esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of Decision-Making Support Method
### Prioritization Decision
#### 4.2.4 Clustering Complaint Processing Rates  
Data clustering was performed based on the weighted processing rates calculated for each complaint type-location combination. Figure 3 presents the overall distribution of weighted average CPR for these combinations (e.g., poor road condition on highway, road-kill/rockslide on local road). Although different rates were observed across combinations, consistent groupings emerged. Among various clustering techniques, this study employed DBSCAN algorithm to capture the groups. The DBSCAN algorithm is a non-parametric technique for clustering spatial data with noise. The algorithm detects clusters of data points that are closely grouped together, using a predefined number of neighboring points. It also identifies any data points that are significantly different from the rest and located in locations with low density. This clustering approach is capable of effectively handling clusters with different forms and noise, unlike other methods method, and hierarchical clustering, which are more suitable for compact and well-separated spherical clusters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Less than 12 h**} & \multicolumn{1}{c}{**-1 d**} & \multicolumn{1}{c}{**-2 d**} & \multicolumn{1}{c}{**-4 d**} & \multicolumn{1}{c}{**-1 Week**} & \multicolumn{1}{c}{**-2 Weeks**} & \multicolumn{1}{c}{**-3 Weeks**} & \multicolumn{1}{c}{**-4 Weeks**} & \multicolumn{1}{c}{**More than 4 Weeks**} \\ \hline Weight & 100 & 90 & 70 & 50 & 30 & 10 & 5 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weighs by CPT section.  
Figure 3: Overall distribution of weight average CPR.  
In contrast to other clustering techniques that require a pre-set number of clusters, DBSCAN allows for flexibility in this aspect. However, as there is no established basis for arbitrarily determining the number of clusters, we experimented with numbers ranging from 2 to 10. It is worth noting that 10 more clusters would not be practically applicable; we decided to apply 10 as the maximum number of clusters. Clustering was performed for each scenario, and the optimal number of clusters was determined using the silhouette score technique [23]. To verify the robustness of the DBSCAN results, we also applied the K-means clustering technique [13] for comparison.
# Research on Ultra-Fast Transient Overvoltage Characteristics of Electric Locomotive
## 5 Conclusions  
In this paper, the operating overvoltage induced by the pantograph lifting, main breaker closing, main breaker opening, and pantograph dropping in HX\({}_{\text{D}}\)1 electric locomotive is analyzed. The formation mechanism, the distribution law, the hazard as well as the influence of arresters on overvoltage is discussed in detail. The conclusions are summarized as follows:  
1. The tested amplitude and steepness of operating overvoltage is higher than in existing reports. The peak operating overvoltage of HX\({}_{\text{D}}\)1 is 328.60 kV and its steepness is 4.21 \(\times\) 10\({}^{4}\) kV/\(\upmu\)s, exhibiting characteristics similar to VFTO. The phenomenon of multiple arc reignition is obvious in the pantograph lifting and dropping process, whereas it is not apparent in the main breaker closing and opening operations.
2. The amplitude of overvoltage during the pantograph lifting and breaker closing exceeds 3\(U_{m}\) of rated voltage considering the equivalent variable capacitance between the pantograph and the catenary. In addition, for pantograph dropping and main breaker opening overvoltage, the amplitude tends to increase in a pattern of 3, 5, 7, 9, and 11 times the rated voltage as the arc reignition.
3. Ultra-fast transient overvoltage is unevenly distributed at high-voltage equipment of the electric locomotive. The overvoltage with high amplitude and high steepness mainly concentrates at the pantograph while other test points are relatively low.
4. Operating overvoltage poses a serious threat to high-voltage equipment, and reasonable layout of the arrester can effectively reduce the overvoltage amplitude and decrease the likelihood of damage to the high-voltage equipment.  
