esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
###### Abstract  
Fault detection and isolation are important tasks in statistical process control. A real-time contrasts (RTC) control chart converts the statistical process-monitoring problem to the real-time classification problem, thus outperforming traditional monitoring techniques. An RTC assigns a class to reference data and the other class to a window of real-time contrasts. However, RTC control charts often fail to detect abnormal states when both normal and abnormal data exist together in the window. To enable more rapid detection of an improved RTC control chart, this paper proposes a multivariate process monitoring system with an improved RTC control chart. Although previous RTC control charts proposed by other studies outperform the original RTC chart, it is still difficult to detect an abnormal state when normal and abnormal data exist together. To overcome this problem, this paper proposes an RTC control chart using novelty detection and variable importance with random forests. Novelty detection and variable importance were used so that fault can be detected when the control limit could not be exceeded despite the abnormal state. The proposed method extracts representative data in the sliding window and adds the extracted data to the window to quickly detect the abnormal state. Experiments demonstrate the proposed method to outperform the original RTC chart.  
Keywords:real-time contrasts (RTC); control chart; novelty detection; variable importance; fault detection; multivariate exponentially weighted moving average (MEWMA) +
Footnote †: journal: Applied sciences
## 4 MAC Protocol Design
### Adaptive Slot Allocation Method
#### 4.4.2 Traffic Adaptive CSMA Slots Division  
Since there are limited numbers of slots that can be borrowed in the TDMA period, the former strategy is sufficient for burst traffic of a few nodes. However, when the traffic of the entire network increases and data packets are queued in many nodes, additional actions are required to relieve the contention. Here, an adaptive CSMA slot division algorithm is proposed. In this algorithm, the queue counters were extracted and used to calculate the network traffic. Also, we assume that the acceptable average queue value is known to the network designer and it is marked as \(Q\) here. When the average value of the queue counter exceeds Q, the length of the CSMA period will increase exponentially. Otherwise, when the received data packets show that all the packet queues are zero, meaning the network traffic is light, the length of the CSMA period will decreases linearly. In extremis, all the inactive slots are divided to the CSMA period.  
As illustrated in Figure 10, in the CSMA period, data packets are queued in several nodes. With the queue information in the received packets, the host node calculates the average value of the queue counters. Then, in the next superframe, more slots are divided to the CSMA period to relieve the heavy traffic. The algorithm is demonstrated in Table 4.  
\begin{table}
\begin{tabular}{l} \hline
**Adaptive Slots Allocation Method** \\ \hline \(K\): Slots division index \\ \(N_{\mathit{borrow\_max}}\): Maximum of borrowed slots \\ \(N_{\mathit{allet}}\): Number of idle slots \\ \(F[M][N]\): Slots occupation matrix in the TDMA period \\ \(S(i)\): List of idle slots in the TDMA period \\ \(q(i)\): Queue counter value of node \(i\) \\ \(\overline{q}\): Average queue counter value of received data packets \\ \(\mathcal{Q}\): acceptable average queue threshold \\ \(L_{\mathit{csma}}\): Slots number in the CSMA period entry 1 \\ \hline \end{tabular}
\end{table}
Table 4: Traffic adaptive CSMA slot division algorithm.  
Figure 10: Traffic adaptive CSMA period division.  
Figure 9: Process of dynamic slot borrowing.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 7 Conclusions  
This paper developed a power system reliability evaluation method which considers the cybersecurity and WPRs of a wind farm. It is concluded that: (i) The instant worst-case attack and long termed imperfect attack lead to non-negligible consequence to the power system in different time scales. The cybersecurity of a wind farm has a great influence on power system reliability. (ii) The failure rate of a wind turbine attacked by an IPA grows rapidly, and its remaining life is shortened by nearly 20%. (iii) The period of peak load is the vulnerable time of power system. The system reliability will benefit from the re-dispatching of power reserves with a reasonable ramping rate, which reduces both the impact of cyber-attacks and WPRs.  
Periodic vulnerabilities patching and intrusion detection are of great importance for cybersecurity. To better estimate the frequency of cyber-attacks, the stochastic attack-defense model can be further improved by using the statistics from the common vulnerability scoring system [21]. Combined with firewalls, encryption, system hardening, and physical security, wind farm owners should pay more attention to the operation states of wind turbines. Equipment operating in critical conditions should be monitored in a timely manner. In future work, a dynamic analysis and detection of an IPA will be further studied.  
Conceptualization, H.W. and J.L. ([PERSON]); methodology, H.W.; software, M.C.; validation, H.W., [PERSON] ([PERSON]) and X.L.; formal analysis, H.W.; investigation, H.G.; resources, M.C.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, H.W.; writing--review and editing, M.C.; visualization, X.L.; supervision, J.L. ([PERSON]); project administration, J.L. ([PERSON]); funding acquisition, J.L. ([PERSON]).  
