esa-sceva/satcom-chunk-collection huggingface dataset:
# Life Cycle Assessment of Substitutive Building Materials for Landfill Capping Systems in Vietnam
### Lca  
Life Cycle Assessment (LCA) is the most widely used holistic methodology, a multi-stage process, whose detailed definition is given in the International Standards in the series ISO 14040. According to ISO 14040, LCA is defined as the \"compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle\" [61]. The UNEP/SETAC Life Cycle Initiative defines LCA as \"a technique that is used to assess the environmental aspects associated with a product over its life cycle\" (UNEP/SETAC, 2011). The combination of individual processes (unit processes) that form a product's life cycle is called the 'product system' [62]. LCA aims to evaluate the environmental burdens of a product or a process. Thus, it can be applied for various purposes, including identifying the source of environmental impacts associated with a product, comparing similar products, designing new products, etc. Other major applications of LCA to products include green purchasing, eco-labelling, and eco (green)-design [62]. LCA studies can also be used in support of complex business strategies and decision-making, government policies, or sector-level initiatives. LCA serves to express the potential environmental impacts and damages associated with a product or service system in a way that supports comparisons between alternatives, both at the level of the individual substance emission and at the level of the entire studied system [63]. Figure 3 illustrates the LCA methodology used in this study [64].  
LCA has a structured four-stage framework:  
* goal and scope definition;
* life cycle inventory;
* life cycle impact assessment;
* interpretation.  
The LCA is especially effective in comparing products, e.g., building materials that differ in their raw materials composition but have the same functionality [65]. In such cases, LCA can serve as a basis for decision-making to improve sustainability in the construction industry [66].  
The life cycle assessment was carried out using the \"Ecoinvent 3.6\" database and the software \"Simapro 9.2\". The software provided a user interface, the environmental information from the Ecoinvent database, and the options for the impact assessment method. The choice of assessment method was \"ReCiPe 2016 Midpoint (H) V1.04/World (2010) H\" because of its wide range of preferences among several LCA studies and guidelines as well [67; 68; 69].The midpoint method also has low variation in understanding the impact categories than the endpoint method or even in a single score indicator [70].  
Figure 3: Life Cycle Assessment Framework Structure (Source: [64]).
## 3 Different Aspects of Ransomware Studies
### Machine Learning Ransomware Attacks  
Machine learning has a great impact on cybersecurity field. It has numerous applications to detect the intrusion, fraud, malware, and ransomware. Machine learning approaches are effective in detecting the hidden patterns. Machine leaning has numerous application in classifying the malware, ransomware, and benign programs [22].  
R-PackDroid, an android based ransomware detection system was presented in [57]. This system performed the static analysis by analyzing the Dalvik bytecode. The Crypto-API calls of applications were examined to extract the features. However, the system was limited in its functionality as it cannot detect the system API calls or encrypted operations that run during the execution.  
EldeRan, a machine learning based system was presented in [58]. This system was able to detect the previously unseen ransomware families. This system captured the significant dynamic features, for the ransomware detection and classification. An evaluation of LR, SVM and NB was also performed. The most relevant features were selected using the mutual information criteria while classification was performed using LR. However, the system ran the samples for only 30 s which made the system inaccurate for the ransomware attacks utilizing the obfuscation techniques.  
A windows based NetConverse system was developed in [59] for the network ransomware detection. Different machine learning classifiers were selected including MLP, BN, DT, KNN, RF and LMT. These classifiers were compared in term of TP and time required to build a detection model. A network protocol analyzer extracted the features from the network traffic.  
In [60], a detection system was proposed which utilized the sequence pattern mining to detect the ransomware. Different classifiers were used including Bagging and MLP, and two machine learning classifiers J48 and RF. Frequent features of ransomware were detected by using the sequence pattern mining. Ransomware logs were built by using the vectors composed of ransomware frequent patterns. This study emphasized on the use of pattern mining techniques for the extraction of best features for the ransomware hunting applications.
