#n= 500 k= 10 p= 0.5
#sigma: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
#beta =  0.4 

#mean of distances:
 Ld= [np.float64(360.6), np.float64(47.7), np.float64(15.8), np.float64(5.0), np.float64(3.6), np.float64(2.7), np.float64(2.2), np.float64(2.2), np.float64(2.8), np.float64(2.2)] 

#variance of distances:
 Lv= [np.float64(51.561031797278844), np.float64(17.66805026028622), np.float64(6.249799996799898), np.float64(1.378404875209022), np.float64(1.019803902718557), np.float64(0.7483314773547882), np.float64(0.9797958971132712), np.float64(1.469693845669907), np.float64(1.16619037896906), np.float64(1.16619037896906)] 

#beta =  0.8 

#mean of distances:
 Ld= [np.float64(365.6), np.float64(39.7), np.float64(14.1), np.float64(3.2), np.float64(0.6), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(90.79063828391118), np.float64(17.895809565370325), np.float64(8.86227961644181), np.float64(4.106093033529563), np.float64(0.4898979485566356), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#beta =  1.2000000000000002 

#mean of distances:
 Ld= [np.float64(415.7), np.float64(26.2), np.float64(18.5), np.float64(1.9), np.float64(0.1), np.float64(0.0), np.float64(0.3), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(134.6642491532181), np.float64(14.288456879593403), np.float64(6.024948132556827), np.float64(1.9078784028338915), np.float64(0.20000000000000004), np.float64(0.0), np.float64(0.6), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

