esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing
### Ship Detection
In [6], this trade-off is not easy to reach because the larger the scale, the better the sensitivity to detect small targets, but the worse the capability to isolate close targets, and vice versa. 2. **Confidence**. According to the characteristics of the local maxima indicating the presence of a potential ship, a confidence parameter C can be defined indicating the percentage of certainty that the algorithm has in labeling the detected spot as a ship. The range of values taken by C permits different situations, for instance: 1. Ship-like sea aberrations causing false alarms (wave crests) \(\mathtt{>}\) C \(<0.4\). 2. Small ships with low dispersion \(\mathtt{>}\) \(0.4<\mathrm{C}<0.7\). 3. Ships visible by eye inspection \(\mathtt{>}\) C \(>0.7\). The confidence parameter has been found to be very useful for supporting classification and defining alarms. It helps the operators to do better decision making as the efforts can be focused to particular targets according to the confidence values that they take. Figure 4 shows an example of ship detection carried out by SIDECAR for an ESA ENVISAT image covering the surroundings of Gibraltar, Spain, with Image Precision Mode at 27-12-2007. As observed, ship detection performs very well because all the ships identified by eye inspection are located as well as other ones with low RCS. In all the cases, the confidence levels given to each detection permit the identification of which type of ship could be behind. Especially interesting are the two spots cruising from South to North and labeled by white rectangles. The associated features and the confidence assigned by the system make them potential candidates to be clandestine boats boarding illegal immigrants. But without ground-truth such an assertion is difficult to formulate.
## 1 Introduction
In addition, in order to consider the partition of polymer particles into the grain boundaries formed by cellulose particles, it is necessary to control the polymer polymerization reaction to adjust the particle size from several tens of nanometers to several micrometers. For example, excellent preparation methods for polymer particles involve emulsion polymerization [24], suspension polymerization [25], and seed polymerization [26]. These methods are very useful for synthesizing particles of a size from 100 nm to several micrometers. However, it is difficult to produce polymer particles of several tens of nanometers. If an O/W emulsion of several tens of nanometers can be prepared from monomers, it is predicted that the desired fine particles can be prepared through suspension polymerization. We have previously reported that a hydrothermal emulsification method using a microcapillary provided the O/W emulsion with a size of at least 30 nm [27; 28]. Therefore, combining the hydrothermal emulsification of a monomer with suspension polymerization is expected to yield polymer particles with the desired size. In this study, we investigated the synthesis of polymer particles combining the hydrothermal emulsification of a monomer and suspension polymerization. Styrene, methyl methacrylate, vinyl acetate, and L-lactic acid were used. Environmentally friendly materials should be used for finishing WTBs. As is well known, L-lactic acid is biodegradable, but styrene and methyl methacrylate are not. However, the reason why we used styrene and methyl methacrylate with high hydrophobicity was that we considered that it is easy to verify whether it is possible to prepare polymer particles by hydrothermal emulsification and to investigate the effect of polymer hydrophobicity on surface modification, because their hydrophobicity, trend of entanglement, and softening properties largely differ from each other (Table 1). In addition, the reason is that these monomers can be easily polymerized by free-radical polymerization [25; 26]. We examined whether the coating of WTBs with polymer particles could be controlled by these properties. The WTBs were obtained from a mixture of cellulose and lignin through a pressure molding method according to a previously described method [16; 19] and modified with polymer particles. The surface state of WTBs treated by polymer particles was, with scanning electron microscopy (SEM) and atomic force microscopy (AFM), observed in terms of the convective assemblies of polymer particles [29; 30]. A water contact angle of WTBs coated with various polymer particles was measured to discuss the relationship between the water repellency and hydrophobicity. Furthermore, the long-term stability of WTBs was examined in terms of water contact angle and the ratio of cracked WTBs.
## 1 Introduction
If a router on the route goes down, the destination may become unreachable. Dynamicrouting allows routing tables in routers to change as the possible routes change. In case of wireless sensor networks dynamic routing is employed because nodes may frequently change their position and die at any moment. The advantages and disadvantages of wireless sensor networks can be summarized as follows:  
Advantages:  
* Network setups can be done without fixed infrastructure. * Ideal for the non-reachable places such as across the sea, mountains, rural areas or deep forests. * Flexible if there is _ad hoc_ situation when additional workstation is required. * Implementation cost is cheap. Disadvantages:  
* Less secure because hackers can enter the access point and get all the information. * Lower speed compared to a wired network. * More complex to configure than a wired network. * Easily affected by surroundings (walls, microwave, large distances due to signal attenuation, _etc._). A Wireless Sensor Network structure is shown in Figure 1.
