esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 1 Introduction
This category includes unmanned excavators for material extraction and transport vehicles involved in logistics. These machines typically follow fixed trajectories between material sites and handling equipment, with tasks often adhering to cyclical patterns. Prompt processing of task requests from mobile devices is crucial to maintaining the safety and efficiency of the overall operation. In the deployment of edge nodes within mining environments, the number of nodes is closely linked to the number of operational devices and sensors. Edge computing facilitates timely real-time task responsiveness, significantly reducing task execution latency. However, it is crucial to account for the cost implications of node deployment throughout the execution process. Current research predominantly focuses on the conceptual placement of edge nodes at the network periphery or near data sources, without offering concrete, context-specific placement strategies. Additionally, the distinct characteristics of mining environments introduce unique challenges, making certain deployment solutions impractical for these applications. Variations in deployment strategies can lead to disparities in deployment costs, server resource utilization, network reliability, and access delays, highlighting the necessity of precise, context-sensitive deployment plans [10]. In the mining context, the strategic deployment of edge nodes to enhance real-time processing capabilities while simultaneously reducing latency and costs remains a critical research area. The primary contributions of this paper are outlined as follows:  
* This study introduces a comprehensive system model for the deployment of edge nodes. The mining machinery is categorized into fixed and mobile equipment, and detailed edge node deployment models are provided for each scenario based on their specific operational requirements. * In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. * Simulation platforms were established in two distinct scenarios, utilizing a production line system and an inspection trolley. These platforms were employed to validate the proposed edge node deployment method. The results clearly demonstrate the superior effectiveness of the proposed strategy over conventional approaches. The subsequent sections of this article are organized as follows. Section 2 reviews the related work. Section 3 introduces the system model and outlines the edge node deployment problem. In Section 4, two enhanced heuristic algorithms are presented. Section 5 provides the experimental validation of the proposed method, followed by a detailed analysis of the results. Finally, the article concludes with a comprehensive summary in Section 6.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### Dataset from the Testbed  
This section provides information about the total dataset obtained as a result of the scenarios performed separately on the testbed. Network traffic packets of each scenario were collected with Wireshark network listening and analysis tool. Then, the packets of these 6 scenarios were collected in a single file and the total dataset was created. The features frequently used in the literature and specific to the Modbus TCP/IP protocol were determined for this dataset [23; 25]. Table 1 shows the features used in this research.  
A new and comprehensive dataset consisting of 30 attributes, 1 deterministic class, and a total of 22.768 samples was obtained. While preparing the dataset, the attacks were observed on the SCADA system and abnormal situations were noticed clearly by the operator. It is detected whether there is a DDoS attack and if there is an attack, which of the 5 different types is determined. This dataset is suitable for training and testing deep learning and machine learning models. Due to these properties, a new perspective and contribution to the literature is presented.
## 7 Phase One Testing and Results  
To avoid exhaustion, for each user, two of the five object manipulation methods were randomly selected. For each of the methods, the user had to complete all seven test cases in one setting. All data set analyses were treated uniformly due to the randomized testing order. Table 1 shows the minimum, maximum, and mean test completion data for all seven tests by all users. The red highlights identify the shortest average completion time for each test. For example, the shortest average completion time for Test 1 was 7.3 seconds for the IVI method. Three observations could be made.
## 3 Data Introduction
### The 1:50,000 Scale Data Source and Analytical Methods  
Since the samples were collected by two different organizations, for ease of subsequent description, the study area is divided into northern and southern regions. The 1:50,000 scale soil geochemical data for the northern area, including L50E021011, L50E022011, and L50E022012, were completed by the Geological Survey Institute of China University of Geosciences (Beijing) from 2009 to 2013, with a total of 3826 data points. The southern area, including L50E023011, L50E023012, and L50E024011, was completed by the Tianjin Geological Survey Research Center from 2010 to 2013, with a total of 6924 data points. The soil geochemical survey was conducted in accordance with the \"Geochemical Survey Specifications\" (1:50,000 scale), with a sampling density of 8-20 points/km\({}^{2}\). The sampling targets were residual slope deposits close to the bedrock surface (Figure 3a).  
A total of 12 elements were analyzed in the samples. Pb, Mo, Sn, Cu, Ag, Zn, Ni, and Co elements across all map sheets were analyzed using electrode spectroscopy (ES); As, Sb, and Bi elements were analyzed using atomic fluorescence spectroscopy (AFS); and W was  
Figure 3: The sampling distribution map: (**a**) 1:50,000 sample distribution map; (**b**) 1:200,000 sample distribution map.  
analyzed using the polarographic method (POL) (Table 1). All geochemical sample testing and analysis were completed by the Inner Mongolia Land and Resources Exploration and Development Institute Laboratory, with an internal inspection pass rate for all samples greater than 93%.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Resource Allocation Algorithms for MWSC  
In this section, we first present a reformulation of P1 as an MILP problem, which helps obtain the optimal solutions efficiently, and then propose a greedy suboptimal algorithm with low computational complexity.
