esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of ICS and SCADA Systems Attacks Using Honeypots
## 2 Related Work
* Insider threats against OT systems refer to any malicious actions taken by individuals within an organization that can compromise the security and availability of critical infrastructure and industrial processes. These actions can be intentional or accidental, and can have serious consequences. To detect OT attacks, researchers use two types of models. The first model uses a real environment with real PLCs, HMIs, historians, etc., and starts to capture, monitor, and correlate these logs based on use cases. The second model uses a low-level interaction honeypot solution to simulate real PLCs, HMIs, and OT protocols such as MODBUS, BACnet, S7 comm, etc., and performs the same functions mentioned previously, capturing, monitoring, and correlating these logs based on use cases. In this paper, the researchers use model two because Conpot is easy to deploy, modify and extend, and is also cost-effective. In this paper, we use model two, because Conpot is easy to deploy, modify, and extend, and is cost-effective. There are several types of supervisory control and data acquisition (SCADA) deployment, which are as follows:  
* Centralized SCADA: All field devices and control panels are connected to a central computer, which acts as the SCADA server. This type of deployment is suitable for large systems with multiple field devices and control panels. * Distributed SCADA: The SCADA system is divided into multiple subsystems, each with its own server and field devices. This type of deployment is suitable for large systems with multiple locations, or for systems that need to be distributed over a wide geographical area. * Hybrid SCADA: The SCADA system is a combination of centralized and distributed SCADA. This type of deployment is suitable for systems that have a combination of centralized and distributed components. * Web-based SCADA: The SCADA system is accessed through a web browser. This type of deployment is suitable for systems that need to be accessed remotely or for systems that need to be accessed by multiple users. * Cloud-based SCADA: The SCADA system is hosted on a cloud computing platform. This type of deployment is suitable for systems that need to be accessed remotely or for systems that need to be accessed by multiple users.
## 3 Structures
### Nanords
The top and side views of the ZnO NRs in Figure 9a are shown in a highly magnified SEM picture, which unmistakably demonstrates that the ZnO NRs have hexagonal cross-sections. The schematic representation of each paper fiber developed with ZnO NRs is shown in Figure 9b. After the ZnO seed layer had coated the paper fiber, the ZnO NRs that had grown on it began to radiate outward along the fiber. The ZPNG can produce an output voltage and current of 10 mV and 10 nA and has high flexibility and piezoelectric sensitivity. The output voltage and current of the ZPNG after several cycles of rapid stretching (FS) and fast releasing are shown in Figure 9c,d (FR). The enlarged view of one cycle of FS and FR is shown in the insets. The output voltage and output current of the ZPNG are shown in Figure 9e,f at different frequencies, i.e., f1\({}^{\prime}\)f2\({}^{\prime}\)f3\({}^{\prime}\), and at an applied strain of 2 cm. Figure 9e,f makes it abundantly evident that as the frequency rises, so do the output voltages and currents. The findings demonstrate that this energy harvesting system offers a straightforward and efficient platform to obtain low-frequency mechanical energy for real-world applications. Figure 7: Schematic diagram of the fabrication process of vertically aligned ZnO NRs arrays; (**a**) screen printing method used to coat the fabric’s surface as the electrode; (**b**) Ag-coated fabric; (**c**) fabric on a PTFE holder; (**d**) ZnO NRs array hydrothermal growth; (**e**) vertically aligned ZnO NRs array on the surface of the fabric coated with Ag; (**f**) structure of ZnO-T-PENG [73]. Figure 8: (**a**) Charge generation process of the PENG; (**b**) output voltage (**c**) output current of the PENG by finger bending and palm clapping; enlarged view of one cycle of (**d**) output voltage and (**e**) output current [73].
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 3 Methods and Technologies for Smart Mobility
### Machine Learning
#### 3.1.1 Predicting Traffic Accidents  
Radar sensors and rear cameras have been used to prevent or reduce accidents. However, research on accident prevention and anomaly detection of vehicles has not yet reached the commercialization stage due to other aspects of the road surface. In this section, we review research on accident prevention methods using smart mobility.  
In [2], the authors proposed SafeDrive to detect anomalous driving behavior. SafeDrive detects abnormal driving habits through unsupervised learning according to features such as rapid acceleration, sudden braking, RPM-speed mismatch, speeding, RPM anomalies, rapid swerving, and neutral taxing. In [3], the Hadoop framework was used to efficiently process and analyze numerous traffic data using sampling methods to solve the data imbalance problems. Accordingly, the prediction system first preprocesses and analyzes big data on traffic to generate data for the learning system. The imbalance in the generated data is corrected using the sampling method. The modified data were classified into several groups, and classification analysis was applied to improve the prediction accuracy. In addition, a MapReduce-based synthetic minority oversampling technique (SMOTE) was devised to solve the data imbalance.  
