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opencc-by-4.0Sep 2022View details →

Non-acoustic speech sensing system based on flexible piezoelectric

The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment. The dataset is available at: https://doi.org/10.5281/zenodo.7185663 The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details. This dataset and its descriptor paper were created by: Shiji Yuan, Ying Sun, Shuai Wang, Xinlei Chen,Ying Ding,Dezhi Zheng , Shangchun Fan For questions or suggestions, please e-mail Shuai Wang <wangshuai@buaa.edu.cn> Description: Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset". The recording was carried on by software named Adobe Audition2022. We set monophonic recording, 16-bit storage format, and 16 kHz sampling frequency before recording and saved the recorded voice in wav format.  The dataset is provided with two storage rules, which are stored by subject number and command number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by command. In the second rule, the speech data of ten commands are stored in different folders, and the names of the folders are the command contents. The subject number, command number and record order are given for each data entry. For example, the data obtained when subject 1 recorded command 10 for the first time was labeled as "1-10_1". After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data was designed to remove problematic data that were very short or very quiet.The script of the data filtering algorithm is provided in this repository.   For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository. File list: Non-acoustic Speech Dataset.zip speech data filtering algorithm.zip Readme.txt

opencc-by-4.0May 2024View details →

Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"

Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in Mammal Research. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a> WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R". WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual.  WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups.  Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com).

opencc-zeroJun 2024View details →

Dataset: First Trust Flexible Municipal High Income ETF (MFLX) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Fig. 2 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris

Fig. 2 Frame shots showing the four feeding modes in the smooth newt. In the aquatic stage: a suction feeding under water and b jaw prehension on land. In the terrestrial stage: c suction feeding under water and d tongue prehension on land. The prey (maggot) is indicated by the arrow.

Fig. 1 Landmarks used for the kinematic analyses. 1 upper jaw tip, 2 lower jaw tip, 3 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris

Fig. 1 Landmarks used for the kinematic analyses. 1 upper jaw tip, 2 lower jaw tip, 3 hyoid (throat), 4 jaw joint, 5 nape, 6 dorsal trunk reference, 7 tongue tip (only digitized when visible)

Fig. 4 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris

Fig. 4 Scatter plot of the first two principal components. Principal component 1 (PC1) and principal component 2 (PC2) are derived from the 12 kinematic variables to illustrate the relationship among kinematic patterns for the four feeding modes coded by symbols and the ten individuals coded by color. Each data point represents one feeding event, and the ellipses indicate 95 % confidence interval in the four feeding modes. P@1 explains 57 % and P@2 explains 15.5 % of the total variance. See Table 3 for complete loadings of each principal component

Fig. 3 in Flexibility is everything: prey capture throughout the seasonal habitat switches in the smooth newt Lissotriton vulgaris

Fig. 3 Kinematic profiles of the four feeding modes. Kinematic means (dark and bold curves)±SD (pale and slim curves) of gape (blue), hyoid (Vreen), head rotation (oranVe), and tongue movement (Vray, only shown

opencc-by-4.0Mar 2024View details →

Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use

This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".

Fig. 4 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 4. Seasonal variation in dry mass (A), state-4respiration (B), and cytochrome c oxidase (C) in the pectoral muscle, heart, liver and kidneys of hwameis (Garrulaxcanorus) captured in either summer or winter in Wenzhou, China. Data are shown as mean ± SEM, *p <0.05, **p <0.01, ***p <0.001.

Fig. 3 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 3. Correlations between body mass and resting metabolic rate (RMR) (A), between body mass and EWL (B), between RMR and EWL (C), and between RMR and thermal conductance (D) in Chinese hwameis (Garrulax canorus) captured in either summer or winter in Wenzhou, China.

Fig. 1 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 1. Minimum, maximum and mean ambient daily summer (July to August 2013) and winter (January to February 2014) temperatures in Wenzhou, China. Mean ambient temperature ranged from 31.3 ± 0.2°C in summer to 8.6 ± 0.4°C in winter.

Fig. 2 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 2. Seasonal variation in body mass (A), resting metabolic rate (B), evaporative water loss (C) and thermal conductance (D) in Chinese hwamei (Garrulax canorus) captured in either summer or winter in Wenzhou, China. Data are shown as mean ± SEM, **p <0.01.

Fig. 5 in Physiological and Biochemical Thermoregulatory Responses in Male Chinese Hwameis to Seasonal Acclimatization: Phenotypic Flexibility in a Small Passerine.

Fig. 5. Correlations between resting metabolic rate (RMR) and state-4 respiration in the pectoral muscle (A), heart (C), liver (E) and kidneys (G), and between RMR and cytochrome c oxidase activity in the pectoral muscle (B), heart (D), liver (F) and kidneys (H), in Chinese hwameis (Garrulax canorus) captured in either summer or winter in Wenzhou, China.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9b. Translation of a tab for all languages

After checking a few (or all!) languages for instance and pressing the Ok button , we obtain the windows shown in figure 9a, or respectively 9b for all languages. Here we can add one or more missing translations, or modify one or more of the existing translations accordingly. All the data within the view cluster can be translated into any language supported by the system.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 8. GoTo -> Translation Option

For example, we select in the view cluster the tab called “Forecasting” and then choose Goto -> Translation (figure 8). After selecting Goto -> Translation, we obtain a selecting window for the desired languages where the user can check one or more languages to translate those tabs or areas into.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 9a. Translation of a tab for certain languages

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 7. Dialog structure of the View Cluster

Field dependency has been generated automatically and as a result the following desired structure has been achieved (figure 7). After populating the database tables with data in different languages with the help of the view cluster, the popup has been adapted in order to support the translation of the tabs and areas. Supplementary internal tables have been defined in the function module POPUP_FLEX, in order to copy data from the translation tables. The corresponding SELECT statements have been embedded in TRY-CATCH blocks, in order to prevent short dumps due to faulty selection processes.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 6. View cluster TAFC

For all these tables and views, table maintenance generators have been created and activated, in order to have the possibility to manage individual datasets in every table and view. The corresponding names of those function groups for the table maintenance generators are the same names as those for the views. The purpose of these maintenance views is only to take care of the input data more efficiently. These views will be used later in the view cluster, which ensures a hierarchical order of the data. Therefore, the maintenance views will also include the predecessor, in order to facilitate linking in the field dependency tab of the view cluster (Swapna, 2007). These three maintenance views are embedded in the following view cluster (figure 6)

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 5. Maintenance Views

Beside the tabs and the areas, the database table FLD also contains the fields TABLENAME and FIELD, which suggest the related parameters, whom input may be updated at runtime. Through standard SAP functionality the tables in BASIS, respectively their fields are by default translated in the login language of the user. So in the fields TABLENAME and FIELD of the FLD table, we will obtain, in the user login language, the names of the tables and fields from BASIS via the foreign keys to the table DD03L for TABLENAME and to the table DD02L for FIELD. Beside the database tables, 3 maintenance views have been created, TABV, AREV and FLDV, for the tabs, areas and fields of the popup (figure 5). We have chosen maintenance views instead of database views to be able to use them in the view cluster.

BRAIN Journal-Redesigning a Flexible Material Master Data Application with Language Dependency-Figure 2. Content of the single database table in the previous implementation

In the previous implementation there has been used a single database table which did not provide a consistent overview of existing tabs, areas and fields, as well as of the languages, in which a specific field of an area or tab was translated. So, it was difficult to maintain this database table by the customizing end-users in different languages, because every update of a tab, area or field required a number of actions in this table which had to be done manually and very carefully, requiring much time and attention. The number of rows of this table was very large and the content looked like the one shown in figure 2.