esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This algorithm has been widely applied in fields such as medical image analysis, remote sensing data processing, social network analysis, public health, security, and transportation, with numerous studies demonstrating its exceptional performance. This research is the first to apply HDBSCAN to the clustering analysis of maritime vessel trajectory traffic, and we hope to contribute to advancements in this field. This study focuses on historical data from the waters near Taichung (TC) Port in Taiwan. To achieve optimal trajectory clustering analysis, it is essential to understand AIS data and the characteristics of vessel navigation deeply. The data must be cleaned, and features must be selected according to these characteristics. Section 2 of this paper reviews the related literature on trajectory data preprocessing and clustering applications. Section 3 describes the characteristics of AIS data and the preprocessing methods in detail, including data cleaning, trajectory segmentation, and simplification. The latter part introduces clustering algorithms and explains the selection and optimization of clustering features and feature normalization. Section 5 presents the results of the experimental data, detailing data processing, analyzing the clustering results of ship trajectories and the causes of noise formation, and finally comparing the clustering performance of HDBSCAN and DBSCAN using evaluation metrics. Finally, Section 6 summarizes the contributions and limitations of this study and identifies directions for future research.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Determination for Inter-Cluster Transmission Modes  
We adopt the same method as that in [3] to determine inter-cluster transmission modes, where the purpose is to decide on an appropriate transmission power and antenna mode between a cluster and each of its adjacent clusters. This process is briefly described as follows.  
Each MCH is already aware of its neighboring MCHs following the overhearing of the \"SCH confirmation messages\" in the cluster formation phase. If the MCH of a given cluster wishes to establish VMIMO links with adjacent clusters, it broadcasts a channel probing request (CPREQ) packet. If the given cluster has a SCH, this SCH will send its own CPREQ after it hears this CPREQ.  
Upon receiving the two CPREQ packets, the receiving MCHs and SCHs estimate the Channel State Information (CSI) between the source MCH/SCH and the receiving MCH/SCH, and communicate such information to each other. Then the receiving MCH/SCH calculates the minimum power needed to communicate between the MCH/SCHs of the transmitting and receiving clusters using one of four possible modes (i.e., SISO, MISO, SIMO, and MIMO).  
The MCH and SCH in each neighboring cluster determine the optimal transmission mode that minimizes the total energy (which includes both transmission and circuit components) among the four modes. Each receiving MCH then sends this information back to the source MCH/SCH (and also to the receiving SCH) via a channel probing response (CPRES) packet.  
A network topology consisting of virtual inter-cluster links is constructed, where each link in this topology represents the transmission mode that requires the least amount of energy. Information about the selected transmission power and mode for each link is flooded throughout the network, so that each cluster will eventually have a complete knowledge of all links' weights, which will be used as input to the path selection algorithm. A minimum-energy routing protocol based on [PERSON]'s algorithm is executed by the source MCH to obtain the path with the minimum total energy among all possible paths between the source MCH/SCH and the sink.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
## 7 Synthetic Experimental Framework  
In most papers that deal with DODME by adding richer ICT traffic measurements to the conventional link flow counts [13; 16; 17], the authors assume specific conditions for controlling the data collection processes, such as those proposed by [17; 34; 35]. These conditions for collecting the data ensure their quality and allow for making assumptions that form the basis of the approaches. This is not always possible with commercial data because the researcher has no access to it, the fleet size is very limited, or the only available data are supplied by commercial companies who prohibit access to the whole raw data set, which they pre-process, depending on their business model. Therefore, it is common to conduct simulation experiments that emulate reality, which is then mimicked by generating synthetic data. Reference [1] provides an experimental framework was widely used by researchers. However, they used specific microscopic simulation models and numerical software (i.e., MATLAB). This is why we propose a synthetic and agnostic software data generation process that fulfills the functional requirements and provides data that are indistinguishable from the actually measured data consisting of:A learning process that is supported by the analysis of a sample of GPS waypoints from an equipped fleet;
* Building a microscopic traffic simulation model of a traffic network that emulates the behavior of individual vehicles, including the class of equipped vehicles;
* Developing an APP to properly emulate the GPS data collection from the equipped vehicles (e.g., replicating the waypoint data collection policies).  
