diff --git a/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/lofar2_unb2c_sdp_station_jesd_pins.tcl b/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/lofar2_unb2c_sdp_station_jesd_pins.tcl
index 4c1458f377e0f097733b6f7c81d1fac5c730a511..68493f020ab05975213c3816ebd9d059da72bc22 100644
--- a/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/lofar2_unb2c_sdp_station_jesd_pins.tcl
+++ b/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/lofar2_unb2c_sdp_station_jesd_pins.tcl
@@ -58,6 +58,34 @@ set_location_assignment PIN_BB5 -to BCK_RX[2]
 set_location_assignment PIN_AY9 -to BCK_RX[1]
 set_location_assignment PIN_BB9 -to BCK_RX[0]
 
+# Set link type to Long Reach (LR) for Backplane communication.
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[11]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[10]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[9]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[8]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[7]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[6]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[5]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[4]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[3]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[2]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[1]
+set_instance_assignment -name XCVR_A10_RX_LINK LR -to BCK_RX[0]
+
+# Set Equalizer to high gain mode.
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[11]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[10]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[9]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[8]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[7]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[6]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[5]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[4]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[3]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[2]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[1]
+set_instance_assignment -name XCVR_A10_RX_ONE_STAGE_ENABLE NON_S1_MODE -to BCK_RX[0]
+
 set_instance_assignment -name IO_STANDARD "HSSI DIFFERENTIAL I/O" -to                   BCK_TX[0]
 set_instance_assignment -name XCVR_A10_TX_VOD_OUTPUT_SWING_CTRL 30 -to                  BCK_TX[0]
 set_instance_assignment -name XCVR_VCCR_VCCT_VOLTAGE 1_0V -to                           BCK_TX[0]
diff --git a/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/qsys_lofar2_unb2c_sdp_station.qsys b/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/qsys_lofar2_unb2c_sdp_station.qsys
index 1e2eb5123971ca96dc706fc9664afb6afaa54ed1..0d55fa4b896fc27be44f91a74f70ee76c2e5b36f 100644
--- a/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/qsys_lofar2_unb2c_sdp_station.qsys
+++ b/applications/lofar2/designs/lofar2_unb2c_sdp_station/quartus/qsys_lofar2_unb2c_sdp_station.qsys
@@ -33260,7 +33260,7 @@
     </fileSets>
 </generationInfoDefinition>]]></parameter>
   <parameter name="hlsFile" value="" />
-  <parameter name="logicalView">ip/qsys_lofar2_unb2c_sdp_station/qsys_lofar2_unb2c_sdp_station_reg_bdo_destinations.ip</parameter>
+  <parameter name="logicalView">../lofar2_unb2c_sdp_station/ip/qsys_lofar2_unb2c_sdp_station/qsys_lofar2_unb2c_sdp_station_reg_bdo_destinations.ip</parameter>
   <parameter name="moduleAssignmentDefinition"><![CDATA[<assignmentDefinition>
     <assignmentValueMap/>
 </assignmentDefinition>]]></parameter>
diff --git a/applications/lofar2/images/images.txt b/applications/lofar2/images/images.txt
index 8b2961a16b1d75adb95dbead3dbe7703ecd7f4c0..4b08358b00403aa6a838af225d6fd0f5d0b38e73 100644
--- a/applications/lofar2/images/images.txt
+++ b/applications/lofar2/images/images.txt
@@ -50,6 +50,7 @@ lofar2_unb2c_sdp_station_full-dbc6375ef         | 2024-02-22 | EK   | See [1]. O
 
 lofar2_unb2c_sdp_station_full-d601da896         | 2024-03-02 | EK   | With 1024 size input buffer
 lofar2_unb2b_sdp_station_full_wg-d601da896      | 2024-03-02 | EK   | With 1024 size input buffer
+lofar2_unb2c_sdp_station_full-3b0adbdd1         | 2024-07-31 | RW   | Added JESD RX assignments LR + non_s1_mode
 
 References:
 [1] https://support.astron.nl/confluence/display/L2M/L3+SDP+Testing+Notebook%3A+SDP+FW+general
diff --git a/applications/lofar2/images/lofar2_unb2c_sdp_station_full-3b0adbdd1.tar.gz b/applications/lofar2/images/lofar2_unb2c_sdp_station_full-3b0adbdd1.tar.gz
new file mode 100644
index 0000000000000000000000000000000000000000..a050afef6adc02c34db3818cb9d9ed592c9c3559
Binary files /dev/null and b/applications/lofar2/images/lofar2_unb2c_sdp_station_full-3b0adbdd1.tar.gz differ
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_beamformer.vhd b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_beamformer.vhd
index a88a102b282e7c909c3500c0d2c5f05d19a40d89..b6b45f5c044202da94dfa2f7551808da33810fc0 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_beamformer.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_beamformer.vhd
@@ -369,9 +369,9 @@ begin
   ---------------------------------------------------------------
   u_sdp_bst_udp_offload: entity work.sdp_statistics_offload
   generic map (
-    g_statistics_type => "BST",
-    g_offload_time    => sel_a_b(g_sim, g_sim_sdp.offload_time, c_sdp_offload_time),
-    g_beamset_id      => g_beamset_id
+    g_statistics_type   => "BST",
+    g_offload_node_time => sel_a_b(g_sim, g_sim_sdp.offload_node_time, c_sdp_offload_node_time),
+    g_beamset_id        => g_beamset_id
   )
   port map (
     mm_clk    => mm_clk,
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_correlator.vhd b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_correlator.vhd
index fa97a9f7d77b10986605677f326a2135b1a0b8e2..b9f657544669a015e92d9e2dbc83f2f27a6aeaf4 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_correlator.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_correlator.vhd
@@ -107,6 +107,8 @@ architecture str of node_sdp_correlator is
   signal quant_sosi_arr                : t_dp_sosi_arr(c_sdp_P_pfb - 1 downto 0) := (others => c_dp_sosi_rst);
   signal xsel_sosi                     : t_dp_sosi := c_dp_sosi_rst;
   signal new_interval                  : std_logic;
+  signal ctrl_interval_size            : natural;
+  signal offload_interval_size         : natural;
 
   signal crosslets_sosi                : t_dp_sosi  := c_dp_sosi_rst;
   signal crosslets_copi                : t_mem_copi := c_mem_copi_rst;
@@ -150,7 +152,7 @@ begin
   u_crosslets_subband_select : entity work.sdp_crosslets_subband_select
   generic map (
     g_N_crosslets            => c_sdp_N_crosslets_max,
-    g_ctrl_interval_size_min => sel_a_b(g_sim, g_sim_sdp.xst_nof_clk_per_sync_min, c_sdp_xst_nof_clk_per_sync_min)
+    g_ctrl_interval_size_min => sel_a_b(g_sim, g_sim_sdp.N_clk_per_sync_min, c_sdp_N_clk_per_sync_min)
   )
   port map(
     dp_clk         => dp_clk,
@@ -159,7 +161,8 @@ begin
     in_sosi_arr    => quant_sosi_arr,
     out_sosi       => xsel_sosi,
 
-    new_interval   => new_interval,
+    new_interval       => new_interval,
+    ctrl_interval_size => ctrl_interval_size,
 
     mm_rst         => mm_rst,
     mm_clk         => mm_clk,
@@ -288,10 +291,15 @@ begin
   ---------------------------------------------------------------
   xst_udp_sosi <= mon_xst_udp_sosi_arr(0);
 
+  offload_interval_size <= sel_a_b(g_sim, g_sim_sdp.N_clk_per_sync, ctrl_interval_size);
+
   u_sdp_xst_udp_offload: entity work.sdp_statistics_offload
   generic map (
     g_statistics_type          => "XST",
-    g_offload_time             => sel_a_b(g_sim, g_sim_sdp.offload_time, c_sdp_offload_time),
+    g_offload_node_time        => sel_a_b(g_sim, g_sim_sdp.offload_node_time, c_sdp_offload_node_time),
+    g_offload_packet_time      => sel_a_b(g_sim, g_sim_sdp.offload_packet_time, c_sdp_offload_packet_time),
+    g_offload_scale_w          => sel_a_b(g_sim, g_sim_sdp.offload_scale_w, c_sdp_offload_scale_w),
+    g_ctrl_interval_size_min   => sel_a_b(g_sim, g_sim_sdp.N_clk_per_sync_min, c_sdp_N_clk_per_sync_min),
     g_P_sq                     => g_P_sq,
     g_crosslets_direction      => 1,  -- = lane direction
     g_bsn_monitor_sync_timeout => c_sdp_N_clk_sync_timeout_xsub
@@ -317,6 +325,7 @@ begin
 
     in_sosi      => crosslets_sosi,
     new_interval => new_interval,
+    ctrl_interval_size => offload_interval_size,
 
     out_sosi  => mon_xst_udp_sosi_arr(0),
     out_siso  => xst_udp_siso,
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_filterbank.vhd b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_filterbank.vhd
index 691a5a916f2e60945d2288c84c4eaaf418767720..b3711f0328354500b83806f233567c850f82f5e9 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_filterbank.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_filterbank.vhd
@@ -404,8 +404,8 @@ begin
 