# Three-Dimensional Formation Control for Robot Swarms
## 5 Covering the Sensing Holes Inside the User-Specified Shape  
There may be a case where the networked ARs entirely cover the user-specified 3D shape, but we want to add more ARs into the shape. In this case, we apply distributed rendezvous controls in Section 4.2, while not moving the leader. After the network begins clustering under the rendezvous controls in Section 4.2, coverage holes are newly formed in the user-specified 3D shape. Additional ARs are further located until they cover all holes in the user-specified shape entirely. Covering the sensing holes in the 3D shape is handled in this section.  
Furthermore, covering the sensing holes is useful when AR failures happen. Suppose that ARs are positioned for covering the user-specified 3D shape through the formation controls in our paper. As time elapses, some ARs may fail. In addition, an intruder may destroy some ARs, hence coverage holes inside the user-specified shape are formed. To tackle this case, we address formation controls for covering all sensing holes inside the user-specified shape.  
Suppose that only \(N_{h}\) ARs are located inside the user-specified shape. Suppose that new \(M\) ARs \(v_{i}\) (\(i\in\{1,2,\ldots,M\}\)) are located such that at least one AR is connected to the existing tree \(T\), which contains \(N_{h}\) ARs.  
We use Algorithm 3 for covering all sensing holes inside the user-specified shape. We locate these new \(M\) ARs, in order to cover all holes in the shape. Initially, \(M\) ARs rendezvous at the leader under the rendezvous algorithms in [9]. Thereafter, a new tree \(T\) is initialized by applying the distributed BFS in Section 4.1 to the new \(M\) ARs. In Algorithm 3, \(T=T_{new}\cup T\) implies that \(T_{new}\) is merged with the existing tree \(T\) to form a new tree \(T\).  
```
1:Suppose that only \(N_{h}\) ARs are located inside the user-specified shape, while forming a tree \(T\);
2:Initially, \(M\) ARs rendezvous at the leader;
3:\(S=[v_{1},v_{2},\ldots,v_{M}]\);
4:New tree \(T_{new}\) is initialized by applying the distributed BFS in Section 4.1 to the new \(M\) ARs;
5:\(T=T\cup T_{new}\);
6:\(i=M\);
7:repeat
8: By applying the distributed BFS in Section 4.1, \(v_{i}\) finds an AR with a frontierPt, which has the smallest hop distance from \(v_{i}\);
9: Let \(u_{s}\) present the found AR;
10: A frontierPt on the senseSphere of \(u_{s}\) is set as \(f_{v_{i}}\);
11: The AR \(v_{i}\) maneuvers along the path to \(u_{s}\), then tracks the edge from \(u_{s}\) to \(f_{v_{i}}\);
12:if\(v_{i}\) arrives at its assigned position \(f_{v_{i}}\)then
13: EnableSensor(\(v_{i}\),\(u_{s}\));
14:endif
15:\(i=i-1\);
16:until the index \(i\) becomes 0 or no frontierPt is detected under the distributed BFS;
```  
**Algorithm 3** Cover the sensing holes
## 6 Results and Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **COMPLEXITY** & **TIME (Second Per Epoch)** \\ \hline Dense & 3 layers; 10, 10, 1 units & 0.431 \\ Stateless RNN & 3 layers; 100, 100, 1 units & 1.272 \\ Stateful RNN & 3 layers; 100, 100, 1 units & 0.575 \\ LSTM & 3 layers; 100, 100, 1 units & 1.038 \\ CNN Hybrid & 4 layers; 32, 32, 32, 1 filters & 0.919 \\ Fully CNN & 7 layers; 32, 32, 32, 32, 1 filters & 0.722 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model complexity and execution time. \begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **MAE**1  & **MSE**2  \\ \hline Dense & 5.5728846 & 61.942547 \\ Stateless RNN & 6.6797476 & **82.98067** \\ Stateful RNN & 7.0399313 & **96.302475** \\ LSTM & 5.3750377 & **59.87254** \\ CNN Hybrid & 5.3894196 & 62.422806 \\ Fully CNN & **1.8986198** & **6.2615194** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **MAE-1**  & **MAE-2**2  \\ \hline Dense & 5.5728846 & 5.9691405 \\ Stateless RNN & 6.6797476 & **6.0903750** \\ Stateful RNN & 7.0399313 & **6.1267543** \\ LSTM & 5.3750377 & **5.3358135** \\ CNN Hybrid & 5.3894196 & 5.9622736 \\ Fully CNN & 1.8986198 & **12275864** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance comparison on 5-day-long windows. test, the wavenet model predicted the last 400 samples with a mean absolute value of 0.665. For future works, the wavenet model, which performed better than the memory models, can be compared by using a public dataset. Conceptualization, M.G. and F.U. ; methodology, M.G. and F.U; software, M.G. ; validation, M.G. and F.U. ; writing--review and editing, M.G. and F.