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Context

ViC dataset is a collection for implementing a Dynamic Spectrum Access(DSA) system testbed in the CBRS band in the USA. This data is a DSA system which consists of a 2-tier user : Incident user: generating a chirp signal with a Radar system, Primary user: LTE-TDD signal with a CBSD base station system, and corresponds to signal waveforms in the band 3.55-3.56 GHz (Ch1), 3.56-3.57 GHz (Ch2) respectively. There are a total of 12 classes, excluding the assumption that two of the 16 cases are used by CBSD base stations, depending on the presence or absence of two users in two channels. The labels of each data have the following meanings :

0000 (0) : All off 0001 (1) : Ch2 - Radar on 0010 (2) : Ch2 - LTE on 0011 (3) : Ch2 – LTE, Radar on 0100 (4) : Ch1 – Radar on 0101 (5) : Ch1 – Radar on / Ch2 – Radar on 0110 (6) : Ch1 – Radar on /Ch2 – LTE on 0111 (7) : Ch1 – Radar on / Ch2 – LTE, Radar on 1000 (8) : Ch1 – LTE on 1001 (9) : Ch1 – LTE on / Ch2 – Radar on (X) 1010 (10) : Ch1 – LTE on / Ch2 – LTE on (X) 1011 (11) : Ch1 – LTE on / Ch2 – LTE, Radar on 1100 (12) : Ch1 – LTE, Radar on 1101 (13) : Ch1 – LTE, Radar on / Ch2 – Radar on (X) 1110 (14) : Ch1 – LTE, Radar on / Ch2 – LTE on (X) 1111 (15) : Ch1 – LTE, Radar on / Ch2 – LTE, Radar on

Content

This dataset has a total of 7 types consisting of one raw dataset expressed in two extensions, 4 processed datasets processed in different ways, and a label. Except for one of the datasets, all are Python version of numpy files, and the other is a csv file.

(Raw) The raw data is a IQ data generated from testbeds created by imitating the SAS system of CBRS in the United States. In the testbeds, the primary user was made using the LabView communication tool and the USRP antenna (Radar), and the secondary user was made by manufacturing the CBSD base station. This has both csv format and numpy format exist.

(Processed) All of these data except one are normalized to values between 0 and 255 and consist of spectrogram, scalogram, and IQ data. The other one is a spectrogram dataset which is not normalized. They are measured between 250us. In the case of spectrograms and scalograms, the figure formed at 3.56 GHz to 3.57 GHz corresponds to channel 1, and at 3.55 GHz to 3.56 GHz corresponds to channel 2. Among them, signals transmitted from the CBSD base station are output in the form of LTE-TDD signals, and signals transmitted from the Radar system are output in the form of Chirp signals.

(Label) All of the above five data share one label. This label has a numpy format.

The global Top Coated Label Films market is experiencing robust growth, driven by increasing demand across diverse sectors including food & beverages, pharmaceuticals, and personal care. The market's expansion is fueled by several key factors: the rising preference for aesthetically appealing and durable labels, technological advancements in film coating techniques leading to enhanced product protection and shelf life, and the burgeoning e-commerce sector necessitating high-quality labels for efficient product identification and tracking. The projected Compound Annual Growth Rate (CAGR) suggests a substantial market expansion over the forecast period (2025-2033). Polypropylene and Polyethylene segments dominate the market due to their cost-effectiveness and versatility, while applications in the food and beverage industry are expected to witness significant growth owing to stringent regulations and increasing consumer awareness regarding product authenticity and safety. However, fluctuations in raw material prices and environmental concerns related to plastic waste pose potential challenges to market growth. Competitive landscape analysis reveals the presence of both established multinational corporations and regional players, indicating a dynamic market with opportunities for both large-scale production and specialized niche applications. Geographic segmentation shows strong growth in the Asia-Pacific region, driven primarily by the expanding economies of China and India, with significant growth also anticipated in North America and Europe due to established markets and increased consumer demand for branded products. The market is segmented by application and type, with a further breakdown by region providing detailed insights for strategic planning and investment decisions. The historical period (2019-2024) indicates steady growth, establishing a firm foundation for the robust expansion predicted for the forecast period. Future growth is likely to be influenced by innovations in sustainable film technologies and increasing adoption of eco-friendly packaging solutions. Companies are likely focusing on expanding their product portfolios to cater to specific industry needs and enhance their market share within this growing sector.