## 3 Positioning Model Establishment and Solution
### Triangular Centroid Position Algorithm  
#### 3.1.1 Traditional Triangular Centroid Position Algorithm  
The traditional centroid position algorithm is the distance-weighted centroid position algorithm [22; 23; 24], that is, to find several reference nodes closest to the unknown node, and to calculate the distance between the three reference nodes and the unknown node as a weight and introduce it into the position algorithm. Then, the centroids of these reference nodes are found as position estimates for the unknown nodes.  
We take two-dimensional planes as an example; that is, the anchor points in the three two-dimensional planes of A, B, and C. After obtaining the distance from the tag to each anchor point, the trilateral positioning method can be used to calculate the position, as shown in Figure 4a. In trilateral positioning, three circles are drawn with each anchor point as the center and the distance from the anchor point to the tag as the radius intersects at a point, and this intersection is the position of the tag. However, the existence of the range error prevents the three circles from intersecting at one point, resulting in no solution for the positioning coordinates. To solve this situation, triangular centroid positioning can be used to obtain a real-time solution, and higher accuracy can be obtained when the ranging error is evenly distributed. The accuracy is shown in Figure 4b. When two circles A and B intersect, two feature points of coordinate estimation can be obtained. The third anchor point C is used as a reference, and the feature points with farther distances are discarded. The feature point D is obtained. When two circles A and C are tangent, the tangent point E is used as the feature point; when the two circles B and C are separated, the center point between the line connecting the circle centers B and C and the intersection of the two circles is taken as the feature point F, then the three feature points D, E, F determine a triangle \(\Delta\)DEF, the coordinates of the three anchor points are known, and the calculated coordinates of D, E, and F are (xD, yD), (xE, yE) and (xF, yF), then the coordinates of the label (xO, yO) are obtained as follows:  
\[\begin{cases}\begin{aligned} \text{x}_{\text{O}}=\frac{\text{x}_{ \text{O}}+\text{x}_{\text{E}}+\text{x}_{\text{E}}}{3}\\ \text{y}_{\text{O}}=\frac{\text{y}_{\text{O}}+\text{y}_{\text{E}}+\text{y}_{ \text{E}}}{3}\end{aligned}\end{cases} \tag{2}\]  
Figure 4: Triangulation and centroid positioning ((**a**)without interference, (**b**)with interference).
## 2 Materials and Methods
### Lead Adsorption Study  
Adsorption measurements were performed by batch technique at 25 \({}^{\circ}\)C. In total, 0.1 g of clay was placed in 250 mL reagent bottles containing 40 mL of lead nitrate ([PERSON], 203580, 99.99%) solution of initial concentration 300 mg L\({}^{-1}\) and the solution was checked for a given time in a thermostatic shaker bath with 130 rpm. The pH of the solution was adjusted by using 0.1 mol L\({}^{-1}\) NaOH or HCl. The solution was centrifuged then the clay was removed. Then, the lead in filtrate was measured by using a lead ion selective electrode (Orion 9682 BN). The adsorption mechanism was investigated by applying the same predefined procedure with 6 h contact time and with the pH of suspension adjusted at 6. Pb (II) concentration varied in range from 20 to 850 mg/L. The adsorption capacity of Pb (II) ions onto clays was calculated using Equation (1).  
\[\mathrm{q_{e}}=\ \frac{(\mathrm{C-C_{e}})\ V}{\mathrm{m}} \tag{1}\]where \(\rm q_{e}\) is the equilibrium capacity concentration of adsorbate on adsorbent (mg/g), C is the initial concentration of adsorbate (lead solution) (mg/L), \(\rm C_{e}\) is the equilibrium concentration of adsorbate (mg/L) in solution, m is the used mass (g) of adsorbent (clay) and V is the volume of lead solution (L).
