (Time Division Duplex).  
based on channel parameters. To estimate the channel parameters for traditional and M-MIMO systems, a training-based and blind channel estimation technique is provided and matches the performance of both the systems. Therefore, by implementing the proposed method, M-MIMO delivers a much better BER count than \(2\times 2\) MIMO systems. However, a more agile algorithm is required to simplify the complexities of M-MIMO more.
# Robust Static Structural System Identification Using Rotations
## 4 Using Redundant Rotations for Parameter Estimation
### Strategies to Use Redundant Measurements  
In the case of redundant measurements with errors, the observability equations cannot be satisfied strictly. Numerical estimations were obtained by using the least squares method. However, the redundant measurement can be divided into several essential sets. For each essential set, a unique solution can be obtained. Hence, four strategies to use redundant rotations are presented as follows.  
Strategy 1: Formulate the observability equations (Equation (6)) employing structural system identification using COM with all rotations in one batch. In this case, the equations cannot be satisfied strictly as measurement errors exist and the equations are solved directly using the least squares method.  
Strategy 2: Derive the geometrical relations (referred to as compatibility conditions) that the nodal displacements should satisfy first [25]. Impose these compatibility conditions using optimization techniques by minimizing the discrepancy between the measured shape and the compatible one. The estimations of the parameters are obtained by providing the compatible displacements in Equation (6).  
Strategy 3: The estimations using the redundant measurement sets are obtained in several batches. In each batch, the target parameters are obtained using one essential set, which is a subset of the redundant set. The final estimations are the average of the estimations from all batches. This strategy is also noted as an averaging method.  
Strategy 4: The averaging method is carried out first. Then, the outliers in the estimations from different batches are detected and removed. The final estimations are the average of the remaining valid estimations. To determine outliers, the first quantile \(Q_{1}\) and the third quantile \(Q_{3}\) of the estimations from different batches are calculated. By the assumption of normal distribution, valid estimations should fall into the interval \([Q_{1}-2.7(Q_{3}-Q_{1}),\)\(Q_{3}+2.7(Q_{3}-Q_{1})]\) with a coverage of 99.7%. Hence, values outside of this range are invalid and ruled out.
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 3 Results
### Effects on Soil
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Unpolluted Soil**} & \multicolumn{4}{c}{**Weakly Polluted**} & \multicolumn{4}{c}{**Highly Polluted**} \\ \hline
**Element** & **Extractant** & **Sampling** & **Control** & **Biochar** & **Control** & **Biochar** & **Control** & **Biochar** & **Control** & **Biochar** \\ \hline \multirow{3}{*}{Cd} & CH\({}_{3}\)COOH & Rhizosphere & 0.09 a & 0.16 & 0.04 b & 0 & 0.67 b & 0.16 & 0.40 b & 0 & 1.20 a & 0 & 1.20 a & 0 \\  & & Bulk soil & 0.09 a & 0.16 & 0.093 a & 0.16 & 1.07 a & 0.16 & 1.07 a & 0.16 & 1.60 b & 0 & 1.60 b & 0 \\  & & Rhizosphere & 0.025 a & 0 & 0.025 a & 0 & 0.50 a & 0 & 0.25 b & 0 & 0.33 a & 0.10 & 0.42 a & 0.10 \\  & & Bulk soil & 0.025 a & 0 & 0.033 a & 0.10 & 0.42 a & 0.10 & 0.33 a & 0.10 & 0.53 a & 0.10 & 0.58 a & 0.10 \\  & & Rhizosphere & 2.59 a & 0.33 & 2.67 a & 0.43 & 29.6 a & 1.30 & 28.50 a & 0.16 & 158.3 a & 9.50 & 1356 b & 0.90 \\ Cu & & Bulk soil & 2.13 b & 0.16 & 2.27 a & 0.16 & 27.10 & 1.07 & 2.69 a & 1.93 & 143 b & 1.8 b & 127.70 b & 10.2 \\  & & Rhizosphere & 1.67 a & 0.10 & 0.75 b & 0.31 & 3.33 a & 0.20 & 1.42 b & 0.62 & 185.0 a & 6.44 & 5.33 b & 3.01 \\  & & Bulk soil & 0.50 b & 0.18 & 0.67 b & 0.27 & 25.0 ab & 0.64 & 11.77 c & 0.10 & 10.08 b & 1.64 & 5.60 b & 0.94 \\  & & Rhizosphere & ND\({}^{*}\) & ND & & 2.53 a & 1.84 & ND & & 11.60 a & 1.02 & 12.10 a & 3.42 \\ Pb & & Bulk soil & ND & & ND & & 3.33 a & 2.57 & ND & & 5.33 a & 0.91 & 6.40 b & 0.75 \\  & & Rhizosphere & 0.92 a & 0.10 & 1.83 a & 0.51 & 2.08 a & 0.51 & 0.83 a & 0.10 & 1.83 a & 0.37 & 0.58 b & 0.20 \\  & & Bulk soil & 0.25 b & 0 & 1.50 a & 0.64 & 2.33 a & 0.82 & 1.67 a & 0.91 & 17.5 a & 0.47 & 0.58 b & 0.10 \\  & & Rhizosphere & 4.00 b & 0 & 5.20 a & 0 & 20.90 a & 0.16 & 187.0 a & 0.82 & 84.50 a & 5.52 & 84.80 a & 3.21 \\ Zn & & Bulk soil & 4.13 b & 0.43 & 5.20 a & 0.49 & 19.70 a & 1.66 & 180.0 b & 0.75 & 86.0 a & 2.21 & 80.4 a & 6.73 \\  & & Rhizosphere & 1.58 a & 0.82 & 1.92 a & 0.27 & 2.08 a & 0.91 & 1.08 ab & 0.10 & 2.83 a & 0.10 & 2.33 b & 0.10 \\  & & Bulk soil & 2.05 a & 0.27 & 1.42 a & 0.41 & 0.75 b & 0.35 & 0.67 b & 0.10 & 1.75 c & 0.10 & 1.42 d & 0.10 \\ \hline \hline \end{tabular}  
* ND. Not detected. Mean metal concentration and SEM (\(\pi=3\)). For each soil, element and extractant, values followed by the same letter are not significantly different ([PERSON]’s text, \(p<0.05\)).  
\end{table} 
Table 6: Metal concentrations (mg/kg and SEM values) in extracts from rhizosphere soil and bulk soil from a pot experiment with barley, and the effects of biochar (2% by weight).
## Appendix A  
The FENE-P equations simplify considerably if we consider 1D, laminar, parallel flow: \(u_{x}=u_{z}=0\) and \(u_{x}=u_{x}(y)\equiv u\). The system becomes:  
\[C_{yy}\frac{du}{dy}-\frac{1}{\lambda}(f(C_{kk})C_{xy})=0 \tag{1}\] \[\frac{1}{\lambda}\left(1-f(C_{kk})C_{yy}\right)=0\] (2) \[\frac{1}{\lambda}\left(1-f(C_{kk})C_{zz}\right)=0\] (3) \[2C_{xy}\frac{du}{dy}-\frac{1}{\lambda}\left(1-f(C_{kk})C_{xx} \right)=0, \tag{4}\]  
where \(f(C_{kk})=\frac{L^{2}}{L^{2}-C_{kk}}\). Introducing the Weissenberg number as \(Wi=\lambda\frac{du}{dy}\) and solving the system of equations above, one finds the following cubic equation in \(f(C_{kk})\equiv f\):  
\[f^{3}-f^{2}-\frac{2 Wi^{2}}{L^{2}}=0, \tag{5}\]  
which omits one real solution, \(f_{R}\), that satisfies the laminar equations applicable at the wall:  
\[C_{xx}=\frac{1}{f_{R}}\left(\frac{2 Wi^{2}}{f_{R}^{2}}+1\right) \tag{6}\] \[C_{yy}=\frac{1}{f_{R}}\] (7) \[C_{zz}=\frac{1}{f_{R}}\] (8) \[C_{xy}=\frac{Wi}{f_{R}^{2}} \tag{9}\]  
where  
\[f_{R}=\frac{1}{3}\left(\frac{B}{2^{1/3}}+\frac{2^{1/3}}{B}+1\right) \tag{10}\]  
with  
\[B=(A+[(A+2)^{2}-4]^{1/2}+2)^{1/3}\quad\text{and}\quad A=54\left(\frac{Wi}{L} \right)^{2}. \tag{11}\]In our numerical simulations, the explicit definition of the wall value using Equations (A6)-(A9) as a Dirichlet boundary condition considerably improves the stability of the solution procedure and is preferred over a zero-flux Neumann boundary condition for the conformation tensor.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Relevant Description
