esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Kinetics  
To offer the difference in adsorption rate, the concentration of 100, 200, and 300 mg L\({}^{-1}\) of the DB15 dye was utilized to carry out kinetic studies at 303, 313, and 323 K. The used kinetics limitations are given in Table 5.  
Figure 8 represents the results of the kinetic models developed for DB15. The kinetic data of adsorption of DB15 on minerals were analyzed using a pseudo-1 st-order [37] and a pseudo-2 nd-order [38].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]-** & **[PERSON]** & **[PERSON]-** & **[PERSON]-** & **[PERSON]-** & **[PERSON]-** & **[PERSON]-** \\  & & & & & & & & & \\ \hline SSE & 321.3 & 1080.3 & 297.4 & 4324.3 & 227.2 & 281.7 & 321.3 & 306.4 & 296.9 \\ \(\chi^{2}\) & 12.14 & 40.80 & 8.02 & 67.55 & 6.42 & 5.59 & 12.14 & 10.79 & 10.15 \\ \(R^{2}\) & 0.94 & 0.80 & 0.94 & 0.97 & 0.96 & 0.95 & 0.94 & 0.94 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the curve fitting for isotherm models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Initial Dye** & \multirow{2}{*}{**Temp** [**K**] [**Mg mL\({}^{-1}\)]} & \multirow{2}{*}{**\(q_{e,expt}\)**} & \multicolumn{4}{c}{**Pseudo-First Order**} & \multicolumn{4}{c}{**Pseudo-Second Order**} \\ \cline{3-10}  & & & & & & & & & & \\ \hline \multirow{3}{*}{100} & 303 & 60 & 50.18 & \(1.40\times 10^{-1}\) & 0.68 & 0.96 & 55.64 & \(3.99\times 10^{-3}\) & 0.87 & 1.72 \\  & 313 & 57 & 48.58 & \(1.40\times 10^{-1}\) & 0.63 & 1.10 & 53.92 & \(4.10\times 10^{-3}\) & 0.84 & 0.46 \\  & 323 & 55 & 45.89 & \(1.54\times 10^{-1}\) & 0.58 & 0.98 & 50.40 & \(5.07\times 10^{-3}\) & 0.81 & 0.42 \\ \hline \multirow{3}{*}{200} & 303 & 80 & 70.67 & \(1.79\times 10^{-1}\) & 0.78 & 0.39 & 75.69 & \(4.78\times 10^{-3}\) & 0.95 & 0.08 \\  & 313 & 76 & 65.34 & \(6.53\times 10^{1}\) & 0.51 & 0.54 & 65.88 & \(1.71\times 10^{-3}\) & 0.76 & 1.65 \\  & 323 & 73 & 64.35 & \(2.19\times 10^{-1}\) & 0.51 & 0.54 & 67.94 & \(7.39\times 10^{-3}\) & 0.80 & 0.22 \\ \hline \multirow{3}{*}{300} & 303 & 86 & 76.28 & \(2.39\times 10^{-1}\) & 0.49 & 0.47 & 79.82 & \(7.72\times 10^{-3}\) & 0.80 & 0.19 \\  & 313 & 80 & 68.28 & \(2.97\times 10^{-1}\) & 0.40 & 0.20 & 70.19 & \(1.54\times 10^{-2}\) & 0.71 & 0.10 \\ \cline{1-1}  & 323 & 75 & 68.07 & \(3.04\timesExamination of the kinetics data (Figure 8) represents raise in adsorption with an increase in adsorbent concentration. Similarly, the adsorption rate increases with time. However, it is realized that the rate of adsorption decreases as the temperature increases.  
Figure 8: (**a–c**) Kinetic model developed for DB15 dye on HNT system at different temperatures.
## 2 Dynamics Modelling and Problem Formulation
### The Desired Landing Trajectory  
In the alignment phase, the aircraft altitude will be around 500 m. Additionally, it keeps at a constant speed. In this case, the approaching speed is selected as 67.4 m/s. After that, the aircraft will enter the glide slope mode, when the approach path reaches the desired glide path. In this mode, the flight path angle should be kept at \(-3\) deg and the gliding velocity should be maintained at a constant value. The aircraft enters the flare phase when the altitude is around 15 m. The aircraft will gradually approach the ground and the vertical speed will be reduced to 0.3 m/s at the touch down point.  
