Common use of LIST OF TABLES Clause in Contracts

LIST OF TABLES. Table Page Table 1-1. Continuous Parameters Monitored at the NAEMS Lagoon Sites 1-4 Table 1-2. Monitoring Sites Under the NAEMS 1-5 Table 2 1. Summary of NAEMS Open-Source Sites 2-11 Table 3 1. NAEMS Emissions and Process Parameter Data Received 3-3 Table 3 2. Reported Emission Rates for NAEMS Lagoon Sites 3-5 Table 3 3. Review of Swine Lagoon Articles Received in Response to EPA’s CFI 3-9 Table 3 4. Review of Dairy Lagoon Articles Received in Response to EPA’s CFI 3-12 Table 3 5. Review of Swine Lagoon Articles Obtained by Previous EPA Literature Searches 3-15 Table 3 6. Review of Dairy Lagoon Articles Obtained by Previous EPA Literature Searches 3-19 Table 4 1. Reported Number of Valid Emissions Days by Site 4-5 Table 4 2. Summary of Intermittent Data 4-7 Table 4 3. Dates of Lagoon Coverage Observations 4-8 Table 4 4. Dates or Animal Inventory Records 4-10 Table 4 5. Negative Emissions Values by Xxxxxxxxx and Measurement Method 4-12 Table 4 6. Number of Valid Emissions Days Available in the Spring and Summer 4-13 Table 4 7. Number of Valid Emissions Days Available in the Fall and Winter 4-13 Table 4 8. NAEMS Data for Swine and Dairy Lagoon Confinement Operations 4-14 Table 4 9. Design and Operating Parameters of the NAEMS Swine and Dairy Lagoon Sites 4-15 Table 4 10. Site-Specific Ambient and Lagoon Conditions 4-16 Table 4 11. Average Daily Emissions for by Site 4-17 Table 5 1. Summary of Symbols and Terms Used in Equation 5 1 5-4 Table 5 2. Number of 30-Minute NH3 Emissions Values by Site 5-7 Table 5 3. Number of 30-Minute Data Values for Continuous Variables 5-8 Table 5 4. Number of 30-Minute Values Available for Intermittent Data by Site 5-9 Table Page Table 5 5. Farm and Lagoon Information by Site 5-10 Table 5 6. Selected Candidate Predictor Variables 5-13 Table 5 7. Meteorological Variable Bin Cut-Offs 5-21 Table 5 8. Summary Statistics for NH3 and Meteorological Variables 5-25 Table 5 9. Summary of Main Effect Mean Trend Variables 5-26 Table 5 10. Summary of Farm-Based Predictor Variables 5-44 Table 5 11. Summary of Data Available by Month and Site 5-52 Table 5 12. Fit Statistics for Subsets of Mean Trend Variables 5-53 Table 5 13. Fit Statistics for Identity, Log and Reciprocal Link Functions 5-55 Table 5 14. Fit Statistics With and Without a Random Effect of Site 5-57 Table 5 15. Fit Statistics for Three Combinations of Two Farm-Based Variables 5-58 Table 5 16. Variables Values Used In Example Calculations 5-58 Table 5 17. Results of the animal/sa EEM Examples 5-60 Table 5 18. Values of Mean Trend Variables for the animal/sa EEM Examples 5-61 Table 5 19. Results of the animal/size EEM Examples 5-63 Table 5 20. Values of Mean Trend Variables for animal/size EEM Examples 5-64 Table 5 21. Results of the sa/size EEM Examples 5-66 Table 5 22. Values of Mean Trend Variables for the sa/size EEM Examples 5-67

LIST OF TABLES. Table Page Table 1-1. Continuous Parameters Monitored at the NAEMS Lagoon Sites 1-4 Table 1-2. Monitoring Sites Under the NAEMS 1-5 1 Summary of Literature in AVs 2 Table 2 1. Summary of NAEMS Open-Source Sites 2-11 Literature on Adoption 6 Table 3 1. NAEMS Emissions and Process Parameter Data Received 3-3 Table 3 2. Reported Emission Rates for NAEMS Lagoon Sites 3-5 Table 3 3. Review of Swine Lagoon Articles Received in Response to EPA’s CFI 3-9 Table 3 4. Review of Dairy Lagoon Articles Received in Response to EPA’s CFI 3-12 Table 3 5. Review of Swine Lagoon Articles Obtained by Previous EPA Literature Searches 3-15 Table 3 6. Review of Dairy Lagoon Articles Obtained by Previous EPA Literature Searches 3-19 Table 4 1. Reported Number of Valid Emissions Days by Site 4-5 Table 4 2. Summary of Intermittent Data 4-7 Table 4 3. Dates of Lagoon Coverage Observations 4-Literature on WTP 8 Table 4 4. Dates or Animal Inventory Records 4-Summary of Literature on Mode Choice 9 Table 5 Summary of Literature on Benefit and Concerns 10 Table 4 5. Negative Emissions Values by Xxxxxxxxx 6 Summary of Literature on Perception of Technology and Measurement Method 4-Operations 12 Table 4 67 Summary of Literature on Travel Demand 14 Table 8 Classification of Studies by Detailed Approach 16 Table 9 PCA result