Game progression and Armory aren't working anymore, so the default game modes will only let you choose from basic default loadouts.
I've made the PvE (Combat Patrol) game mode as a pack, which let you add your script to it (e.g. Arsenal box to pick and creates loadouts on the fly in-game).
You can find the "Custom Combat Patrol" game mode here: Customizable Combat Patrol game mode
And a tutorial on how to add a Virtual Arsenal box following there: Add Virtual Arsenal (loadouts)
Hello guys,
Since a few months or so, it seems that people that don't already have Argo in their Library are now unbale to add it using the Steam's Support trick.
I unfortunately believe that Argo have been completely removed from Steam, making it "impossible" to add it to your Steam's library anymore.
Even the FREE games come with a unique CD Key when they are added to our library, binding our Steam Account to Argo... so without that CD Key, it mostly impossible to start the game,
even if you have all the files required to run the game.
That was probably a demand from Bohemia directly to remove the "archive". I don't see why some older games would still be available through the Support of Steam, but not Argo anymore.
Sorry for the bad news and I wish best for the last Argo's Survivors.
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The following animations, clips and movies all pertain to Argo and many of them are available on the Argo YouTube Channel (argoproject).
When available, direct links to high resolution video and more information about the video are provided.
Check out other movies using the menu on the right.
Also available in SpanishEarth Reporter Dr. Susan Wijffels, discuss the Argo Program
Synopsis and direct access to 30 minute episodeScripps Institution of Oceanography video on the Argo array and why it is important
A few months ago, we announced that we wanted to make Zero Trust security accessible to everyone, regardless of size, scale, or resources. Argo Tunnel, our secure method of connecting resources directly to Cloudflare, is the next piece of the puzzle.
Argo Tunnel creates a secure, outbound-only connection between your services and Cloudflare by deploying a lightweight connector in your environment. With this model, your team does not need to go through the hassle of poking holes in your firewall or validating that traffic originated from Cloudflare IPs.
In 2018, Cloudflare introduced Argo Tunnel, a private, secure connection between your origin and Cloudflare. Traditionally, from the moment an Internet property is deployed, developers spend an exhaustive amount of time and energy locking it down through access control lists, rotating ip addresses, or clunky solutions like GRE tunnels.
With Tunnel, users can create a private link from their origin server directly to Cloudflare without a publicly routable IP address. Instead, this private connection is established by running a lightweight daemon, cloudflared, on your origin, which creates a secure, outbound-only connection. This means that only traffic that routes through Cloudflare can reach your origin.
Originally, we built Tunnel to solve a straightforward problem. It was unnecessarily difficult to connect a server to the Internet. Instead of implementing other legacy models, we wanted to create a frictionless way to establish a private connection directly to Cloudflare. This was of particular interest to us as we also wanted to solve what was a key pain point for many of our own customers, too.
With that, suppose you are working on a local development environment for a new web application and want to securely share updates with a friend or collaborator. You would first install cloudflared to connect your origin to Cloudflare. Then, you would create your Tunnel and generate a hostname in the Cloudflare dashboard using your Tunnel UUID so that users can reach your resource and run your Tunnel. You can also add a Zero Trust policy with Cloudflare Access to your DNS record so that only friends and collaborators can view your resource.
We also wanted to focus efforts on persistence. Previously, if cloudflared needed to restart for any reason, we treated each restart as a new Tunnel. This meant creating a new DNS record as well as establishing a connection to Cloudflare.
In our latest feature release, we introduced the concept of Named Tunnels. With Named Tunnels, users can assign a Tunnel with a permanent name which then creates a direct relationship with your Tunnel UUID. This model allows these two identifiers to become persistent records which can enable autonomous reconnection. Now in the event your Named Tunnel does need to restart, your cloudflared instance can reference this UUID address to reconnect rather than starting each restart from the ground up.
Hello,
I have an existing system with a starter battery (12V AGM 100Ah), a bow thuster battery (12V AGM 280Ah) and a new service battery bank (12V LiFePO4 560Ah with REC Smart BMS). Additionally this will be charged via Victron Multiplus using shore power or a VICTRON MPPT solar charge controller (only for the service battery bank) or it all can be charged by an alternator, which is connected via a diode (VICTRON ARGOFET 200-3) to all three batteries.
Is this really necessary or cannot the ARGOFET still make sure the LiFePO4 batteries are charged by the alternator, especially since the SmartBMS will shut down the loading process, as soon as there is bad voltage/current coming to the system?
