7200 .csv files, each containing a 10 kHz recording of a 1 ms lasting 100 hz sound, recorded centimeterwise in a 20 cm x 60 cm locating range on a table. 3600 files (3 at each of the 1200 different positions) are without an obstacle between the loudspeaker and the microphone, 3600 RIR recordings are affected by the changes of the object (a book). The OOLA is initially trained offline in batch mode by the first instance of the RIR recordings without the book. Then it learns online in an incremental mode how the RIR changes by the book.


The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used.


Blended Learning has been widely used in current basic education as a new teaching model, and how to improve the acceptance of students in Blended Learning is a hot issue that needs to be solved in the practice of teaching. 


There is a paper for the dataset:


Primary science curriculum student acceptance of blended learning: structural equation modeling and visual analytics

doi: 10.1007/s40692-021-00206-8


Full Text: https://rdcu.be/cAooZ





These are OMNeT++ flooding simulation results of four-UAV FANET in three different topologies, as mentioned in "The Broadcast Storm Problem in FANETs and the Dynamic Neighborhood-Based Algorithm as a Countermeasure".

Please, check the paper for more information.


This study seeks to obtain data which will help to address machine learning based malware research gaps. The specific objective of this study is to build a benchmark dataset for Windows operating system API calls of various malware. This is the first study to undertake metamorphic malware to build sequential API calls. It is hoped that this research will contribute to a deeper understanding of how metamorphic malware change their behavior (i.e. API calls) by adding meaningless opcodes with their own dissembler/assembler parts.


Monitoring cell viability and proliferation in real-time provides a more comprehensive picture of the changes cells undergo during their lifecycle than can be achieved using traditional end-point assays. Our lab has developed a CMOS biosensor that monitors cell viability through high-resolution capacitance measurements of cell adhesion quality. The system consists of a 3 × 3 mm2 chip with an array of 16 sensors, on-chip digitization, and serial data output that can be interfaced with inexpensive off-the-shelf components.


This dataset uses a newly introduced method for the analysis of the effects on WCET of the toolchain configurations used in real-time systems. It is the result of a full factorial experiment comparing SCADE code generation tools (SCADE 5 and 6), compilers (Wind River, GCC, Code Warrior), optimization settings, and WCET analysis tools (high water mark measurement, Rapita RVS, Otawa, AbsInt aiT). SCADE generated software is targeted due to its prevalence in hard real-time systems that require WCET analysis for scheduling.


Data set contains logs from OptiTrack motion camera system and flex sensor information from a smart glove. Participants performed finger taps for 10 secs.


These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion.


The rawdata.csv profile indicates the traffic analysis based mobility patterns. we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset, we map each trip record to the zones with the same origin zones and destination zones. After  this, we can obtain this dataset. This dataset stores the hourly number of departure and arrival trips in each traffic analysis zone.

The POI-importance.csv profile indicates the term frequency-inverse doument frequency(TF-IDF) of each category of poi the in each traffic analysis zone.