esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Contribution  
In the previous section, the shortcomings of recent state-of-the-art research were assessed. In this section, the following contributions will be presented with the aim of addressing these shortcomings:  
1. Technical and economic integration: This paper effectively combines technical aspects of the power grid, such as voltage stability, system losses, grid reliability, sustainability, and integration challenges, with economic goals in the planning process of battery swapping stations. This study aims to ensure efficient and reliable operation within the broader power grid infrastructure.
2. Dynamic network reconfiguration: The importance of dynamically reconfiguring power distribution networks to optimize battery swapping station planning is highlighted. By adjusting network configurations based on real-time demand and supply conditions, the study aims to improve grid flexibility, reliability, and efficiency while accommodating EV integration.
3. Multiobjective optimization: This paper explores techniques for optimizing battery swapping station planning considering multiple objectives such as station capacity, grid constraints, renewable energy integration, and operational scheduling. By adopting a multiobjective approach, this study aims to find optimal solutions that maximize overall system performance.
4. Risk assessment and resilience analysis: Risk assessments and resilience analyses are conducted to identify vulnerabilities, mitigate risks, and improve the ability of battery swapping stations to withstand disruptions. Addressing these aspects contributes to long-term reliability and operational continuity in the face of uncertainties.
5. Environmental impact assessment: Environmental impact assessments are carried out to evaluate the sustainability of battery swapping stations and their potential for reducing carbon emissions in transportation. By assessing the environmental footprint, this study aims to support environmentally friendly infrastructure development and sustainable transportation practices.  
In summary, this paper takes a holistic approach to optimizing battery swapping station planning by integrating dynamic network reconfiguration and considering technical aspects of the power grid. Through comprehensive analysis and synthesis of the relevant literature, this study aims to offer insights and recommendations for developing efficient, reliable, and sustainable infrastructure solutions for EV integration within the power grid.
## 3 Results and Discussion
### Adsorption Experiments of Pb(II)/Cd(II) with O-MWCNT\({}_{S}\)  
#### 3.2.1 pH  
The pH affects the degree of protonation and the existence forms of Pb(II) as well as Cd(II) of O-MWCNT\({}_{\rm S}\). Figure 3 illustrates the dynamics data of the removal of metal ions Pb(II)/Cd(II) at various pH levels (pH = 2\(\sim\)6) and at a stationary concentration (10 mg\(\cdot\)L\({}^{-1}\)) of metal ions Pb(II)/Cd(II). As illustrated in the figure, the minimum removal of Pb(II) and Cd(II) was 10.27 and 0.36% at pH 2, respectively. And the results can be explained as follows: when the pH value is less than 3.1, which is the zeta potential of O-MWCNT\({}_{\rm S}\)[34], a substantial amount of H\({}^{+}\) is easier to occupy the adsorption sites of O-MWCNT\({}_{\rm S}\) than metal ions Pb(II)/Cd(II). The adsorption removal of metal ions Pb(II)/Cd(II) rises to 90.15 and 31.47% with the increase in pH from 3 to 6. Importantly, at pH 6, a significantly greater amount of lead is adsorbed onto the surface of O-MWCNT\({}_{\rm S}\). This observation indicates the strong adsorption (surface precipitation) of Pb(II) on O-MWCNT\({}_{\rm S}\). The conclusion is in agreement with the report of [PERSON] et al. [35], in which it is found that as pH increased to values greater than 5.5, a fraction of Pb(OH)\({}_{2}\) appeared on O-MWCNT\({}_{\rm S}\).  
Figure 1: TEM analysis of MWCNT\({}_{\rm S}\) before and after hydrogen peroxide oxidation. (**a,b**) Carbon nanotubes before oxidation; (**c,d**) carbon nanotubes after oxidation.
applied sciences  
A Comparison of Different Methods for Rainfall Imputation: A Galician Case Study [PERSON] 1,*\({}^{\text{\tiny{(c)}}}\), [PERSON] 2 and [PERSON] 3  
Footnote 10: footnot  
the ten-minute data, possibly due to the difficulty involved in handling the high variability that rainfall presents on this time scale.  
