esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of SPME
#### 3.4.3 Effect of Addition of NaCl  
The addition of NaCl to water samples could promote the extraction of these oxygenase compounds during SPME preparation process. NaCl in solution could increase the ionic strength of water samples significantly, meanwhile, the aqueous solubility of oxygenase compounds would reduce in NaCl solution [33; 34]. The effect of addition of NaCl on the extraction efficiency of these analytes is shown Figure 6a,b.  
As shown in Figure 6, the effect of NaCl added into water samples was especially obvious for TBA and TAME. In order to suppress interference, three replicate water samples were extracted in SPME system and detected in GC-QqQ-MS/MS system at each NaCl concentration. The responses intensities of TBA and benzene were greatly increased as NaCl dosage increased from 2.0 to 2.8 g. However, for other analytes, the responses intensity in GC-QqQ-MS/MS system was almost the same with different NaCl dosage. All further method evaluation was therefore made with 2.0 g added NaCl.  
Figure 5: Effect of extraction time on signal intensities of the twelve analytes (20 \(\upmu\)g/L). The peak area at the extraction time of 10 min is used as a reference (100%). (**a**) the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; (**b**) the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.
## 1 Introduction
### Motivation  
The efficient integration (nodal location and nominal size) and operation (on an hourly basis) of FACTS in electrical distribution networks pose fundamental challenges regarding the nature of this optimization problem as it belongs to the family of mixed-integer nonlinear programming (MINLP) [17]. Given the complexity of the exact model, this research aims to propose an efficient solution methodology to locate and size FACTS in medium-voltage distribution networks by reformulating the MINLP model as a mixed-integer convex approximation [18]. The main advantage of convex optimization is that it allows representing optimization problems in engineering and science while ensuring a global optimal solution [19]. This research aims to provide the literature and the industry with an efficient and reliable solution methodology to address the studied problem, with superior performance compared to MINLP solvers in commercial software or metaheuristic algorithms [18].  
It is important to mention that studying the problem regarding the optimal location and sizing of FACTS in electrical distribution networks is necessary since:  
1. Dynamic reactive power compensation is an area of continuous development due to the quality impositions of regulatory offices on distribution companies, which aim to make their medium-voltage distribution grids efficient, reliable, and secure [20]. To this effect, effective optimization algorithms must be proposed;
2. Most existing solution methodologies focus on metaheuristic optimization algorithms to deal with the exact MINLP formulation via decoupling-based approaches that work under a master-slave optimization strategy [21]. However, even though these master-slave approaches are efficient and easily implemented in multiple programming languages, they do not allow ensuring a global optimum, given the random nature of heuristic-based optimizers [22]. Therefore, this research takes advantage of mixed-integer convex programming to propose an efficient solution methodology to locate and size FACTS in electrical networks while allowing us to find the global optimum [23].
## 1 Introduction
The associated voltage and current waveforms showed multiple arc-like transient disturbances. [PERSON] et al. [24] investigated the leakage current, partial discharge, and arcing generated in oil-impregnated paper insulation cables in water immersion conditions and the effect of core temperature on fault signals. [PERSON] [25] discussed the degradation mechanism of cable insulation under different factors such as liquid conductivity, soil type, and insulation material, and confirmed that cable insulation degradation is mainly caused by arcing under wet conditions. It can be seen that research on low-voltage cables remains insufficient, particularly regarding the voltage and current characteristics during the development of incipient faults. Moreover, no widely accepted incipient fault model has been established so far, hindering the development of effective detection methods. To address these gaps, this study investigates the voltage and current characteristics of single-phase incipient faults commonly occurring in cross-linked polyethylene (XLPE) cables with insulation defects. These characteristics are correlated with the typical stages of initial cable faults, while also considering the influence of various factors on fault characteristics. Thus, cable incipient models are proposed based on the experimental results for further research on cable incipient fault detection methods. This paper is arranged as follows. Section 2 explains the experimental setup and the parameters of the cable samples and the experimental conditions. Section 3 presents the experimental results and analyzes the main findings. Section 4 presents the simulation results and analysis, which support the experimental findings. Section 5 proposes incipient fault models for LV XLPE cables. Section 6 provides the application results of the proposed model. Section 7 concludes the paper.