Figure 2 depicts the conceptual diagram of the proposed methodology. To conduct the computational experiments that are reported in this paper, this methodological framework was implemented in PTV Vissim and PTV Vissim, where the DTA used was the SBA implemented in Visum [36]. It was applied to a model of the city of Hillsboro, USA, consisting of 618 links and 58 zones. The simulation ran over a time horizon from 08:00 am to 09:00 am in three periods of 20 min; 120 days were generated in Vissim in order to obtain enough GPS trips with different random seeds and time latencies based on the learning process from the physical data. The full GPS data set contained 9.1 M waypoints, which is approx. 109 k trips. This data set was transformed to PrT paths in Visum using the GPX import tool and its map matching techniques to obtain the interpolated travel times at the link level from the sequence of waypoints.
# Effects of Vibration on Adjacent Pipelines under Blasting Excavation
## 5 Soil Vibration Response above the Buried Pipeline
### Soil Response Characteristics around the Pipeline  
The explosion stress wave theory shows when a wave propagates in surrounding rock [28], the propagation medium peak effective stress and peak particle velocity have the following relationship:  
\[\sigma=\rho cv \tag{14}\]  
where \(\sigma\) is the peak effective stress, \(\rho\) is the soil's density, \(c\) is the longitudinal blast velocity, and \(v\) is the _PPV_.  
Figure 11: Distribution of PES on the pipeline.  
Figure 12: Pipeline cross-section dynamic response characteristics under hazardous construction conditions: (**a**) peak particle velocity; (**b**) peak effective stress.  
The ratio of pipe to soil is given by the following Equation:  
\[\frac{\sigma_{p}}{\sigma_{s}}=\frac{\rho_{p}c_{p}v_{p}}{\rho_{s}c_{s}v_{s}} \tag{15}\]  
Since the topsoil around the pipe vibrates at a similar speed to the pipe's surface, the above equation is simplified to the following Equation:  
\[\frac{\sigma_{p}}{\sigma_{s}}=\frac{\rho_{p}c_{p}}{\rho_{s}c_{s}} \tag{16}\]  
The seismic wave's propagation velocity is relevant to some of the physical properties of the medium. It can be expressed as the following Equation:  
\[c=\sqrt{\frac{E(1-\mu)}{\rho(1+\mu)(1-2\mu)}} \tag{17}\]  
where \(\mu\) is the Poisson's ratio of the medium.  
Therefore, the value of the pipe-to-soil relationship in this study can be expressed as:  
\[\frac{\sigma_{p}}{\sigma_{s}}=341.144 \tag{18}\]  
The results gained from simulations were compared with those obtained from empirical formulas to verify the scheme's feasibility further. Table 8 shows the calculation results. It is clear the calculation results are in agreement with the empirical equation of the blast stress wave theory. This proves the numerical simulation effectively reflects the effects of tunnel blast vibration on the pipe.
## 3 Results  
### Digital Filters Theoretical Comparative Analysis  
For a comparative analysis of the technical characteristics of FIR DFs based on known MAC units [28] and DFs based on proposed TMAC units, we alternately fix parameters \(K\) and \(k\). Let us first consider the case of a 15 th-order filter (i.e., \(K=15\)). For the considered case, we will change the  
Figure 5: The proposed truncated MAC (TMAC) unit structure.  
Figure 6: The proposed architecture of a \(K\)-th order FIR DF based on TMAC units.  
bit width \(k\), sorting through the most popular data formats 8, 16, 32 and 64 bits. Table 1 shows the obtained values of the parameters \(U_{delay}\) and \(U_{area}\) for the corresponding devices. After that, we fix the capacity \(k=16\) bits, and we sort through the orders 3, 7, 15 and 31 for the FIR DF. Table 2 shows the obtained values of the parameters \(U_{delay}\) and \(U_{area}\) for the corresponding devices.  