From above analysis, two variables need to be controlled, such that the aircraft follows the desired trajectory: one variable is the longitudinal speed \(V\) and another is the altitude \(h\). According to the civil aircraft landing requirements, the desired longitudinal speed \(V_{d}\) is almost constant during the landing stage. As the vertical speed is very small, the horizontal speed \(V_{x}\) is assumed to be equal to the longitudinal speed \(V_{d}\), that is  
\[V_{x}\approx V_{d}=67.4\;\mathrm{m/s} \tag{9}\]  
The desired altitude trajectory \(h_{d}\) includes two parts: glide slope and flare segment. In the glide slope, the flight path angle is \(-3\) deg, so the desired trajectory is  
\[h_{d}(t)=-\tan(3^{\circ})V_{x}t=-\tan(3^{\circ})V_{d}t \tag{10}\]  
In the flare segment, the desired trajectory is chosen as [28]  
\[h_{d}(t)=\frac{k_{1}}{k_{2}^{2}}e^{-k_{2}V_{x}t}-\frac{k_{1}}{4k_{2}^{2}}e^{- 2k_{2}V_{x}t}+k_{3}V_{x}t+k_{4}=\frac{k_{1}}{k_{2}^{2}}e^{-k_{2}V_{d}t}-\frac{ k_{1}}{4k_{2}^{2}}e^{-2k_{2}V_{d}t}+k_{3}V_{d}t+k_{4} \tag{11}\]  
where the \(k_{1},\cdot\cdot\cdot,k_{4}\) meet the following equations:  
\[\begin{split}\dot{h}_{d}(0)=V_{d}\sin(3^{\circ}),h_{d}(0)=15m\\ \dot{h}_{d}(t_{a})=0.3m/s,h_{d}(t_{a})=0\end{split} \tag{12}\]  
where \(t_{a}\) is the total time of the flare segment, which is chosen as \(t_{a}=10\)s. We choose the initial \(k_{0}=\left[k_{1_{0}},k_{2_{0}},k_{3_{0}},k_{4_{0}}\right]=\left[0.0001,0.01,0,5\right]\) and the allowable error is \(10^{-4}\) in order to find \(k_{1},\cdot\cdot\cdot,k_{4}\). Using the function \(fsolve(\cdot)\) in _Matlab_ to get the following results:  
\[\begin{split} k_{1}=0.0006,k_{2}=0.0056\\ k_{3}=-0.0031,k_{4}=1.7933\end{split}\]  
Figure 2 shows the desired trajectory of the altitude \(h_{d}\).
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion  
### Data Sources  
The sentiment data utilized in this study is sourced from the Financial News Database of Chinese Listed Companies (CFND), available through the CNRDS China Research Data Service platform. This comprehensive database encompasses over 400 online media outlets and more than 600 newspaper publications, ensuring a robust representation of sentiment trends in the financial market. Specifically, the dataset includes news reports from twenty leading online financial media platforms, such as Hexun, Sina Finance, East Money,Tencent Finance, NetEase Finance, Phoenix Finance, China Economic Net, Sohu Finance, JRJ, Huaxun Finance, FT Chinese, Panorama, CNSTOCK, China Securities Journal, Stockstar, Caixin, The Paper, Yicai, 21 CN Finance, and Caijing. These platforms are recognized for their authoritative coverage, high-quality reporting, and widespread influence among investors, making their content particularly valuable for sentiment analysis in financial markets. By leveraging this extensive and reliable dataset, our study aims to provide a more accurate and comprehensive reflection of market sentiment dynamics.  
The financial sentiment for the daily stock index is derived using the BERT model, which quantifies sentiment by extracting the number of positive sentiment news headlines, the number of negative sentiment news headlines, and the total count of financial news headlines. The sentiment indicator is then computed based on the following formula:  
\[S=\frac{S_{p}-S_{N}}{News_{all}} \tag{39}\]  
In this formulation, \(S_{p}\) denotes the number of news headlines with positive sentiment, \(S_{N}\) represents the number of news headlines with negative sentiment, and \(News_{all}\) corresponds to the total number of news headlines.  
This study focuses on the CSI 300 Index, the SSE 500 Index, and the CSI A50 Index as the primary research subjects. Daily, 30-min, and 15-min trading data for these indices were collected via the Tushare API of stock block trading. The dataset include key features such as opening price, closing price, highest price, lowest price, trading volume, and transaction value. The selected data span from 15 January 2007 to 31 December 2020.  
