Scenario Description Clause Examples

Scenario Description. In this third scenario a public permissioned blockchain called Hyperledger Fabric by IBM [5] is used. This blockchain tracks certificates in a supply chain of table grapes. In this scenario [11], a farmer in South Africa produces organic grapes, and presents such a claim to a certification authority. This authority issues a certificate to the farm, allowing the farm to certify its grapes. Grapes are stored in boxes, which are identified by a unique barcode. To ensure a correct certification process, certification authorities are accred- ited by an accreditation authority. The certification authority stores the certifi- cate it receives from an accreditation authority on the blockchain. Additionally, details of the certification authority are stored on the blockchain, so that anyone may see which party certified a farm. This entire process is audited. An audi- tor may revoke the certificate issued by the certification authority, for example, after the discovery of unauthorized pesticides [31] being used in the production of the fruits. An auditor also may revoke accreditations made by the accreditation authority. Here, both revocation types are recorded on the blockchain. The grape boxes are shipped to resellers in Europe, after which the grapes are sold to supermarkets, and eventually to customers. Since it is unknown who may purchase the grapes, public verifiability is required. This allows all parties involved to query the blockchain for the validity of the organic certificate. Also, change of ownership is recorded in the blockchain, and provenance of the labeled boxes can be determined. From this description we observe that there are mul- tiple, known writers. However, these writers are not trusted, as can be observed from the cascading audit trail from farmer to auditor.

Scenario Description. ‌ The idea behind the scenario we discuss here is that of an autonomic cloud computing platform; or, in other words, a distributed software system which is able to execute applications in the presence of certain difficulties such as leaving and joining nodes, fluctuating load, and different requirements of applications to be satisfied. The cloud is based on voluntary computing and using peer-to-peer technology to provide a platform- as-a-service. We call this cloud the Science Cloud Platform (SCP) since the cloud is intended to run in an academic environment (although this is not crucial for the approach). The interaction of these three topics mentioned is discussed in the next section. An illustrative picture of how such a cloud may be composed is shown in Figure 11. In our cloud scenario, we assume the following properties of nodes: Nodes have vastly different hardware, which includes CPU speed, available memory and also additional hardware like specialized graphics processing etc. Also, a node may have different security levels. With regard to the applications, we assume that: An application has requirements on hardware, i.e. where it can and wants to be run (CPU speed, available memory, other hardware) An application is not a batch task. Rather, it has a user interface which is directly used by clients in a request-based fashion. The main scenario of the science cloud is based on what the cloud is supposed to do, i.e. run, and continue running in the case of changing nodes and load, applications. The document [ASC12] has listed three smaller scenarios which we combine here to a general scenario which describes how the cloud manages adaptation. On top of this basic scenario, other scenarios may be imagined which improve specific aspects such as how to distribute load based on particular kinds of data or how to improve response times. The basic cloud scenario focuses on application resilience, load distribution and energy saving. In this scenario, we imagine apps being deployed in the cloud which need to be started on an appropriate node based on its SLA (requirements). The requirements may include things like CPU speed of the node to be run on, memory requirements, or similar things. Once the app is started, we can imagine that problems occur, such as that a node is no longer able to execute an app due to high load (in which case it must move the app somewhere else) or due to a complete node failure (in which case another node must realize this and take...

Scenario Description. The scenarios considered in our analysis result from the combination of both the traditional and new TST management rules and different water trading alternatives, resulting in 5 different scenarios:  Scenario 1a: traditional TST management rule without water trading;  Scenario 1b: traditional TST management rule with spot water purchases in drought periods;  Scenario 2a: new TST management rules without water trading; 1 The high water values in the ▇▇▇▇▇▇ are, in part, due to the concentration of horticultural crops and greenhouses, and also to the widespread modernization of irrigation systems (Calatrava and ▇▇▇▇▇▇▇▇- ▇▇▇▇▇▇▇▇ 2012). The agricultural sector that depends on the transferred volumes from the Tagus basin generates 1268 € million to the GDP of the ▇▇▇▇▇▇ basin (PwC 2013). The cancellation of the TST would lead to a reduction of the GDP close to 7.1% (▇▇▇▇▇▇ 2008).  Scenario 2b: new TST management rule with spot water purchases in drought periods;  Scenario 2c: new TST management rule with the proposed option contract (different parameterizations).

