esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Results
#### 4.4.1 General Tracking  
First, we focus on the two most general hashtags, adopted by politicians, media, and general public to tag their messages related to both electoral periods. As the Spanish general elections were held on 28 April 2019, the adopted hashtag was #28a. On the other hand, the European and local elections were held on 26 May, using an equivalent hashtag (#26m). Figure 3 shows how our method tracked both hashtags consistently in their relevant periods (April and May, respectively), resulting in 3.9 million tweets being extracted. Noticeably, two related hashtags were also captured in very specific subperiods: both right at the end of the respective voting day: #eleccionesgenerales28a (general elections 28A) and #elecciones26m (elections 26M). Upon further inspection, we found out that #eleccionesgenerales28a and #elecciones26m were the hashtags used in media for the TV shows that tracked the recount as well as for the subsequent talk-shows that analyzed the results. Opposite to what happened with the previous tags--#28a and #26m, probably easier to predict, short, and thus fast to type--here there is not a complete consistency between both hashtags, hindering the chances of predicting the second hashtag once the first one (#eleccionesgenerales28a) is known. Naively, one could expect something like #eleccioneseuropeas26m (European elections 26M), which did not happen. In any case, the relevancy of these unexpectedly captured hashtags (#eleccionesgenerales28a and #elecciones26m) should not be underestimated, as in the day after both electoral processes, they reach a higher relevancy than their general counterparts (more details in Figure 4). For reference, the translation to absolute figures means that a total of 662,413 tweets--442,379 discussing the results of the general elections and 220,034 for the European/local ones--would have been lost had it not been for the automated approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hashtag** & **Periods among the Top 10** \\ \hline \#espanáviva & 1142 out of 2928 (39.0\%) \\ \#28a & 952 (32.5\%) \\ \#absolucio & 686 (23.4\%) \\ \#psoe & 657 (22.4\%) \\ \#freetothom & 656 (22.4\%) \\ \#26m & 654 (22.3\%) \\ \#porespña & 645 (22.0\%) \\ \#votapsoe & 593 (20.2\%) \\ \#hazquez & 566 (19.3\%) \\ \#siemprehaciadelante & 474 (16.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hashtags that appear in the largest number of hourly extraction lists.
## 3 Discussion
### Effects of Power Coefficient on Metering Error  
The mean value, upper and lower curves of metering error at currents \(I_{\text{max}}\), \(I_{b}\) and 0.5 \(I_{b}\) are presented in Figure 7a-c, respectively.  
As shown in Figure 7, the mean value, upper and lower bounds sinusoidally fluctuated with calendar time both for power coefficients 1.0 and 0.8C. For example, the minimum and maximum of mean values were 0.0% and 0.12% and the fluctuant center was 0.06%. However, the metering error was disturbed more significantly under power coefficient 0.5L than under 1.0 and 0.8C. The mean value and standard deviation were computed at three specific times and were plotted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\Omega\) & \(\psi\) & \(\tau\) & \(a_{\Omega,\psi,\tau,2}\) & \(a_{\Omega,\psi,\tau,1}\) & \(a_{\Omega,\psi,\tau,0}\) & \(R^{2}\) \\ \hline \multirow{8}{*}{Mean} & \multirow{4}{*}{1.0} & 1 & 0.1898 & \(-\)0.1367 & 0.0716 & 0.8600 \\  & & 2 & 0.1233 & \(-\)0.1154 & 