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The raw dataset, which was extracted from the StackOverflow website, and the time series extracted from this dataset are presented at [[https://github.com/mesuttguven/Trend-Prediction-on-Stack-Overflow-Dataset](https://github.com/mesuttguven/Trend-Prediction-on-Stack-Overflow-Dataset)]([https://github.com/mesuttguven/Trend-Prediction-on-Stack-Overflow-Dataset](https://github.com/mesuttguven/Trend-Prediction-on-Stack-Overflow-Dataset)) (accessed on 7 April 2023). The codes used in this paper are also presented on the GitHub page. The authors declare no conflict of interest.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 7 Results and Discussion
### DRAMBR Accuracy  
In this ensemble classification, we evaluate DRAMBR's accuracy across the three scenarios and the overall system performance. We combine the Random Forest and XGBoost models to analyze the accuracy in two ways:  
1. Noise-Based Accuracy: Focused only on noise points refined by DBSCAN and Isolation Forest. This classification results in 72%.
2. Full System Accuracy: Includes all MRs (honest and noise) across all scenarios. This evaluation results in 98%.  
Figure 16: Reporter classification results.  
Figure 17 illustrates the noise-based classification with 72% accuracy from Random Forest and XGBoost, highlighting the performance of the DBSCAN, iForest, and GMM models applied to the noise points.  
In Figure 18, we show the Random Forest and XGBoost results in the three scenarios, achieving a total accuracy of 98%, which measures the effectiveness of the entire system.  
The total system accuracy, as shown in Table 11, reflects the system's overall ability to classify MRs and \(Rep_{\text{Vehs}}\) correctly across the three scenarios, including 1--consistent MRs from DBSCAN (True Positives); 2--refined classifications of noise points through iForest and GMM.  
The results in Figure 19 compare accuracy, precision, recall, and F1-score in each scenario and the overall system, highlighting the system's robustness in distinguishing between honest, malicious, and erroneous reporters across all scenarios.  
The (ACD-OCC) scenario demonstrates the system's ability to achieve perfect classification. The other scenarios (ACD-ABS and ACD-RSL) show consistently high  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Scenario** & \multicolumn{2}{c}{**MRs**} & **Consistent** & **Noise** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \hline _ACD-OCC_ & 100 & 83 & 17 & 1 & 1 & 1 & 1 \\ \hline _ACD-ABS_ & 100 & 80 & 20 & 1 & 0.97 & 0.98 & 0.96667 \\ \hline _ACD-RSL_ & 100 & 92 & 8 & 0.97 & 0.97 & 0.97 & 0.96667 \\ \hline Overall & 300 & - & - & 0.99 & 0.98 & 0.98333 & 0.9778 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance Accuracy Across Scenarios.  
Figure 17: Confusion matrix representing DRAMBR performance in noise-based accuracy.  
Figure 18: Confusion matrix Rrpresenting DRAMBR performance in _ACD-OCC_ (**Left**), _ACD-ABS_ (**Middle**), _ACD-RSL_ (**Right**).  
accuracy of approximately 97%, even under varying conditions. The overall accuracy of around 98% indicates that the DRAMBR system correctly classified (approximately) most of the MRs across all scenarios.  
It is worth noting that as the system benefits from larger datasets to make more reliable decisions, the system accuracy improves with an increasing scale of reporters, Through our findings, we show how the system effectively distinguishes between honest, malicious, and erroneous reporters. This approach ensures accurate reputation updating for both reporters and targets, enhancing trust and accountability in disconnected vehicular networks, as it ensures reliable decision-making even under constraints.
