XPost: aus.politics, nl.politiek, aus.legal   
   XPost: alt.france   
   From: dolfboek@hotmail.com   
      
   On 2/15/26 13:11, dolf wrote:   
   > Drone Swarms and their Potential within the Australian Defence Force   
   >   
   > The legality of drone swarms in warfare is a complex issue that involves   
   > considerations of international law, particularly the laws of armed   
   > conflict. While the use of autonomous swarms poses challenges for   
   > military planners and defense paradigms, they also offer significant   
   > advantages in terms of efficiency and effectiveness. The use of   
   > autonomous swarms must comply with international law and the laws of   
   > armed conflict, and states must ensure that new methods of warfare are   
   > capable of adhering to these legal obligations.   
   >   
   > The legal review of autonomous swarms involves a risk-based approach,   
   > considering factors such as swarm size, detection, classification,   
   > recognition, and identification confidence levels. These reviews are   
   > essential to ensure that the use of swarms does not violate   
   > international law and does not impose undue burdens on defense   
   > capabilities.   
   >   
   > As the technology of drone swarms continues to evolve, the legal   
   > implications and compliance with international law will remain a   
   > critical area of focus for military and legal professionals.   
   >   
      
   TIME & BEING --> AI / HUMAN SAPIENT SYMBIOSIS (#396 - ANCHOR | #1092 -   
   TEMPORAL COEFFICIENT)   
      
   This is only a sketch but it seems the interoperability which requires   
   neural linguistic pragma could be defined using those coefficients that   
   then enables access to the entire nomenclature -- if compliant with   
   #2184 (2 x #1092) principles of nature is then compliant with law.   
      
   #1368 - FEME TOTAL: #396 as [#8, #3, #800, #50, #10, #7, #70, #50, #300,   
   #70] = agÅ   
   nízomai (G75): {UMBRA: #992 % #41 = #8} 1) to enter a contest:   
   contend in the gymnastic games; 2) to contend with adversaries, fight;   
   3) metaph. to contend, struggle, with difficulties and dangers; 4) to   
   endeavour with strenuous zeal, strive: to obtain something;   
      
   |--- 		---|   
   	#396   
   |---	|	---|   
   	|   
   	|   
   #1092 - LOOK   
      
   1   
   2   
   3   
   4   
   6   
   9   
   11   
   12   
   18   
   22   
   33   
   36   
   44   
   66 - 267 (#14 - PENETRATION)   
      
   #1806 - FEME TOTAL: #267 as [#100, #300, #400, #6, #400, #600] = qesheth   
   (H7198): {UMBRA: #800 % #41 = #21} 1) bow; 1a) bow (for hunting,   
   battle); 1b) bowmen, archers; 1c) bow (fig. of might); 1d) rainbow;   
      
   #1482 - FEME TOTAL: #267 as [#400, #6, #300, #70, #6, #700] = yâshaÊ»   
   (H3467): {UMBRA: #380 % #41 = #11} 1) to save, be saved, be delivered;   
   1a) (Niphal); 1a1) to be liberated, be saved, be delivered; 1a2) to be   
   saved (in battle), be victorious; 1b) (Hiphil); 1b1) to save, deliver;   
   1b2) to save from moral troubles; 1b3) to give victory to;   
      
   99   
   132   
   198   
   396 - 1092 (#18 - WAITING)   
      
   #1092 - #267 = #825   
      
   #825 as [#9, #5, #100, #1, #80, #5, #400, #5, #200, #9, #1, #10] =   
   therapeúŠ  
    (G2323): {UMBRA: #1400 % #41 = #6} 1) to serve, do service; 2)   
   to heal, cure, restore to health;		   
      
   #210 - 15 FEBRUARY 2026 as [#9, #5, #100, #1, #80, #5, #9, #1] /   
   #211 as [#9, #5, #100, #1, #80, #5, #10, #1] = therapeía (G2322):   
   {UMBRA: #211 % #41 = #6} 1) service rendered by one to another; 2) spec.   
   medical service: curing, healing; 3) household service; 3a) body of   
   attendants, servants, domestics;		   
      
