esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 6 Simulation Results and Discussion
### DC/AC Converter  
In Table 10, the effect of the DC/AC converter efficiency on the optimal size and cost of system execution is shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Low Cut-Off Speed of Wind Turbines (m/s)** & 1 & 3 & 5 \\
**Number of Wind Turbines** & 329 & 225 & 142 \\
**Number of Photovoltaic Units** & 939 & 1364 & 1774 \\
**Number of Electrolyzers** & 2017 & 2290 & 2673 \\
**Number of Hydrogen Tanks** & 1327 & 1635 & 1875 \\
**Number of Fuel Cells** & 313 & 370 & 396 \\
**Number of Batteries** & 23 & 27 & 46 \\
**Incidental Costs (\$)** & \(1.201\times 10^{4}\) & \(1.29\times 10^{4}\) & \(1.76\times 10^{4}\) \\
**Transmission and Transformer Lines Cost(\$)** & \(1.19\times 10^{6}\) & \(1.282\times 10^{6}\) & \(1.372\times 10^{6}\) \\
**(5)Cost of Electricity Cut-Off** & \(3.43\times 10^{5}\) & \(5.17\times 10^{5}\) & \(6.92\times 10^{5}\) \\
**Electricity Revenue from Sales to the Upstream Network (\$)** & \(8.821\times 10^{6}\) & \(9.472\times 10^{6}\) & \(1.141\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of cut-off wind turbine speed on the optimum size and cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Initial Investment Cost of Solar Arrays (\$/kw) & 6000 & 7000 & 8000 \\ Number of Wind Turbines & 184 & 225 & 321 \\ Number of Photovoltaics Units & 1419 & 1364 & 1265 \\ Number of Electrolyzers & 2137 & 2290 & 3494 \\ Number of Hydrogen Tanks & 1554 & 1635 & 1948 \\ Number of Fuel Cells & 329 & 370 & 427 \\ Number of Batteries & 20 & 27 & 41 \\ Incidental Costs (\$) & \(1.03\times 10^{4}\) & \(1.29\times 10^{4}\) & \(1.411\times 10^{4}\) \\ Transmission and Transformer Lines Cost (\$) & \(1.04\times 10^{4}\) & \(1.282\times 10^{6}\) & \(1.299\times 10^{6}\) \\ Cost of Electricity Cut-Off (\$) & \(5.05\times 10^{5}\) & \(5.17\times 10^{5}\) & \(2.1009\times 10^{5}\) \\ Electricity Revenue from Sales to the Upstream Network (\$) & \(1.009\times 10^{7}\) & \(9.472\times 10^{6}\) & \(8.753\times 10^{6}\) \\ Total Cost (\$) & \(1.613\times 10^{7}\) & \(1.772\times 10^{7}\) & \(1.962\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effect of initial investment cost of solar panels on the optimum size and cost.  
According to Table 10, it is expected to decrease costs by increasing the efficiency of the converter. As the converter efficiency rises, the power losses are reduced, correspondingly. Then, with lower energy production, all demands will be supplied sufficiently.
## 6 Conclusions  
### Research Summary  
This study conducted a systematic comparative study of deep learning methods and shallow learning methods for the key problem of urban road travel time prediction in intelligent transportation systems. Through experimental analysis on urban road travel time data from Shenzhen, China, this study provides empirical contributions and methodological guidance for this field.  
The main research contributions were as follows:  
(1) Deep learning model performance on MAPE: Deep learning models demonstrated superior performance on MAPE metrics, with LSTM-DNN achieving optimal MAPE values across all prediction scenarios using 30 min sliding time windows.  
(2) Model performance variations: The research revealed significant performance differences across evaluation metrics. While deep learning models excelled on MAPE, random forest models achieved superior RMSE performance in most prediction scenarios, demonstrating that model selection depends critically on the specific evaluation criteria and prediction requirements.  
(3) Prediction scale impact: Experimental results showed that as prediction scale increased from 2 to 60 min, all models experienced performance degradation, but with varying degrees of decline. Deep learning models demonstrated relatively better stability under shorter prediction scales, while shallow learning models showed more robust performance at longer prediction scales.  
(4) Multi-run training effectiveness: The implementation of five independent training runs for each hyperparameter configuration successfully addressed the randomness problem in deep learning models, providing reliable performance evaluation across 66,240 data samples and 10 prediction scenarios.  
