esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Application and Result Analysis
### Feature Analysis  
In this research, the decision tree-based distributed gradient lifting framework (LightGBM) is used to assess the importance of the features. LightGBM is a lightweight framework of the gradient boosting decision tree algorithm. It uses decision tree iterative training to obtain the optimal model, which has the advantages of excellent training effects and difficulty to over-fit. Compared with the extreme gradient boosting, LightGBM uses a gradient-based one-side sampling algorithm to accelerate the training speed of the model, without compromising accuracy [22].  
Six parameters, including DEPTH, CAL, NPHI, GR, DT, and RT, from Well A1 are input into the LightGBM model to obtain the influence of each parameter on RHOB and order the characteristic importance. The quantitative results are shown in Table 2:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Well Name**} & \multirow{2}{*}{**Index**} & \multicolumn{6}{c}{**Petrophysical Well Logs**} \\ \cline{3-10}  & & **DEPTH** & **CAL** & **DT** & **GR** & **NPHI** & **RT** & **RHOB** \\  & & **(m)** & **(cm)** & **(μs/m)** & **(API)** & **(_v/v_)** & **(ohmm)** & **(g  
The features are added one by one in descending order of relevance to the ordinary model (KELM) in order to choose the significant sample features. Figure 5 displays the change curve of model errors (RMSE) that is determined by the comparison test. The declining trend of RMSE shows a substantial change when the model contains four inputs, as shown in Figure 5. Therefore, to reduce the computational complexity and the time duration, a model with four inputs is applied to estimate the \"missing\" RHOB logs. The four input variables are DT, NPHI, CAL, and GR.
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 5 Test Results
### Test Results
By estimating the throughput of the 300 validation samples (100 per Operator), we get similar conclusions, the throughput of \(G_{1}\) is closer to the throughput estimated by ML\({}_{3}\) in the end-to-end connection in almost all the cases and the two Utility functions, \(G_{1}\) and \(G_{2}\), select the same Operator chosen by ML\({}_{3}\). The reason of this almost unanimity of results is that in all cases link 1 is much worse than link 2, and, because of this, the values of end-to-end connection throughput are similar to link 1 throughputs (bottleneck). In this situation, \(G_{1}\) is securely the best hypothesis of the hypothesis space \(G\). So, in normal functioning of the routing selection tool, ML\({}_{1}\) and ML\({}_{2}\) will estimate (after training) the value of throughput for each Operator and \(G_{1}\) will determine the estimated end-to-end throughput. Based on this estimations, \(G_{1}\) will select the best end-to-end Operator. \begin{table}
\begin{tabular}{c c} \hline \hline
**Machine Learning Algorithm** & **Accuracy** \\ \hline ML\({}_{1}\) (UAV to antenna) & 84\% \(\pm\) 2\% \\ ML\({}_{2}\) (control station to antenna) & 82\% \(\pm\) 3\% \\ ML\({}_{3}\) (UAV to control station) & 81\% \(\pm\) 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the ML algorithms for 3 tests following 3-fold cross-validation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Link**} & \multicolumn{3}{c}{**Estimated Throughput [Mbps]**} \\ \cline{2-4}  & **Operator 1** & **Operator 2** & **Operator 3** \\ \hline Link 1—ML\({}_{1}\) & 8.3 & 11.3 & 10.6 \\ Link 2—ML\({}_{2}\) & 14.7 & 16.8 & 15.4 \\ End-to-end—ML\({}_{3}\) & 9.1 & 11.1 & 10.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated values of throughput in link 1, link 2, and end-to-end connection.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 3 Channel Access Mechanisms
### Carrier Sensing Adaptive Transmission (Csat)
Hence, the channel selection for the LTE's operation in the unlicensed band discussed next is critical to avoid interference and ensure fair co-existence.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids
### Direct Impact Ionization
#### 2.1.1 Theory of Gas Bubbles  
Numerous studies [22; 23; 24; 25] have shown that the liquid phase may be unstable during the impulse discharges. Plasma channels are formed along the electric field gradients [6; 16]. Such channels, because of their lower density and different dielectric permittivity, allow easier development of discharges(especially local discharges) leading to the electrical breakdown inside the liquid [16; 26]. Models that use gas bubbles theory usually assume the presence of micro-bubbles in non-gaseous media or the self-ignition of gas bubbles. The generation of gas bubbles in liquids may result from local overheating of the medium because of Joule effect in areas characterized by high intensity of electric field (near electrodes), electrostatic expansion of micro-bubbles occurring in liquid or chemical processes [20; 27; 28]. The process of gas bubble development has been schematically shown in Figure 1. However, in fast discharges (aprox.10 ns), characterized by fast pulses rise times (approx. 150 ps), gas bubbles are not observed. As it was mentioned in [26], gas bubbles can exist in such conditions, but their dimensions are lower than the visible size. It has been generally accepted that the theory of gas bubbles can be used to describe discharges with significant durations, larger than microseconds [16].
