esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results analysis
### Optimized Robust Solutions for Energy and Environment Systems
#### 4.2.2 Optimized schemes of electricity supply  
Figure 7a shows the optimized electricity generation scheme between 2020 and 2030. The major electricity power conversion technologies in the BTH region include gas-based, coal-based, wind, and solar power. Optimized electricity generation is defined by the following equation: \(OEG_{\textit{kth\ opt}}=W_{\textit{kft\ opt}}^{\pm}+Y_{\textit{kth\ opt}}\), where \(W_{\textit{ktop}}^{\pm}\) and \(Y_{\textit{ktop}}\) are the amounts of electricity generated in the first and second stages. Coal-based power is expected to play the dominant role, contributing what would be 259.06 \(\times\) 10\({}^{9}\) kWh, 265.41 \(\times\) 10\({}^{9}\) kWh, and 250.97 \(\times\) 10\({}^{9}\) kWh in periods 1, 2 and 3 (h = 3), respectively. With the implementation of a series of energy mix policies for pollutant emission mitigation, such as the \"Paris Agreement\" and \"Chinese Action Plan of Air Pollution Prevention and Control\", the ratio of coal-based power generation would decrease from 68.79% in 2020 to 55.48% in 2030. The utilization of natural gas-based and renewable energy would rapidly develop from 2020 to 2030. Natural gas-based power, wind power, solar power and generation are projected to increase by 53.86%, 87.96%, and 266.34% over the planning horizon, contributing 22.65%, 20.44%, and 1.44%, respectively to the total power generation by 2030.  
Further, the amount of locally generated electricity is insufficient for the needs of the BTH region, with approximately 36% of the consumed electricity imported from other regions. Figure 7b describes imported electricity under different electricity demand levels between 2020 and 2030. Imported electricity is expected to increase from 202.47 \(\times\) 10\({}^{9}\) to 254.47 \(\times\) 10\({}^{9}\) kWh over the planning horizon under a high electricity demand level. This indicates that imported electricity is expected to play an increasingly important role from period 1 to period 3, especially at a high demand level.  
Figure 6: Optimized solutions of energy input amounts for energy processing and electricity generation between 2020 and 2030. CFO represents crude oil input to oil refining; CFH represents coal input to heat processing; NFH represents natural gas input to heat processing; CFC represents coal input to coke processing; CFF represents coal input to coal-fired power; NFC represents natural gas input to coal-fired power.
## 2 Methodology
### Bi-directional Gated Recurrent Unit  
Conventional RNNs have a limited feature extraction ability for time series and a weak ability to mine deep-level features. Hence, increasing the number of network layers is often necessary to make up for this deficiency. However, increasing the number of network layers may lead to problems such as gradient disappearance and gradient explosion. To address these challenges, researchers proposed a model called GRU [29], which is a simplified LSTM model [30; 31; 32]. The GRU model contains only two gating mechanisms: reset gate and update gate. This gating mechanism can effectively extract temporal information, has fewer parameters, and requires fewer computational resources than the LSTM model. The basic structure of GRU is shown in Figure 3, and the formulas are (9)-(12).  
\[z_{t}=\sigma(W^{x}x_{t}+U^{x}h_{t-1}) \tag{9}\]  
\[r_{t}=\sigma(W^{x}x_{t}+U^{x}h_{t-1}) \tag{10}\]  
\[\widetilde{h}_{t}=\tanh(r\odot Uh_{t-1}+Wx_{t}) \tag{11}\]  
\[h_{t}=(1-z_{t})\odot\widetilde{h}_{t}+z_{t}\odot h_{t-1} \tag{12}\]  
where \(x_{t}\) and \(h_{t-1}\) are the input information at the current moment and the hidden state at the previous moment, respectively, \(\sigma\) is the sigmoid function, \(W^{z}\) and \(U^{z}\) are the weights of the update gate, \(W^{r}\) and \(U^{r}\) are the weights of the reset gate, \(U\) and \(W\) are the weights of \(\widetilde{h}_{t}\), \(r_{t}\) is the state of the reset gate, and \(\odot\) denotes the Hadmard product.  