0.0727 & 0.5964 \\  & & 3 & 0.1039 & \(-\)0.1039 & 0.0570 & 0.4012 \\ \cline{2-6}  & \multirow{4}{*}{0.8C} & 1 & 0.1751 & \(-\)0.1682 & 0.0737 & 1.0000 \\  & & 2 & 0.0878 & \(-\)0.0951 & 0.0590 & 1.0000 \\  & & 3 & 0.0955 & \(-\)0.1101 & 0.0309 & 1.0000 \\ \cline{2-6}  & \multirow{4}{*}{0.5L} & 1 & 0.0635 & \(-\)0.0675 & 0.0496 & 1.0000 \\  & & 2 & \(-\)0.0533 & 0.0655 & \(-\)0.0051 & 1.0000 \\  & & 3 & \(-\)0.0683 & 0.0743 & 0.0178 & 1.0000 \\ \hline \multirow{8}{*}{\(\Omega=1\), \(3\sigma\)} & \multirow{4}{*}{0.8C} & 1 & \(-\)0.1175 & 0.1535 & 0.2778 & 0.6302 \\  & & 2 & \(-\)0.1201 & 0.1776 & 0.1648 & 0.9273 \\ \cline{1-1}  & & 3 & \(-\)0.1703 & 0.2527 & 0.0789 & 0.9940 \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{0.8C} & 1 & 0.0120 & \(-\)0.0176 & 0.3215 & 1.0000 \\ \cline{1-1}  & & 2 & 0.0028 & \(-\)0.0005 & 0.1950 & 1.0000 \\ \cline{1-1}  & & 3 & 0.0754 & \(-\)0.0835 & 0.1339 & 1.0000 \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{0.5L} & 1 & 0.0347 & \(-\)0.0789 & 0.3885 & 1.0000 \\ \cline{1-1}  & & 2 & 0.3434 & \(-\)0.5009 & 0.3758 & 1.0000 \\ \cline{1-1}  & & 3 & 0.1229 & \(-\)0.1887 & 0.2203 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter estimates for measurement error/current model.  
As shown in Figure 8a-c, the mean value exhibited a declining trend when the power coefficient changed from 1.0 to 0.8C and then to 0.5L. The maximum alteration of the mean value reached 0.07% at current \(I_{\text{b}}\), whereas the maximum change of mean value at current 0.5 \(I_{\text{b}}\) and 0.1 \(I_{\text{b}}\) was around 0.03% and less than the value at current \(I_{\text{b}}\). However, the standard deviation of the metering error presented an increased trend when the power coefficient changed from 1.0 to 0.8C and then to 0.5L. When the current was 0.1 \(I_{\text{b}}\), the maximum change of 3\(\sigma\) changed significantly by about 0.12%, but it varied slightly around 0.04% when the currents were \(I_{\text{b}}\) and 0.5 \(I_{\text{b}}\).
## 2 Materials and Methods
### Objective Functions
#### 2.2.1 Linear Independency of Mode Shapes  
To distinguish the various modes in a modal test, the measured modal vectors of the structure should be as linearly independent as possible. Moreover, to match the identified mode shapes with the modes obtained through finite-element analysis, the modes of the structure must be distinguished. The MAC matrix proposed by [PERSON] and [PERSON] [5] can be used to evaluate the degree of correlation between modes.  
\[MAC_{i,j}=\frac{\Phi_{i}^{T}\Phi_{j}}{\sqrt{\left(\Phi_{i}^{T}\Phi_{i}\right) \left(\Phi_{j}^{T}\Phi_{j}\right)}}, \tag{2}\]  
where \(\Phi\) is the mode shape matrix, which is calculated using the reduced order model in this study. The subscripts \(i\) and \(j\) denote the \(i\)th row and \(j\)th column of \(\Phi\), respectively, which are indicators of the order of the selected modes.  
Two of the most commonly used MAC matrix optimization methods were compared in [6]. In this study, we used the least squares method, which minimizes the sum of the off-diagonal-element least squares in the \(MAC\) matrix as the first objective function for evaluating the linear independence of modal shapes, as presented in Equation (3).  
\[f_{1}=\sum\limits^{s}\begin{aligned} & i=1,j=1\ \ \left(MAC_{i,j}\right)^{2},\\ &(i\
eq j)\end{aligned} \tag{3}\]  
where \(s\) is the total number of selected modes for OSP. The subscripts \(i\) and \(j\) denote the \(i\)th row and \(j\)th column of the \(MAC\) matrix, respectively.
