esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 2 Access Sharing Models
### Local Loop Unbundling (LLU)
#### 2.1.1 Line Sharing  
In line sharing, shown in Figure 1a, the incumbent operator provides some services to the subscriber while maintaining control over the copper pair. New entrants (i.e., OLOs) can lease a selected part of the copper pair spectrum. For example, the OLO could lease non-voice spectrum, i.e., at frequencies above 4 kHz, which are used for DSL broadband access. In this case, the incumbent continues to provide voice (or other) services to its customers as the shared access loop is connected to its network, while the Digital Subscriber Line Access Multiplexer (DSLAM) is operated by the new entrant. The Custom Premises Equipment (CPE) is typically provided by the OLO.  
One of the main challenges for line sharing was the hardware interface incompatibility. For example, the implementation of Asymmetric Digital Subscriber Line (ADSL) and Integrated Services Digital Network (ISDN) with telephony line required distinct spectrum allocations. Consequently, additional equipment was, at times, necessary for ADSL and splitting.
## 3 Results
### Regression Analysis  
The regression analysis (curve estimation) found that there is a significant (\(p\) < 0.001) linear relationship between PP2-PP1 and MM2-MM1, which can be presented by the following equation:  
PP2-PP1 = 0.719 \(\times\) (MM2-MM1) + 0.900  
The coefficient 0.719 in front of the independent variable (MM2-MM1) points out that when it is increased by one unit (in this case, 1 mm), the dependent variable PP2-PP1 increases on average by about 0.7 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Variables** & **PP2-PP1** & **L2-L1** & **l2-l1** & **aP2-aP1** & **PA2-PA1** \\ \hline  & MM2-MM & 0.749 *** & \(-\)0.051 & \(-\)0.098 & 0.662 *** & 0.683 *** \\  & PP2-PP & & \(-\)0.023 & \(-\)0.050 & 0.672 *** & 0.752 *** \\  & L2-L1 & & & 0.714 *** & 0.337 & 0.157 \\  & l2-l1 & & & & 0.351 * & 0.195 \\  & aP2-aP1 & & & & & 0.829 *** \\ \hline \hline \end{tabular} \({}^{*}\)=\(p\) < 0.05; ***=\(p\) < 0.001.  
\end{table}
Table 4: Correlation analysis between variables MM2-MM1, PP2-PP1, L2-L1, l2-l1, aP2-aP1 and PA2-PA1 in the RME device group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Variables** & **PP2-PP1** & **aP2-aP1** & **PA2-PA1** \\ \hline Treated with & MM2-MM1 & 0.719 *** & 0.726 *** & 0.932 *** \\  & RME & PP2-PP1 & & & 0.960 *** \\ \hline \hline \end{tabular} \({}^{***}\)=\(p\) < 0.001.  
\end{table}
Table 5: Coefficients between variables MM2-MM1, PP2-PP1, aP2-aP1, and PA2-PA1 in the RME device group, obtained by linear regression analysis.  
The linear regression model between PP2-PP1 and MM2-MM1 is presented graphically in Figure 4.  
To follow the dynamics of the change of PA2-PA1 as a consequence of the change of MM2-MM1, a regression equation with statistical reliability (\(p<0.001\)) was derived. This is shown graphically on Figure 5:  
PA2-PA1 = 0.932 \(\times\) (MM2-MM) \(-\) 0.289  
Figure 4: Scatter plot and linear regression model between PP2-PP1 and MM2-MM1. (R\({}^{2}\) = 0.561).  
Figure 5: Scatter plot and linear regression model between PA2-PA1 and MM2-MM1. (R\({}^{2}\) = 0.474).  
The coefficient 0.932 before the variable (MM2-MM1) means that when it increases by one unit (in this case, 1 mm), the dependent variable PA2-PA1 increases on average by about 0.9 mm.  
To follow the dynamics of the change of PA2-PA1 and PP2-PP1, a regression equation with statistical reliability (\(p<0.001\)) was derived. This is expressed by the equation:  
\[\text{PA2-PA1}=0.960\ \times\ \text{(PP2-PP1)}-0.212\]  
The coefficient 0.960 in front of the independent variable (PP2-PP1) means that when it increases by one of its measurement units (in this case, 1 mm), the dependent variable PA2-PA1 increases on average by about 1.0 mm. A scatterplot and linear regression model between PA2-PA1 and PP2-PP1 are shown in Figure 6.
