esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### CLP Data Preparation  
The CLP data preparation process consists of normalization, imputing missing values, removing factors with zero variance, and eliminating outliers.  
Mostly, CLP data have varying scales that lead to increased training time and biases in predictive models and affect convergence in prediction [33]. Hence, the experimental data are normalized using Equation (2) in a process called \"max-min normalization\", where \(x_{ij}\) is the value of instance \(i\) from factor \(j\); \(x_{jmin}\) and \(x_{jmax}\) are the minimum and maximum values of factor \(j\), respectively; and \(r_{ij}\) is the normalized value of instance \(i\) from factor \(j\). Max-min normalization guarantees that all features have the exact same scale.  
\[r_{ij}=\frac{x_{ij}-x_{jmin}}{x_{jmax}-x_{jmin}} \tag{2}\]  
Data sets often have some missing values, due to human error or non-availability of real data. Imputation methods use ML algorithms to help estimate missing values. Based on [PERSON] and [PERSON] [46], the neural network-based imputation method is able to train a data set containing incomplete samples and identify instances similar to instances with missing values. Based on the results of several studies [46; 47; 48], neural network imputation was applied in the present study in order to impute missing values of CLP.  
Standard deviation is a measure of the variance of each factor in a data set. Removing factors with no variation in data instances is a pre-processing step for data sets [49]. In this study, CLP factors with zero standard deviation were removed from the data set.  
Detecting and eliminating outliers is another essential step in data preparation. Although outliers are part of a data set, they are significantly different from other observations. In this study, [PERSON]'s method, which utilizes the median, upper, and lower quartiles of a data set, was applied as an outlier detection method [50]. Since quartiles are resistant to farthest data of the data set, [PERSON]'s method is less sensitive, compared to methods using mean and standard variance [50].
# Stability Analysis of Buck Converter Based on Passivity-Based Stability Criterion
## 5 Simulation Validation  
Computer simulations played a key role in the process of designing the Buck converter [33]. Different simulation software and models of the components may need different system control parameters, which may need to be fine-tuned. In this paper, a Buck-CPL simulation model was built using MATLAB R2008. In order to use the presented model in practice, it should be included in the analyzed specific parameters of the system. The specific parameters of the Buck converter are shown in Table 2.  
Figure 10 shows the waveforms of the bus voltage and inductance current under the traditional control and the improved control of the system, respectively. The comparison shows that the improved control strategy improves the stability margin of the system. The controller parameters of the Buck converter are shown in Table 3. Figure 10a shows the divergent oscillations of the system with traditional control and Figure 10b,c show the waveforms with different values of \(k_{\text{F}}\) under the improved control strategy. It is concluded that the system continues experiencing divergent oscillations when \(k_{\text{F}}=50\text{e}^{-7}\). When the value of \(k_{\text{F}}=1\text{e}^{-3}\) satisfies the passive condition, the system begins to converge, and the improved control strategy can effectively improve the system stability and enhance the system stability margin.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Input voltage \(v_{\text{s}}\) & 100 (V) \\ Output voltage \(v_{\text{dc}}\) & 60 (V) \\ Parasitic resistance \(r\) & 0.12 (\(\Omega\)) \\ Inductance \(L\) & 8 (mH) \\ Capacitance \(C\) & 4600 (uF) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The Buck converter parameters.  
Figure 10: Comparison waveform diagram. (**a**) Traditional control; (**b**) improved control \(k_{\text{F}}=50\text{e}^{-5}\); (**c**) improved control \(k_{\text{F}}=10\text{e}^{-4}\).