On the other hand, the previous imputation methods can be considered as single imputations, since they replace each missing data point with a single estimated data point. This can lead to a loss of variability in the samples. To solve this problem, [PERSON] (1966) proposed the multiple imputation method. What is more, previous research works ([PERSON], 1966; [PERSON], 1966; [PERSON], 1966) demonstrated that multiple imputations were more appropriate when the data pattern was arbitrary, as is the case with rainfall. Consequently, the multiple imputation method is of interest for this case study. In this method, each missing data item can be replaced by a set of plausible values, thereby resulting in several complete data sets. We can find several methods that implement the multiple imputation procedure, such as MICE, which uses the fully conditional specification approach (FCS), or Amelia, which uses a joint modelling (JM) approach; in ([PERSON], 1966), a discussion about both approaches can be found.  
In this study, a comparison between the four imputation methods has been carried out to fill in the rainfall time series data in multiple meteorological stations in Galicia, which takes as reference the data from their nearest neighbouring stations. Methods implemented in the R package have been chosen, such as missForest ([PERSON], 1966), which is based on the Random Forest algorithm ([PERSON], 1966), MICE (Multiple Imputation by Chained Equations ([PERSON], 1966)), and Amelia II ([PERSON], 1966)--that use a combination of the classic EM algorithm method with a bootstrap approach to impute missing values--as well as the IDW as a classic imputation method to be able to compare the previous ones. A crossvalidation methodology has been proposed for the evaluation of the estimates provided by these imputation methods, which consists of creating incomplete time series data from real data loss patterns for each month and imputing the missing data in different circumstances.  
The main aim of this study is to determine the best imputation method to fill in the rainfall data series of the network of meteorological stations in Galicia. The results obtained show that the missForest method provided better results in the imputation of this meteorological variable than the MICE, Amelia, or IDW.
# An Energy-Balanced Geographic Routing Algorithm for Mobile Ad Hoc Networks
## 4 Simulation
Comparing the remaining lifetime of the link to select the candidate set, this can improve the stability of the data transmission link, reduce the frequency of re-transmission, and thus reduce the end-to-end delay. Figure 10: First node death time versus the velocity of a mobile node. Figure 13 shows the curve of the packet delivery rate varies with node packet delivery speed for different routing algorithms. As can be seen from Figure 13, the packet delivery rate decreases with the increase of the node's packet delivery rate, since the network load increases as the packet delivery rate increases, while the node's transmission capacity and cache are limited. Therefore, some packets are dropped or outdated due to waiting longer than the maximum lifetime. The performance of the GPSR algorithm is the worst, mainly due to the fact that GPSR does not consider the mobility of nodes. When the data transmission link suddenly breaks, the node needs to send data packets again, and if the number of re-transmissions reaches the upper limit, the data packets are discarded. The proposed EM-GPSR algorithm gives priority to the next-hop node with the more stable link, which can reduce the number of re-transmissions, and so the packet delivery rate is the highest and the performance is better. Figure 14 shows the curve of the death time of the first node in the network versus the packet transmitting rate under different routing algorithms. It can be seen from the figure that as the packet transmitting rate increases, the dead time of the first node in the network decreases. It also shows that the GPSR has the worst performance because the GPSR does not take into account the energy consumption of a node. When a node is selected as the next hop, the link will continue to be used until the node is not reachable or the link is broken. A single node consequently consumes energy too fast and fails. However, SU-GPSR and EM-GPSR proposed in this paper are obviously superior to GPSR, mainly because the next-hop selection takes into account the remaining energy of nodes, and nodes with more remaining energy are preferentially selected. The first node death time of EM-GPSR is a little longer than that of SU-GPSR, owing to a more accurate measurement of the energy consumption of nodes. Figure 14: First node death time versus transmit rate of data packets. Figure 13: Packet delivery rates versus transmit rate of data packets. Figure 15 compares the number of dead nodes at different packet delivery rates under different routing algorithms. As can be seen from the figure, the number of dead nodes increases with the increase of the packet delivery rate. Because GPSR does not consider energy balance, the number of dead nodes is the largest. Besides, the proposed EM-GPSR further introduces the remaining lifetime of the node and makes the energy consumption of the network more balanced, thus, reduces the number of dead nodes.
