Study and modeling of the server application for monitoring embedded systems of vehicle fleet in agribusiness

Daniel Mezzalira1 and Luis C. Trevelin1

{daniel.mezzalira, trevelin}@dc.ufscar.br

1Computing Department, Federal University of São Carlos, São Carlos, SP 13565-905 Brazil

Abstract—The management of multiple systems such as machine tools, vehicles, aircraft, among others, results in a very intense flow of data between the server and embedded systems, using wired and/or radiofrequency structures, demanding performance and interest in real time systems. The objective of this study is to propose a low cost scalable architecture for embedded applications, using pools of personal computers for high performance storage, retrieval and processing of information through the study of traces and real solutions for companies operating in this niche market.

Index Terms—Embedded Systems, Distributed Systems, Queuing Systems, Simulation, Performance.

I. INTRODUCTION HE MECHANIZATION of various economic sectors and the increase of the vehicles fleet, heavy and light, has

grown in world scope, with Brazil being a country of growing attention.. This fact motivated by the optimization of resources has created a demand sensing and telemetry to obtain valuable information for decision making and especially to reduce costs with fuel, which represent a large portion of the operation [1]. The profitability of a sector attracts investment and technological innovation in many contexts. Some of these sectors require process automation, using the resources of mechanization and different sizes of vehicles fleet. These resources optimize production and lower costs, increasing competitiveness and adding profit to the owner [2].

This telemetry of mechanized assets held by embedded systems generate large volumes of data, some of which must be recorded and viewed immediately and/or subordinated to a hierarchy of priority, in order to correctly populate Enterprise Resource Planning (ERP) and ensure an effective business intelligence.

The Brazilian context imposes an infrastructural barrier of communication, in which signal shadows forces the equipment to store information and download them abruptly when they return to areas with connectivity. This submission of information is often costly, requiring fault tolerance techniques and delays as the DTN networks [3], which implies in embedded information storage and transmission of all content when there is connectivity. This generates large peaks characteristic to the server, which often must be oversized to meet this demand, which occurs sparsely in time. To meet these peaks, powerful and expensive servers should be allocated to often be required in a few well-defined periods of the month, for example, in the days leading up to the reporting of monthly balance.

This article analyzes the performance of the vehicle

embedded systems under general, deepening in the context of the sugarcane sector, since the scenario of this operation imposes great challenges for the computerization of equipment such as an environment with high concentrations of suspended particles (dust), moisture, impact, high temperatures. Besides these, the lack of communication infrastructure cited before also represents one of these challenges and will be widely discussed here.

The aim of this research is to conduct the study and modeling of a server architecture, through the theory of queues, in order to provide a model that has the best average behavior of the events described, using the mathematical model MVA [4] to represent the behavior of server upon demand by the embedded systems and users, prizing scalability, performance and low cost. This includes the failures in communication and the receipt, processing and storage of information, and user interaction with information, for example, using an application of queries the fleet.

II. COMMUNICATIONS TECHNOLOGIES

A. Scenario Overview The motivation for studying an architecture that best suits

an application server for embedded systems is due to consents to certain characteristics of the communication infrastructure of the region that the fleet operates. In addition, it is safe to say that the intermittent signal effectively implies the data storage for embedded system, so abruptly sent when it finds connectivity. Analyzing this phenomenon in the Brazilian agricultural context, it is feasible to show the amplitude of the wave of information that floods the server, since usually the fleet moves in a planned and jointly on different areas that may or not have connectivity.

To parameterize the simulations, the study had available real traces of two companies that operate with the monitoring of fleets and surveyed the infrastructure present in the Brazilian territory, described in the topics of this section.

B. Infrastructural Analysis Ascertaining the availability of areas with connectivity, it is

possible to delve into the problem and better understand the unique event of bursts, as evidenced in the analysis of traffic. The different scenarios of operation have challenges to obtain the information with the lowest possible latency, quality and consistency. The lack of infrastructure in large areas is common, with constantly shadows and edges (boundaries of quality, with little sign for the transfer, causing abrupt interruptions) of connectivity.