## 1 Introduction  
Activated carbon is an excellent adsorbent that has been applied in many applications [1]. Due to its stable chemical properties, high mechanical strength, acid and alkali resistance, heat resistance, insolubility in water and organic solvents, and renewability, activated carbon has been widely used in the chemical industry, environmental protection, food processing, refined, metallurgy, medicine, military chemical protection, and other fields [2; 3; 4]. Biomass is the main raw material of activated carbon. Several reviews report a great deal of work done on applications for the removal of specific pollutants from an aqueous phase [5]. Under the condition of high temperature and low oxygen, biomass could turn to biochar. However, the biochar often has poor quality, ash and other impurities are in the microporous, thus the adsorption performance is not good. Further improvement of the structure is needed to standardize biochar into activated carbon [6]. In recent years, microwave pyrolysis has gained increased attention from many researchers because of its unique heating mode and higher application prospect. Biochar made by microwave pyrolysis has a better structure than those made by traditional electric pyrolysis [7; 8; 9; 10; 11].  
Artemisia selengensis, as compositae sagebrush and perennial herbaceous plant, was planted in shoals and moist areas. Due to its developmental nutrition, the cultivated area of artemisia selengensisin Nanjing, Jiangsu province has reached \(2.33\times 10^{9}\) hectares, and the annual production is more than 500,000 tons [12]. After two seasons, the aged stems of artemisinin selengensis have no edible value, and they become a big problem that needs to be solved. High humidity and high fiber content made those aging stems difficult to mush. There has been little research focusing on the processing of aged stems. Traditional thermochemical conversion is not suitable for this material because it is highly humid and fibrous. However, microwave pyrolysis can effectively be used for the process because microwaves can affect biomass itself directly [13], and there are not many limitations on its size [14; 15]. Water is a good kind of microwave absorber, so high moisture content also brings a high heating rate. Thus, the microwave pyrolysis technique is suitable to convert aged stems into biochar.  
In this research, artemisinin selengensis was used to produce biochar by using microwave pyrolysis technology. The influence of pyrolysis temperature, heating rates, temperature holding time and additive on the biochar yield and adsorbability were investigated. An orthogonal experiment was designed to find the optimum condition to produce biochar for the maximum adsorbability.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 4 An Overview of Research Challenges
### Edge Computing-Based Vehicular ICN  
Edge computing [31] is deployed to bring computing resources near data subscribers. The architecture of Edge computing is decentralized that uses network nodes to jointly execute a considerable amount of processing, data caching, and controlling [32; 33]. With the help of Edge nodes and network connections, processing overhead is largely decreased and bandwidth restrictions are surmounted for consolidated services [19]. The attraction of Edge computing increases with the provision of on-demand services and availability of resources near consumer devices, which result in low response time and greater consumer satisfaction [34].  
Data sharing in vehicular network has been a growing concern for the last few years. Three types of data sharing are deemed the most prominent practices in vehicular network, i.e., sharing warning messages about an accident, reminder for the prevention of vehicles crash, and notice about the road congestion [35]. Besides, infotainment content availability for passengers has also attracted the attention of vehicular research forums. In this regard, Edge computing, which brings the storage capacity and computational processes near the customers for content retrieval with minimum delays, is integrated with vehicular networks [36]. However, the integration of ICN with vehicular Edge computing is a challenging issue due to consistent mobility of vehicles. That is, if content, which is stored in an Edge node (a vehicle), is accessed by a passenger in a moving car, that can be accessed somehow easily in the IP-based Internet model. However, relating it with the ICN, which believes in names rather than IPs, is quite complicated job. This can be resolved by storing an accessed content at several RSUs thereby ignoring the redundancy factor. Yet, according to the Cisco Visual Networking Index (VNI) [13], mobile traffic by 2021, with 5.5 billion users, will reach seven times more than the current traffic, which introduces further complication for vehicular ICN. In other words, the storage of _RSUs_ would exhaust in a moment, which requires replacement of the _already-stored_ contents. In this scenario, the accessing sensor node (vehicle) will not find the previously cached content. This scenario motivates researchers to design such an intelligent and sophisticated mechanism that paves the way to integrate Edge and ICN with vehicular networks.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 3 Materials and Methods
### Specifications of Resolution Targets
#### 3.1.2 Slanted Edge Target  
The image quality verification method using a slanted edge target has been widely used over the past 10 years. It has been adopted by several international standards, including the International Organization for Standardization (ISO). As shown in Figure 8, the angle of the slanted edge of the target was designed to be inclined at 5\({}^{\circ}\), as stipulated in ISO 12233 [22]. In addition, the contrast between the black and white parts of the slanted edge target must be at least 40:1 in ISO 12233. However, the recently revised black and white contrast ratio is as 4:1 [22].  