# A Comparison of Energy Management System for a DC Microgrid
## 2 Mathematical Modeling for DC Microgrid Components
### Solar Radiation  
The solar radiation incident on the panel is modeled to have a better estimate of the energy production of the photovoltaic panel so that the EMS performs correct optimization. Radiation is a parameter that depends on many variables, both temporal and position, therefore, its modeling is somewhat more complex than the panel model. To model the incident solar radiation will have to be characterized from the equations that govern the behavior of radiation on an inclined surface [19].  
The global irradiance \(G(\alpha,\beta)\) on an arbitrarily oriented and inclined surface: It is calculated with the components of direct irradiance on an inclined \(B(\alpha,\beta)\), irradiance diffused \(D(\alpha,\beta)\), and irradiance reflected \(R(\alpha,\beta)\) plane, as shown in Equation (9).  
\[G(\alpha,\beta)=B(\alpha,\beta)+D(\alpha,\beta)+R(\alpha,\beta) \tag{9}\]  
Direct radiation on an arbitrarily oriented and inclined surface \(B(\alpha,\beta)\) is calculated through the direct radiation measured on a horizontal surface \(B(0)\) and the geometric terms such as angle of incidence \(\delta_{S}\) and the angle or zenith distance \(\delta_{ZS}\), as shown in Equation (10).  
\[B(\alpha,\beta)=B(0)\frac{cos\delta_{S}}{cos\delta_{ZS}} \tag{10}\]  
Radiation reflected on arbitrarily oriented and inclined surface \(R(\alpha,\beta)\) is calculated through the reflected radiation measured on horizontal surface \(R(0)\) and the geometric terms such as the angle of inclination \(\beta\), as shown in Equation (11).  
\[R(\alpha,\beta)=R(0)\left(\frac{1-cos\beta}{2}\right) \tag{11}\]  
Diffuse radiation on an arbitrarily oriented and inclined surface \(D(\alpha,\beta)\) is calculated through the reflected radiation measured on a horizontal surface \(R(0)\), the geometric terms such as the direct irradiance transmission index \(A_{i}\) and others mentioned in the previous paragraphs, as shown in Equation (12).  
\[D(\alpha,\beta)=D(0)\left(\frac{1-cos\beta}{2}\right)(1-A_{i})+D(0)\frac{ cos\delta_{S}}{cos\delta_{ZS}}A_{i} \tag{12}\]
## 6 Conclusions and Future Work  
The case study in Section 5 highlights the use of the REIP SDK in the design of an application pipeline for a multimodal smart filter for urban bicycle accident data collection. A finding from this case study was that the REIP workflow (see Section 3) allows the sensor architect to design the data pipeline from an abstract, top-down view and have it translate directly into software components without the extraneous complications caused by software and hardware-specific details. The modular blocks that were combined to make up the case study's functionality abstracted a lot of the complexity away from the traditional software development process. Moreover, it allows hardware decisions to be pushed to a later stage in the process which, if made too early, can unnecessarily constrain the application.  
The REIP SDK includes dozens of blocks for the commonly required tasks of: data acquisition, processing and storage. Advanced data serialisation and block connection strategies (Sections 4.1.2 and 3.4.3) offered by the REIP SDK can then be used to quickly find the right balance between multi-threading and multi-processing for optimal performance, which is crucial for real-time data processing at high rates with minimal loss of samples. It is also important to note that the REIP SDK can handle data of varying structure and size, and does not add any significant overhead (Section 4.2.2) so the user application can maximise the utilisation of available computational resources.  
To further increase the usability and accessibility of the REIP platform, we are currently working on a Graphical User Interface (GUI) for application pipeline creation using the Node-RED [52] platform. This browser-based interface will provide a visual representation of the functional blocks. It will be used to wire-up blocks and create application pipelines for less technical users. This GUI can take into account the constraints of the proposed system, as well as allow users to define custom application-specific hardware/software constraints. The resulting application pipeline can then be exported in the form of a deployable script that is ready to be executed on sensor nodes with our run-time installed. In addition, for embedded devices such as the Raspberry Pi and NVIDIA Jetson, the compilation steps for dependency libraries can be long, difficult and error prone. In order to ease this pain (for ourselves and others) we also plan to provide working Docker images that already contain the core library dependencies of the REIP SDK, such as PyArrow, and others that are typically difficult/time-consuming to build on resource constrained devices.  
