esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology  
Accordingly, the proposed study introduces an effective extended the OLSR protocol with a deep-learning model for enabling effective video streaming in MANETs. The steps involved in the proposed study are data collection, black-hole detection with trust-based optimal reliable routing, blockchain storage, and validation. Initially, the input video data are gathered from publicly available sources. Then, for identifying black-hole nodes, the proposed study introduces a new twin-attention-based dense convolutional bidirectional gated network (SA_DCBiGNet) model. Then, a trust value is generated to analyze the reliability of neighboring nodes, based on the trust values. After, the routing is performed using the extended osprey-assisted optimized link state routing protocol (EO_OLSRP). Figure 1 shows the block diagram of the proposed method.  
Here, the osprey optimization algorithm (OOA) selects the optimal path based on parameters such as node-stability degree and link-stability degree. Finally, blockchain storage is enabled to enhance the security of MANET information through interplanetary file system (IPFS) technology. The validation process of the proposed blockchain framework is performed by utilizing the delegated proof-of-stake (DPoS) method. Thus, the proposed study effectively secures MANETs against unauthorized parties and thereby enhances confidentiality. The attained superior performance in the proposed study states that the developed extended OLSR is a suitable protocol for MANET video transmission applications. An effective open-source framework for simulating different network situations, including mobile devices is ns-3. It is an important tool for researchers and developers since it offers an extensive collection of models and modules to precisely depict the operation of mobile networks. Also, this study proposes the performance of mobile networks can be better understood by using ns-3 mobile simulation models. Through precise simulation of mobility, channel propagation, MAC protocols, and applications, we can obtain significant insights and enhance network architectures for enhanced mobile experiences.
# Effects of Oxidized Pyrenes on the Biological Responses in the Human Bronchial Epithelial Cells
## 1 Introduction  
Short- and long-term exposure to particulate matter (PM) in polluted air can cause serious respiratory problems. In particular, fine particulate matter (aerodynamic diameter \(\leq\) 2.5 um, PM2.5) can penetrate the bronchial and alveolar layers of the airway and respiratory tract, resulting in lung and cardiovascular diseases [1, 2]. Previous experimental and epidemiological studies have shown that PM2.5 causes several respiratory illnesses, including pneumonia, asthma, acute bronchitis, and lung cancer [3, 4]. However, despite the available information on the respiratory toxicity of PM, the contribution of each PM component remains unclear. Atmospheric PM is a mixture of inorganic compounds (including oxides of transition metals), dust, smoke, elemental metals, various liquid and solid substances, and biological agents including bacteria, fungi, and viruses [5]. Among these, organic components, such as polycyclic aromatic hydrocarbons (PAHs), created by the combustion of fossil fuels, have been studied [6, 7]. In our previous study [8], we used a simulated experimental system to demonstrate that PAHs are nitrated on the surface of yellow sand, a form of PM, by the catalytic action of clay minerals. In this study, PAHs, especially pyrenes, in PMs are assumed to be readily oxidized in the same manner by ozone, a major oxidant produced from high concentrations of precursors generated in East Asia and transported beyond its borders. Furthermore, oxidized metabolites of pyrenes, such as 4,5-PyQ and 1,8-PyQ + 1,6-PyQ, also known as pyrene quinones, might have potential cytotoxic effects, as pyrene is considered an important toxic PAH [9].  
Previously, we examined the respiratory effects of several quinone compounds and found that trans-airway exposure to some of these compounds induced [10] and exacerbated allergic airway inflammation [11; 12; 13]. However, comprehensive respiratory toxicity, including cell viability, cell death pattern, inflammatory traits, and oxidative stress of other environmental quinone compounds remain to be investigated; in addition, the hazardous effects of PAHs and those of their quinone forms remain to be compared.  
Therefore, in this study, we evaluated the respiratory toxicity of pyrene, a PAH, and its quinone forms using in vitro experiments.
# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
## 4 Results and Discussion  
Several experiments were conducted to evaluate the performance of the proposed model. All the experiments were conducted based on videos recorded by a drone under various conditions, including different lighting levels and random obstacles along the path of the drone. In this study, there was no predefined target or path for the drone flight in the scene. These videos are the ground truth of the scenario where the drone was seamlessly flying directly toward a clear path. We assumed that the drone was flying autonomously in an unknown environment to find a clear path between the obstacles. The video data collected are based on the front-view aerial images taken by the drone. Thus, our model took the video sequences as the input to develop scene understanding and reasoning. At first, several tests were conducted to select a good model of Faster R-CNN for object detection. Once the model was confirmed, simulation tests were conducted based on the waypoint generated by the model from the video data to verify our approach in a scenario of a drone flying through trees in a remote environment. This experiment simulated autonomous drone navigation in a jungle without relying on a GPS.