Figure 8: Specifications of the slanted edge target for UAV photogrammetry.  
Figure 6: ESF graph of edge target (a) and slanted edge target (b).  
Figure 7: Specification of simple resolution bar target for UAV photogrammetry.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.3 Solar-Aware Distributed LEACH  
Since battery-driven sensor nodes run out of power faster, [PERSON] et al. [74] proposed solar-operated WSN and imposed it on a LEACH environment. The preferable candidates for CHs are given to solar-driven nodes only, while battery-driven nodes act as member nodes [75]. Hence, the threshold for CH selection needs to be changed to increase the probability of solar-operated nodes and is given as:  
\[T(n)=sf(n)\times\frac{P}{1-\Big{(}\frac{CHads}{nunNodes}\Big{)}} \tag{5}\]  
where _CHeads_ is the number of CHs since the start of the last (_1/P_) round and _numNodes_ is the total number of nodes, \(sf(n)\) is a scaling factor higher than 1 for solar-operated nodes and is set to reciprocal for battery-operated nodes. The introduction of solar-aware WSN increased the network lifetime for typical scenarios as compared to centralized LEACH.
## 2 Atmospheric mmW Propagation in the Presence of Water Suspension
The phase dispersion leads to a frequency dependent group delay given via the derivative:  
\[\tau_{d}(f)=-\frac{1}{2\pi}\frac{d\phi(f)}{df}=-\underbrace{\frac{1}{2\pi} \frac{d\phi_{0}(f)}{df}}_{\tau_{0}}-\underbrace{\frac{1}{2\pi}\frac{d}{df} \Delta\phi(f)}_{\Delta\tau_{d}(f)}=\underbrace{\frac{d}{c}}_{\tau_{0}}+ \underbrace{\frac{d}{c}\left[N_{0}+N^{\prime}(f)+f\frac{dN^{\prime}(f)}{df} \right]\cdot 10^{-6}}_{\Delta\tau_{d}(f)} \tag{7}\]  
We define \(\tau_{0}=d/c\) as the time delay in vacuum and identify the incremental group delay as:  
\[n(f)=1+[N_{0}+N^{\prime}(f)-jN^{\prime\prime}(f)]\cdot 10^{-6} \tag{1}\]0  
In Figure 1, graphs are drawn for the expected attenuation \(L(f)\), phase shift \(\Delta\phi(f)\), and incremental group delay \(\Delta\tau_{d}(f)\) for different water vapor concentrations W (and resulted visibilities Vis). Note that the absorption peaks appearing in 22 GHz and in 60 GHz are due to resonant absorptions in water (H\({}_{2}\)O) and oxygen (O\({}_{2}\)) molecules.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 6 Experiments  
### Environmental Setup  
We conducted experiments in a mock apartment, as shown in Figure 8. The monitored space of each sensor node is approximately a cone with a 3-m radius. There are in total five sensor nodes attached on the ceiling 3 m above the ground, forming a star topology WSN [35]. The positions of these five sensor nodes are (2,2), (2, -2), (-2,-2), (-2,2), and (0,0), respectively. The sensor nodes can be regarded as being located on four corners and the center of a square. Such a deployment is based on the consideration that the sensor nodes should cover as much of the monitored region while having some region overlap to enhance localization accuracy. The monitored region of our WSN is 6 m \(\times\) 6 m. The sampling rate of each PIR sensor is 15 Hz. The sensor data collected by the CC2530 on sensor nodes will be sent to the sink node based on the ZigBee (IEEE 802.15.4) protocol with a 250-kbps data rate [36]. The sink will transport the data to the PC host for further data processing.  
We collected 67 datasets from three volunteers. The first volunteer is a female, age 23, 42 kg, 160 cm; the second volunteer is a male, age 22, 64 kg, 169 cm; the third volunteer is a male, age 37, 174 cm, 70 kg. Each volunteer performed five kinds of daily activities: walking, sitting, standing, lying and transitional activities. The transitional activities include: sit-to-lie, lie-to-sit, sit-to-stand, stand-to-sit, etc. The duration of each dataset is about four minutes. There are a total of 16,328 data frames for all datasets. Our system does not need to be re-parameterized for different human subjects. The same system configuration is able to recognize the activities performed by volunteers with their own style. We used a web cam to record the process of experiments as the ground truth.