In [4], the authors predicted the exact arrival time using linear regression, naive Bayes, and distributed random forest to reflect it in the algorithm before accidents and congestion occur in order to minimize the expected arrival time variation due to these variables. The authors of [5] proposed a real-time autonomous accident detection system based on computer intelligence technology. The proposed system uses k-nearest neighbor (KNN), regression trees, and feed-forward neural network models to predict the likelihood of accidents.  
Finally, in [6], the authors implemented various classification models, such as logistic regression, artificial neural networks, decision trees, k-nearest neighbors, and random forests, to predict incident severity and demonstrate the performance of the proposed model with significant accuracy through experimental results. They also implemented a web-based message alert system to warn users through IoT devices.
## 2 MDD/GNSS Installations and Measurement Methods
### Determination of Reference Point Coordinates  
The exact coordinates (WGS84) of the fixed point (base station) were determined by the Virtual Reference Station (VRS) technique supported by the Korea National Geographic Information Institute (NGII) as shown in Table 1. The virtual reference station (VRS) method is an effective way of transmitting correction data through a data link to users for real-time kinematic (RTK) positioning. The Network-based Real Time Kinematic (NRTK) technique was used to determine the coordinates of the base station based on ENU (east-north-up coordinates). This technique consists of Virtual Reference Stations (VRS) and Flachen Korrektur Parameters (FKP) and is operated by the National Geographic Information Institute (NGII) of Korea [27]. Most reference stations in Korea are densely located, with baselines ranging from 30 to 50 km, which provides a robust infrastructure for determining the current position of the base station (fixed point).
## 3 Results and Discussion of Data Analysis
### Granger Causality Test Analysis
By restricting the analysis to events with Kp < 4, the influence of geomagnetic activity as a confounding variable is minimized, ensuring that the observed relationships between signal strength changes and XRA are predominantly driven by ionospheric dynamics. In the negative lag intervals, early changes in signal strength are observed as an ionospheric pre-response to the impending XRA disturbance, demonstrating the predictive potential of these signal strength variations. In the positive lag intervals, the significant influence of XRA on low-frequency signal strength is concentrated in the 0-10 min period following the event, demonstrating the immediate impact of the X-ray event on the ionosphere. This analysis confirms that C-class XRA events have a considerable effect on low-frequency signals, with this effect exhibiting both a time delay and predictive potential. Figure 7: Average Significant Granger Causality Percentage.
## 5 Discussion
### Analysis of Driving Factors  
This study, based on the XGBoost model and SHAP value analysis, explored the impact of different driving factors on WUE. The results show that the NDVI, TMAX, and VPD have a significant positive effect on WUE, indicating that the improvement in WUE in the study area is closely linked to the contributions of these factors. This is because the increase in the NDVI, under the influence of temperature and VPD, enhances the ecosystem's carbon sequestration capacity and improves WUE [32]. Notably, the effects of TMAX and VPD are more complex. TMAX exhibits a quadratic relationship with WUE, as moderate temperatures promote WUE, whereas excessively high temperatures can severely impact photosynthesis, stopping organic matter production and transpiration [33; 34]. On the other hand, SRAD and the PDSI have a suppressive effect on WUE. High SRAD increases evaporation, which in turn reduces WUE [35]. The PDSI shows a suppressive effect on WUE because, in environments with low drought severity, vegetation can increase productivity, but in persistent drought conditions, vegetation struggles to absorb nutrients and grow [36]. In sum, the findings of this study highlight the intricate mechanisms through which different driving factors influence WUE, underscoring that the relationship between these factors and WUE is not merely linear. Additionally, given the complex impacts of temperature and VPD on WUE, regional climate change management should focus on how to reduce the negative effects of extreme temperatures and droughts on water resources through adaptive management. For instance, water resource reserves and improvements in irrigation technology can help cope with extreme drought conditions, thereby enhancing the adaptability of crops and natural ecosystems. Establishing a remote sensing-based monitoring system to track key factors in real time can provide early warnings for ecosystem management and help decision-makers take timely action in response to increasing climate change and environmental pressures.
