Hero section update

vr01/package.json

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     "react-dom": "^18.2.0",
     "react-router-dom": "^6.22.3",
     "react-scripts": "5.0.1",
+    "react-scroll": "^1.9.0",
     "web-vitals": "^2.1.4"
   },
   "scripts": {

vr01/public/img/Downloads/bike+sharing+dataset (1)/Readme.txt

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+==========================================
+Bike Sharing Dataset
+==========================================
+
+Hadi Fanaee-T
+
+Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto
+INESC Porto, Campus da FEUP
+Rua Dr. Roberto Frias, 378
+4200 - 465 Porto, Portugal
+
+
+=========================================
+Background 
+=========================================
+
+Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return 
+back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return 
+back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of 
+over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, 
+environmental and health issues. 
+
+Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by
+these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration
+of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into
+a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important
+events in the city could be detected via monitoring these data.
+
+=========================================
+Data Set
+=========================================
+Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions,
+precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to  
+the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is 
+publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then 
+extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com. 
+
+=========================================
+Associated tasks
+=========================================
+
+	- Regression: 
+		Predication of bike rental count hourly or daily based on the environmental and seasonal settings.
+
+	- Event and Anomaly Detection:  
+		Count of rented bikes are also correlated to some events in the town which easily are traceable via search engines.
+		For instance, query like "2012-10-30 washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are 
+		identified in [1]. Therefore the data can be used for validation of anomaly or event detection algorithms as well.
+
+
+=========================================
+Files
+=========================================
+
+	- Readme.txt
+	- hour.csv : bike sharing counts aggregated on hourly basis. Records: 17379 hours
+	- day.csv - bike sharing counts aggregated on daily basis. Records: 731 days
+
+
+=========================================
+Dataset characteristics
+=========================================	
+Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv
+
+	- instant: record index
+	- dteday : date
+	- season : season (1:springer, 2:summer, 3:fall, 4:winter)
+	- yr : year (0: 2011, 1:2012)
+	- mnth : month ( 1 to 12)
+	- hr : hour (0 to 23)
+	- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
+	- weekday : day of the week
+	- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
+	+ weathersit : 
+		- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
+		- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
+		- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
+		- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
+	- temp : Normalized temperature in Celsius. The values are divided to 41 (max)
+	- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
+	- hum: Normalized humidity. The values are divided to 100 (max)
+	- windspeed: Normalized wind speed. The values are divided to 67 (max)
+	- casual: count of casual users
+	- registered: count of registered users
+	- cnt: count of total rental bikes including both casual and registered
+
+=========================================
+License
+=========================================
+Use of this dataset in publications must be cited to the following publication:
+
+[1] Fanaee-T, Hadi, and Gama, Joao, "Event labeling combining ensemble detectors and background knowledge", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.
+
+@article{
+	year={2013},
+	issn={2192-6352},
+	journal={Progress in Artificial Intelligence},
+	doi={10.1007/s13748-013-0040-3},
+	title={Event labeling combining ensemble detectors and background knowledge},
+	url={http://dx.doi.org/10.1007/s13748-013-0040-3},
+	publisher={Springer Berlin Heidelberg},
+	keywords={Event labeling; Event detection; Ensemble learning; Background knowledge},
+	author={Fanaee-T, Hadi and Gama, Joao},
+	pages={1-15}
+}
+
+=========================================
+Contact
+=========================================
+
+For further information about this dataset please contact Hadi Fanaee-T (hadi.fanaee@fe.up.pt)