# GNSS-Based Monitoring Methods for Mining Headframes
## 2 Overview of the GNSS Monitoring Scheme Design and Monitoring Platform for the Headframe
### Overview of the Monitoring Platform  
Liuzhuang Mine monitoring employs the \"Beidou + Typical Geological Hazard Intelligent Monitoring and Rapid Warning Cloud Platform\" (referred to as the BIMRWP platform), which was developed by the School of Spatial Information and Surveying Engineering at Anhui University of Science and Technology. This platform is an innovative system that integrates BDS/GNSS technology. It achieves real-time monitoring and early warning of geological disasters through integrated data management and analysis tools, providing scientific decision support for engineering technicians and managers. The BIMRWP platform is suitable not only for monitoring and early warning of geological disasters such as mine subsidence, landslides, and collapses but also for real-time monitoring and timely early warning in various scenarios including reservoir dams, high-rise buildings, large bridges, and urban surfaces, providing technical support for disaster prevention and geological disaster reduction.  
After logging into the BIMRWP platform, users will enter an intuitive main interface that displays regional information in the form of maps, including urban and geographical boundaries and major topographical features. Users can quickly obtain geographical information of the area and access detailed project engineering through the project application  
Figure 1: Overview of the Liuzhuang Mine headframe and schematic of the monitoring stations on the upper and lower sheave wheel platforms.  
Figure 2: Installation site of the GNSS reference stations and structural component diagram of the GNSS monitoring dedicated receivers.  
summary list on the left. The map displays the specific distribution of project locations with blue markers, showing specific geographic locations, project names, latitude, and longitude when the mouse hovers over. Users can click on any marker to enter the project and obtain additional information.  
The BIMRWP platform attains a planar measurement accuracy within \(\pm\)2.5 mm, thereby fully satisfying the criteria for planar position measurement in the context of mining subsidence monitoring. Regarding elevation measurement, the mean error in elevation differences, when compared to precise leveling measurements, is \(\pm\)8.4 mm, which adequately fulfills the requirements for elevation measurement in subsidence monitoring. The system demonstrates efficiency and rapidity in the collection of surface movement and deformation data, thereby offering dependable technical support for subsidence monitoring in coal mining [27].  
Figure 3 shows the working base map of Liuzhuang Mine West District, detailing the distribution of monitoring station receivers and base stations, providing an intuitive reference for users to view the real-time data streams and satellite connection status of monitoring points further.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 1 Introduction
The main contributions of our work can be summarized as follows:  
* We provide a learning-based method for selecting an optimal MPR-based policy according to the time-varying satellite elevation angle. We also provide a mechanism for reliable estimation of the non-stationary LOS probability. * By including MPR capabilities in our transmission policy, we allow the UE to transmit on multiple satellite links to improve link availability and data rates and minimize end-to-end (E2E) loss. * The novelty in this work is that we provide a self-learning-based LOS tracking mechanism that can work in scenarios with non-stationary link state transition probabilities, which is a challenge in NTN mobile systems. In the following part of the paper, we review the literature related to our research and describe our system model and the architecture of the proposed AC agent. We then present and discuss the simulation results and finally conclude the paper by setting the direction for future research.
# Evaluation of NOx Reduction Effect and Impact on Asphalt
## 2 Materials and Test Methods  
### Materials  
#### 2.1.1 Photocatalyst  
In this study, the photocatalyst used as the surface treatment agent was TiO\({}_{2}\), and three TiO\({}_{2}\) types with different specific surface areas were used to compare the differences in the NOx decomposition effect. The crystal structures of TiO\({}_{2}\) are anatase-type, with excellent light efficiency, and a combination of anatase and rutile, with an average particle size of 20-30 nm. The properties of the TiO\({}_{2}\) used are listed in Table 1.