   u_sdp_sst_udp_offload: entity work.sdp_statistics_offload
   generic map (
-    g_statistics_type => "SST",
-    g_offload_time    => sel_a_b(g_sim, g_sim_sdp.offload_time, c_sdp_offload_time)
+    g_statistics_type   => "SST",
+    g_offload_node_time => sel_a_b(g_sim, g_sim_sdp.offload_node_time, c_sdp_offload_node_time)
   )
   port map (
     mm_clk    => mm_clk,
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_oversampled_filterbank.vhd b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_oversampled_filterbank.vhd
index 3e9de6c4baba26d5f0883cd46a1de604b0f50b4f..be5959997943e1b997138934c514bda52e710d98 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_oversampled_filterbank.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/node_sdp_oversampled_filterbank.vhd
@@ -740,8 +740,8 @@ begin
 
   u_sdp_sst_udp_offload: entity work.sdp_statistics_offload
   generic map (
-    g_statistics_type => "SST_OS",
-    g_offload_time    => sel_a_b(g_sim, g_sim_sdp.offload_time, c_sdp_offload_time)
+    g_statistics_type   => "SST_OS",
+    g_offload_node_time => sel_a_b(g_sim, g_sim_sdp.offload_node_time, c_sdp_offload_node_time)
   )
   port map (
     mm_clk    => mm_clk,
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/sdp_crosslets_subband_select.vhd b/applications/lofar2/libraries/sdp/src/vhdl/sdp_crosslets_subband_select.vhd
index 9296c034921d37dd48dfd5199116263e881872a5..bd28652ee0aa5a5c118cf55cdf1ccf6508742471 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/sdp_crosslets_subband_select.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/sdp_crosslets_subband_select.vhd
@@ -55,7 +55,7 @@ use work.sdp_pkg.all;
 entity sdp_crosslets_subband_select is
   generic (
     g_N_crosslets : natural := c_sdp_N_crosslets_max;
-    g_ctrl_interval_size_min : natural := c_sdp_xst_nof_clk_per_sync_min
+    g_ctrl_interval_size_min : natural := c_sdp_N_clk_per_sync_min
   );
   port (
     dp_clk         : in  std_logic;
@@ -64,7 +64,8 @@ entity sdp_crosslets_subband_select is
     in_sosi_arr    : in  t_dp_sosi_arr(c_sdp_P_pfb - 1 downto 0);
     out_sosi       : out t_dp_sosi;
 
-    new_interval   : out std_logic;
+    new_interval       : out std_logic;
+    ctrl_interval_size : out natural;
 
     mm_rst         : in  std_logic;
     mm_clk         : in  std_logic;
@@ -143,6 +144,7 @@ begin
 
     reg_mosi => reg_bsn_sync_scheduler_xsub_mosi,
     reg_miso => reg_bsn_sync_scheduler_xsub_miso,
+    reg_ctrl_interval_size => ctrl_interval_size,
 
     in_sosi_arr  => in_sosi_arr,
     out_sosi_arr => dp_bsn_sync_scheduler_src_out_arr,
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd b/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
index c789c12710c2ac13f6039ff101a29e7953ff3179..fb42c311a0c57a2ae01469afbc05cb89dee15aeb 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/sdp_pkg.vhd
@@ -120,12 +120,14 @@ package sdp_pkg is
   constant c_sdp_wg_ampl_lsb             : real := c_diag_wg_ampl_unit / real(c_sdp_FS_adc);  -- WG amplitude in number of LSbit resolution steps
   constant c_sdp_wg_subband_freq_unit    : real := c_diag_wg_freq_unit / real(c_sdp_N_fft);  -- subband freq = Fs/1024 = 200 MSps/1024 = 195312.5 Hz sinus
   constant c_sdp_N_clk_per_second        : natural := c_sdp_f_adc_MHz * 10**6;  -- Default 200M clock cycles per second
-  constant c_sdp_N_clk_per_sync          : natural := c_sdp_f_adc_MHz * 10**6;  -- Default 200M clock cycles per sync interval of 1 second
-  constant c_sdp_N_clk_sync_timeout      : natural := c_sdp_f_adc_MHz * 10**6 + c_sdp_f_adc_MHz * 10**5;  -- 10% margin.
+  constant c_sdp_N_clk_per_sync          : natural := c_sdp_N_clk_per_second;  -- Default 200M clock cycles per sync interval of 1 second
+  constant c_sdp_N_clk_per_sync_max      : natural := c_sdp_N_clk_per_second * 10;  -- 10 seconds
+  constant c_sdp_N_clk_per_sync_min      : natural := c_sdp_N_clk_per_second / 10;  -- 0.1 second
+  constant c_sdp_N_clk_sync_timeout      : natural := c_sdp_N_clk_per_second + c_sdp_N_clk_per_second / 10;  -- 10% margin.
   constant c_sdp_N_clk_sync_timeout_xsub : natural := 2147483647;  -- = 2**31 - 1 = largest value for NATURAL for 10.7 seconds. Do not use 2*31 to avoid Modelsim NATURAL overflow warning.
   constant c_sdp_N_sync_jesd             : natural := c_sdp_S_pn * c_sdp_N_sync_rcu / c_sdp_S_rcu;  -- = 4, nof JESD IP sync outputs per PN
-  constant c_sdp_f_sub_Hz                : real := real(c_sdp_f_adc_MHz * 10**6) / real(c_sdp_N_fft);  -- = 195312.5
-  constant c_sdp_N_int                   : natural := c_sdp_f_adc_MHz * 10**6;  -- nof ADC sample periods per 1 s integration interval
+  constant c_sdp_f_sub_Hz                : real := real(c_sdp_N_clk_per_second) / real(c_sdp_N_fft);  -- = 195312.5
+  constant c_sdp_N_int                   : natural := c_sdp_N_clk_per_second;  -- nof ADC sample periods per 1 s integration interval
   constant c_sdp_N_int_sub               : real := c_sdp_f_sub_Hz;  -- nof subband sample periods per 1 s integration interval
   constant c_sdp_N_int_sub_lo            : natural := natural(FLOOR(c_sdp_N_int_sub));  -- = 195312
   constant c_sdp_N_int_sub_hi            : natural := natural(CEIL(c_sdp_N_int_sub));  -- = 195313
@@ -273,8 +275,15 @@ package sdp_pkg is
 
   -- Can use same offload time for all statistics, because 1GbE mux will combine them
   -- see https://support.astron.nl/confluence/display/L2M/L3+SDP+Testing+Notebook%3A+Statistics+offload
-  --CONSTANT c_sdp_offload_time : NATURAL := 13000;  -- from wave window 62855nS / 5nS = 12571 cycles.
-  constant c_sdp_offload_time : natural := 600000;  -- 600000 * 5 ns = 3 ms, so gn 31 starts after 93 ms
+  -- Intra node time of 600000 * 5 ns = 3 ms, so gn 31 starts after 31 * 3 = 93 ms
+  constant c_sdp_offload_node_time    : natural := 600000;
+  -- Inter packet time of 3100 * 5 ns = 15.5 us, so maximum 9 * 7 = 63 XST packets will yield total gap time of
+  -- about 63 * 15.5 us = 0.98 ms. The 63 XST packets take about 63 * 2344 octets * 8 bits / 1GbE = 1.2 ms.
+  -- Hence in total the XST offload takes about 0.98 + 1.2 = 2.2 ms, which fits in the budgetted 3 ms per node.
+  constant c_sdp_offload_packet_time  : natural := 3100;
+  -- Scale factor to implement offload time dependent on ctrl_interval_size, see sdp_statistics_offload.vhd
+  -- for more description.
+  constant c_sdp_offload_scale_w : natural := 15;
 
   -- packet lengths, see ICD SC-SDP
   constant c_sdp_nof_bytes_per_statistic : natural := 8;  -- c_sdp_W_statistic_sz * c_word_sz = 2 * 4 = 8
@@ -625,8 +634,6 @@ package sdp_pkg is
                                                      init_sl  => '0');  -- Default = 1
   constant c_sdp_nof_crosslets_reg_w : natural := c_sdp_mm_reg_nof_crosslets.nof_dat * c_sdp_mm_reg_nof_crosslets.dat_w;
 
-  constant c_sdp_xst_nof_clk_per_sync_min : natural := c_sdp_N_clk_per_sync / 10;  -- 0.1 second
-
   -- XSUB MM address widths
   constant c_sdp_reg_crosslets_info_addr_w                     : natural := c_sdp_mm_reg_crosslets_info.adr_w;
   constant c_sdp_reg_nof_crosslets_addr_w                      : natural := c_sdp_mm_reg_nof_crosslets.adr_w;
@@ -661,14 +668,17 @@ package sdp_pkg is
   -- SDP simulation constants record, to use instead of HW default when g_sim = TRUE
   -------------------------------------------------
   type t_sdp_sim is record
-    xst_nof_clk_per_sync_min : natural;
-    offload_time             : natural;  -- select > 0 and gn_index > 0 to see effect of offload_time on statistics offload
-    sync_timeout             : natural;
-    unb_nr                   : natural;
-    node_nr                  : natural;
+    N_clk_per_sync       : natural;  -- for statistics offload timing
+    N_clk_per_sync_min   : natural;  -- for statistics offload timing
+    offload_node_time    : natural;  -- select > 0 and gn_index > 0 to see effect on statistics offload
+    offload_packet_time  : natural;  -- select > 0 to see effect on statistics offload
+    offload_scale_w      : natural;  -- for statistics offload timing
+    sync_timeout         : natural;
+    unb_nr               : natural;
+    node_nr              : natural;
   end record;
 