The REIP SDK was designed to facilitate and streamline the process of environmental sensing deployments, providing researchers of varying experience levels with tools and best practices for designing and building sensor networks (Section 3.2). Implementing sensor networks can end up being an enormous engineering burden and often takes away valuable time from the actual research that the researchers are seeking to perform. We hope that the REIP SDK presented can alleviate this burden.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 6 Numerical Results  
In this section, we evaluate the performance of the proposed UVCS virtual coordinate system on the top of the Geo routing algorithm based on (i) topological metrics, describing the similarity between virtual and physical topology, and (ii) routing metrics, reflecting the routing performance. We also compare the proposed approach to other routing schemes such as the Gradient Assisted Routing (GAR) algorithm [19] with virtual coordinates, as well as the Geo routing algorithm with known physical coordinates and the Dijkstra algorithm as a benchmark.
## 1 Introduction
To achieve this, it continuously fine-tunes its decision thresholds based on the statistical likelihoods generated by various detection models, as well as on historical context drawn from the surrounding environment. This approach is designed to be broadly compatible, allowing for seamless integration into established data workflows and analytical engines that may incorporate additional, and often more resource-intensive, detection capabilities. More specifically, it consolidates outputs from different detection tools--whether these operate in batch mode or deliver results in real time--serving as a coordinator to streamline decision-making and cut down expenses. Consider, for instance, a cloud-based security solution comparable to Microsoft Defender. In such a layered detection setting, the cloud-driven detection functions would represent costlier tiers of the proposed architecture, supplementing on-device scanning and analysis. By doing so, our contextual bandit-based approach ensures that organizations can strategically allocate their budget, incurring higher costs only when more sophisticated and, thus, more expensive cloud-based detection services are genuinely needed. This paper offers several key contributions. First, it introduces a contextual bandit-based reinforcement learning framework: a supervised machine learning-driven, cost-sensitive solution designed for detecting cyber threats. By employing a layered detection structure, our approach identifies and adjusts an optimal set of decision thresholds, dynamically adapting them to reflect varying environmental factors, such as shifts in model confidence, across different datasets. Second, this approach leverages multi-armed bandit algorithms along with newly devised reward mechanisms that quantify the decision certainty of the chosen detection models. These mechanisms were purposefully crafted to implicitly maintain an effective balance between exploration and exploitation, all while minimizing expenses and enhancing detection accuracy. When benchmarked against existing cutting-edge solutions, these reward strategies delivered superior results in terms of both cost reduction and threat detection capabilities. Third, our approach is tested using two representative datasets that capture today's most common threat vectors: phishing and malware attacks. By examining how the framework's decision boundaries respond to variations in parameters, reward configurations, and selection methods, we identify the optimal operational settings of our contextual bandit-based solution with respect to cost-efficiency and detection effectiveness. The remainder of this paper is structured as follows: Section 2 reviews related work. Section 3 introduces our contextual bandit-based reinforcement learning approach. Section 4 presents the evaluation process and results. Section 5 discusses and interprets the findings. Finally, Section 6 concludes the paper and outlines directions for future research.
## 6 Conclusions  
In this study, an innovative dual-path design optimization framework of \"demand mapping-contradiction resolution\" is proposed by integrating several theoretical models: KE, AHP, QFD, and TRIZ. This significantly improves the efficiency of the ordering interface design, its visual attractiveness, and the satisfaction of users' perceptual needs. Firstly, KE provides demand-oriented theoretical support for interface design by capturing users' emotional needs and aesthetic preferences; AHP analyzes complex problems hierarchically and clarifies the weight of each user's demand, thus ensuring scientific and precise design decisions; QFD accurately transforms user demands into design elements, effectively optimizing resource allocation and improving design efficiency; and TRIZ provides innovative solutions to design conflicts and ensures a good balance between functionality and aesthetics in interface design. This combination of multi-theoretical models not only provides a systematic methodology for ordering interface design but also provides a useful reference for research in other digital interface design fields.  
Second, this study innovatively introduces AIGC technology into the design process, specifically applying the MidJourney tool. We combine users' perceptual needs and design weights to quickly generate diversified design solutions, which not only greatly improves the design efficiency, but also quickly produces results that meet the users' psychological expectations when responding to the diverse user needs. In addition, through weight locking and parameter adjustment, this study ensures the stability and repeatability of the AIGC results, which provides a practical basis for the wide application of AIGC technology in the design field.  
Finally, the study verifies the effectiveness of the optimized ordering interface through user evaluations and feedback mechanisms. Questionnaire surveys and statistical analyses clarify user preferences across different design solutions, offering robust data support for further design refinement.  
