Arma 2 Epoch Download
Dibe Naro
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Created: 2020-09-01 15:29:27
Published: 2020-09-01 15:30:52
## [1.3.3.1] - 2020-09-01### Added- Clear message that the server is not fully loaded when Players login to early- Optional Black Market Traders- Build in RyanZ Zombiespawner (when RyanZ is enabled on the Server)- Trader Filter for useable items on currently equipped weapons### Fixed- On farming wracks / cinder, sometimes the more far away object was looted instead of the nearest- Purchased Boats from Traders sometimes spawned damaged- In some cases, purchased Vehicles spawned on top of already existing vehicles -> crashed### Changed### Server Owners- Added missing predefined variable "Epoch_BaseSpawnSkips" (no issues, just a rpt error)- Krypto Limit from 250000 to 1000000 to prevent unwanted bans- Some loot positions were not in correct syntax- Black Market Traders can be configured within CfgBlackMarket.hpp (within the mission file)- RyanZ Zombiespawner can be configured within epoch_server_RyanZ_Spawner.pbo (server side) - To disable this spawner, you can remove this pbo from your Server
This will be executed before the insert in the table dayz_epoch.character_data (so don't remove the new one). This will remove every line with the PlayerUID of the inserted line. If you want to add some security, you could add the and Alive= 0 in the condition.
GMSAI: provides location-based spawnning of infantry, vehicle, UGV, UAV and air patrols. Spawn points may be either Arma3 map locations or user-defined locations. Support is included for player rewards (crypto, tabs, respect, karma). Vehicles can optionally be claimed. We have been running this on two severs for about 6 months and I feel it is bug free at present. -DbD-/GMSAI
Generally I loved how epoch was. Its loot distribution felt alright as well. Maybe overpowered compared to how Dayz SA is meant, but compared to those Overpoch servers that later arrived to Arma 2, it was perfect.
Arma 3: Epoch brings ground breaking graphics to the Arma series. We are investing lots time, research and development into our arma3 hosting features. The future is Arma3 with epoch being released in a few months. We are striving to be at the front of the pack when it comes to reliable Armed Assault 3 hosting.
In this paper, we first present an algorithm that minimizes energy consumption by not only reducing the number of sensor sampling operations but also by reducing message transmissions. The basic idea is to use time-series forecasting to try and predict future sensor readings. When the trend of a particular sensor reading is fairly constant and thus predictable, the sensor sampling frequency and message transmission rate are reduced. Conversely, when the trend changes, both the sampling rate and message transmission rates are increased. Additionally the paper describes how a randomized wake-up scheme can be used to improve temporal coverage so as to minimize the time that elapses between the occurrence of an event and its detection. The randomized algorithm eliminates the need for additional communication between nodes thus improving overall energy consumption. We show that using our randomized algorithm, in certain cases, the delay between the occurrence of an event and its detection can be reduced to just one epoch (Note: An epoch refers to the time period between two consecutive samples.)
As the AS algorithm disregards this spatial correlation between adjacent nodes, the temporal coverage of a particular node is only attributed to the node's own sampling frequency. In other words, if a node skips x samples, it is assumed that any event that occurs during these x epochs will not be detected. Figure 7(a) shows a histogram of the frequency of the maximum sequence of consecutive uncovered epochs that are attained by all the five nodes in the network based on the data set from Nelly Bay collected over 17 days.
(a) Histogram showing spread of maximum sequence of consecutive uncovered epochs for AS algorithm; (b) Histogram showing maximum sequence of consecutive uncovered epochs when spatial correlations of adjacent nodes is considered.
However, if the sampling schedules of all adjacent nodes are combined, then the probability of all sensor nodes within a single hop missing an event due to a large SS value is greatly diminished. Figure 5 illustrates this concept. We can see clearly from Figure 7(b) that not only are long sequences of uncovered epochs completely eliminated, but the area of the graph in Figure 7(b) is significantly lower than that of Figure 7(a). This implies that combining sampling schedules greatly reduces the chances of missing an event.
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