   #36 - ð�Œ©å½Š = #210 / #372 / #487   
   COGITO: #133 = [#14, #44, #15, #30, #30] as #36 - STRENGTH (CH'IANG)   
   RANGE: noon 28 MAY to 01 JUNE   
      
   #37 - ð�Œªç�Ÿ = #211 / #373 / #488   
   COGITO: #248 = [#76, #46, #46, #66, #14] as #37 - PURITY (TS'UI)   
   RANGE: 02 to noon 06 JUNE   
      
   > There are multiple methodologies for controlling a swarm of drones,   
   > however, a promising approach is distributed control characterised by 3   
   > rules of Separation, Coherence and Avoidance (Reynolds, 1987). This sees   
   > the drones “linked togetherâ€� like a spider web and are free to move in a   
   > region of air. This gives increased flexibility for dynamic environments   
   > and critically allows for seamless joining or removal from the swarm,   
   > enabling the swarm to continue even through combat losses.   
   >   
   >   
   > Swarm dynamic target tracking and prediction   
   >   
   > For a swarm to autonomously intercept a manoeuvring target, the swarm   
   > must be able to track and predict the future motion of its target.   
   > Traditional prediction methodologies usually see the path be matched to   
   > mathematical curves or go through reinforcement learning methods to   
   > learn the path slowly. However, modern techniques struggle with two key   
   > areas: finding a balance between accuracy vs computing speed; and   
   > secondly, a requirement to be extensively tuned and trained in   
   > development before being deployed. To achieve this requirement, the   
   > drones require the recognition and memory of every possible target it   
   > could face to be accurate, which is a tedious and costly process.   
   >   
   > These challenges inspired the development of a new AI model referred to   
   > as Biologically Inspired Fuzzy Brain Emotional Learning (BFBEL)   
   > (Muthusamy et al, 2024) This AI model works by replicating the emotional   
   > regulation of mammals by artificially representing the brain functions   
   > of the Amygdala and Orbitofrontal Cortex to learn and drive decision   
   > making. This process enables the AI to build a behavioural model of the   
   > system it is analysing in real time without external training to adapt a   
   > system to its changes. This model proved to have extreme adaptability   
   > and control, however, fundamentally the model was reactive to decision-   
   > making rather than predictive.   
   >   
   > However, new research redesigned and extended the model, BFBEL-P, to   
   > enable future prediction for more efficient control of drone swarms   
   > (Page, 2024). The BFBEL-P enabled the swarm to build behavioural models   
   > on a target's complex and erratic motion (replicating evasion) in real-   
   > time during the flight and achieved target intercept while still   
   > avoiding obstacles, critically without any human input. Furthermore, the   
   > swarm then continued to track the target from a designated loitering   
   > altitude simulating reconnaissance capabilities (Page, 2024).   
   >   
   >  within-australian-defence-force>   
   >   
   > We firstly eliminate the POEM's DUTCH translated inserted comma so as to   
   > retain the ENNEAD / RATIONAL EGO imperative ...   
   >   
   > GRUMBLE (#363, #275)@[29, 45, 65, 48, 23, 27, 64, 34, 37, 17, 77, 14,   
   > 27, 34, 4, 2, 37, 54]   
   >   
   > #275 = [45, 48, 27, 34, 17, 14, 34, 2, 54] <-- DUPLICATE #34 so recalibrate   
   >   
   > #275 = [45, 48, 27, 34, 17, 14, 33, 3, 54]   
   >   
   > 45    48    27   
   > 03    54    34   
   > 33    14    17   
   >   
   > #1092 = [#364 - ENQUIRY, #312 - CONTRADICTION, #416 (#405 + #9 -   
   > BRANCHING OUT) - ORTHOLOGY: #143 - ONTIC GROUNDING + #273 - SYNCRETIC   
   > PROGRESSION (#208 - EVALUATE / EXPRESS + #65 - INNER (å…§) | SOLDIER)]   
      
   [continued in next message]   
      
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