## 2 Database Establishment
### Power Quantization  
In mmWave systems, the basic structure cannot receive an accurate value of the acquired power through feedback by the channel after beam scanning.  
In general, the results obtained by the infrastructure feedback were conducted only by the quantized CQIs and the corresponding pairs-of-beams indices. Therefore, the simulated continuous acquired power obtained from Equation (2) needs to be quantized before proceeding to the next step, which is denoted by i in the first row of Table 1. A large number of scholars have studied the mapping scheme from the acquired power to CQI [16; 17]. In long-term evolution (LTE), the UE receives the modulation and coding scheme (MCS), and it transmits blocks reliably through the CQI. More specifically, within the bandwidth of the received CSI signal, when the block error rate in the MCS is lower than 10%, the UE can receive the MCS with the highest accuracy rate. The goal of this experiment is different from the above, since, at this point, our main focus is not on the MCS, but on predicting the beam power. After the beam power is obtained, this information will be regressed on the infrastructure. Therefore, in this paper, the main indicator of the reference-signal-acquired power (RSRP) is the CQI. The quantization scheme in this paper as follows: the quantization scheme is uniform, the upper limit of power is \(P_{u}\), and the lower limit is \(P_{\ell}\). The acquired power (P) and the CQI can be described by the following formula:  
\[Q(p)=[\min\{\max\{\frac{p-P_{\ell}}{r_{CQI}},0\}\,,\frac{P_{u}-P_{\ell}}{r_{CQI }}\}], \tag{3}\]  
where \(r_{CQI}\) represents the CQI granularity and defines the ceiling acquired power (\(P_{u}(CQI=Q(P_{u}))\)) and the floor-level acquired power (\(P(CQI=0)\)). The quantized power is in the interval \([P_{\ell},P_{u}\) ]. Correspondingly, the inverse process of quantization is given by:  
\[r(q)=r_{CQI}q+P_{\ell}, \tag{4}\]  
Since quantization reduces the information entropy, it will produce inevitable errors, and especially when the acquired power exceeds our defined interval (\([P_{\ell},P_{u}]\)). Therefore, in Section 4, we illustrate how the learning accuracy depends on \(P_{u}\), \(P_{\ell}\), and the quantization accuracy. Moreover, the results show that the performance will not be significantly reduced because of the reduction in the quantization accuracy.
## 4 Experimental Simulation and Analysis
### Simulation Result Analysis
#### 4.2.1 Performance Index  
(1) System utility value  
System utility value refers to the benefits obtained by the system when users communicate using bandwidth resources allocated by algorithms. As shown in Equation (5).  
(2) Bandwidth utilization  
Bandwidth utilization rate refers to the ratio of the actual bandwidth of data transmission of the total available bandwidth, expressed as:  
\[\eta=\frac{\sum\limits_{i=1}^{N}x_{i}}{x_{all}} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Type of Service**} & \multicolumn{2}{c}{**Business Priority q**} \\ \hline \multicolumn{2}{c}{Conversational services} & \multicolumn{2}{c}{2} \\ Stream services & & 1.75 \\ Interactive services & & 1.25 \\ Background services & & 1 \\ \hline \end{tabular}
\end{table}
Table 5: Modulation scheme and related parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Type of Service**} & \multicolumn{2}{c}{**Business Priority q**} \\ \hline \multicolumn{2}{c}{Conversational services} & \multicolumn{2}{c}{2} \\ Stream services & & 1.75 \\ Interactive services & & 1.25 \\ Background services & & 1.25 \\ \hline \end{tabular}
\end{table}
Table 4: Business priority parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Type of Service**} & \multicolumn{2}{c}{**Business Priority q**} \\ \hline \multicolumn{2}{c}{**Type of Service**} & \multicolumn{2}{c}{**Business Priority q**} \\ \hline \multicolumn{2}{c}{Conversational services} & \multicolumn{2}{c}{2} \\ Stream services & & 1.75 \\ Interactive services & & 1.25 \\ Background services & & 1.25 \\ \hline \end{tabular}
\end{table}
Table 5: Modulation scheme and related parameters.  