## 3 Metrics for Jamming Attack Detection
### Packet Send Ratio (PSR)  
[PERSON] _et al._[1] define PSR of a node as the ratio of the number of packets actually sent by the node during a given time period to the number of packets intended to be sent by the node during that given period. 'The number of packets intended to be sent during a given time period' is found by calculating the time of the channel's availability to the node during the given period, much in the same way as in the case of CST, and then by multiplying this available time with the packet transmission rate. Finally, the PSR is calculated as defined above. The PSR-calculation is cumbersome and accordingly, we do not find it suitable for our system either.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Framework  
The framework of the MODMSA is shown in Figure 2. In the MODMSA, a minimum perturbation random initialization is first adopted to generate the population with least perturbation while maintaining the diversity of the population. Then, evolutionary operators in the SPEA2, such as fitness assignment, environment selection, multi-point crossover, and greedy mutation, are performed to search for better scheduling solutions, and the profit of the current population is further improved by the profit-oriented local search algorithm at the end of each iteration. The minimum perturbation random initialization, multi-point crossover, and greedy mutation are improved based on the characteristic of the DMP-PP, while other operators such as environment selection are the same as the SPEA2 [40]. Afterwards, an adaptive perturbation control strategy is performed to limit the maximum allowable perturbation, and the archive sets are updated based on the obtained non-dominated solutions at the current iteration. The above procedures repeat until the termination condition is met and output the non-dominated solutions in the archive.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 3 Results and Discussion
### Comprehensive Analysis of Drought Risk Assessment
At 24-month time scale under RCP 4.5, the high risk level extended further from the 3-month time scale to the areas of Mueang District, i.e., Na Kham Hai (Sd-9), Non Khamin (Sd-21), Nong Wa (Sd-22), and Ban Pharo (Sd-24) Sub-districts, Nong Bua Lamphu Province, while under RCP 8.5, most of the areas were at a low level except Nong Bua Sub-district (Sd-20), Mueang District, Nong Bua Lamphu Province, which were moderate level. During 2081-2100 (Figure 8d), at the 3-month time scale, the same risk levels were observed for both RCP 4.5 and 8.5, except for moderate risk under RCP 4.5 at Nong Bua Sub-district (Sd-20), Mueang District, Nong Bua Lamphu Province, which turned to high risk under RCP 8.5. At the 12-month time scale under RCP 4.5, moderate risk was found at Na Wang District, i.e., Na Kae (Sd-3), Wang Pla Pom (Sd-5), and Wang Thong (Sd-7) Sub-districts, Na Klang District, i.e., Dan Chang (Sd-6) and Uthai Sawan (Sd-16) Sub-districts, Mueang District, i.e., Nong Bua Sub-district (Sd-20), Nong Bua Lamphu Province, and the remaining areas were observed as low risk. Regarding the 24-month time scale under RCP 4.5, moderate risk turned to high risk at Na Wang District, i.e., Na Kae (Sd-3) and Wang Thong (Sd-7) Sub-districts and Mueang District, i.e., Nong Bua Sub-district (Sd-20), Nong Bua Lamphu Province, while at the 12- and 24-month time scales under RCP 8.5, only Nong Bua Sub-district (Sd-20), Mueang District, and Nong Bua Lamphu Province remained at moderate level and others remained at low level. When considering the period 2061-2100, the overall drought risk levels under both RCPs 4.5 and 8.5 mostly varied between low to moderate with the exception of the period 2061-2080 under RCP 4.5, where the drought risk level at the midstream increased to high. Referring to Table 5, a large picture of drought risk based on DRI in the entire Lam Phaniang River Basin was delineated for evaluating long-term drought trends, as well as tracking directly how drought levels have changed over time. Overall, drought risk has a decreasing trend throughout the future time periods. In details, the results for the 3-month time scale showed moderate drought risk for the baseline and the period 2021-2060 under both RCPs, and low drought risk for the period 2061-2100, with the exception of RCP 4.5 during 2041-2060 and 2061-2080 where drought risks turn to low and moderate categories, respectively. At the 12-month time scale, the severity of drought was projected from low risk at baseline to moderate risk during 2021-2060, and remains at low risk during 2061-2100, with the exception of RCP 4.5 during 2041-2060 and 2061-2080 where low and moderate risks were found, respectively. When considering the 24-month time scale, the drought risks were most likely to be the same as the case of 12-month time scale, except the high drought risk which seems to have an upsurge during 2021-2040 under RCP 8.5.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 2 Track Condition Monitoring System
### Evaluation Value Used in the Track Condition Diagnostic System  
The concept of a \"track condition diagram\", which provides a visual representation of the results of a track irregularity analysis using measurement data obtained from an onboard sensing device, has been proposed. We used this concept in this study for track condition diagnosis.  
Car-body vibrations are correlated significantly with track irregularity. Therefore, the single-sided maximum amplitude of vibration in a 10-m-long section was calculated based on the measured car-body vibration acceleration and then utilised in the evaluation. Here, the single-sided maximum amplitude used as a feature value was the absolute value of the half amplitude of the highest vibration acceleration in the section. The method used to calculate the single-sided maximum amplitude is illustrated in Figure 2.  
