esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis
### OP of the Secondary Network
\Bigg{]}dv_{1}.\] (50) Next, substituting the PDF of \(V_{1}\) given in (1) into (50), we obtain \(\mathcal{J}_{1}\) as \[\mathcal{J}_{1}=\sum_{p=1}^{M}\bigg{(}\frac{B_{1}\Omega_{\text{PTS}_{1}}}{ \Omega_{\text{PTS}_{1}}+\beta_{1}}-\frac{B_{2}\Omega_{\text{PTS}_{1}}}{ \Omega_{\text{PTS}_{1}}+\beta_{3}}-\frac{B_{3}\Omega_{\text{PTS}_{1}}}{ \Omega_{\text{PTS}_{1}}+\beta_{2}}+\frac{B_{4}\Omega_{\text{PTS}_{1}}}{ \Omega_{\text{PTS}_{1}}+\beta_{4}}\bigg{)}.\] (51) Similarly, we can exactly calculate \(\mathcal{J}_{2}\) in (49) as \[\mathcal{J}_{2} =\sum_{p=1}^{M}A_{3}\frac{\Omega_{\text{PTS}}}{\Omega_{\text{PTS }}+\chi_{3}}.\frac{\Omega_{\text{PTS}_{1}}\alpha_{8}}{\Omega_{\text{PTS}_{1}} \alpha_{8}+(\Omega_{\text{PTS}}+\chi_{3})\alpha_{6}}\exp\biggl{(}-(\Omega_{ \text{PTS}}+\chi_{3})\frac{\alpha_{5}-\alpha_{7}}{\alpha_{8}}\biggr{)}\] \[-\sum_{p=1}^{M}A_{4}\frac{\Omega_{\text{PTS}}}{\Omega_{\text{PTS }}+\chi_{4}}\frac{\Omega_{\text{PTS}_{1}}\alpha_{8}}{\Omega_{\text{PTS}_{1}} \alpha_{8}+(\Omega_{\text{PTS}}+\chi_{4})\alpha_{6}}\exp\biggl{(}-(\Omega_{ \text{PTS}}+\chi_{4})\frac{\alpha_{5}-\alpha_{7}}{\alpha_{8}}\biggr{)}.\] (52)
* **Case 2**: \(P_{\text{SS}_{1}}\leq(1+\theta_{\text{Sth}})P_{\text{SR}_{b}}\) or \(\alpha_{5}\leq\alpha_{7}\) Similarly to Case 1, we can formulate \(\mathcal{I}_{3}\) in this case as follows: \[\mathcal{I}_{3} =\underbrace{\int_{0}^{+\infty}f_{U}(u)\Bigg{[}\int_{\frac{ \alpha_{5}\alpha_{1}+\alpha_{7}-\alpha_{8}}{\alpha_{6}}}^{+\infty}f_{V_{1}}(v_{ 1})\mathcal{I}_{3}(u,v_{1}|C_{1})dv_{1}\Bigg{]}du}_{\mathcal{J}_{3}}\] \[+\underbrace{\int_{0}^{+\infty}f_{U}(u)\Bigg{[}\int_{0}^{\frac{ \alpha_{5}\alpha_{1}+\alpha_{7}-\alpha_{5}}{\alpha_{6}}}^{+\infty}f_{V_{1}}(v _{1})\mathcal{I}_{3}(u,v_{1}|C_{2})dv_{1}\Bigg{]}du}_{\mathcal{J}_{4}}.\] (53)Similarly to the derivation of \(\mathcal{J}_{1}\) and \(\mathcal{J}_{2}\), we can obtain \(\mathcal{J}_{3}\) and \(\mathcal{J}_{4}\) in (53), respectively, as  
\[\mathcal{J}_{3}=\sum_{p=1}^{M}\bigg{[}\frac{C_{1}\Omega_{\text{PTSR}}}{\Omega_{ \text{PTSR}}+\beta_{5}}-\frac{C_{2}\Omega_{\text{PTSR}}}{\Omega_{\text{PTSR}}+ \beta_{6}}\bigg{]}, \tag{54}\]  
\[\mathcal{J}_{4}=\sum_{p=1}^{M}\frac{A_{3}\Omega_{\text{PTSR}}}{ \Omega_{\text{PTSR}}+\chi_{3}}-\sum_{p=1}^{M}\frac{A_{3}\Omega_{\text{PTSR}} \alpha_{6}}{(\Omega_{\text{PTSR}}+\chi_{3})\alpha_{6}+\Omega_{\text{PTSS}_{1}} \alpha_{8}}\exp\biggl{(}-\Omega_{\text{PTSS}_{1}}\frac{\alpha_{7}-\alpha_{5}} {\alpha_{6}}\biggr{)}\] \[-\sum_{p=1}^{M}\frac{A_{4}\Omega_{\text{PTSR}}}{\Omega_{\text{ PTSR}}+\chi_{4}}+\sum_{p=1}^{M}\frac{A_{4}\Omega_{\text{PTSR}}\alpha_{6}}{( \Omega_{\text{PTSR}}+\chi_{4})\alpha_{6}+\Omega_{\text{PTSS}_{1}}\alpha_{8}} \exp\biggl{(}-\Omega_{\text{PTSS}_{1}}\frac{\alpha_{7}-\alpha_{5}}{\alpha_{6} }\biggr{)}. \tag{55}\]  
Substituting (49), (51), and (52) into (37), and substituting (53)-(55) into (37), we obtain \(\text{OP}_{\text{SS}_{1}}\) in Case 1 and in Case 2, respectively. Therefore, we finish the proof here.
# Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
## 3 Results
### Clustering of City Transport Areas According to the Criterion of Population Mobility Structure
As the transport area moves away from the city center and the travel time increases, the share of trips by car increases and by PT decreases. When using averaged parameters by groups of areas, the influence of the average travel time by private transport on the trips share is described by a quadratic model, and for PT, by a linear model. These research results can be used in the formation of a construction policy and the definition of a city development strategy--towards suburbanization and expansion of the city borders, with urbanization and an increase in the density of resettlement in the central part of the city or an increase in the density of resettlement in the peripheral areas of the city.
## 2 Materials and Methods
### Satellite Imagery Datasets  
Sentinel-1 and 2 time-series data were retrieved from January 2019 to December 2020 from the Google Earth Engine (GEE) catalogue. Table 1 presents the characteristics of the Sentinel-1 and 2 data used in this study.  
Sentinel-1 ground-range-detected (S1_GRD) images with an interferometric wide swath (IW) instrument mode were used in this study. The IW mode images were provided in dual polarization with vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH). Only VH-polarized backscatter data were used in this study because they are more sensitive to rice fields than VV backscatter [22; 50].  
We only used Sentinel-1A data, as Sentinel-1B data were not available in the study area. The spatial resolution of the images was 10 m. These images were pre-processed with the Sentinel-1 Toolbox, employing thermal noise removal, radiometric calibration, and terrain correction using the SRTM 30 DEM. After pre-processing, each scene was calibrated and ortho-corrected [51].  
Sentinel-2 Level-1C (Top-of-Atmosphere Reflectance, TOA) was used instead of Sentinel-2 Level-2A (Surface Reflectance, SR) because artefacts appear in Sentinel-2 Level-2A SR. In addition, Sentinel-2 Level-1C (Top-of-Atmosphere Reflectance, TOA) have been successfully used in numerous studies for surface classification, such as [28; 30; 32; 34; 52; 53]. Band 4 (Red) and band 8 (NIR) from Sentinel-2 multispectral image (MSI) level 1C data were used to calculate the normalized difference vegetation index (NDVI). The spatial resolution of the images was 10 m. A total of 883 scenes from Sentinel-1A and 3,825 scenes from Sentinel-2A and B were accessed from the GEE.
## 3 Malicious Document Classification
### Feature Extraction
#### 3.3.3 Vectorization  
In order to apply a classification model to detect malicious documents, we need to represent the documents as vectors. To this end, existing text analysis techniques first build a vector representation of each word in the corpus, aggregating vectors of words included in a document to build a vector representation of the document. We use the three following well-known techniques in our framework for this document vectorization.  