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 2 Methodology
### YOLO (You Only Look Once)  
YOLO approaches object detection as a single regression task, directly predicting bounding boxes and class probabilities from the input image in one forward pass. A convolutional backbone, typically CSPDarknet-inspired modules, is used to extract rich spatial features. These features are then processed by a feature aggregation neck, such as a Feature Pyramid Network (FPN) or Path Aggregation Network (PAN), which enhances multi-scale detection by combining spatial and semantic information across different resolutions. The resulting feature maps are fed into multiple detection heads that simultaneously predict object classes, bounding box coordinates, and objectness scores. Recent iterations, such as YOLOv8, introduce anchor-free detection and partially decoupled heads for classification and regression, improving performance on small or overlapping objects while maintaining real-time inference capabilities.  
Figure 1 illustrates the general architecture of the YOLOv8 model, highlighting a convolutional backbone based on Concatenate-to-Fuse (C2F) modules for feature extraction, a PAN-style neck for multiscale feature fusion, and detection heads operating at three different resolutions. In the following subsections, we provide detailed descriptions of the YOLO variants used in this comparative study. The information presented is based on the official documentation from the Ultralytics repository [18].  
Figure 1: General high-level architecture of YOLOv8.
## 3 Mobile Object-Following System Using LiDAR  
### Object Classification Using Density-Based Clustering  
The density-based clustering technique identifies objects by clustering data points from the LiDAR sensor based on spatial density, determining the continuity of points around a data point using density within a defined radius. This approach is particularly effective when using data from low-specification LiDAR sensors [23]. As shown in Figure 3, a given data point 'p' is considered a neighbor if at least one additional 'p' exists within a radius '\(\epsilon\)'.  
By setting '\(\epsilon\)' to a value within 0.05 m for the target object in front of the LiDAR sensor, continuous objects could be identified, as confirmed by the distribution of points shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Specification** \\ \hline Measurement frequency & 10,000 Hz\(-\)18,000 Hz \\ Scan frequency & 5 Hz\(-\)12 Hz \\ Detection range & 0.12 m\(-\)16 m \\ Scanning angle & 360\({}^{\circ}\) \\ Angular resolution & 0.1\({}^{\circ}\)\(-\)0.24\({}^{\circ}\) \\ Supply voltage & 4.8 V\(-\)5.2 V \\ Operating current & 350 mA (average), max 500 mA \\ Peak current (startup) & 1000 mA \\ Laser wavelength & 775 nm\(-\)800 nm (infrared) \\ Laser output & Average 3.5 mW, max 6 mW (Class I eye safety) \\ Operating temperature & 0 \({}^{\circ}\)C\(-\)50 \({}^{\circ}\)C \\ Storage temperature & \(-\)10 \({}^{\circ}\)C\(-\)60 \({}^{\circ}\)C \\ Weight & 214 g \\ Data communication & 3.3 V level UART, baud rate 512,000 bps \\ Interface & PH2.0-5P connector for power and data communication \\ Optical communication and power & Wireless optical\(-\)magnetic fusion technology for wireless communication and power supply \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the YDLiDAR G6 sensor used in this study. From a comprehensive evaluation standpoint, the LiDAR’s maximum power draw (approximately 2.5 W) amounts to only about 0.17 percent of the SCOUT 2.0’s total battery capacity.  
Figure 4: LiDAR sensor data point distribution and density-based clustering method.
# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 2 Previous Work  
Computerized adaptive testing (CAT) has been widely explored as a promising approach to improve the efficiency and accuracy of tests. Several approaches have been developed to tailor the test content to individual test takers based on their performance, resulting in more precise and informative evaluations [7]. Previous studies using CAT have focused on techniques such as item selection algorithms [8], response models [9], and stopping rules [10]. For instance, Item Response Theory (IRT) has been extensively utilized to estimate the subject's ability level and guide the selection of subsequent test items. These approaches, however, often assume specific characteristics of the data or their distribution (e.g., the normality of the distribution) and do not perform well when these assumptions are not met. The ML-based CAT algorithm introduced in this paper is free of these restrictions.  