Methodology, Y.G.; Software, Y.G.; Formal analysis, Y.G.; Writing--original draft, Y.G.; Writing--review & editing, F.T.; Visualization, H.L.; Supervision, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 6 Discussion and Future Considerations
### Packet Delivery Errors  
It is common to assume that update packets are always delivered to the destination successfully; however, in reality, packet delivery errors may occur [82]. Numerous factors related to the physical surroundings and the communication route may cause this. In this case, it would be beneficial to study the impact of different communication impairments on transmission and AoI. For instance, pathloss, obstacles, fading, etc., are some factors that can impair communication among the sensor nodes themselves and the A2G channel between the UAV and ground sensor nodes. It is also important to note that there are other reasons that could impair the quality and AoI due to transmission failures. For example,if the actions of the mobile agent (UAV) in a time slot are not properly learned using efficient ML techniques, this could lead to erroneous decisions with respect to UAV movement [8]. Hence, effective learning techniques are required to ensure that the UAV can accurately adjust its altitude and speed to reduce transmission failures.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Data Collection  
This step is defined as a process of data extraction based on general information of selected articles where units of analysis proposed, justification of the study, sampling strategy, methodology, publication year, and others are compared. It was focused on the selected primary studies and addressed the proposed research questions.  
Figure 4 represents topics identified within the 51 relevant studies, where researchers have placed greater emphasis on identified problems for at least one of the four identified areas, and solutions have been prioritized for the following topics. Seismic data acquisition (30 articles) is the most researched area, followed by data processing (25 articles), most of which are based on the development of algorithms (23 articles). Furthermore, several authors have emphasized solutions to optimize seismic monitoring networks and ad hoc alternatives for data transmission (19 articles). On the other hand, it can be seen that the number of studies for the development of communication protocols (15 articles) is less because several investigations assume ideal systems without a loss of information; in addition, redundancy for seismic data is not considered. Regarding specialized equipment, there is great progress and diversity among sensors and dataloggers that meet quality standards, robustness, low energy consumption, storage capacity, and compatibility with communication formats and acquisition systems for more than a decade; therefore, this field is currently developed (less than nine articles). Finally, there are specific studies (11 articles) in the EEW field related to exclusively to find different methods for data processing time reduction and generate early warnings with greater precision. However, from the EEW approach, greater emphasis has not been placed on the stages behind the processing, such as data transmission and acquisition, which contribute significantly to the availability of information in the SDCs.  
Figure 3: Study quality assessment.
# Low-Order Electrochemical State Estimation for Li-Ion Batteries
## 3 Evaluation Study
### Discussion  
As can be seen from the numerical evaluation study, the proposed approach is able to reconstruct microscopic electrochemical states (concentration and potential profiles) together with macroscopic electrical states (SOC, output voltage) in an efficient way, i.e., with low computational requirements and enabling potential real-time applications.  
It should be noted that the data used for the identification of the ROM considered the cell temperature as an external signal, so the resulted model is capable to tackle changes on temperature making a good agreement for testing scenarios besides the standard 25\({}^{\circ}\)C. The ECM and CC with optimized parameters could not consider this effect, leading to a faster increasing discrepance to the actual states if the external temperature was shows deviations from the standard temperature.  
The average root square error of the ROM is promising supporting the helpful use of the DMDc for complex systems that present a low-dimensional behaviour between sampling times. The RMSE for the concentration is less than 3\(\times\)10\({}^{-3}\) (lets recall that the SOC depends of the concentration on the solid phase) and for the terminal voltage is less than 100 \(\upmu\)V. In contrast, the standard equivalent circuit model (ECM) has a RMSE of around 40 mV for the terminal voltage. Additionally, the dimension of the ROM is only about 7% of the numerical model.  
The processing time of the numerical model for the discharge/charge simulation study was of 75.9 s while the ROM only needed 1.6 s, i.e., around only 2% of the original time, enabling its potential future implementation in real-time applications.  
It should be highlighted once more, that the ROM proceeds from equation free data, implying the resulted ROM depends only from the data produced during the numerical simulation of training data scenarios. Accordingly, different simulations would come up with slightly to considerably different truncation factors, dimensions and values for the ROM. Thus, it is important to define _a priori_ what phenomena would be required to be reflected in the ROM and create an appropriate test scenario in terms of current and temperature.
## 2 Model Setup
### Model Establishment
#### 2.2.2 Spring Elements  
Accurate prediction of joint deformations is important, as it influences the overall structural deformations and consequently the overall internal forces to a great extent. Controlling the structural deformations is an essential performance assessment index, which often governs the thickness of the lining structure.  
Figure 2: Outline of a typical lining structure of the Ningbo quasi-rectangular tunnel (unit: mm): (a) pattern A; (b) pattern B.  
Traditionally, in the BSM method, the spring element is given a fixed stiffness value, or given fixed positive and negative stiffness values for all joints. However, the joint's rotational stiffness value varies under different combinations of bending moments and axial forces, and additionally, the joint's shear stiffness value is also affected by the acting compressive forces. Examples of such behavior are given in Section 2.3.1.  
