esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
The Backbone-Assisted Hop Greedy (BAHG) protocol is a position-based \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Beacon** & **MaxJitter** & **N. Validity** \\  & **Validity Time** & & \\ \hline
1 & 2 s & 0.1 s & 20 s \\
2 & 2 s & 0.5 s & 30 s \\
3 & 2 s & 1 s & 40 s \\
4 & 10 s & 0.1 s & 30 s \\
5 & 10 s & 0.5 s & 40 s \\
6 & 10 s & 1 s & 20 s \\
7 & 20 s & 1 s & 40 s \\
8 & 20 s & 0.5 s & 20 s \\
9 & 20 s & 0.1 s & 30 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Orthogonal Array of GPSR Experiment Design: The table shows the experimental layout using an Orthogonal Array for the GPSR protocol, which enabled efficient analysis of multiple parameter combinations to determine their impact on routing performance metrics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**EXP. NO** & \begin{tabular}{c} **Beacon** \\ **MaxJitter, N.** \\ **Validity Time** \\ \end{tabular}  & **T1** & **T2** & **T3** & **T4** & **T5** & **Equation** \\ \hline
1 & (1,0,1,20) s & 0.04994 & 0.05242 & 0.05244 & 0.05268 & 0.04925 \\ \hline
2 & (1,0,5,30) s & 0.06386 & 0.07142 & 0.0642  
routing approach designed to enhance routing efficiency in urban VANET environments by minimizing the number of intermediate intersections along a communication path. BAHG operates using a reactive strategy to first determine the destination's position and then establish a route over selected intersections using designated backbone nodes. These backbone nodes, which form a superset of coordinator nodes, are classified into three types: stable, primary, and secondary. Stable backbones are vehicles that remain stationary near intersections during red traffic signals and are prioritized for their reliability. Primary and secondary backbones are dynamically chosen from moving vehicles crossing the intersection when the signal turns green. This structured categorization ensures consistent path stability and facilitates efficient routing across urban grids.
## 2 Materials and Methods
### Electromagnetic Interference (EMI) Shielding Properties  
The EMI shielding effectiveness properties were evaluated using a P9372A Keysight Streamline Vector Network Analyzer (Keysight, Santa Rosa, CA, USA) and two sets of microwave standard 15 dB gain waveguides (WR 187 and WR 147, respectively, obtained from Advanced Technical Materials Inc. (ATM), Patchogue, NY, USA) covering a broad C frequency band in the range of 3.5-9.0 GHz, which is typical for wireless communications, such as satellite communications, Wi-Fi devices, cordless telephones, weather radar systems, etc. In particular, every sample was placed in the middle of each set of waveguides, and its scattering parameters (S-parameters; S\({}_{11}\), S\({}_{12}\), S\({}_{22}\), S\({}_{21}\)) were recorded.
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 3 Task Allocation  
### Framework of the Mission Planning Method  
Inspired by the hierarchical control framework [25; 26; 27] of a multi-UAV system. The mission planning method was divided into three layers in this paper, as shown in Figure 1. The first layer was the path estimation process, a Particle Swarm Optimization-based method (shown in Section 4.2) was employed to generate a PH path for each UAV as an estimated path. The length of the estimated path was seen as the input for the task allocation process, the estimated path was used to decouple the task allocation and path planning problems. In the second layer, the task allocation process assigned targets to UAVs that optimized the system performance. A task allocation algorithm was proposed to assign the targets. In the task allocation process, the resource requirement of targets, resource consumption and task sequence of UAVs were taken into account. In the last layer, according to the task allocation strategy, a path planning method was proposed to generate flyable and safe paths for the UAVs considering the task sequence of each UAV. In this layer, the simultaneous arrival, UAV's kinematic and collision avoidance constraints were considered (detailed explanation can be found in Section 4.3). Finally, a path following process was used to generate the control signals for the UAV team. As the path following was not the focus of this paper, a path following method based on the nonlinear model predictive control [28] (NMPC) was used to track the generated PH paths.