## 4 Results and Discussion
### Experimental Facilities  
Our evaluation was conducted using several Python-based packages including Sklearn [Version Number: 1.3.1], Pandas [Version Number: 2.1.0], Numpy [Version Number: 1.25.0], and SkFeature [Version Number: 1.0.0]. TensorFlow [Version Number: 2.3] was used to build both the generator and discriminator networks of the GAN model. Anaconda software [Version Number: 3] was used as a Python IDE. Specialized libraries like Keras simplified the architecture development process, allowing us to focus on optimizing the model rather than dealing with lower-level details. For data preprocessing and feature extraction, tools such as scikit-learn, Numpy, and Pandas were utilized.
## 2 Materials and Methods
### Methodology  
#### 2.3.1 Spatial Identification of Ecosystems  
(1) Spatial distribution of ecosystems  
The spatial distribution of forest and grassland in the Altai Mountains National Park Candidate Area was analyzed at the pixel level for the period from 2000 to 2020. The frequency of forest and grassland occurrence within each pixel during this 21-year period was calculated and classified into four categories: low-frequency presence (0-6 years), medium-frequency presence (7-13 years), high-frequency presence (14-20 years), and stable presence (21 years).  
(2) Center of gravity migration  
For the gravity center migration analysis, a 500 \(\times\) 500 m grid was chosen to strike a balance between spatial resolution and data compatibility. Similar regional-scale studies typically employ spatial resolutions ranging from 500 to 2500 m [36; 48; 49]. The driving factor data used in this study have a spatial resolution between 500 and 1000 m. Synthesizing the data within each grid allows for detailed insights while also capturing the broader distribution patterns of ecosystem changes through spatial analysis. This grid size ensures compatibility with the spatial accuracy of the available data, fulfilling the requirements of the research objectives. Consequently, a 500 \(\times\) 500 m grid was used to calculate the density of forest and grassland within each grid cell from 2000 to 2020 using the tabulate area function. This resolution was also applied when analyzing long-term trends and the influence of various driving factors on ecosystem dynamics. The spatial migration of the center of gravity for forest and grassland was analyzed using the standard deviation ellipse model, with all analyses conducted in ArcGIS Pro 3.0.1. The standard deviation ellipse, a spatial statistical method, provides insights into the spatial distribution characteristics of forest and grassland ecosystems. The center of the ellipse represents the mean location of the ecosystem distribution, while the dimensions of the ellipse indicate the degree of spatial dispersion and the directional trends of the ecosystems [50; 51]. This helps in understanding how the distribution of these ecosystems shifts over time and highlights any patterns of spatial expansion or contraction.  
(3) Trend analysis of ecosystem change  
The Theil-Sen median method, a nonparametric estimation technique, was employed to analyze long-term trends in forest and grassland ecosystems. This method is particularly beneficial because of its robustness to outliers, allowing for the more reliable detection of subtle, long-term trends in a heterogeneous mountainous environment [52; 53]. A \(\beta\) value greater than 0 (\(\beta>0\)) indicates an increasing trend in the proportion of forest and grassland, whereas a \(\beta\) value less than 0 (\(\beta<0\)) signifies a decreasing trend. To assess the statistical significance of these changes, the Mann-Kendall test was applied. The test statistic (Z) with absolute values greater than 1.65, 1.96, and 2.58 corresponds to significance levels of 90%, 95%, and 99%, respectively [54].
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
## 3 Anode Rod Sequence Fluctuation Detection Algorithm Based on Sliding Window  
The current 400 kA aluminum electrolytic cell has six feeding points evenly distributed over the space, and each port has the greatest impact on the nearest surrounding area. In order to reduce the time and computational effort in the process of tracking and predicting the distributed anode current sequence, the 400 kA aluminum electrolytic cell is divided into six areas according to the feeding point, as shown in Figure 2. Each area contains four anode rods on the A side and four on the B side. The current sequence of each area is divided into several window current segments through the sliding window [19], and the current of each sliding window is analyzed to monitor the current fluctuation.
