esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 3 Concept Drift Adaptation Methods under Deep Learning
### Concept Drift Adaptation Methods Based on Hybrid Learning
SAE-DNN [76] actively detects the occurrence of concept drift using the STEPD. If a concept drift occurs, the top level of SAE-DNN is extended by means of random vector function linking (RVFL). The parameters in the extension layer are dynamically assigned to new data through Lasso regularization and L2 regularization. Figure 7: The general process of concept drift adaptation methods based on hybrid learning. However, there is a certain amount of noise interference. Adaptive online ensemble learning with RNN and ARIMA (OARIMA-RNN) [39] uses RNN models to capture temporal dependencies and implement online learning modeling. Then, it dynamically adapts to concept drift by adding ARIMA to the set and RNN hyperparameters being optimized with each new batch. It has better accuracy than traditional offline models. However, there was no quantification of conceptual drift or performance during the drift. RACE [77] uses the concept of processing recycling, which uses an MLP, J48 decision tree, and support vector machine as basic learners to process training instances of time-series data. Then, the training instances are processed by the incremental learning algorithm, and the concept is used to detect the occurrence of concept drift. When concept drift occurs, it is updated and retrained. The algorithm requires a large amount of memory to run and slows down convergence as the size of the integration increases. From the summary of typical algorithms in Table 3, it can be seen that \"LSTM\" + \"other models\" is a common hybrid approach, which is mainly applicable to long-term streaming data and can overcome the forgetting problem and improve the accuracy to a certain extent. In summary, for hybrid learning methods, multi-model integration is mainly tuned using dynamic weighting between models. So, it is essentially parameter updating, and there are also embedded model combinations that are mainly applied in process industries, such as power forecasting.
## Literature Review
### Review of Classification Using Machine Learning  
Researchers have conducted extensive studies on methods to manage and operate networks by analyzing network traffic and user behaviors. Hence, the widespread use of network technologies incorporating artificial-intelligence (AI) technologies has gained attention. Extensive research has been conducted on the knowledge-defined-networking paradigm, in which AI technology is incorporated into network routing, resource management, log analysis, and planning. Several companies have already applied AI data analysis to network operations [23]. In turn, multiple studies have been conducted to determine how network providers can leverage machine learning to analyze user traffic. [PERSON] and [PERSON] [24] exploited machine learning to classify IP traffic in support of QoS guarantees. [PERSON] et al. [25] proposed the classification of Chinese mobile internet users into heavy and high-mobility users by analyzing the network traffic of 2G and 3G services. Various other studies proposed methods, such as decision trees [26; 27], support-vector machine (SVM) [28; 29; 30], \(k\)-nearest neighbor (KNN) [31; 32], hidden Markov model (HMM) [33; 34], and K-Means [35; 36] for traffic classification. The application targets of these methods differed depending on whether the analysis was performed on wired, wireless, or encrypted traffic. However, these techniques demonstrated the following structure: They captured and analyzed network traffic; they applied machine learning by using traffic characteristics as features; and they classified the traffic data. The traffic data were diverse, ranging from captured packets to public datasets. However, data related to OTT usage, which is a recent trend, were rarely considered. Few studies have proposed methods to classify users based on OTT consumption [9; 37]. Those that did classified consumers into three consumption categories (i.e., high, medium, and low) by using various machine-learning methods to analyze OTT traffic. The current study is significant in that it is the first to use deep learning in an attempt to classify users in terms of OTT usage. However, deep learning has the disadvantage of requiring large numbers of calculations. On the other hand, it has the advantage of high accuracy. Hence, it has been widely utilized for similar classification problems [38; 39; 40]. Therefore, in this study, to overcome the demerits of excessive time-consumption, we propose a time-aware user-analysis framework that applies the MetaCost method [41]. Based on [PERSON] risk theory, MetaCost can reduce specific classification errors while setting the cost of misclassification differently. By using these properties, we can reduce the load on deep learning through cost adjustment.