The spring used in this paper is a unidirectional element, with a nonlinear generalized force-displacement capability. It has longitudinal or torsional functions in three degrees of freedom. The longitudinal capability is a uniaxial tension-compression function and the torsional capability is a purely rotational function. In a state of plain strain, this spring element consists of a pair of nodes, which are at the same spatial position, as shown in Figure 4. Each node has three degrees of freedom, i.e., two translational modes and one rotational mode, all in plane. \(K_{\theta}\), \(K_{n}\) and \(K_{s}\), respectively, represent the rotational, tangential and radial stiffness. In the current case, a rotational spring and a translational spring are considered in the longitudinal joints and two translational springs in different directions in the circumferential joints.
# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Comprehensive Satisfaction of Tour Itinerary
#### 3.2.1 Scoring Based on the Time Utilization Ratio  
People typically prefer to allocate more of their limited travel time to visiting scenic spots rather than spending it on travel or waiting. We define the Time Utilization Score \((TUS)\) to evaluate the proportion of actual time spent on visiting scenic spots in the tour itinerary \(I\). It is described using Equation (1):  
\[TUS(I)=\frac{\sum_{a_{i}\in I}AVDur_{a_{i}}}{AvailableTime} \tag{1}\]  
where \(AVDur_{a_{i}}\) represents the actual visit duration of scenic spot \(a_{i}\) in the itinerary plan. The duration of visiting each scenic spot will not exceed the expected time. \(AvailableTime\) represents the available time, which equals the planned journey duration minus the total time for meals and rest expected during the journey.
# Risk Assessment for the Development of Emergency Scenarios for Tram Driver Training
###### Abstract  
(1) Background: This article presents research results from a project to develop a simulator and training program for tram drivers based on virtual reality tools. This article aims to present the research results on the risk assessment of adverse events to develop training scenarios for tram drivers. (2) Methods: The research methodology involved four steps, including the identification of adverse events based on interviews with experts, estimation of risk parameters, risk assessment using fuzzy logic, and risk evaluation to identify events recommended for the training program. (3) Results: Thirteen adverse events related to tram driving were identified and ranked according to the proposed classification. A risk assessment was then performed for the selected group of events using fuzzy logic models. The results made it possible to recommend situations that should be mapped in the virtual world as part of a tram driver training program. (4) Conclusions: The results made it possible to distinguish a group of events that should constitute the training area dedicated to tram drivers. Including the developed emergency scenarios in the training program allows employees to examine their reactions to stressful or dangerous situations and better prepare themselves for future duties.  
emergency scenarios; fuzzy logic; hazardous situations; tram driver training; VR tools Article
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 3 Security Threats for IoT Systems
### Application Specific Threats  
Although routing forms an essential component of the IoT system, the IoT devices are expected to host application software required by the function envisaged to be performed, such as temperature monitoring and supply chain management. Being software applications, these can introduce additional vulnerabilities to the overall attack surface for an IoT system. We categorize these threats as application-specific and present them below.  
_Denial of Service attack:_ Historically, Denial of Service (DoS) attacks are targeted at making the victim unavailable for legitimate service. This can be achieved by flooding the victim with extraordinarily large volume of requests or by exhausting resources, such as memory and computational power available to the victim. Within IoT, the threat of a DoS attack is two-fold; the victim can be part of the network under threat that an attacker wishes to make unavailable, or the victim can be used as a zombie to launch a Distributed DoS (DDoS) on a target IoT network. In this context, Botnet attacks targeting IoT devices have recently attracted significant attention with Mirai [37] the pioneer of Botnet attack. The significance of these threats within IoT systems has been identified by [38; 39; 40].  
_Malicious code injection:_ As identified by [39; 41], malicious code injection is another application-specific threat to IoT systems. The attacker, in this case, attempts to inject malicious code to get privileged access to the victim. Consequently, the attacker can damage the normal operation by causing threat to the data or to the network using one of the routing specific attacks described in the previous section.
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 6 Conclusions  
The primary goal of this research was to compare the quality of synthetic images generated by a DCGAN and an LSGAN. The study combined actual SLD images with synthetic images. Various types and quantities of images were used for training purposes. During the experiments, sophisticated bounding-box detection techniques, such as YOLO V5, were utilized and successfully detected symbols from 16 different categories, despite some com  
Figure 16: Detection results for the (**a**) original dataset and (**b**) the augmented dataset.  
ponents having minimal differences. These results indicated the accuracy of the detection technique in challenging tasks.  
