esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### The Result of Re Based on Different Methods  
To verify the accuracy of the analytical model formulated as Equation (17) presented in this paper, regardless of the limitations of transponder reply rate and DME capacity, we simulate and calculate _Re_ based on Equations (16), (17) and a Monte Carlo method. For simplicity, we assume that DME signals are thought to be valid unless they are collided. Simultaneously, DME signals can be received and processed as long as they are valid signals. Simulation parameters are shown as follows:  
* \(f_{t}\ =15\) Hz, \(f_{s}\ =150\) Hz.
* 95% of aircraft are in track mode and others are in search mode.
* DME operates in mode X, \(\tau_{m,i}\ =\ \tau_{m,r}=15.5\) us and \(t_{d}\ =60\) us.
* PRF of identification pulse: \(f_{id}\ =169\) Hz.
* Inter-system interference is absent.
* Simulation time: 1 s.
* Number of aircraft: variation from 5 to 600 every 3.
* Number of Monte Carlo simulation: 1000.  
Figure 5 plots the analytic and simulation results of RE (equivalent to probability of reply) versus the number of aircraft. The numbers on the curve are interrogation pulse density \(\lambda_{i}\) as defined in Figure 4, for example, the first number 75 means \(\lambda_{i}=75\) pulses per second. Figure 5 shows that \(\lambda_{i}\) increases with the number of aircraft; accordingly, DME intra-system interference increases with \(\lambda_{i}\); as a result, RE decreases with a rise in the number of aircraft just as shown in Figure 5. It can be seen that the results based on Equation (16) are consistent with simulations only when the number of aircraft is less than about 100, the difference of results based on Equation (16) and simulations increases with the number of aircraft, which results from the assumption in the derivation of Equation (16). As noted in the previous section, Equation (16) assumes that none of the signals overlap with dead time. However, collision probability increases with the number of aircraft, such assumptions are not supported at all; therefore the error rises with the increase of number of aircraft.  
Figure 5 shows that the difference between results based on Equation (17) and simulations increases with the number of aircraft, but the results based on Equation (17) are only 0.6% and 2.1% less than simulation when the number of aircraft are 100 and 575, respectively; however, the result based on Equation (16) is 12.3% less than simulation when the number of aircraft is 575. In general, the results based on Equation (17) agree well with simulation; the reason is that queueing theory model solves the problem of intra-system interference compared to Equation (16); hence, the error of Equation (17) is less than that of Equation (16) when the number of aircraft is large. Note, Equation (17) is based on a large number of aircraft; in fact, owning to the small duty cycle and PRF of DME interrogation, when the number of aircraft is small, intra-system interference is very little; in conclusion, Equation (17) can be used whether the number of aircraft is small or not.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
###### Abstract  
High precision position control is essential in the process of parts manufacturing and assembling, where eddy current displacement sensors (ECDSs) are widely used owing to the advantages of non-contact sensing, compact volume, and resistance to harsh conditions. To solve the nonlinear characteristics of the sensors, a high-accuracy calibration method based on linearity adjustment is proposed for ECDSs in this paper, which markedly improves the calibration accuracy and then the measurement accuracy. After matching the displacement value and the output voltage of the sensors, firstly, the sensitivity is adjusted according to the specified output range. Then, the weighted support vector adjustment models with the optimal weight of the zero-scale, mid-scale and full-scale are established respectively to cyclically adjust the linearity of the output characteristic curve. Finally, the final linearity adjustment model is obtained, and both the calibration accuracy and precision are verified by the established calibration system. Experimental results show that the linearity of the output characteristic curve of ECDS adjusted by the calibration method reaches over 99.9%, increasing by 1.9-5.0% more than the one of the original. In addition, the measurement accuracy improves from 11-25 \(\upmu\)m to 1-10 \(\upmu\)m in the range of 6 mm, which provides a reliable guarantee for high accuracy displacement measurement.  