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators  
As discussed in the system model, the nonlinear estimator has a key role in eliminating DME interference in the LDACS GS receiver. This paper deals with simple and improved designs of some nonlinear estimators \(f(.)\) and comparison of its performance with the pulse blanking method. These nonlinear estimators perform instantaneous operation on the received sample (after CP removal) \(y_{t}[n]\) by utilizing the statistical information from the signals \(x_{t}[n](n=0,1,\ldots N-1)\) and the noise component \(i_{t}[n]\). For a sufficient number of subcarriers, the time domain OFDM symbol \(x_{t}\) can be modeled by Gaussian pdf [44]. As the channel selected is AWGN, the useful component \(y_{t}^{\prime}[n]\) is also Gaussian distributed.  
Figures 5 and 6 explain how 2-GMM estimation is utilized in actual GAE/OBE and GAE/OBE enhanced pulse peak processors. In GAE/OBE, the parameters obtained after 2-GMM estimation are used to calculate the scaling factor (\(\mu\)). The scaling factor is \(\rho_{k}\) for GAE and \(\beta_{o}(y)\) for OBE. The GAE/OBE enhanced pulse peak processors perform selective nonlinear scaling with the help of a noise detector. It is possible to propose different types of nonlinear devices with various combinations of noise detectors, data estimators, and scaling factors.  
In this paper, we selected single and double threshold noise detection methods. The amplitude of the received signal is compared with predefined threshold values. The different types of data estimators employed are GAE and OBE. Pulse peak attenuators use the same scaling factors used in GAE and OBE. Pulse peak limiters use different algorithm to find out different scaling factors in an iterative way. The various combinations of the above mentioned blocks result in proposing different pulse peak attenuators and limiters. The following section discusses the mathematical functioning and formulation of GAE, GAE enhanced PPA, GAE enhanced PPL, joint GAE enhanced PPA, joint GAE enhanced PPL, OBE, OBE enhanced PPA, OBE enhanced PPL, joint OBE enhanced PPA and joint OBE enhanced PPL in detail.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 4 LEO-SCS and Its Uncertain Evolution Model
### Uncertain Satellite Network Evolution Model
#### 4.2.3 Dynamic Uncertain Satellite Network Model  
**Definition 10**.: _A network is a dynamic uncertain network if its topology changes with time, and the weights of its edges are uncertain variables that vary with time._  
A dynamic uncertain network can be denoted as \(G(V,E(t),W(t))\), where \(V\) is the node set, \(E(t)\) is the edge set that varies over time, and \(W(t)\) is the weight set composed of uncertain variables that vary with time, which can be the cost, distance, or capacity of edges.  
In an LEO-SCS, the end-to-end delay, which depends on the propagation delay and processing delay [22], is the key performance parameter [28; 29; 30; 31]. Propagation delay is the time used by electromagnetic waves to transmit a certain distance and it depends on the lengths of links (i.e., ISLs, ULs, and FLs). Processing delay is the time required for nodes (i.e., satellites, user terminals, and gateways) to check the frame's header and determine where to forward the frame. In an LEO-SCS, since the lengths of links periodically change with time, the propagation delay is a deterministic variable that varies with time. However, because limited data on processing delays can be obtained before LEO-SCS deployment, the processing delay has epistemic uncertainty. So, the end-to-end delay of an LEO-SCS is an uncertain variable that varies with time.  