## 2 Materials and Methods
### Statistics  
For all data obtained through testing, basic central and distributional parameters were calculated, including the mean, standard deviation, minimum, maximum, and range. To assess the normality of the distribution of results, skewness and kurtosis were used. [PERSON]'s correlation analysis was employed to explore the univariate association between physiological parameters and MRP. The classification of correlation coefficients followed the suggested guidelines: an r value of \(\leq\)0.35 denoted a low or weak correlation, an r value ranging from 0.36 to 0.67 indicated a modest or moderate correlation, an r value from 0.68 to 1.0 represented a strong or high correlation, and an r value greater than 0.90 signified a very high correlation [43]. The residual statistics diagram was used to assess the independence of residuals. Multivariate relationships among the predictors and criteria were evaluated using multiple regression analysis. Based on the formula n = 20 + 5k (where k represents the number of predictors), which was proposed by the authors [44] as optimal, considering the principle based on the rate of change in reliability criteria relative to the number of participants, this study calculated that it is optimal to use 4 predictor variables. The coefficients R, R2, and \(p\) were calculated for linear multiple regression. Evaluation of the model was assessed through R2, which indicates the proportion of variance in the dependent variable explained by the model. Meanwhile, the statistical significance of the model was evaluated in the ANOVA test section. The assessment of each independent variable in the model was calculated based on standardized coefficients (Beta) to determine the contribution of each variable to the final equation [45]. Statistical significance was set at \(p\) < 0.05. Data analysis was performed using IBM SPSS Statistics software, version 26 (Statistical Package for Social Sciences, v26.0, SPSS Inc., Chicago, IL, USA).
## 3 Results and Discussion
### Moisture Susceptibility
#### 3.2.3 Freeze--thaw Splitting Test  
As shown in Figure 10, the freeze--thaw splitting strength ratios \(TSR\) of asphalt mixtures composed of five aggregates are ranked in order of magnitude as type B limestone, type A limestone, basalt, diabase, and granite. In comparison to basalt and diabase asphalt mixtures, limestone-based asphalt mixtures showed greater \(MS_{0}\) and \(TSR\) values, with no appreciable difference between the two values. Combining Figures 9 and 10, it can be seen that the outcomes, despite the low \(TSR\), were very similar to those of the immersion Marshall test. This result is due to the vacuum saturation process that the freeze--thaw  
Figure 10: Freeze—thaw splitting test.  
Figure 9: Immersion Marshall test.  
splitting test goes through to enable moisture to enter the spaces in the asphalt mixture. During the freeze--thaw cycle, water expands as it freezes, increasing its volume. The voids in the asphalt mixture may enlarge as a result of this process, making it more vulnerable to harm.
# On the Design and Implementation of a Blockchain-Based Data Management System for ETO Manufacturing
## 1 Introduction
### Research Objective  
Compared with other production types of manufacturing enterprises, ETO manufacturing usually starts with sales business, followed by product design, supporting procurement, online production, delivery, and other business processes, and ends with after-sales service business. The specific process and the roles involved are shown in Figure 1. The sales department generates contract orders according to user requirements, while the technical department is responsible for the product design of the orders and generates the BOM list and production work orders. After that, the purchasing department purchases raw materials according to the BOM list to be prepared for production. The manufacturing center plans and produces work orders, while product quality inspections are completed. After the product is delivered to the user, the sales department is responsible for the after-sales service of the contract order.  
The production of a finished product in the manufacturing process includes two stages: semi-finished product processing and product assembly. In addition, each stage also has quality inspection. Importantly, the production process routes of different customized products are entirely different, while the process routes of semi-finished products are almost discrete.  
Manufacturers using ETO production have realized that each product they produce has its own problems and challenges, which are mainly the following:  
1. Tracking data in engineering production. Generally speaking, manufacturing companies that use made-to-order (MTO) production will standardize production. Once the customer order is received, it can be produced directly according to the existing design. However, ETO manufacturing requires the completion of a new design of a product to meet a customer's unique specifications. This results in production process data constantly changing between customers and manufacturers, even during production. Therefore, there is a need for a data management platform that can track all the changes the product undergoes throughout the process.  
2. Security risks of data being tampered with or misused. Due to the numerous and discrete steps of ETO-type production, the business process between production departments is complicated. When business data are transferred from one department to the next, workers may violate company security regulations by tampering with or stealing data. Therefore, each data node in the production process needs to have the ability to trace the data to ensure its validity and consistency.  
3. Lack of effective validation of the execution of the process design. Since ETO manufacturing is completely designed and customized to customer specifications, even  
Figure 1: Production process of ETO manufacturing.  
similar products may have different production routes. This requires a method to determine whether the production routing is correctly executed.
## 3 Methodology
### Description of Representative Infrastructure
#### 3.1.1 Power Grid Topology  
The test network's power grid part is a representation of a section of the actual distribution network in the Czech Republic, structured with a ring topology, which can be viewed in Figure 2.  