Assuming there's a direct alternator to starter battery connection, your lithium is charged by the alternator when the dc:dc chargers are connected direct to the starter battery. You configure them to start charging when the starter battery is being charged. There's effectively no load on the starter battery at any time.
I have a different setup but have charged our 300Ah 4 cell LiFePO4 battery for the last 7 years of full-time travel via our Canter 100A rated alternator at 70-85A until full. No smoke yet. Yes I have seen those YouTube videos where they show smoke with low revving alternators. Mine spins >3000 rpm even at fast idle.
Argoverse 2 datasets share a common HD map format that is richer than the HD maps in Argoverse 1. Argoverse 2 datasets also share a common API, which allows users to easily access and visualize the data and maps.
We created Argoverse to support advancements in 3D tracking, motion forecasting, and other perception tasks for self-driving vehicles. We offer it free of charge under a creative commons share-alike license. Please visit our Terms of Use for details on licenses and all applicable terms and conditions.
The Argoverse 2 datasets are described in our publications, Argoverse 2 and Trust, but Verify, in the NeurIPS 2021 dataset track. When referencing these datasets or any of the materials we provide, please use the following citations
The data in Argoverse 2 comes from six U.S. cities with complex, unique driving environments: Miami, Austin, Washington DC, Pittsburgh, Palo Alto, and Detroit. We include recorded logs of sensor data, or "scenarios," across different seasons, weather conditions, and times of day.
We collected all of our data using a fleet of identical Ford Fusion Hybrids, fully integrated with Argo AI self-driving technology. We include data from two lidar sensors, seven ring cameras, and two front-facing stereo cameras. All sensors are roof-mounted:
Our semantic vector map contains 3D lane-level details, such as lane boundaries, lane marking types, traffic direction, crosswalks, driveable area polygons, and intersection annotations. These map attributes are powerful priors for perception and forecasting. For example, vehicle heading tends to follow lane direction, drivers are more likely to make lane changes where there are dashed lane boundaries, and pedestrians are more likely to cross the street at designated crosswalks.
The Argoverse 2 Lidar Dataset is a collection of 20,000 scenarios with lidar sensor data, HD maps, and ego-vehicle pose. It does not include imagery or 3D annotations. The dataset is designed to support research into self-supervised learning in the lidar domain, as well as point cloud forecasting.
All Argoverse datasets contain lidar data from two out-of-phase 32 beam sensors rotating at 10 Hz. While this can be aggregated into 64 beam frames at 10 Hz, it is also reasonable to think of this as 32 beam frames at 20 Hz. Furthermore, all Argoverse datasets contain raw lidar returns with per-point timestamps, so the data does not need to be interpreted in quantized frames.
The Lidar Dataset contains 6 million lidar frames, one of the largest open-source collections in the autonomous driving industry to date. Those frames are sampled at high temporal resolution to support learning about scene dynamics.
The Argoverse 2 Motion Forecasting Dataset is a curated collection of 250,000 scenarios for training and validation. Each scenario is 11 seconds long and contains the 2D, birds-eye-view centroid and heading of each tracked object sampled at 10 Hz.
Spanning 2,000+ km over six geographically diverse cities, Argoverse 2 covers a large geographic area. Argoverse 2 also contains a large object taxonomy with 10 non-overlapping classes that encompass a broad range of actors, both static and dynamic. In comparison to the Argoverse 1 Motion Forecasting Dataset, the scenarios in this dataset are approximately twice as long and more diverse.
The Argoverse 2 Map Change Dataset is a collection of 1,000 scenarios with ring camera imagery, lidar, and HD maps. Two hundred of the scenarios include changes in the real-world environment that are not yet reflected in the HD map, such as new crosswalks or repainted lanes. By sharing a map dataset that labels the instances in which there are discrepancies with sensor data, we encourage the development of novel methods for detecting out-of-date map regions.
The Map Change Dataset does not include 3D object annotations (which is a point of differentiation from the Argoverse 2 Sensor Dataset). Instead, it includes temporal annotations that indicate whether there is a map change within 30 meters of the autonomous vehicle at a particular timestamp. Additionally, the scenarios tend to be longer than the scenarios in the Sensor Dataset. To avoid making the dataset excessively large, the bitrate of the imagery is reduced. 152ee80cbc
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