For comparative experiments, consistent datasets and model parameters were utilized, with an 8:2 ratio for the training and testing sets. The objective of this study is to predict the future closing prices of various stock indices. To ensure a rigorous evaluation of model performance, we employed a rolling-window time-series cross-validation approach. The input and output sequence lengths were set to 1, 3, 5, 7, 10, and 15, respectively. To enhance the robustness of our findings, each comparative experiment was independently repeated 10 times, and the average values were reported as the final results.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 3 LRI Ranging Phase
### Model Errors: Scale Factor and Light Propagation Time  
The formulas used for converting phase to range so far assume error-free knowledge of the laser frequency (\(\
u_{\mathrm{R}}\)) and light travel time (\(\Delta t^{\mathrm{GPS}}_{\mathrm{RTR}}=\Delta t_{\mathrm{RTR}}\)). In order to assess the effect of inaccuracies in these quantities, one can replace  
\[\
u_{\mathrm{R}}(t)\longrightarrow\
u_{\mathrm{R}}(t)+\delta\
u_{ \mathrm{R}}(t) \tag{32}\] \[\Delta t^{\mathrm{GPS}}_{\mathrm{RTR}}(t)\longrightarrow\Delta t ^{\mathrm{GPS}}_{\mathrm{RTR}}(t)+\Delta t^{\mathrm{GPS,e}}_{\mathrm{RTR}}(t) \tag{33}\]  
such that \(\delta\
u_{\mathrm{R}}\) and \(\Delta t^{\mathrm{GPS,e}}_{\mathrm{RTR}}\) account for errors in the absolute laser frequency and propagation time, respectively. There are two ways to apply the laser frequency errors. One way is to consider them in the phase domain (cf. Equation (9))  
\[\varphi^{\mathrm{Noisy}}_{\mathrm{TWR}}(t)-\varphi^{\mathrm{Noise Free}}_{\mathrm{TWR}}(t) =\Phi_{\mathrm{R,Noise}}(t)-\Phi_{\mathrm{R,Noise}}(t-\Delta t _{\mathrm{RTR}})=\int_{t-\Delta t_{\mathrm{RTR}}}^{t}\delta\hat{\
u}_{\mathrm{ R}}(t^{\prime})\,\mathrm{d}t^{\prime} \tag{34}\] \[\approx\delta\hat{\
u}_{\mathrm{R}}(t)\cdot\Delta t_{\mathrm{RTR }}(t), \tag{35}\]  
Figure 2: Evaluation of accuracy for different formulas and approximations \(\rho_{\mathrm{TWR,...}}\left(t\right)\) shown in red for Equation (21), in green for Equation (22) and in blue for Equation (27). The simulation parameters for a time-series of 86,400 s length are given in Appendix A and Table A therein. The error is defined as \(\left|\rho_{\mathrm{TWR,...}}\left(t\right)-\left(L(t)-L_{0}\right)\right|\), where \(\left(L(t)-L_{0}\right)\) denotes the true (debiased) range (cf. Appendix A). The dashed traces assume a drifting frequency \(\
u_{\mathrm{R}}\), while the solid lines assume an oscillating frequency \(\
u_{\mathrm{R}}\). The dashed-blue trace is not visible because the error is zero. \(\
u_{\mathrm{R}}\) is assumed to be known without any error. where we just renamed \(\delta v\) into \(\delta\varPhi_{\text{R}}\). Equation (34) illustrates that the (cavity) phase variations \(\Phi_{\text{R,Noise}}\) are suppressed in the TWR combination due to the self-comparison with a delayed instance, which can be expressed in the frequency domain with a transfer function \((1-e^{-2\pi i\beta\Delta t_{\text{RTRTR}}})\). Similar equations apply to the KBR, where this function is called a ranging filter [11] (Equation (4.20)) and [10] (Equation (3.38)). The second way of applying the errors in Equations (32) and (33) is to consider them in the quantities used to convert the phase to range.
## 3 Materials and Methods  
### Data Acquisition  
We collected satellite images of tailing ponds located in China by Gaofen-7 satellite imagery [29]. The Gaofen-7 satellite has a resolution of 0.65 m and an elevation accuracy of 0.1 m, meeting the needs for 1:10,000 scale mapping precision [30]. Three-channel RGB images were acquired as raw data with a resolution of about 2 m and a size of 732 \(\times\) 732 pixels, and a total of 325 images were taken. The dataset was expanded using four data augmentation methods from OpenCV(Version 4.8.1): mirroring, darkening, rotating, and adding Gaussian noise [31]. This was done to increase sample diversity, enhance model robustness, and improve generalization [32]. An example of this augmentation is illustrated in Figure 3. Changes in image brightness can replicate satellite shooting scenes with varying lighting conditions, mirroring and rotation can replicate varied shooting angles and positions of satellites, and Gaussian noise can replicate artifacts that may occur during image collecting and processing. A dataset consisting of 1625 improved images was ultimately created. To ensure assessment reliability, the dataset is split into two parts: a training set and a validation set. The training set has 80% of the data, and the validation set has the other 20% [33]. This permits complete training and effective validation of datasets [34]. The annotation schematic may be shown in Figure 4. The picture annotation produces a JSON file with details like the label's outline position and name.  