## 3 Model
### Event-Based Supply Chain Partnership Integration Model
#### 3.2.1 Orders  
At time \(t_{a}\), enterprise \(s_{i,j}\)\((i=2,3,\cdots,n\)\(j=1,2,\cdots,m)\) collects orders from its downstream partner enterprises. The set of customers of the enterprise \(s_{i,j}\) from stage \(i+1\) is \(k_{i,j}(t_{a})\) and the order placed by a downstream enterprise \(s_{i-1,l}\) to enterprise \(s_{i,j}\) is \(D_{i,j}^{i-1,l}(t_{a})\). The orders from downstream customers to enterprise \(s_{i,j}\) is \(D_{i,j}(t_{a})\), defined as:  
\[D_{i,j}(t_{a})=\sum_{l\in k_{i,j}(t_{a})}D_{i,j}^{i-1,l}(t_{a}) \tag{1}\]  
After handling the orders from the downstream enterprises, enterprise \(s_{i,j}\) will place orders to upstream suppliers. Order quantity can be influenced by numerous factors. Time factor should be what the enterprise must first take into account, since in practice, seasons and holidays can lead to the increasing of order quantity. In order to avoid the occurrence of risk events, enterprises often need to consider the resilience factor in the process of planning of their order quantity. Accordingly, enterprise \(s_{i,j}\)'s actual order quantity \(O_{i,j}(t_{a})\) can be given as:  
\[O_{i,j}(t_{a})=D_{i,j}(t_{a})+e_{i,j}\Big{(}I_{i,j}(t_{a})-D_{i,j}(t_{a}) \Big{)}t_{i,j} \tag{2}\]  
where \(I_{i,j}(t_{k})\) is the enterprise \(s_{i,j}\)'s production capacity, \(e_{i,j}\) and \(t_{i,j}\) are the random coefficients which describe the effect of the time factor and resilience factor respectively.  
After receiving the orders from the downstream customers, enterprise \(s_{i,j}\) will evenly place order to suppliers according its demand. Denote \(J\) as the set of enterprise \(s_{i,j}\)'s suppliers, and \(|J|\) as the number of suppliers. The enterprise \(s_{i,j}\) places the order \(O_{i,j}^{i+1,v}(t_{a})\) with its upstream supplier \(s_{i+1,v}\) according to the following rule:  
\[O_{i,j}^{i+1,v}(t_{a})=\frac{O_{i,j}(t_{a})}{|J|} \tag{3}\]  
The quantity of orders from enterprise \(s_{i,j}\) equals the order quantity took by supplier \(s_{i+1,v}\):  
\[O_{i,j}^{i+1,v}(t_{a})=\mathrm{D}_{i+1,v}^{i,j}(t_{a}) \tag{4}\]
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods
### Dataset
#### 2.1.3 Ground-Based PWV Data  
Aerosol Robotic Network (AERONET) is a global atmospheric aerosol observation network currently consisting of hundreds of solar photometers [29]. AERONET has been in continuous operation for more than 25 years. Although its major task is to monitor aerosol properties, it can also provide high-precision PWV data. AERONET PWV data have an uncertainty of about 10% [30; 31]. Because AERONET can provide high-precision PWV data for long time series and AERONET sites are widely distributed around the world, AERONET PWV data are widely used as verification data for water vapor remote sensing retrieval results [32; 33; 34]. In this study, remote sensing PWV data were validated using level 1.5 AERONET data version 3 (data after cloud masking and quality control) [35], which can be downloaded from the AERONET homepage ([[https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov)]([https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov)), accessed on last accessed on 5 March 2022).