## 7 Synthetic Biology Methodologies to Enhance Biofuel Yields
### RNA Interference Approach for Controlling Transcription and CRISPR/CRISPRRa  
Controlling transcription is also an alternative effective method involved in genetic engineering, which is most frequently applied in metabolic engineering as well. RNAi as well as CRISPR interference (CRISPRi) are the approaches that are employed for precise gene knockouts [174; 175]. The RNAi approach is a naturally occurring transcription controlling mechanism observed in most of the eukaryotes. Recently, it has been used for the heterologous expression of argonaute as well as dicer in _S. cerevisiae_. This is further optimized to be used as an important tool for metabolically engineering the organisms. In _S. cerevisiae_, a test for putative gene targets was performed using the synthetic RNAi approach to enhance the production of itaconic acid [176], along with tolerance towards acetic acid [175]. CRISPR, as the name suggests is a spin-off from the CRISPR/Cas9 approach; here, the nuclease coupled with the guiding RNA is absent (dCas). This dCas has the potential to physically obstruct the initiation and elongation steps of transcription [174]. In _E. coli_, this interference approach has also been applied to increase the production of flavonoids by adjusting the control networks related to crucial metabolic pathways like the TCA cycle and fatty acid biosynthesis [177]. Furthermore, CRISPR protein that is deficient of nuclease is coupled with an activator domain and can be used to activate the required gene (CRISPRa) [178]. By fusing dCas9 with RNA polymerase's \(\omega\) subunit as well as VP64 activator, the CRISPR approach was successfully applied to activate the reported genes in the two hosts _S. cerevisiae_[178] and _E. coli_[179]. The above discussed approaches can upregulate the required genes and even downregulate the unnecessary genes for enhancing the production of biofuel and its related compounds.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications  
In this section, we show how the AT paradigm can be applied to achieve coordination in various real-world problems. Depending on the structure and characteristics of each domain, different technologies from the AT sandbox need to be selected and combined so as to meet the requirements for each case. Section 3.1 highlights the use of techniques related to norms and organisations (in particular, auction protocols and market-based control) in an open domain, where flows of autonomous vehicles, controlled by individually rational driver agents, are coordinated through a network of intelligent interactions. Section 3.2 is dedicated to the problem of evacuation guidance in smart buildings, where evacuees, suffering from significant levels of uncertainty concerning the state of an emergency, are provided with individualised route recommendations in a coordinated manner. In this context, issues related to situation-awareness and semantics play a major role. Section 3.3 addresses the coordination of fleets of ambulance vehicles. Even though this is a primarily closed scenario, we address it with techniques from the field of AT, applying an algorithm that _simulates_ multiple concurrent computational auctions. Section 3.4 focuses on coordination of emergency medical services for angioplasty patients--a problem similar to the previous one, even though its internal structure (different types of agents, etc.) leads to a more complex coordination mechanism. Finally, Section 3.5 also addresses a coordination problem related to fleet management, but applied to the field of taxi services. Here, we again have a higher degree of openness, as taxis are conceived of as autonomous agents, so coordination needs to be induced by incentives, targeted at influencing the choices of drivers whose actions are not fully determined by organisational rules and protocols.
# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 5 Conclusions and Recommendations
### Applications and Development Recommendations
#### 5.2.4 Develop End-to-End Cross-Border E-Commerce Supply Chain Optimization Solutions  
Enhance supply chain data transparency by building an integrated supply chain management system. Collect and analyze data in real-time across the entire supply chain, from suppliers to end consumers, creating a continuously updated and stable big data set. Utilize big data technologies and multimodal learning models to provide deep insights, predicting market trends and adjusting production plans accordingly. Gain a comprehensive understanding of demand characteristics in different markets and customer groups. Use predictive analytics to optimize inventory levels and implement precise inventory management strategies. Employ efficient logistics planning to reduce delivery times and costs. Dynamically adjust the supply chain to build a flexible network, adapting quickly to real-time data and market demands. For example, if there is a surge in demand in a specific region, the system can automatically adjust production priorities and logistics resources to ensure the timely fulfillment of demand.  
Utilize big data-driven analytical tools to assess and categorize potential risks within the supply chain. By constructing a comprehensive risk-management framework, implement effective risk prevention and mitigation measures to ensure the stability and sustainability of the supply chain. Enhance customer engagement and feedback mechanisms by using multimodal data analysis to continuously understand customer behavior and feedback. This ongoing analysis helps optimize supply chain operations, ultimately improving customer satisfaction. Establish a customer feedback system that allows consumers to directly influence supply chain decisions, such as affecting inventory and production through their reviews and feedback.  
