esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pairing T3 SD with DEVS
#### 3.3.2 T3 SD Association with DEVS  
The objective of this section is to provide mapping of some of the concepts between T3 SD and DEVS such that we can derive a framework that contains the advantages of each. In doing so, it is necessary to completely map all concepts from both frameworks, but those  
Figure 13: A description of the relationship between T3 SD and simulation. The problem is an input to T3 SD from stakeholders. The output from T3 SD is the specification of required input/output (I/O), the required performance of the systems design (Performance Index), plan for testing the system design (Test Plan), and discrete event representations of the system design (System Design).  
that conceptually overlap. As such, we have found some alignment between the hierarchy of system specification of DEVS and some concepts of T3 SD. Concepts from DEVS that do not inherently map to T3 SD include the system modeling formalism formalisms for a multi-component system and structured systems. Concepts from T3 SD that do not inherently map to DEVS include the requirements for performance (PR), cost (CR), and tradeoff (TR). These distinctions stem from DEVS having a defined purpose of system model specification, which includes a need for additional formalism beyond what is included in T3 SD, and T3 SD having a defined purpose of modeling the engineering of system, which includes a need for formal modeling of the problem space that allows for distinguishing between system models that acceptable from those that are not. As stated previously, system testing is something we will explore in future research.  
From the DEVS and Hierarchy of System Specification review and [PERSON]'s system theoretic foundations for modeling and simulation [44], we claim that a partial mapping between T3 SD and DEVS is possible. A conceptual overlay of some of the concepts used in T3 SD and DEVS to define the hierarchy of system specification is shown in Figure 14.  
The input/output requirement (IOR) of T3 SD can be mapped to the I/O frame, I/O relation, and I/O function of DEVS; the functional system model (Z), functional system design (FSD), buildable resultant system model (Z@), and implementable system mode (ZS) can be mapped to the I/O system of DEVS; and the buildable system design (BSD) and implementable system design (ISD) can be mapped to the Network of systems of DEVS. As stated previously in this section, there does not appear to be a mapping from T3 SD to the structured system or multi-component system models of DEVS. With this conceptual mapping between the T3 SD framework and the DEVS formalism, we are able to set the foundation for the convergence.
## 3 Results and Discussion
### Engine Performance
#### 3.1.2 Combustion Phasing  
The mass fraction burned (MFB) was calculated using the heat release rate as shown in Figure 5. The combustion condition in the cylinder can be verified in this manner. The crank position where the MFB is 10% from the start of the pilot injection is denoted as CA10, 50% is CA50, and the point where the MFB becomes 90% is denoted as CA90. The difference between the fuel injection start point and CA10 is called the flame-development angle (or duration), and the difference between CA10 and CA90 is called the rapid-burning angle or combustion duration. CA50 shows where the MFB is 50%, which means that 50% of the injected fuel is converted to energy [57; 58]. Table 6 shows the rapid-burning angle and rapid-burning angle calculated by analyzing the MFB at each condition. Figure 4: Combustion pressure and heat release rate of (**a**) 0 Nm, and (**b**) 40 Nm; combustion pressure rise rate of (**c**) 0 Nm, and (**d**) 40 Nm. The CA10 of all blend ratios at an engine load of 0 Nm were at approximately -9 \({}^{\circ}\)CA ATDC, and the flame-development durations of all blends varied from 2.40 ms to 2.49 ms. As described above, the pilot injection quantity was small compared with the total injection, and the oxygen content and poor atomization influence each other to offset this result so that the ignition delays of pilot injections were similar at idle. At an engine load 40 Nm, the CA10 of PD0, PD10, and PD30 were similar at -7 \({}^{\circ}\)CA ATDC, but CA10 retarded to -5 \({}^{\circ}\)CA ATDC for PD100. As seen in Figures 4d and 5b, the combustion phases of the pilot injection of PD0, PD10, and PD30 dramatically improved due to increased pressure in the cylinder with increased engine loads of 40 Nm, but the combustion phase was slower in PD100. The flame-development durations of PD0, PD10, and PD30 at 40 Nm engine load were about 2.87 ms, and the rapid-burning durations varied from 4.0 ms to 4.82 ms. For PD100, the flame-development duration decreased to 3.33 ms, and the rapid-burning duration decreased sharply to 4.53 ms. Also, CA50 of PD100 at an engine load of 40 Nm was delayed from 7.4 ms for PD30 to 8.3 ms. This may be because the combustion conditions were improved by the increased pressure in the cylinder, but the combustion reaction did not improve because of the poor atomization of PD100. After the main injection, the MFB of PD100 was delayed as a whole, but it rose rapidly. This is because the ignition delay of the main injection was increased by the slower and deteriorated combustion of the pilot injection due to the poor atomization of PD100. [PERSON] et al. [49] also reported that the combustion of the main injection was delayed due to deterioration of the pilot injection. Thus, when using PD100 under idle conditions, the combustion characteristics were more affected by the poor atomization due to the higher viscosity than by the effect of the oxygen.