## 7 Discussions and Future Directions  
We take an information-theoretic approach to study the flying base station positioning, channel performances, beneficialness/utility, and dependence on the stationary base station. Our work provides the fundamental results about flying base stations, which are generally applicable across communication processing and mobility control operations, to guide future research and development in flying base stations for wireless networking. While adding mobility on the base station through the flying base station can provide significant benefits to meet the performance and coverage demands/requirements of future communications, to actualize it to reap such benefits requires further research and development to advance the technology and address the limitations and challenges. This section describes some of those future directions, and we call for greater R&D to actualize and advance flying base station.
## 3 Methods
### Encoder
#### 3.2.2 Atrous Spatial Pyramid Pooling  
The shape and sizes of buildings vary across different scenes, making it difficult for single-scale features to fully represent building areas. Meanwhile, the pooling layers in CNNs cause the loss of small-scale building area information during feature extraction. To address the issues, we incorporated atrous spatial pyramid pooling (ASPP) [25] during the encoding stage. ASPP utilizes three parallel dilated convolutions, with dilated rates of [6; 12; 18] selected based on reference [25], to extract features at different receptive fields, thereby enhancing the ability to capture features from targets of varying scales. The structure of ASPP is shown in Figure 3.  
In practice, \(F_{18,34}^{5}\) was first processed through a \(1\times 1\) convolutional layer, three \(3\times 3\) convolutional layers with dilation rates of 6, 12, and 18, and ASPP pooling, which included an average pooling layer, a \(1\times 1\) convolutional layer, and an upsampling operation. Subsequently, the outputs were concatenated, and a \(1\times 1\) convolutional layer was applied to reduce the channel number of the concatenated feature maps to match the channel count of \(F_{18,34}^{5}\), generating the processed feature map \(F_{18,34}^{(5)}\). Formally,  
\[F_{18,34}^{(5)}=f^{1\times 1}(C(f^{3\times 3}(F_{18,34}^{5}),f^{3\times 3}(F_{18,34}^ {5}),f^{3\times 3}(F_{18,34}^{5}),f^{1\times 1}(F_{18,34}^{5}),Up(f^{1\times 1}( GAP(F_{18,34}^{5})))) \tag{5}\]  
where \(Up(\cdot)\) denotes the upsampling operation, and \(GAP(\cdot)\) denotes the global average pooling operation.
## 2 Methods
### Statistical Analysis  
Descriptive data were expressed as the means (M) \(\pm\) standard deviations (SD) of the measurements. The student's paired t-test was applied to all biomechanical variables to determine any significant effects during fast and preferred RSJs. Due to the sample size and participant's ability was limited in this research. [PERSON]'s d of effect size (EZ) for the differences was calculated to indicate the practical relevance of the significance (Table 1). A Bonferroni correction was used to adjust the \(p\)-value for multiple comparisons in tests, the alpha level for the statistical tests was set at \(\alpha\) = 0.005 after Bonferroni correction adjust. Pearson product-moment correlation (r) of jump height with all the predictor variables were provided to understand the correlation of the biomechanical parameters to RSJ performance (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fast Approach**} & \multicolumn{2}{c}{**Prefer Approach**} & \multicolumn{2}{c}{\multirow{2}{*}{\(t\)}} & \multicolumn{2}{c}{\multirow{2}{*}{\(p\)}} & \multicolumn{2}{c}{\multirow{2}{*}{\(r\)}} & \multicolumn{2}{c}{\multirow{2}{*}{_Cohen’s d_}} \\ \cline{2-2} \cline{5-6}  & **M** & **SD** & **M** & **SD** & & & & \\ \hline Jump height (m) & 0.78 & 0.08 & 0.77 & 0.09 & 0.57 & 0.583 & 0.74 & 0.55 & 0.161 \\ Horizontal GRF (BW) * & 1.58 & 0.24 & 1.36 & 0.24 & \(-\)7.11 & \(<\)0.000 & 0.91 & 0.82 & 2.139 \\ Vertical GRF (BW) & 3.46 & 0.31 & 3.27 & 0.34 & 2.40 & 0.035 & 0.65 & 0.42 & 0.695 \\ Horizontal touchdown velocity (m/s) & 4.31 & 0.17 & 3.96 & 0.28 & \(-\)2.91 & 0.059 & 0.61 & 0.37 & 1.577 \\ Vertical touchdown velocity (m/s) & 0.32 & 0.17 & 0.30 & 0.17 & \(-\)0.42 & 0.682 & 0.60 & 0.36 & 0.132 \\ Concentric time (ms) & 115 & 16 & 124 & 13 & \(-\)3.25 & 0.008 & 0.82 & 0.67 & 0.983 \\ Eccentric time (ms) & 127 & 16 & 144 & 22 & \(-\)3.15 & 0.009 & 0.56 & 0.32 & 0.914 \\ \hline \hline \end{tabular}
* Indicates significant difference found between fast and prefer RSJ, \(p\) \(<\) 0.005.  