## 2 Model Methodology
### Notations  
The main parameters and variables used in the model development are presented as follows.  
\begin{tabular}{l l} \hline \hline _Indices:_ \\ \(M\) & Set of customer nodes, indexed by \(i\); \\ \(N\) & Set of potential facilities, indexed by \(j\); \\ \(S\) & Set of scenarios, indexed by \(s\); \\ _Parameters:_ \\ \(c_{ij}\) & the cost of supplying one unit to customer node \(i\) from facility \(j\); \\ \(\sigma_{is}\) & the demand of customer point \(i\) under scenario \(s\); \\ \(p\) & the number of facilities that can be opened; \\ \(q_{s}\) & the probability that scenario \(s\) may occur; \\ \(\beta\) & a constant that represents the maximum relative regret permitted for each scenario; \\ \(z_{s}^{*}\) & the optimal objective function value of the problem under scenario \(s\); \\ _Decision variables:_ \\ \(Y_{j}\) & is 1, if facility \(j\) is opened in the robust optimization problem; otherwise 0; \\ \(Y_{js}\) & is 1, if facility \(j\) is opened in the problem under scenario \(s\); otherwise 0; \\ \(X_{ijs}\) & is 1, if the demand of customer node \(i\) is supplied by facility \(j\) under scenario \(s\); otherwise 0. \\ \hline \hline \end{tabular}
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 3 Modeling MANETs
### Stress Level  
We model a node-specific performance value which we refer to as _stress_. This value represents the overall quality of service experienced by the clients connected to the node.  
\[stress_{i}=\frac{n}{\sum_{j=1}^{n}LQM_{j}} \tag{3}\]  
The stress level, an internal value derived from locally available information only, is used as a trigger (see Section 4, specifically Section 4.2.1 for the mechanism to deploy additional drones). The threshold for this trigger is set by default to 1.5, which, for a node serving as an access point (and therefore with only a single other node as a client), would be reached when the client's LQM drops below \(\frac{2}{3}\). For nodes connected to clients, this trigger is reached when the average LQM of all associated clients drops below \(\frac{2}{3}\).
## 5 Future Prospects and Recommendations
Organizations should stay informed about evolving regulations and actively participate in industry efforts to develop international standards for AI use in maritime operations. Ethical guidelines should be established to ensure transparency, fairness, and accountability in AI systems. 6. Continuous Monitoring and Improvement: AI systems should be continuously monitored to assess their performance and identify areas for improvement. Implementing feedback loops where insights from real-world operations are used to refine AI models can enhance their accuracy and reliability over time. Regular audits and evaluations can help ensure that AI systems remain aligned with safety standards and operational goals. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Future Development** & **Description** & **Potential Impact** \\ \hline Enhanced Machine Learning Algorithms & Development of more sophisticated AI models capable of deeper analysis and more accurate predictions. & Improved decision-making and operational efficiency [62; 63] \\ \hline Integration with IoT and Blockchain & Combining AI with IoT for real-time data collection and blockchain transactions. & Increased data reliability and operational transparency [66; 67; 68] \\ \hline Autonomous Vessels & AI-driven autonomous ships capable of operating with minimal human intervention. & Significant reduction in human error, enhanced safety, and operational efficiency [79; 80] \\ \hline Advanced Predictive Maintenance & Improved predictive maintenance systems using advanced AI and sensor technologies. & Reduced maintenance costs and downtime, extended equipment lifespan [85; 86] \\ \hline Real-Time Environmental Monitoring & AI systems providing real-time analysis of environmental conditions for better navigation and operational decisions. & Enhanced safety and reduced environmental impact [74; 75] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Future prospects of AI in maritime operations. By embracing these recommendations and staying abreast of technological advancements, the maritime industry can leverage AI to significantly enhance safety, efficiency, and sustainability. The future of maritime operations, empowered by AI, holds the potential for safer seas and more efficient global trade.