# Last-Mile Optimization Using Neural Networks
## 1 Introduction
semble classification approach using multiple ML models and MLP models, alongside the automation of last-mile logistics processes. These contributions aim to enhance delivery times and supply chain sustainability by optimizing product flows within the city. Utilizing the developed model extends its application to diverse scenarios, showcasing its versatility and effectiveness. One prominent application involves the categorization of financial transactions, providing a streamlined approach to accounting processes and delivering comprehensive insights into expenditure patterns. The model's adaptability further proves valuable in the realm of business strategies, where it can be employed to categorize products based on regional preferences. This enables businesses to tailor their product offerings to specific markets, enhancing market responsiveness. Additionally, the model finds relevance in the digital landscape by contributing to content categorization in online platforms. Whether applied to articles, blogs, or multimedia, the model facilitates efficient organization and categorization, ultimately improving user accessibility to relevant information across various online channels. The subsequent sections of the paper are structured as follows: Section 2 introduces related work. Section 3 provides an in-depth overview of our proposed method's system. Section 4 delves into the findings and evaluation of the proposed method. In conclusion, Section 5 summarizes and concludes the paper.
## 3 Experimental Results and Discussions
### AC-FL Simulation Results  
The proposed AC-FL model was trained with the data from 2012, and tested with the data from 2013 for the same monthly period. The simulation results showed a downward trend for the MAPE value of the optimization system since the first step. The system learned with both negative and positive information. When the MAPE value was worse, it showed that the existing choices on the ant path route were not proper for the fuzzy model, that this route was punished by giving fewer weight values, and that it consequently decreased the selection possibility of the path in the next step. When the MAPE value was better, it showed that the existing choices on the ant path route were more proper for the fuzzy model; thus, this route was rewarded with higher weight values, and it increased the selection possibility of the path in the next step.  
Figure 19: Training results of the GA–FL forecasting model for 1–5 February 2013.  
Figure 20: Testing results of the GA–FL forecasting model for 7–12 February 2014.  
After a while, in the late phase of the iteration process, as shown in Figure 21, differences in the MAPE values decreased dramatically. This situation showed that the route was optimized, the agent ants almost followed the same path, and similar rule sets were applied to the system in each step of the iteration process. The route optimization process and MAPE values of the ant rule base sets are seen in Figure 21.  
The proposed AC-FL model was trained with 1000 virtual agent ants. In each step of the iteration, when an agent ant followed a route, which meant a new knowledge base of the fuzzy model, the weight matrix was updated. This route was also constantly updated with other pheromone amounts, which were calculated using the tolerance value that was defined at the beginning of the iteration process in the script. The pheromone constant in each iteration was calculated according to the performance of the virtual agent ant.  