This study contributes systematic method comparison analysis and practical technical guidance to the field of urban road travel time prediction. The research findings hold important theoretical value and practical significance for promoting the development and optimization of travel time prediction modules in intelligent transportation systems. Through objective and comprehensive experimental analysis, this study provides a solid empirical foundation for method selection and application practice in this field.
# Implanting Intelligence in 5G Mobile Networks--A Practical Approach
## 2 Tutorial & Background
### ETSI NFV MANO Reference Architecture  
The ETSI introduces the NFV MANO architecture [14] a fundamental base solution to build the softwarized 5G networks. The 3 GPP leverages this mechanism to create the network slices enabling the dynamic deployment of network functions as virtual network functions (VNFs) completely decoupled from the underlying hardware running these functions. The VNF life cycle management together with dynamic resource allocation to the network slices implemented as a set of interconnected VNFs are performed and managed by ETSI NFV Management and Orchestration (MANO). MANO is mainly responsible to manage and orchestrate the network services, VNFs, and other resources such as networking, computation, and storage.  
The ETSI NFV MANO architecture is comprised of three main building blocks:  
* **Management and Orchestrator (MANO):** responsible for managing and allocating resources to NFV architecture such as creation and deletion of slices, slice selection and resource allocation. The MANO architecture has three main components: (_i_) _Virtualized Infrastructure Manager (VIM)_: controls the physical and virtual infrastructures. Furthermore, it manages the interaction of a VNF with computing, storage and network resources; (_ii_) _VNF Manager:_ configure and manages the VNFs and takes care of the life cycle of the VNFs. A VNF manager can be deployed for each VNF or a single VNF manager can serve multiple VNFs; (_iii_) _Orchestrator:_ manages the VIM and the VNF manager.
* **Virtualized Network Functions (VNFs):** VNF is the software implementation of network functions deployed on one or more virtual machines running over the common NFVI. A set of VNFs executed in a specific order is referred to as Service Function Chain (SFC). The MANO can communicate with VNFs via the Ve-Vnfm connection point.
* **NFV Infrastructure (NFVI):** a set of physical hardware and software resources building the environment to host and connect the VNFs. NFVI creates a virtualized environment for all the VNFs allowing them to communicate with the MANO via the NF-Vi connection point.
# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## 1 Introduction
### Research Objective  
The principal goal of our research was to design, develop and implement a health-monitoring platform to obtain clinical measurements from elderly or chronically ill patients through non-invasive devices to be sent to healthcare providers in real time. The healthcare monitoring system is comprised of a wearable sensor device to constantly monitor heart electrical activity, heart rate, body temperature and fall data, working in conjunction with a fixed measurement station the patient can periodically access to monitor blood oxygen saturation, respiration rate and galvanic resistance. The objectives of this work included developing:  
1. A framework to manage and analyze information collected by PlaIMoS platform;
2. A wireless sensor network (WSN) infrastructure that includes a scalable network algorithm;
3. A mobile application to provide real-time monitoring and information access for patients, doctors and caregivers.
## 3 Boundary Conditions and Scattering Properties of Two-Dimensional Surfaces  
In this section, impedance boundary conditions (IBCs) and generalized sheet transition conditions (GSTCs) will be reviewed first, compared with conventional boundary conditions. Then scattering coefficients of two-dimensional surfaces are defined, and related formulas derived from IBCs and GSTCs are given. Finally, relationship between sheet impedances and surface susceptibilities will be discussed.
## 3 System Model and Detail Design
### Adaptive UE Aggregation  
By combining Case 2 with the combination scheme with UE aggregation, the integration of UE transmission and cooperation nodes become more effective, and this is called the multi-path transmission scheme. In this case, the BS can determine whether it is more advantageous to transmit data through both the cooperation nodes and UE or solely through the UE based on their positions. This approach saves resources and eliminates the need for intermediate calculations. While this scheme can reduce latency to a certain extent, it increases the overall energy consumption of the system. The system model of the multi-path transmission scheme is Case 3 in Figure 1.  
