chore: fix spelling issues and small corrections

DataLake_provideAccessToken/readme.md

@@ -25,7 +25,7 @@ This small flow triggers sets up an API endpoint which can be called from extern
 The function delivers the body for the API request against the `/api/datalake/v3/generateAccessToken` endpoint. From here, the Access Token is provided and feedback to the user via the `http`-Node
 
 ## Result
-Once the API endpoint is accessed, the user is presented with a JSON respose containing the Access Token for IDL.  
+Once the API endpoint is accessed, the user is presented with a JSON response containing the Access Token for IDL.  
 This can then be used in native S3-Tools to be worked with and connect to the IDL: 
 In this example, calling the full API endpoint https://presiot-visualflowcreatorhttp.eu1.mindsphere.io/public/presiot/getAccessTokenForIDL?key=1a03090a3cc44d56c57d5cd7d545899219523b5127079317fcc2c637b3a0cab23cc1fcadce75196b36f761428e6ee80dacc64e4349928b23a2cf8e0e6495b897 results in a JSON response like shown below.
 ![image](./doc/result_IDL_AccessToken.png)

README.md

@@ -1,6 +1,5 @@
 # Insights Hub Visual Flow Creator Examples
 
-[![The MDL License](https://img.shields.io/badge/license-MDL-009999.svg?style=flat)](./LICENSE.md)
 [![Documentation](https://img.shields.io/badge/mindsphere-documentation-%23009999.svg)](https://opensource.mindsphere.io/docs/mindconnect-nodejs/index.html)
 [![Forum](https://img.shields.io/badge/mindsphere-community-%23009999.svg)](https://community.plm.automation.siemens.com/t5/Developer-Space/bd-p/MindSphere-platform-forum)
 

commandMqttDevice_MindConnect/readme.md

@@ -27,7 +27,7 @@ Commanding can be initiated via a dedicated API. Here we will use VFC to initiat
 A dedicated commanding node was added to the standard nodes of Visual Flow Creator.  
 https://documentation.mindsphere.io/MindSphere/apps/visual-flow-creator/mindconnect-nodes.html#command-mindconnect
 
-Manual confuguration towards the API endpoint for commanding is no longer needed but still shown in depth in the description below. 
+Manual configuration towards the API endpoint for commanding is no longer needed but still shown in depth in the description below. 
 With "MindConnect" nodes, you can send a command to MindConnect MQTT device and check the status of the sent command. For more information about MindConnect Elements, refer to [Connectivity](https://documentation.mindsphere.io/MindSphere/connectivity/overview.html).
 
 

createCustomEndpoint/readme.md

@@ -7,7 +7,7 @@ The example shows how you can
 - choose who has access to this endpoint (only users of the flow, all users on the tenant, public access)
 - develop the API functionality using VFC nodes
 
-This flow demonstrates a simple getting-started example on how to provide machine data via an API for external access of the last 15min intervall of machine data as JSON object. 
+This flow demonstrates a simple getting-started example on how to provide machine data via an API for external access of the last 15min interval of machine data as JSON object. 
 ![image](./doc/createCustomEndpoint.png)
 
 
@@ -20,7 +20,7 @@ This flow demonstrates a simple getting-started example on how to provide machin
     - generate key in case of public API access
     - the URL shown in the node setup is the direct URL to access the service
     ![image](./doc/setup_KeyGeneration.png)
-3. Selet an Asset / Aspect / Variable(s) where you want to read the time series data from (yellow node)
+3. Select an Asset / Aspect / Variable(s) where you want to read the time series data from (yellow node)
 ![image](./doc/setup_selectAssetToRead.png)
 4.  Save the flow 
 
@@ -31,7 +31,7 @@ https://[TenantName]-visualflowcreatorhttp.eu1.mindsphere.io/public/presiot/mach
 
 ## Result
 
-When querying the service URL e.g. via a Browser/Postman/Pyhton/... the JSON-object with the timeseries data will be received. 
+When querying the service URL e.g. via a Browser/Postman/Python/... the JSON-object with the timeseries data will be received. 
 ![image](./doc/result.png)
 
 

generateSampleData/readme.md

@@ -9,7 +9,7 @@ With this example you are able to do that.
 ![image](./doc/generateSampleData.png)
 
 - Import the flow in Visual Flow Creator
-- Selet an asset / aspect / variable where you want to write time series data
-- Doubleclick on the datatype node like `Integer` and rename the `Parameter out` value with the name of your data variable
+- Select an asset / aspect / variable where you want to write time series data
+- Double click on the datatype node like `Integer` and rename the `Parameter out` value with the name of your data variable
 - Save the flow
 - Trigger the flow

integrateExternalApi/readme.md

@@ -18,9 +18,9 @@ Your business case may require to use external services over HTTP. Visual Flow C
 
  - The inject node passes the `CITY` and `API_KEY` information to HTTP Request node through `msg` object.
  - (**Get Weather**) node is the HTTP Request node and it contains the URL with query parameters.
- - This node performs the substitution of variables with the values it recieves from the inject node.
+ - This node performs the substitution of variables with the values it receives from the inject node.
  - Then HTTP request node fetches the data synchronously from Openweather service endpoint.
- - It converts the recieved response to JSON format and sets it as value for the payload field of the msg object.
+ - It converts the received response to JSON format and sets it as value for the payload field of the msg object.
  - Debug node in the end displays the contents of payload field to the debug window. 
 
 

virtualMachineSimulator/readme.md

@@ -8,7 +8,7 @@ It can be used if no physical assets are available or to learn about the feature
 ![image](./doc/virtualMachineSimulator.png)
 
 ## Prerequisites
-- access to Asset-Managment 
+- access to Asset-Management 
 
 ## Setup & Configuration
 ### Setup machine asset
@@ -27,23 +27,15 @@ e.g. `my virtual machine`
 2. Open the settings for the **upper** write-timeseries node select the previously generated asset instance `my virtual machine` and aspect `machine_states`. Do not select any variables. 
     ![image](./doc/VFC_setup_Asset-Aspect.png)
 4. Repeat this for the **lower** write-timeseries node and select `my virtual machine` -> `product_counter`. Do not select any variables.  
-5. Adjust the execution intervall for the simulation as desired in the link-in node.
+5. Adjust the execution interval for the simulation as desired in the link-in node.
 6. Save the flow 
 
 :cloud: :heavy_check_mark: You're done, your machine simulation is now running - enjoy!
 
 
 ## Result
-After saving the flow, the output of the simulation is written to the asset and you can monitor the results e.g. in Fleet Manager. Based on this you can also now start calculating the machine KPIs (OEE, Availability, Quality, ...) using the VFC or other apps. 
+After saving the flow, the output of the simulation is written to the asset and you can monitor the results e.g. in Insights Hub Monitor. Based on this you can also now start calculating the machine KPIs (OEE, Availability, Quality, ...) using the VFC or other apps. 
 ![image](./doc/FleetManager_Results.png)
 
 ## See also
 - [Asset Manager Documentation](https://documentation.mindsphere.io/resources/html/asset-manager/en-US/index.html)
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