The training and testing values of the proposed AC-FL model are given in Figures 22 and 23. The proposed model was trained with the monthly-based dataset for the same weekday periods in 2013 and 2014.  
The ant colony optimization process is aimed at finding the best rule base set that will be used for testing with the target data. At the end of the optimization process, two different rule base sets were improved at the same time. One of these rule base sets gave the best training error value, which was  
Figure 21: Rule base path learning process during optimization.  
Figure 22: Training results of the ant colony (AC) best rule base set, which was generated considering the maximum weighted rule base for 1–5 February 2013.
# Blockchain Applications in Agribbusiness: A Systematic Review
## 3 Results and Discussion
### A Qualitative Systematic Review
#### 3.2.5 Blockchain Application for Agriculture (Farm) in Agribusiness
According to the authors, key information about citrus farms is stored in digital profiles including the environment and cultivation, soil, water, area, season, plant quality, growing conditions, planting season, and information on the fertilizers and pesticides used for growing oranges. Then, the product is marketed after a digital contract is signed and stored in blockchain. [PERSON] [70] tested a traceability system for the food supply chain using blockchain, helping the agricultural cooperatives improve transparency on the origin and processes incorporated into the products. They developed a proof-of-concept blockchain model (PoC) in the agri-food field for the traceability of the supply chain, proving the origin of berry production, and personalizing the roles of each actor in the supply chain. Blockchain, developed with this PoC concept, is being implemented and tested in a Spanish Agricultural Cooperative that uses a book with permissions (hyperledger), based on a smart contract. The demonstration was based on a previous analysis of the berry chain and the interactions between farmers, cooperatives, their certifiers, suppliers, and supermarkets to allow the digital representation of a large number of berries to be associated with a single digital certification. [PERSON] et al. [71] suggested a system of sequence diagrams based on blockchain for soybean traceability. They also created an algorithm model for selling soy among the several participants, using smart contracts to track and control all interactions and transactions of the participants involved in the ecosystem of the supply chain. It is inferred that blockchain is being used in the agricultural sector to improve food security, the production process, processing, and transport, besides being applied in a management system of the supply chain to provide transparency, safety, neutrality, and reliability in all the operations of the supply chains. Blockchain, together with IoT, contributes to the resolution of challenges related to operation safety, quality certification, and product origin.
###### Abstract  
During the operation of wind turbines, flow separation appears at the blade roots, which reduces the aerodynamic efficiency of the wind turbine. In order to effectively apply vortex generators (VGs) to blade flow control, the effect of the VG spacing (\(\lambda\)) on flow control is studied via numerical calculations and wind tunnel experiments. First, the large eddy simulation (LES) method was used to calculate the flow separation in the boundary layer of a flat plate under an adverse pressure gradient. The large-scale coherent structure of the boundary layer separation and its evolution process in the turbulent flow field were analyzed, and the effect of different VG spacings on suppressing the boundary layer separation were compared based on the distance between vortex cores, the fluid kinetic energy in the boundary layer, and the pressure loss coefficient. Then, the DU93-W-210 airfoil was taken as the research object, and wind tunnel experiments were performed to study the effect of the VG spacing on the lift-drag characteristics of the airfoil. It was found that when the VG spacing was \(\lambda/H=5\) (\(H\) represents the VG's height), the distance between vortex cores and the vortex core radius were approximately equal, which was more beneficial for flow control. The fluid kinetic energy in the boundary layer was basically inversely proportional to the VG spacing. However, if the spacing was too small, the vortex was further away from the wall, which was not conducive to flow control. The wind tunnel experimental results demonstrated that the stall angle-of-attack (AoA) of the airfoil with the VGs increased by \(10^{\circ}\) compared to that of the airfoil without VGs. When the VG spacing was \(\lambda/H=5\), the maximum lift coefficient of the airfoil with VGs increased by 48.77% compared to that of the airfoil without VGs, the drag coefficient decreased by 83.28%, and the lift-to-drag ratio increased by 821.86%.  