Figure 1: Track condition monitoring system.
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 2 The Emergence of Privacy Impact Assessment
### The Current Landscape in PIA Frameworks & Tools
It tackles one of the current field's shortfalls, which is the lack of a risk scoring system that adequately considers the context and the primary goal, i.e., the privacy preservation, of the environment for identified vulnerabilities, as this can lead the organisations to improperly prioritise their mitigation efforts. To this end, our approach for supporting a PIA, goes beyond a mere documentation of an organisation's data and procedures, but offers a direct connection between the data processing flows and the existing cybersecurity status of the organisation in order to identify the magnitude of privacy risks. The developed scoring system complements the systematic approach for identifying data processing flows based on Inderdependency Graphs, by annotating them with the risk scores of the supporting assets. APSIA is assisted by asset inventory to offer a great level of automation in the process, while the vulnerability detection capabilities contribute to the dynamicity of the tool in detecting new threats. One could argue that, APSIA is only applicable in the context of GDPR privacy impact assessment. Indeed, our method and tool have instantiated based \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{l} \\ \end{tabular} } & ENISA Tool & - & - & - & - & & ✓ & - & GDPR \\  & GS1 EPC/RFID PIA Tool & - & - & - & - & ✓ & - & - & N/A \\  & SPIA Tool & - & - & - & ✓ & ✓ & - & - & HIPAA \\  & CNIL Tool & - & - & ✓ 1 & - & ✓ & ✓ & - & GDPR \\  & APSIA & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & \& GDPR \\ \hline \hline \end{tabular}  
* Offers a level of automation but not dynamicity when it comes to ICT asset management and vulnerabilities detection.  
\end{table} 
Table 1: Comparison of tools. on GDPR. Nonetheless, APSIA is based on a generic methodology and technical artifacts that can be adjusted to comply with other legal frameworks and standards and we aim to extend our tool in the future towards this direction. Overall, the GDPR requirements, along with the cybersecurity status and the privacy levels of the organisation are offered in a unified manner under the umbrella of the methodology presented in the following section.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 4 Network Architecture
### RF Uplink  
In this work, a Wireless Body Area Network (WBAN) is proposed, which is a network that operates in the miner's body, as shown in Figure 5. In this figure, the two RF antennas represent the transmitting and receiving antennas. The notation (\(Pt_{RF}\), \(G_{t}\)) refers to the power transmitted and the gain of the transmitting antenna. In the receiving antenna, the notation (\(Pr_{RF}\), \(G_{r}\)) represents the minimum power received and the gain of the receiving antenna. This notation is consistent with the Friis Transmission Equation used for the link budget calculation.  
On the other hand, the WBAN network works with small batteries and has low transmission power, very low power consumption, typically below 10 mW, and low data throughput of around 10 Kbps. The proposed WBAN interconnects small nodes with vital sign monitoring devices; the three primary vital signs, body temperature, pulse rate, and respiratory rate, are taken by a wearable device attached to the miner. All of these monitoring devices, in turn, are connected to a central processor, which processes the information, including the Cell Identifier and the Miner's Identifier, working in a specific format and converting the values to binary numbers and transmitting them using RF technology. These data are transferred through the infrastructure to a medical center where doctors evaluate the data received and assess the worker's state of health. This WBAN enables the integration of automated monitoring results, diagnostics, alarms, emergencies, and electronic patient records databases for better patient health management.  
To send the primary vital signs data, a single-chip radio transceiver for the 433/868/915 MHz ISM band is used. The current consumption is very low, in transmission only 11 mA at an output power of \(-10\) dBm, and reception mode 12.5 mA. The module uses Gaussian frequency-shift keying (GFSK) modulation and Manchester encoding. The transmission mode is ShockBurst which transfers the frame as seen in Figure 6([PERSON] et al., 2018).  
Figure 5: RF uplink.  
Figure 6: RF Frame.  
The preamble is used to obtain the synchronization, an address field to establish communication, the payload field where the data of the acquired vital signs are sent, and a CRC field for error detection.  
For a link budget, we use the Friis Transmission Equation that relates the power received \(Pr_{RF}\) to the power transmitted \(Pt_{RF}\) between two antennas separated by a distance \(R\).  
\[Pr_{RF}=Pt_{RF}G_{t}G_{r}\big{(}\frac{\lambda}{4\pi R}\big{)}^{n}, \tag{8}\]  
where \(G_{t}\) is the gain of the transmitting antenna, \(G_{r}\) is the gain of the receiving antenna, and the term \((\frac{\lambda}{4\pi R})^{2}\) is called the free-space loss factor where \(n\) can be modified depend of the environment.