#### 3.1.2 Item-Item Graph  
Following the approach outlined in [24], we utilize the \(k\)-nearest neighbors (\(k\)NN) method to construct an item-item graph \(S^{m}\) for modality \(m\) based on the original modal features. Initially, we assess the similarity between items by calculating the similarity of their respective original features, which results in the formation of a similarity matrix \(S^{m}\in\mathbb{R}^{N\times N}\) representing the item-item graph. The cosine similarity function is employed to compute the values within the similarity matrix \(S^{m}\):  
\[S^{m}_{ab}=\frac{(x^{m}_{a})^{T}x^{m}_{b}}{\left\|x^{m}_{a}\right\|\left\|x^{m }_{b}\right\|} \tag{3}\]  
where \(S^{m}_{ab}\) refers to the similarity between item \(a\) and \(b\) in modality \(m\). \(x^{m}_{a}\) denotes the original features of item \(a\) in modality \(m\), and \(x^{m}_{b}\) denotes the original features of item \(b\) in modality \(m\). To obtain more precise node representations and more relevant neighboring features in the execution of graph convolution procedures, we will use the \(k\)NN sparse method [41] to sparse the original adjacency matrix \(S^{m}\), which means that for each item, only the top-k edges are retained:  
\[\hat{S}^{m}_{ab}=\begin{cases}1,\text{ }S^{m}_{ab}\in\text{top-k}(S^{m}_{a}),\\ 0,\text{ otherwise.}\end{cases} \tag{4}\]  
where \(\hat{S}^{m}_{ab}\) is the element of the resulting sparse matrix \(\hat{S}^{m}\). Different from [24], we do not use the similarity between items as the element value of the sparse matrix. Instead, we follow the approach of [25] and set the top-k highest-correlated edges to 1, while setting the remaining lower-correlated or unrelated edges to 0. Furthermore, we normalize the discretized matrix \(\hat{S}^{m}\) to avoid the gradient explosion problem:\[\hat{S}^{m}=(D^{m})^{-\frac{1}{2}}\hat{S}^{m}(D^{m})^{-\frac{1}{2}} \tag{5}\]  
where \(D^{m}\in\mathbb{R}^{N\times N}\) is the diagonal degree matrix of \(\hat{S}^{m}\) and \(D^{m}_{aa}=\sum_{b}\hat{S}^{m}_{ab}\). At this point, we have obtained the normalized item-item graph \(\hat{S}^{m}\) for \(m\) modality. The next step is to aggregate the item-item graphs across various modalities to obtain the latent item-item graph. Following [25], we also freeze the updates of the latent item-item graph. To be specific, we have modal set \(M=\{v,t\}\) where \(v\) and \(t\), respectively, denote the visual modality and textual modality. The latent item-item graph can be derived from the normalized item-item graphs for different modalities using the following formula:  
\[S=\alpha_{v}\hat{S}^{v}+\alpha_{t}\hat{S}^{t} \tag{6}\]  
where \(S\in\mathbb{R}^{N\times N}\), \(\alpha_{v}\), and \(\alpha_{t}\) are the weight coefficients for the visual modality and textual modality, respectively. Additionally, we set \(\alpha_{t}=1-\alpha_{v}\), and based on the findings of [25], we empirically set \(\alpha_{v}\) to 0.1.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Stability Performance Evaluation
#### 4.1.2 Performance Analysis for BER  
The magnitude of the BER is related to the SNR, and it can be seen from Figure 8 that the BER decreases as the SNR increases. When the SNR is almost zero, the BER of the DCO-OFDM scheme is 0.3096, and the BER of the ACO-OFDM scheme is 0.4346. As the SNR increases to 20 dB, the BER of DCO-OFDM is 0.1605. While the BER of the ACO-OFDM scheme is \(5.722\times 10^{-6}\). The lower BER also means that the received signal error is smaller and the communication stability is better. Therefore, it can be seen that  
Figure 7: Comparison of the handover failure probabilities of the proposed I-VHO, D-VHO, and CBDHVHO schemes.