By implementing these strategies, cross-border e-commerce enterprises can build a highly optimized and flexible supply chain system that quickly adapts to market changes and significantly enhances customer satisfaction, thereby gaining a competitive advantage in the global market. This end-to-end supply chain optimization solution will leverage big data and artificial intelligence technologies to revolutionize supply chain management and drive continuous business growth.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 4 System Design and Simulation Configuration
### Simulation Tool, Models and Parameters
#### 4.3.1 3 GPP Antenna Model  
In this study, macro cellular communication antennas are considered to be a source of the ambient signal. In a considered layout of the cellular network shown in Figure 4, each cell is equipped with directional antenna, and we have used the extended 3 GPP antenna model that is presented in [51] to model the antenna radiation pattern in azimuth and elevation plane. The antenna gain \(G(\varphi,\theta)\) for any horizontal direction \(\varphi\) and vertical direction \(\theta\) is given by Equation (15), where \(G_{H}(\varphi)\) and \(G_{V}(\theta)\) are gains of the antenna in the azimuth and elevation plane in \(dB\) scale, respectively. In Equation (16), \(-180^{\circ}\leq\varphi\leq 180^{\circ}\), \(HPBW_{H}\) is the half power beamwidth (HPBW) of the antenna in degrees in the horizontal plane, \(FBR_{H}\) is the front to back ratio (FBR) in \(dB\) scale, and \(\varphi_{az}\) is the reference azimuth direction of the antenna in degrees, and \(A_{Max}\) is the maximum gain of the antenna [\({\it dBi}\)]. Whereas, in Equation (17), \(-90^{\circ}\leq\theta\leq 90^{\circ}\), \(HPBW_{V}\) is the HPBW of the antenna in degrees in the vertical plane, \(SLLV\) is the side lobe level (SLL) of the radiation pattern in vertical domain in dBs, and \(\theta_{tilt}\) is the reference downtilt angle of the main beam of the antenna. Table 2 provides the antenna radiation modeling parameters used in our simulations.  
\[G(\varphi,\theta)=G_{H}(\varphi)+G_{V}(\theta) \tag{15}\]  
\[G_{H}(\varphi)=-\min\biggl{[}12\biggl{(}\frac{\varphi-\varphi_{az}}{HPBW_{H}} \biggr{)},FBR_{H}\biggr{]}+A_{Max} \tag{16}\]  
\[G_{V}(\theta)=-\min\biggl{[}12\biggl{(}\frac{\theta-\theta_{tilt}}{HPBW_{V}} \biggr{)},SLLV\biggr{]} \tag{17}\]
## 3 Results and Discussion
As for all television camera tubes, the main TSS parameter was television resolution capability (determined with the use of the linear line target by the number of lines per 1 mm at twenty percent VS modulation). AR is determined by the shape and finite sizes of a scanning element, i.e., by its aperture. In the MS the scanning element is the scanning boundary or the line of zero potential. In the TSS there are volt \(U_{0}\) and linear \(b\) apertures:  
\[U_{0}=4 KT/q \tag{3}\]  
Figure 3: Structure diagram of the noise-proof multiscan-based device for measuring the geometric parameters of objects. \[b=U_{0}\cdot I/E_{0} \tag{4}\]  
Equation (3) is a volt expression of the aperture of the scanning boundary determined by the value of the smearing of the I-V characteristics of the MS photodiode cell equal to the quadruple temperature potential. The linear aperture (expression (4)) is the minimum size of the projection of a measured object, at which the VS edges are not distorted yet, and consequently, the correspondence between the size of the object projection and the video pulse duration is retained. The analysis of the modelling results has shown that a VS is actually a smeared profile of the LZ, and the task of its analysis is to determine the zone parameters (coordinates, width, and illumination) by the smeared-out bell-shaped VS from the MS. Figure 4c,d illustrate the stages of the conventional processing of the VS with the use of its first and second derivatives. It can be seen that the width of the wide LZ can be determined using the difference of the moments of the time of zero crossings of the trapezoidal VS second derivative; it can also be determined directly by the VS (without its differentiation) if the measured moments of time correspond to the VS crossing half its maximum. In this case, for the wide LZ, the VS amplitudes and first and second derivatives are directly proportional to illumination. However, for narrow zones, the moments when the sign of the second derivative of the bell-shaped VS change do not correspond to the boundaries of these zones since the TSS cannot register their width with a high accuracy, due to the VS smearing out by its volt aperture. The time coordinate of the middle of the narrow LZ is definitely determined by the moment of the VS first-derivative zero crossing. Thus, the conventional processing of the VS of small objects does not permit the provision of a high potential accuracy of the MOD; therefore, it is practical to study the mechanism of  