## 3 Results and Discussion
### Mechanical Strength  
Four trendlines indicating the mechanical strength of the SCL hydrogels and Ca\({}^{2+}\) hydrogels are shown in Figure 2A,B, respectively. Figure 2A illustrates that the 2% SCL hydrogel had the highest mechanical strength (0.57 \(\times\) 10\({}^{4}\) Pa), followed by slightly lower strength in the 1.5% SCL hydrogel (0.53 \(\times\) 10\({}^{4}\) Pa), the considerably lower strength in the 1.1% SCL hydrogel (0.28 \(\times\) 10\({}^{4}\) Pa), and the lowest strength in the 0.8% SCL hydrogel (0.76 \(\times\) 10\({}^{3}\) Pa). Figure 2B shows that Ca\({}^{2+}\) hydrogels follow a similar trend in mechanical strength; the 2% Ca\({}^{2+}\) hydrogel had the highest strength (0.23 \(\times\) 10\({}^{5}\) Pa), followed by the 1.5%, 1.1%, and 0.8% Ca\({}^{2+}\) hydrogels (at 0.216 \(\times\) 10\({}^{5}\), 0.14 \(\times\) 10\({}^{5}\), and 0.68 \(\times\) 10\({}^{4}\) Pa, respectively). The mechanical strength results indicate that Ca\({}^{2+}\) hydrogels are stronger than SCL hydrogels; the weakest Ca\({}^{2+}\) hydrogel (0.8%) had a similar strength to the strongest SCL hydrogel (2%).  
Figure 1: SEM images illustrating the morphology of SCL and Ca\({}^{2+}\) hydrogels: Without cells (**A** for SCL hydrogel and **a** for Ca\({}^{2+}\) hydrogels) and with cells (**B–E** for SCL hydrogel and **b–e** for Ca\({}^{2+}\) hydrogels, both with varying concentrations of 0.8%, 1.1%, 1.5%, and 2%, respectively).
## 3 Results and Discussion
### Selecting the Configuration of ANNs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Hidden** \\ **Neuron** \\ \end{tabular} } & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Statistical Index**} \\ \cline{3-6}  & & **AARD\%** & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{1} & Train & 41.35 & 77,007.54 & 277.502 & 0.9338 \\  & Test & 43.68 & 72,265.35 & 268.822 & 0.9546 \\  & Total & 41.59 & 76,531.88 & 276.644 & 0.936 \\ \hline \multirow{4}{*}{2} & Train & 36.82 & 57,041.61 & 238.834 & 0.949 \\  & Test & 31.08 & 106,981.54 & 327.08 & 0.9572 \\  & Total & 36.24 & 62,050.78 & 249.1 & 0.9482 \\ \hline \multirow{4}{*}{3} & Train & 33.54 & 49,877.14 & 223.332 & 0.9573 \\  & Test & 48.86 & 87,864.31 & 296.419 & 0.941 \\  & Total & 35.08 & 53,687.40 & 231.705 & 0.9553 \\ \hline \multirow{4}{*}{4} & Train & 27.2 & 52,898.02 & 229.996 & 0.9572 \\  & Test & 41.79 & 31,827.33 & 178.402 & 0.9651 \\  & Total & 28.66 & 50,784.55 & 225.354 & 0.9577 \\ \hline \multirow{4}{*}{5} & Train & 23.85 & 33,742.42 & 183.691 & 0.9721 \\  & Test & 23.06 & 54,227.72 & 232.868 & 0.9552 \\  & Total & 23.77 & 35,797.18 & 189.201 & 0.9704 \\ \hline \multirow{4}{*}{6} & Train & 19.92 & 10,395.52 & 101.958 & 0.9913 \\  & Test & 33.18 & 25,249.53 & 158.901 & 0.9845 \\  & Total & 21.25 & 118,885.43 & 109.02 & 0.9904 \\ \hline \multirow{4}{*}{7} & Train & 13.88 & 5371.999 & 73.294 & 0.9961 \\  & Test & 20.71 & 5675.702 & 75.337 & 0.9934 \\  & Total & 14.57 & 5402.462 & 73.501 & 0.9957 \\ \hline \multirow{4}{*}{8} & Train & 12.99 & 5365.915 & 73.252 & 0.9958 \\  & Test & 22.3 & 4866.778 & 69.762 & 0.9946 \\ \cline{1-1}  & Total & 13.92 & 5315.849 & 72.91 & 0.9957 \\ \hline \multirow{4}{*}{9} & Train & 11.48 & 5250.163 & 72.458 & 0.9958 \\  & Test & 23.74 & 1027.986 & 101.39 & 0.9911 \\ \cline{1-1}  & Total & 12.71 & 5754.662 & 75.859 & 0.9953 \\ \hline \multirow{4}{*}{**10**} & Train & **9.78** & **2434.457** & **49.34** & **0.998** \\ \cline{1-1}  & Test & **18.48** & **7820.685** & **88.435** & **0.9926** \\ \cline{1-1}  & Total & **10.65** & **2974.717** & **54.541** & **0.9976** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of cascade feedforward (CF) model with different hidden neurons. The hidden neurons were not significant in the GR, therefore the spread value needed to be modified to reach the significant hidden neurons in GR. Accordingly, one hundred statistical indicators of various GRs were compared with regards to the spread value changes within 0.1-10, with 0.1 interval steps. Table 8 lists the values of AARD%, MSE, RMSE, and R\({}^{2}\) obtained from different ANN models with the best topology.
