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The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council. This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council. The dataset includes a row for each Council action on any given facility. Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein. The public is advised that the records upon which the information in this database is based are kept in the Siting Council’s offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday. Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation. Cingular: SNET, SCLP, and New Cingular after merger with AT&T T-Mobile: Omni (Omnipoint), VoiceStream Verizon: BAM, Cellco AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T Nextel: Smart SMR

This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

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Analysis of ‘Wireless Telecommunication Tower Sites Under Siting Council Jurisdiction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/650b9b39-3a9b-4d42-a395-b233ddae16e2 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council.

This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council.

Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein.

The public is advised that the records upon which the information in this database is based are kept in the Siting Council’s offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday.

Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation.

Cingular: SNET, SCLP, and New Cingular after merger with AT&T

T-Mobile: Omni (Omnipoint), VoiceStream

Verizon: BAM, Cellco

AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T

Nextel: Smart SMR

--- Original source retains full ownership of the source dataset ---

Dataset of commercial microwave link (CML) data for the Netherlands. This can be used to estimate path-average rainfall between telephone towers. It contains microwave frequency, end date & time of reading, minimum & maximum received power, path length, coordinates, link identifier, errored seconds, and severely errored seconds. The network consists of on average ~3070 sub-links over ~1818 unique link paths covering the Netherlands, having a temporal resolution of 15 min. The transmitted signal levels were not available and may be considered constant for each individual link. The dataset spans the period 13 January 2011 up to and including 15 March 2015, although data gaps exist. The dataset contains part of the network from one of the three mobile network operators in the Netherlands during this 4-year period. The current network is expected to be very different and have a lower density. The data have been provided by the mobile network operator (MNO) T-Mobile NL (since 5 September 2023 called Odido).

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior.

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz).

For details of the dataset, see arXiv preprint https://arxiv.org/abs/2307.03401

Researchers may use this dataset for publications and reports, as long as: 1) Users shall not carry out activities that involve unethical usage of the data, including attempts at re-identifying data subjects, harming individuals, or damaging companies, and 2) The Data Descriptor paper (https://arxiv.org/abs/2307.03401; citation below) needs to be cited when using the data for research and/or commercial purposes. Downloading this dataset implies agreement with the above two conditions.

• Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2023). Metropolitan scale and longitudinal dataset of anonymized human mobility trajectories. arXiv preprint arXiv:2307.03401. https://arxiv.org/abs/2307.03401

--- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) ---

The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space.

The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior.

There are 2 tasks in the Human Mobility Prediction Challenge.

In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz).

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz).

For more details, see https://connection.mit.edu/humob-challenge-2023

Here are a few use cases for this project:

1. Public Health Surveillance: In the era of pandemics, the model can assist public health authorities in monitoring mask compliance in public places like parks, markets, and shopping malls. It can be applied to CCTV footage to identify instances of mask non-compliance, which can help in taking necessary precautions.

2. Smart Buildings: The model can be incorporated in corporate or institutional buildings with IoT integration. Entrance doors or turnstiles could use this model to ensure that individuals are wearing masks correctly before they're allowed entry, promoting a safer work or learning environment.

3. Public Transport Monitoring: This model can be used in metros, buses or trains to ensure passengers are following mask guidelines correctly. It can assist in automated passenger monitoring and provide live updates to authorities for immediate action, if required.

4. Personal Safety Gadgets: Rapid advancements in AR glasses or smart glasses can leverage this model. Its mask detection feature can provide realtime feedback to the users or wearers about the surrounding safety levels based on people's mask-wearing habits.

5. Social Distancing Apps: A mobile application, using this model, can notify users if they are in an environment where people aren't wearing masks properly, or at all, allowing them to avoid potentially unsafe situations.

ABSTRACT

This is the data set of nine MOMAA weather stations operated during the field campaign of the research project PIANO (Penetration and Interruption of Alpine Foehn) in the Inn Valley and Wipp Valley near Innsbruck, Austria, during fall and early winter 2017. The goal of the campaign was to study the erosion of cold air pools during south foehn and the associated foehn breakthrough at the valley floor in the vicinity of Innsbruck as well as the subsequent foehn breakdown. The campaign comprises seven Intensive Observation Periods (IOPs), more specifically six south foehn events (IOP 2 to IOP 7) and one west foehn (IOP 1).