To describe an LEO-SCS in the normal state, a dynamic uncertain satellite network model \(G(V,E(t),D(t))\) can be built, where \(V=\{V_{s},V_{g},V_{v}\}\) is the node collection of satellites, gateways, and VNs; \(E(t)=\{E_{s}(t),E_{u}(t),E_{f}(t)\}\) is the edge collection of ISLs, ULs, and FLs; and \(D(t)=\{D_{s}(t),D_{u}(t),D_{f}(t)\}\) is the delay of edges. Using this model, both the dynamics of network topology and the epistemic uncertainty of performance parameters can be expressed. Here, we use an LEO-SCS dynamic adjacency matrix to represent the dynamic, uncertain satellite network model, and it can be divided into an inter-satellite adjacency matrix \(S_{m}(t)\), a satellite-gateway adjacency matrix \(G_{m}(t)\), and a satellite-VN adjacency matrix \(U_{m}(t)\) according to the types of links. The LEO-SCS dynamic adjacency matrix is  
\[A_{G}(t)=\begin{bmatrix}S_{m}(t)&G_{m}(t)&U_{m}(t)\\ G_{m}^{\prime}(t)&0_{Y\times Y}&0_{Y\times Z}\\ U_{m}^{\prime}(t)&0_{Z\times Y}&0_{Z\times Z}\end{bmatrix}_{(X+Y+Z)\times(X+Y+Z)} \tag{13}\]  
where \(X\), \(Y\), and \(Z\) are the numbers of satellites, gateways, and VNs, respectively, in the system. We describe \(S_{m}(t)\), \(G_{m}(t)\), and \(U_{m}(t)\) in Table 1.  
Faced with insufficient data, uncertainty statistics methods can be used to obtain the uncertainty distributions of processing delay on the satellite, gateway, and VN.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 3 Hybrid Representation Approach
### Rule-Guided Event Detection  
Rules can be formulated to express domain knowledge in the area of driving behaviour. In this light, it has been suggested that simple thresholds can be used to reliably detect basic events, such as acceleration, braking and turning [1; 2]. Table 1 presents the thresholds proposed by [PERSON] et al. [1] for each type of event and three levels of intensity: low, medium, and high. The rule-guided event detection component uses these thresholds to calculate the frequency of occurrence for each pair of event type and intensity. The derived histogram reflects long-term driving behaviour. Intuitively, both normal and aggressive drivers are expected to use brakes at some point, yet aggressive drivers use brakes much more frequently.
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 5 Discussion
At the component point of view, hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are critical than others and which call for special attention of designers and operators. More in detailed conclusions can be reached in Table 5.  
Moreover, failure modes are observable consequences (failures) of a system. In this paper, the failure modes' criticality analysis of the C\({}^{4}\)ISR system is carried out, as shown in Table 6.  
In Table 6, the criticality rank of each failure mode in a decreased order indicated that: (i) The criticality ranks of application software failures in the system are high such as information integrated management module 1 and software 2 failure (F48), CPEX main processor module application software 1 and software 2 failure (F54), and Information exchange module application software (F43), which means that the application software is the weak link in the entire C\({}^{4}\)ISR system; (ii) Failure mode F50, F48, F49, F45 ranks the highest in their importance, addition to application software failures already mentioned, information management application software are critical as well and which needs the particular attention in the C\({}^{4}\)ISR system upgrading. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Importance** & **Failure** & **Probability** & **Importance** & **Failure** & **Probability** & **Importance** & **Failure** & **Probability** \\
**Order** & **Mode** & **Failure** & **Order** & **Mode** & **Failure** & **Order** & **Mode** & **of System** \\ \hline
1 & F50 & 0.419885 & 17 & F36 & 0.263031 & 33 & F10, F29 & 0.181204 \\ \hline
2 & F48, F49 & 0.400207 & 18 & F38 & 0.262967 & 34 & F21 & 0.162382 \\ \hline
3 & F45 & 0.384112 & 19 & F40 & 0.244182 & 35 & F3, F25 & 0.157688 \\ \hline
4 & F47 & 0.379861 & 20 & F32 & 0.244151 & 36 & F19 & 0.147996 \\ \hline
5 & F43 & 0.349425 & 21 & F42 & 0.244116 & 37 & F7 & 0.147769 \\ \hline
6 & F54 & 0.34004 & 22 & F34 & 0.244086 & 38 & F8, F22 & 0.147535 \\ \hline
7 & F37 & 0.338387 & 23 & F5 & 0.235423 & 39 & F13, F26 & 0.147486 \\ \hline
8 & F52, F53 & 0.334847 & 24 & F58 & 0.213435 & 40 & F2, F24 & 0.137338 \\ \hline
9 & F51 & 0.329612 & 25 & F6 & 0.212961 & 41 & F59 & 0.136363 \\ \hline
10 & F41 & 0.321465 & 26 & F11, F30 & 0.210006 & 42 & F20 & 0.126622 \\ \hline
11 & F33 & 0.321437 & 27 & F56 & 0.202748 & 43 & B & 0.12501 \\ \hline
12 & F44 & 0.313964 & 28 & F9, F23 & 0.198131 & 44 & F55 & 0.124862 \\ \hline
13 & F46 & 0.313905 & 29 & F14, F27 & 0.194326 & 45 & F15 & 0.124772 \\ \hline
14 & F35 & 0.301125 & 30 & F57 & 0.192215 & 46 & F1 & 0.007962 \\ \hline
15 & F39 & 0.28325 & 31 & F16 & 0.18442 & & & \\ \hline
16 & F31 & 0.283221 & 32 & F4 & 0.181351 & & B: F12, F17, F18, F28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Failure mode importance ranking table.
# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
###### Abstract
First, use a deep autoencoder model to extract a low dimensional representation from the high dimensional input space. The architecture of the autoencoder model consists of two symmetrical deep neural networks--an encoder and a decoder that applies backpropagation, setting the target values to be equal to the inputs. It attempts to copy its input to its output and anomalies are harder to reconstruct compared with normal samples [16]. In DAE-DBC, low dimensional representation of data includes bottleneck hidden layer in autoencoder network and reconstruction error of each point. Based on reconstruction error, we estimate the first outlier threshold and distinguish data is normal or abnormal using it. But in this step, data cannot be divided clearly. Then, the autoencoder model is retrained only with normal data and low dimensional representation and optimal outlier threshold are calculated again using by the retrained model. The low dimensional data from retrained autoencoder model is classified into groups using the Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. The optimal value of eps parameter (minimum distance between two points) of DBSCAN is estimated by DMDBSCAN algorithm [19]. Based on the optimal outlier threshold from retrained autoencoder model, identify novelty. If most of the instances in a group exceed our threshold, all instances in this cluster are considered as abnormal and labeled by novelty. We have tested DAE-DBC on several public benchmark datasets, it presents higher AUC score than the state-of-the-art techniques. We propose the unsupervised novelty detection method over high dimensional data based on deep autoencoder and density-based clustering. The main contribution of the proposed method are as follows:  
* We derive a new DAE-DBC method for unsupervised novelty detection that is not domain specific. * The number of clusters is unlimited and each cluster will be labeled by novelty depending on what percentage of objects that it contains exceed the error threshold. In other words, identifying novelty is based on the reconstruction error threshold without considering the sparse, or far, or small ones. Therefore, large-scale clusters are also possible to be novelty. * Our extensive experiment shows that DAE-DBC has a greater performance than other state-of-the-art unsupervised anomaly detection methods. This article consists of 5 sections. In Section 2, we provide a detailed survey of related work on unsupervised anomaly detection. Our proposed method is explained in Section 3. Section 4 presents the experimental dataset, compared methods, the evaluation metric and the result and comparison of experiments. Finally, Section 5 concludes the paper.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Algorithm Evaluation for \(Lb=92\) dB
#### 7.1.2 Algorithm Results for \(d=7.2209\) km  
A new scenario targets the increase in the distance between the gateway and the served WSN to \(d=7.2209\). For the same number of UAVs and USVs (19 and 2, respectively) and the same technical solution of \(LB=92\) dB as in previous scenario, the number of virtual nodes that would cover this new larger distance must increase to 19. Table 11 presents the evaluation of the algorithm.  
To highlight the improvements made after profiling the implemented code, we included the time execution before profiling (\(t_{1}\)) and after the improvements (\(t_{2}\)) in Table 11. The execution time for Algorithm 1 for the scenario presented in Table 11 is 0.0418 s, while the positioning algorithm requires between 8.982 s and 24.073 s to be executed.  
We notice that the best results for \(\sigma\) are obtained for \(\rho=0.1\) and the execution time \(t_{2}=14.347\). Nevertheless, a good tradeoff is \(\rho=0.2\), where the execution time decreases to 8.982 s.  
The cases where \(\sigma\) is marked with * are for unsuccessful link \(\sum_{i=0}^{n_{tm}}c_{i}\
eq n_{tm}+1\).  
Comparing the results corresponding to the scenarios proposed in Tables 10 and 11, one can notice that the best value of distance step for these scenarios remains \(\rho=0.2\).  
parameter \(\rho=0.12\) should be avoided for both scenarios, whereas for the scenario in Table 12, \(\rho\) should not exceed 0.1.