# Comprehensive Evaluation of NIMBY Phenomenon with Fuzzy Analytic Hierarchy Process and Radar Chart
## 2 Risk Evaluation System of NIMBY Phenomenon  
### Evaluation Process  
The selection of evaluation indicators is a crucial step in the risk evaluation of the NIMBY phenomenon, as it directly affects the evaluation results. The risk evaluation indicators should follow the principles of systematicity, scientific quality, comparability and practicability [36; 37; 38]. In addition, each of these principles needs to be reflected in the evaluation results through a combination of qualitative and quantitative principles which allow for more specific and accurate evaluation results.  
The risk model of the NIMBY phenomenon of transmission and transformation projects and the evaluation method are mainly carried out in the following steps: (1) Firstly, analyze and determine the major elements affecting the NIMBY phenomenon of transmission and transformation projects, and construct a three-level evaluation index system including the target layer, unit layer and element layer. Secondly, define the evaluation grade characteristics and evaluation value ranges. Finally, estimate the risk model of the NIMBY phenomenon of transmission and transformation projects. (2) Secondly, use the expert evaluation method and radar chart method to evaluate different elements of every layer. Then, use the minimum area method to calculate the risk level. Finally, use the fuzzy comprehensive evaluation method to calculate the overall risk level. The construction of the risk model and the flow of the evaluation method are shown in Figure 1.  
A combination of multiple evaluation methods is adopted to evaluate the risk of the NIMBY phenomenon of transmission and transformation projects, making the results more accurate and reasonable. Meanwhile, a factor-analysis method is an objective and scientific mathematical method which can be used to select the main evaluation indexes of the NIMBY phenomenon and quantify the evaluation indexes through the Likert scale [39]. In addition, the expert evaluation method is scored with the help of experts, which is more professional and authoritative.  
Figure 1: Risk model and the flow of the risk evaluation method.
## 2 Materials and Methods  
As the starting materials, PbO (99.9%, Daejung, Sibeung-si, Korea), ZrO\({}_{2}\) (99.6%, Daejung, Sibeung-si, Korea) TiO\({}_{2}\) (Daejung, Sibeung-si, Korea), La\({}_{2}\)O\({}_{3}\) (99.7%, Daejung, Sibeung-si, Korea), and SnO\({}_{2}\) (99.8%, Daejung, Sibeung-si, Korea) were used. The raw materials were weighed according to the composition of [(Pb\({}_{0.88}\), La\({}_{0.12}\))(Zr\({}_{1-x}\), Sn\({}_{x}\))\({}_{0.86}\)Ti\({}_{0.14}\)]O\({}_{3}\), then 0.4 wt% of the dispersant (DISPERBYK-111, BYK, Wesel, Germany) was added to the composition, which went through a wet ball-milling process at 120 RPM for 24 h. After that, it was dried in an oven at 100 \({}^{\circ}\)C for about one day, and the powder was placed into the alumina crucible and calcinated at 880 \({}^{\circ}\)C.  