\end{table}
Table 1: Descriptive data on biomechanical parameters of lower extremity. Based on means (M) and standard deviations (SD).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**Fast Approach**} & \multicolumn{2}{c}{**Prefer Approach**} \\ \hline  & \(r\) & \(p\) & \(r\) & \(p\) \\ \hline Horizontal GRF (BW) & 0.082 & 0.801 & 0.480 & 0.114 \\ Vertical GRF (BW) & 0.229 & 0.474 & 0.200 & 0.533 \\ Horizontal touchdown velocity (m/s) & 0.358 & 0.253 & 0.223 & 0.486 \\ Vertical touchdown velocity (m/s) & 0.215 & 0.502 & 0.315 & 0.319 \\ Concentric time (ms) & 0.366 & 0.242 & 0.340 & 0.280 \\ Eccentric time (ms) & 0.574 & 0.051 & 0.221 & 0.490 \\ ELR & 0.188 & 0.558 & 0.167 & 0.604 \\ GA\%RVC in pre-activation (\%) & 0.278 & 0.382 & 0.125 & 0.699 \\ TA\%RVC in push-off (\%) & 0.213 & 0.506 & 0.255 & 0.424 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson product-moment correlation (r) of jump height of fast and prefer approach.
# Analytical Modeling for Underground Risk Assessment in Smart Cities
## 4 Approximation Schemes for Underground Risk Index
### Linear Approximation  
First, we assume linear approximation for the unknown function in Equation (3) as below \[H(p_{k}(x_{i},y_{i},t),r_{k}(x_{i},y_{i}))=\alpha_{1}.p_{k}(x_{i},y_{i},t)+\alpha_ {2}.r_{k}(x_{i},y_{i}) \tag{7}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the weight assigned to risk probability and risk severity, respectively, such that \(\alpha_{1}+\alpha_{2}=1\). \(p_{k}\) is the risk probability calculated from sensors readings for \(k^{th}\) factor at time \(t\) and \(r_{k}\) is the impact severity of the \(k^{th}\) risk factor at location \((x_{i},y_{i})\). After calculating individual risk for every parameter, we can now express a linear approximation for the final risk index on location \((x_{i},y_{i})\) at time \(t\), i.e., \(Risk_{T}(x_{i},y_{i},t)\) given in Equation (6) as below.  
\[\begin{split} F(M_{1}(x_{i},y_{i},t),M_{2}(x_{i},y_{i},t),\dots M _{m}(x_{i},y_{i},t))=\beta_{0}+\beta_{1}.M_{1}(x_{i},y_{i},t)+\\ \beta_{2}.M_{2}(x_{i},y_{i},t)+\dots\beta_{m}.M_{m}(x_{i},y_{i},t )\end{split} \tag{8}\]  
where \(\beta_{k}\) is contribution coefficient of \(k^{th}\) factor towards final risk index and its values can be determined using multiple linear regression analysis.  