for AT1 (preferences for lifestyle and mobility options) 22 Table 10 PCA result for AT2 (perceived benefits and concerns of shared mobility) 23 Table 11 PCA result for AT3 (reasons toward or against private vehicle ownership) 24 Table 12 PCA result for AT4 (motivations for and desired features of AV) 24 Table 13 Result of Measurement Equations for AV Adoption and WTP 44 Table 14 Result of Structural Equations 45 Table 15 Result of Measurement Equations 50 Table 16 Result of Structural Model 52 Table 17 Results of Factor Analysis for Mode Dependency 56 Table 18 Linear SVM Model Performances 57 Table 19 Linear SVM Model Coefficients 59 Table 20 Mode Choice Model Results for Regular Trips (t-ratios in brackets) 68 Table 21 Mode Choice Model Results for Occasional Trips (t-ratios in brackets) 70 Table 22 Identified Latent Attitude Factors 72 Table 23 Model Results for Transit Users 74 Table 24 Model Results for Car Users 76 Table 25 Summary of Influential Attitudes to Emerging Mobility Options 81 Table 26 Summary of Influential Variables to Emerging Mobility Options 81 Table 27 Potential Model Changes for ACES Considerations 85 1 INTRODUCTION Today’s world is deeply influenced by the way new technology evolves. Number Advances in information and communication technologies have played an important role in how we live and travel and will continue to do so. Rapidly emerging mobile apps have contributed to the quick expansion of Valid Emissions Days Available car sharing, ridesourcing, and various other on-demand services around the world. Similarly, connected and autonomous vehicle technologies are expected to bring a paradigm shift in how we define mobility. It is essential to incorporate ridesourcing and automated vehicle (AV) considerations into current long-range transportation planning efforts, which usually extends to the next 20 to 30 years. On the other hand, there are a lot of uncertainties with respect to technology development, regulations, and user acceptance that make it challenging to draw a clear picture of how shared mobility and AVs may affect our daily travel and the potential implications on the society as a whole. To address these challenges, a stated preference (SP) survey was designed and implemented in the Spring and Summer 4-13 Table 4 7. Number first phase of Valid Emissions Days Available this research effort, to examine travelers’ mode choice behavior in the Fall upcoming age of automated, connected, electric, and Winter 4-13 Table 4 8shared vehicles (ACES). NAEMS Data The nationwide survey engaged in carefully designed choice experiments to measure the likelihood and extent of behavioral changes. Multiple scenario types were developed to gauge user response under different circumstances. The survey data provided useful insights into travelers’ mobility choice behavior from several aspects, including the willingness to shift to shared mobility at varying cost and time incentives, willingness to pay (WTP) for Swine advanced vehicle technologies, views and Dairy Lagoon Confinement Operations 4-14 Table 4 9concerns of vehicle automation, and attitudes and perceptions toward mobility options. Design Using these survey data, this study intends to investigate the factors that influence people’s mobility choice behavior facing emerging mobility options, with a focus on exploring the role of user attitudes and Operating Parameters perceptions. Advanced econometric models and data analytic methods will be explored to fuse multi- dimensional information and provide an approach to understand the likelihood and magnitude of behavior shifts toward AVs and shared mobility options. This report is organized as follows. The next chapter summarizes recent literature in ACES analysis. The following chapter introduces the NAEMS Swine survey data and Dairy Lagoon Sites 4-15 Table 4 10attitude analysis. Site-Specific Ambient The next chapter describes the study methodology, followed by modeling results from three main perspectives: AV adoption and Lagoon Conditions 4-16 Table 4 11WTP, shared mobility adoption, and mode choice behavior. Average Daily Emissions for by Site 4-17 Table 5 1Then recommendations on how to incorporate ACES considerations into the modeling framework are