The data analysis obtained in Tables 1 and 2 shows that the transition from MAC units to TMAC units as the basis for FIR DF implementation can theoretically reduce filtering time by 22.39-29.86% and reduce hardware costs by 2.41-6.32%, depending on the filter order and bit width of the processed data.
# Full-Scale Model Test of Subway Contact Channel under Mechanical Construction +
## 4 Strain Results and Analysis
### Strain Results for the Standard Ring Segment  
Figure 16 shows the steel bar strain variation at the waist position of the standard ring segment. In the initial condition, the steel bar at the waist position of the internal arc was strained gradually as the initial loading increased. The steel bar strain of the waist position of the internal and external arcs leaned toward compressive strain. During the pre-support process of the internal support, the strain fluctuated slightly at first but then stabilized after a steep increase. Overall, the strain decreased unnoticeably with the cutting construction. Additionally, the steel bar at the waist position of the external arc was strained by each stage under the initial condition. Then, the strain fluctuated at the 10 th stage, with a fluctuation range of no more than 30 \(\mu\epsilon\). In the pre-support condition, the strain fluctuated with the alternating loading, and there appeared to be some slight increase in strain. Overall, the steel bar strain at the waist position of the external arc changed from compression to tension with cutting construction, and the compressive strain was reduced to 0.  
Figure 15: Variation of the concrete strain at the top position.  
Figure 17 shows the concrete segment strain variation at the waist position of the standard ring. In this figure, the concrete segment strain varied consistently at the waist position and the 225\({}^{\circ}\) direction of the standard ring. In the initial condition, the concrete was strained and the stress increased with the number of loading stages. In the pre-support condition, the concrete segment strain fluctuated slightly within a range, which was mainly related to the loading method of the internal support. This indicated that the alternate loading of the support at the top and the front and rear waist positions affected the concrete strain at the waist of the segment. In the cutting condition, with the increasing thrust of the shield machine, the concrete segment strain gradually decreased. In the support\(-\)dismantling condition, the concrete segment strain increased significantly, although it was not larger than the variation under the initial and pre-support conditions.
# Optimization of the Weighted Multi-Facility Location Problem Using MS Excel
## 3 Performance Evaluation
### Results
#### 3.3.1 Histograms and Solutions for Benchmark A  
From the data given in Tables 5-10 and on the histograms for benchmark A (see Figure 4), the influence of the used method and hardware configuration can be deduced. Unlike the metaheuristic method, the results of the evolutionary method depend on the performance of the hardware. The metaheuristic algorithm is often very close to the optimum value; however, in a minority number of cases it remained stuck in some local optima.
# Designing a Novel High-Performance FBG-OADM Based on Finite Element and Eigenmode Expansion Methods
## 3 Finite Element Method  
The calculation principles underlying the FEM [12; 16; 17; 18] are briefly explained here. The FEM can be applied to solve PDEs and it can be combined with numerical simulation methods for evaluating boundary conditions. The FEM is based on the variational principle, region segmentation, and interpolation functions. In the FEM, the variational principle is used to convert original problems (i.e., PDEs and boundary conditions) into functional functions to find the extrema. Next, geometric regions are divided, and hypothetically known element nodes are substituted into interpolation functions to describe the unknown functions in the elements. Then, multivariate linear equations are substituted into the functional functions, and boundary conditions are incorporated to identify the interpolation functions of all elements. When solutions to the interpolation functions are obtained, unknown nodes and the solutions of an entire region can be derived. The FEM procedure is summarized as follows: (1) convert the problems of PDEs and boundary conditions into quadratic functional functions to identify the extrema; (2) divide the geometric regions into subblocks, which are referred to as elements (e.g., triangular or quadrilateral elements); (3) assuming that the nodes in a triangular element are known, substitute the known nodes into the interpolation functions to create polynomial linear equations and describe the unknown variables in the elements; and (4) substitute the polynomial linear equations into the extrema-based functional functions while incorporating the boundary conditions to identify the solutions to the interpolation functions. By obtaining the unknown nodes using the interpolation function, calculate the solutions of the entire region.  