eddy current displacement sensor; calibration; linearity adjustment; weighted support vector machine +
Footnote †: journal: Sensors
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 1 Introduction
### Principles of Wound Healing  
Wound healing is a complex biological process that restores the integrity and function of damaged tissue. It involves a coordinated series of cellular and molecular events divided into distinct but overlapping phases: hemostasis, inflammation, proliferation, and remodeling (maturation). Each phase plays a crucial role in achieving successful wound closure and tissue repair. The hemostasis phase is characterized by vasoconstriction, platelet activation, and the formation of a fibrin clot. Platelets aggregate at the injury site, releasing clotting factors and signaling molecules, such as platelet-derived growth factor (PDGF) and transforming growth factor-beta (TGF-\(\beta\)). These factors recruit other cells essential for subsequent phases of healing. The fibrin clot not only halts bleeding but also provides a scaffold for cellular migration. The proliferative phase is marked by the formation of new tissue and typically lasts several days to weeks. Fibroblasts, a key cell type in this phase, migrate into the wound and deposit extracellular matrix (ECM) proteins, including collagen. Angiogenesis, driven by VEGF, occurs as endothelial cells form new capillaries to supply oxygen and nutrients. Concurrently, keratinocytes proliferate and migrate across the wound bed, facilitating re-epithelialization. The granulation tissue formed during this phase provides a foundation for further remodeling. The final phase of wound healing can last weeks to months, depending on the wound size and systemic factors. During remodeling, type III collagen in the ECM is replaced by type I collagen, which has greater tensile strength. Myofibroblasts contract the wound edges, reducing its size. Blood vesselsthat were formed in excess during the proliferative phase undergo apoptosis, leaving a more organized vascular network [17].  
This review offers an in-depth examination of wound dressings, highlighting their critical features for effective wound healing, particularly focusing on data from the last five years. It provides an exploration of various natural and synthetic polymers, detailing their origins, structures, and inherent properties. Additionally, the review presents a critical analysis of antimicrobial dressings. This manuscript stands out by not only delivering comprehensive information but also offering a high-quality discussion of their key attributes, which are essential for the successful development of wound-healing dressings.
## 4 Theoretical Calculation and Analysis
### Finite Element Model
#### 4.3.1 Parameter Definition  
The main parameters included elastic modulus, specific heat, and thermal conductivity. As shown in Table 3, two material models were defined as the foundation and the ground (the strength grade of concrete is C35).
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
###### Abstract  
Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.  
Keywords:source localization; optimization algorithm; data mining; complex networks +
Footnote †: journal: Applied Sciences
## 4 Experimental Results
### Discussion about the Hyper-Parameters of the Proposed Semi-Supervised Learning
#### 4.5.2 Different Batch Size for Self-Supervised Learning  
We discussed the effect of different batch size in the self-supervised stage. In this experiment, according to the linear scaling rule [62], the learning rate changed according to the batch size. The rule of learning rate change was as follows:  
\[\text{lr}=\frac{\text{base\_lr}\times\text{BatchSize}}{256} \tag{11}\]  
where the base_lr denotes base learning rate, which was set to \(5\times 10^{-2}\). The remaining experimental settings were the same as the previous experiment.  
The results with a batch size for self-supervised learning from 16 to 256 are reported in Table 6. This result shows that the optimal fine-tuning accuracy is obtained when the batch size was set to 32 for self-supervised learning. We also observe that there is a serious performance drop when the batch size increases from 64 to 256. When the batch size decreases to 16, there is a small performance change within the level of random variations. It is worth noting that these results are obtained from our training on the FeatureNet machining feature dataset and do not represent a consistent performance on other datasets.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results  
The following subsections present the results and show how accurate and stable S-1A and S-1B are in terms of measuring radiometric backscatterer. The comparison of the results between S-1A and S-1B with the same geometric conditions (e.g., elevation angle) shows the intercomparability between both SAR instruments. The different target types focus on different backscatter regimes covering point targets with high SNR but also distributed targets such as the Amazon rainforest with medium SNR, down to backscatter near noise found in ice areas and calm waters.
## 2 Materials and Methods
### Anthropometric Parameters  
Height and body mass were assessed using a stadiometer with a resolution of 0.1 mm (AlturaExata(r), Belo Horizonte, Brazil) and a digital scale with a resolution of 0.01 kg (Tanita(r) BF683W, Arlington Heights, IL, USA). Participants were instructed to wear light clothing and be barefoot during the test. BMI was calculated using a body mass and height ratio (BMI = body mass/height2) and z-scores were applied to investigate the participants' weight status, their height-for-age and their weight-for-age. BMI was classified using the World Health Organization (WHO) growth charts [13; 14].  
