A pull request workflow often needs two things that should be kept separate: untrusted code to test and trusted repository credentials to comment, label, publish, or update a status. GitHub’s June 2026 actions/checkout changes made one dangerous combination harder to enable by accident, but they do not turn every pull request workflow into a safe deployment workflow.

This guide builds a review workflow that tests pull-request code with the least useful token possible, then shows the narrow case where pull_request_target is appropriate. The successful result is easy to inspect: a pull request runs the contributor’s code without repository write permissions, while a trusted follow-up job can add a label without checking out that code.

Quick answer: to convert Keplerian orbital elements to heliocentric ecliptic coordinates, compute mean anomaly at the target time, solve Kepler’s equation for eccentric anomaly, derive true anomaly and radius, form the perifocal vector, then rotate it by the argument of periapsis, inclination, and longitude of ascending node. The orbital mechanics primer explains the Earth gravitational parameter used for Earth-centered versions of these calculations.

This cheat sheet is based on “Heliospheric Coordinate Systems” by Franz & Harper (2002, corrected 2017). It’s 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 — and visualize it in Grafana.

In this post, we’ll walk through how to:

• Create a lightweight GPS fix monitoring script
• Use Telegraf’s inputs.exec plugin to ingest the data
• Store it in InfluxDB
• Visualize GPS lock status in Grafana

🚀 Why Monitor GPS Fix Status?

When 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 client‑server 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.

1. Overview and Architecture

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.

What Is Dependency Injection?

At its core, dependency injection involves passing (or “injecting”) an object’s dependencies rather than instantiating them internally. The main benefits include:

This guide explains how to turn a fresh Raspberry Pi OS installation into a Raspberry Pi stratum 1 NTP server using GPS, PPS, and Chrony. It also covers PTP hardware timestamping and Telegraf monitoring; pair it with GPS fix monitoring in Grafana to make receiver lock status visible before it becomes a timing incident.

1. Overview

In this project you’ll build a stratum‑1 NTP server using:

• Raspberry Pi 5 as the host.
• A GPS module (via the serial UART port) for an absolute time reference.
• A PPS (Pulse Per Second) signal from the same GPS for sub-microsecond timing accuracy.
• The PTP hardware timestamping on the Ethernet interface for improved precision on PTP packets.
• Chrony for NTP time synchronization.
• gpsd to interface with your GPS receiver.
• Telegraf for monitoring various metrics (including Chrony statistics) sent to an InfluxDB backend.

2. Prerequisites and Required Hardware

Before you begin, ensure you have the following:

Quick answer: Earth’s standard gravitational parameter is μ = 3.986004418 × 10^14 m³/s² (equivalently 398600.4418 km³/s²). In an Earth-centered two-body calculation, μ = GM is the constant used in the vis-viva equation, mean motion, escape velocity, and orbital-elements calculations. JPL publishes planetary physical parameters and the underlying constants in its Planetary Physical Parameters reference.

For a Sun-centered orbital-elements-to-position workflow and heliocentric ecliptic coordinates, see the companion heliospheric coordinate systems guide.

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.

The Crystal Ball function is a probability-density function used in particle physics when a measured peak is approximately Gaussian on one side but has a power-law tail on the other. It is commonly used to model detector response and energy-loss effects such as bremsstrahlung. It is not an array-prediction or “crystal ball problem” algorithm.

At a practical level, the function has four shape and location parameters: the peak location μ, width σ, tail-transition location α, and tail exponent n. A fit uses the Gaussian core to represent the central detector resolution and the tail to represent asymmetric non-Gaussian loss or reconstruction effects. ROOT provides a maintained Crystal Ball PDF example and implementation reference.

Version: 1.2

Store all DotFiles in a bare git repository

Setup

1. git init –bare $HOME/.cfg
2. alias config=’usr/bin/git –git-dir=$HOME.cfg/ –work-tree=$HOME'
3. echo “alias config=’usr/local/bin/git –git-dir=$HOME.cfg/ –work-tree=$HOME’” >> $HOME/.zsh/aliases
4. config config –local status.showUntrackedFiles no

Adding files

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

Add the github repository

1. config remote add origin <remote-url>
2. config push -u origin main

Restore files from github

1. echo “.cfg” >> .gitignore
2. git clone <remote-git-repo-url> $HOME/.cfg
3. alias config=’/usr/local/bin/git –git-dir=<path to .cfg’s Git directory> –work-tree=$HOME'
4. config config –local status.showUntrackedFiles no
5. config checkout

Date: <2024-04-03 Wed>

Transforming Enterprise Networking:

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.

Understanding Juniper Mist and Its AI-Driven Approach

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.