# Combining a Universal OBD-II Module with Deep Learning to Develop an Eco-Driving Analysis System
## 3 Materials and Methods
### Neural Network Algorithm
#### 3.5.2 Neural Network Model Selection and Processing  
Because consecutive changes in vehicle statuses may influence fuel consumption, recurrent neural networks (RNNs) were employed as the optimal model for predicting fuel consumption. The Elman neural network (Figure 7), an RNN, is a dynamic system; it comprises the input layer, the information-providing context layer, the hidden layer, and the output layer. The Elman neural network uses its context layer to copy the hidden layer's output from the previous time unit [35]; that is, the current condition of the vehicle is determined by the output of the previous time unit. The neurons of each layer transmit nonlinear function data to the next layer through calculation and weighting. The main function is presented as follows [33]:  
\[s_{i}(t)=\sigma\{\left(\ \sum_{k=1}^{K}v_{ik}s_{k}(t-1)+\sum_{j=1}^{I}w_{ij}I_{j}( t-1)\ \right)\} \tag{3}\]  
where \(s_{k}(t-1)\) represents the output from the previous time unit, \(I_{j}(t)\) is the input, and \(w_{ij}\) and \(v_{ik}\) are individual weights.  
The activation function is a nonlinear function. Through repeated stacking of the activation function, the neural network can acquire the data's characteristics. This sigmoid function was employed in this study as the activation function. The activation function is presented as follows:  
\[\sigma_{(\mathrm{x})}=\frac{1}{1+\mathrm{e}^{-\mathrm{x}}} \tag{4}\]  
In addition to the Elman neural network, the present study adopted other networks used in a relevant study [30] to predict fuel consumption--namely, the feed-forward backprop (FFB) and layer RNN. MATLAB (MathWorks, The MathWorks co., USA) was employed as the neural network analysis software. For a fair comparison, the FFB and layer RNN models both consisted of an input layer, an output layer, and two hidden layers; they had the same number of neurons in the hidden layers. TRAINLM was employed as the training function, whereas TANSIG (i.e., sigmoid function) was used as the activation function.  
Finally, the theoretically calculated values and the results predicted by the neural network were compared to verify the prediction results and to evaluate the model's performance. The evaluation indicators were the root mean squared error (RMSE) and correlation coefficient (\(\gamma\)). These indicators were derived using the following equations:  
\[MSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{5}\]  
\[\gamma=\frac{\sum_{i=1}^{N}\left(\hat{y_{i}}-\overline{\hat{y_{i}}}\right)(y_{ i}-\overline{y_{i}})}{\sqrt{\sum_{i=1}^{N}\left(\hat{y_{i}}-\overline{\hat{y_{i}}} \right)^{2}}*\sqrt{\sum_{i=1}^{N}\left(y_{i}-\overline{y_{i}}\right)^{2}}} \tag{6}\]  
where n represents data length, \(y_{i}\) is the initial data, and \(\hat{y_{i}}\) is the prediction results of the neural network.
# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 5 Simulation and Performance Analysis
### Results and Discussion
#### 5.3.3 Normalized Routing Load  
Figure 8 shows the NRL performance against the node density with a variance in the traffic load. The proposed BOLSR-PSO and OLSR-PSO demonstrate the lowest routing load especially in high density network compared to the OLSR RFC-3626, OLSR-Gomez, and OLSR-RAND algorithms. This is because of reliable configuration parameters are obtained using PSO algorithm. Evidently, the low NRL experimented in these parameters enhanced the functionality of routing operation, so they can even reduce the possibility of network failure and avoid conjunction in high routing loads. Table 7 shows that normalized routing load results were the best compared to other algorithms excluding  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **BOLSR-PSO** & **OLSR-RFC** & **OLSR-RAND** & **OLSR-Gomez** & **OLSR-PSO** \\ \hline Mean & 53.3 & 52.8 & 52.6 & 53.6 & 28.2 \\ Median & 53.6 & 53.5 & 53.7 & 54.1 & 28.2 \\ Mode & 96.9 & 97.2 & 97.2 & 96.4 & 45.2 \\ Best & 99.7035 & 99.5027 & 99.6027 & 99.9157 & 51.7402 \\ Worst & 6.0021 & 5.7274 & 4.7432 & 4.7502 & 2.6205 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical Result of PDR Performance Analysis.  