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2012 Second Brazilian Conference on Critical Embedded Systems

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Large distances between equipment and the physical structure of the network define a difficult and costly logistics for collecting, updating and maintenance of solutions, making wireless communication technologies attractive. The main modes of transmission in the current market are: General Packet Radio Service (GPRS, provided by cell phone companies) and low-orbit satellites such as Iridium network (the use of geostationary satellites require expensive and sensitive to vibrations antennas and despite having a better cost of the data packet is unfeasible for the context of vehicle fleets).

The use of these technologies is basically attributed to the price of communications hardware and hiring their services (cost versus data packet). To opt for GPRS technology involves cheap hardware, better cost per megabyte and better coverage in urban perimeters. By contrast, the use of satellites implies a higher cost of hardware, more expensive and limited data plans, but provides a wide coverage area, including remote locations. Other technologies complement this scenario, such as WiMAX[5], WiFi and Zigbee, using techniques of passing information to other devices in a proprietary infrastructure, but are used on specific points of a locale and are used in embedded systems confined to well-defined places and therefore not considered in this project. Items 3 and 4 are a brief discussion on the use of key technologies for embedded telemetry systems.

C. GPRS This part of the research addressed the antennas regulated

by the Brazilian Communication Agency (Anatel[6]), where it was possible to obtain the characteristics and spatial coordinates of each sensor installed in Brazil. Using this information and applying the results of the study made by Teleco[7], it was possible to generate a georeferenced map of coverage area estimated for Brazil, as shown in Figure 1.

This figure highlights the coverage area of São Paulo state shows the strategy of the operators to cover the urban area and the main roads, in order to provide signal for a large portion of users. However, it is also clear that the coverage area represents a small portion of the territory (in this example, the state of São Paulo has approximately 6.9% of signal coverage), penalizing the use of this technology in other fields, such as agribusiness. This map ignores the distinction of operators, since there are GPRS modems that allow the use of up to four different operators simultaneously, minimizing the

problem of lack of connectivity for commercial reasons, analyzing only the physical aspects. Table I shows the territory versus connectivity area relation for each Brazilian state:

This table makes clear that the technology for sending packets using cellular networks is only effective for embedded systems that operate in urban perimeters and roads due to the low percentage of the area effectively covered by the signal. The cost of using this technology is very attractive for the installation of hardware and hiring of data packets and enables the inclusion of a solution with broadband, if the embedded system had access to 3G technologies.

D. Communication by Low Orbit Satellite (LEO) The use of low-orbit satellites provides an excellent

alternative for sending data from the embedded systems in remote areas. The cost of installing hardware and data packet are more expensive than GPRS technology, but are still viable.

Despite the constellation of satellites provide full coverage of the Brazilian territory, this does not guarantee 100% availability of connectivity. Several factors contribute to failures in the transmission of information, being two of the most significant: rain clouds and tilt of the antenna above

Fig. 1. Highlight coverage area estimated for the antennas with transmission technology packages the state of Sao Paulo.

TABLE I RELATIONSHIP BETWEEN TERRITORY AND COVERAGE SIGNAL AREA

State Territory (Km²) Signal Area (Km²) Percent (%)

AC 152.581,388 282,871853 0,001853908 AL 27.767,661 1189,463768 0,042836297 AP 142.814,585 265,897134 0,001861835 AM 1.570.745,68 823,285326 0,000524137 BA 564.692,669 4582,270278 0,008114627 CE 148.825,602 2597,065660 0,017450396 DF 5.801,937 1094,629974 0,188666298 ES 46.077,519 1969,014150 0,042732643 GO 340.086,698 3549,164460 0,010436058 MA 331.983,293 1958,240923 0,005898613 MT 903.357,908 1843,154658 0,002040337 MS 357.124,962 1355,961124 0,003796881 MG 586.528,293 10983,724669 0,018726675 PA 1.247.689,515 2202,807697 0,00176551 PB 56.439,838 1902,223504 0,033703561 PR 199.314,85 5229,733599 0,026238555 PE 98.311,616 2697,861316 0,027441938 PI 251.529,186 1759,594323 0,006995587 RJ 43.696,054 5230,736810 0,119707304 RN 52.796,791 1658,708319 0,03141684 RS 281.748,538 7098,377223 0,025194016 RO 237.576,167 631,653052 0,002658739 RR 224.298,98 191,535414 0,000853929 SC 95.346,181 3804,065005 0,039897403 SP 248.209,426 17228,508983 0,069411179 SE 21.910,348 826,421488 0,037718319 TO 277.620,914 1116,221134 0,004020667

TOTAL 8.514.876,599 84073,191844 0,009873683

This table shows the relationship between the area coverage and the signal for each state Brazilian.