Figure 4: Timing diagrams illustrating the operation of a noise-proof multiscan-based measuring device. (**a**) the action \(f(\hat{x})\) is the profile of the light zone; (**b**) the response \(V(\hat{t})\) is the MOD video signal (solid line); (**c,d**) illustrate the stages of the conventional processing of the video signal with the use of its first and second derivatives. the formation of a smeared-out VS for improving the metrological characteristics of measuring devices.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 2 Review on Chaotic Time Series Forecasting  
In the past three decades, many researchers have rigorously researched forecasting of chaos in various areas, such as wind farms, photovoltaic systems, hydrological systems, communication systems, and oil and gas fields, using ANN. Thus, there is a scope for a critical review of chaotic time series forecasting in various areas using machine learning techniques. This manuscript critically reviews various works published from 1992 to 2021. The decade-wise research contributions to chaotic time series forecasting during this period are shown in Figure 1.  
Figure 1: Decade-wise research contributions to chaotic time series forecasting from 1992 to 2021. According to the literature review collected from Table 1, 43% of works have employed the ANN-based approaches in the literature for chaotic time series forecasting. The additional techniques are based on the following: FNN 24%, optimization algorithms 15%, WNN 6%, and other approaches 4%. In the 15% of optimization-algorithm-based techniques, the various algorithms used are GA, PSO, SSA, SA, SOM, CGO, GWO, CBAS, etc. The objective of these techniques is to improve accuracy, computational efficiency, and concerns due to the presence of uncertainties in various applications. Some novel techniques reported in Table 1 focused on efficiently tackling multiple objectives. Table 1 also shows that these articles have dealt with several forecasting parameters, such as load, power, speed, traffic flow, signals, etc. In some of these works, real-time data were also collected from various countries, including Australia, Belgium, Canada, China, Iran, Laos, Morocco, Thailand, and the USA, as shown in Figure 2. In the first decade, research on chaotic time series forecasting relied on statistical data to forecast the system's future behavior. The rest of the decades used artificial intelligence and other novel models for chaotic time series forecasting in various applications. The detailed analysis of various forecasting approaches in different applications is explained in the following sections. Figure 2: Locations of real-time data collected from various parts of the world.
## 2 Materials and Experimental Procedure  
### Materials  
#### 2.1.1 Fly Ash (FA)  
Fly ash is a significant byproduct transported through flue gases resulting from the combustion of pulverized coal in thermal power plants, collected in cyclones or electrostatic filters. The molten material formed due to the high-temperature combustion of coal cools down and transforms, through gas flow, into partially or completely spherical ash particles. These ash particles are very fine (0.5-150 microns) and are referred to as fly ash, due to their being carried by flue gases. The major components found in fly ash include silicon dioxide (SiO\({}_{2}\)), aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), iron (III) oxide (Fe\({}_{2}\)O\({}_{3}\)), and CaO, and their quantities vary depending on the type of fly ash. The fly ash used in this study is class C fly ash, containing high calcium oxide according to American Material Test Standard (ASTM) C618 [13]. The class C fly ash used in this investigation comes from the Soma Thermal Power Plant in Manisa, Turkey, as shown in Figure 1. In terms of grain size, 11.7% of FA passes through a 90 \(\upmu\)m sieve, while 26.6% passes through a 45 \(\upmu\)m sieve; the density of FA used in this study was 2.40 g/cm\({}^{3}\). Its chemical and physical properties are provided in Table 1.  
Figure 1: The geopolymer mortar’s waste aggregate: (**a**) FA, (**b**) BFS, and (**c**) LP.