This research was founded by China national key research and development program, grant number 2018 YFB0905200.  
The authors declare no conflict of interest.
## 3 Results
### Electrical Transport  
Figure 4 shows the plots of ln\(\sigma_{e}T\) versus 1000/\(T\) for La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics synthesized by the different methods (where \(\sigma_{e}\) is the electrical conductivity and T is the absolute temperature). The relative densities of the two specimens were generally the same, an adequate density value of about 93%. It was found that La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) synthesized by the combustion method displayed a higher electrical conductivity of 31.6 5-cm\({}^{-1}\) at 800 \({}^{\circ}\)C, while only 11.33 S-cm\({}^{-1}\) for that by the SSR method. In addition, although the two specimen curves are not very linear, they are very close to linear, which indicates that small polaron-hopping is the main mechanism of conductivity behavior, as shown in the following equation [18; 19]:  
\[\sigma_{e}=\left(\frac{C}{T}\right)exp(E_{a}/KT)\]  
Figure 3: SEM micrographs of fracture surfaces of La\({}_{0.8}\)Ca\({}_{0.2}\)CrO\({}_{3}\) ceramics. (**a**) Sintered at 1400 \({}^{\circ}\)C and by the combustion method; (**b**) sintered at 1400 \({}^{\circ}\)C and by the high temperature SSR method; (**c**) sintered at 1500 \({}^{\circ}\)C and by the high temperature SSR method.  
where \(C\) is a material constant containing the carrier concentration term, \(T\) is the absolute temperature, \(E_{a}\) is the activation energy for small polaron-hopping and \(K\) is the Boltzmann constant. Moreover, the activation energies of small polarons hopping were calculated from the linear fit of the plots, 0.136 eV for the combustion method while 0.172 eV for the SSR methods samples. However, the difference in energy activation is not very large, a significant difference in conductivity can be observed.  
The excellent conductivity for the combustion method sample is probably related to its microstructure. The grain growth appeared perfectly straight along the boundary, and a clear grain boundary was observed (Figure 3a). However, in examining Figure 3c--in addition to several holes on the surface of ceramics grains--the grains size of ceramics prepared by high temperature solid state method is obviously smaller. It shows that there are more discontinuous grain boundaries in ceramics. Generally speaking, the electronic conduction of the lanthanum chromates is essentially a bulk electronic transport property. Grain boundaries and holes can be considered as inhomogeneous components in ceramics, which hinder the transport of electron carriers. As a result, it is considered that the microstructure of the ceramics prepared by combustion method is mainly assumed to be responsible for its superior electronic conducting properties.
###### Abstract  
This work proposes a high-efficiency High-Brightness LED (HB-LED) driver for Visible Light Communication (VLC) based on a Two-Input Buck (TIBuck) DC/DC converter. This solution not only outperforms previous approaches based on Buck DC/DC converters, but also simplifies previous proposals for VLC drivers that use the split power technique with two DC/DC converters: one is in charge of the communication tasks and the other controls the biasing of the HB-LED (i.e., lighting tasks). The real implementation of this scheme requires either two input voltage sources, one of which is isolated, or one DC/DC converter with galvanic isolation. The proposed implementation of splitting the power is based on a TIBuck DC/DC converter that avoids the isolation requirement, overcoming the major drawback of this technique, keeping high-efficiency and high communication capability thanks to the lower voltage stress both across the switches and at the switching node. This fact allows for the operation at very high frequency for communication purposes, minimizing switching power losses, achieving high efficiency and providing lower filtering effort. Moreover, the duty ratio range can also be adapted to the useful voltage range of the HB-LED load to maximize the resolution on the tracking of the output voltage. The power is split by means of an auxiliary Buck DC/DC converter operating at low switching frequency, which generates the secondary voltage source needed by the TIBuck DC/DC converter. This defines a natural split of power by only processing the power delivered for communications purposes at high frequency. A 7 W output-power experimental prototype of the proposed VLC driver was built and tested. Based on the experimental results, the prototype achieved 94% efficiency, reproducing a 64-QAM digital modulation scheme and achieving a bit rate of 1.5 Mbps with error in communication of 12%.  