**Keywords:** vortex generators; wind turbine; airfoil; aerodynamic performance; large eddy simulation
_Applied_  
_sciences_  
_Review_  
A Review of Distributed Fibre Optic Sensors for Geo-Hydroological Applications  
[PERSON] (c)  
National Research Council, Research Institute for Geo-Hydroological Protection, Corso Stati Uniti 4,  
I-35127 Padova, Italy; [EMAIL_ADDRESS]; Tel.: +39-049-829-5812  
Academic Editor: [PERSON]  
Received: 5 August 2017; Accepted: 27 August 2017; Published: 1 September 2017  
**Featureurd Application: Distributed fibre optic sensors for geo-hydroological applications: a comprehensive review about methodology, weaknesses, and strengths.**  
**Abstract: Distributed optical fibre sensing, employing either Rayleigh, Raman, or Brillouin scattering, is the only physical-contact sensor technology capable of accurately estimating physical fields with spatial continuity along the fibre. This unique feature and the other features of standard optical fibre sensors (e.g., minimal invasiveness and lightweight, remote powering/interrogating capabilities) have for many years promoted the technology to be a promising candidate for geo-hydroological monitoring. Relentless research efforts are being undertaken to bring the technology to complete maturity through laboratory, physical models, and in-situ tests. The application of distributed optical fibre sensors to geo-hydroological monitoring is here reviewed and discussed, along with basic principles and main acquisition techniques. Among the many existing geo-hydroological processes, the emphasis is placed on those related to soil levees, slopes/landslide, and ground subsidence that constitute a significant percentage of current geohazards.**  
**Keywords: distributed optical fibre sensors; landslide; soil erosion; subsidence; levees**
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 3 Introduction to GNSS  
Global Navigation Satellite Systems, abbreviated as GNSS is a term that refers to a number of multi-satellite systems that are owned, originated or being developed by different nations in the world and are (or would be) used to provide navigation and positioning data up to a certain level of accuracy for domestic, commercial, military and research purposes. Every GNSS has a constellation design that enables it to achieve a global coverage. The Global Positioning System (GPS)-USA, Galileo-Europe, GLObal'naya NAvigatsionnaya Sputnikovaya Sistema (GLONASS)-Russia, Compass Navigation Satellite System (CNSS)-China are the examples of GNSS. At the moment, GPS is the only GNSS which is fully operational. All other systems are either being developed or are partially operational. Table 1 shows difference parameters of various GNSS.  
A GNSS is divided into three segments namely Space Segment, Ground Segment and User Segment. The space segment consists of the orbit constellation and the satellites (Space Vehicles). The ground segment consists of several monitoring stations on the ground, which send the corrected data about clocks and orbits to the satellites. The user segment consists of the GNSS receiver which can be of many different types.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline
**Treatment Stage** & **Metric** & **Moroceli** & **PF200** \\ \hline Raw water & Mean (NTU) & 13.86 & 10.14 \\ \hline Median (NTU) & 2.14 & 6.52 \\ \hline
5 th–95 th percentile (NTU) & 0.97–49.35 & 2.40–31.17 \\ \hline Std. Dev (NTU) & 49.79 & 17.28 \\ \hline Clarified water & Mean (NTU) & 0.58 & 1.62 \\ \hline Median (NTU) & 0.43 & 1.18 \\ \hline
5 th–95 th percentile (NTU) & 0.02–1.69 & 0.37–4.55 \\ \hline Std. Dev (NTU) & 0.68 & 1.54 \\ \hline Filtered water & Mean (NTU) & 0.21 & 0.54 \\ \hline Median (NTU) & 0.02 & 0.32 \\ \hline
5 th–95 th percentile (NTU) & 0.02–0.92 & 0.07–1.88 \\ \hline Std. Dev (NTU) & 0.32 & 0.93 \\ \hline Sampling period & 1 year & 5 months \\ \hline \end{tabular}
\end{table} 
