Class-8 Heavy Truck Duty Cycle Project Final Report - Center for ...

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For those cases in which an instrumented tractor was mated to a non-instrumented trailer, a procedure was developed to assess the type of tires that was on that trailer. Schrader provided ORNL with a database (called the trailer-tire database) of all of the Schrader trailers. This database included the trailer ID number as well as the type of tires that were mounted on that trailer. The company also provided ORNL with a database (called the trailer-trip database) of all the trips that each one of the six instrumented tractors undertook during the data collection period. This database contained the starting and ending date and time of each trip, its origin and destination (city and state), the trailer ID of the trailer that was assigned to that trip, information about fuel tickets (if any), and information about vehicle weight (if any) obtained at a commercial vehicle scale. For each record in the data collected for this project, the time stamp of that record (which included date and time) was used to access the trailer-trip database to extract the ID of the trailer that was utilized in that trip. This trailer ID, in turn, was used to enter the trailer-tire database to determine the type of tires that the trailer had. This information was subsequently added to the database of data collected in this project. This procedure was followed until all of the records had been assigned a type of tire for the trailer that was used when the information in that record was collected. The tractor and trailer tire information, which was critical for the determination of the effect of tires in fuel efficiency, was archived, and three copies were made and stored at three different locations to minimize the risk of data loss. 6.1.3 Tractor and Trailer Weight Information Weight plays a main role in Class-8 truck fuel economy variations. It was therefore paramount that this type of information be collected in this study. To do so, a device (AirWeigh) which is described elsewhere in this report, was mounted on the six participating tractors, and provided instantaneous weight measurements at the steer and drive axels7, which were saved as two of the 60 data channels of the DAS. The AirWeigh device was also mounted on the ten instrumented trailers; therefore, when one of these ten trailers was mated with one of the six tractors, the DAS collected weight data for the entire truck. However, as discussed above, the mating of an instrumented tractor with an instrumented trailer was not a very common occurrence (less than 6% of the time), and as a result, most of the time (94%) the six tractors were mated with non-instrumented trailers (i.e., trailers without an AirWeigh device on board). This situation therefore did not provide weight data for about 94% of the trips engaged in by the instrumented tractors, and a remedy for this was developed by the ORNL staff. Based on the data collected for the cases in which the instrumented tractors and trailers were mated, ORNL developed a model that predicted the trailer weight when only the tractor was equipped with an AirWeigh device. This model, which is described in detail later in this report, was used to assign trailer weight to each one of the records in the database of data collected in this project. Other Information Other information relevant to the data analysis task was also generated and added to the database of data collected in this project. This included roadway slope (in %), which was computed using the altitude data gathered with the help of the GPS device, and filtered to smooth out local fluctuations due to GPS errors; relative wind speed (raw and filtered); and filtered vehicle speed which was later used to determine acceleration, deceleration and constant speed intervals8. The filter used was a 7 As described earlier, the AirWeigh device uses inputs from the vehicle air-suspension system. For the tractor, only the drive axle weight is measured; the steer axle weight is computed by the AirWeigh device. 8Filtering was needed because of the inherent fluctuations in the data, which if not filtered would have introduced errors into the data analyses. 66
sliding exponential smoothing filter that considered the median of eleven observations before, and eleven observations after the current point. 6.1.4 Information Indexing As discussed earlier, the size of the final database surpassed 295GB. In order to efficiently find and retrieve specific information from such a large database it was necessary to index the information and to create a database of pointers that would allow for the quick retrieval of data records that comply with possibly multiple search criteria specified by the analyst. Recalling that the final database was analogous to a stack of double-sided paper 110 stories tall, searching for specific criteria would be very labor intensive. However, creating a database of pointers, i.e., an index of the information contained in the main database, would expedite the search process since these pointers would indicate exactly where (e.g., file and records within that file) to find a specific set of data. For each one of the main attributes of the collected information that might play a relevant role in the data analysis (for example, • a truck moving in an urban area vs. a truck moving in a rural area; • a truck moving on a freeway vs. a truck moving on a surface street; • a truck moving under rainy conditions vs. a truck moving under clear weather; • a truck moving during the morning peak hour vs. a truck moving during mid-morning offpeak hours; • a truck moving vs. a truck in a stationary position; and many others). The ORNL-developed software identified, within the daily data files previously generated, the records in which each attribute is valid, and where it is not valid. This indexing allowed for a very efficient management of the information and was used in the data analysis task to allow for the selection of files that contained only the specific information of interest; for example, a truck traveling in urban areas, during the morning peak-hour, during rainy weather. A search engine, that uses the index pointers was developed and included with the DCGenT Prototype to help with the identification and retrieval of information that satisfies one or more userselected criteria simultaneously. Fig. 56 shows a screen capture of the data selection criteria that is part of the DCGenT Prototype. Those criteria include: 1) time of day (peak- and off-peak periods), 2) air temperature, 3) precipitation status, 4) roadway type (freeways or surface streets), 5) spatial location (urban or rural areas), 6) congestion level as a function of traveling speed, 7) topography (different type of terrains from upslope to flat to down slope), 8) truck tire configuration, 9) instrumented (i.e., both tractor and trailer equipped with AirWeigh devices) or noninstrumented (i.e., trailers with no AirWeigh devices) vehicles, 10) type of transmission and tires for the tractor, 11) specific tractors (one or more of the six instrumented tractors), 12) wind speed range, 13) vehicle dynamic condition (e.g., truck stationary, truck accelerating, etc), and 14) vehicle weight (in four levels: empty; light load, medium load, and heavy load). 67