This study used FEM to solve the guided modes in an optical fiber structure. As shown in Figure 2, the OMM was used to obtain the standard mesh size, which was based on the geometric size of the objects [12]. Then, the BMM was used to obtain boundary meshes with a higher resolution. Since the numerical simulations were limited by memory and computation ability constraints, achieving pairwise orthogonality between modes was impossible. Thus, errors were unavoidable. Therefore, the acceptable maximum error value (i.e., the orthogonal value) was set at \(-40\) dB as the review standard. In other words, all mesh-cutting resolutions were required to produce modes with an orthogonal value of less than \(-40\) dB [9; 12; 19]. Mesh sizes that satisfied the review standard were obtained through adjusting the mesh-cutting resolutions and testing the orthogonal values. The orthogonal value equation is shown as follows [15; 19]:  
\[\int_{A\infty}E_{tv}\times H_{t\mu}\cdot\hat{z}dA=\int_{A\infty}E_{t\mu}\times H _{t\
u}\cdot\hat{z}dA=0\;for\;\
u\
eq\mu \tag{2}\]  
The mesh size used in this study was 1:5:17:43 (i.e., the object boundary/small object/medium object/large object ratio).  
Figure 2: BMM- and OMM-derived meshes for the FBG-OADM simulation.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Reverse Engineering  
As many embedded devices do not open their source code, we need to reverse the firmware to find bugs. The main challenge is in addressing the different instrument sets and identifying the logic. Recent studies have performed interesting work in IoT firmware binaries and protocols.  
Many smart home platforms connect tens of IoT devices via mobile apps and IoT clouds, but they do not provide enough security safeguards. [PERSON] et al. [58] analyzed five widely used smart home platforms and found that the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) have not been seriously considered. Combining firmware reverse engineering, network traffic interception, and blackbox testing, they identified a set of unexpected state transitions that could lead to remote device substitution, remote hijacking, remote DoS, illegal occupation, and firmware theft. Several new vulnerabilities were discovered in real-world smart home platforms.  
ICSREF [93] is a reverse engineering framework for PLC binaries and is widely used in industrial control systems (ICSs). It can reverse varied PLC binaries without previous knowledge and provide the fingerprint of a binary. Therefore, ICSREF can be deployed to analyze PLC malware and identify malicious code authorship attribution through fingerprinting. To evaluate the correctness of ICSREF, the authors built a database of PLC program binaries with source code, which can be used as benchmarks by the community.  
MISMO [26] is used to reverse embedded binary code in IoT control application domains. Specifically, reverse engineering performs a symbolic semantic-matching algorithm to match the target subroutine. The outcomes can also be used in vulnerability analysis and security mitigation. The authors evaluated MSIMO on more than 2k real-world firmware binaries from six application domains (drones, autonomous cars, smart homes, robotics, 3D printers, and the Linux kernel). The experimental results proved that MSIMO can accurately obtain a binary code's algorithm-level semantics and found a zero-day vulnerability in the most recent Linux kernel.  
PIE [112] is a parse and processing logic identifier that checks the security of closed source embedded devices. It combines data flow and template matching to analyze parser-like binary code. When used correctly, it can identify the user input of firmware componentsor extract protocols and discover memory-related bugs in input processing programs. The authors evaluates PIE on four real-world devices, and it extracted all commands from their protocol parsers, including hidden commands (backdoor).