-  constant c_sdp_sim : t_sdp_sim := (1, 10, 3 * 1024, 0, 0);
+  constant c_sdp_sim : t_sdp_sim := (2, 1, 10, 5, 1, 3 * 1024, 0, 0);
 
   -------------------------------------------------
   -- SDP functions
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/sdp_statistics_offload.vhd b/applications/lofar2/libraries/sdp/src/vhdl/sdp_statistics_offload.vhd
index 858a12ce9f8e979dbd3e1828ce5c5b972007d6fa..91ef17b9a47ba4e6741989a0cb89a02ef86adf7d 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/sdp_statistics_offload.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/sdp_statistics_offload.vhd
@@ -110,7 +110,10 @@ use work.sdp_pkg.all;
 entity sdp_statistics_offload is
   generic (
     g_statistics_type          : string  := "SST";
-    g_offload_time             : natural := c_sdp_offload_time;
+    g_offload_node_time        : natural := c_sdp_offload_node_time;
+    g_offload_packet_time      : natural := c_sdp_offload_packet_time;
+    g_offload_scale_w          : natural := 0;  -- 0 = default
+    g_ctrl_interval_size_min   : natural := 1;  -- 1 = default
     g_beamset_id               : natural := 0;
     g_P_sq                     : natural := c_sdp_P_sq;  -- number of available correlator cells,
     g_crosslets_direction      : natural := 1;  -- > 0 for crosslet transport in positive direction (incrementing RN), else 0 for negative direction
@@ -141,8 +144,9 @@ entity sdp_statistics_offload is
     reg_bsn_monitor_v2_offload_cipo : out t_mem_cipo;
 
     -- Input timing regarding the integration interval of the statistics
-    in_sosi          : in t_dp_sosi;
-    new_interval     : in std_logic;
+    in_sosi            : in t_dp_sosi;
+    new_interval       : in std_logic;
+    ctrl_interval_size : in natural := 1;  -- 1 = default
 
     -- Streaming output of offloaded statistics packets
     out_sosi         : out t_dp_sosi;
@@ -166,6 +170,15 @@ end sdp_statistics_offload;
 architecture str of sdp_statistics_offload is
   constant c_nof_streams               : natural := 1;
 
+  -- Offload timing
+  constant c_offload_node_time_scaled : natural := (g_offload_node_time * 2**g_offload_scale_w) /
+                                                    g_ctrl_interval_size_min;
+  constant c_offload_packet_time_scaled : natural := (g_offload_packet_time * 2**g_offload_scale_w) /
+                                                      g_ctrl_interval_size_min;
+  constant c_offload_packet_time_max : natural := g_offload_packet_time *
+                                                  (c_sdp_N_clk_per_sync_max / c_sdp_N_clk_per_sync_min + 1) + 1;
+                                                  -- +1 for margin and to ensure > 0
+
   -- header fields
   constant c_marker                    : natural := func_sdp_get_stat_marker(g_statistics_type);
   constant c_nof_signal_inputs         : natural := func_sdp_get_stat_nof_signal_inputs(g_statistics_type);
@@ -193,6 +206,8 @@ architecture str of sdp_statistics_offload is
     remote_gn            : natural;  -- index of remote global node
     remote_pn            : natural;  -- index of remote node in antenna band
     remote_si_offset     : natural;  -- index of first signal input on remote node
+    offload_node_time    : natural;  -- unit offload delay between nodes
+    offload_packet_time  : natural;  -- unit offload delay between packets per node
     base_dly             : natural;  -- same base offload delay for nof_cycles_dly per node
     nodes_dly            : natural;  -- incremental offload delay for nof_cycles_dly per node
     nof_cycles_dly       : natural;  -- trigger_offload delay for this node
@@ -218,6 +233,7 @@ architecture str of sdp_statistics_offload is
   type t_reg is record
     packet_count         : natural range 0 to c_nof_packets_max;
     start_address        : natural range 0 to c_mm_ram_size;
+    start_timer          : natural range 0 to c_offload_packet_time_max;
     start_pulse          : std_logic;
     start_sync           : std_logic;
     dp_header_info       : std_logic_vector(1023 downto 0);
@@ -228,7 +244,7 @@ architecture str of sdp_statistics_offload is
     instance_address     : natural range 0 to c_mm_ram_size;
   end record;
 
-  constant c_reg_rst : t_reg := (0, 0, '0', '0', (others => '0'), 0, 0, 0, 0, 0);
+  constant c_reg_rst : t_reg := (0, 0, 0, '0', '0', (others => '0'), 0, 0, 0, 0, 0);
 
   signal p                        : t_parameters;
   signal p_gn_id                  : std_logic_vector(c_byte_w - 1 downto 0);
@@ -242,6 +258,7 @@ architecture str of sdp_statistics_offload is
   signal nxt_r                    : t_reg;
 
   signal reg_new_interval         : std_logic;
+  signal ctrl_interval_scaled     : natural;
 
   signal data_id_rec              : t_sdp_stat_data_id;
   signal data_id_slv              : std_logic_vector(31 downto 0) := (others => '0');
@@ -266,6 +283,8 @@ architecture str of sdp_statistics_offload is
   signal station_info             : std_logic_vector(15 downto 0) := (others => '0');
 