Balance assessment tests are necessary to evaluate the risk of fall, especially among older adults. It is highly beneficial to shorten these testing sequences. The lengthy BESTest [11] assessment of fall risk has been manually shortened, resulting in shorter tests such as the Mini-BESTest [12] and the Brief-BESTest [13]. Nevertheless, these approaches are non-adaptive and do not tailor the testing process to an individual's performance. Consequently, there remains a need for an adaptive approach that dynamically adjusts the testing sequence based on the individual's responses and abilities.  
Machine learning (ML) involves complex data-driven algorithms that rely heavily on training data and are challenged in generalizing to novel inputs. This is especially true when the number of samples in the training dataset is small, and overfitting may occur [14]. When analyzing data collected from human subjects performing motor tasks, we typically encounter the problem of a small sample size [15]. Some ML algorithms are more attuned to dealing with these problems than others, e.g., decision trees [16] and random forest [17,18]. These and other ML algorithms also allow for a process of feature ranking [19] so that features with high predictive power can be determined. This, in turn, can guide feature reduction to overcome overfitting due to the small sample size [20,21]. In [22], the authors developed an adaptive algorithm that classifies the risk of fall into three categories (low, middle, or high). Building upon their work, the current study aims to enhance the performance of the method in [22] by developing an efficient and adaptive fall risk assessment tool that predicts actual balance test scores. The proposed ML-based CAT approach (ML-CAT) determines the most accurate, concise, and feasible components from among a large battery of balance tests that are best suited for each individual in real-time balance testing. Furthermore, the approach is also shown to be effective in overcoming the limitations of remote testing by eliminating tests that are not safe or reliable to perform in a home setting.
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 3 Results and Discussions
### Corrosion Rate, V\({}_{corr}\), of Zr2.5 Nb Alloy in Ringer's Solution  
The possibility of measuring the corrosion rate, V\({}_{corr}\), directly and in real time (\(\mu\)m/year) can be achieved exclusively by analyzing the evolution of the polarization resistance (\(R_{p}\)). The densities of the corrosion currents determined by the relation \(i_{corr}=I_{corr}/S\) were estimated by the inverse proportionality of the values collected from the linear polarization curves (\(R_{p}\)) and Equations (1) and (2), the corrosion rate as the penetration rate resulted after applying [PERSON]'s law [20]. In other words, the higher the polarization resistance values, the lower the corrosion rates.  
The diagram with the corrosion rates at different immersion periods for the Zr2.5 Nb alloy immersed in the Ringer's solution are shown in Figure 5.  
Figure 4: Evolution of the polarization resistance of the Zr2.5 Nb alloy in Ringer’s solution: (**a**) the polarization resistance segment (Rp1), (**b**) the polarization resistance segment (Rp2), and (**c**) the polarization resistance segment (Rp3).  
Figure 5: Corrosion rate (V\({}_{corr}\)) for Zr2.5 Nb alloy immersed in [PERSON]’s solution: (**a**) primary segment (V\({}_{corr1}\)), (**b**) secondary segment (V\({}_{corr2}\)), and (**c**) tertiary segment (V\({}_{corr3}\)).  
Figure 5a shows that the corrosion rate recorded for the Zr2.5 Nb alloy in the first segment reaches a value of Vcorr = 2.37 \(\pm\) 0.9 \(\upmu\)m/year. After about 21 h, the value of the corrosion rate reaches 2.31 \(\pm\) 0.5 \(\upmu\)m/year. At the end of the 37 h, it has a value of 2.29 \(\upmu\)m/year. This decrease is a relatively small difference between the value of Vcorr after 6 h and at the end of the 37 h, which is 0.08 \(\upmu\)m/year.  
In conclusion, the values of the corrosion rate recorded on the surface of the Zr2.5 Nb alloy classify the studied material within the group with good stability [14].