presented in the next chapter. Summary of Symbols The last chapter summarizes the study with major findings and Terms Used in Equation 5 1 5-4 Table 5 2. Number of 30-Minute NH3 Emissions Values by Site 5-7 Table 5 3. Number of 30-Minute Data Values for Continuous Variables 5-8 Table 5 4. Number of 30-Minute Values Available for Intermittent Data by Site 5-9 Table Page Table 5 5. Farm and Lagoon Information by Site 5-10 Table 5 6. Selected Candidate Predictor Variables 5-13 Table 5 7. Meteorological Variable Bin Cut-Offs 5-21 Table 5 8. Summary Statistics for NH3 and Meteorological Variables 5-25 Table 5 9. Summary of Main Effect Mean Trend Variables 5-26 Table 5 10. Summary of Farm-Based Predictor Variables 5-44 Table 5 11. Summary of Data Available by Month and Site 5-52 Table 5 12. Fit Statistics for Subsets of Mean Trend Variables 5-53 Table 5 13. Fit Statistics for Identity, Log and Reciprocal Link Functions 5-55 Table 5 14. Fit Statistics With and Without a Random Effect of Site 5-57 Table 5 15. Fit Statistics for Three Combinations of Two Farm-Based Variables 5-58 Table 5 16. Variables Values Used In Example Calculations 5-58 Table 5 17. Results of the animal/sa EEM Examples 5-60 Table 5 18. Values of Mean Trend Variables for the animal/sa EEM Examples 5-61 Table 5 19. Results of the animal/size EEM Examples 5-63 Table 5 20. Values of Mean Trend Variables for animal/size EEM Examples 5-64 Table 5 21. Results of the sa/size EEM Examples 5-66 Table 5 22. Values of Mean Trend Variables for the sa/size EEM Examples 5-67conclusions.

LIST OF TABLES. Table Page Table 1-1. Continuous Parameters Monitored at the NAEMS Lagoon Sites 1-4 Table 1-2. Monitoring Sites Under the NAEMS 1-5 1 Contribution of partners 10 Table 2 1. Summary of NAEMS Open-Source Sites 2-11 Data Models Developed 30 Table 3 1. NAEMS Emissions ANTALYA Interventions and Process Parameter FIWARE Data Received 3-3 Table 3 2. Reported Emission Rates for NAEMS Lagoon Sites 3-5 Table 3 3. Review of Swine Lagoon Articles Received in Response to EPA’s CFI 3-9 Table 3 4. Review of Dairy Lagoon Articles Received in Response to EPA’s CFI 3-12 Table 3 5. Review of Swine Lagoon Articles Obtained by Previous EPA Literature Searches 3-15 Table 3 6. Review of Dairy Lagoon Articles Obtained by Previous EPA Literature Searches 3-19 Model Dependency 31 Table 4 1. Reported Number EU level legislation for MAtchUP Antalya lighthouse project 44 List of Valid Emissions Days by Site 4Figures Figure 1 Vertical Interoperability 12 Figure 2 Horizontal Interoperability 12 Figure 3 Antalya Smart City Framework 15 Figure 4 Antalya Urban Platform 16 Figure 5 Instrumented Layer 17 Figure 6 Conceptual Big Data Visualization Architecture for Antalya 18 Figure 7 AUP Components and Actions 19 Figure 8 General System Architecture for the Urban Platform 21 Figure 9 - All communication links in ELK cluster are encrypted 22 Figure 10 Xxxxx Connectors with Elasticsearch 23 Figure 11 Confluent Schema Registry for storing and retrieving schemas 25 Figure 12 NGSI Context Information Model 27 Figure 13 Antalya Open Data Portal 38 Figure 14 Antalya Open Data Portal-5 Table 4 2. Summary Organization List 39 Figure 15 Antalya Open Data Portal-Datasets 40 Figure 16 Open Data Portal Graph View 41 Figure 17 Antalya Open Data Portal-Map View 42 Figure 18 Antalya Open Data Portal-API 42 Figure 19 Big Data Ingestion & Data flow 46 Figure 20 Data Pseudonymization 47 Figure 21 Data anonymization 48 Figure 22 Antalya Interoperability & Integration Action Plans for M38-M48 52 Abbreviations and Acronyms Acronym Description API Application Programming Interface AUP Antalya Urban Platform BI Business Intelligence CKAN Comprehensive Knowledge Archive Network CSW Catalogue Services for the Web DAQ Data Acquisition DCAT-AP Data Catalogue vocabulary – Application Profile EIP European Innovation Partnership EMT Municipal Transport Company (Empresa Municipal de Transporte) ESB Enterprise Service Bus EV Electric Vehicle GDPR EU General Data Protection Regulation HTTP HyperText Transfer Protocol ICT Information and Communications Technology IDABC Interoperable Delivery of Intermittent European eGovernment Services to public Administrations, Businesses and Citizens IoT Internet of