The meat consumption at the current level is highly unsustainable. Because of the problems that meat production causes to the environment, it is considered as one of the main problems. Vegetarian and vegan private label products represent a new challenging trend in addressing the customers within sustainable food consumption at affordable prices. The submitted paper aimed to find out whether Slovak consumers know and subsequently buy products of the private brand targeted on vegans and vegetarians, in which product categories they do so, how they perceive them and what attracts and discourages them. The research was carried out in the period from September to December 2020, when a total of 2,011 respondents from all over Slovakia took part. As we have focused only on consumers who know the product line of private labels targeted on vegans and vegetarians (product line of vegan and vegetarian products), we have further analyzed and interpreted only the answers of 978 respondents. For the need to obtain the main aim of the research, we have formulated four theoretical assumptions and five hypotheses, whose veracity was verified with the use of selected statistical methods and techniques processed out at statistical programs XL Stat, SAS Enterprise Guide 7.1. and SAS 9.4. The key finding of our research is, that even if it could be assumed that the products of this specific private label will be bought only by respondents from the vegan or vegetarian category, the opposite is true—the private label is known and bought by the respondents from the category “I eat everything,” which means that it is necessary to think about this product line, to wider it and continue in the improvement of its quality as this is what the customers want.

Urban Sound & Sight (Urbansas):

Version 1.0, May 2022

Created by
Magdalena Fuentes (1, 2), Bea Steers (1, 2), Pablo Zinemanas (3), Martín Rocamora (4), Luca Bondi (5), Julia Wilkins (1, 2), Qianyi Shi (2), Yao Hou (2), Samarjit Das (5), Xavier Serra (3), Juan Pablo Bello (1, 2)
1. Music and Audio Research Lab, New York University
2. Center for Urban Science and Progress, New York University
3. Universitat Pompeu Fabra, Barcelona, Spain
4. Universidad de la República, Montevideo, Uruguay
5. Bosch Research, Pittsburgh, PA, USA

Publication

If using this data in academic work, please cite the following paper, which presented this dataset:
M. Fuentes, B. Steers, P. Zinemanas, M. Rocamora, L. Bondi, J. Wilkins, Q. Shi, Y. Hou, S. Das, X. Serra, J. Bello. “Urban Sound & Sight: Dataset and Benchmark for Audio-Visual Urban Scene Understanding”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.

Description

Urbansas is a dataset for the development and evaluation of machine listening systems for audiovisual spatial urban understanding. One of the main challenges to this field of study is a lack of realistic, labeled data to train and evaluate models on their ability to localize using a combination of audio and video.
We set four main goals for creating this dataset:
1. To compile a set of real-field audio-visual recordings;
2. The recordings should be stereo to allow exploring sound localization in the wild;
3. The compilation should be varied in terms of scenes and recording conditions to be meaningful for training and evaluation of machine learning models;
4. The labeled collection should be accompanied by a bigger unlabeled collection with similar characteristics to allow exploring self-supervised learning in urban contexts.
Audiovisual data
We have compiled and manually annotated Urbansas from two publicly available datasets, plus the addition of unreleased material. The public datasets are the TAU Urban Audio-Visual Scenes 2021 Development dataset (street-traffic subset) and the Montevideo Audio-Visual Dataset (MAVD):

Wang, Shanshan, et al. "A curated dataset of urban scenes for audio-visual scene analysis." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.

Zinemanas, Pablo, Pablo Cancela, and Martín Rocamora. "MAVD: A dataset for sound event detection in urban environments." Detection and Classification of Acoustic Scenes and Events, DCASE 2019, New York, NY, USA, 25–26 oct, page 263--267 (2019).

The TAU dataset consists of 10-second segments of audio and video from different scenes across European cities, traffic being one of the scenes. Only the scenes labeled as traffic were included in Urbansas. MAVD is an audio-visual traffic dataset curated in different locations of Montevideo, Uruguay, with annotations of vehicles and vehicle components sounds (e.g. engine, brakes) for sound event detection. Besides the published datasets, we include a total of 9.5 hours of unpublished material recorded in Montevideo, with the same recording devices of MAVD but including new locations and scenes.