## 3 Results and Discussion
### Thermogravimetric and Crystal Structure Analysis  
The XRD and TGA results are shown in Figure 8. From the XRD analysis, the pattern for the anatase crystal structure of TiO\({}_{2}\) was confirmed using indexing from (JCPDS 73-1764). The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) crystal structure was also observed and was indexed using (JCPDS 33-0664). The TGA analysis on the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fibers was performed by heating the sample to 750 \({}^{\circ}\)C in air at 3 \({}^{\circ}\)C/min to completely decompose the carbon component in the sample; the observed weight loss is equal to the carbon content [52].  
Figure 7: Survey XPS spectrum of the Fe\({}_{3}\)O\({}_{4}\)/C composite nanofibers (**A**), deconvoluted spectra of nanocomposite structure for Fe 2p (**B**), Ti 2p (**C**), O 1s (**D**), and C 1s (**E**).  
The weight loss that occurred below 100 \({}^{\circ}\)C was caused by the loss of adsorbed water in the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fibers. As shown in Figure 8a, the TGA results showed a carbon content of 53.5% while the Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fibers exhibit a weight loss of 59.9%, thereby confirming that the amount of the active material in the composite-fiber anode is about 40.1% of Fe\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) nanoparticles.
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 2 Materials and Methods
### Antioxidant Activity of Mycelium  
#### 2.8.1 DPPH-Free Radical Scavenging Activity  
Total antioxidant capacity was measured based on the DPPH (2,2-diphenyl-1-picrylhydrazyl)-free radical scavenging method adapted from [PERSON] et al. [42]. A total of 22 \(\upmu\)L of mycelial extract and 200 \(\upmu\)L of 120 \(\upmu\)M DPPH solution dissolved in 70% methanol were added to a 96-well microplate and left in the dark at room temperature for 30 min. The absorbance was measured at a wavelength of 517 nm (**A**1). The absorbance of the solvent was measured as **A**0. Each sample was determined in quintupplicate. Trolox was used as standard to determine the calibration curve (0-1 mg/mL). The percentage of scavenging activity was also determined using Equation (1):  
\[\textbf{scavening}\ \%=\left(\frac{\textbf{A}0-\textbf{A}1}{\textbf{A}0} \right)\times 100. \tag{1}\]
## 3 Inversion Results  
In total, the bottom parameters, including \(c_{0}\), \(K\), and \(H_{2}\), were examined in 24 locations, distributed in the regions of our study area that had relatively simple geology (Figure 1; red dashed lines). These were composed of 12 adjacently located pairs in order to verify the consistency of the results. The results of our geoacoustic inversion are summarized in Table 1, and the distributions of these parameters is investigated in Figures 11 and 12. Figure 10: A reduced time vs offset plot of the amplitude envelope of the experimental source gather #2290 (**a**) and its zoomed portion (**b**); and the amplitude envelope of the simulated source gather #2290 for \(c_{0}\) = 1525 m s\({}^{-1}\), \(K\) = 0.75, and \(H_{2}\) = 1000 m (**c**), and its zoomed portion (**d**). The measured times are reduced with a velocity of 2000 m s\({}^{-1}\). A is a possible caustic that is found both in the experiment (**a**,**b**) and model (**c**,**d**) data. B is another arrival of strong intensity that can possibly be another caustic or a reflection from sub-bottom layering. The constrained seafloor-velocity \(c_{0}\) values show a bi-modal distribution (Figure 11a,b) that is associated with two main parts of the study area (C and D in Figure 12). These two areas correspond to bottom depths of 1030-1250 m and 1320-1360 m, respectively. In area C, sound speed was distributed between 1500 and 1600 m s\({}^{-1}\), with an average of \(1537\pm 13\) m s\({}^{-1}\). In area D, sound speed was markedly higher and was distributed between 1595 and 1625 m s\({}^{-1}\), with an average of 1613 \(\pm\) 12 m s\({}^{-1}\) (Figure 11a,b). The average sound speed over the whole studied area was 1556 \(\pm\) 13 m s\({}^{-1}\). Thus, \(c_{0}\) seems to increase with the bottom depth \(H_{1}\) (Figure 12a). \(K\) was normally distributed over the whole study area between 0.75 and 0.85 m s\({}^{-1}\) with an average of 0.80 \(\pm\) 0.035 s\({}^{-1}\) (Figure 11c) and seems to slightly decrease with bottom depth \(H_{1}\) (Figures 11b and 12b).