The circumferences of the hip, waist and mid-upper arm were measured with an anthropometric tape of 0.1 cm resolution ([PERSON](r), Sao Paulo, Brazil). Hip and waist circumferences were used to calculate the waist-to-hip ratio (waist-to-hip ratio = waist circumference [cm]/hip circumference [cm]), and height and waist circumferences were used to calculate the waist-to-height ratio (waist-to-height ratio = waist circumference [cm]/height [cm]). The triceps and the subscapular, abdominal and calf skinfold were measured with a 0.01 mm-resolution caliper ([PERSON](r), Porto Alegre, Brazil), and the sum of skinfolds was calculated using the values obtained for the four measured skinfolds (sum of four skinfolds = abdominal skinfold [mm] + triceps skinfold [mm] + subscapular skinfold [mm] + calf skinfold [mm]). Bone diameters, humeral and femoral, were measured with a 0.1 mm-resolution digital caliper ([PERSON](r), Sao Paulo, Brazil), and the sum of diameters was calculated using the two measured diameters (sum of diameters = humeral diameter [cm] + femoral diameter [cm]) (Supplementary Table S1). The choice of anthropometric parameters involved the consideration of previous studies that investigated the following: (1) the association between anthropometric parameters and health parameters in HIV-diagnosed children and adolescents [40; 41; 42; 43] as well as their association with alterations in body composition [44; 45]; (2) the validity of equations used for estimating body fat [30] and bone parameters [32] in this population. For quality control, all anthropometric evaluations were carried out in accordance with the recommendations of the International Society for the Advancement of Kinanthropometry (ISAK) [46] by ISAK-certified evaluators. Inter-evaluator and intra-evaluator technical errors of measurement were estimated at 0.33% and 0.23%, respectively. These estimates were calculated for waist circumference measurements in a population of a similar age range to that of the present study.
## 4 Results
### Comparative Analysis of DVS and RGB Sensor Security  
Our experiments provide valuable insights into the relative security characteristics of traditional RGB cameras and emerging DVS technology:  
* **Research Context**: It is important to emphasize that the DVS cameras in our study were used exclusively for research purposes and were not integrated into the vehicle's driving decision model. The data collected from DVS cameras was analyzed separately from the main autonomous driving pipeline.
* **Defense Effectiveness**: Our defense mechanisms were evaluated separately for each sensor type. RGB cameras, which are actively used in the driving model, required more aggressive filtering that sometimes reduced image quality. DVS defenses were studied in isolation as a research component.
* **Anomaly Detection**: The anomaly detection models for both sensors were designed to work exclusively with their respective sensor data. The RGB model showed clearer separation between normal and attack conditions and directly impacted vehicle safety, while the DVS model's patterns were analyzed purely for research insights.
* **Future Potential**: While not currently used for driving decisions, the complementary nature of RGB and DVS cameras suggests that sensor fusion approaches could potentially enhance security in future implementations.  
As shown in Table 1, these findings suggest that incorporating DVS technology alongside traditional sensors can enhance the security posture of autonomous vehicles, particularly in high-dynamic-range scenarios where RGB cameras are more likely to be compromised by environmental factors or deliberate attacks.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 7 Discussion  
As most of the current research points out, the upcoming 6G technology is anticipated to create a highly connected society with diverse applications and services. The distributed ML technology has acquired a central place in the 6G research and is expected to play an important role in shaping the 6G world. However, it is important to analyze the various distributed learning solutions available in the literature for using them for various 6G applications. Through surveying the current distributed learning solutions, we have individuated how they can be effectively used in the 6G technology. As analyzed, DL and RL are two of the main ML approaches, and have gained lots of attention in the last decade. However, distributed learning approaches are becoming increasingly considered; in particular, FL and MARL are two of the main distributed learning techniques used to solve the different challenging problems in wireless communication research currently. In particular, FL and MARL are very important for solving various 6G technology scenarios. As highlighted, there could be various innovative solutions associated with distributed learning-based research. These solutions are expected to play key roles in the upcoming days in 6G related solutions.