Bag of words (BoW) is a document representation technique widely used in NLP and information retrieval fields. In BoW vectorization, each word included in the corpus is encoded as a _one-hot vector_ with a length equal to the number of words included in the corpus [27]. Each document becomes a sum of vectors of words included in the document. In the document vector obtained with the BoW technique, the value corresponding to a word appearing in the document is set as 1, and the value corresponding to a word not appearing in the document is set as 0. The time complexity of BoW vectorization is \(O(Nl+NV)\), where \(N\) is the size of the dataset, \(l\) is the number of tokens in each sample, and \(V\) is the size of the global vocabulary (e.g., the size of feature vector dimension).  
Term Frequency-Inverse Document Frequency (TF-IDF) is another vectorization technique that considers the importance of each word within the corpus [28; 29]. In TF-IDF, a document is represented as a vector with the length same as the number of words included in the corpus. An element of the document vector has a value multiplied by \(TF_{w,d}\), which indicates the frequency with which the corresponding word appears in the document, and \(IDF_{w,D}\), which indicates the importance of the word in the entire corpus. \(TF_{w,d}\) is computed by counting the occurrences of word \(w\) in the document \(d\), and \(IDF_{w,D}\) is done by counting the documents containing \(w\), which is scaled logarithmically. The time complexity of TF-IDF vectorization is \(O(2 Nl+NV)\).  
Word2 Vec is a technique for learning the vector representation of each word in the corpus by using a neural network [30; 31]. The vectors of words learned from Word2 Vec are located more closely if they have similar meanings within the corpus. Word2 Vec extracts a continuous skip-gram [32] from the corpus and then uses it to train a shallow neural network to predict the surrounding words of a given word [30; 31]. A vector corresponding to each word is extracted from the projection layer of the neural network learned in this way. Since Word2 Vec is a technique for obtaining a vector representation of a word, an additional aggregation step is required to obtain the final document vector. In this paper, we use the sum and average of the word vectors included in a document as the vector representation of the document. The time complexity of vectorization using Word2 Vec could be represented as \(O(NC(D+D\log_{2}V))\), where \(C\) is the size of the sliding window, \(D\) represents the size of the resulting vectors.
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
## 2 Problem Formulation
### Optimization Tool
The optimization task applied to the antenna system can be summarized as follows: basically, the DE optimizer randomly generates vectors as possible solutions. The candidate vectors of real numbers denote the variables in the optimizer--in this particular optimization case, the radii of the array \(\vec{r}=[r_{1},r_{2},\ldots,r_{n}]\). Hence, each suggested solution vector proposes a dual-beam radiation pattern to be evaluated that meets certain characteristics of side lobe level and directivity for specific directions of interest in the array. Subsequently, the mutation operator increases the search in the \(d\)-dimensional space, followed by the crossover sub-process that handles the components inherited from the mutation operation with a fixed probability crossover rate (Cr), improving the diversity in the population but minimizing convergence speed. Afterwards, the selection operator protects the survival of the fittest solutions in the following generations and reduces the probability of population stagnation. At the end, the algorithm reaches a global best population that conforms a dual-beam power pattern with the predefined design parameters, i.e., a dual-beam pattern with maximum directivity and minimum sidelobe level in a fixed location in our numerical examples. ```
Input: G: Maximum number of iterations, \(N_{pop}\): Population size, F: Scale factor, Cr: Crossover rate Output:\(P_{G}\): Global best solution
1 Generate \(I_{pop}\) individuals of the initial population pseudo-randomly;
2 while(\(G\
eq True\))do
3 for\(i=1\): \(I_{pop}\)do
4 Evaluate population \(f(\chi_{i})\)
5
6 end for
7 for\(i=1\): \(I_{pop}\)do
8 **Mutation**
9 Select 3 individuals \(\chi_{r1}\), \(\chi_{r2}\), \(\chi_{r3}\);
10 compute Equation (7)
11 **Crossover**
12\(v_{d}=\chi_{off}\)
13 for\(j=1\): \(n\)do
14 generate \(rand\)(0,1);
15 if\(rand\)(0,1) \(<Cr\)then
16\(\chi_{off,j}=\chi_{i,j}\);
17
18 end if
19 **Selection**
20 if\(f(\chi_{off})\leq f(\chi_{i})\)then
21 save index for replacement \(\chi_{i}=\chi_{off}\);
22
23 end if
24
25 end for
```  
**Algorithm 1**DE/rand/1/bin pseudocode used in the optimization process
## 2 Materials and Methods
### Biological Studies  
#### 2.5.1 Cell Viability Assay  
The viability of human osteoblast ATCC hFOB1.19 cultured on the chitosan film was determined using the MTS assay. Before cell seeding, 10 mg samples (\(n\) = 5) were sterilized by UV irradiation for 15 min each side and then seeded at 2 \(\times\) 10\({}^{3}\) cells/scaffolds in a 96-well plate. The seeded cells were incubated at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\), and 95% humidity for 48 h. A positive control was included, which comprised cells with only culture medium (C+) and hydrogen peroxide as negative control (C\(-\)) with the target with MTS solution.  