Our study indicates that incorporating a mix of genuine and synthetic images in the training process enhances the capacity to recognize symbols. We have drawn the following conclusions based on our findings: (1) During the experiment, the dataset that yielded the best results was obtained from Group 2. This dataset involved the combination of authentic images with synthetic images generated through the utilization of DCGAN and LSGAN techniques. (2) Through the integration of real and synthesized images, there was a significant enhancement in recognition performance, resulting in a notable accuracy rate of 95% with YOLO V5. (3) The inclusion of additional samples during the training phase has the potential to enhance performance and minimize errors. To improve object recognition, the dataset should incorporate diverse real and synthetic images.  
In the future, our study will concentrate on utilizing GANs to produce symbols in the context of diagrams. This approach is expected to considerably reduce the human effort required for data annotation. Additionally, we will develop a comprehensive system based on the suggested methods to enable the complete processing and analysis of engineering diagrams such as SLDs. We anticipate that the findings presented in this article will make subsequent tasks, such as line detection and text localization, much easier. Moreover, future research will involve combining Explainable AI (XAI) and other GAN techniques, such as WGAN, MCGAN, MFCGAN, and StyleGAN, with additional detection methods.  
Conceptualization, H.B. and Y.K.H.; Methodology, H.B., Y.K.H. and Z.H.A.; Software, H.B.; Validation, H.B., W.K. and Z.H.A.; Formal analysis, Y.K.H. and W.K.; Investigation, Y.K.H., W.K. and Z.H.A.; Resources, Y.K.H. and W.K.; Data curation, H.B., Y.K.H.; Writing--original draft, H.B.; Writing--review & editing, Z.H.A.; Supervision, Y.K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Yayasan UTP PRG (YUTP-PRG) (grant number: 015 PBC-005) and the Computer and Information Science Department of Universiti Teknologi PETRONAS.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to authorized access to data.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Methodology  
The study followed the workflow shown in Figure 3. The workflow commenced with well correlation and synthetic seismogram generation to establish a robust seismic-to-well tie. Subsequently, horizons and faults were manually picked throughout the entire 3D volume. Two horizons, namely the Top and Base of the Plover Formation, representing the interval of interest within the highly faulted synrift deposits, were interpreted. Additionally, faults that intersected with the interval of interest were picked. The variance attribute, a widely used discontinuity attribute for highlighting faults, fractures, and stratigraphic boundaries, was generated in Petrel Software (version 2020) with an IL 3 by XL 3 and 15 samples of vertical smoothing with no dip correction and displayed along the top of the Plover Formation to provide constraints and assist in fault interpretation. A 3D Gaussian filter was applied prior to the computation of the variance to reduce random noise. A time structure map was then constructed using the manually interpreted faults and horizons to illustrate the faulting system and structural configuration of the rifted interval. This part of the study is predominantly interpreter-driven, relying on knowledge and experience as it has a lot of uncertainties and is time-consuming.  
Figure 3: The workflow used in this study.  
The second part of the study was entirely data-driven, involving the application of pre-trained deep-learning models to filtered seismic amplitude data for the automatic prediction of faults within the entire seismic volume. The final step in the workflow was to validate and compare the outputs of both the traditional interpreter-guided and the CNN-driven approaches.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Representation Methods
#### 5.2.6 Rule-Based Representation  
Some of the studies in the literature focused on the rule-based representation method for representing malicious software behavior. There are two main mechanisms for generating the rule-based behaviors, which are: manually and automatically. Manual rulesnecessitate significant effort on the part of malware analysts to observe the features by which malicious behaviors are represented, whereas tools such as the YARA tool have been used to generate automatic rules. The rule-based constructed behaviors can be utilized in the detection phase by matching them with the generated rule-based behavior of the test file.  
The majority voting decision-making mechanism has been widely used in the previous studies to decide if the test file's rule-based behaviour is more in line with benign or malware rule-based behaviour. However, rule-based malware detection and classification models are limited to recognizing only the malicious activities that are represented in their generated rules. The authors of [54] represented malware and benign behaviors using dynamic API calls, API sequences, and network traffic features by building the rules that are stored in a database to describe malware and benign behaviors, and then leveraging a majority voting method when their model matched the extracted testing files' behaviors with those rules.