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Realtime Earth Satellite object tracking and orbit data for STARS-ME. NORAD Identifier: 43640.

Is my phone listening to me? According to a survey of internet users conducted between February and March of 2023, 50 percent of users in the United Kingdom reported having the feeling of being "followed" online after discussing a product or seeing an ad on TV. Around 46 percent of users in Canada reported the same feeling, while around 40 percent of users in the United States reported feeling "followed" by their connected devices after discussing a topic in the presence of their devices.

Market Overview The global period tracking app market is projected to grow at a CAGR of XX% over the forecast period 2025-2033. The market is primarily driven by increasing awareness about women's health, rising adoption of mobile devices, and technological advancements in health tech. The growing number of women seeking personalized health solutions and the increasing incidence of menstrual disorders further contribute to the market growth. The market is segmented by application and type, with female users dominating the application segment and Android holding the largest share in the type segment. Competitive Landscape The period tracking app market is highly competitive, with key players including Natural Cycles, Stardust App, Biowink (Clue), Perigee, Flo Health, Glow, Ovia, ABISHKKING, Me v PMDD, GP Apps, FitrWoman, and Flatcracker Software. These companies offer a wide range of features, including cycle tracking, fertility prediction, symptom management, and educational content. The market is consolidated, with a few major players controlling a significant share. However, smaller players are gaining market share by offering innovative features and targeting specific segments of the female population. Partnerships and collaborations are common strategies employed by companies to expand their reach and enhance their offerings.

The set contains reports on tracking the effectiveness of regulatory acts of the National Bank of Ukraine. The entry in each row of the table is a separate tracking report and contains, in particular, the type, name, name and identifier of the publisher, a link on the Internet to the text of the tracking report.

In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).

For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see XSENS manual for a detailed description of avaiable measurements).

Data organization

There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:

Segment Orientation - Quat

Segment Orientation - Euler

Segment Position

Segment Velocity

Segment Acceleration

Segment Angular Velocity

Segment Angular Acceleration

Joint Angles ZXY

Joint Angles XZY

Ergonomic Joint Angles ZXY

Ergonomic Joint Angles XZY

Center of Mass

Sensor Free Acceleration

Sensor Magnetic Field

Sensor Orientation - Quat

Sensor Orientation - Euler

See also: https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US

For more information on each specific data and/or sensors please see the xsens manual (Link above)

Data Annotation

In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.

The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).

Items for Trust perception scale - HRI, Schaefer 2016:

Which % of time does the robot

Function successfully

Act consistently

Communicate with people

Provide feedback

Malfunction

Follow directions

Meet the needs of the mission

Perform exactly as instructed

Have errors

Which % of the time is the robot:

Unresponsive

Dependable

Reliable

Predictable

Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:

The way the robot moved made me uncomfortable

I felt I could rely on the robot to do what it was supposed to do

The speed at which the gripper picked up and released the components made me uneasy

I felt safe interacting with the robot

I knew the gripper would not drop the components

The size of the robot did not intimidate me

The robot gripper did not look reliable

I was comfortable the robot would not hurt me

I trusted that the robot was safe to cooperate with

The gripper seemed like it could be trusted

K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.

G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.

Montenegro ME: Logistics Performance Index: 1=Low To 5=High: Ability to Track and Trace Consignments data was reported at 2.371 NA in 2016. This records a decrease from the previous number of 2.763 NA for 2014. Montenegro ME: Logistics Performance Index: 1=Low To 5=High: Ability to Track and Trace Consignments data is updated yearly, averaging 2.530 NA from Dec 2010 (Median) to 2016, with 4 observations. The data reached an all-time high of 2.763 NA in 2014 and a record low of 2.371 NA in 2016. Montenegro ME: Logistics Performance Index: 1=Low To 5=High: Ability to Track and Trace Consignments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Montenegro – Table ME.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the ability to track and trace consignments when shipping to the market, on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;