1 Abbreviations of Brazilian States.

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ground. The modems which use L-band network Iridium, for example, operate in the range of 1.6GHz, so that the radio wave interacts with water molecules, so large cumulus nimbus clouds drastically attenuate the signal.

The second factor is the characteristics of the antennas commercially available to the use this modems (Figure 2 emphasizes the low tolerance to slant). A fleet equipped with embedded systems operating on land with a slope cannot transmit the signal, creating an area without connectivity similar to the shadow of GPRS.

III. METHODOLOGY The study follows three clear stages: achievement

parameters of a real solution for monitoring vehicle fleets in the market, building of environment of simulations with the previously obtained parameters and proposal a distributed architecture with a high degree of scalability, performance and low cost, which was subjected to traffic based on real trace obtained.

Using the Java Modeling Tools (v.0.8.0) [9] it was possible to generate and submit to testing various hypotheses of architectures servants and in which firstly the conventional architecture installed on the client was checked. After identifying the bottlenecks, we tested the same architecture by changing the average times of services in order to simulate a speed-up for the application. Then, a distributed architecture with the original values was designed to theoretically give the same speed-up, and again subjected to tests (i.e., a speed up to 500% of a node architecture was designated with the same parameters containing five nodes).

A. Study of conventional Architecture The conventional architecture consists of a rack server with

the configuration described in Table II (Quad). Although the server is not modern, the purpose of this study is exactly to evaluate the performance of these models, which are proportionately cheaper. Through management tool provided by Oracle® and the insertion of passages to capture logs were possible to extract the parameters also described in tables III and VI, necessary to calibrate the simulation.

To demonstrate the operation of architecture, a simulation that will serve as a basis for comparison for other was set at 10 customers and 20 machines, operating at a total coverage area of signal, without bursts in the stream server. Figure 3

illustrates the conventional architecture implemented in Java Modelling Tools software.

B. Study of Conventional with Speed up Getting the results in the first stage was possible to identify

bottlenecks and propose an improvement. We submitted the same architecture to the same input rate (20 machines and 10 users, with and without burst) with speed up of 500% to the services related to the identified bottleneck.

This step is important to check the advantages and disadvantages of opting for a distributed conventional architecture, analyzing mainly the cost item, because with large investments it is possible to get a powerful enough hardware for the monitoring of large fleets, however, the goal is to find an architecture with high scalability, performance and low cost.

An example of how to get more expensive and powerful hardware does not necessarily imply significant speed ups is demonstrated by a simple experiment, where the messages generated by embedded systems are processed by four different computers, as the table II shows. The messages are loaded into memory and then the decoding process each message and insert them into the database, resulting in the performance chart shown in Figure 4.

Comparing Quad and Dirac computer, we have an investment three times bigger to a speed up of approximately 30% and comparing the Quad and Quantica, the investment becomes 3.65 times of a speed up of approximately 45%.

A disadvantage of this approach is the limitation imposed by the hardware purchased, in which it is not possible to continue indefinitely increases the speed up, reaching a point where all the hardware must be replaced, creating a cost on the order of thousands of dollars.

TABLE II CONFIGURATION

Quantica Dirac Quad PC Home

Processor Xeon 5620 Xeon 5410 Intel 2-

Quad Atlon XP

4200 Frequency

(MHz) 2400 2333 2333 2200

Cores 8 (Hiper Threads) 8 4 2

RAM (GB) 16 8 8 1,5

Operation System

Open Suse Linux

Arch Linux Mandriva Linux Arch Linux

Value (R$) 7.300 6.000 2.000 300 Table of computers configuration tested.

Fig. 3– Conventional Architecture.

Fig. 2– Characteristics of an antenna used by the Iridium network [8].