visible light communication (VLC); high-brightness LED; two-input buck (TIBuck) DC/DC converter; split power +
Footnote †: journal: _Sensors_**2024**, 24, 6392. [[https://doi.org/10.3390/s24196392](https://doi.org/10.3390/s24196392)]([https://doi.org/10.3390/s24196392](https://doi.org/10.3390/s24196392))
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Datasets
#### 4.2.2 CHB-MIT Scalp EEG Database  
The database was obtained in 2010 at children's hospital Boston (CHB-MIT) [38]. EEG signals were recorded from 22 patients with intractable seizures, and aged between 1.5 and 22 years. The duration of EEG signals was standardized to 60 min for the entire dataset, utilizing 23 channels. A European data format (EDF) file from the CHB-MIT dataset, along with its signals, is depicted in Figure 4.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method  
We propose a novel collaborative mechanism to reduce the cumulative error of localization by communicating between smartphones when two users encounter each other. As shown in Figure 2, the proposed method comprises three modules: encounter communication, localization comparison at the encounter location, and localization correction.  
Figure 2: An overview of the proposed method.  
We acquire walking data, stride length, and heading direction using SmartPDR [11]. Then, the indoor position is estimated by projecting the walking data to an indoor map obtained from the information board image. Inspired by localization using indoor maps [7], a particle filter is used to project walking data to the map image. If the encounter of users is detected, we compare the trajectories of estimated locations by a decision tree model trained on synthesized walking data. Finally, the positions are corrected according to the comparison result.
## 3 Results
### Comparison with Other Scenarios Worldwide
This approach allows the element presence to be correlated with respect to the same element abundance in the upper continental crust [49; 57]. The EFs were calculated according to the equations reported in [PERSON] et al. [58] and [PERSON] et al. [59] using La as the normalizing element: if the EF value is below 10, the element has a crustal origin and therefore is defined as \"no enriched element\"; in contrast, if the EF value is higher than 40-50, the elements are of anthropogenic origin and are called \"elements enriched\"; values between these two thresholds show a mixed origin of the element investigated (long-transport phenomena from other natural and/or anthropogenic sources). Figure 2 shows the EF profile for all of the elements investigated in the three fractions. As can be seen, most of the considerations above reported were confirmed: Ni, Cr, Fe, Rb, Ni, Br and As can be considered as no enriched elements (their EFs were below 10), in other words, having a crustal origin. Actually, the As in the PM\({}_{1}\) fraction showed an EF almost at the border (i.e., EF 44.5), meaning a contribution from anthropogenic sources. A similar condition was found for Hg, whose values were in the range 10-50. On the other hand, elements such as Zn, Sb, Ag, Sc, Cd, and Au showing EFs >50 (up to 3413 for Au in the PM\({}_{10}\)) came from anthropogenic sources (e.g., the waste plant, the heavy traffic): among them, Sb and Cd involve problems related to human health, but their low levels, compared with their relevant limit/guideline values, should not raise an alarm in the population.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## Appendix A Taxonomy of Malware Analysis
### Malware Analysis  
Major objectives of malware analysis include:  
* To gain the capability of responding to network intrusion
* To determine how can systems and files be infected
* To analyze the potential of suspected binaries/PE
* To devise the mechanism for identifying malware
* To find host-based signatures or indicators
* To find network-based signatures or indicators* The scale of devastation that malware can pose Normally in the case of malware what we get hold of for the sake of analysis are binary files or executables which are not easily understandable by humans. Therefore, different analysis techniques have been proposed to get full insights into malware. Broad categories of these techniques are shown in Figure A1.  
**Static Analysis:** It refers to the phenomena of analyzing a file without executing it to keep the process of analysis safe. This approach includes the extraction of low-level information such as CFGs (Control Flow Graphs), DFGs (Data Flow Graphs), and system call analysis. Different tools can aid in static analysis such as IDAPro for disassembling the file. The static analysis gets failed when malware is obfuscated as it cannot penetrate through the packed samples as explained by [18].  
**Basic Static Analysis:** It can confirm the maliciousness of the file. It can provide information about the functionality of malware, but it can't work with diligently programmed malware because of the lack of understanding of sophisticated malware's behavior.  
**Advanced Static Analysis:** It refers to reverse engineering, which can be performed through a disassembler to understand the instruction code of the malware.  
**Dynamic Analysis:** When the file is executed in the safe/virtual environment for the sake of analysis then, it is called dynamic analysis It should be conducted by hiding the virtual environment from malware otherwise, malware can hibernate itself. This approach gets failed, when a particular triggering condition doesn't occur on which malware executes in its malicious state.  
**Basic Dynamic Analysis:** It executes the malware in a safe environment to observe behavior to find any signature. It provides low-level information so cannot work with sophisticatedly programmed malware.  
**Advanced Dynamic Analysis:** It uses a debugger to investigate the internal state of running malicious executables. It extracts detailed information, which helps in understanding the code as shown by [85].  