In order to effectively mine the laws of forward and backward information in time series data, the BiGRU [33; 34; 35] model employs a structure consisting of a bi-directional recurrent neural network with forward and backward propagation. The structure is shown in Figure 4. In contrast to the unidirectional GRU model, BiGRU can simultaneously  
Figure 3: Gated recurrent unit.  
consider the changing pattern of the data before and after to mine the time series features more comprehensively. The formula of BiGRU is as follows:  
\[\overset{\rightarrow}{h}_{t}=GRU\left(x_{t},\overset{\rightarrow}{h}_{t-1}\right) \tag{13}\]  
\[\overset{\leftarrow}{h}_{t}=GRU\left(x_{t},\overset{\leftarrow}{h}_{t-1}\right) \tag{14}\]  
\[h_{t}=W_{\overset{\rightarrow}{h}_{t}}\overset{\rightarrow}{h}_{t}+W_{ \overset{\leftarrow}{h}_{t}}\overset{\leftarrow}{h}_{t}+b_{t} \tag{15}\]  
where \(W_{\overset{\rightarrow}{h}_{t}}\) and \(\overset{\rightarrow}{h}_{t}\) are the state and weight of the forward hidden layer at time \(t\), \(W_{\overset{\leftarrow}{h}_{t}}\) and \(\overset{\leftarrow}{h}_{t}\) are the state and weight of the backward hidden layer at time \(t\), and \(b_{t}\) is the bias at time \(t\).
# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 1 Introduction
In our study, we collected data from the smartcard system for the bus industry and considered other external factors that affected the ride. In this article, we propose an integrated-weight time-series model to forecast passenger flow and detail our comparison with the listing methods. In summary, the goals of this study are as follows:  
1. To identify the important attributes that affect passenger flow from a total of 42 attributes in the smartcard system;
2. To add other variables that affect passenger flow, such as climate, time, space, and lag period, to establish a prediction model;
3. To apply multilayer perceptron (MLP), support vector regression (SVR), radial basis function (RBF) neural network, and long short-term memory network (LSTM) methods to forecast passenger flow with different types of time data series (weeks, weekdays, and holidays);
4. To propose an integrated-weight time-series forecast model that uses forecast data from the top three of the 80 intelligent forecast models as the adaptive factors;
5. To provide results that can be used as a reference by the government, industry, and related personnel. The remaining sections are organized as follows: Section 2 is a literature review. In Section 3, we describe the research model and discuss the research design and methodology. Section 4 shows the results and findings. Finally, Section 5 presents the implications, limitations, and future work.
## 4 Application Fields of the A25 Integrated MIoT
### Marine Resource Management  
The use of the MIoT can help to better manage fisheries and develop marine resources, reduce resource waste, and promote the sustainable development of fisheries. Specific uses are as follows:  
1. Fishing vessel positioning and monitoring: By installing position sensors and monitoring equipment on fishing vessels, information such as the position, speed, and heading of the fishing vessels can be tracked in real time. This helps to improve the safety and the management efficiency of the fishing vessels, and enables the identification of unusual behavior, such as illegal fishing activities or fishing vessel entry into restricted areas.
2. Marine environmental monitoring: Various sensors, such as water quality sensors and meteorological sensors, are used to collect data in the ocean, including water temperature, salinity, oxygen content, ocean currents, and more. These data can help scientists to better understand marine ecosystems, assess the health of marine resources, and predict natural disasters such as storms and red tides.
3. Fishery resource management: With the help of MIoT technology, fishery resources can be intelligently managed. By monitoring data such as fishery activities and fish stock distribution and trends, it is possible to monitor fishing in real time and assess resources. This contributes to sustainable fishery management policies, protects fishery resources, and ensures the sustainable development of fisheries.