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C. Proposal for a Distributed Architecture The distributed architecture assumes that hardware twice as

expensive does not mean it will be twice as fast [10], as demonstrated in the test section B. Following the assumption of a system with well-defined classes of services for embedded systems (receiving messages) and customers (queries to the database), the work creates the possibility that processing of these classes can be conducted in a distributed way with increase in performance proportional to the addition of new nodes to the server pool.

This hypothesis creates an attractive solution for economic and maintenance of the solution, since it is possible to integrate heterogeneous server (i.e., with times of services described above differ from each other). Through the analysis of each node, a task scheduler forwards the request to the node with lower load, to ensure the shortest queue for each request, as shown in Figure 5.

With this architecture, you can expand the number of nodes to increase performance of the solution, which allows the replacement of the hardware easily, keeping the proposal for a scalable low cost. Another interesting point is the growing demand for green computing [11], in which idle resources can be temporarily reallocated or even shutter down, helping to reduce energy consumption of the cluster.

This architecture was resubmitted to the same input rate (20 machines and 10 users, with and without burst) and was compared with the conventional architecture to speed up.

IV. REAL TRACERS Analyzing the real traces of vehicular embedded systems, it

was possible to identify three types of messages: control, productivity and operational messages, all georeferenced. The

analysis of real data was performed on three fronts: a study of the behavior of a vehicle, study of the flow of information coming to the server and achievement of the average rate for each service class. All information was obtained from a real system, operating under normal conditions, as will be discussed further in the following topics.

A. Harvester Trace The trace was obtained from an embedded system installed

on a sugar cane harvester with an acquisition rate of one second. Include the period of 07.09.2011-11h03min42s - 09.14.2011- 08h43min26s containing 12.743.145 messages, enabling the distinction of three well-defined classes: urgent messages containing alarms signaling non-conformity of any kind (user-defined events) and control (used by the system); productivity messages, containing the information needed to calculate the yield of the monitored activity, such as the harvested area, weight collected, among others, operational messages, containing information concerning the management of operation, as the current operator, current activity, among others.

The control messages have the highest priority of the system, since they indicate non-conformity that can result risks and losses, such as the radiator temperature alarm and lock from a row of planter. The first message can prevent breakage and fire equipment, the second prevents large losses of planted area productivity.

From the raw data it was possible to determine also the rate of generation of such messages by the embedded systems, as the following Table III. The analyzed vehicle was a sugar cane harvester and the amount of generation of the alarm message can vary depending on the type of the vehicle and set of events which generate such message.

B. Geospatial Analysis of the Harvester Traces From the connectivity Map, described in the topic II, it was

possible to determine the index points generated in areas of connectivity for embedded systems. Submitting the trace to spatial database, we found that a small portion was actually

Fig. 4– Test decoding and storage of messages per second.

Fig. 5– Distributed Architecture proposed.

TABLE III RATE OF MESSAGES

Type of Message Priority1 Rate (λ)

Control 4 0,075148122 /second3

Productivity 32 1/second Operational 22 1/second

Table of message types and their rates generation rates. 1The higher the value, the higher the priority; 2As required, these priorities can be reversed. As the generation rate is the

same, does not change the outcome of the study; 3This rate may vary significantly according to the type of tracked vehicle

on which this event generates message. The figure was obtained from the study of a sugar cane harvester.

TABLE IV POINTS GEOREFERENCEDS

Generated with Quant Percent (%) Signal 6544 0.000513531

Non Signal 12736601 0.999486469 Zero Point1 451061 0.035396364

Table of message types and their generation rates. 1Points with GPS coordinates equal to zero.

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Fig. 7– Server traffic for the period described analysis of the small server.

generated in areas of connectivity, as shown in Table IV. Another relevant factor was the number of GPS points with

null values, occurrence reported by machine operators and found in trace, which suggests that the lack of infrastructure in remote regions transposes the connectivity question. This phenomenon can be explained by the high concentration of global positioning modules in neighboring states (São Paulo and Minas Gerais).

Following the customer's desire in not to reveal the position of the farm and fleet, the Figure 6 shows the trace without this informations. Table V demonstrates how the variability behaves when an application starts to monitor large fleets in different companies.