Scenario Description. ‌ For the Smart Supply Chain, the main objective is the improvement of the efficiency of the transportation of components from the supplier plants to FCA production plants, monitoring parameters related to the conditions of the containers during the transportation, in order to be able to react to events than can happen during the travel, that can impact on the physical condition of the components or on the expected delivering date. To reach this goal, travelling containers conditions will be monitored using an HW product prototype called “Outdoor LOGistic TrackER” (OLOGER from now), developed by Cefriel, that will be integrated with MIDIH platform. The first round of experiment (end by M18) will be focused on logistic data coming from these devices. The second round of Experiment (M27), will extend data sources, including other data sources like weather and traffic information, and will require to use other FIWARE lane components of the MIDIH platform. In the first round, the data acquisition system, including transmission, management and storage of IoT industrial logistic data (DiM, Data in Motion), will rely on the MindShpere/FIWARE lane. - Data Ingestion: the ingestion of raw data from the field to FIWARE /MindSphere will leverage on Data Collector modules. MIDIH foreground component MASAI. - Data Processing: the analysis of logistic data (DaR, Data at Rest) in order to produce useful insight and information about the logistic process, will leverage on MindSphere components and ad-hoc logic. - Data Persistence: Mongo DB to manage the storage and loading of data - Data Visualization: visualization of output data will be done leveraging on a Production Logistic Optimization application developed within MIDIH by Cefriel (CC6). [For more details concerning the Business Scenarios and Objectives, please refer to D5.1] The background and foreground components in this scenario (first round) are shown in the following Table 4. MongoDB T4.2, T4.3, T4.4 FIWARE DONE BACKGROUND MASAI MindSphere T4.2 FIWARE DONE3 FOREGROUND

Scenario Description. We have developed a scenario for the purpose of illustrating how these clauses could work in practice A single buyer is purchasing 15,000 Woodland Carbon Pending Issuance Units (PIUs) and 10 Biodiversity Net Gain Units (BUs) The buyer is purchasing these credits from a single landholding The restoration project is a woodland creation project, being designed and implemented by a tenant farmer with the consent of their landlord The tenant is the only tenant on the land, and the landlord and tenant are sharing the benefits and responsibilities of the restoration project As this is a long-term project with commitments of 30+ years, the landlord is expected to be involved in the project The restoration site is in England Project Name/Number: Landlord Supplier Name: Tenant Supplier Name: Landlord Supplier Address: Tenant Supplier Address: Buyer Name: Buyer Address: Effective Date of this Agreement: Termination Date of this Agreement: Jurisdiction: Signed by the duly authorised Representative of Landlord: Signed by the duly authorised Representative of Tenant: Name: … Click here to enter text.
Name: … Click here to enter text.
Position: … Click here to enter text.
Position: Click here to enter text.
Date: Click here to enter a date.
Date: Click here to enter a date.
Signed by the duly authorised Representative of Buyer: Name: … Click here to enter text.
Position: … Click here to enter text.
Date: Click here to enter a date.
Clause Name: Overview of planned works Purpose: Provides a clear description of the project / project plan The Supplier (Tenant) with the consent of the Landlord will conduct a woodland creation project across X hectares of land, with expected works beginning [XX Date] and ending [XX Date]. The woodland will be mixed broadleaf and will replace intensive agricultural land used for dairy farming. The project has been reviewed and approved by [XX authorities]. Comments The authorities will vary dependent on the ecosystem services being generated and the location of the project. For Biodiversity Net Gain, permission will be required from the local council. Clause Name: Sales Purpose: Provides a clear overview of the exact services being sold Subject to the satisfaction of the conditions specified in clause [4.2] below, The Supplier shall, no later than ten (10) Business Days from the date 15,000 PIUs for the Project (“Buyer PIUs”) are registered on the Registry, offer the Buyer PIUs to the Buyer and the Buyer commits to purchase the Buyer...

Scenario Description. ‌ In the Smart Factory scenario, the proposed solution is the development of a system to control and analyse the quality control and process control data. The aim is to provide capabilities of visualization and predictive maintenance to the production line. For this, MIDIH will develop a solution to provide the blue-collar workers and plant supervisors with the capability to visualize and prevent the factory production. In addition to this quality control, a machine and tooling status control module will be developed. Smart Factory scenario background consists of a FIWARE and APACHE lanes with several components: • Data ingestion: Data Collector to enable physical level to FIWARE (i.e. OPC UA, non OPC UA, etc.). • Data bus: Orion Context Broker to manage context information or ▇▇▇▇▇ to integrate data streams. • Data processing: CEP Siddhi, Logstash and TensorFlow to analyse events and create complex events or to elaborate files with information when services are executed. • Data persistence: Druid to manage which data must be loaded. • Data visualization: Ruby on Rails and Nginx to present the data. The background and foreground components in this scenario are shown in the following Table 5. Orion Context Broker (OCB) T4.2, T4.3, T4.4 FIWARE DONE BACKGROUND HADOOP T4.4 APACHE DONE HIVE T4.4 APACHE DONE IDAS T4.2, T4.3, T4.4 FIWARE DONE MIDIH Connectors T4.3 FIWARE DONE FOREGROUND