DATA SET DESCRIPTION

1. Spatial coverage and locations

Nine portable automatic weather stations (AWS), so-called MOMAA weather stations, were operated during the PIANO field campaign in the greater Innsbruck area, Austria. MOMAA stands for mobile measurement network for Alpine atmospheric research, which is an infrastructure that belongs to the Innsbruck Atmospheric Observatory (IAO). Most of the weather stations were operated at the floor of the Inn Valley between the villages Inzing (west of Innsbruck) and Volders (east of Innsbruck). One station was installed in Innsbruck on the rooftop of the former Hotel Hilton about 50 m above street level. Two stations were deployed at the slope of the Inn Valley at Ölberg in the north and Bergisel in the south of Innsbruck. One station was operated in the Wipp Valley at the village of Patsch close to the radiosonde site and not far from an eddy covariance station. The station coordinates and heights are listed in the data file and a description of the locations is given here (see also momaa_locations.png):

M02: Völs

M03: Innsbruck/Bergisel

M04: Patsch/Pfaffenbichl

M05: Innsbruck/Ölberg

M06: Innsbruck/Hotel Hilton

M07: Innsbruck/Saggen/Kettenbrücke

M08: Volders

M09: Unterperfuss

M10: Inzing/Zirl/Modellflugplatz

The MOMAA stations were installed to complement the operational AWS networks of the national weather service ZAMG and other institutions.

1. Temporal coverage

The PIANO field campaign took place during fall and early winter 2017. However, the MOMAA data set is available for the period from about mid August 2017 to mid December 2017. Two stations (M04 and M06) remained in operation until the beginning of February 2018. Due to malfunctions, short data gaps occurred in early September 2017. Moreover, precipitation data is missing for M07 on 27 and 28 October due to a blocked rain gauge and soil temperature data for the same station is missing in mid September due to a damaged sensor. The exact operating times are:

M02: 1300 UTC 15 Aug 2017 to 1359 UTC 11 Dec 2017

M03: 1230 UTC 12 Aug 2017 to 1059 UTC 11 Dec 2017

M04: 1200 UTC 15 Aug 2017 to 1459 UTC 08 Feb 2018

M05: 0630 UTC 13 Aug 2017 to 1359 UTC 11 Dec 2017

M06: 1100 UTC 13 Aug 2017 to 1459 UTC 08 Deb 2018

M07: 1730 UTC 29 Aug 2017 to 1429 UTC 11 Dec 2017

M08: 0830 UTC 18 Aug 2017 to 1429 UTC 11 Dec 2017

M09: 0800 UTC 28 Aug 2017 to 1329 UTC 11 Dec 2017

M10: 1330 UTC 29 Aug 2017 to 1329 UTC 11 Dec 2017

1. Instrument details

Sensor type

Measurements were taken with nine automatic weather stations manufactured by the Swiss company SensAlpin. Each station consists of the following sensors and hardware:

Data logger (Campbell CR1000)

Air temperature and humidity sensor (Rotronic HC2A-S3) mounted in an actively ventilated radiation shield (Rotronic RS12T)

2D ultra sonic anemometer (Gill WindSonic)

Net radiometer (Kipp&Zonen NR Lite)

Barometer (Campbell CS100, Sera model 278)

Tipping bucket rain gauge (RM Young model 520202 or 52203)

Two soil temperature sensors (Campbell thermistor 107)

GSM modem

Battery and solar panel

Aluminum telescopic mast, side boom and tripod (Letrona)

Two stations (M05 at Ölberg and M06 at Hotel Hilton) had no rain gauge, no net radiometer and no soil temperature sensors.

MOMAA ID number and location

The MOMAA ID numbers mentioned in the data file (02 to 10) correspond to the ID numbers (M02 to M10) used internally at ACINN (they are indicated on each logger box). Station M01 was not used during PIANO. GPS coordinates and heights of all MOMAA stations are provided in the data file.

Time and measurement interval

Time in the data file is in UTC. The measurement interval is 1 minute. A measurement value represents either a 1-minute average (arithmetic average for scalar quantities and vector average for wind quantities), a maximum within 1 minute or a sum over 1 minute.

Installation height

The wind sensor was mounted 3.5 m above ground (AGL). The air temperature and humidity sensor was installed at 2 m (AGL). The rain gauge and the net radiometer were mounted on a side boom at 1.8 m AGL. The height of the barometer is different for each station (between 0.5 and 1 m AGL) and is listed in the data file. Where possible, the two soil temperature sensors were placed at 2 and 6 cm below the surface, respectively. Note that station M06 was deployed at the rooftop of the former Hotel Hilton (now AC Hotel Innsbruck). Hence, the sensor heights for M06 are relative to the height of the rooftop, which is at about 50 m above street level.