Table 2: PF200 vs. Moroceli: main turbidity statistics for both systems. below 1 NTU, meeting WHO recommendations for safe drinking water even during variable weather conditions. Additionally, all the systems studied show a decrease in the _E. coli_ counts below the detection limit, independently of the raw water quality. Future work with the PF200 will need further testing to improve turbidity removal, including experimenting with coagulant dosing. Additionally, testing should be performed with the PF200 for other key parameters dictated by legislators, like biological contamination (e.g., _E. coli_) and emerging contaminants. The use of a sludge blanket can potentially provide a nutrient-rich growth environment for the opportunistic pathogens characteristic of the water treatment and distribution systems, such as _Legionella_. Microorganisms grown at the stage of the process would not be removed by the clarifier and the filter, posing a threat of directly contaminating the distribution network. In such cases, the entire process would have to be redesigned to incorporate other means of protection against microbiological contamination. A similar set of factors should be taken into account for other aspects of the design as well. The performance of the tested technology in terms of nutrient removal, capturing microplastics, per- and polyfluoroalkyl substances, as well as other parameters regulated by the drinking water legislation, should be evaluated, as no similarities between the technologies should be assumed without evidence. These aspects are highly important, given the fact that at the time of the tests, the studied technology was not validated by the EPA as an approved method of water treatment for human consumption. Despite the similarities between the conventional process and PF200, it is unclear to what degree it replicates performance on the established treatment methods. Further research is necessary to answer those questions.
## 3 Calculation Condition  
The fluid computing domain is 8 m \(\times\) 1.1 m \(\times\) 0.5 m. The simulation model of the underwater vehicle is 100 mm away from the left inflow surface, 7000 mm away from the right outflow surface, 100 mm away from the bottom, 300 mm away from the top (200 mm away from the water surface), 300 mm away from the back of the fluid computing domain and 700 mm away from the front of the fluid computing domain. The fluid computing domain is shown in Figure 1.  
In the setting of boundary conditions, the discharge port is the velocity inlet. The inflow surface is the velocity inlet. The outflow surface is the pressure outlet. The top of the fluid computing domain is the pressure outlet. The inflow surface temperature is 293 K, and the inflow velocity is 0.1 m/s. The thermal jet temperature is 333 K, and the speed is 0.3 m/s [25].  
In this paper, the Fluent software (Fluent 2020R1, Wuhan, China) is adopted for simulation calculations. In the solution setting, considering the water surface infrared characteristics of an underground vehicle thermal jet, the air layer is divided out in the fluid computing domain, and the VOF fluid volume function is used to track the gas-liquid interface layer. The thickness of the air layer is 100 mm on the water surface. The solving method is the SIMPLE algorithm based on a finite volume method. The solver type is pressure-based, and Transient is adopted for the time attribute. Moreover, the influence of gravity on fluid movement is reflected in the solution process. The reliability requirements of the calculations and solution are reflected in the limitation of the convergence residual of  
Figure 1: Diagram of fluid computing domain.  
each parameter. The convergence order of the energy residual is less than \(10^{-7}\), and the convergence order of the residual of other parameters is less than \(10^{-4}\). The results can be considered reliable if the residual convergence is achieved [25].  
In order to eliminate the influence of the number of grids on the calculation results, the central maximum temperature values at the horizontal distances of 0.1 m, 0.3 m and 0.5 m between the thermal jet and the center of the discharge port are selected as verification data, and the central maximum temperature of the underwater vehicle thermal jet at the same position and under different grid numbers is compared and verified. The verification results are shown in Figure 2. The data in the figure shows that, when the number of grids exceeds 1 million, the maximum temperature at the center of the thermal jet at different distances from the center of the discharge port is relatively consistent, with a small error within 0.01 K. Therefore, it is appropriate to control the number of grids at about 1.5 million when setting the local size and global size of meshes.