After 3 h of MTS incubation with the cells, the light absorbance at 490 nm was measured by a multi-well spectrophotometer (Thermo Scientific(tm)) and subtracted from that of the controls to yield the corrected absorbance.  
Cell adhesion was followed by SEM using both direct and indirect methods. Osteoblast (2 \(\times\) 10\({}^{4}\) cells/mL) were seeded in a 24-well plate and then either grown on 10 mg films for direct contact analysis or by using 300 \(\upmu\)L of extracts, obtained from each sample, and deposited on glass discs for indirect contact assay. For this, they were incubated at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) and 95% humidity for 48 h. After osteoblast culture, samples were washed twice with PBS, and fixed in 25% glutaraldehyde, for 2 h. Subsequently, the samples were washed three times for 10 min with PBS and dehydrated in gradual series of ethanol (70, 80, 90% and absolute ethanol) for 1 h for each percentage. The fixed cells were observed in a JEOL brand Scanning Electron Microscope, JSM-6360 LV, at different amplifications (500\(\times\)) using a voltage of 20 kV, on a cold stage at \(-\)20 \({}^{\circ}\)C.
# Study on the Aging Behavior of Asphalt Binder Exposed to Different Environmental Factors
## 3 Results
### Analysis of Outdoor Exposure on the Chemical Composition of Asphalt Binder
#### 3.3.2 Effect of Outdoor Exposure on the Functional Groups of Different Asphalt Binder
Conversely, O-aging had a minor impact on virgin asphalt binder. Natural aging had a lesser effect on the characteristic functional groups of SBS-modified asphalt binder. Among these, All-aging exerted the most substantial influence on the sulfoxide index of SBS-modified asphalt binder, though it represented only 64.5% of the effect of PAV aging on SBS-modified asphalt binder. Additionally, O+UV-aging had the most profound impact on the carbonyl index of SBS-modified asphalt binder, accounting for only 39.2% of that of SBS-modified asphalt binder.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 5 Design Procedure and Application Example
### Selection of the Bus Capacitance  
The third step is to design the bus capacitance to fulfill both the maximum ripple \(\delta v_{dc}/v_{dc}<0.5\) [\(\%\)] and the maximum perturbation of the DC bus voltage \(\Delta v_{dc}/v_{dc}<3.5\) [\(\%\)]. Therefore, expressions (16), (18), (44), and (49) are used to analyze the \(\delta v_{dc}/v_{dc}\) and \(\Delta v_{dc}/v_{dc}\) values for different bus capacitances, and those results are synthesized in Figure 8.  
Figure 6: Effect of \(L_{m}\) on both \(\delta i_{m}\) and \(F_{suc}\) for turn-ratio values of some commercial HFT. (**a**) \(n=1.4\) and XFRMS HFT; (**b**) \(n=5.4\) and Vitee HFT; (**c**) \(n=8.0\) and Nascent HFT; (**d**) \(n=12.0\) and Pulse HFT.  
Figure 7: Average duty cycle achieved with the HFT of Table 2.  