C. Application Server Trace After analyzing an individual entity, the whole system was

analyzed in the same period, checking every second the amount of information from each fleet monitored by embedded systems that reaches the server. It is clear how a monitoring server fleet is liable to large load variations, due to

no connectivity areas, as shown in the graph in Figure 7 and 8. However, an interesting fact occurs when the number of

embedded systems increases considerably. Looking again the input flow of another company, responsible for the monitoring of thousands of vehicles, the peaks of the graph are attenuated by the purely probabilistic distribution geographical are vehicles, i.e., a large fleet distributed over a large area tends to be variability smaller and more uniform, becoming the scatter plot more uniform, as shown in figure 8. We note that, despite the graphic display a stability greater than the flow represented by a small company (which has a lower fleet) monitor a large fleet also causes the most significant peaks, viewed in the diagram in a few moments, but representing a sample flow greater than six times the average. See again Table V for a statistical analysis of samples collected on both servers.

Analyzing the server was also possible to determine three types of services classes by the user interaction. Basically, fleet tracking systems feed Geographic Information System (GIS) for better visualization of the information recorded. Monitoring the queries requested by users and GIS, it was possible to identify three classes of queries, with their approximate rates in Table VI:

D. Considerations Describe the scenario is a fundamental step to understand

the peculiarities that occur in a server application for monitoring systems embedded vehicle fleets. Basically, this section demonstrates how complex is to provide for a fleet a 100% of connectivity, what is not possible even using satellite communication technology.

V. RESULTS The simulation result of conventional architecture achieved

the same basic behavior of the real scenario monitored in small company, the server is saturated and the queue time increases gradually until not decreasing the load (what happens when employees complete the work shift). It is possible to identify the bottleneck of the solution is the database, responsible for providing the information for any type of query. From this information, we found that a service database five times faster would be sufficient to meet demand with low levels of queuing, however this cost is much more than 500% and can occur from reaching the limits of the hardware, requiring your full and costly replace.

Comparing the results of the simulations in table VII, it is advantageous that over architecture for a node when the

Fig. 8– Server traffic for the period described analysis of a large server.

Fig. 6– Trail of the harvester and highlight area of the edge signal. Thesetraces were generated by uDig (GIS software) [12]. TABLE VI

RATE OF CONSULTS

Type of Consult Priority1 Rate (λ)

Simple2 1 1 /second

Median3 0 0.1/second Complex4 0 0.25/hour

Table of types of queries and their generation rates. 1The higher the value, the higher the priority; 2 Queries requested by the GIS automatically, for updating information; 3Represents some queries coming from the automatic and other users; 4Costly and complex queries, performing large queries and processing by

the band, for example, comparative monthly income between two harvesters.

TABLE V SERVERS STATISTICS

Variable Small Server(Bytes) Big Server(Bytes)

Quartile(0.00) 1 10000

Quartile(0.25) 722 31500 Quartile(0.50) 1064 36250 Quartile(0.75) 1338 40750 Quartile(1.00) 5254 304250

Standard Deviation 480,1128 7.0662e+03 Mean 1079 36514

Statistical analysis of the flow recorded in servers of different sizes (The size of the server matches the number of machines and users in the system.)

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complex queries are required, since it takes up station for a long period. While a node is busy with this complex query, the other classes use the other nodes, resulting in a small time queue.

The response time found in test 3 is justified by the interactive response time law, described in equation 1, where M represents the number of customers in the system, X the throughput of the system and the Z the thinking time. As Z is smaller for the database to speed up and all the other results follow similar values.

ZXMR

0

(1)

The response time of the line 1 tends to increase during the simulation, since the arrival rate is bigger than the service rate. The results of the line 4 and 5 show an interesting amount of throughput. The complex queries, which have much larger service time than the others, are responsible for the disruption of the system. Where they enter, they holding station for a long period of time, providing a significant advantage to a distributed architecture. While a station is only a complex query, the other dealing with the other classes, increasing the throughput even with an average response time less than the architecture with speedup. The results of the line 4 and 5 show an interesting amount of throughput. The complex queries, which have much larger service time than the others, are responsible for the disruption of the system. Where they enter, they holding station for a long period of time, providing a significant advantage to a distributed architecture. While a station is only a complex query, the other dealing with the other classes, increasing the throughput even with an average response time less than the architecture with speed up.