## 2 Overview of Modern Technologies and Frameworks in Smart Healthcare Systems
### Internet of Things-Based Smart Healthcare Systems  
Previously, because the separate components of the healthcare system were not interconnected, the system was not capable of continuously delivering medical services to patients. Hence, it was challenging for medical practitioners to continue monitoring their patient's health and give recommendations accordingly. The incorporating of IoT technology in the healthcare sector allows doctors to remotely diagnose and treat their patients and enables medical staff to provide high-level care to their patients. This is accomplished by lightweight IoT devices (sensors) that are deployed in the healthcare environment, where each sensor has the potential to accumulate data from the surrounding environment (e.g., patient's body) and forward it to various healthcare applications via the Internet. IoT allows patients to send their real-time medical data, such as blood pressure, oxygen levels, calorie consumption, and heart rate, using wearable sensors attached to the body. With the help of sensors placed on the body, the doctors can pervasively monitor them (continuous monitoring) and suggest recommendations as per their live medical data. Here, the patients do not have to come to the hospital; instead, it directly connects to the doctor via a remote connection from their home. Due to this, the vital bed is free for other patients who need emergency intensive care. Moreover, the healthcare system has many physical assets, such as oxygen cylinders, nebulizers, wheelchairs, oxygen meters, heart monitors, personal protective equipment, and surgical tools that must be properly managed so that one can easily track down their numbers and locations in the emergency situations [23; 24]. IoT-based healthcare asset management helps to track these assets in no time. For example, in [25], the authors discuss the COVID-19 global pandemic that requires advanced medical facilities in a short span of time. To this end, they proposed an IoT-based healthcare monitoring architecture that offers hybrid communication, colossal medical screening, and cloud-based data centres to help hospitals, rescue teams, and first aid units. Further, [PERSON] et al. in [26] presented an edge-enabled IoT healthcare management system intended to improve the performance of the monitoring, diagnosis, and management of the patient's data. They proposed a system that monitors different medical tasks and manages various healthcare activities using database systems. However, because of the system's centralized storage, the attacker can breach its security.
# Improvement of Dung Beetle Optimization Algorithm Application to Robot Path Planning
###### Abstract  
We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5-7.2% on a 10 \(\times\) 10 grid and by 11.9-14.6% on a 20 \(\times\) 20 grid.  
Keywords:dung beetle optimization algorithm; dynamic spiral search; adaptive t-distribution perturbation; single-objective optimization; path planning +
Footnote †: journal: Applied sciences
# Map API-Based Evacuation Route Guidance System for Floods
###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**1980–1999** & **Exthquake** & **Extreme** & **Flood** & **Landslide** & **Mass** & **Storm** & **Volcanic** & **Wildfire** \\ \hline
263 & 445 & 130 & 1380 & 263 & 27 & 1457 & 84 & 163 \\ \hline
**2000–2019** & **Total number of disasters by type** & & & & & & \\ \hline
**Drought** & **Earthquake** & **Extreme** & **Flood** & **Landslide** & **Mass** & **Storm** & **Volcanic** & **Wildfire** \\ \hline
338 & 552 & 432 & 3254 & 376 & 13 & 2043 & 102 & 238 \\ \hline
**Difference in total number of accidents by type between 1980–1999 and 2000–2019 (increase or decrease)** & **Flood** & **Landslide** & **Mass** & **Storm** & **Volcanic** & **Wildfire** \\ \hline
75 & 107 & 302 & 1874 & 113 & \(-\)14 & 586 & 18 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total number of disasters by type: 1980–1999 vs. 2000–2019 [11]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**1980–1999** & **(Unit: 1 million \$)** & **US\$ Economic** \\
**Reported disasters** & **Total deaths** & **Total affected** & **doses** \\ \hline
4212 & 1.19 T & 3.25 T & 1.63 T \\ \hline
**2000–2019** & & & & \\ \hline
**Reported disasters** & **Total deaths** & **Total affected** & **US\$ Economic losses** \\ \hline
7348 & 1.23 M & 4.03 B & 2.97 T \\ \hline
**Change in disaster impact (increase or decrease)** & & & \\ \hline
**Reported disasters** & **Total deaths** & **Total affected** & **US\$ Economic losses** \\ \hline
3136 & \(-\)1.18 T & \(-\)3.24 T & 1.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Disaster impact: 1980–1999 vs. 2000–2019 [11]. level, and meteorological data were used as input data. Based on this, we propose an evacuation route system that predicts flash floods and simultaneously uses a map API to identify the nearest shelter by identifying the user's current location route. To develop the shelter system, an appropriate testbed was selected through field investigation; and shelter data in the testbed area were created, stored, and utilized as a dataset. In this regard, we propose an evacuation system that creates a route-search environment using a map API and provides users with the shortest route search. Section 2 presents the research methodology, Section 3 presents the system design of this study, and Section 4 presents the system implementation and considerations. Finally, Section 5 presents the conclusions of the study.