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Class-8 Heavy Truck Duty Cycle Proj
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Vehicle Systems Program ORNL/TM-200
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2.7 LAUNCH OF THE FOT (OCTOBER 23,
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40 Connection and Support Structure
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LIST OF TABLES Table Page 1 Pilot T
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ACKNOWLEDGEMENTS The Oak Ridge Nati
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Sixty channels of data were collect
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xviii
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The data analyses performed in Phas
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Fig. 2. HHDDT Transient Mode In add
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vehicle; 3) a transit bus; 4) a nei
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payload and accessory load would al
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Data # Performance Measure Benefit
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Data # Performance Measure Benefit
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Fig. 10. NGK NOx/O2 Sensors Fig. 11
Page 36 and 37: 1.3.4 De-instrumentation: Following
Page 38 and 39: Fig. 16. Pilot Test North-South Rou
Page 40 and 41: GPS information collected with the
Page 42 and 43: 2.1 REVIEW OF PERFORMANCE MEASURE R
Page 44 and 45: 2.3.2 Performance Measures Fig. 18.
Page 46 and 47: Signal Sensor 53 Road Grade Calcula
Page 48 and 49: 2.3.6 Tractor and Trailer Details F
Page 50 and 51: determined that the particular CANo
Page 52 and 53: Cable runs to and from sensors were
Page 54 and 55: 2.8 CONDUCTION OF FOT The FOT laste
Page 57 and 58: 3. INTERNET DATA ACCESS TOOL The HT
Page 59: Fig. 30. Search Results The file se
Page 62 and 63: When the button “Extract Data”
Page 64 and 65: Data Fig. 33. Segment Merging In ad
Page 66 and 67: Fig. 36 shows the synthetic cycle g
Page 68 and 69: Fig. 39. A Synthetic Duty Cycle wit
Page 70 and 71: tractors) as well as the maximum to
Page 72 and 73: vehicle was carrying a heavy load o
Page 74 and 75: Vehicle Speed [mph] Vehicle Speed [
Page 76 and 77: 5.2 REAL-WORLD DUTY CYCLES Informat
Page 78 and 79: Vehicle Speed [mph] 70 60 50 40 30
Page 80 and 81: Given the size and complexity of th
Page 82 and 83: data or out-of-range data. Therefor
Page 84 and 85: 6.1.1 Spatial Information Using the
Page 88 and 89: Fig. 56. DCGenT Prototype Search Cr
Page 90 and 91: at constant speed. Parameters m, b,
Page 92 and 93: weight. Notice that, as expected, m
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Page 98 and 99: q q ( − ) ( feb'− feb) 100 '
Page 100 and 101: Table 15. General Statistics: Total
Page 102 and 103: 70% 60% 50% 40% 30% 20% 10% 0% Idli
Page 104 and 105: Fig. 68. All Trips with Dual Tires
Page 106 and 107: The almost 700,000 miles logged dur
Page 108 and 109: Frequency 300 250 200 150 100 50 0
Page 110 and 111: Speed [mph] 80 70 60 50 40 30 20 10
Page 112 and 113: vehicle is hauling light or heavy l
Page 114 and 115: Frequency 350 300 250 200 150 100 5
Page 116 and 117: A summary of the statistics describ
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Page 120 and 121: 6.3.2.4 Summary of Fuel Efficiency
Page 122 and 123: Lesson Learned: A technology progre
Page 124 and 125: Lesson Learned: Adding external sen
Page 126 and 127: know if the vehicle was “filled u
Page 129 and 130: 8. SUMMARY OF RESULTS AND CONCLUSIO
Page 131 and 132: 9. FUTURE DIRECTIONS The Heavy Truc
Page 133: Appendix A PILOT TEST LETTER REPORT
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1.0 BACKGROUND 2.0 PROJECT OVERVIEW
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ACKNOWLEDGEMENTS The authors wish t
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evaluate the data collection system
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performance of their next generatio
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March and provided to the ORNL Prog
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Between January, 2005 and May, 2005
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Figure 4: NGK Sensor Figure 5: Omeg
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o Date and Time o Wind Speed o Wind
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Figures 28 through 31 show the ball
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Figure 22: Airweigh System Figure 2
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DVD. Figures 35 and 36 show some of
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3.3.7.1 Pre-Analysis Efforts: As th
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Task 1: Identification of Fleet(s)
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Data # Performance Measure Performa
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Sensor or eDaq Performance Performa
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45 46 47 48 49 50 51 52 53 Measure
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Data # 62 63 64 65-67 68-69 70-71 7
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Data # Performance Measure 87 Headi
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Appendix A-B Final Pilot Test Plan
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• DAS/sensor suite/cabling operab
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9 10 11 12 13 14 15 Lateral Velocit
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34 Rain Intensity 35 36 37 38 39 40
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59 60 61 62 63 64 65-67 68-69 70-71
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85 Longitude 86 Velocity 87 Heading
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3.2 Sensors The PMs listed in Table
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vehicles will be dedicated to the p