The powder was pulverized after the calcination process. Low-temperature sintering additives such as ZnO (99.5%, Daejung, Sibeung-si, Korea), NiO (99.7%, Daejung, Sibeung-si, Korea), and PbO (99.8%, Daejung, Sibeung-si, Korea) were added to the composition. Thereafter, the mixture was pulverized and mixed by a wet ball-milling process in ethanol solvent. For sintering, the dried powder was mixed with an aqueous binder (PVA), and bulk was pressed at 10 \(\mathcal{O}\) using a uniaxial pressure press. To remove the binder before sintering, the sample was heated at 600 \({}^{\circ}\)C for 2 h and sintered from 950 to 1100 \({}^{\circ}\)C for 3 h.  
Density was measured using the Archimedian method to confirm whether low-temperature sintering was performed. For crystal structure analysis, X-ray diffractometer (DMAX2500, Rigaku, Tokyo, Japan) was used, and the microstructure was observed using a field emission scanning electron microscope (JSM-6700F, JEOL, Tokyo, Japan). In addition, to measure the dielectric constant, Ag electrodes were printed on both sides and annealed at 600 \({}^{\circ}\)C. The electrical characteristics were analyzed using an impedance analyzer (E4990A, Keysight, Santa Rosa, CA, USA) and a multiferroic tester (Precision LC, Radiant, Renton, WA, USA).
## 3 Proposed Methods
### Model Overview
#### 3.3.2 Long- and Short-Memory Neural Network (LSTM)  
LSTM network is a recurrent neural network (RNN) [17]. Like all recurrent neural networks, LSTM can calculate the data of traditional computers with sufficient network elements, especially for time-series data. Its general structure is shown in Figure 3.  
The three module frame parts in the figure can be seen as three cell structures. The front and rear two cells represent the cell states at the previous and later times. The middle cell is the state at the current time. It can be divided into three gating parts, representing the forgetting gate, the input gate, and the output gate 18] The three gates receive the LSTM output value \(h_{t-1}\) of the previous time state and the input data \(x_{t}\) of the current time as inputs [19]. The \(f_{t}\) of the forgetting gate part can be regarded as obtained from the input \(x_{t}\) and \(h_{t-1}\), which are used to control the forgetting degree of the information in \(c_{t-1}\). Each value in \(f_{t}\) belongs to the range of [0,1]. The lower bound value 0 represents complete forgetting, and the upper bound value 1 represents completely unchanged, which can be retained. The forgetting gate determines the forgetting degree of the state information in the previous period, and the function of the input gate is to add new content to the current state information. Similarly, the input gate part obtains the current one from the input \(x_{t}\) and \(h_{t-1}\) to control the update degree of the current state information Here, the current state information \(c_{g}\) is also calculated from the input \(x_{t}\) and \(h_{t-1}\). Then, the current new state information \(c_{t}\) can be calculated by the following formula. It means forgetting some old information and updating some new information [22]. The last part is the output gate. Similarly, according to \(x_{t}\) and \(h_{t-1}\), \(o_{t}\) can be calculated to control which information needs to be output. The specific calculation formula is as follows:  
\[\begin{array}{l}f_{t}=\sigma\Big{(}w_{f}[h_{t-1},x_{t}]\Big{)}+b_{f}\\ i_{t}=\sigma\Big{(}w_{f}[h_{t-1},x_{t}]\Big{)}+b_{i}\\ h_{t}=\tan h\Big{(}w_{g}[h_{t-1},x_{t}]\Big{)}+b_{g}\\ c_{t}=f_{t}\times c_{t}-1+i_{t}\times g_{t}\\ o_{t}=\sigma(w_{o}[h_{t-1},x_{t}])+b_{o}\\ h_{t}=o_{t}\times\tan h(c_{t})\end{array} \tag{12}\]  
where \(w\) and \(\mathbf{b}\) represent the weight matrix and bias vector of the above gate, \(c_{t}\) represents the memory cell, and \(\sigma\) and \(\tan h\) represent \(S\)-type functions and hyperbolic tangent activation functions, respectively.