# Electric Drive with an Adaptive Controller and Wireless Communication System
## 1 Introduction
### Related Work  
Different approaches can be observed in the published papers. Most of the research is focused on the control structures and does not provide sufficient discussion on experimental data acquisition and visualization. On the other hand, some papers present complex designs of the front-end applications for data visualization without the examination of plant control. The authors mainly deal with proper data presentation [45; 46], interaction with the user by using haptic-feedback features [47; 48] and the optimization of required hardware resources [49; 50]. Both of the approaches lack the integrity of the process, the control structure, and its state variables. This makes them difficult to compare directly, as different targets are defined for various purposes. Because the idea of Industry 5.0 is becoming very popular, it is essential to focus on interactive smart products and enable human-machine and human-robot collaboration [51; 52; 53]. This can only be achieved with user-friendly interfaces and smart control structures (using artificial intelligence). Thus, the presented research addresses the smart control structure of the neural speed controller extended with online data presentation.  
In this paper, a neural adaptive control structure is applied to control the speed of a two-mass drive. The experimental results obtained with an incremental encoder are sent to the end user via an ESP32 device. The visualization of the data is performed by a web-based HMI. In the first section, the mathematical description of the plant and the control structure is presented. Next, the working principle of the ADALINE neural predictor implemented in the proposed controller is discussed. The ABC algorithm is described later. The following section focuses on the numerical tests of the control structure performed in Matlab(tm)/Simulink. Afterward, the experimental results are presented. They serve as a practical verification of the simulation studies. Then, ESP32-based data transmission and visualization HMI are shown. The article is concluded with a brief analysis of the results obtained and final comments.
# Exceptional Points in Non-Hermitian Photonic Crystals Incorporated With a Defect
###### Abstract  
We explored exceptional points (EPs) in one dimensional non-Hermitian photonic crystals incorporated with a defect. The defect was asymmetric with respect to the center. Two EPs could be derived by modulating the normalized frequency and the gain-loss coefficient of defect. The reflection coefficient complex phase changed dramatically around EPs, and the change in complex phase was \(\pi\) at EPs. The electric field of EPs was mainly restricted to the defect, which can induce a giant Goos-Hanchen (GH) shift. Moreover, we found a coherent perfect absorption-laser point (CPA-LP) in the structure. A giant GH shift also existed around the CPA-LP. The study may have found applications in highly sensitive sensors.  
exceptional point; non-Hermite; photonic crystal; defect; Goos-Hanchen shift +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
In this paper, we investigated a stacked dual-patch antenna with 3D printed thick quasi-air substrates and a cavity wall for wideband applications. In order to achieve the theoretical maximum bandwidth of the patch antenna, a stacked dual-patch configuration was employed to the proposed antenna to extend the area of the conductive radiator. In addition, the square-pipe resin frames manufactured using the 3D printing method were applied to the proposed antenna to implement a quasi-air substrate structure that has a low permittivity close to 1. Therefore, the proposed antenna had a broad bandwidth of 620 MHz with a fractional bandwidth of 20.7%. Then, the bore-sight gain of the proposed antenna is 7.9 dBi, and the HPBWs in the E- and H-planes are 48\({}^{\circ}\) and 55\({}^{\circ}\), respectively. The results demonstrate that by using the proposed antenna structure, broadband characteristics close to the fundamental bandwidth limit of the patch antenna can be achieved, and at the same time, the durability of the antenna can be improved for various applications such as military radar systems, mobile communication systems, and wireless transfer systems.  
Conceptualization, D.J., H.C., and J.-Y.L.; formal analysis, D.J. and H.C.; funding acquisition, H.C.; investigation, D.J. and J.-Y.L.; methodology, D.J. and J.-Y.L.; project administration, H.C.; software, D.J. and J.-Y.L.; supervision, H.C.; validation, D.J., J.-Y.L., and H.C.; visualization, D.J.; writing--original draft, D.J.; writing--review and editing, D.J., [PERSON]-Y.L., and H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Research Institute for defense Technology planning and advancement (KRIT) grant funded by the Korea government (DAPA (Defense Acquisition Program Administration)) (No. KRIT-CT-22-021, Space Signal Intelligence Research Laboratory, 2022).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.