Within this grid, there are three distinct voltage levels: the HV segment (110 kV), the MV segment (22 kV), and the LV segment (0.4 kV). Power is supplied to this network from a substation that is equipped with a 110/22 kV transformer. Furthermore, the network consists of five individual sections of overhead lines (L1-L5). Each of these sections is outfitted with a recloser (R1-R4) to interrupt fault currents. The overhead line (L5) is partitioned by a section switch named LBS1. Under normal (fault-free) conditions, this LBS1 is typically in an open state. Hence, the network runs in a radial manner to reduce short-circuit currents. However, if a fault occurs (for instance, in Section L1), section LBS facilitates the DSO to initiate feeding from the other direction. This LBS is operated remotely, and its communication is orchestrated by an RTU, specifically labeled as RTU2.5 in Figure 1. This positions RTU2.5 as a pivotal component for the operation of LBS1. In the event of communication or if RTU2.5 malfunctions, immediate restoration of the supply is impossible due to a fault in the distribution network. Such a scenario results in an extension of the fault duration, subsequently impacting the SAIDI and SAIFI metrics. Moreover, the network houses four DTs (22/0.4 kV) that transform MV to LV, ensuring the demands of the end consumers are met via subterranean LV cables (C1-3). These cables diverge at the LV level busbar and are intended for different consumer groups. This node highlights the dependency of the power network on the communication network, as previously discussed.
## 2 Related Work
### QoE-Oriented Papers  
The paper by [10] focuses on QoE, using offline deep reinforcement learning to optimize caching and meet user preferences. It uses an offline deep reinforcement learning algorithm for cache optimization, which is the main stage of the process.  
Ref. [6] focuses on QoE and video streaming, improving 5G mobile performance via edge intelligence and TCP and VoD optimization. It does not mention a specific resource orchestration but an MEC service framework. Ref. [13] considers both QoS and QoE, aiming to optimize execution time and energy consumption, as well as meeting user preferences. It uses a two-phase probabilistic model-checking algorithm for offloading and bandwidth selection.  
Ref. [23] aims to maximize QoE by minimizing costs through a reverse auction mechanism and two greedy algorithms, IMaxQ and IMinC. In an Ubunye auction, multiple buyers compete by bidding higher prices for an item a single seller offers, increasing offers and maximizing the seller's revenue. Conversely, in a reverse auction, multiple sellers compete to offer the lowest price to one buyer, benefiting them with lower costs. However, this may lead sellers to cut prices excessively, potentially compromising QoS.  
Ref. [24] proposes a mechanism for preloading dynamic adaptive streaming over HTTP (DASH) video segments in MEC environments over 5G networks, using an ML model to predict which segments should be preloaded. The random forest classifier was selected for its superior accuracy among the tested models. The paper improves QoS and QoE in the delivery of video content by reducing latency and redundant traffic. The study aims to use the classification model rather than a comprehensive orchestration system to improve the efficiency of preloading media segments.  
Table 1 compares our proposal with related work. The Ubunye service stands out by implementing service classification to identify the source of user requests and distinguish between high-bandwidth and low-latency services. This classification improves resource allocation, optimizes bandwidth-intensive services, and enhances the overall user experience (QoE). Ubunye offers a more user-centered approach to managing MEC resources by addressing QoS and QoE, setting it apart from other services.
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
## 4 Finite Element Analysis
### Steady-State Rolling Analysis  
A commercial software, ABAQUS, was applied for finite element analysis. A uniform pressure of 0.21 MPa was applied to the tire inner liner to simulate the tire inflation process. The \"SYMMETRIC MODEL GENERATION command was used in Abaqus to rotate the two-dimensional model into a three-dimensional finite element model. The 3D model had a total of 201,403 elements and 213,903 nodes. The rim and road surface were defined as analytical rigid bodies. The vertical load was set to 9000N. To simulate the steady-state rolling state of the tire, the ALE (arbitrary Lagrangian-Eulerian) method, an implicit algorithm provided by ABAQUS, was used [22]. All geometric grid nodes did not move, and the physical change from the rotation was calculated as changes in other variables (energy, material properties, etc.) in the tire circumferential direction [5]. That is to say, this method transforms the contact problem of free rolling into a simple space problem. The rolling speed was set to 30 km/h. The free rolling angular velocity of the tire was 26.97 rad/s, and the direction was clockwise. The steady-state rolling analysis procedure of tire is shown in Figure 13.  
Based on the simulation results, the \(\Delta SED_{max}\) (the maximum strain energy density range) values of the rubber compounds (carcass, apex, and abrasion) in the circumferential direction of the bead region were obtained, as shown in Table 2.  
Figure 12: The fitting curve of abrasion by Yeoh model.  
Figure 13: Steady-state rolling analysis procedure.