Figure 2: Diagram of the research framework.
## 3 Proposed PBFT Architecture and Model
### Roles of Nodes and Network Architecture
#### 3.1.2 GPN (Group Primary Node)  
The GPN is the representative node responsible for group management within the cluster selected by the CPN. The GPN broadcasts the consensus message received from the CPN to the lower layer for consensus. It plays an important role in achieving consensus within the group. In summary, the GPN manages the consensus process within the group it manages and aggregates the consensus results from within the group. To explain the role of the GPN in more detail, it is as follows.  
After receiving the consensus message from the CPN, if there is a GPN in the lower layer, the GPN broadcasts the received consensus message to the GPNs in the lower layer. If there is no GPN to forward the consensus message to a lower layer, each GPN performs the PBFT-based consensus algorithm with the NNs within its group. In this process, the GPN is responsible for achieving consensus within the group and serves as the primary node of the typical PBFT protocol. Once the consensus within the group is complete, the GPN aggregates the consensus results and returns them to the CPN.  
As we described, GPNs play an important role in starting and finishing the consensus algorithm in each group. Therefore, problems may occur if the GPN behaves abnormally due to network errors, malicious activity, or other reasons. To mitigate these risks, we can leverage the nature of private blockchains to select GPNs based on trusted nodes.  
Additionally, institutions and operators can impose severe penalties if abnormal behavior occurs, including long-term cancellation of GPN status.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Profit-oriented Local Search Algorithm  
After decoding the individual into the multiple single-orbit observation plan, a profit-oriented local search algorithm is adopted to improve the profit of each single-orbit observation plan. The detailed procedures of the profit-oriented local search algorithm are summarized in Algorithm 1. In lines 3-4, for each orbit, the maximum observation profit \(P_{max}\), the best single-orbit observation plan \(\Pi_{ij}^{\mathrm{B}}\), and the current single-orbit observation plan \(\Pi_{ij}^{\mathrm{C}}\) are initialized. Then, destroy operation and repair operation are executed iteratively to search for the solution with the highest profit. In each iteration, the indicator removal and random removal are performed first (line 6-7), and then, the insertion operation is executed only when the repair probability \(P_{R}\) exceeds its insertion execution probability (line 8-16). Finally, \(P_{max}\) and \(\Pi_{ij}^{B}\) are greedily updated according to their profits (line 17-21).  
```
0: Single-orbit observation plan set \(\Pi\), maximum iteration \(K\)
0: Improved single-orbit observation plan set \(\Pi^{\prime}\)
1:for\(i=1\) to \(N\)do
2:for\(j=1\) to \(m_{i}\)do
3: Calculate the profit of single-orbit observation plan \(\Pi_{ij}^{O}\) as the maximum observation profit \(P_{max}\)
4: Initialize the initial single-orbit observation plan \(\Pi_{ij}^{O}\) as the best single-orbit observation plan \(\Pi_{ij}^{B}\) and the current single-orbit observation plan \(\Pi_{ij}^{C}\)
5:for\(k=1\) to \(K\)do
6: Perform indicator removal on \(\Pi_{ij}^{C}\)
7: Perform random removal on \(\Pi_{ij}^{C}\)
8:if\(P_{R}\geq P_{I1}\)then
9: Perform direct insertion on \(\Pi_{ij}^{C}\)
10:elseif\(P_{R}\geq P_{I2}\)then
11: Perform direct insertion and shifting insertion on \(\Pi_{ij}^{C}\)
12:elseif\(P_{R}\geq P_{I3}\)then
13: Perform direct insertion, shifting insertion, and backtracking insertion on \(\Pi_{ij}^{C}\)
14:elseif\(P_{R}\geq P_{I4}\)then
15: Perform direct insertion, shifting insertion, backtracking insertion, and deletion insertion on \(\Pi_{ij}^{C}\)
16:endif
17: Calculate the profit \(P^{C}\) of the current single-orbit observation plan \(\Pi_{ij}^{C}\)
18:if\(P^{C}\geq P_{max}\)then
19:\(P_{\max}=P^{C}\), \(\Pi_{ij}^{O}=\Pi_{ij}^{C}\)
20:else
21:\(\Pi_{ij}^{C}=\Pi_{ij}^{O}\)