## 5 Conclusions
**Nomenclature**  
_Constants_  
\(\rho\) Air density (=1.205 kg/m\({}^{2}\))  
\(\gamma_{w,opt}\) Optimal tip speed ratio  
\(G_{p,opt}\) Optimal power coefficient of wind turbine  
\(R\) Rotator radius  
\(\omega_{m,opt}\) Optimal rotational speed  
\(S_{cut-in}\) Cut-in speed of wind turbine  
\(S_{rated}\) Rated speed of wind turbine  
\(S_{cut-out}\) Cut-out speed of wind turbine  
\(P_{\max},P_{\min}\) Maximum and minimum limits of possible power  
\(\lambda_{c_{max}},\lambda_{c_{min}}\) Maximum and minimum limits of loading margin  
\(P_{S_{max}},P_{S_{min}}\) Maximum and minimum limits of supply bid  
\(P_{D_{max}},P_{D_{min}}\) Maximum and minimum limits of demand bid  
\(Q_{G_{max}},Q_{G_{min}}\) Maximum and minimum limits of reactive power  
\(I_{ij_{max}}\) Maximum limit of line currents between nodes \(i\) and \(j\)  
\(V_{\max},V_{\min}\) Maximum and minimum limits of voltage magnitude  
\(\mu_{\max},\mu_{\min}\) Maximum and minimum limits of shadow price  
\(T\) Scheduling time (e.g., 24 h)  
\(c_{1},c_{2}\) Acceleration coefficients  
_Variables_  
\(\beta\) Blade pitch angle  
\(S_{w}\) Wind speed  
\(b\) Scale parameter of Rayleigh distribution  
\(k,a\) Shape and scale parameter of Weibull distribution  
\(\sigma_{L}\) Standard deviation of load  
\(e_{L}\) Expected value of probability variable  
\(w\) Weighting factor  
\(P_{w,rated}\) Rated power of wind turbine  
\(P_{W,F,j}\) Active power output of wind farm at bus \(i\)  
\(P_{W,P}\) Active power of \(n\)th wind turbine  
\(\Delta P_{n}\) Variation in active power of \(n\)th bus  
\(P_{G},P_{L}\) Power outputs of generator and load, respectively  
\(P_{G_{x}},P_{L_{v}}\) Current power outputs of generator and, respectively  
\(P_{S},P_{D}\) Supply and demand bid volumes (in MW), respectively  
\(C_{S},C_{D}\) Bid prices for supply and demand (in $/MWh)  
\(Q_{G}\) Generator reactive power  
\(\Delta P_{i,j}\) Variation of power flow in critical condition  
\(P_{ij},P_{ji}\) Power flowing through lines in both directions  
\(V_{i},V_{j}\) Voltage magnitude at buses \(i\) and \(j\), respectively  
\(I_{ij},I_{ji}\) Line currents in both directions  
\(P_{\mathrm{SNB}}\) Power output of SNB point for voltage collapse  
\(P_{base}\) Power output at base operating point  
\(\delta_{i},\delta_{j}\) Voltage angle at buses \(i\) and \(j\), respectively  
\(\theta_{ij},Y_{ij}\) Angle and magnitude of \(j\)th element of \(Y_{bus}\), respectively
## 1 Introduction
Recently, there has been increasing attention given to \(\mathcal{F}\) distribution [21]. For instance, \(\mathcal{F}\)-distributed fading was used to model a dual-hop relaying communication system, in which ideal or non-ideal hardware is considered [22]. [PERSON] et al. provide the performance analysis of the \(\mathcal{F}\) fading model with the maximum ratio combining (MRC) scheme for device-to-device communication systems [23]. [PERSON] et al. utilize the \(\mathcal{F}\) distribution to model the effect of multipath and shadowing in RF links [24]. The results in [24] show that the \(\mathcal{F}\) fading model provides a better fit compared to the generalized-\(K\) model. In contrast to generalized-\(K\) and log-normal distribution, the expressions for PDFs and CDFs are less complex using the \(\mathcal{F}\) distribution. More recently, \(\mathcal{F}\) distribution was used in the FSO channel to model turbulence fading for further exploration [25]. From this work, we know that the \(\mathcal{F}\) distribution provides an improved fit to simulation data in different practical propagation scenarios and tractable expressions compared with well-known Gamma-Gamma and \(\mathcal{M}\) distributions. Moreover, in the FSO communication scenario, the authors analyze hybrid mmWave/FSO systems over the \(\mathcal{F}\)-distribution fading channel [26]. In order to better characterize the situation of FSO communication, the pointing error impairment is considered in the \(\mathcal{F}\) distribution model [27]. Motivated by the aspects mentioned above, we propose an AF-based mixed RF-FSO communication system, where the RF channel experiences Rayleigh fading and the FSO channel is assumed to operate over \(\mathcal{F}\) fading in the presence of pointing errors. The main contributions of this paper are given as follows: (i) the closed-form expressions of CDF, MGF, and the moments for AF relaying are derived; (ii) the closed-form expressions of the end-to-end performance metrics are derived, including outage probability, average BER, and ergodic capacity; (iii) asymptotic outage probability expressions are derived for both relay protocols. The rest of the paper is organized as follows: Section 2 details the system and channel model. End-to-end SNR statistics of AF relaying are presented in Section 3. In Sections 4 and 5, the closed-form expressions for the outage probability, average BER, and ergodic capacity are derived. Asymptotic outage probability analysis is given in Section 6. Section 7 presents the simulation and analytical results. Finally, Section 8 presents the conclusions.