   -- Debug signals for view in Wave window
+  signal dbg_g_offload_node_time         : natural := g_offload_node_time;
+  signal dbg_g_offload_packet_time       : natural := g_offload_packet_time;
   signal dbg_c_marker                    : natural := c_marker;
   signal dbg_c_nof_signal_inputs         : natural := c_nof_signal_inputs;
   signal dbg_c_nof_statistics_per_packet : natural := c_nof_statistics_per_packet;
@@ -374,34 +393,73 @@ begin
   -- . gn_index and ring_info.O_rn are in full GN = ID = 0:255 range defined by c_sdp_W_gn_id = 8
   -- . pn_index = gn_index MOD c_sdp_N_pn_max = gn_index[3:0] = 0:15, with c_sdp_N_pn_max = 16
   -- . O_rn is first GN index in ring, so O_rn <= gn_index
-  -- . nof_cycles_dly for statistics offload start per node:
+  -- . Avoid offload bursts between nodes via g_offload_node_time:
+  --   - The nof_cycles_dly delays the start of statistics offload per node. The nof_cycles_dly
+  --     increases as function of the GN index, so that the multiple nodes do their offload after
+  --     eachother, to avoid bursts from multiple nodes.
   --   - c_sdp_N_band * c_sdp_N_pn_max = 32 nodes is maximum for a LOFAR2 Station.
-  --   - p.nodes_dly: g_offload_time = c_sdp_offload_time = 600000 * 5 ns = 3 ms, so for max
+  --   - p.nodes_dly: g_offload_node_time = c_sdp_offload_node_time = 600000 * 5 ns = 3 ms, so for max
   --     gn_index[4:0] = 31 the offload starts after 93 ms, to just fit within XST T_int min is 100 ms.
-  --   - p.base_dly: use gn_index[7:5] to add a small extra dly g_offload_time / 8 of per group of
-  --     32 nodes, so 0:g_offload_time in 8 steps of g_offload_time/8, to remain within 32 * 3 = 96 ms
-  --     < 100 ms for any set of 32 nodes within full GN range.
+  --   - p.base_dly: use gn_index[7:5] to add a small extra dly g_offload_node_time / 8 of per group of
+  --     32 nodes, so 0:g_offload_node_time in 8 steps of g_offload_node_time / 8, to remain within
+  --     32 * 3 = 96 ms < 100 ms for any set of 32 nodes within full GN range.
   --   - use +1 for nof_cycles_dly to ensure that hdr_input.integration_interval gets the correct
   --     value also for node 0 with zero delay. Otherwise node 0 will read an integration_interval
   --     value that depends on when the remaining sop_cnt of the last interval in case of a XST
   --     processing restart.
+  --   - g_offload_node_time = c_sdp_offload_node_time = 600000 suits g_ctrl_interval_size_min. For
+  --     larger ctrl_interval_size the p.offload_node_time can be > g_offload_node_time by factor
+  --     ctrl_interval_size / g_ctrl_interval_size_min. Use g_offload_scale_w to be able to implement
+  --     this scaling by using right shift instead of integer divide:
+  --       p.offload_node_time = (g_offload_node_time * 2**g_offload_scale_w / g_ctrl_interval_size_min) *
+  --                             (ctrl_interval_size / 2**g_offload_scale_w)
+  --                          ~= g_offload_node_time * ctrl_interval_size / g_ctrl_interval_size_min
+  -- . Avoid offload bursts per node via g_offload_packet_time:
+  --   - Each node offloads multiple packets per integration interval. For SST nof_packets = S_pn = 12,
+  --     for BST nof_packets = 1 (per beamset), and for XST maximum nof_packets = P_sq *
+  --     c_sdp_N_crosslets_max = 9 * 7 = 63.
+  --   - g_offload_packet_time = c_sdp_offload_packet_time = 3100 suits g_ctrl_interval_size_min. For
+  --     larger ctrl_interval_size the p.offload_packet_time can be > g_offload_packet_time by factor
+  --     ctrl_interval_size / g_ctrl_interval_size_min. Use g_offload_scale_w to be able to implement
+  --     this scaling by using right shift instead of integer divide:
+  --       p.offload_packet_time = (g_offload_packet_time * 2**g_offload_scale_w / g_ctrl_interval_size_min) *
+  --                               (ctrl_interval_size / 2**g_offload_scale_w)
+  --                            ~= g_offload_packet_time * ctrl_interval_size / g_ctrl_interval_size_min
+  -- . Fit g_offload_scale_w for offload_node_time and offload_packet_time.
+  --   - For c_sdp_offload_node_time / g_ctrl_interval_size_min = 600000 / 20000000 = 0.03 choose
+  --     g_offload_scale_w = 15 to have c_offload_node_time_scaled = 0.03 * 2**15 = 983.04 --> 983 and
+  --     ctrl_interval_scaled = 20000000 / 2**15 = 610.35 --> 610, and 983 * 610 = 599630, which is
+  --     close enough to c_sdp_offload_node_time = 600000.
+  --   - For c_sdp_offload_packet_time / g_ctrl_interval_size_min = 3100 / 20000000 = 0.000155 choose
+  --     g_offload_scale_w = 15 to have c_offload_node_time_scaled = 0.000155 * 2**15 = 5.08 --> 5 and
+  --     ctrl_interval_scaled = 20000000 / 2**15 = 610.35 --> 610, and 5 * 610 = 3050, which is close
+  --     enough to c_sdp_offload_packet_time = 3100.
+  --   - Default use g_offload_scale_w = 0, ctrl_interval_size = g_ctrl_interval_size_min = 1 to have
+  --     p.offload_node_time = g_offload_node_time
+  --     p.offload_packet_time = g_offload_packet_time
+
+  ctrl_interval_scaled <= SHIFT_UINT(ctrl_interval_size, g_offload_scale_w);
+
   p_reg_parameters : process(dp_clk)
   begin
     if rising_edge(dp_clk) then
-      p.gn_index         <= gn_index;  -- gn_index[7:0] full GN range
-      p.pn_index         <= func_sdp_gn_index_to_pn_index(p.gn_index);  -- pn_index = 0:15
-      p.offset_rn        <= TO_UINT(ring_info.O_rn);
-      p.rn_index         <= p.gn_index - p.offset_rn;
-      p.local_si_offset  <= p.pn_index * c_sdp_S_pn;
-      p.base_dly         <= TO_UINT(p_gn_id(7 downto 5)) * (g_offload_time / 8);
-      p.nodes_dly        <= TO_UINT(p_gn_id(4 downto 0)) * g_offload_time;
-      p.nof_cycles_dly   <= p.base_dly + p.nodes_dly + 1;  -- + 1 to ensure proper hdr_input.integration_interval also on node 0
-      p.nof_rn           <= TO_UINT(ring_info.N_rn);
-      p.nof_used_P_sq    <= smallest(p.nof_rn / 2 + 1, g_P_sq);
-      p.remote_rn        <= func_ring_nof_hops_to_source_rn(r.instance_count, p.rn_index, p.nof_rn, g_crosslets_direction);
-      p.remote_gn        <= p.offset_rn + p.remote_rn;
-      p.remote_pn        <= func_sdp_gn_index_to_pn_index(p.remote_gn);
-      p.remote_si_offset <= p.remote_pn * c_sdp_S_pn;
+      p.gn_index            <= gn_index;  -- gn_index[7:0] full GN range
+      p.pn_index            <= func_sdp_gn_index_to_pn_index(p.gn_index);  -- pn_index = 0:15
+      p.offset_rn           <= TO_UINT(ring_info.O_rn);
+      p.rn_index            <= p.gn_index - p.offset_rn;
+      p.local_si_offset     <= p.pn_index * c_sdp_S_pn;
+      p.offload_node_time   <= c_offload_node_time_scaled * ctrl_interval_scaled;
+      p.offload_packet_time <= c_offload_packet_time_scaled * ctrl_interval_scaled + 1;  -- + 1 to ensure > 0
+      p.base_dly            <= TO_UINT(p_gn_id(7 downto 5)) * SHIFT_UINT(p.offload_node_time, 3);  -- divide by 2**3 = 8
+      p.nodes_dly           <= TO_UINT(p_gn_id(4 downto 0)) * p.offload_node_time;
+      p.nof_cycles_dly      <= p.base_dly + p.nodes_dly + 1;  -- + 1 to ensure > 0, to have proper
+                                                              -- hdr_input.integration_interval also on node 0
+      p.nof_rn              <= TO_UINT(ring_info.N_rn);
+      p.nof_used_P_sq       <= smallest(p.nof_rn / 2 + 1, g_P_sq);
+      p.remote_rn           <= func_ring_nof_hops_to_source_rn(r.instance_count, p.rn_index, p.nof_rn, g_crosslets_direction);
+      p.remote_gn           <= p.offset_rn + p.remote_rn;
+      p.remote_pn           <= func_sdp_gn_index_to_pn_index(p.remote_gn);
+      p.remote_si_offset    <= p.remote_pn * c_sdp_S_pn;
     end if;
   end process;
 
@@ -487,7 +545,7 @@ begin
   data_id_slv <= func_sdp_map_stat_data_id(g_statistics_type, data_id_rec);
 
   -- sensitivity list in order of appearance
-  p_control_packet_offload : process(r, trigger_offload, dp_sop, dp_header_info, reg_input)
+  p_control_packet_offload : process(r, p, trigger_offload, dp_sop, dp_header_info, reg_input)
     variable v       : t_reg;
     variable v_index : natural;
   begin
@@ -497,9 +555,9 @@ begin
 
     -- The trigger_offload occurs p.nof_cycles_dly after the in_sosi.sync and
     -- the offload will have finished before the next in_sosi.sync, because
-    -- c_sdp_offload_time is such that all offload will finish within 100 ms
+    -- c_sdp_offload_node_time is such that all offload will finish within 100 ms
     -- and the integration interval (= sync interval) is 1 s for SST and BST
-    -- and minimal 0.1s (= c_sdp_xst_nof_clk_per_sync_min) for XST.
+    -- and minimal 0.1s (= c_sdp_N_clk_per_sync_min) for XST.
     -- The trigger_offload initializes the control for the first packet offload
     -- in every sync interval.
     -- . Issue a start_pulse per packet offload. The start_pulse is used by
@@ -522,8 +580,7 @@ begin
     -- u_dp_block_from_mm_dc. This ensures that the dp_sop identifies the
     -- sop of the offload packet. At the dp_sop:
     -- . the dp_header_info per packet offload can be released
-    -- . the next packet offload can be prepared
-    --
+    -- . the next packet offload with inter packet gap can be prepared
     elsif dp_sop = '1' then
       -- Release dp_header_info for current packet offload
       v.dp_header_info := dp_header_info;
@@ -541,7 +598,7 @@ begin
             v.interleave_count := 0;
             v.interleave_address := v.start_address;
           end if;
-          v.start_pulse := '1';
+          v.start_timer := p.offload_packet_time;
           v.packet_count := r.packet_count + 1;
 
         elsif g_statistics_type = "BST" then
@@ -569,13 +626,21 @@ begin
             v.instance_count := r.instance_count + 1;
             v.instance_address := v.start_address;  -- use v.start_address to avoid multipier needed in (r.instance_count + 1) * 2**c_sdp_ram_st_xsq_addr_w
           end if;
-          v.start_pulse := '1';
+          v.start_timer := p.offload_packet_time;
           v.packet_count := r.packet_count + 1;
 
         else
           null;  -- do nothing in case of unknown g_statistics_type
         end if;
       end if;
+
+    -- Use start_timer to have p.offload_packet_time gap between packets, and issue start_pulse when
+    -- the start_timer expires
+    elsif r.start_timer > 0 then
+      v.start_timer := r.start_timer - 1;
+      if v.start_timer = 0 then
+        v.start_pulse := '1';
+      end if;
     end if;
 