**Hybrid Analysis:** This approach is a combination of both static and dynamic approaches. Researchers are trying to make use of the beneficial features of both approaches.  
Table A1 refers to the summary of the advantages and disadvantages of static and dynamic approaches in malware analysis.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 1 Introduction
* Second, we propose an edge-based data dissemination protocol which addresses efficiency and scalability in vehicular networking environments. Our protocol accommodates the two major classes of vehicular networking applications: traffic safety and travel convenience. * Third, we provide a simulation-based implementation for evaluating our protocol at the application level in comparison with existing approaches. The remainder of this paper is organized as follows: a literature review on integrating MEC in vehicular environments is presented in Section 2. In Section 3, we classify the potential applications. In Section 4, we describe our proposed protocol, whereas in Section 5, we evaluate its performance via simulation results. In Section 6, we highlight the associated challenges and issues. The last section concludes the paper.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 4 Experiment  
### Dataset Description  
This study utilizes a publicly available aircraft dataset tailored for large-scale and complex scenarios, comprising 1479 original images. To simulate satellite imagery from multiple viewing angles, each image is rotated to four orientations (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)), resulting in a total of 5916 images. Specifically, the 0\({}^{\circ}\) images are from the publicly available dataset, while the other angles are generated by rotating the 0\({}^{\circ}\) images. The dataset has a spatial resolution of 1 m and includes 11 aircraft categories: A220, A321, A330, A350, ARJ21, Boeing 737, Boeing 747, Boeing 777, Boeing 787, C919, and other-airplane.  
The images were primarily collected from three major civilian airports: Shanghai Hongqiao International Airport, Beijing Capital International Airport, and Taiwan Taoyuan International Airport. The dataset features a variety of image sizes to reflect diverse real-world conditions.  
For edge-side model training, the dataset is split into 81% for training, 9% for validation, and 10% for testing. The number of samples per aircraft category is detailed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categories** & **Quantities** \\ \hline
**A220** & **10,420** \\
**A321** & **4159** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Aircraft categories and quantities in the dataset.
# Recent Progress of Metal-Air Batteries--A Mini Review
## 4 Electrolytes
### Aqueous Electrolyte  
**Alkaline solutions** (7 \(<\) pH \(\leq\) 14): Alkaline electrolytes are the most applied electrolyte in aqueous-based MABs, because the ORR is more favorable with faster reaction kinetics and a lower overpotential, compared to acidic electrolytes. Alkaline electrolytes have a shortcoming that CO\({}_{2}\) (from air atmosphere) reacts with electrolyte and forms a carbonate surrounding the cathode. The large amount of carbonate will block the porous structure of the positive electrode material and decrease cathode efficiency.  
**Neutral salt solution** (pH = 7): Al alloy-air batteries can discharge in a neutral salt solution with a lower corrosion rate and higher activity than in an alkaline electrolyte.  
**Acidic solutions** (2 \(\leq\) pH \(<\) 7): Acidic electrolytes are rarely used in aqueous-based MABs because a large amount of H\({}^{+}\) in solution could directly react with metal and reducing battery efficiency. [PERSON] et al. [63] reported Al-Zn alloy anode performance changed with various types of acids at the same concentration, pH, and operating temperature. Electrochemical test results indicted Al-Zn alloy in 0.5 M HCl was more negative than that in 0.5 M HAc: \(-\)1.02 V and \(-\)0.80 V, respectively.  
**Hybrid electrolyte**: In Figure 14b,c, a novel type of aqueous FABs was equipped with an alkaline anode electrolyte (anolyte) and an acidic cathode electrolyte (catholyte). The anolyte and catholyte are separated by an alkali metallic ion (Li\({}^{+}\) or Na\({}^{+}\)) solid-state electrolyte separator. The alkali metal ion serves as an ionic mediator to sustain the redox reactions at both the anode and cathode [64].  
Figure 14: (**a**) Schematic illustration of theoretical voltages of Fe\(-\)air batteries operated with an alkaline or an acidic cathode electrolyte. Two types of Fe\(-\)air batteries with a Na\({}^{+}\)- or a Li\({}^{+}\)-ion solid electrolyte, for (**b**) a Fe(LiOH)//Li-SSE/O\({}_{2}\)(H\({}_{3}\)PO\({}_{4}\)/LiH\({}_{2}\)PO\({}_{4}\)) cell, and (**c**) a Fe(NaOH)//Na-SS/O\({}_{2}\)(H\({}_{3}\)PO\({}_{4}\)/NaH\({}_{2}\)PO\({}_{4}\)) cell. SSE represents solid-state electrolyte [64] (Copyright © 2017, American Chemical Society).  
Figure 13: Schematic diagrams of various electrolytes of MABs.