The previous figure shows that any capacitor higher than 48.85 uF, which is the minimum \(C_{dc}\) acceptable, fulfills both the maximum ripple and maximum perturbation of the DC bus voltage. From the figure, it is evident that such a limit \(C_{dc}\) value fulfills the maximum \(\Delta v_{dc}/v_{dc}\), and provides a much smaller ripple \(\delta v_{dc}/v_{dc}\). Finally, the bus capacitor is selected as the near commercial value \(C_{dc}=\) 50 [uF], which provides \(\Delta v_{dc}/v_{dc}=\) 3.4 [%] and \(\delta v_{dc}/v_{dc}=\) 0.35 [%], thus fulfilling both maximum ripple and maximum perturbation of the DC bus voltage.
# Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects
## 2 Framework of an Integrated Decision Support System for Scaffolding in Construction Projects
### Simulation Modules
#### 2.2.2 Phase 2: Alternative Ranking Module
Alternative Ranking  
TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) was developed in 1981 [43]. The essential concept of the TOPSIS approach is that the alternative with the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution is the best alternative [43; 44]. In the TOPSIS implementation process, the normalized decision matrix is firstly computed through Equation (7). \[r_{ij}=\frac{a_{ij}}{\sqrt{\sum_{i=1}^{n}a_{ij}^{2}}},i=1,2,\ldots,I,j=1,2, \ldots,J \tag{7}\]where \(r_{ij}\) represents the normalized decision matrix, \(a_{ij}\) represents the assessment value of attribute \(j\) for alternative \(i\), \(I\) indicates the total number of all potential alternatives, and \(J\) indicates the total number of attributes in the decision-making problem. Then the weighted normalized decision matrix \(v_{ij}\) is calculated through Equation (8). \[v_{ij}=w_{j}\times r_{ij},i=1,2,\ldots,I,j=1,2,\ldots,J \tag{8}\]  
where \(w_{j}\) is the normalized weight of attribute \(j\) obtained from FAHP as addressed in Section 2.2.2, and \(r_{ij}\) is the normalized decision matrix obtained from Equation (7). The positive ideal solution (\(A^{+}\)) and negative ideal solution (\(A^{-}\)) are determined through Equations (9) and (10). \[A^{+}=\{v_{1}^{*},v_{2}^{*},v_{3}^{*},\ldots,v_{I}^{*}\}=\{(\max{{}_{i}}v_{ij} |j\in J_{1}),(\min{{}_{i}}v_{ij}|j\in J_{2})\,|i=1,2,\ldots,I\} \tag{9}\]  
\[A^{-}=\{v_{1}^{-},v_{2}^{-},v_{3}^{-},\ldots,v_{I}^{-}\}=\{(\min{{}_{i}}v_{ij} |j\in J_{1}),(\max{{}_{i}}v_{ij}|j\in J_{2})\,|i=1,2,\ldots,I\} \tag{10}\]  
where \(A^{+}\) and \(A^{-}\), respectively, denote the positive ideal solution and the negative ideal solution, \(J_{1}\) represents the set of the benefits criteria, and \(J_{2}\) represents the set of cost criteria. Then the Euclidean distance from the positive ideal solution (\(A^{+}\)) and the negative ideal solution (\(A^{-}\)) for each alternative can be calculated through Equations (11) and (12). \[S_{i}^{+}=\sqrt{\sum_{j=1}^{n}{(v_{ij}-v_{j}^{*})^{2}}},(i=1,2,\ldots,I,j=1,2, \ldots,J) \tag{11}\]  
\[S_{i}^{-}=\sqrt{\sum_{j=1}^{n}{(v_{ij}-v_{j}^{-})^{2}}},(i=1,2,\ldots,I,j=1,2, \ldots,J) \tag{12}\]  
where \(S^{+}\) represents the distance from the maximum ideal point, and \(S^{-}\) represents the distance from the minimum ideal point. Finally, the closeness coefficient (\(CC_{i}\)) for each alternative can be determined through Equation (13). The closeness coefficient reflects the relative distance of the alternative to the ideal solution. Based on the values of \(CC_{i}\), the ranking results are determined. The alternative with the maximum value of \(CC_{i}\) achieves the best performance. \[CC_{i}=\frac{S_{i}^{-}}{S_{i}^{-}+S_{i}^{+}},i=1,2,3,\ldots,I \tag{13}\]
## 3 Proposed System
### Energy Efficiency Problem Formulation  
This optimization problem's purpose is to maximize total energy efficiency (\(EE^{T}\)). As illustrated below, the restrictions are the same as in the preceding optimization problem:  
\[\underset{a_{k},u,n,P_{k},u,n}{max}EE^{T} \tag{1}\]  
Figure 2: The proposed DEELB method’s architecture.  
