Personal blog
This is a part of study described in the dedicated post.
We will compare “out-of-sample” error of MSU SINP model, Wind Pulses simulator and machine learning regression model. In this post we will assess current SINP solar wind predictions.
Continue reading “MSU SINP Solar Wind Velocity Forecast”
This is a part of study described in the dedicated post.
We have per minute solar wind observations (density, temperature and velocity) recorded by EPAM instrument of Advanced Composition Explorer (ACE) spacecraft. (see this link for data files)
This data from ACE can be used for two purposes. First, we can use this particular archive of observations (e.g. 2015 year) to fit the prediction model parameters. Second, we can use very recent measurements coming from ACE as predictors for forecasting the wind velocity for near future.
Continue reading “Solar wind observations by EPAM ACE”
This is a part of study described in the dedicated post.
I am going to perform data clean up and feature extraction for Solar wind model fitting. The major predictor of the solar wind is considered to be coronal holes characteristics (e.g. see this paper)
I’ve got two CSV data sets that contain quantitative features extracted from the Sun images with computer vision algorithms.
One file is “green” (193nm) spectrum portion originated features, another one is “red” (211nm) spectrum portion originated features
Continue reading “Extracting possible solar wind predictors”
I’m going to do an experiment of predicting the solar wind speed near the Earth based on the series of the Sun images.
Skobeltsyn Institute of Nuclear Physics of Moscow State University publishes observation and prediction data on space weather. Solar wind prediction is also published there. My experiment is to try to build more accurate prediction based on the same initial data from SINP MSU.
The experiment is the following:
For each of this experiment phases I will publish a separate post.
Today my RFc package was accepted and published on CRAN.
With this package you can right now fetch the following environmental parameters
The parameters above can be fetched for the point set of for the geo grid specified.
The original service providing data is collaborative project of Microsoft Research Cambridge and Information Technologies in Science lab where I currently work.
Today I check whether the background radiation level correlates with the elevation from sea surface.
I show that the correlation between background radiation level and elevation is about 0.03 (uSv/h per km) and this slope explains only 3% of radiation level variation.
While almost all sites with IP cam specifications say that Kadymay KDM-6828A RTSP URL must be
rtsp://IPADDRESS:554/ch0_0.h264
In my case the URL appeared to be
rtsp://IPADDRESS:554/user=cctv&password=cctv&channel=1&stream=0.sdp?real_stream–rtp-caching=100
for the HD stream and
rtsp://IPADDRESS:554/user=cctv&password=cctv&channel=1&stream=1.sdp?real_stream–rtp-caching=100
for the preview stream.
Note that I’ve set up separate user with login cctv and password cctv respectively. Use your credentials in the URL above.
Hi,
Today I got acquainted with recent years wi-fi penetration techniques (e.g. brutefrocing WPS pin). I realized that the strongest security measures at my home are set for WAN side at the Internet gateway. For years I considered my LAN absolutely safe. But WPA PSK with long random key-phrase turned out to be not enough.
Taking measures, after disabling WPS at my access point I decided to add lightweight intrusion detection for LAN (as setting up such heavy intrusion detection systems like Snort is overkill for my home environment. I think the same is true for most of the home and SOHO (small office home office) environments). Link layer seemed the most appropriate level to monitor as almost every intruder action will touch it.
I desired to monitor arp and IPv6 neighbourhood tables at my Internet gateway as primary goal for me is preventing the intruder from doing illegal actions using my connection.
To monitor the tables I wrote a script which notifies me upon unknown PC connects to my home LAN. It polls the tables comparing the records with known PCs MAC addresses.
The script is at github: https://github.com/dgrechka/LLIDS
Now I fill more safe =)
Deploy the script in your homes. Let’s control our networks =)
As the world moves toward the Internet of Things there are lots of cheap environmental sensors available at the market.
When it just started several years ago I spotted the Toradex company that sells embedded devices. I caught sight of the sensors series called Toradex Oak sensors. The Toradex supplied Microsoft Robotics Studio libraries for them which was right enough for my student project. So I ordered two.
Now I’m building the monitoring system for a summer house based on Raspberry PI. And these sensors made by Toradex suits well for gathering environmental data.
The official site provides a sample of using the sensors on linux. But I have a FreeBSD.
So I started to think about constructing a simple solution to gather the data on BSD.
The sensors identify themselves as HID devices. After short investigation I found that FreeBSD provides usbhidctl utility to communicate with HID. That looked promising as it did not require linux emulation. With a single command we are able to fetch all the immediate values from the sensor!
Another task was data storage engine. My colleague Eugene suggested me using collectd or statsd to organize storage. Both of them appeared to be able to store the data and to stream the data to remote host for further storage. I decided to use collecd as it is in C so my Rapberry PI box will have minimal package set.
Finally I ended up with the script that is invoked by collectd. The script enumerates HID USB devices, finds Toradex sensors, gets the values from them, applies proper units transformation and returns the data as the string compatible with collectd.
I share it here. So you can download it, modify and extend for your needs.
Open the post to access downloads.
The Global Historical Climatology Network-Monthly (GHCN-M) dataset by NCDC is particularly important data set if your research deals with climate data. It is widely accepted. Its major advantage is quality control and a variety of data sources combined together. I used it several times as reference data for validation of calculated climate surfaces. It is also great for uncertainty assessment of climate interpolation methods.
But it is distributed as text files of specific format only. And you will have to write a parser to fetch the data.
This week I decided to load the GHCNv3 into MySQL to make it flexible for fetching. I can fetch different subsets of the data into CSV files just with composing a proper select query. That made a significant speed up in experiments with interpolation techniques.
I share these SQL scripts to enable others researchers to load GHCN v3 into their own SQL servers. You can restore GHCN at your server and perform requests to it. Just download the script, execute it. And you are able to get the data you need. Fast :)
The scripts do not contain CREATE DATABASE statements. Thus create an empty database by hand and then execute the proper script.