## 1 Introduction
[13] considered the impact of ramp factors on emissions in the vehicle routing problem (VRP) and proposed a two-objective strategy for energy consumption minimizing low-carbon Vehicle routing problems (ECM-LCVRP) in different road gradient environments. Based on the classical TDVRP, [PERSON] and [PERSON]. [14] constructed a model of the urban distribution problem that comprehensively considered energy conservation, low carbon and cost saving and further minimized economic cost, including the above three factors; the goal was to plan the vehicle routing problem. On the basis of studying the vehicle fuel consumption model, time-window penalty function and speed optimization strategy, [PERSON] et al. [15] proposed a variable-speed vehicle routing optimization model with a time window so as to solve the problem of the difficulty of a vehicle traveling with constant speed to meet the time-to-service requirement of its customers. A low-carbon pickup and delivery vehicle routing problem was proposed by [PERSON] et al. [16], the adaptive genetic hill-climbing algorithm was designed to solve the optimization model, which considers the carbon tax policy. [PERSON] et al. [17] analyzed the pickup and delivery vehicle pollution routing problem with multi-objectives, and the total traveling time, the emission of greenhouse gases and the number of customers were considered in the model. An evolutionary algorithm was designed to solve this problem. The multi-objective regional low-carbon location routing problem was proposed by [PERSON], [PERSON] [18]. The total cost, time and service duration were considered in the model, three multi-objective evolutionary algorithms were designed based on the complexity of the proposed problem. [PERSON], [PERSON], et al. [19] described an open vehicle routing problem with time windows, and the low-carbon open vehicle routing problem with time-windows model was established, and the goal was minimum total costs. A two-phase algorithm was designed to handle the model. [PERSON], et al. [20] analyzed the green open vehicle routing problem with time windows. The comprehensive modal emission model (CMEM) was established, and a hybrid tabu search algorithm with several neighborhood search strategies was designed to handle this problem. Based on the analysis above, it can be concluded that the former research on vehicle routing problems with time windows (VRPTW) and PEP focused on the factors of traffic congestion, vehicle composition and vehicle load, etc., rather than multi-path vehicle routing problems [21; 22; 23]. Therefore, a multi-path mixed-integer mathematical programming model with a time window was established,which aimed to optimize fuel cost, driver cost, vehicle depreciation and time-window penalty cost and determine the exact solution using CPLEX in Java. Finally, the applicability and feasibility of the model were verified with the example of Jingdong's (JD) logistics in the main urban area of Chongqing, and sensitivity analyses were performed on this model.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### 50-Node Scenario
#### 6.1.2 GA-Based Approach  
This subsection evaluates performance of a GA-based solution. Figure 4 demonstrates the GA's achieved path planning solution in 1000 iterations in the scenario in Figure 2.  
In Figure 4, the GA-based approach achieves a path length of 6227 m in 1000 iterations as a metaheuristic which approaches the solution iteratively.
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 3 Results  
In this study, a series of statistical analyses have been performed to evaluate the difference between the delays of LI and OI. In order to perform the analyses correctly, firstly, the Shapiro-Wilk test was applied to determine whether the delay data were normally distributed. According to the results of the Shapiro-Wilk test, both the LI delay data (Shapiro-Wilk test statistic = 0.370, \(p\) < 0.05) and the OI delay data (Shapiro-Wilk test statistic = 0.322, \(p\) < 0.05) did not fit a normal distribution. Time difference distributions are shown in Figure 3.  
Based on these results, it was concluded that parametric statistical tests could not be used and the Mann-Whitney U test, a non-parametric test, was preferred. The results of the Mann-Whitney U test showed a statistically significant difference between local and online latency (U = 794.0, \(p\) < 0.05). Table 1 shows the Mann-Whitney U test results.  
According to descriptive statistics, the MAD value for the LI delay was 0.004, the median value was 0.064, the minimum value was 0.020, and the maximum value was 0.660. Similarly, the MAD value for the OI delay was 0.006, the median value was 0.244, the minimum value was 0.101, and the maximum value was 1.123. All the results of the descriptive statistics are shown in Table 2.  
These findings indicate that there is a statistically significant difference between LI and OI delays and that there is a significant difference in their performance.  
In addition, analysis of the memory usage data revealed interesting results (see Table 3). The average increase between seconds was measured as 0.0037 MB. The average memory usage during the session was measured as 72.62 MB with a minimum of 63.74 MB, and the maximum memory usage was 83.62. The standard deviation of memory usage was calculated at 3.48 MB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **U-Statistic** & \(p\)**-Value** \\ \hline Local vs. online & 794.0 & 3.317643 \(\times\) 10\({}^{-25}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mann-Whitney U test results regarding the local and online conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **MAD** & **Median** & **Min** & **Max** \\ \hline Local & 0.004 & 0.064 & 0.020 & 0.660 \\ Online & 0.006 & 0.244 & 0.101 & 1.123 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics.  
Figure 3: Comparison of time differences between the online and local systems.  
Figure 4 shows an initial low level of memory usage that increases over time, with a steady increase over a period of time. Although there are occasional fluctuations, the average memory usage (red dashed line) is generally above the curve and shows a steadily increasing trend. These results show that the memory usage of the system varies over time and reaches a stable level over a period of time.