Figure 7: Packet Delivery Ratio vs. Number of Nodes.  
OLSR-PSO which was fairly bad in packet delivery ratio and the number of packet loss. Obtained mean was 220, median 189, mode 8, best 0.06 and worst 596.7. OLSR-Gomez results were fairly good in packet delivery ratio and packet loss, however, the normalized routing load results was not favorable. We noticed a slight advantage in the OLSR-PSO configuration when the vehicle density increased in high traffic load. In addition, the BOLSR-PSO respects the balancing of the trade-off model requirement, which selects a stable path according to the cost of the QoS metrics and the constraints of the network topology. Globally in routing, the lower NRL, the higher the ability to maintain routing table.
# Energy Communities Design Optimization in the Italian Framework
## 2 Materials and Methods
### Energy Community Model
The total annual results are computed by weighting the typical days values, according to the fraction of the year that they represent. Hence, the OPEX part of the objective function can be formulated as follows:  
\[\min\sum_{d\in D}N_{d}\left[\sum_{h}\sum_{i}(\xi_{i,d,h}^{EB}+\xi_{i,d,h}^{NG}- \varphi_{i,d,h}^{ER})\right] \tag{6}\]  
where: the \(t\in T\) time set is replaced by \(d\in D\) and \(h\in\{1\dots 24\}\), which are the sets of typical days and hours of the day; \(i\in PODs\) is the set of points of delivery; \(N_{d}\) is the number of days represented by typical day \(d\); \(\xi_{i,d,h}^{EB}\) and \(\varphi_{i,d,h}^{ER}\) are the electricity bill and the electricity remuneration, respectively, computed accordingly to the considered configuration; \(\xi_{i,d,h}^{NG}\) is the natural gas bill, whose computation method is independent of the configuration. The electricity bill features an energy, a power, and a fixed quota, plus taxes paid on total consumption, and hence \(\xi_{i,d,h}^{EB}\) is calculated as the hourly contribution to the total amount paid in the bill (further details on the electricity remuneration will be given in the following sections). Similarly, \(\xi_{i,d,h}^{NG}\) comprises the fixed and the energy quota, plus taxes for the natural gas bill. The constraints of the model enforce the energy balances, the dependence of the energy conversion systems on the weather conditions (e.g., the specific photovoltaic production on the irradiation and ambient temperature, the coefficient of performance of the electric heat pumps on the ambient temperature), the storage management, the exchange profiles with the grid, and the bills' calculation. To sum up, the optimization problem used in this study can be stated as follows: given (i) a set of available technologies (in terms of installation and operating costs, their characteristic performance and parameters), which depends on the scenario considered (see the case study below); (ii) the electricity, heating, and cooling demands of the residents (see the case study below for the total values and profiles); (iii) the prices and structure of the bills of natural gas and electricity; (iv) the production profiles of the intermittent RES; and (v) the renewable self-consumption configuration (condominium members as single users or part of a community, see the case study below for further details), determine the optimal design of the energy conversion and storage units and exchange with the grid so as to minimize the annualized investment and operating costs of the condominium as a whole.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods  
The components of the system include a fixed node (RiverCore), a mobile node (Drifter), a drone mountable sniffer, and weather stations, along with a web-based data acquisition platform, all integrated with IoT techniques to retrieve data through 3G cellular networks.  
The developed architecture uses the Message Queuing Telemetry Transport Protocol (\"MQTT is an OASIS standard messaging protocol for the Internet of Things (IoT)\" [17]), to send real-time data packages from fixed nodes to a server that stores retrieved data in a non-relational database. From this, data can be accessed and displayed through different customizable queries and graphical representations, allowing future use in flood analysis and prediction systems. The parts of the system can be seen in Figure 1.