     nxt_r <= v;
diff --git a/applications/lofar2/libraries/sdp/tb/vhdl/tb_sdp_statistics_offload.vhd b/applications/lofar2/libraries/sdp/tb/vhdl/tb_sdp_statistics_offload.vhd
index 5b2da154df7b5384886e388f7446c58a361cecf3..f10e1a83b477577e362304edb7f287124dc87ac4 100644
--- a/applications/lofar2/libraries/sdp/tb/vhdl/tb_sdp_statistics_offload.vhd
+++ b/applications/lofar2/libraries/sdp/tb/vhdl/tb_sdp_statistics_offload.vhd
@@ -53,12 +53,18 @@ use work.tb_sdp_pkg.all;
 entity tb_sdp_statistics_offload is
   generic (
     -- All
-    g_fast_mm_clk              : boolean := true;  -- When TRUE use 1 GHz mm_clk  to speed up simulation, else use 100 MHz mm_clk
-                                                   -- for real speed of u_dp_block_from_mm_dc in sdp_statistics_offload
+    g_fast_mm_clk              : boolean := true;  -- When TRUE use 1 GHz mm_clk  to speed up simulation, else use
+                                                   -- 100 MHz mm_clk for real speed of u_dp_block_from_mm_dc in
+                                                   -- sdp_statistics_offload
     g_statistics_type          : string := "XST";
-    g_offload_time             : natural := 50;
+    g_offload_node_time        : natural := 50;
+    g_offload_packet_time      : natural := 5;
+    g_offload_scale_w          : natural := 0;  -- 0 = default
+    g_ctrl_interval_size       : natural := 1;  -- 1 = default
+    g_ctrl_interval_size_min   : natural := 1;  -- 1 = default
     g_reverse_word_order       : boolean := true;  -- when TRUE then stream LSB word after MSB word.
-    g_gn_index                 : natural := 4;  -- global node (GN) index, must be in range(O_rn, O_rn + N_rn), use > 0 to see effect of g_offload_time
+    g_gn_index                 : natural := 4;  -- global node (GN) index, must be in range(O_rn, O_rn + N_rn),
+                                                -- use > 0 to see effect of g_offload_node_time
     g_nof_sync                 : natural := 3;  -- simulate some sync periods, choose >= 3
     -- BST
     g_beamset_id               : natural := 0;  -- < c_sdp_N_beamsets
@@ -66,8 +72,9 @@ entity tb_sdp_statistics_offload is
     g_O_rn                     : natural := 0;  -- GN index of first ring node (RN)
     g_N_rn                     : natural := 8;  -- <= c_sdp_N_rn_max = 16, number of nodes in ring
     g_P_sq                     : natural := 9;  -- <= c_sdp_P_sq, nof available correlator cells
-    g_nof_crosslets            : natural := 4;  -- <= c_sdp_N_crosslets_max
-    g_crosslets_direction      : natural := 1  -- > 0 for crosslet transport in positive direction (incrementing RN), else 0 for negative direction
+    g_nof_crosslets            : natural := 7;  -- <= c_sdp_N_crosslets_max
+    g_crosslets_direction      : natural := 1  -- > 0 for crosslet transport in positive direction (incrementing RN),
+                                               -- else 0 for negative direction
   );
 end tb_sdp_statistics_offload;
 
@@ -78,7 +85,7 @@ architecture tb of tb_sdp_statistics_offload is
 
   constant c_cross_clock_domain_latency : natural := 20;
 
-  constant c_offload_time              : natural := g_offload_time * g_gn_index;
+  constant c_offload_node_time         : natural := g_offload_node_time * g_gn_index;
 
   -- Use sim default dst and src MAC, IP, UDP port from sdp_pkg.vhd and based on g_gn_index
   constant c_node_eth_src_mac          : std_logic_vector(47 downto 0) := c_sdp_stat_eth_src_mac_47_16 & func_sdp_gn_index_to_mac_15_0(g_gn_index);
@@ -149,7 +156,7 @@ architecture tb of tb_sdp_statistics_offload is
   -- Define block timing.
   constant c_bsn_init            : natural := 0;
   -- Sufficient c_nof_block_per_sync to fit more than c_nof_packets_max offload packets per sync interval.
-  constant c_nof_block_per_sync  : natural := 3 + c_mm_dp_clk_ratio * (ceil_div(c_offload_time, c_packet_size) + c_nof_packets_max);
+  constant c_nof_block_per_sync  : natural := 3 + c_mm_dp_clk_ratio * (ceil_div(c_offload_node_time, c_packet_size) + c_nof_packets_max);
   constant c_nof_clk_per_block   : natural := c_packet_size;
   constant c_nof_clk_per_sync    : natural := c_nof_block_per_sync * c_nof_clk_per_block;
 
@@ -655,12 +662,15 @@ begin
   -- SDP info
   u_dut: entity work.sdp_statistics_offload
   generic map (
-    g_statistics_type     => g_statistics_type,
-    g_offload_time        => g_offload_time,
-    g_reverse_word_order  => g_reverse_word_order,
-    g_beamset_id          => g_beamset_id,
-    g_P_sq                => g_P_sq,
-    g_crosslets_direction => g_crosslets_direction
+    g_statistics_type        => g_statistics_type,
+    g_offload_node_time      => g_offload_node_time,
+    g_offload_packet_time    => g_offload_packet_time,
+    g_offload_scale_w        => g_offload_scale_w,
+    g_ctrl_interval_size_min => g_ctrl_interval_size_min,
+    g_reverse_word_order     => g_reverse_word_order,
+    g_beamset_id             => g_beamset_id,
+    g_P_sq                   => g_P_sq,
+    g_crosslets_direction    => g_crosslets_direction
   )
   port map (
     mm_clk => mm_clk,
@@ -680,8 +690,9 @@ begin
     reg_hdr_dat_miso => hdr_dat_miso,
 
     -- ST
-    in_sosi          => in_sosi,
-    new_interval     => new_interval,
+    in_sosi            => in_sosi,
+    new_interval       => new_interval,
+    ctrl_interval_size => g_ctrl_interval_size,
 
     out_sosi         => sdp_offload_sosi,
     out_siso         => sdp_offload_siso,
diff --git a/applications/lofar2/libraries/sdp/tb/vhdl/tb_tb_sdp_statistics_offload.vhd b/applications/lofar2/libraries/sdp/tb/vhdl/tb_tb_sdp_statistics_offload.vhd
index c325ba9be21b0f986b274e1060af40f0e48548b3..9851c36b6fa80cc7818689151fc2f44d7ae1a959 100644
--- a/applications/lofar2/libraries/sdp/tb/vhdl/tb_tb_sdp_statistics_offload.vhd
+++ b/applications/lofar2/libraries/sdp/tb/vhdl/tb_tb_sdp_statistics_offload.vhd
@@ -37,12 +37,18 @@ architecture tb of tb_tb_sdp_statistics_offload is
   signal tb_end : std_logic := '0';  -- declare tb_end to avoid 'No objects found' error on 'when -label tb_end'
 begin
 --    -- All
---    g_fast_mm_clk              : BOOLEAN := TRUE;  -- When TRUE use 1 GHz mm_clk  to speed up simulation, else use 100 MHz mm_clk
---                                                   -- for real speed of u_dp_block_from_mm_dc in sdp_statistics_offload
+--    g_fast_mm_clk              : BOOLEAN := TRUE;  -- When TRUE use 1 GHz mm_clk  to speed up simulation, else use
+--                                                   -- 100 MHz mm_clk for real speed of u_dp_block_from_mm_dc in
+--                                                   -- sdp_statistics_offload
 --    g_statistics_type          : STRING := "SST";
---    g_offload_time             : NATURAL := 500;
+--    g_offload_node_time        : NATURAL := 50;
+--    g_offload_packet_time      : natural := 5;
+--    g_offload_scale_w          : natural := 0;  -- 0 = default
+--    g_ctrl_interval_size       : natural := 1;  -- 1 = default
+--    g_ctrl_interval_size_min   : natural := 1;  -- 1 = default
 --    g_reverse_word_order       : BOOLEAN := TRUE  -- when TRUE then stream LSB word after MSB word.
---    g_gn_index                 : NATURAL := 1;  -- global node (GN) index, use > 0 to see effect of g_offload_time
+--    g_gn_index                 : NATURAL := 1;  -- global node (GN) index, use > 0 to see effect of
+--                                                -- g_offload_node_time
 --    g_nof_sync                 : NATURAL := 3;
 --    -- BST
 --    g_beamset_id               : NATURAL := 0;
@@ -53,22 +59,24 @@ begin
 --    g_nof_crosslets            : NATURAL := 1;
 --    g_crosslets_direction      : INTEGER := 1;  -- +1 or -1
 