Furthermore, the main idea of AUA is based on single-path transmission and multi-path transmission schemes. On the one hand, it is certain that the latency of the system under a multi-path transmission scheme is less than that of a single-path transmission scheme. On the other hand, the overall energy consumption of the system in a multi-path transmission scheme is greater than that under a single-path transmission scheme. In other words, if the UE is far away from the BS compared with the cooperation node, then this means that the SNR between the UE and BS (\(\gamma_{\text{UB}}\)) is very small compared with the SNR between the cooperation node and BS (\(\gamma_{\text{CB}}\)). In this case, there is no need to send information to the UE because the contribution of the UE is negligible. Similarly, if the cooperation node is far away from the BS compared with the UE, then this means that the SNR between the cooperation node and BS (\(\gamma_{\text{CB}}\)) is quite small compared with the SNR between the UE and BS (\(\gamma_{\text{UB}}\)). In this case, there is no need to send information to the cooperation node because the contribution of the cooperation node is negligible. Based on this consideration, we propose two proportional coefficients \(\alpha\) (in dB) and \(\beta\) (in dB) to specify that the UE chooses the transmission scheme. To be clear, the flowcharts of the UE aggregation and adaptive UE aggregation scheme are shown in Figure 2. First of all, it is necessary to determine whether the location of the UE is close to the BS. If it is close, then there is no need to assist transmission through the cooperation nodes. Then, the SNR between any two devices can be calculated using Equation (1). After finishing the relative parameters, if the effect of the cooperation node is tiny, then the direct link is chosen in this situation. This can be distinguished by the following equation:  
\[\gamma_{\text{UB}}>\alpha+\gamma_{\text{CB}} \tag{2}\]  
If the effect of the UE is tiny, then the indirect link is chosen in this situation. This can be distinguished by the following equation:  
\[\beta+\gamma_{\text{UB}}<\gamma_{\text{CB}} \tag{3}\]  
Moreover, the other UEs that do not satisfy the above two inequalities will choose Case 3, in which the UE and cooperation nodes transmit information at the same time. The pseudocode of Case 2 with the combination is shown as Algorithm 3.
## 3 Wheel Flat Recognition and Positioning Algorithm Based on Multisensor Arrays
### Step III: Which Wheel is Responsible for the Abnormal Impact  
The first target of this step is to calculate the average speed of the wheels. Since the distance between the sensors is fixed once installed, the average speed of the wheels can be obtained by calculating the sensor spacing divided by the interval of troughs. The average speed of the wheels is denoted as \(v_{w}\).  
Indeed, there can only be one wheel at a specific time and position. Therefore, as long as the positions of the wheels are determined, it is possible to find out the source of impacts. The position of \(S_{1}\) is regarded as the origin of the coordinate. The time when the front and rear wheels arrive at the origin are denoted as \(t_{ini,\ front}\) and \(t_{ini,\ rear}\). For the \(j\)-th (\(j=1,\,2,\,\ldots\,\,N_{I}\)) impact process, the locations of the front and rear wheels can be described by the following equations, respectively:  
\[S_{j,\ front}=v_{w}\times\left(t_{j,\ imp}-t_{ini,\ front}\right) \tag{10}\]  
\[S_{j,\ rear}=v_{w}\times\left(t_{j,\ imp}-t_{ini,\ rear}\right) \tag{11}\]  
where \(t_{j,\ imp}\) is the middle time of the \(j\)-th impact process IMP\({}_{j}\).  
According to the above method, the following result is obtained: During IMP\({}_{1}\) (0.1878-0.225 s), the front wheel is between \(S_{5}\) and \(S_{6}\), while the rear wheel is between \(S_{2}\) and \(S_{3}\). During IMP\({}_{2}\) (0.325-0.35 s), the front wheel is between \(S_{10}\) and \(S_{11}\), while the rear wheel is between \(S_{6}\) and \(S_{7}\). According to the impact positions detected in Section 3.3, all the impact processes are identified as being generated by the front wheel, which is consistent with the predefined condition.  
Figure 19: The relationship of response amplitudes and sensor location: (**a**) Movement rule of the parameter flag; (**b**) filtered signals during IMP\({}_{1}\) (0.1875–0.225 s); and (**c**) filtered signals during IMP\({}_{2}\) (0.325–0.35 s).