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### ANN (Artificial Neural Network)-Based MPPT Method
#### 3.1.1 Algorithm and Working Principle  
The ANN-based MPPT algorithm uses a multi-layer perceptron (MLP) architecture. The structure includes the following:  
* Input Layer: Takes environmental and system parameters as inputs, such as the following:
* Solar irradiance.
* Load power.
* Hidden Layers: Processes input data through weights and activation functions. These layers learn the nonlinear relationships in the system.
* Output Layer: predicts the voltage or current at the MPP.  
The ANN is trained using historical data, and the learning process minimizes the error function to optimize the model. Once trained, the ANN can predict the maximum power point under various environmental conditions.
## 3 Proposed System
### Dog Tracking  
After a dog is detected, it is tracked to determine its movement trajectory. The dog-tracking system identifies the position of the same dog in consecutive images and plots these positions to form an action path. The system uses a DeepDogTrack model for dog tracking. In addition to using a Kalman filter to predict the dog's position in the next frame, the model also uses a CNN to extract and match the dog's features in consecutive frames to determine the dog's motion status. DeepDogTrack is an improved DeepTrack pedestrian tracking model. The DeepSORT model integrates simple online and real-time tracking (SORT) [44] and CNN technology to extract and match each pedestrian's features and analyze the location and appearance information of each pedestrian to achieve accurate tracking. To reduce the computation time of the system and improve the accuracy of dog tracking, the system adopts our novel DeepDogTrack model, which contains improvements in the processing flow and adjustment of parameters.
## 4 Generalized Weighted Fairness (GWF)
### DRRF and GWF  
To apply the generalized weighted fairness (GWF) concept into IEEE 802.16e Mobile WiMAX Networks, we modified the Deficit Round Robin with Fragmentation (DRRF) algorithm so that the resource allocation follows the GWF criterion. We first calculate the pre-quantum number derived from the GWF equation (See Figure 3b). Then, similar to the quantum derivation in DRRF, the finalized quantum number is updated based on number of free slots, queue length, and its deficit counter (See also Figure 2).
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 3 Characterizations/Properties of DLC Coatings
### Recent Manufacturing Process  
In the above subsections, we described the properties and characterizations of DLC coatings. However, little recent advancement of manufacturing characteristics was also noted. The significant major part of this discussion is focused on the latest investigation of spark and arc sources, high power impulse magnetron sputtering (HiPMS), structure and nonlinear optical properties, and texturing. Vacuum electro-spark alloying (VESA)  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Adhesion** & **Type of Film** & **Deposition** & **Measurement** & **Reference** \\
**Strength (Pa)** & **Process** & **Decision** & **Technique** & **Reference** \\ \hline
1 to 3 × 10\({}^{\circ}\) & & & e-beam heated C & \\ (0.1 to 0.3 J m\({}^{\circ}\)) & & & rod & \\
4 to 7 × 10\({}^{\circ}\) & & & Electron and ion & \\
4 to 7 J m\({}^{\circ}\)) & & & beam source & \\ \(>2.\) to 2.8 × 10\({}^{\circ}\) & & & \\ (failure occurred in substrate) & & & & \\ \(
\begin{tabular}{l} 1000\ A\ \text{Ge}\ \text{or}\ \text{Si}\end{tabular}\) & & & \\ interlayer) & & & & \\ \(2\times 10^{\circ}\) & DLC on glass & & & \\  & & & sputtering & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adhesion strength of various films and their fabrication procedures.  
and pulsed cathode arc evaporation (PCAE) were investigated for the deposition of TiC-based coatings in inert (Ar) and reactive (C:H\({}_{4}\)) atmospheric conditions [143]. An investigation was done by using high-power impulse magnetron sputtering (HiPIMS) for the deposition of micrometer-thick DLC coatings over Si and steel substrates [144]. The adhesion on both sorts of substrates was ensured with an easy Ti interlayer, while the energy of contact ions was adjusted by using RF (radio frequency) biasing on the substrate at 100 V DC self-bias.  
Recently published work revealed DLC films that contain 4-29% of Si and were deposited by reactive magnetron sputtering of C target [145]. H-free DLC films with embedded Cu nanoparticles, such as DLC: Cu, were increased by simultaneous DC magnetron sputtering with graphite and Cu points of view [146].