Data correction

Here we provide so-called "level 1" data that includes a bias correction for air temperature and relative humidity. Based on a preliminary data analysis after the PIANO campaign, a warm bias was detected in the time series of air temperature that was caused by high-frequency noise induced by the ventilation of the Rotronic radiation shield due to electromagnetic induction. This warm bias is proportional to the battery voltage as long as the ventilation is active. The higher the battery voltage, the higher the rotation speed of the fan and, hence, the larger the error due to induction. The status of the ventilation is indicated by the parameter vent_flag in the data file (off=0, on=1). The ventilation is automatically switched off if the battery voltage drops below a certain threshold. This only occurred a few times during the field campaign (see overview figure momaa_overview_ventilation_flag.png).

In order to correct this bias, we performed a sensor intercomparison study in December 2018 with all MOMAA stations and an independent reference sensor (the same type of sensor but with a separate wiring for the temperature sensor and the ventilation). Based on these measurements, the air temperature bias was quantified for each station with the linear function (\Delta T_\mathrm{bias} = k V_\mathrm{bat} + d). Here, (k) and (d) are slope and intercept of the linear regression. The values of these coefficients are different for each station. (V_\mathrm{bat}) is the battery voltage. The bias is about 1 to 2 K. In the data file we provide values for (k) and (d) as well as corrected temperature ta and uncorrected (raw) temperature ta_raw. We strongly recommend to use corrected temperature.

Based on the same intercomparison measurements, a notable bias in relative humidity of about 4 to 8 % was found for station M04. This bias is proportional to the measured humidity. The bias is much smaller for the other stations (about 0 to 2 %). Nevertheless, we applied a bias correction for all stations based on (\Delta \mathrm{RH}_\mathrm{bias} = k \mathrm{RH}_\mathrm{raw} + d). In the data file we provide values for (k) and (d) as well as corrected relative humidity rh and uncorrected (raw) temperature rh_raw.

We did not calibrate the pressure sensors. Hence, values for air pressure may exhibit a station-dependent offset from the truth. In order to correct these potential offsets, the user may want to follow the approach of Muschinski et al. (2020; section 4.5).

1. Data file structure

File format

Provided are data in netCDF format as well as overview figures in PNG format.

Data

MOMAA_lev1_FINAL.nc is a netCDF file that contains the whole MOMAA data set. Time in the data file is given in UTC as a UNIX timestamp (seconds since 00:00 UTC 01 January 1970) and the measurement interval is 1 minute. The following parameters can be found in the data file: corrected and uncorrected air temperature (ta and ta_raw), corrected and uncorrected relative humidity (rh and rh_raw), mean and maximum horizontal wind speed (wspeed and wspeed_max; the latter is the maximum 3-second gust wind speed measured in 1 minute), mean wind direction and its standard deviation (wdir and wdir_stddev), net radiation (nr), air pressure (pa), precipitation accumulated over 1 minute and maximum precipitation intensity (prec and rrmax), soil temperature at two different depths (ts1 and ts2), ventilation flag (vent_flag), and battery voltage (batt_volt). The data file also contains information on the location and the absolute height of each station as well as the sensor heights above ground level.

Overview figures

momaa_locations.png illustrates the locations of the MOMAA stations on a terrain map.

momaa_overview_[parameter].png illustrates the whole data set of a certain parameter. It is useful for assessing data availability. Overview figures for several different parameters are provided.

1. Publications

MOMAA data of the PIANO field campaign have been analyzed in following publications: Muschinski (2019), Muschinski et al. (2020), Haid et al. (2020), Umek et al. (2021).

1. Contact

Contact alexander.gohm (at) uibk.ac.at for any questions regarding the data set.

1. Acknowledgements

The PIANO field campaign was supported by the Austrian Science Fund (FWF) and the Weiss Science Foundation under Grant P29746-N32, by KIT IMK-IFU, Austro Control GmbH, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), the Hydrographic Service of Tyrol, Innsbrucker Kommunalbetriebe AG (IKB), Bergisel Betriebsgesellschaft m.b.H., Innsbrucker Nordkettenbahnen Betriebs GmbH, T-Mobile Austria GmbH, Unser Lagerhaus Warenhandelsgesellschaft, PEMA

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