Using Table II, it was possible to simulate the estimated parameters of bursts for the same application in different states of Brazil, as shown in Table VIII. Sao Paulo has more signal coverage, resulting in a more constant flow of data. The state of Acre has little coverage, resulting in long periods of idleness and peak flow when the fleet is an area with signal.

These factors reflect the mean values shown in Table VIII. Sao Paulo (SP) has a more intensive use of the server, with longer periods of flow near the middle, so there are fewer burdens, making the response time and throughput higher than compared to the fleet operating in the Acre (AC), which has

the smallest area connectivity, resulting in long idle times and large peak loads.

VI. CONCLUSION By obtaining values of a real system it was possible to

perform simulations and verify that a distributed architecture offers great advantages for monitoring vehicular embedded systems. The model allows the use of heterogeneous servers, maintaining the assumption of a scalable architecture, low cost and achieving high performance by adding more nodes to the server pool. Another advantage is to enable the adoption of Green Computing, reducing the monthly cost of the cluster through the efficient management of resources.

REFERENCES [1] A. ARAÚJO. Indicadores da função motomecanização aplicados em

usina de açúcar e álcool em um ambiente gerenciado por processos: um estudo de caso, master’s dissertation, Federal Univ. of Santa Catarina, Florianópolis. Dept. Production Engineering 2002.

[2] F. FAVARETTO, Uma contribuição ao processo de gestão da produção pelo uso da coleta automática de dados de chão de fábrica, doctoral thesis, Univ. of São Paulo, São Carlos, Dept. Mechanical Engineering, 2001.

[3] F. WARTHMAN, Delay-Tolerant Networks (DTNs) – A Tutorial. Based on V. Cerf, S. Burleigh, A. Hooke, L. Torgerson, R. Durst, K. Scott, K. Fall, H. Weiss, Delay-Tolerant Network Architecture, DTN Research Group Internet Draft, 2003.

[4] Bogardi-Meszoly, A.; Levendovszky, T.; Charaf, H.; , "Extending the Mean-Value Analysis Algorithm According to the Thread Pool Investigation," Industrial Informatics, 2007 5th IEEE International Conference on , vol.2, no., pp.731-736, 23-27 June 2007

[5] Chen, J.-L.; Chang, Y.-C.; Du, H.-W.;, "Embedded worldwide interoperability for microwave access-based vehicular router for telematics computing," Communications, IET , vol.4, no.7, pp.861-869, April 30 2010.

[6] Agência Nacional de Telecomunicações, http://www.anatel.gov.br (current Mar. 19, 2012).

[7] Teleco, http://www.teleco.com.br/tutoriais/tutorialavaltrans/pagina_2.asp (current Mar. 19, 2012).

[8] SATCOM, http://www.sensorantennas.com/antenna_pdf/GPS/S67-1575-168.pdf (current Mar. 19, 2012).

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TABLE VII RESULTS

S1 Queue Time(s)2

Server Utilization

Database Utilization

Response Time(s) Throughput

1 0.1152 0.1939 1.0000 4.343e+5 31.4635 2 0.1150 0.1937 0.2653 0.5036 41.7647 3 0.0431 0.03883 0.26534 1.7130 41.7670 4 0.2488 0.2356 0.6456 1.9248 638.1423 5 0.1028 0.04863 0.73974 21.7800 759.9519

1Simulation. 2For the Control messages (Waiting time to be served). 3Mean of the five servers. 4Mean of the five databases. 1 – Simulation of the Conventional Architecture; 2 – Simulation of the Conventional Architecture with speed up; 3 – Simulation of the Distributed Architecture; 4 – Simulation of the Conventional Architecture with speed up and burst; 5 – Simulation of the Distributed Architecture and burst in AC.

TABLE VIII RESULTS 2

S1 Queue Time(s)2

Server Utilization

Database Utilization

Response Time(s) Throughput

1 0.1085 0.0543 0.9875 15.7876 1177.9492 2 0.1028 0.0486 0.7397 21.7800 759.9519 Table of results. Comparing the results of different states. 1Simulation. 2For the Control messages (Waiting time to be served). 1 – Simulation of the Distributed Architecture and burst in SP; 2 – Simulation of the Distributed Architecture and burst in AC calculated

in table VII.

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