-  u_sst                     : entity work.tb_sdp_statistics_offload generic map( true, "SST", 50,  true, 3, 3);
-  u_sst_no_reverse          : entity work.tb_sdp_statistics_offload generic map( true, "SST", 50, false, 3, 3);
-  u_sst_os                  : entity work.tb_sdp_statistics_offload generic map( true, "SST_OS", 50,  true, 3, 3);
-  u_sst_os_no_reverse       : entity work.tb_sdp_statistics_offload generic map( true, "SST_OS", 50, false, 3, 3);
-  u_bst_0                   : entity work.tb_sdp_statistics_offload generic map( true, "BST", 50,  true, 1, 3);
-  u_bst_0_no_reverse        : entity work.tb_sdp_statistics_offload generic map( true, "BST", 50, false, 1, 3, 0);
-  u_bst_1                   : entity work.tb_sdp_statistics_offload generic map( true, "BST", 50,  true, 1, 3, 1);
-  u_xst_P1                  : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 0, 16,  1, 1, 1);
-  u_xst_P1_N3               : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 0, 16,  1, 3, 1);
-  u_xst_P9                  : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 0, 16,  9, 1, 1);
-  u_xst_P9_N3               : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 0, 16,  9, 3, 1);
-  u_xst_P9_N3_no_reverse    : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50, false, 1, 3, 0, 0, 16,  9, 3, 1);
-  u_xst_P9_N3_neg_dir       : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 0, 16,  9, 3, 0);
-  u_xst_P8_N7_RN1_15        : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 1, 3, 0, 1, 15,  8, 7, 0);
-  u_xst_P1_N7_RN0_7         : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 3, 3, 0, 0,  8,  1, 7, 1);  -- P_sq = 1 < N_rn/2+1 = 5
-  u_xst_P9_N7_RN0_7         : entity work.tb_sdp_statistics_offload generic map( true, "XST", 50,  true, 3, 3, 0, 0,  8,  9, 7, 1);  -- P_sq = 9 > N_rn/2+1 = 5
-  u_xst_P9_N4_RN0_7_slow_mm : entity work.tb_sdp_statistics_offload generic map(false, "XST", 50,  true, 3, 3, 0, 0,  8,  9, 4, 1);  -- P_sq = 9 > N_rn/2+1 = 5
-  u_xst_P9_N7_RN0_7_slow_mm : entity work.tb_sdp_statistics_offload generic map(false, "XST", 50,  true, 3, 3, 0, 0,  8,  9, 7, 1);  -- P_sq = 9 > N_rn/2+1 = 5
+  u_sst                     : entity work.tb_sdp_statistics_offload generic map( true, "SST",    50, 5, 0, 1, 1,  true, 3, 3);
+  u_sst_time                : entity work.tb_sdp_statistics_offload generic map( true, "SST",    50, 8, 1, 6, 2,  true, 3, 3);
+  u_sst_no_reverse          : entity work.tb_sdp_statistics_offload generic map( true, "SST",    50, 5, 0, 1, 1, false, 3, 3);
+  u_sst_os                  : entity work.tb_sdp_statistics_offload generic map( true, "SST_OS", 50, 5, 0, 1, 1,  true, 3, 3);
+  u_sst_os_no_reverse       : entity work.tb_sdp_statistics_offload generic map( true, "SST_OS", 50, 5, 0, 1, 1, false, 3, 3);
+  u_bst_0                   : entity work.tb_sdp_statistics_offload generic map( true, "BST",    50, 5, 0, 1, 1,  true, 1, 3);
+  u_bst_0_no_reverse        : entity work.tb_sdp_statistics_offload generic map( true, "BST",    50, 5, 0, 1, 1, false, 1, 3, 0);
+  u_bst_1                   : entity work.tb_sdp_statistics_offload generic map( true, "BST",    50, 5, 0, 1, 1,  true, 1, 3, 1);
+  u_xst_P1                  : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 0, 16,  1, 1, 1);
+  u_xst_P1_N3               : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 0, 16,  1, 3, 1);
+  u_xst_P9                  : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 0, 16,  9, 1, 1);
+  u_xst_P9_N3               : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 0, 16,  9, 3, 1);
+  u_xst_P9_N3_no_reverse    : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1, false, 1, 3, 0, 0, 16,  9, 3, 1);
+  u_xst_P9_N3_neg_dir       : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 0, 16,  9, 3, 0);
+  u_xst_P8_N7_RN1_15        : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 1, 3, 0, 1, 15,  8, 7, 0);
+  u_xst_P1_N7_RN0_7         : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 3, 3, 0, 0,  8,  1, 7, 1);  -- P_sq = 1 < N_rn/2+1 = 5
+  u_xst_P9_N7_RN0_7         : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 0, 1, 1,  true, 3, 3, 0, 0,  8,  9, 7, 1);  -- P_sq = 9 > N_rn/2+1 = 5
+  u_xst_P9_N7_RN0_7_time    : entity work.tb_sdp_statistics_offload generic map( true, "XST",    50, 5, 1, 3, 2,  true, 3, 3, 0, 0,  8,  9, 7, 1);  -- P_sq = 9 > N_rn/2+1 = 5
+  u_xst_P9_N4_RN0_7_slow_mm : entity work.tb_sdp_statistics_offload generic map(false, "XST",    50, 5, 0, 1, 1,  true, 3, 3, 0, 0,  8,  9, 4, 1);  -- P_sq = 9 > N_rn/2+1 = 5
+  u_xst_P9_N7_RN0_7_slow_mm : entity work.tb_sdp_statistics_offload generic map(false, "XST",    50, 5, 0, 1, 1,  true, 3, 3, 0, 0,  8,  9, 7, 1);  -- P_sq = 9 > N_rn/2+1 = 5
 end tb;
diff --git a/applications/lofar2/model/pfb_os/dsp_study_erko.txt b/applications/lofar2/model/pfb_os/dsp_study_erko.txt
index e3756e2303ba6eca1a05778a6368ea6f37411af1..34b5b1f82b347aa6fbab38f76bf31e08df4cf6e5 100644
--- a/applications/lofar2/model/pfb_os/dsp_study_erko.txt
+++ b/applications/lofar2/model/pfb_os/dsp_study_erko.txt
@@ -31,6 +31,7 @@
 # * [JOS2] Introduction to Digital Filters, 2007
 # * [JOS3] Physical Audio Signal Processing, 2010
 # * [JOS4] Spectral Audio Signal Processing, 2011
+# * [SP4COMM] Signal Processing for Communications, 2008, Paolo Prandoni and Martin Vetterli
 #
 # * [WIKI] https://en.wikipedia.org/wiki/Bilinear_transform
 # * [WIKI] https://en.wikipedia.org/wiki/Discrete_cosine_transform
@@ -48,13 +49,21 @@
 #   . Applied DSP No. 9: The z-Domain and Parametric Filter Design
 #     https://www.youtube.com/watch?v=xIN5Mnj_MAk
 # * [NOISESHAPING]
-#   . "Digital Signal Processing Oversampled Analog to Digital Conversion with
-#      Noise Shaping", D.R. Brown --> ADC
-#   . "Noise shaping", Markus Nentwig, December 9, 2012 --> DAC
-#      https://www.dsprelated.com/showarticle/184.php
-#   . "Realisering van Digitale Signaalbewerkende Systemen, Toepassingen",
-#      5N290, TUE, P.C.M. Sommen, --> DAC slide 19, 20,
+#   - ADC:
+#     . "Digital Signal Processing Oversampled Analog to Digital Conversion
+#        with Noise Shaping, D.R. Brown
+#        https://spinlab.wpi.edu/courses/ece503_2014/5-5oversampled_adc_shaped.pdf
+#   - DAC:
+#     . "Digital Signal Processing Oversampled Digital to Analog Conversion
+#        with Noise Shaping", D.R. Brown
+#        https://spinlab.wpi.edu/courses/ece503_2014/5-6oversampled_dac_shaped.pdf
+#     . "Noise shaping", Markus Nentwig, December 9, 2012
+#        https://www.dsprelated.com/showarticle/184.php
+#     . "Realisering van Digitale Signaalbewerkende Systemen, Toepassingen",
+#        5N290, TUE, P.C.M. Sommen, --> DAC slide 19, 20,
 # * [PM-REMEZ] https://pm-remez.readthedocs.io/en/latest/
+# * [SELESNICK] Ivan Selesnick
+#   . https://eeweb.engineering.nyu.edu/iselesni/EL713/zoom/mrate.pdf
 #
 # https://ocw.mit.edu/courses/6-341-discrete-time-signal-processing-fall-2005/
 # Youtube: Guitars 4RL
@@ -160,7 +169,7 @@ a) DTFT [LYONS 3.52, MATLAB]
 
 - Discrete time to continuous frequency domain:
 
-           +inf
+          +inf
     X(w) = sum x[n] exp(-jw n)
           n=-inf
 
@@ -168,7 +177,7 @@ a) DTFT [LYONS 3.52, MATLAB]
 b) z-transform [LYONS 6.3, MATLAB]
 - Decrete time to z-domain:
 
-           +inf
+          +inf
     X(z) = sum x[n] z^-n,  z = r exp(jw) = r (cos(w) + j sin(w)),
           n=-inf
 
@@ -563,7 +572,7 @@ c) s-plane and z-plane
 - For FIR b = h. For IIR it is not possible to directly derive b, a from h
   [LYONS 6.1]. Therefor use z-transform [LYONS 6.3]:
 
-           +inf             +inf
+          +inf             +inf
     H(z) = sum h[n] z^-n  = sum h[n] r^-n exp(-j w n)
           n=-inf           n=-inf
 