## 2 Study Area and Datasets
### Datasets  
To obtain the deformation for a long-term series of the Xiaomojiu landslide, we collected 96 scenes of SAR data (Tables 1 and 2) over two years, including 86 scenes collected from ESA Sentinel-1 and 10 scenes from ALOS PALSAR-2 (Figure 1), respectively. The pixel spacing of Sentinel-1 SAR data in the range and azimuth directions are 2.3 and 14 m, respectively. The resolution of PALSAR-2 SAR data is about 4 m. SAR data from three observation directions can determine the three-dimensional deformation of landslides. The SRTM DEM, acquired from USGS, provides 30 m-resolution elevation information. To analyze and identify landslide factors and external geometric changes, rainfall and NDVI were obtained from NOAA (National Oceanic and Atmospheric Administration). Water level data of the Jinsha River were acquired from CGS (China Geological Survey).
###### Abstract  
An effective post-processing algorithm is essential for achieving high rates of secret key generation in quantum key distribution. This work introduces an approach to quantum key distribution post-processing by integrating the three main steps into a unified procedure: syndrome-based error estimation, rate-adaptive reconciliation, and subblock confirmation. The proposed scheme employs low-density parity-check codes to estimate the quantum bit error rate using the syndrome information, and to optimize the channel coding rates based on the Slepian-Wolf coding scheme for the rate-adaptive method. Additionally, this scheme incorporates polynomial-based hash verification in the subblock confirmation process. The numerical results show that the syndrome-based estimation significantly enhances the accuracy and consistency of the estimated quantum bit error rate, enabling effective code rate optimization for rate-adaptive reconciliation. The unified approach, which integrates rate-adaptive reconciliation with syndrome-based estimation and subblock confirmation, exhibits superior efficiency, minimizes practical information leakage, reduces communication rounds, and guarantees convergence to the identical key. Furthermore, the simulations indicate that the secret key throughput of this approach achieves the theoretical limit in the context of a BB84 quantum key distribution system.  
quantum key distribution; post-processing; low-density parity-check codes; rate-adaptive information reconciliation; syndrome-based error estimation; subblock confirmation Article  
Efficient Integration of Rate-Adaptive Reconciliation with Syndrome-Based Error Estimation and Subblock Confirmation  
20242653103390/e26010053  
1
# A Study on a Mechanism of Lateral Pedestrian-Footbridge Interaction
## 4 Amplitude Frequency Curve
### Amplitude Frequency Curve of Footbridge  
According to Equation (38), if \(a_{p}=0\), \(a_{s}=0\), then it can be illustrated that the lateral vibration displacement of pedestrians and the lateral vibration displacement of the footbridge are both 0, which is the trivial solution of the footbridge.  
Equation \(A_{p2,3}=\frac{-k_{2}}{2k_{1}}\) is inserted into Equation (38) with the assumption that \(\Delta_{p}=0\); as a result, one gets  
\[a_{s}=\sqrt{-\frac{64\eta_{r}^{2}\omega_{p}^{4}k_{1}^{2}k_{2}+64m_{r}^{2}\eta^ {2}\omega_{p}^{2}k_{1}^{2}k_{2}+16m_{r}^{2}\eta\zeta\omega_{p}^{2}k_{1}k_{2}^ {2}+m_{r}^{2}\zeta^{2}\omega_{p}^{2}k_{2}^{3}}{512k_{1}^{3}\zeta_{s}^{2}\omega_ {s}^{4}}}. \tag{49}\]When equation \(A_{p2,3}=\frac{-k_{2}\pm\sqrt{k_{2}^{2}-4k_{3}k_{3}}}{2k_{1}}\) is inserted into Equation (38) in the case of \(\Delta_{p}>0\), we can obtain:  