## 5 Verification Measurements  
### Measurements Scenario and Setup  
The results of the following outdoor measurement campaign investigate the performance of the proposed, novel cross-range enhancing radar method. The measurements have been performed at the ballpark of the local baseball club. As a radar device, we utilized an X-band FMCW Radar system with separate transmit and receive paths. Because of its perfect 3 dB beamwidth of 90 degrees, we choose a WR-90, open-ended, rectangular waveguide as the TX antenna. In order to cover the whole AOI, the main beam direction was pointed to the field's center direction, i.e., 2 nd base. Moreover, two of the above-mentioned Vivaldi antennas were used as RX antennas. Together, they cover the 90-degree azimuth angle of the field by subdividing it into three beam areas namely: Center-Beam 1 (CB1) and Center-Beam 2 (CB2) for the fringe areas providing each a single coding filter function. Their overlap area, which results in the coding of both filters, is denoted as Overlap-Beam (OB) further on. Within the infield, five target positions have been identified that are located in the different beams as well as in different LOS-range distances. Figure 14 shows drone footage of the test field with overlaid measurement sectors, indicated target positions, and corresponding coordinates. Here, the origin of the coordinates was set to the radar device at the home base.  
Furthermore, Table 1 lists all targets T1-T5 and their properties including corresponding field coordinates, LOS distance to the radar device, and their sector beam locations.  
Figure 14: Drone footage of the measurement scenario with overlaid antenna beam areas and indicated target locations. Coordinates (x,y) of the targets T1–T5 are given in meters.
###### Abstract
The remainder of this paper is organized as follows: the system model and related problems are formulated in Section 2, which introduces the power system and DoS attack model, and describes the frequency regulation as well; Section 3 elaborates control structure and control law design by sparse neural network based reinforcement learning; Stackelberg game model and the optimization scheme of control structure are derived under DoS attacks in Section 4; and Section 5 demonstrates the simulation results to verify the proposed algorithm.
# Security Challenges for Light Emitting Systems
###### Abstract  
Although visible light communication (VLC) channels are more secure than radio frequency channels, the broadcast nature of VLC links renders them open to eavesdropping. As a result, VLC networks must provide security in order to safeguard the user's data from eavesdroppers. In the literature, keyless security techniques have been developed to offer security for VLC. Even though these techniques provide strong security against eavesdroppers, they are difficult to deploy. Key generation algorithms are critical for securing wireless connections. Nonetheless, in many situations, the typical key generation methods may be quite complicated and costly. They consume scarce resources, such as bandwidth. In this paper, we propose a novel key extraction procedure that uses error-correcting coding and one time pad (OTP) to improve the security of VLC networks and the validity of data. This system will not have any interference problems with other devices. We also explain error correction while sending a message across a network, and suggest a change to the Berlekamp-Massey (BM) algorithm for error identification and assessment. Because each OOK signal frame is encrypted by a different key, the proposed protocol provides high physical layer security; it allows for key extraction based on the messages sent, so an intruder can never break the encryption system, even if the latter knows the protocol with which we encrypted the message; our protocol also enables for error transmission rate correction and bit mismatch rates with on-the-fly key fetch. The results presented in this paper were performed using MATLAB.  