@@ -830,30 +839,47 @@ c) s-plane and z-plane
       X(m) = sin(pi * m) / sin(pi * m / K)
           ~= K * sinc(m) for K = N >~ 10
 
-- Fourier transform theorems [JOS4 B]
+- DTFT properties [JOS4 B, PROAKIS 4.3]
+  . Linearity: a1 x1[n] + a2 x2[n] <==> a1 X1(w) + a2 X2(w)
   . Scaling: x(t / a) <==> |a| X(a w)
-  . Shift: x(t - T) <==> exp(-j w T) X(w)
-  . Modulation: x(t) exp(j v t) <==> X(w - v), is dual of shift
+  . Time shift: x(t - T) <==> X(w) exp(-j w T)
+                x[n - k] <==> X(w) exp(-j w k), t = n Ts
+  . Frequency shift (complex modulation): x[n] exp(+j v n) <==> X(w - v), is
+      dual of time shift
+  . Real modulation: x[n] cos(v n) <==> 1/2 [X(w + v) + X(w - v)]
+  . Conjugation: x*[n] <==> X*(-w)
   . Convolution:
       x * y <==> X Y
       x y <==> 1 / (2 pi) X * Y
-  . flip(x) <==> flip(X)
-  . d(t) <==> 1, dirac pulse with area 1 at t = 0
+  . flip(x) <==> flip(X), so when signal is folded (time reversed) about the
+      origin in time, then its magnitude spectrum remains unchanged, and the
+      phase spectrum changes sign (phase reversal).
+  . d[n] <==> 1, dirac pulse with area 1 at n = 0
+    d[n - k] <==> exp(-j w k), dirac pulse with area 1 at n = k
 
                   +inf
-  . d_train_P(t) = sum d(t - m P), period P
-                  m=-inf
+  . d_train_P[n] = sum d(n - k P), period P
+                  k=-inf
     <==>
-                         +inf
-    d_train_P(f) = 1 / P  sum d(f - m / P)
-                         m=-inf
+                            +inf
+    d_train_P(w) = 2 pi / P  sum d(w - 2 pi k / P)
+                            k=-inf
+
+  . Sampling: x_d(t) = x_a(t) d_train_Ts(t)
+              <==>                             +inf
+              X_d(f) = X_a * d_train_fs(f) = fs sum X_a(f - k fs)
+                                               k=-inf
+
+    - The sampling theorem [PROAKIS 4.2.9, CROCHIERE 2.1]:
+        The digital spectrum is a periodic repetition of the scaled analogue
+        spectrum with period fs. If spectrum X_a = 0 for |f| >= B, then for
+        fs >= 2 B there is no overlapping aliasing (= spectral folding) and
+        then it is possible to reconstruct x_a from x_d using an LPF.
+    - The sinc() is the ideal interpolation formula:
 
-  . sampling: x_d(t) = x(t) d_train_Ts(t)
-              <==>
-              X_d(f) = X * d_train_fs(f)
-                         +inf
-                     = fs sum X(f - k fs)
-                         k=-inf
+              +inf        sin(pi (t - nT) / T)  +inf
+      x_a(t) = sum x_d[n] -------------------- = sum x_d[n] sinc((t - nT) / T)
+              n=-inf        (pi (t - nT) / T)   n=-inf
 
 
 10) Short Term Fourier Transform (STFT) [JOS4 7, 8]
@@ -978,64 +1004,168 @@ c) s-plane and z-plane
 
 12) Multirate processing:
 - Linear Time Variant (LTV) process, because it depends on when the
-  downsampling and upsampling start.
+  downsampling and upsampling start. This causes that order of operations
+  matters [LYONS 10.3.1]
+- Sampling and sampling rate conversion can be viewed as a modulation process
+  in which the spectrum of the digital signal contains periodic repetitions of
+  the baseband signal (images) spaced at harmonics of the sampling frequency.
+  This property can be used to advantage when dealing with bandpass signals by
+  associating the bandpass signal with one of these images instead of with the
+  baseband [CROCHIERE 2.4.2].
 - Polyphase filtering ensures that only the values that remain are calculated,
   so there are D or U phases [LYONS 10.7]. The LPF with all phases is called
-  the protype filter.
+  the prototype filter. Do not calculate samples that will be:
+  . discarded,
+  . inserted as zeros.
 - For large D or U use two stage D = D1 * D2 or U = U1 * U2, where D1 > D2 and
   U1 < U2 [LYONS 10.8.2]
 
-- Polyphase decomposition of H(z) [VAIDYANATHAN 4.3]:
+- Sampling, downsampling and upsampling
+  . Sampling causes the analoge spectrum to alias around k 2pi, similar for
+    downsamping the the digital spectrum aliases around k 2pi / D, as if the
+    analogue signal was sampled directly at the downsampled rate [LYONS 10.1].
+  . Downsampled spectrum [LYONS 10.3.2]
+    1. Draw original spectrum beyond -2pi to + 2pi, to show 0 and at least one
+       spectral replication (alias) for both negative (-2pi) and positive
+       (+2pi) frequency directions of the original sample frequency fs_old =
+       2pi.
+    2. Draw D - 1 copies of the original spectrum shifted by k 2pi / D, note
+       k = 0 is the original spectrum of step 1
+    3. Scale up the frequency axis of the new spectrum by factor D to get the
+       frequency axis for the down sample frequency. The downsampled spectrum
+       now ranges from -pi to pi for fs_new = 2pi.
+    4. Scale down magnitude of new spectrum by factor D. The time domain
+       amplitude of downsampled signal remains the same, but the frequency
+       domain magnitude decreases by factor D, because the DFT magnitude is
+       proportional to number of time-domain samples used in the
+       transformation [LYONS 10.3.1].
+  . Upsampled spectrum [LYONS 10.5.2]
+    1. Draw original spectrum beyond -U 2pi to +U 2pi, to show at least U
+       spectral replications of the original spectrum in both negative and
+       positive frequency directions of the original sample frequency fs_old =
+       2pi. That is all, because  inserting U - 1 zeros merely increases the
+       effective sample frequency to fs_new = U fs_old. It does not change the
+       spectrum, but it does cause that U - 1 spectral replications (aliases)
+       are now also in the 2pi range of fs_new. Hence it looks like inserting
+       zeros replicates the spectrum around multiples of 2pi / U, but it is
+       easier to understand as that increasing fs_new now includes U - 1
+       replications of the original spectrum.
+    2. Scale down the frequency axis of the new spectrum by factor U to get the
+       frequency axis for the up sample frequency. The upsampled spectrum now
+       ranges from -pi to pi for fs_new = 2pi.
+    3. The magnitude of new spectrum remains the same.
+  . Decimation = LPF + Downsampling [LYONS 10.1]:
+      To avoid overlapping aliasing after downsampling to fs_new an LPF needs
+      to band limit the original spectrum to pi / D = fs_old/2 / D.
+  . Interpolation = Upsampling + LPF:
+      To interpolate the zero values for fs_new an LPF needs to band limit the
+      new spectrum to pi / U = fs_new/2 / D.
+      Using zero order hold would be a naive approach, because then all samples
+      need to be calculated and the LPF then needs to compensate for the
+      non-flat pass band of sin(x)/x [LYONS 10.5.1].
+  . Decimation and interpolation can also use a BPF to select another part of
+    the band [HARRIS 2.2, VAIDYANATHAN 4.1.1].
+
+- Downsampling [LYONS 10.1, PROAKIS 10, CROCHIERE Fig 3.2, VAIDYANATHAN Fig
+  4.1.4, JOS4 11.1, SP4COMM 11.1.2]:
+
+      x_D[n] = x[nD], because x_D removes D-1 values from x
+
+    Define x' at x time grid, but with x' = 0 for samples in x that will be
+    discarded. This operation has no name in literature, probably because it
+    is a conceptual step and not an implementation operation:
+
+      x'[n] = d[n] x[n],   with d[n] = 1, for n % D = 0
+                                     = 0, otherwise
+
+    Use x' to first express z-transform X_D(z) in X'(z) and then z-transform
+    of X'(z) in X(z). The z-transform X_D(z) = X'(z^(1/D)), because x' is 0
+    when k is not a multiple of D:
 
-  H(z) = H0(z^N) + H1(z^N) z^-1 + H2(z^N) z^-2 + ... + Hi(z^N) z^-i
+               +inf
+      X_D(z) = sum x_D[n] z^(-n)
+              n=-inf
 