\[a_{s2}= \left\lceil\begin{array}{l}\frac{-64m_{s}^{2}\omega_{p}^{4}k_{2} +64m_{s}^{2}\omega_{p}^{4}k_{1}^{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}-64m_{s}^{2}\eta ^{2}\omega_{p}^{2}k_{1}^{2}k_{2}+64m_{s}^{2}\eta^{2}\omega_{p}^{2}k_{1}^{2} \sqrt{k_{2}^{2}-4k_{1}k_{3}}}}{512k_{1}^{3}\zeta_{s}^{2}\omega_{s}^{4}}\\ -\frac{16m_{s}^{2}\eta\omega_{p}^{2}k_{1}k_{2}^{2}-32m_{s}^{2}\eta\omega_{p}^{2 }k_{1}\lambda_{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}-16m_{s}^{2}\eta\omega_{p}^{2}k_{1} ^{2}(k_{2}^{2}-4k_{1}k_{3})+m_{s}^{2}\zeta^{2}\omega_{p}^{2}k_{2}^{3}}}{512k_{ 1}^{3}\zeta_{s}^{2}\omega_{s}^{4}}\\ \sqrt{+\frac{m_{s}^{2}\zeta^{2}\omega_{p}^{2}(k_{2}^{2}-4k_{1}k_{3})^{\frac{3 }{2}}-3m_{s}^{2}\zeta^{2}\omega_{p}^{2}(k_{2}^{2}-4k_{1}k_{3})k_{2}+3m_{s}^{2} \zeta^{2}\omega_{p}^{2}\lambda_{2}^{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}}}{512k_{1}^ {3}\zeta_{s}^{2}\omega_{s}^{4}}}\end{array}\right. \tag{50}\] \[a_{s3}= \left\lceil\begin{array}{l}-\frac{64m_{s}^{2}\omega_{p}^{4}k_{2} +64m_{s}^{2}\omega_{p}^{4}k_{1}^{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}+64m_{s}^{2}\eta ^{2}\omega_{p}^{2}k_{1}^{2}+64m_{s}^{2}\eta^{2}\omega_{p}^{2}k_{1}^{2}\sqrt{k_ {2}^{2}-4k_{1}k_{3}}}}{512k_{1}^{3}\zeta_{s}^{2}\omega_{s}^{4}}\\ -\frac{16m_{s}^{2}\eta\omega_{p}^{2}k_{1}^{2}+32m_{s}^{2}\eta\omega_{p}^{2}k_{ 1}\lambda_{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}+16m_{s}^{2}\eta\omega_{p}^{2}k_{1}^{ 2}(k_{2}^{2}-4k_{1}k_{3})+m_{s}^{2}\zeta^{2}\omega_{p}^{2}k_{2}^{3}}}{512k_{1 }^{3}\zeta_{s}^{2}\omega_{s}^{4}}\\ +\frac{m_{s}^{2}\zeta^{2}\omega_{p}^{2}(k_{2}^{2}-4k_{1}k_{3})^{\frac{3}{2}}+3 m_{s}^{2}\zeta^{2}\omega_{p}^{2}(k_{2}^{2}-4k_{1}k_{3})k_{2}+3m_{s}^{2}\eta ^{2}\zeta^{2}\omega_{p}^{2}\lambda_{2}^{2}\sqrt{k_{2}^{2}-4k_{1}k_{3}}}{512M2k_ {1}^{3}\zeta_{s}^{2}\omega_{s}^{4}}\end{array}\right. \tag{51}\]  
Here, Equation (49) can be regarded as a trivial solution for the lateral vibration of the footbridge, and Equations (50) and (51) are nontrivial solutions for the lateral vibration of the footbridge.  
Therefore, the first approximate solution of the lateral steady-state response of the pedestrians and footbridge is written as:  
\[U_{s}=a_{s}(T_{1})\cos(\omega_{s}t) \tag{52}\]  
\[u_{p}=a_{p}(T_{1})\cos[\omega_{p}t+\theta_{p}(T_{1})]. \tag{53}\]
# Fatigue Assessment of Explosive Bolts Considering Vibration of Fixtures
## 6 Discussion  
Since the missile vehicle and the release device are complex systems, the fatigue strength of the explosive bolts will be affected by the pretension force, the depth of the weaken groove, the road roughness, the fatigue life curve with a certain failure probability, and so on. Based on the above results, we can find when the pretension force of explosive bolts are assigned as \(F_{\text{pre}}=0.4\sigma_{b}A_{s}\), the vibration amplitude is relatively small; i.e., maximum amplitude is about 14 MPa and the corresponding stress ratio is 0.9777.  
In order to ensure the explosive bolts will not break during the missile transportation on the vehicle, we analyze the effects of pretension force on the vibration behavior of the structure, as illustrated in Figure 13. It reveals that the minimum stress ratio obviously decreases with the decreasing of the pretension force (from \(0.4\sigma_{b}A_{s}\) to \(0.2\sigma_{b}A_{s}\)) in the whole vibration response, while the maximum vibration amplitude increases linearly. Still following the analysis process in Section 5, we obtain that these explosive bolts can undergo more than 6000 km carrying distance on Class A to E roads.  