Combining Equations (6) and (8), we get  
\[Risk_{T}(x_{i},y_{i},t)=\beta_{0}+\sum_{s\in S}\beta_{s}.M_{s}(x_{i},y_{i},t) \in[0,1] \tag{9}\]  
where \(S\) is set of risk contributing factors under consideration, e.g., sewerage, ground status, etc.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 4 Circuit Implementation
### Eight-Phase 12.5% Duty Cycle Clock Generator
#### 4.2.1 Divider  
The divide-by-four ring counter uses a D flip-flop, as shown in Figure 11. The divide-by-four on the input signal \(4\times f_{LO}\) quadrature generates a frequency of \(f_{LO}\) and a 50% duty cycle signal; after eight D flip-flops, it generates an eight-phase \(45^{\circ}\) phase-shifted, 50% duty cycle from the intermediate signal. The D flip-flop consists of a differential clock-controlled transmission gate in series with an inverter to achieve the highest inversion speeds, in which the size of NMOS is 5 \(\upmu\)m/120 nm, and the size of PMOS is 10 \(\upmu\)m/120 nm. The eight-phase signal becomes eight NAND to generate an eight-phase, \(45^{\circ}\) phase-shifted, 12.5% duty cycle from the clock signals. Normally, adding an intermediate dummy d flip-flop is necessary to balance the phase error; clock phase differences affect the differential loss of filters.  
Figure 11: The divide-by-four ring counter circuit.  
Figure 10: The schematic of the clock generator.
# Emotion Recognition in Horses with Convolutional Neural Networks
## 2 Materials and Methods
### Defining a Horse Emotion Tracking Framework
EquiFACS provides a method that can be used to document the facial movements associated with different social contexts and thus to address questions relevant to understanding social cognition and comparative psychology, as well as informing current veterinary and animal welfare practices [22; 38; 39]. These previous results indicate that a combination of head orientation with facial expression, specifically involving both the eyes and ears, is necessary for communicating social attention. The earlier findings emphasize that in order to understand how attention is communicated in non-human animals, it is essential to consider a broad range of cues [22; 38; 39]. Recent developments in understanding horse facial expression suggest that they have many detailed ways of expressing intention and communication. The use of such detailed facial coding systems like EquiFACS and Pain Face helps develop even better mechanisms for determining levels of stress and pain, especially in close proximity when the nuances of emotional and physical expression need more attention. In order to simplify the emotional coding tool for this experiment and to test a novel system that could be both portable and useful for the layperson, we broke these up into four emotions (Figure 1), which were defined with respect to neck and facial cues that could be judged from a distance:  
**Alarmed**  
* Eyes: open eyes with little or no sclera
* Ears: stiffly forward
* Nose: open nostrils, usually slightly tense mouth or muzzle
* Neck: above parallel, head higher than back  
**Annoyed**
* Eyes: open with perhaps some sclera
* Ears: stiffly back or pinned back, close to the horse's head
* Nose: nostrils slightly closed, tense mouth or muzzle  
Figure 1: Horse emotions. * Neck: usually parallel or above parallel Curious
* Eyes: open with little or no sclera
* Ears: pointing forward/sides but relaxed
* Nose: open nostrils, relaxed mouth and muzzle
* Neck: usually parallel to ground but may be slightly below or above  
**Relaxed**
* Eyes: partially to mostly shut
* Ears: relaxed, opening pointing to the sides
* Nose: relaxed mouth and muzzle
* Neck: approximately parallel or below  
Once the emotions were defined, the second step was to collect the data. In this case, a total of 440 images of horses were collected from private sources where the horses were familiar and the context of the photo was known to help guide the coding of expressions where all four criteria were met. There were a total of 110 images per emotion labeled by two of the authors based on the coding system above that was derived from the aforementioned research on behavioral ethograms. Images were of horses at liberty in large paddocks or pastures with no equipment or observed human presence or interactions. These images were split in training and validation sets in order to train the detector and the model.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 5 Simulation Results and Analyses
### Validation of LSHADE-SPACMA Technique to Solve the TNEP Problem  
#### 5.1.1 Garver 6-Bus Test System  
In the pioneering paper [4], the Garver network was discussed and has since been used by many other researchers to compare various TNEP approaches. Figure 2 shows that it comprises 6 buses and 6 lines. Bus 6 is not connected to the rest of the network in the initial configuration, and the current demand for buses 1-5 is 760 MW, while the installed generation capacity for these interconnected nodes is only 215 MW. As a result, to avoid power from not being supplied, the TNEP would have to facilitate interconnecting bus 6 to the rest of the network.  