## 3 Nature of Input Dimension and Overfitting
### Explanation for the Input Dimension Based on Vibration Signals  
#### 3.2.1 Data Description  
In this section, the bearing dataset [33] provided by Case Western Reserve University were employed for analysis. The vibration signals were collected using accreometers from the drive end of a motor under four different operating conditions: normal condition (NC), inner race fault (IF), outer race fault (OF), and roller fault (RF). There were three different severity levels (0.18, 0.36, and 0.53 mm) for IF, OF, and RF cases, respectively. All the samples were collected under four different loads (0, 1, 2, and 3 hp) and the sampling frequency was 12 kHz. Therefore, the dataset included ten health states under four loads, and we treated the same health state under different loads as one class.
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
###### Abstract  
Data-driven fault diagnosis is considered a modern technique in Industry 4.0. In the area of urban rail transit, researchers focus on the fault diagnosis of railway point machines as failures of the point machine may cause serious accidents, such as the derailment of a train, leading to significant personnel and property loss. This paper presents a novel data-driven fault diagnosis scheme for railway point machines using current signals. Different from any handcrafted feature extraction approach, the proposed scheme employs a locally connected autoencoder to automatically capture high-order features. To enhance the temporal characteristic, the current signals are segmented and blended into some subsequences. These subsequences are then fed to the proposed autoencoder. With the help of a weighting strategy, the seized features are weight averaged into a final representation. At last, different from the existing classification methods, we employ the local outlier factor algorithm to solve the fault diagnosis problem without any training steps, as the accurate data labels that indicate a healthy or unhealthy state are difficult to acquire. To verify the effectiveness of the proposed fault diagnosis scheme, a fault dataset termed \"Cu-3300\" is created by collecting 3300 in-field current signals. Using Cu-3300, we perform comprehensive analysis to demonstrate that the proposed scheme outperforms the existing methods. We have made the dataset Cu-3300 and the code file freely accessible as open source files. To the best of our knowledge, the dataset Cu-3300 is the first open source dataset in the area of railway point machines and our conducted research is the first to investigate the use of autoencoders for fault diagnosis of point machines.  
Keywords:railway point machines; urban rail transit systems; fault diagnosis; autoencoder +
Footnote †: journal: Applied Sciences
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 2 Preliminaries
### Weaknesses of [PERSON]'s Scheme  
[PERSON]'s scheme has two weaknesses. First, because ID\({}_{\rm HA}\) is not properly hidden, an attacker can easily intercept it and determine the relationships among the MU, FA, and HA. In addition, it does not use an authentication mechanism between the MU and the FA, and therefore, for any message m\({}_{1}\) to the FA, the FA performs the appropriate processing and transfers the results to HA. Because of this feature, the attacker can flood a specific target (HA), intercept other people's message (m\({}_{1}\)) over the wireless network, and change the message timestamp T\({}_{\rm MU}\). Although the message will not be verified through the HA, the attacker can do enough to cripple a specific target host (HA), namely, the attacker can perform a distributed denial-of-service attack. Second, [PERSON]'s scheme does not clearly define or address some important issues, such as the assignment of session key computation during the roaming phase and how to manage the various session keys for a large number of users. If there are hundreds of thousands of people in a wireless network environment, and each person's session key is different, managing the keys is not trivial. Table 2 shows a detailed description of the weaknesses of [PERSON]'s scheme [18].