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An understanding of local walking context plays an important role in the analysis of gait in humans and in the high level control systems of robotic prostheses. Laboratory analysis on its own can constrain the ability of researchers to properly assess clinical gait in patients and robotic prostheses to function well in many contexts, therefore study in diverse walking environments is warranted. A ground-truth understanding of the walking terrain is traditionally identified from simple visual data.

This data set contains: 

- Training dataset: 271 CT-scans of inner ears used for optimization and training of the model. 

- Validation dataset: 70 CT-scans of inner ears used for external validation. 

- U-net architecture deep-learning model's weight after optimized training. 

- All manual segmentations performed for both datasets. 

- All post-processed automated segmentations performed by the model for bothd atasets. 

The dataset contained walking data of 41 volunteers, including 20 women and 21 men. Each volunteer walked on asphalt and SLATE roads for six times, each time for less than one minute. In addition, the dataset also included gait data on stairs and stairs. The data acquisition frequency is 100HZ, and a total of four sensors are used to collect data. The sensor numbered 001 is located on the left knee, the sensor numbered 001 is located on the right wrist, the sensor numbered 003 is located on the left ankle, and the sensor 004 is located on the back of the waist.

In the realm of real-time communications, WebRTC-based multimedia applications are increasingly prevalent as these can be smoothly integrated within Web browsing sessions. The browsing experience is then significantly improved concerning scenarios where browser add-ons and/or plug-ins are used; still, the end user's Quality of Experience (QoE) in WebRTC sessions may be affected by network impairments, such as delays and losses.

This data set contains measurements on reading and writing data to OPC UA servers directly and via REST and GraphQL interfaces. Each measurement is conducted 1000 times. Measurements include reading a single value and reading 50 values. Measurements using cache server were also performed. Measurement data is collected with Wireshark and the .csv files are exported from it. in addition, .txt files contain request execution times recorded by the client.