## 4 Experiment  
### Evaluation Indicators  
In order to quantify the difference between \(x_{i}\) and \(\hat{x}_{i}=h_{s}(y_{i}),\ i=1,\ldots,s\), we choose to use the training and the testing mean squared error as metrics to assess the reconstruction performance, defined as follows:  
\[T_{\text{train}}=\frac{1}{s}\sum_{i=1}^{s}\lVert h(y_{i})-x_{i}\rVert_{2}^{2} \tag{28}\]  
\[T_{\text{test}}=\frac{1}{d}\sum_{i=1}^{d}\lVert h(\widetilde{y}_{i})-\tilde{ x}_{i}\rVert_{2}^{2} \tag{29}\]  
\(D=\{(\widetilde{y}_{i},\tilde{x}_{i})\}_{i=1}^{d}\) is a set of data used for testing, containing \(d\) samples that were not used during the training phase. The data center reconstructs the outdoor collection data using the network model trained on historical data, and the closer the outdoor data reconstruction effect is to the training reconstruction effect the stronger the network generalization ability. Therefore, the generalization ability of the network by comparing the difference \(T_{\text{gen}}\) between the average training mean square error and the average testing mean square error, the \(T_{\text{gen}}\) is defined as follows:  
\[T_{\text{gen}}=\left|T_{\text{test}}-T_{\text{train}}\right| \tag{30}\]  
Peak signal-to-noise ratio PSNR (in dB), compression ratio \(r\) and algorithm iteration Time (\(t\)) are used as evaluation metrics to measure the other performance of the network, PSNR is defined as follows:  
\[\text{PSNR}=10\text{lg}\left[f_{\text{max}}^{2}\middle/\left(\frac{1}{N}\sum_ {i=1}^{N}\left(f_{i}-\hat{f}_{i}^{2}\right)^{2}\right)\right] \tag{31}\]  
where \(f\) denotes the original signal, \(\hat{f}\) denotes the reconstructed signal, and \(f_{\text{max}}\) denotes the largest component of the vector \(f\). The higher the value of the reconstruction signal-to-noise ratio, the better the reconstruction effect. The compression ratio \(r\) is defined as follows:  
\[r=m/n \tag{32}\]  
where \(n\) is the original signal length and \(m\) is the compressed signal length. The smaller the compression rate \(r\), the higher the degree of signal compression.  
The time taken by the algorithm to reconstruct the signal called the algorithm convergence Time (\(t\)), is used to characterize the complexity of the algorithm and as a reference for comparing the performance of different algorithms.
## 4 Simulation Verification and Analysis
### Optimization for the Current Control Loop  
In this section, the control parameters of VSC correspond to the case of \(\lambda_{2-4}\)(s), where the grid-connected VSC is unstable. Figure 21 shows the VSC output current when \(m=0\), 0.1 and 1, respectively. Table 2 gives the THD and oscillations of grid-connected currents at different \(m\). When \(m=0\), which means the active damping is disabled, the VSC output current is severely distorted due to oscillation. The oscillation frequencies are 175 Hz and 75 Hz, and the THD of the VSC output current is 91.3%. After enabling the active damping, when \(m=0.1\), the system is still oscillating. If m is too small, the oscillation cannot be suppressed which corresponds to the conclusion of Figure 14. When \(m=1\), VSC can operate stably, and the THD is reduced to 0.18%.  
Figure 22 shows the dynamic response characteristics of VSC in the process of current regulation when \(m\) takes different values. At 1s, the d-axis current reference is adjusted from 4A to 8A. When \(m=0\), the transient response time is 0.05 s. When \(m=0.1\), the transient response time is 0.08 s. In addition, when \(m=1\), the transient response time increases to 0.08 s. Therefore, a larger m is beneficial to the stability, but the dynamic response speed of the system will decrease with the increase of \(m\).  
It can be concluded that without modifying the control parameters, the proposed stability evaluation method can contribute to selecting the damping coefficient for the active damping to effectively suppress the oscillation of the system, thereby expanding the range of control parameter selection.  
Figure 21: VSC output current when \(m=0\), 0.1 and 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Value of \(m\)** & **THD of \(i_{\text{o}abc}\)** & **Oscillation Frequency of \(i_{\text{o}abc}\)** \\ \hline
0 & 91.3\% & 175 Hz/75 Hz \\ \hline
0.1 & 90.2\% & 175 Hz/75 Hz \\ \hline
1 & 0.18\% & Stable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of grid-connected currents under different \(m\).  
Figure 22: Current regulation command from 4A to 8A, when \(m=0\), 0.1 and 1.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### CN Slicing  
The impact of network slicing in the CN domain can be summarized into three main topics: slice identity management, slice-aware device connectivity and the allocation of separate 5 GC functions.  
Figure 3: On the deployment of network slices.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 4 Electrochemical Biofilm Control
### Electrochemical Communication during Biofilm Formation  
In general, all bacterial biofilms can be defined as microbial communities within which the individual bacterial cells communicate with each other. This communication affects the rise of biofilm, the coordination of biofilm growth depending on nutrient availability and environmental conditions, and interspecies interactions as well.  