Recordings for TAU were acquired using a GoPro Hero 5 (30fps, 1280x720) and a Soundman OKM II Klassik/studio A3 electret binaural in-ear microphone with a Zoom F8 audio recorder (48kHz, 24 bits, stereo). Recordings for MAVD were collected using a GoPro Hero 3 (24fps, 1920x1080) and a SONY PCM-D50 recorder (48kHz, 24 bits, stereo).

When compiled in Urbansas, it includes 15 hours of stereo audio and video, stored in separate 10 second MPEG4 (1280x720, 24fps) and WAV (48kHz, 24 bit, 2 channel) files. Both released video datasets are already anonymized to obscure people and license plates, the unpublished MAVD data was anonymized similarly using this anonymizer. We also distribute the 2fps video used for producing the annotations.

The audio and video files both share the same filename stem, meaning that they can be associated after removing the parent directory and extension.

MAVD:
video/

TAU:
video/

where location_id in both cases includes the city and an ID number.

city & places & clips & mins & frames & labeled mins \\
Montevideo & 8 & 4085 & 681 & 980400 & 92 \\
Stockholm & 3 & 91 & 15 & 21840 & 2 \\
Barcelona & 4 & 144 & 24 & 34560 & 24 \\
Helsinki & 4 & 144 & 24 & 34560 & 16 \\
Lisbon & 4 & 144 & 24 & 34560 & 19 \\
Lyon & 4 & 144 & 24 & 34560 & 6 \\
Paris & 4 & 144 & 24 & 34560 & 2 \\
Prague & 4 & 144 & 24 & 34560 & 2 \\
Vienna & 4 & 144 & 24 & 34560 & 6 \\
London & 5 & 144 & 24 & 34560 & 4 \\
Milan & 6 & 144 & 24 & 34560 & 6 \\
\midrule
Total & 50 & 5472 & 912 & 1.3M & 180 \\

Annotations

Of the 15 hours of audio and video, 3 hours of data (1.5 hours TAU, 1.5 hours MAVD) are manually annotated by our team both in audio and image, along with 12 hours of unlabeled data (2.5 hours TAU, 9.5 hours of unpublished material) for the benefit of unsupervised models. The distribution of clips across locations was selected to maximize variance across different scenes. The annotations were collected at 2 frames per second (FPS) as it provided a balance between temporal granularity and clip coverage.

The annotation data is contained in video_annotations.csv and audio_annotations.csv.

Video Annotations

Each row in the video annotations represents a single object in a single frame of the video. The annotation schema is as follows:

• frame_id: The index of the frame within the clip the annotation is associated with. This index is 0-based and goes up to 19 (assuming 10-second clips with annotations at 2 FPS)
• track_id: The ID of the detected instance that identifies the same object across different frames. These IDs are guaranteed to be unique within a clip.
• x, y, w, h: The top-left corner and width and height of the object’s bounding box in the video. The values are given in absolute coordinates with respect to the image size (1280x720).
• class_id: The index of the class corresponding to: [0, 1, 2, 3, -1] — see label for the index mapping. The -1 value corresponds to the case where there are no events, but still clip-level annotations, like night and city. When operating on bounding boxes, class_id of -1 should be filtered.
• label: The label text. This is equivalent to LABELS[class_id], where LABELS=[car, bus, motorbike, truck, -1]. The label -1 has the same role as above.
• visibility: The visibility of the object. This is 1 unless the object becomes obstructed, where it changes to 0.
• filename: The file ID of the associated file. This is the file’s path minus the parent directory and extension.
• city: The city where the clip was collected in.
• location_id: The specific name of the location. This may include an integer ID following the city name for cases where there are multiple collection points.
• time: The time (in seconds) of the annotation, relative to the start of the file. Equivalent to frame_id / fps .
• night: Whether the clip takes place during the day or at night. This value is singular per clip.
• subset: Which data source the data originally belongs to (TAU or MAVD).

Audio Annotations

Each row represents a single object instance, along with the time range that it exists within the clip. The annotation schema is as follows:

• filename: The file ID odd the associated audio file. See filename above.
• class_id, label: See above. Audio has an additional class_id of 4 (label=offscreen) which indicates an off-screen vehicle - meaning a vehicle that is heard but not seen. A class_id of -1 indicates a clip-level annotation for a clip that has no object annotations (an empty scene).
• non_identifiable_vehicle_sound: True if the region contains the sound of vehicles where individual instances cannot be uniquely identified.
• start, end: The start and end times (in seconds) of the annotation relative to the file.

