This cheat sheet is based on âHeliospheric Coordinate Systemsâ by Franz & Harper (2002, corrected 2017). Itâs written for readers comfortable with high-school algebra, geometry, trigonometry, and a bit of Calculus I.
If you’re running a Raspberry Pi-based time server or GPS-equipped system and want visibility into the GPS signal status (2D, 3D fix, or no lock), you can use a simple Bash script and Telegraf to bring that data into your InfluxDB time series database â and visualize it in Grafana.
In this post, weâll walk through how to:
inputs.exec plugin to ingest the dataWhen using GPS for time synchronization (especially with chrony or ntpd), it’s useful to know whether the GPS module actually has a signal lock:
In this post, we dive into the mechanics behind a DNS exfiltration tool written in Python. This project demonstrates how data can be covertly transmitted via DNS queries. I built this clientâserver system to study DNS exfiltration techniques in a controlled, educational environment. Below, I detail the architecture, key design decisions, and provide annotated code snippets from both the client and server implementations.
The project is divided into two main components:
Dependency Injection (DI) is a software design pattern that helps create loosely coupled, testable, and maintainable code. Instead of having classes create their own dependencies, those dependencies are “injected” from the outside. This blog post will walk you through a concrete Python example that demonstrates DI, along with alternate techniques like the Service Locator and Factory patterns.
At its core, dependency injection involves passing (or “injecting”) an objectâs dependencies rather than instantiating them internally. The main benefits include:
This guide explains how to transform a fresh installation of Raspberry Pi OS into a stratumâ1 NTP server. The project leverages a GPIOâbased GPS module with PPS, the PTP hardware timestamping capability of the Ethernet interface, Chrony for NTP synchronization, and Telegraf with InfluxDB for monitoring.
In this project you’ll build a stratumâ1 NTP server using:
Before you begin, ensure you have the following:
Welcome to this interactive curriculum on the mathematics and physics behind launching and recovering space vehicles. In this notebook, we will build a comprehensive understanding of the underlying math, coordinate systems, and physics that allow engineers to determine launch windows, orbital trajectories, reentry paths, and landing coordinates for real-world spacecraft.
This notebook is structured to guide you step-by-step through the essential topics required for space mission planning. We begin with fundamental mathematical concepts such as algebra, trigonometry, and coordinate systems, and progressively build up to advanced topics like orbital mechanics, trajectory optimization, and reentry dynamics. Each section includes interactive Python examples and visualizations to help reinforce the concepts and illustrate how real-world physics is applied in space missions.
Exploring the intricacies of the Crystal Ball Problem as it applies to arrays, this piece delves into the challenge of predicting future elements based on current sequences. It underscores the significance of pattern recognition and algorithmic forecasting in computational analysis and real-world applications.
The Crystal Ball Problem is a metaphor for predicting future elements or trends in an array based solely on its current sequence. Imagine you have an array of numbers representing data points over timeâsuch as stock prices or weather dataâand your task is to forecast the upcoming values. The âcrystal ballâ is our metaphor for the ability to foresee these future values.
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Store all DotFiles in a bare git repository
Manually add files to repository by using config add <filename>. Use this to add all your files manually, this way only updates to your tracked files will show
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In the ever-evolving realm of enterprise networking, the integration of Artificial Intelligence (AI) technologies has become a game-changer. One of the most notable advancements in this area is Juniper Mist, a platform that leverages AI to revolutionize how networks are managed and operated. This article delves into how AI, particularly through solutions like Juniper Mist, is reshaping the enterprise networking landscape.
Juniper Mist is an AI-driven network solution that offers a unique combination of cloud management, advanced AI capabilities, and programmable APIs to deliver a full-stack solution for wireless, wired, and WAN networks. The platform is designed to simplify operations, improve user experiences, and provide unprecedented insight into network performance.
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Welcome to 2024, a year that continues to witness revolutionary changes in the world of networking. As a network engineer, I’ve seen firsthand the rapid evolution of technology in our field. This year is no exception, with several trends emerging that are reshaping how we think about and implement network infrastructure. Let’s dive into some of the top trends in networking for 2024.
Although 5G has just recently become mainstream, the buzz around 6G technology is already gaining momentum. Expected to be commercially available by 2030, research and development in 6G are heating up in 2024. This next-generation technology promises even faster speeds, lower latency, and higher reliability, paving the way for more advanced applications like holographic communications and augmented reality.