## 2 Materials and Methods
### Spectral Coherence  
The CoP-target motion coupling was assessed using spectral analysis in the frequency band between 0-1 Hz, based on the methods of [PERSON] et al. [20]. For this purpose, a customized version of a freely available software (NeuroSpec 2.0) was used. Both the CoP and target time series were interpolated at 64 Hz in order to achieve an appropriate frequency resolution for the spectral analysis, as we wanted to get a coherence, phase, and gain value of 0.25 Hz (frequency of the sinewave) and the neighborhood frequencies (for the Lorenz signal). Spectral analysis was performed in the interpolated time series of 7680 data points which were sampled at 64 Hz. Setting the segment length power at 2\({}^{10}\) (1024 data points) gave a segment duration of 16 s. This resulted in a frequency resolution of 0.0625 Hz. The software output the means and confidence limits across the entire frequency band (0-1 Hz) according to three variables, i.e., spectral coherence, spectral phase, and spectral gain. The spectral coherence was used as a metric of correlation between the two signals (CoP-target position) in the frequency domain, illustrating their linear relationship. The spectral phase illustrated the temporal relationship between the two signals, expressed in degrees (\({}^{\circ}\)). The absolute synchronization between the two signals was illustrated by 0\({}^{\circ}\) phase lag, while positive and negative values indicated that the sway led or followed the target motion respectively. The spectral gain revealed information regarding the spatial (amplitude) coupling between the two signals (CoP-target), while a gain of 1 indicated an absolute spatial coupling at a given frequency point. Gain values less than or greater than 1 indicated target undershooting or overshooting (the CoP moved with smaller or greater amplitude than the target), respectively. For the sinewave, the coherence, phase, and gain values at the target's dominant frequency (0.25 Hz) were computed and registered for statistical analysis. Similarly, for the Lorenz  
Figure 1: Graphical illustration of the apparatus and task. (**A**) Participants were asked to track the visual (red) target dot moving in the vertical direction with their body by shifting their center of pressure (CoP) (yellow dot) in the antero-posterior direction. An upward target motion was tracked by a forward CoP shift, while a downward target motion was tracked by a backward CoP shift. (**B**) The target’s (red dot) motion amplitude was normalized to 60% of the participants foot length. Tracking lasted 120 s and was performed either with feedback (yellow dot was visible) or without feedback (yellow dot was not visible).  
target signal, the average of the coherence, phase, and gain values at 3 adjacent frequency bins (0.1875, 0.25, and 0.3125 Hz) was calculated.
## 3 GNSS Data Collection and Processing Methodology
Figure 3: _Cont._  
Figure 2: The number of processed satellites (**Left**) and PDOP factor (**Right**). The computed C/N0 values were further analyzed using the epoch-by-epoch domain, which is illustrated in Figures 4 and 5. It can be seen that there are some obvious fluctuations in the C/N0 values for the GPS L\({}_{5}\) and Galileo E\({}_{5a}\) signals over the three examined days; on the contrary, the C/N0 values for the GPS L\({}_{1}\) and Galileo E\({}_{1}\) signals are stable. An exception with L\({}_{1}\) and E\({}_{1}\) signals transmitted by satellites on DOY 71 and 72. Based on data quality analysis, the acquired smartphone-GNSS raw measurements are processed in the PPP approach in three different scenarios, including GPS-only (G), Galileo-only (E), and combined GPS/Galileo (GE) in real-time and post-processing. For real-time solutions, the archived real-time CNES products are utilized [36], while the final IGS products from GFZ, GRG, and WUM analysis centers are employed [37] for post-processing solutions. Additionally, based on the aforementioned C/N0 analysis, both the satellite elevation-dependent and the signal-noise-ratio GNSS weighting model are employed for weighting analysis, which follows the equation:  
\[\sigma=\frac{1}{\sqrt{sinE}}+\left(a+b\cdot 10^{(-\frac{1}{2}\cdot\frac{C/N0}{10})}\right) \tag{5}\]  
Figure 4: C/N0 values over time for GPS L\({}_{1}\), GPS L\({}_{5}\), (**Up**) Galileo E\({}_{1}\) and Galileo E\({}_{9}\) (**Down**) for DOY 70. Figure 5: C/N0 values over time for GPS L\({}_{1}\), GPS L\({}_{5}\), (**Up**) Galileo E\({}_{1}\) and Galileo E\({}_{9a}\) (**Down**) for DOY 71.  
where \(\sigma\) represent signal uncertainty; \(a\) and \(b\) are coefficients estimated from filter residuals, \(E\) is the elevation angle of the satellite above the horizon, and C/N0 is the carrier-to-noise density ratio, in dB-Hz (signal quality metric). Table 3 summarizes the ionosphere-free PPP parameters using the Net-Diff GNSS software.