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DATE TEST ENGINEER: DRIVER: DEPARTI
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Jim Ridge, Dana Product Engineer, w
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Table 5.1 October Route and Schedul
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Table 5.2 December Route and Schedu
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Appendix A-C Field Test Signals 64
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9 Performance Measure Performance M
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Performance Measure 25 Rain Duratio
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Letter Report SUMMARY OF RELEVANT H
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Sensor or Performance Data Sensor o
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GAWR 44 lb AirWeigh 5800 Road Surfa
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Appendix B SCHRADER MEMORANDUM OF U
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MOU-UTB-2007001 Schrader Trucking C
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MOU-UTB-2007001 Schrader Trucking C
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Appendix C FIELD OPERATIONAL TEST P
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Collection, Analysis, and Archiving
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4.0 APPROACH Phases 2 and 3 of the
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Signal Sensor 60 Tractor-Trailer Ma
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Schrader Trucking will provide six
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Sensitive Information] [Partner Sen
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4.6 Sensor and DAS Integration to T
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5.5 Vehicle Deployment Methodology
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Start address: 2360 Cherahala Blvd
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Figure 6.2: Schrader Shop, Far Left
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Figure 6.7: eDAQ Lite with two Vehi
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6.5 Safety Information For Student
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Appendix D SYSTEM DESIGN AND OPERAT
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TABLE OF CONTENTS Collection, Analy
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9.1.2. Shutdown Procedure 9.1.3. SI
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Signal Sensor 26 Cruise Control Acc
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Figure 4.1: Air-Weigh System Compon
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4.5. EDAQ The eDAQ Lite is connecte
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the equipment inside from overheati
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5.4. Weather Station Connections Fi
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Figure 5.5.3: VBOX Cable Table 5.5.
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Figure 6.3: Weather Station Locatio
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6.6 Power Supply Use a second cable
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can also be changed from this windo
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on and the yellow light should begi
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Figure 7.1.3-1: Weather Station and
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Speed unit: m/s Sampling frequency:
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iii) Click on Browse to find and op
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c) Power Controller (Power) - no co
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1) Select the Transducer and Messag
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Figure 7.2.4-2: Adding Transducer C
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Figure 7.2.4-3: Editing DIO Vehicle
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A. Changing Units - Converting tran
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C. Coordinates - Assign correct sig
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switch) Table 7.2.5: Contents of Ti
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Trigger Delay Setup: Check Enable d
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Figure 7.2.5-6: Time Base Shifter S
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Units: Full Scale Estimate - Min: 0
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Figure 7.2.6-2: Burst History Data
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Press Ctrl+3 (or click Run in the T
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8.2. Procedures for Heavy Truck Dut
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d) Turn on the ignition. e) Turn on
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4) Change the IP address field to t
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5) Check to see that the computer c
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e) When all SIC files have been sav
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4) To verify the time setting, go t
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e) Alter the id, mask, value, etc.
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Longitudinal CAN 0x87FF 0x0304 Long
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ULSB 0x98EAFFFBC1FE00 MaxVehSpLm CA
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Figure 9.1.6-2: Performing a ColdSt
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Table 9.2.2-1: Descriptions and Uni
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Channel ID Sensor Format Integer? P
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Channel ID eDAQ Invalid Data Value
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Channel ID Resolution Accuracy Note
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9.3. Appendix C: Additional DAS Not
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e replicated and saved as five iden
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2) Type in “cmd” 3) Type “ipc
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Producing multiple setup files 1) G
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9.4. Appendix D: Checklists and For
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D-96
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9.5. Appendix E: References and Web
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E-3
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E-5
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E-11
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