Scenario Description. The safety of large civil engineering structures like dams requires a comprehensive set of efforts, which must consider the structural safety, the structural monitoring, the operational safety and maintenance, and the emergency planning [1]. The consequences of failure of one of these structures may be catastrophic in many areas, such as: loss of life (minimizing human casualties is the top priority of emergency planning), environmental damage, property damage (e.g., dam flood plain), damage of other infrastructures, energy power loss, socio-economic impact, among others. The risks associated with these scenarios can be mitigated by a number of structural and non- structural preventive measures, essentially to try to detect in advance any signs of abnormal behaviour, allowing the execution of corrective actions in time. The structural measures are mainly related to the physical safety of the structures, while the non-structural measures can comprise a broad set of concerns, such as operation guidelines, emergency action plans, alarm systems, insurance coverage, etc. In order to improve the structural safety of large civil engineering structures, a substantial technical effort has been made to implement or improve automatic data acquisition systems able to perform real-time monitoring and trigger automatic alarms. This paradigm creates an imminent deluge of data captured by automatic monitoring systems (sensors), along with data generated by large mathematical simulations (theoretical models). Besides the fact that these monitoring systems can save lives and protect goods, they can also prevent costly repairs and help to save money in maintenance. Figure 1 Schematic representation of the instruments’ location

Scenario Description. ‌ The robotics scenario consists of a structured environment of width W and depth D, initially un- known to the robots. The structure of the environment mimics that of a building floor. A team of R robots called rescuers (Fig. 1(a)) is deployed in a special area called the deployment area within the environment. The size of the deployment area is always assumed sufficient to house all the robots. We imagine that some kind of disaster has happened, and the environment is occasionally ob- structed by debris (Fig. 1(b)) that the robots can move. In addition, a portion of the environment is dangerous for robot navigation due to the presence of radiation (Fig. 1(c)). We assume that prolonged exposition to radiation damages the robots. Short-term exposition increases a robot’s sensory noise. Long-term damage eventually disables the robot completely. To avoid damage, the robots can use debris to build a protective wall, thus reaching new areas of the environment. Damage is simulated through a function dr(t) that increases with exposition time t from 1 to 10. The value of dr(t) is used as a scale factor for the natural sensory noise of a robot, until it reaches the value 10, which corresponds to a disabled robot. We imagine that a number V of victims (Fig. 1(d)) are trapped in the environment and must be rescued by the robots. Each victim is suffering a different injury characterized by a gravity Gv. The health hv(t; Gv) of each victim, initially in the range (0,1], deteriorates over time. When hv = 0, the victim is dead. The robots must calculate a suitable rescuing behavior that maximizes the number S of victims rescued. This can be seen as a problem of distributed consensus. A victim is considered rescued when it is deposited in the deployment area alive. In addition, each victim has a different mass Mv. The higher the mass, the larger the number of robots required to carry it to the deployment area. To perform its activities, a robot r must take into account that it has limited energy er. As the robot works, its energy level decreases according to a function er(t). If the energy reaches 0, the robot A reference of all the symbols and their meaning is reported in Table 1. 2.4.1 and 2.4.2, we sketch two possible variants that focus on different behaviors. (a) (b) (c) (d) Figure 1: (a) A rescuer robot. (b) Debris is simulated with grippable cylinders. (c) Radiation is simulated with lights (the yellow blobs in the picture). (d) Victims are simulated with robots.

Scenario Description. ‌ The technical approach consists of the creation of a data space as defined in the IDS Reference Architecture Model4. The software architecture represents the reference architecture of the Industrial Data Space. The elements Connector and Broker from the Industrial Data Space are used as well as the IDS Identity Provider (mandatory and not described in detail as this is a standard component in the Reference Architecture Model5). For the customer, the distribution network organizes itself via apps in the Industrial Data Space. Within MIDIH, a distribution planning app is installed in the manufacturer's IDS connector. The Service Application transferring data through the Supply Chain partners, in this case between the manufacturer and the logistics service provider resp. the transport service provider. The scenario is described in detail in D.5.1 ▇▇▇▇▇ T4.4 APACHE DONE BACKGROUND SPARK T4.4 APACHE DONE