Conditions of use

Dataset created by Magdalena Fuentes, Bea Steers, Pablo Zinemanas, Martín Rocamora, Luca Bondi, Julia Wilkins, Qianyi Shi, Yao Hou, Samarjit Das, Xavier Serra, and Juan Pablo Bello.

The Urbansas dataset is offered free of charge under the following terms:

• Urbansas annotations are release under the CC BY 4.0 license
• Urbansas video and audio replicates the original sources licenses:
  • MAVD subset is released under CC BY 4.0
  • TAU subset is released under a Non-Commercial license

Feedback

Please help us improve Urbansas by sending your feedback to:

• Magdalena Fuentes: mfuentes@nyu.edu
• Bea Steers: bsteers@nyu.edu

In case of a problem, please include as many details as possible.

Acknowledgments

This work was partially supported by the National Science

The aim of this study is to demonstrate bioequivalence of fixed dose combination Dapagliflozin + Metformin modified -release, film-coated tablets, 10 mg + 1000 mg, (AstraZeneca AB, Sweden) versus Forxiga™ (Dapagliflozin), film-coated tablets, 10 mg (Bristol Myers Squibb Company, USA) and Glucophage® long (Metformin), ER tablets, 1000 mg (2 x 500 mg) (Merck Santé S.A.S., France) which are already registered in the Russian Federation.

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per LABEL S.A.S. DI REGALDO MARIA ELENA & C. in CASELLE TORINESE (TO)

The meat consumption at the current level is highly unsustainable. Because of the problems that meat production causes to the environment, it is considered as one of the main problems. Vegetarian and vegan private label products represent a new challenging trend in addressing the customers within sustainable food consumption at affordable prices. The submitted paper aimed to find out whether Slovak consumers know and subsequently buy products of the private brand targeted on vegans and vegetarians, in which product categories they do so, how they perceive them and what attracts and discourages them. The research was carried out in the period from September to December 2020, when a total of 2,011 respondents from all over Slovakia took part. As we have focused only on consumers who know the product line of private labels targeted on vegans and vegetarians (product line of vegan and vegetarian products), we have further analyzed and interpreted only the answers of 978 respondents. For the need to obtain the main aim of the research, we have formulated four theoretical assumptions and five hypotheses, whose veracity was verified with the use of selected statistical methods and techniques processed out at statistical programs XL Stat, SAS Enterprise Guide 7.1. and SAS 9.4. The key finding of our research is, that even if it could be assumed that the products of this specific private label will be bought only by respondents from the vegan or vegetarian category, the opposite is true—the private label is known and bought by the respondents from the category “I eat everything,” which means that it is necessary to think about this product line, to wider it and continue in the improvement of its quality as this is what the customers want.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Teenage adolescent females residing in Baltimore, Maryland who were involved in a relationship with a history of violence were sought after to participate in this research study. Respondents were interviewed and then followed through daily diary entries for several months. The aim of the research was to understand the context regarding teen dating violence (TDV). Prior research on relationship context has not focused on minority populations; therefore, the focus of this project was urban, predominantly African American females. The available data in this collection includes three SAS (.sas7bdat) files and a single SAS formats file that contains variable and value label information for all three data files. The three data files are: final_baseline.sas7bdat (157 cases / 252 variables) final_partnergrid.sas7bdat (156 cases / 76 variables) hart_final_sas7bdata (7004 cases / 23 variables)

Abstract (en): This data collection was designed to ascertain the nutritional status of mid-nineteenth century Americans. These data supply information on cadet's last name, age, height, weight, state of birth, state of appointment, and the date of the physical examination. In addition, family background data, such as whether parents were alive, father's occupation, income of parents, head of household, and urban/rural location of family residence, are provided. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. 2010-02-24 SAS, SPSS, and Stata setups have been added to this data collection. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (2732-HD-07168). The data contain alphabetics.

Unweighted and weighted percentages for demographic and smoking-related variables.

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per UMBRA LABEL S.A.S. DI BERIOLI MARCELLO in Magione (PG)

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per LABEL PRO S.A.S. DI SMORTI MARIA & C. in VENARIA REALE (TO)

Weighted logistic regression results, for the entire sample and current smokers only.