(3) System throughput  
System throughput refers to the number of successful transactions processed by the system per unit of time, expressed as:  
\[\mathrm{T}=\sum\limits_{i=1}^{N}x_{i}.M_{i}.R_{i} \tag{11}\]  
(4) User satisfaction  
The indicators that reflect the fairness of distribution results mainly include absolute fairness and relative fairness. Absolute fairness refers to the equal allocation of bandwidth to every user as much as possible, only considering the absolute amount of bandwidth allocated by users, ignoring the bandwidth needs of users with different priority levels. Therefore, considering relative fairness and utilizing user satisfaction to reflect the relative fairness of bandwidth allocation, the ratio of the actual number of time slots allocated to users to the number of requested time slots is defined as user satisfaction. The calculation method is shown in (12):  
\[c=\frac{\sum\limits_{i=1}^{N}\frac{x_{i}}{x_{\max_{i}}}}{N} \tag{12}\]
## 2 Machine Learning Applications in Big Data
### Aerospace and Defense  
In the aerospace and defense sectors, ML is driving advancements in predictive maintenance, mission planning, and autonomous systems. Predictive maintenance is one of the most widely adopted applications, where ML models analyze data from aircraft sensors and maintenance records, as well as operational data, to predict equipment failures and optimize maintenance schedules. This approach reduces downtime, extends equipment life, and improves safety [175; 176; 177].  
Mission planning is another critical application where ML models analyze data from satellite imagery, weather models, and operational data to optimize flight paths, mission objectives, and resource allocation. These models enable more efficient and effective mission planning, improving the success rate of military and aerospace operations [178; 179; 180].  
ML is also applied in the development of autonomous systems, including unmanned aerial vehicles (UAVs), autonomous ships, and robotic systems. These models enable autonomous systems to perceive their environment, make real-time decisions, and execute complex tasks with minimal human intervention. The integration of ML with big data from sensors, cameras, and other sources enhances the capabilities of autonomous systems, enabling them to operate in dynamic and uncertain environments [181; 182; 183; 184].  
Moreover, ML is used in cybersecurity within the aerospace and defense sectors, where models analyze network traffic, user behavior, and threat intelligence data to detect and prevent cyberattacks. These models help organizations protect critical infrastructure, secure sensitive data, and ensure the integrity of their operations [185; 186; 187].
# Polar Cap Patches Scaling Properties: Insights from Swarm Data
## 2 Data and Methods
### Electron Density Scaling Exponents Estimation
The value of this parameter sets the maximum time scale \(\tau_{\text{max}}\) that can be resolved. In this case the width of the moving window is chosen to be of 40 s, a value that guarantees the statistical robustness of the computation of the \(q\)th-order scaling exponents. The minimum time scale \(\tau_{\text{min}}\) is, instead, dictated by the time series resolution--1 s in the case of the considered electron density dataset. Given these values of \(\tau_{\text{min}}\) and \(\tau_{\text{max}}\), and considering that Swarm A satellite has an orbital velocity of \(\sim\)7.6 km/s, this method allows investigating ionospheric irregularities with spatial scales between approximately 8 and 400 km. These spatial scales are consistent with what shown in Figure 3. Technical details on DSFA method as well as on different kinds of its application on Swarm data can be found, e.g., in [PERSON] et al. [20; 28], [PERSON] et al. [44], to cite just a few. Moreover, the linear combination \(\delta\gamma_{2,1}\) of the first- and second-order scaling exponents, i.e.,  
\[\delta\gamma_{2,1}=2\gamma(1)-\gamma(2) \tag{2}\]  
provides a measure of the intermittent character of the fluctuations, i.e, of the occurrence of anomalous scaling features as observed in fluid and magnetohydrodynamic (MHD) turbulence [39; 45]. Indeed, in the case of global scale invariance the scaling exponents, \(\gamma(q)\) are expected to have a linear dependence on the moment order \(q\), i.e., \(\gamma(q)=\gamma(1)q\), so that only one scaling exponent is sufficient to characterize the complexity of the fluctuation field. We remark that the quantity \(\delta\gamma_{2,1}\) is just a measure of the departure from a linear dependence of the scaling exponents on the moment order. A more exhaustive analysis of the intermittent character would require an extensive study of the convex nature of \(\gamma(q)\) on \(q\). However, in what follows we will call the quantity in Equation (2) as intermittency, for brevity.