## 3 Optimization Algorithm
### Modified Dynamic Programming (MDP)  
The Modified Dynamic Programming (MDP) algorithm is an advancement developed by our research team based on deterministic Dynamic Programming (DP). Recognized for its optimal methodology in managing time-varying systems, such as satellite mission planning, DP faces challenges when the number of variables and the problem space  
Figure 6: BPMN diagram of interval data preprocessor.  
expand, leading to an exponential increase in computational demand (curse of dimensionality) [43; 44]. To address this, our team developed the MDP algorithm, which has been successfully implemented in mission planning for multiple agile satellites equipped with EO/IR payloads [41]. Further refining this approach, the current paper extends the MDP algorithm's application to mission planning for satellite constellations carrying SAR payloads, demonstrating its adaptability and enhanced performance in complex operational contexts.  
Figure 7 shows the overall procedure of MDP. The operation is performed based on the interval \(T_{j}\), and the target connection information between neighboring intervals. MDP employs a recursive strategy until a predefined termination criterion is met, delving into the interval's depth. Upon meeting the termination criterion, MDP reverses its exploration order, updating the schedule by leveraging the connection data. Upon completion of this reverse exploration, the schedule with the highest profit is selected as the final result. The pseudo code of the MDP algorithm is provided in Appendix A.2, Algorithm A2.  
Figure 7: BPMN diagram of MDP algorithm.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 4 Experimental Results And Analysis
### Quantitative Comparisons
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Scenario 1**} & \multicolumn{3}{c}{**Scenario 2**} \\ \cline{2-7}  & **PSNR** & **SSIM** & **Run Time** & **PSNR** & **SSIM** & **Run Time** \\ \hline He’s [11] & 10.65 & 0.44 & 1.8 & 16.5 & 0.79 & 1.7 \\ \hline Ours & 15.15 & 0.63 & 1.5 & 17.19 & 0.81 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of [PERSON]’s algorithm [11] and our proposed methods in the images of Figure 6.  
Moreover, following [11], 5000 images from the dataset are used in our experiments. Here, we use 4000 images to select the hyper-parameters in our proposed method, and use the remaining 1000 images to verify the effectiveness of our method. The evaluation results are shown in Table 3. From Table 3, we can see that our method surpasses other methods by a large margin in PSNR and SSIM respectively, which further demonstrates the effectiveness of our proposed methods.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
## 3 Results
In this case, a general expression for the convergence calculation, given by the \(L_{2}\) standard, was used for the convergence, provided by:  
\[L_{2}=\sqrt{\sum_{i,j}\bigl{(}f_{ij}-F_{ij}\bigr{)}^{2}} \tag{13}\]  
where \(f_{ij}\) represents the analytical acoustic pressure derived from the solution of the equation. \[\frac{\partial^{2}p}{\partial z^{2}}-C\frac{\partial^{2}p}{\partial t^{2}}=0 \tag{14}\]  
where \(C\) includes all the constants of Equation (5), and \(F_{ij}\) is the numerical solution obtained from the numerical implementation of Equation (10), keeping only the dependence on the \(z\) coordinate (14). Making \(L_{2}\) = error, by plotting Log(error) vs. Log(\(\Delta x\)), changing the number of points in \(x\), one has a straight line of slope 1.8, in agreement with the expected second order, as shown in Figure 4.  
Backward and forward finite differences were also implemented in the developed software. In verifying the stability and convergence, it was observed that it was stable only when using centered differences. A case of particular importance was when time-forward schemes were used as a numerical solution. Consequently, in many simulations, the time step had to be less than a characteristic value. Otherwise, the code may have been unstable and did not yield the correct results. The CFL condition is commonly related to the numerical propagation speed. For the numerical solution to be stable, the following stability criterion for the standard second-order finite difference scheme for the wave equation is established, as reported in [9; 10], and is given by (15):  
\[\frac{c\Delta t}{\Delta x\Delta z}\leq\frac{1}{\sqrt{2}} \tag{15}\]  
At each time step, the wavefield propagated at the internal nodes according to Equation (10), and then propagated along the lateral and bottom boundaries employing the difference relations given in [10]. In this work, the relation max (\(\Delta x\), \(\Delta z\)) \(\leq\lambda/4\) was used, where \(\lambda\) represents the wavelength [11]. Figure 4: Convergence for the numerical solution of the wave equation for acoustic pressure. The logarithm of the error, calculated with the \(L_{2}\) standard, is shown with the difference between the numerical and analytical solutions as a function of the logarithm of \(\mathrm{dx}\).