## 4 Modeling and Prediction
### Evolution Model
Due to these results, it was necessary to make certain corrections to the model, including batteries 4, 5, 6, and 7 as part of the data available to train the algorithm, although it continued to give more weight to batteries 1, 2, and 3 (Table 3 and Figure 25). Particularly, batteries 1, 2, and 3 weighted a value of 3, batteries 4 and 6 weighted 1.5, and, finally, batteries 5 and 7 had a 0.5 value. This model has a lower accuracy in batteries 1, 2, and 3: R\({}^{2}\)(B1) = 0.8767; R\({}^{2}\)(B2) = 0.9052; R\({}^{2}\)(B3) = 0.9088. However, it makes a more suitable approximation for the rest of the batteries: R2(B4) = 0.7205; R2(B5) = 0. 3101; R2(B6) = 0.8586; R2(B7) = 0.4138. The worst approximations occur in batteries 5 and 7, which is normal given that they have the most exceptional internal resistance evolution of all the batteries under study (Table 3). It can be inferred that the model draws very irregular internal resistance evolutions compared to the \"real\" results. This happens because of the simplicity of the equation used to develop the prediction model, in which complex corrections may be needed to increase its accuracy. Furthermore, the predictions in this model are made from data of six different parameters extracted from real use measurements. Therefore, the evolution of these parameters tends to be very anarchic in every cycle, causing these extreme fluctuations. For these reasons, in this case, the proximity of each result tendency to the real evolution should be considered rather than their actual shape comparison, also considering that the real behavior of the internal resistance is not as regular as these graphs show. It is important to understand that these types of black box models do not really understand the physics inside the battery [33]. This can be observed very clearly in the results obtained, where the constants that multiply the number of cycles and the discharged capacity are negative. This means that the internal resistance decreases as the battery ages or if the discharge depth increases, which is completely contrary to what was seen in the previous analysis. Therefore, although the model designed is accurate enough for primary predictions, it is important to understand the physical interpretation that causes this evolution of internal resistance. Figure 25: Results of final model including batteries 1–7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**A** & **B** & **C** & **D** & **E** & **F** & **G** \\ \hline \(-\)0.00198 & 0.06902 & \(-\)3.80387 & 15.85996 & 1.64249 & 0.06715 & \(-\)4.31879 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solution parameter results including batteries 1–7.
## 4 Experiment and Analysis
### Equipment and Solutions
To effectively compare the performance of the LC scheme and the TC scheme proposed in this paper, we divide the experimental results into two parts: sections of the road with normal GNSS signals and the entire route, which includes the aforementioned long period of GNSS denial. These are presented and analyzed separately. Table 3 summarizes the root mean square error (RMSE) of the two coupling schemes in terms of horizontal attitude, vertical attitude, velocity, and position estimation. This provides a quantitative basis for the assessment and a comparison of the performance of the two schemes. From Table 3, it can be seen that the experimental results for both types of road sections show that the TC scheme provided in this paper performs better than the LC scheme in terms of attitude, velocity, and position estimation accuracy. The specific improvements are as follows:  
1. For the GNSS normal road section experimental results, compared with LC, the TC scheme reduced the RMSE of the horizontal attitude from 0.210 degrees to 0.078 degrees, the RMSE of the vertical attitude from 1.59 degrees to 0.76 degrees, the RMSE of velocity from 0.032 m/s to 0.026 m/s, and the RMSE of position from 2.81 m to 2.00 m. These improvements correspond to approximately 62.68%, 52.20%, 18.75%, and 28.82% increases in accuracy, respectively. 2. For the experimental results of the road section containing GNSS denial, the TC scheme also shows a significant improvement over the LC scheme. In terms of velocity and position estimation, the TC scheme achieved approximately 20.00% and 35.82% increases in accuracy compared to the LC scheme. Figure 6: Performance comparison between LC and TC schemes. Comparison chart of positional errors in the three spatial dimensions of longitude, latitude, and altitude. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **GNSS Normal Section** & **GNSS Denial Section** \\ \hline \multicolumn{3}{c}{Horizontal attitude (deg): 0.210} \\ \multicolumn{3}{c}{Lose} & Vertical attitude (deg): 1.59 \\ \multicolumn{3}{c}{Velocity (m/s): 0.032} \\ \multicolumn{3}{c}{Position (m): 2.81} \\ \hline \multicolumn{3}{c}{Horizontal attitude (deg): 0.078} \\ \multicolumn{3}{c}{Tight} & Vertical attitude (deg): 0.76 \\ \multicolumn{3}{c}{Velocity (m/s): 0.026} \\ \multicolumn{3}{c}{Position (m): 2.49} \\ \hline \end{tabular}