As can be predicted, microbial communication is associated with the transfer of chemical substances secreted by cells during quorum sensing. However, it is evident that chemicals are diluted out when they are secreted into the environment, thus decreasing the concentration of chemicals with distance from the cell reflecting the decrease in the signal. Moreover, it is important to keep in mind that these signals can only be perceived by specific receptors.  
In the second half of the previous century, it was found out that intercellular communication is mediated through potassium (K\({}^{+}\)) channels, which were discovered in _Escherichia coli_ in 1994 [156]. The secretion of K\({}^{+}\) ions increases the movement of motile cells toward the biofilm. The K\({}^{+}\) ions on the surface of the motile cells cause their depolarization via opening the K\({}^{+}\) channels, which results in hyperpolarization and an increase in proton motive force [157]. The use of a microfluidic approach helped to reveal an interesting finding that the biofilm of bacteria _Bacillus subtilis_ was able to attract distant motile cells of _Pseudomonas aeruginosa_ attached and subsequently incorporated into the biofilm structure. Therefore, it seems that bacteria are capable of cross-species interactions by using K\({}^{+}\) ion channel-mediated electrical signalization [158; 159].  
In addition to the mentioned concentration gradient of K\({}^{+}\) ions, bacteria cells of _Myxococcus xanthus_ can perform intercellular communication via outer membrane vesicle chains and membrane tubes that interconnect the individual cells. These sophisticated structures range between 30 and 60 nm in width and up to 5 um in length. There is an assumption that the resulting network enables the transfer of specific molecules between cells, helping the coordination of their social activities [160].  
In 2019, [PERSON] et al. demonstrated a biocompatibility of an ion-selective sensing platform for potentiometric measurements of potassium in _Escherichia coli_ biofilms [161]. They showed the possibility of how to electrochemically detect biofilm formation through the determination of potassium concentration.
## 5 Results
### Image Restoration
#### 5.3.1 Image Quality Metrics  
We use three standard image quality/similarity metrics for quantitative evaluation: (1) mean square error (MSE) [2], (2) peak signal-to-noise ratio (PSNR) [46], (3) structural similarity index (SSIM) [47], in which the expressions of MSE, PSNR and SSIM are, respectively,  
MSE \[= \frac{\sum\limits_{i=1}^{w}\sum\limits_{j=1}^{h}[F(i,j)-I(i,j)]^{2}}{w \times h},\] (45) \[\text{PSNR}=10\log_{10}\left(\frac{\max(I)^{2}}{\text{MSE}} \right),\] (46) \[\text{SSIM}= \frac{(2u_{F}u_{I}+c_{1})(2\sigma_{FI}+c_{2})}{(u_{F}^{2}+u_{I}^{ 2}+c_{1})(\sigma_{F}^{2}+\sigma_{I}^{2}+c_{2})}.\] (47)  
where \(I\) is the ground-truth image. \(\max(\cdot)\) denotes the maximum possible value. \(u_{F}\) and \(u_{I}\) represent the means of the image \(F\) and the image \(I\), respectively. \(\sigma_{F}^{2}\) and \(\sigma_{I}^{2}\) represent the variance of \(F\) and \(I\), respectively. \(\sigma_{FI}\) is the covariance of \(F\) and \(I\). \(c_{1}=(0.01\times L)\) and \(c_{2}=(0.03\times L)\) are constants, in which L represents the dynamic range of pixel values.  
Figure 14: The RMSE and the maximum absolute error (\(A\epsilon_{\text{max}}\)) as a function of the zenith angle of the sunray using the method in Ref. [23].
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 3 Results  
This section disseminates the results obtained following the implementation and testing of the HADA system for monitoring elderly individuals. The data was collected over two years, and the analysis focused on anomaly detection and alert generation based on the monitored parameters: AR, mR, MR, S, DST, and SST.
# Numerical Investigation of a Novel Grinding Device
## 3 Results  
### Preliminary Stability Analysis  
Figure 2 shows the temporal trend of the total translational and rotational kinetic energy of all the particles in the system (\(ke=0.5\sum m_{i}u_{i}^{2}\) and \(ke_{\mathrm{rot}}=0.5\sum l_{i}\omega_{i}^{2}\)) at different stirring velocities \(\Omega\). As explained in the previous section, each curve represents the average over six simulations with different random seeds. Moreover, the fast fluctuations have been filtered with a moving average of radius \(5\).  