Figure 12: Constant life curve of \(N=10^{7}\) with different survival rate.  
Figure 13: The variation of (**a**) the minimum stress ratio \(R\); and (**b**) the maximum stress and stress amplitude with the decreasing of the pretension force in the whole vibration response of the bolts.  
Except for the pretension force, there are still other factors that will influence structural safety, such as the design of the wall thicknesses of the weaken groove and the charge hole, and the dispersion of mechanical behavior of the material. From this work, we provide a possible process to assess the fatigue safety of this complex system. When there is a possibility of facing fatigue fracture, it is recommended that the diagram of Figure 14 be followed to conduct fatigue evaluations or redesign the structure to improve safety. Also, further detailed material tests and structural optimization analyses are needed to ensure high-reliability safety.
## 4 Experiments  
### Dataset  
The SimpleQuestions (SQ) dataset [40] is a KBQA dataset of single-relation questions. It provides 108,442 single-relation questions with their answer facts, which are organized as subject-relation-object tuples in Freebase. The whole dataset is split into a training set, a validation set, and a test set, with 75,910, 10,845, and 21,687 samples, respectively. Among all samples in the test set, there are 1385 abnormal samples where the golden subject is not in FB5M (a subset of Freebase which provides \(E\) in our experiments) or different from the golden entity in the question. To these samples, even if golden entities could be recognized, they could not be strictly matched to any subject in the KB. In our experiments, the test set is divided into Dataset I, which contains 20,302 normal samples, and Dataset II, which contains 1385 abnormal samples.  
The WebQuestionsSP (WQSP) dataset [41] is a KBQA dataset of complex questions (also based on Freebase), which contains 3098 samples in the training set and 1639 samples in the test set. In our experiments, the test set is also divided into Dataset III, which contains 1233 normal samples, and Dataset IV, which contains 406 abnormal samples in the similar way.  
In addition, to further evaluate the robustness of our model, we generate Dataset V and Dataset VI by replacing 5% and 10% of characters randomly in all samples in WQSP. For example, the sample \"who plays ken barlow in coronation street\" in WQSP is converted into \"who plays ktg barlpc in coronation street\" in Dataset V and \"who plais ken barloo in coronatign street\" in Dataset VI.
## 4 Experimental Results
### Data Augmentation Evaluation  
Due to the limited amount of data provided by the FER-2013 dataset, we employed data augmentation techniques to expand the dataset. In data augmentation, flipping and color adjustment are among the most common methods; thus, we utilized these two methods as the foundation for data augmentation. The flipping method includes both horizontal and vertical flipping modes, although in practical applications, it is uncommon for human faces to be upside down. Obviously, vertical flipping was not an appropriate method for our system. Therefore, we only employed horizontal flipping for data augmentation in our experiments.  
Table 3 shows the effectiveness of data augmentation on the FES-2013 dataset. In practice, the model with data augmentation exhibited improvements of 7.76% and 3.85% compared to the baseline model. We attribute this improvement to two factors. The first factor is the diversification of image extraction. Through data augmentation, the dataset incorporates a wider variety of facial expression images, providing the model with more opportunities to learn features. The second factor is the outcome of color adjustments. Because of saturation adjustment, the facial features within the images were highlighted, making it easier for the CNN model to extract useful features. However, excessive increase in saturation leads to blurring of facial features. In our data augmentation, we observed that some images initially had a lighter color tone, and color adjustments resulted in the blurring of facial features. This explains the phenomenon where using both methods of horizontal flipping and color adjustment led to a decrease in accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Filter Composition** & **Accuracy** \\ \hline Baseline (CNN) & (64, 128, 256) & 60.60\% \\ \hline CNN & (64, 128, 256, 512) & 62.31\% \\ \hline CNN + Haar & (64, 128, 256, 512) & **63.67**\% \\ \hline \end{tabular} The optimal results are highlighted in bold.  