Future research may continue exploring how to apply this framework in evolving practical contexts, ensuring that interface designs consistently align with user expectations and market trends--thereby fostering sustained innovation and development across various design domains.  
Methodology, Z.H. and H.S.; Formal analysis, Z.H.; Investigation, H.S.; Writing--original draft, H.S. and Z.H.; Writing--review & editing, X.H. and Z.H.; Supervision, X.H.; Project administration, X.H.; Funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Social Science Fund of China: 21 BGL067; Key Graduate Education Reform Project of Fuzhou University: FYJG2024012.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 4 Undetectability Analysis  
In this section, we present a comparison of the undetectability of the Gaussian-coded time-frequency modulation and binary modulation methods in terms of detection probability for Eve. To measure undetectability, a privacy rate was proposed in [2]. The privacy rate is defined by the number of bits that can be transmitted covertly through the use of \(N\) channels. According to [2], only \(O\!\left(\sqrt{N}\right)\) bits can be transmitted covertly; thus, the privacy rate cannot be a constant. However, [3; 34] determined that a constant privacy rate is achievable if [PERSON] is uncertain of the channel noise level. Conversely, detection probability is a simple and useful metric of undetectability. For example, the detection probability of chaotic-sequence DSSS signals was studied in [13; 14]. The authors assumed that [PERSON] knows most of the protocols used by target communication systems, such as carrier frequency, modulation, length of the spread sequence, and symbol duration, but not the exact spreading sequence. Then, she can attempt matched filtering with all possible spreading sequences for the received samples to determine if a signal exists. The studies determined the detection probability of chaotic DSSS signals for several types of chaotic sequences and compared the probability with that of conventional binary sequence DSSS signals.  
To measure undetectability, we considered the detection probability as a metric. The eavesdropping problem was formulated as a hypothesis test. We explored the problem by dividing it into three cases according to uncertainty of noise for [PERSON]. In the first case, [PERSON] is assumed to have no information on the noise level. That is, [PERSON] has an infinitely large uncertainty in terms of the noise level. In such extreme cases, the only option for [PERSON] is to test whether the received samples are from a white Gaussian distribution or from other random distributions, i.e., [PERSON] aims to determine the existence of a signal using a normality test. The second case is the other extreme scenario on the opposite side. In the second case, [PERSON] has no uncertainty regarding the noise level. Thus, [PERSON] knows the exact noise energy. In such a case, [PERSON] can apply a hypothesis test based on a threshold of the symbol energy to distinguish whether the signal exists or not. However, the two extreme cases rarely occur in practice. Thus, in the third case, we define a parameter \(\rho\) to quantify the amount of uncertainty [PERSON] has on the noise level. In the following subsections, we measure and compare the undetectability of the proposed signal and conventional signals.
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 7 Conclusions  
Overturning accidents involving mobile cranes account for about half of all crane-related incidents, leading to severe outcomes, including casualties and property damage. These accidents are primarily caused by operational errors, such as excessive weight transfer, and ground subsidence from factors like sinkholes, groundwater, and soft soil at construction sites. While there has been significant research on preventing operational errors, studies addressing ground subsidence remain limited.  
To address these issues, our research team developed an Electrical Resistivity Measurement System (ERMS) to assess ground subsidence and enhance mobile crane safety. The ERMS aims to prevent overturning by analyzing ground conditions for potential subsidence. We validated the system's reliability by comparing it with commercial equipment, ensuring data accuracy. A graphical user interface was also developed for improved scalability and user convenience.  
Field tests demonstrated the ERMS's effectiveness alongside commercial equipment in identical environments, further validating its applicability when mounted on a mobile crane. However, integrated operation with mobile cranes, experiments across diverse environments, and enhancements using various measurement methods remain necessary. In the future, through continued research and improvements, we anticipate broader applicability in enhancing the safety of mobile crane operations.  
**Author Contributions:** Conceptualization, S.C. and J.P.; methodology, H.J., Y.L. and H.L.; software, Y.L.; validation, H.J. and S.C.; formal analysis, Y.C.; investigation, H.J.; resources, Y.L.; data curation, Y.C.; writing--original draft preparation, H.J.; writing--review and editing, H.J., S.C. and J.P.; visualization, H.J.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (20000226, Development of 250-ton All Terrain Crane with AI-based Safety and User-friendliness) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
Not applicable.  
The data used in this paper were collected through the design/test supported by our project. Currently, it is difficult to disclose the data for additional testing and research. In the future, we will strive to share our data by securing more data and conducting further research.  
The authors declare no conflicts of interest.