Energy efficiency is a metric that assesses how well resources are used. In bits/Hz/joule, the spectral efficiency over power consumption is assessed. This is, in some senses, a multi-objective problem in which both throughput and power are maximized [39]. As a result, this challenge aims to transport data at the maximum possible data rate while spending the least amount of power while meeting the constraints.
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 5 Discussion
### Motivation and Contribution of the Study  
The mechanical and electromagnetic environment of the penetration fuze signal processing system is complex and harsh during the high-speed penetration of the projectile into the reinforced concrete target. In the past, experts in the field of penetration fuze mainly used the actual sensor signals to identify the flight status and control the detonation. In recent years, with the complex and changeable targets, the measured signals are more oscillatory, mixed and fuzzy, and the fuze initiation control is facing more severe challenges. With the development of technology, the chip processing ability has improved and new algorithms have emerged, and the penetration overload prediction has gradually attracted the attention of experts in the field. Through the use of dynamic prediction method, combined with real-time overload data, flight status identification and detonation control can be carried out. Even if the projectile body has a severe deflection, it can detonate after the appropriate delay time through this method to prevent the phenomenon of no explosion or early explosion, and improve the damage effect. Therefore, penetration overload prediction is of great significance in the field of penetration ammunition control, and is also one of the research focuses in this field this year.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 4 Role of Energy Internet in Smart Grid
### Energy Internet as a Smart Grid Enabler  
The vision of IOT is to connect anytime, anything, and anywhere to the global information network. The goal of the smart grid is to monitor and control the energy generating units by providing two-way communication, anyplace and anytime, between the grid and IEM system [58]. To communicate with large-scale remote areas, a wireless system is efficient. The integration of the vision of IOT and SG is enabled by EI. Sensing and measurement are the backbone of smart grid. Smart meters and advanced metering infrastructures (AMIs) will support, monitor, and control energy [59]. EI will ease the wide distributed area management for enabling smart grid services. Also, the recent advances in low-power integrated circuits and wireless communications, offers efficient, low cost, and low power consumption devices for use in remote sensing applications [60].  
An EI system decentralizes the areas with large-scale distributed renewable generation, in order to balance the supply and demand, whereas a smart grid follows a localized approach. EI provides  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Design Feature** & **Design Parameter** & **Energy Flow** & **Information Flow** & **Two-Way Communication** & **Plug-n-Play Interfacing** & **Integration of Multiple DRERs and DESDs** \\ \hline Basic ER design [22, 28] & \begin{tabular}{c} Acts as intelligent energy \\ node with three-layer \\ functions shared among SST, \\ IEM, and DGL. All DRERs and DESDs are connected directly with the ER having Plug-n-Play interfacing. \\ \hline Energy hub-based design [51, 55] & \begin{tabular}{c} Swiss Science team proposed \\ it as unit of energy generation, conversion and storage. \\ \end{tabular} & Yes & No & No & Yes & Yes \\ \hline Digital grid router design [56] & \
\begin{tabular}{c} Japan power grid proposed it \\ networks by allocating the IP \\ address to the various \\ electrical equipment \\ connected to the power \\ system. Base station will \\ identify the equipment with \\ respective IP to allocate the \\ power. \\ \end{tabular} & Yes & Yes & Yes & No & No \\ \hline E-router [52] &
\begin{tabular}{c} Designed for dispatch and \\ control of low voltage \\ equipment. \\ \end{tabular} & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between multiple designs of ER. DRER—distributed renewable energy resources; DESD— distributed energy storage devices.  
a bidirectional exchange of power supply between the power grid and customers through a single infrastructure based on ER. It offers a platform of intra energy exchange for multiple forms (thermal, chemical, and so on) for distributed energy sources [51]. As compared in Table 2, the SG objectives of integration, interoperability, and security can be achieved through the concept of EI.