Li-Fi; visible light communication; security; Berlekamp-Massey algorithm; Reed-Solomon; error correcting; OTP
## 2 Materials and Methods
### Experimental Study Design  
The purpose of the following investigation was the analysis of G-N2- and G-N10-coated equine bone graft and melatonin effects on hDPSC proliferation and differentiation in an osteogenic pattern. The design of the study is shown in Figure 1. Human DPSCs were plated in three different time points: An amount of \(\sim\)40.000 cells were placed for 7, \(\sim\)20.000 cells set for 14, and \(\sim\)10.000 cells for 21 days of culture. After 24 h, G-N2 and G-N10 bone blocks, previously sterilized, were placed in the presence of DM or GM with the addition of HEPES (#H4034 Sigma-Aldrich, Milan, Italy) at the concentration of 15 nM. In some experimental conditions (#M5250, Sigma-Aldrich, Milano, Italy) 100 mM of melatonin was added due to its specific properties in bone formation and stimulation. Melatonin was freshly prepared in 100% ethanol in order to avoid an immediate decomposition, according to the guidelines. The procedure was set up as following described:  
Group 1: hDPSCs + equine bone block (Ctrl);  
Group 2: hDPSCs + equine bone block + melatonin (Ctrl + Mel);  
Group 3: hDPSCs + equine bone block G-N-coated 2 \(\mu\)g/mL (G-N2);  
Group 4: hDPSCs + equine bone block G-N-coated 2 \(\mu\)g/mL + melatonin (G-N2 + Mel);  
Group 5: hDPSCs + equine bone block G-N-coated 10 \(\mu\)g/mL (G-N10);  
Group 6: hDPSCs + equine bone block G-N-coated 10 \(\mu\)g/mL + melatonin (G-N10 + Mel).  
After 7, 14, and 21 days of culture, cells were detached and harvested in order to evaluate miRNAs (miR-133a/b, miR-135a, miR-206, miR-29b, and miR-let-7b) and gene expression of RUNX2 and SMAD5, and osteoclastin levels.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 6 Conclusions  
In this paper, a new method based on the principle of dimensional analysis is proposed to predict the ground-borne vibration caused by blasting. The Sadovsky formula is deduced by dimensional analysis, and the uncertainty of its parameters is pointed out. To solve this problem, \(K\) and \(\partial\) are separated by the dimensional analysis method, and a new prediction method is established which comprehensively considers the maximum charge per delay, initiation time per delay, initial rock density, distance from initiation center, number of free surfaces, and other parameters. The case study shows that for the prediction of vibration in the near blasting area, it is necessary to distinguish the vibration data according to the type of blasting holes and the position of the measuring points relative to the working face before using the Sadovsky formula to predict, so as to improve the prediction accuracy. However, its prediction results of the near blasting area are still not ideal. Compared with the best prediction results of the Sadovsky formula, the new method does not need to  
Figure 8: Relationship between mean absolute error and distance.  
distinguish data, and its prediction \(R^{2}\) values in the \(X\), \(Y\), and \(Z\) directions have increased by 0.02, 0.15, and 0.17, respectively. The new method considers more influencing factors, so the prediction results obtained are less affected by the monitoring data error and have higher applicability to the prediction of the near blasting area. Therefore, within the allowable error range, it can better guide the actual construction. The engineering geological conditions studied in this case are mainly weakly weathered granite strata, and the prediction object is ground-borne vibration. The new method can be fully extended to engineering applications under most other geological conditions, and the prediction objects can also be transformed into buildings or underground pipelines.  
Conceptualization, X.R. and Y.G.; methodology, X.R.; validation, Y.G.; formal analysis, Y.G.; investigation, X.R.; resources, H.F.; data curation, Y.G. and X.R.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G. and G.P.; supervision, H.F.; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Scholarship Council (202107090076) and the Rail Transit Safety Collaborative Innovation Center of China (2022 JBXT007, Service safety and intelligent maintenance of rail transit operating tunnels).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Numerical Model
### Governing Equations

Table 1: Parameters of general equations. where the pressure drop coefficient \(k_{\rm v}\) is a constant computed from the following experimental correlation [32] with a value of 0.81. Heat transferred from the finned tube bundle radiators to air is expressed as an energy source term in the air energy equation:  
\[Q_{\rm air}=k(T_{\rm w}-T_{\rm a})A_{\rm v} \tag{10}\]  
where \(k\) is the overall heat transfer coefficient of the radiator. \(T_{\rm w}\) is the water temperature, and \(T_{\rm a}\) is the air temperature. \(A_{\rm v}\) is the heat transfer area of the radiators. The overall heat transfer coefficient \(k\) is approximately 1600 W/(m\({}^{2}\cdot\)K) according to the experimental data.