Things JSON JavaScript Object Notation KML Keyhole Markup Language KPI Key Performance Indicator LOPD Organic Law on the Protection of Personal Data 4LTS Long Term Support NGSI Next Generation Service Interfaces OMA Open Mobile Alliance POI Point of Interest REST Representational State Transfer SQL Structured Query Language STH Short-7 Table 4 3. Dates Term Historic VM Virtual Machine WMS Warehouse Management System XML Extensible Markup Language IPG Interoperability Principles Guide KDEP Short Term Action Plan for Digital Transformation of Lagoon Coverage Observations 4-8 Table 4 4. Dates or Animal Inventory Records 4-10 Table 4 5. Negative Emissions Values by Xxxxxxxxx Turkey Abstract This deliverable is reporting on the current state of development of the Urban Platform integration and Measurement Method 4-12 Table 4 6. Number of Valid Emissions Days Available interoperability in Antalya in the Spring third year of MAtchUP Project (M25 – M38). This task includes the new projects and Summer 4services to improve city’s operations. Also another important task is to increase the connection between the City of Antalya and its citizens. The developments during MAtchUP follow the same principles: ensuring open data, interoperability through open APIs developments and assessing the evaluation process by considering the requirements of Antalya’s monitoring plan. Therefore, new operations and services must guarantee interoperability between the different components involved. Moreover, it is needed to take into consideration the new European General Data Protection Regulation (GDPR). Security and privacy aspects should be taken into account. It is important to publish non-13 Table 4 7sensible and anonymized data for learned by citizens. Number Also developers want to make use when they will start creating innovative services for the city. Integration architecture is one of Valid Emissions Days Available the most critical aspects of the urban platform to sustain consistency and communication between several internal and 3rd party components. Integration architecture and methodologies are introduced in this version of the platform integration and interoperability. As far as the cities in the Fall project share a common objective, this deliverable D4.24 shares a common structure with the analogous deliverables of WP2, which is D2.24, and Winter 4-13 Table 4 8WP3, which is D3.24. NAEMS Data for Swine Furthermore, these deliverables are due in M38 as a third and Dairy Lagoon Confinement Operations 4-14 Table 4 9. Design and Operating Parameters final version of the NAEMS Swine documents DX.10 and Dairy Lagoon Sites 4-15 Table 4 10. Site-Specific Ambient and Lagoon Conditions 4-16 Table 4 11. Average Daily Emissions for by Site 4-17 Table 5 1. Summary of Symbols and Terms Used in Equation 5 1 5-4 Table 5 2. Number of 30-Minute NH3 Emissions Values by Site 5-7 Table 5 3. Number of 30-Minute Data Values for Continuous Variables 5-8 Table 5 4. Number of 30-Minute Values Available for Intermittent Data by Site 5-9 Table Page Table 5 5. Farm and Lagoon Information by Site 5-10 Table 5 6. Selected Candidate Predictor Variables 5-13 Table 5 7. Meteorological Variable Bin Cut-Offs 5-21 Table 5 8. Summary Statistics for NH3 and Meteorological Variables 5-25 Table 5 9. Summary of Main Effect Mean Trend Variables 5-26 Table 5 10. Summary of Farm-Based Predictor Variables 5-44 Table 5 11. Summary of Data Available by Month and Site 5-52 Table 5 12. Fit Statistics for Subsets of Mean Trend Variables 5-53 Table 5 13. Fit Statistics for IdentityDX.23, Log and Reciprocal Link Functions 5-55 Table 5 14. Fit Statistics With and Without a Random Effect of Site 5-57 Table 5 15. Fit Statistics for Three Combinations of Two Farm-Based Variables 5-58 Table 5 16. Variables Values Used In Example Calculations 5-58 Table 5 17. Results of the animal/sa EEM Examples 5-60 Table 5 18. Values of Mean Trend Variables for the animal/sa EEM Examples 5-61 Table 5 19. Results of the animal/size EEM Examples 5-63 Table 5 20. Values of Mean Trend Variables for animal/size EEM Examples 5-64 Table 5 21. Results of the sa/size EEM Examples 5-66 Table 5 22. Values of Mean Trend Variables for the sa/size EEM Examples 5-67respectively.