\end{table}
Table 2: Different filter performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Augmentation Method** & **Accuracy** \\ \hline Baseline (CNN) & - & 60.60\% \\ \hline CNN + Haar & - & 63.67\% \\ \hline CNN + Haar & Horizontal flipping & **68.36\%** \\ \hline CNN + Haar & Horizontal flipping + color adjustment & 64.21\% \\ \hline \end{tabular} \({}^{\pi}\)-\({}^{\pi}\) denotes the model without augmentation. The optimal results are highlighted in bold.  
\end{table}
Table 3: Augmentation performance.
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 3 Proposed Method
### Calculate the Optimal Number of Cluster Heads \((k)\)  
In this paper, we use the simulated annealing algorithm to calculate the optimal number of cluster heads [28]. This algorithm reduces the energy consumption of nodes that transfer their data to cluster heads and does this by reducing the total distance between the nodes and closest cluster heads. We suppose that \(N\) nodes are in the environment. If the number of clusters is \(k\), each cluster has \(\dfrac{N}{k}\) nodes (one cluster and \(\dfrac{N}{k}-1\) nodes).  
As mentioned previously, the task of cluster heads is to send the integrated data to the base station, and the base station is far away from the nodes. Therefore, wasted energy is calculated by the multipath model. Consequently, cluster heads' energy consumption for a signal frame is calculated as follows:  
\[E_{CH}=l\times E_{elec}(\dfrac{N}{k}-l)+l\times E_{DA}\times(\dfrac{N}{k})+l \times E_{elec}+l\times\epsilon_{mp}d_{toBS}^{4} \tag{10}\]  
In Equation (10), \(l\) is the number of data bits, and \(d_{toBS}\) is the distance between the cluster head and base station.  
Each node sends its data to the cluster head. Hence, the energy consumption is calculated by the FRISfree space model as follows:  
\[E_{non-CH}=l\times E_{elec}+l\times\epsilon_{fs}\times d_{toCH}^{2} \tag{11}\]We assume that nodes are distributed in an \(M\times M\) environment, and the area for each cluster is almost \(\dfrac{M^{2}}{k}\). Furthermore, the cluster head is in the centre of the cluster, and the nodes' distance from the cluster head is computed as follows:  
\[d_{toCH}^{2}=\int\int(x^{2}+y^{2})\rho(x,y)dxdy=\int\int r^{2}\rho(r,\theta)rdrd\theta \tag{12}\]  
If the area is the circle with radius \(R=(\dfrac{M}{\sqrt{\pi\times k}})\), Equation (12) will be changed as Equation (13):  
\[d_{toCH}^{2}=\rho\int_{0}^{2\pi}\int\dfrac{\dfrac{M}{\sqrt{\pi k}}}{\sqrt{\pi k }}\ r^{3}drd\theta=\dfrac{\rho}{2\pi}\times\dfrac{M^{4}}{k^{2}} \tag{13}\]  
If the density of nodes in the cluster is the same, then \(\rho=\dfrac{1}{\dfrac{M^{2}}{k}}\), and we will have Equation (14).  
\[d_{toCH}^{2}=\dfrac{1}{2\pi}\times\dfrac{M^{2}}{k} \tag{14}\]  
Therefore, we have Equation (11) as follows:  
\[E_{non-CH}=l\times E_{elec}+l\times\epsilon_{fs}\times\dfrac{1}{2\pi}\times \dfrac{M^{2}}{k} \tag{15}\]  
Energy consumption for each cluster is computed as follows:  
\[\begin{split} E_{cluster}&=E_{CH}+(\dfrac{N}{k}- 1)\times E_{non-CH}\\ &\approx E_{CH}+(\dfrac{N}{k})\times E_{non-CH}\end{split} \tag{16}\]  
Overall energy consumption per each frame is computed as follows:  
\[\begin{split} E_{total}&=k\times E_{cluster}\\ &=l(2\times E_{elec}\times N+E_{DA}\times N+k\times\epsilon_{mp} \times d_{toBS}^{4}+E_{elec}\times N+\epsilon_{fs}\times\dfrac{1}{2\pi} \times\dfrac{M^{2}}{k}\times N)\end{split} \tag{17}\]  
Finally, the optimum cluster head will be computed as follows:  
\[k=\dfrac{\sqrt{N}}{\sqrt{2\pi}}\times\dfrac{\sqrt{sf^{e}}}{\sqrt{pm^{e}}} \times\dfrac{M}{d_{toBS}^{2}} \tag{18}\]
## 3 Selected MPC Algorithms
### Predictive-Fixed Control Technique Based on Space Vector Modulation (SVM) (M\({}^{2}\)Pc)  
In space vector modulation (SVM) [8; 19; 20], it is possible to define each available vector for the 2L-VSC in (\(\alpha\beta\)) plan as shown in Figure 2. The control technique calculates the prediction of the two active vectors that compose each sector at every sampling time (\(T_{s}\)) and evaluates the cost function.  