22:endif
23:endfor
24:endfor
```  
**Algorithm 1** Profit-oriented local search algorithm.
## 2 Detailed Analysis of the Literature
### Prosumer Relationships
#### 2.2.2 Consumer Engagement
The socio-psychological aspect of the integration of the new technologies should garner more attention to manage the resistance from involved participants. One approach is to adopt a communication strategy through campaigns, advertising, and education for instilling public awareness of the significant benefits of the smart grid deployment [78]. Another fundamental building block to address stakeholder resistance and develop a sustainable prosumer engagement is to have mechanisms for trust management and consumer confidence. Trust management must adapt to the continuous progress of the smart grid considering the smart grid'sdynamic nature [79]. However, very limited research exists in this area. Studies on trust in social network and online engagement can be a starting point for investigation. Experiments on a dataset from real social networks examined two types of trust measures, i.e., reliability based on the direct interaction from experience, and local reputation or suggestions by friends and friends of friends based on their past experiences [80; 81]. Results reveal that reliability and local reputation strengthens group cohesion and homogeneity in the social network setting. A study compared online social network versus smart grid network to understand how prosumer based smart grids can grow and flourish [14]. The comparison involved three dimensions, i.e., community formation, community growth, and overall community management, and highlights the need for investigating trust and loyalty in a prosumer network. Two ideas are proposed here, firstly implementing mechanisms for management of breach of trust within the community, and secondly, investigating policy-based solutions to prevent disloyal behaviors. No significant work on trust exists since the publication of the article in 2012. Only one paper discussed trust in the smart grid [82]. The authors argue that trust and confidence, including lack of it, can affect sustainability in energy systems. Based on the above, we propose the following:  
**Proposition 7**. : _(a) Consumer engagement enhances the dynamics within the prosumer market, and facilitates prosumer market objectives, resulting in the sustainable deployment of smart grid; (b) Drivers of consumer acceptance and engagement include comfort, control of innovative devices, environmental benefits, financial incentives, information on billing and electricity use, security, and other social aspects, including social media technologies; (c) Mitigating consumer's anxiety towards risks attributed to smart grid deployment can increase consumer engagement; (d) Stakeholder resistance can be managed through communication strategies to uphold public awareness of the smart grid benefits; (e) Trust management is essential to improve consumer engagement._
## 4 Smart Grid Architecture
### Generation Domain  
The generation sector is responsible for delivering electricity to customers. Bulk electricity is generated from renewable and non-renewable energy sources. The bulk power generation areas are electrically connected to the transmission area via a cable and share a common interface with the markets, operations, and transmission area, as shown in Figure 7. Communication with the transmission area is very important because power is delivered to customers through the transmission area. With good coordination between the generation domain and the transmission domain, the power supply deficit can be addressed directly by operations and indirectly by markets. The requirement for the generation domain is to minimize greenhouse gas emissions, provide storage to handle variable generation from renewable energy sources (RESs), and increase the inclusion of RESs. Various devices such as protective relays, remote terminal units (RTUs), programmable logic controllers (PLCs), fault recorders, and user interfaces (UIs) are used in this area. The power generation domain is further divided into two sub-domains, namely the bulk power generation domain and the non-bulk power generation domain. The participants of the generation domain and their functions are summarized in Table 6.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Participator** & **Functions** \\ \hline Control & Manage the system’s power flow and reliability \\ \hline Measure & Analog and digital measurements collected via the SCADA system \\ \hline Protect & Protect the system from various abnormal events \\ \hline Record & Keep the records of the system status for forecasting purposes \\ \hline Asset management & Determining equipment maintenance schedule \\  & Finding the life expectancy of the device \\ \hline \hline \end{tabular}
\end{table}
Table 6: Generation domain participators and their function.  
Figure 7: Generation domain.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.9 Multi-Area Microgrid  
Multi-area microgrid refers to a distributed energy system consisting of multiple interconnected microgrids that can operate independently or in coordination. This type of microgrid is often used to provide energy to various buildings or communities, and it can be designed with self-healing capabilities to improve its resiliency and reliability. A two-area microgrid was proposed by [26] with modes that stand independently. The multi-area microgrid is used to analyse a multi-machine system. A multi-area microgrid was selected to separate the core system into sections, in a fault event simulation, in a manner that allows the application to adopt distributed control. Each area of the multi-area microgrid is equipped with dispatchable and non-dispatchable distributed generators, respectively. The multi-area microgrid implementation separates the system into sections, making asserting control in a system fault event easier. In an experiment, Ref. [26] deployed three diesel power plants and one hydropower plant, all based on synchronous generators. Two types of power load implementation on power plants were deployed: controlled and uncontrolled. The proposed approach utilises machine-learning techniques to detect event signatures in the power system features. A multiclass classification algorithm was then applied to the generated feature data and facilitated self-healing functionality through postfault decision-making that restored the standalone microgrid system without the need for intervention by the central power station.