Regarding the standard Garver system, it is important to stress that the optimal identified solution is the same as the solution mentioned in the literature to validate the proposed algorithm's efficiency in solving the TNEP problem. The final configuration and the optimal solution of TNEP for the Garver 6-bus network system using the LSHADE-SPACMA technique are depicted in Table 2 and Figure 2. This solution includes one new branch between nodes 3 and 5, two new branches between nodes 4 and 6, and four new branches between nodes 2 and 6. The proposed method gives 13 paths with costs of 200 million USD. It provides the same results obtained by [PERSON] [4] using the linear programming (LP) method in 6.68 s, decreasing the computational burden.  
Figure 2: Garver system: (**a**) New configuration using LSHADE-SPACMA; (**b**) New configuration using LP [4].
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Classification Based on Architecture  
In this subsection, we present a detailed classification of IoHT based on the IoHT architecture in Figure 2. A generic IoHT architecture consists of IoHT devices [22; 23; 24; 25; 26; 27; 28; 29; 30], communication protocols [31; 32; 33; 34; 35; 36], and networks [37; 38; 39; 40]. The IoHT devices are further classified as wearable devices and implant devices. The data of IoHT devices are then transmitted by using different communication protocols like IEEE 802.15.6, WBAN, IEEE 802.15.1 Bluetooth, IEEE 802.11 WiFi, and LoRaWAN. The destination of this medical data is a cloud service, or a remote server designed for intensive processing tasks. Finally, data can be transmitted to a physician or an Emergency Response Team (ERT) via a mobile communication service such as 4G or 5G.  
Once the information is collected from different IoHT devices where the sensors exist, it travels toward the near and far edge of the network to be stored, analyzed, and additionally processing [41].  
Using a fog node network can give a health care system more computing power that smaller and battery-oriented IoHT devices might not be able to achieve. In the IoHT architecture, data operations, such as classification and compression, are completed on the intermediate devices or often single remote servers that allow for the fast processing of data that mobile devices cannot do. In cloud-based networks, the majority of the computation is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
performed on the cloud servers because the cloud has a higher computing capacity than the fog node networks. Cloud-based networks utilize multiple servers for parallel computing and data analysis. Moreover, the cloud has data centers that allow for more data storage that is sometimes needed for patient records.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
###### Abstract  
Salt caverns serve as underground storage for crude oil, natural gas, compressed air, carbon dioxide, and hydrogen. Key stages of cavern development for storage purposes include design, construction, storage, and abandonment. The design phase addresses optimal cavern shape, size, pillar dimensions, number of caverns, the impact of interbeds, and cyclic loading while considering the creep behavior of salt and the mechanical behavior of surrounding layers. During this phase, geological factors such as depth, thickness, and the quality of salt are considered. For construction, two main methods--direct leaching and reverse leaching--are chosen based on design specifications. The storage stage includes the injection and withdrawal of gases in a cyclic manner with specific injection rates and pressures. After 30 to 50 years, the caverns are plugged and abandoned. The geological limitation of salt domes makes it essential to look for more bedded evaporites. This study provides a comprehensive review of bedded evaporites, including their origin and depositional environment. The stability of caverns in all these stages heavily relies on geomechanical analysis. Factors affecting the geomechanics of bedded salts such as mineralogy, physical properties, and mechanical properties are reviewed. A list of bedded evaporites in the U.S. and Canada, including their depth, thickness, and existing caverns, is provided. Additionally, this study discusses the main geomechanical considerations influencing design, solution mining, cyclic loading, and abandonment of caverns in bedded salt caverns.  
bedded evaporites; evaporite properties; salt caverns; geomechanics; cavern stages 2020
## 5 Simulation Results and Performance Analyses  
This section simulates the CLCQ in three aspects. Firstly, the convergence speed is compared with that of QELAR. Subsequently, the impact of varying parameters on the CLCQ is assessed. Finally, the four performance indicators of energy consumption, residual energy variance, average end-to-end delay, and PDR are compared with QELAR and GEDAR.