\end{table} 
Table 3: RMSE comparison of loose and tight coupling schemes.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 4 Tools and Methodology  
In Section 3.2, several real deployments were presented. All of these deployments include only one gateway and a limited number of end nodes. The measurements at different points were carried out with a single end device at a time. In case of large-scale deployments, interference will be the main cause of packet loss and coverage outage. In all the aforementioned studies, only the communication between a single end device and the network was assessed. The network behavior will become different once many devices will simultaneously start using the same network. However, the deployments are good enough to predict the coverage boundaries for certain SFs, but they cannot be used for network scalability assessment. Simulators and test-beds can be used to make repetitive experiments, assess network performance and check the impact of each transmission parameter (transmit power and SF distribution, ADR mechanism, retransmission schemes, etc.).
###### Abstract
Additionally, bidirectional reflectance, transmittance, and ChlF have not been compared with experimental data for validation. The objective of this research is to thoroughly test and verify whether the Fluspect model can simulate the fluorescence in natural conditions and propose a new FQE retrieval method coupled with the Fluspect model to improve the simulation accuracy. The efficiency of the measured data for simulating fluorescence has been verified [30; 46]. Thus, the research questions herein focus on: (1) whether the simulated and measured fluorescence have a good correlation under natural conditions; (2) whether the peak values around 687 nm and 740 nm in the simulated fluorescence curves match the measured data; and (3) whether the Fluspect model derives more accurate simulations of fluorescence when coupled with the new FQE retrieval method with the FQE inversion code, especially reducing RE at the peak values around 687 nm and 740 nm, and whether they make sense physiologically.
## 2 Materials and Methods
### Dataset Collection from the IRIS
#### 2.1.2 Data Selection Criteria  
To construct a high-quality seismic event database, the following selection criteria were applied:  
1. Each recorded event included 120 s of data before and 120 s of data after the P-wave arrival.  
Figure 3: A diagram illustrating the monthly distribution of seismic events. The blue bars represent the number of recorded earthquakes, while the red numerical labels indicate the maximum recorded magnitudes (ranging from 4.1 to 7.0).  
Figure 2: Fragment of the map with events. Blue dots indicate the station’s coordinates on the map, and red dots are events from 1 January 2023 to 31 December 2024.  
2. To eliminate duplicate events, a minimum interval of 120 s was enforced between consecutive detections. If two seismic events were recorded within this 120-s window, one was removed from the dataset. This approach prevented multiple registrations of P waves from the same or temporally close seismic events, thereby improving the precision of seismic data analysis.  
Following applying these criteria, the initial dataset of 1262 recorded events was filtered down to 1147 events, ensuring a more accurate and reliable dataset for subsequent model training and analysis. Data are presented with a minimum signal-to-noise ratio of 2 dB. Figure 4 presents a detailed statistical analysis of the quality of seismic event recordings. It illustrates the distribution of recorded events across key seismic stations for 2023-2024.  
The graph shows that most stations recorded a comparable number of seismic events. However, the BAET station detected significantly fewer events (only 12). This discrepancy is attributed to the fact that BAET was commissioned only in 2024, resulting in a much shorter observation period than the other stations.