Figure 2: Total (**a**) translational and (**b**) rotational kinetic energy at different stirring velocities. Each curve is the average of six simulations with different random seeds.  
In all the cases, it is possible to distinguish a fast initial transient and a pseudo-steady-state regime. The former is connected to the initialization procedure and lasts less than 0.5 s (dashed gray line in Figure 2a). The latter is apparent from a nearly constant value proportional to the stirring velocity (\(ke_{\text{steady}}\propto\Omega^{1.5}\)).  
Comparing both kinetic energies with the related values of the corresponding IWM configuration [23], an increase by a factor of around 2 was found, ascribable to the longer stirring arm and consequent higher tip speed.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 4 Results
### Landing Mission Overview  
The case of a South Pole lander is analysed here considering only the main braking phase of the landing. Starting from the apocentre of an elliptical orbit (\(100\times 15\,\mathrm{km}\) altitude), the lander follows a natural path up to the periselene, at half of its orbital period, i.e., after roughly \(3400\,\mathrm{s}\). Here it starts the thrusted arcs from an altitude of \(15\,\mathrm{km}\), which ends at \(1\,\mathrm{km}\), before relative navigation hand-over. The terminal landing phase is not included in this analysis, since it cannot be separated from the exploitation of a fully relative navigation strategy.  
The trajectory followed by the lander can be seen in Figure 5, superposed to the landing guidance trajectory. A zoomed trajectory of the propelled arc is instead shown in Figure 6, with the view taken directly on the landing trajectory plane. The lander is assumed to have a mass of \(1600\,\mathrm{kg}\), a ballistic coefficient of \(150\,\mathrm{kg}\,\mathrm{m}^{-2}\), a maximum thrust of \(2.5\,\mathrm{kN}\), and a specific impulse of \(320\,\mathrm{s}\).
## 5 Results
In the climate scenarios projected in GCM (CSIRO 3.0, CGCM3.1, ECHAM5, CCSM3.0, HACDM3), a 10% decrease in runoff during the rainy season and a 12% decrease during the dry season is estimated for the coming 10 years; until 2100 the runoff will decrease even further, down to \(-18\%\) in the rainy season and \(-20\%\) in the dry season. A lower runoff will immediately affect hydropower production. The total electricity generation will decrease in accordance with the available flow to the reservoirs. Taking Cahora Bassa as an example, the total capacity of the plant is at present 2075 MW and the yearly standard/normal generation of electricity for the plant is almost 19 TWh. The Zambezi River flow is however never that high all year round. For instance, the electricity generation in 2016 was 15.6 TWh and due to drought in 2017, the electricity generation decreased that year by 11.5%, down to 13.8 TWh. A further reduction in flow to the Cahora Bassa dam by 20% would decrease the electricity generation further, maybe down to 12.5 TWh per year. Assuming a steady growth of electricity consumption in Mozambique requires a continuous development of hydropower stations. Mozambique has a large number of economically suitable rivers and gorges that could be exploited for hydropower generation. Table 2 lists some already existing and some planned hydropower plants in Mozambique which contribute to the electricity supply in the entire southern African region. What is notable from a power supply point of view is that with reduced runoff to the hydropower stations, their maximum capacities cannot be utilized all year round. Instead, the total power generation available during a year will be cut by 10-20% in the 21 st century due to climate change, increased evapotranspiration and decreased runoff. The combined capacity for the hydropower stations in Table 2 is 4685 MW, which theoretically could produce 41 TWh electricity. A more realistic generation is 70-80% of the theoretical capacity which thus is reduced due to climate change. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hydropower Plant** & **Project** & **Province** & **Capacity MW** \\ \hline Cahora Bassa & Operations & Tete & 2075 \\ Mpanda Nkuwa & Project Ongoing & Tete & 1500 \\ Boroma & Project Ongoing & Tete & 400 \\ Lupata & Project Ongoing & Tete & 612 \\ Mavuzi & Operations & Manica & 42 \\ Chicamba & Operations & Manica & 38 \\ Curumana & Operations & Maputo & 16 \\ Lichinga & Operations & Niassa & 0.75 \\ Cuamba & Operations & Niassa & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Major hydropower plants in Mozambique. Source: own illustrations. From [1,2,18]. Figure 6: Projected average monthly temperature for the Ara-Centro region.