The cost function, defined by (15), is evaluated for each sector. Each prediction is evaluated based on (6), where the duty cycles for the two active vectors are calculated by solving the following equation:  
\[d_{i}=\frac{\delta}{g_{i}} \tag{20}\]  
where \(\delta\) is the constant of proportionality, the subscript \(i\) denotes the adjacent vectors, in this case (\(i=1;2\)) and \(i=0\) correspond to the duty cycle of a zero vector which is evaluated only one time,  
\[d_{o}+d_{1}+d_{2}=T_{s}. \tag{21}\]  
By solving the previous equations, it is possible to obtain the value of \(\delta\) and the duty cycles for each vector as given:  
\[d_{o}=\frac{T_{s}g_{1}g_{2}}{J_{o}g_{1}+g_{1}g_{2}+g_{o}g_{2}} \tag{22}\]  
\[d_{1}=\frac{T_{s}g_{o}g_{2}}{g_{o}g_{1}+g_{1}g_{2}+g_{o}g_{2}} \tag{23}\]  
\[d_{2}=\frac{T_{s}g_{o}g_{1}}{g_{o}g_{1}+g_{1}g_{2}+g_{o}g_{2}}. \tag{24}\]  
Considering these expressions, as the new cost function, which is evaluated at every (\(T_{s}\)), and defined as:  
\[g(k+1)=d_{1}g_{1}+d_{2}g_{2}. \tag{25}\]  
As seen in Figure 3b, the two vectors that minimize the CF and the zero vector are selected and applied to the 2L-VSC at the next sampling time. After obtaining the duty cycles and selecting the optimal two vectors, the following equations are defining the times for each vector:  
\[\begin{split} T_{o}&=T_{s}d_{o}\\ T_{1}&=T_{s}d_{1}\\ T_{2}&=T_{s}d_{2}.\end{split} \tag{26}\]
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 3 Methodology
### TOPSIS for Supplier Ranking
#### 3.3.3 Calculation of Weighted Standardized Decision Matrix  
Weighted standardized decision matrix, \(\mathrm{v_{ij}}\) is formed by multiplying the standardized decision matrix with the criteria weights, \(\mathrm{w_{j}}\).  
\[\mathrm{v_{ij}}=\mathrm{U_{ij}}\mathrm{w_{j}}\quad(1\leq\mathrm{i}\leq\mathrm{ m},\;1\leq\mathrm{j}\leq\mathrm{n}) \tag{12}\]  
where \(\mathrm{w_{j}}\) represents criteria weight and \(\sum_{j=1}^{n}\mathrm{w_{j}}=1\).
###### Abstract  
The energy crisis and environmental concerns have accelerated the development of the active distribution network (ADN) with a high proportion of renewable energy, which poses a challenge to the operation of the power system. Moreover, using active management means to promote the consumption of renewable energy is an important task of ADN. Therefore, as an important operation means, the network reconfiguration is used to enhance the adjustable capacity of the power system at the planning stage. Firstly, a \"wind-light-load\" uncertain scenario set is constructed to address the uncertainty of wind speed, lighting, and load. On this basis, a robust optimization model for distributed power generation taking into account network reconstruction and in ADN is proposed. In addition, the distributed generator (_DG_) permeability indicator is introduced in the planning model to improve the ADN ability of absorbing renewable energy. A linearized AC power flow model is utilized to calculate the power flow. Finally, via simulation in an IEEE 33-bus system and IEEE 69-bus system, the influence of network reconfiguration and robustness on distributed generator planning, economy and reliability of ADN is analyzed, and the validity of the model is verified.  
active distribution network; distributed generator planning; DG permeability; network reconfiguration; robust planning; bi-level optimization