-  . Hi(z^N ) is the z-transform of h(mN + i)
-  . Phase i of h(n) with N - 1 zeros
+               +inf                +inf
+             = sum x'[nD] z^(-n) = sum x'[k] z^(-k/D) = X'(z^(1/D))
+              n=-inf              k=-inf
+
+    Make use of the identity that the selector d[n] is equal to the normalized
+    sum of the D roots of unity:
+
+                 D-1
+      d[n] = 1/D sum W_D^(-kn),  with W_D = exp(-j 2pi/D)
+                 k=0
+
+    so:
+
+              +inf                       D-1  +inf
+      X'(z) = sum d[n] x[n] z^(-n) = 1/D sum  sum x[n] (z W_D^k)^(-n)
+             n=-inf                      k=0 n=-inf
+
+                  D-1
+            = 1/D sum X(z W_D^k)
+                  k=0
+
+    Combining the results yields the z-transform of downsampled signal in
+    terms of z-transform of the original signal:
+
+                   D-1
+      X_D(z) = 1/D sum X(z^(1/D) W_D^k)
+                   k=0
+
+    The Fourier transform of the downsampled signal is obtained by evaluating
+    X_D(z) on the unit circle with z = exp(jw) [PROAKIS Eq 10.2.9]:
+
+                      D-1
+      X_D(e^jw) = 1/D sum X(exp(j (w / D - k 2pi / D)))
+                      k=0
+
+                  w in [0:2pi>
+                  summation terms for k != 0 are aliasing terms
+
+    The resulting spectrum is the scaled sum of D superimposed copies of the
+    original spectrum X(e^jω), and each copy is shifted in frequency by a
+    multiple of 2pi/D and the result is stretched by a factor of D. This is
+    similar to sampling of an analogue signal that creates a periodization of
+    the analogue spectrum, but now the spectra are already inherently
+    2pi periodic, and downsampling creates D − 1 additional interleaved copies.
+
+- Upsampling:
 
-- Noble identities [LYONS Fig 10.20], [VAIDYANATHAN Fig 4.2.3]
+      x_U[n] = x[n / U], for n % U = 0
+             = 0, otherwise, because x_U inserts U-1 zeros in x
 
-  up sampling   : x[n] --> up Q --> H(z^Q) --> y[m], is equivalent to:
-                           H(z) --> up Q
+              +inf              +inf
+      X_U(z) = sum x_U[n] z^-n = sum x[k] z^-kU = X(z^U), with n = kU
+              n=-inf            k=-inf
 
-  down sampling : x[n] --> H(z^Q) --> down --> y[m], is equivalent to:
-                  x[n] --> down Q --> H(z) --> y[m]
+  . Spectrum, evaluate X_U(z) on unit circle [PROAKIS Eq 10.3.3]:
 
-  . Hi(z^Q) is upsampled-by-Q version of H(z), so with Q-1 zero coefficients in
-    the H(z) power series, starting at phase i
+      X_U(e^jw) = X(exp(jw U)), w in [0:2pi>
+                                X(jwL) traverses unit circle U times
 
-- LPF + downsampling = decimation:
-  . Do not calculate samples that will be thrown away.
-  . Discarding samples folds the spectrum, first the LPF has to remove all
-    folds.
-  . Sequence w(m) is an upsampled-by-D version of sequence x(n), and sequence
-    x(n) is a downsampled-by-D version of sequence w(m) [LYONS 10.9].
-    . Downsampling: W(z) = X(z^D)
+- Polyphase decomposition of H(z) [VAIDYANATHAN 4.3]:
 
-      w(m) = x(m / D), when m is multiple of D else 0
+  H(z) = H0(z^N) + H1(z^N) z^-1 + H2(z^N) z^-2 + ... + Hi(z^N) z^-i
 
-              +inf             +inf               +inf
-      W(z) =  sum w(m) z^-m =  sum w(Dk) z^-Dk =  sum x(k) z^-Dk = X(z^D)
-             m=-inf           k=-inf             k=-inf
+  . Hi(z^N ) is the z-transform of h(mN + i)
+  . Phase i of h(n) with N - 1 zeros
 
-    . Upsampling: W(z^1/D) = X(z)
+- Noble identities [LYONS Fig 10.20], HARRIS 2.2.1, VAIDYANATHAN Fig 4.2.3]
 
-      w(u) = x(m), when u is Dm else 0
+  . Down sampling : x[n] --> H(z^Q) --> Q:1  --> y[m], is equivalent to:
+                    x[n] --> Q:1    --> H(z) --> y[m]
 
-              +inf             +inf               +inf
-      W(z) =  sum w(u) z^-u =  sum w(Dm) z^-Dm =  sum x(m) z^-Dm = X(z^D)
-             u=-inf           m=-inf             m=-inf
+  . Up sampling   : x[n] --> 1:Q  --> H(z^Q) --> y[m], is equivalent to:
+                             H(z) --> 1:Q
 
-- Upsampling + LPF = interpolation:
-  . Do not calculate samples that will be inserted as zeros.
-  . Inserting zeros replicates the spectrum, the LPF remove all replicas and by
-    that it interpolates to fill in the zeros.
-  . Using zero order hold would be a naive approach, because then all samples
-    need to be calculated and the LPF then needs to compensate for the non-flat
-    pass band of sin(x)/x [LYONS 10.5.1]
+  . H_i(z^Q) is upsampled-by-Q version of H(z), so with Q-1 zero coefficients
+    in the H(z) power series, starting at phase i.
 
 - Fractional time delay [CROCHIERE 6.3]
-  . Up sampling M --> LPF --> z^(-L) --> down sampling M yields semi
-    allpass filter and delay of L / M samples
+  . Up sampling Q --> LPF --> z^(-d) --> down sampling Q yields semi allpass
+    filter and delay of d / Q samples.
 
 - Oversampling ADC and DAC
   . Every oversampling factor of 4 yields 1 extra bit, because then 1 / 4 of the
@@ -1053,6 +1183,8 @@ c) s-plane and z-plane
     frequencies. An LPF filters the higher frequencies and thus increases the
     effective number of bits of the low pass output, by about 1 bit for first
     order feedback.
+  . Noise shaping implies oversampling, because the noise is pushed outside
+    the application bandwidth.
   . ADC: [NOISESHAPING]
     For an ADC noise shaping requires feedback in the analogue domain, because
     after quantization the error information is only available when comparing
@@ -1067,11 +1199,60 @@ c) s-plane and z-plane
     more detailed accuracy on the analogue input signal level that leads to the
     extra bits for oversampling.
   . DAC: [NOISESHAPING]
-    For a DAC noise shaping uses feedback in the digital domain. The round
-    off LSbits of the LPF output are fed back to the input, so that the noise
-    power is shaped towards higher frequencies. This noise power at higher
-    frequences will be filtered by the analoge LPF that filters the DAC
-    output.
+    For a DAC noise shaping uses feedback in the digital domain. A digital
+    LPF filters the low pass band of the oversampled data and has more bits
+    than the DAC. The round off LSbits of the LPF output are fed back to the
+    input, so that the noise power is shaped towards higher frequencies. This
+    noise power at higher frequences will be filtered by the analoge LPF that
+    filters the DAC output.
+
+
+13) Quadrature Mirror Filter (QMF) [CROCHIERE 7.7, PROAKIS 10.9.6]
+
+          |-- h0[n] --> Down Q --> x0[m] --> Up Q --> f0[n] --|
+   x[n] --|                                                   +--> x^[n]
+          |-- h1[n] --> Down Q --> x1[m] --> Up Q --> f1[n] --|
+
+           Q = 2
+
+          X^(z) = T(z)X(z) + A(z)X(-z), with:
+                . T(z) = H0(z)F0(z) + H1(z)F1(z), transfer part
+                . A(z) = H0(-z)F0(z) + H1(-z)F1(z), aliasing part
+
+- Choose:
+    h0[n] = h[n]           <==>  H0(w) = H(w), prototype LPF
+    h1[n] = (-1)^n h[n]    <==>  H1(w) = H(w - pi), mirror image HPF
+    f0[n] = Q h[n]         <==>  F0(w) = Q H(w)
+    f1[n] = -Q (-1)^n h[n] <==>  F1(w) = -Q H(w - pi), to eliminate aliasing,
+                                 so A(w) = 0
+
+    then: X^(w) = T(w) X(w), with T(w) = H^2(w) - H^2(w - pi)
+
+- Get HPF from LPF using frequency shift (complex modulation) by pi = fNyquist,
+  so: h1[n] = h[n] exp(+j pi n) <==> H(w - pi)
+            = h[n] cos(j pi n)
+            = h[n] (-1)^n
+- For perfect reconstruction T(w) = 1. This can only be achieved for a two tap
+  FIR filter, because Q = 2, so each phase then becomes a delay.
+- Choose linear phase (= symmetric) FIR filter:
+
+   h[n] = h[N - 1 - n), for n = 0,1,...,N-1
+
+   H(w) = Hr(w) exp(-j w (N - 1) / 2), where Hr is real function, so:
+
+      Hr^2(w) = |H(w)|^2
+
+      H^2(w)      =            |H(w)     |^2 exp(-j w (N - 1))
+      H^2(w - pi) = (-1)^(N-1) |H(w - pi)|^2 exp(-j w (N - 1))
+
+      T(w) = |H(w)|^2 - (-1)^(N-1) |H(w - pi)|^2
+
+   For N is odd, T(pi / 2) = 0, therefore choose N is even:
+
+      T(w) = |H(w)|^2 + |H(w - pi)|^2
+
+   For approximate reconstruction optimize for both maximum attenuation in
+   stop band of H(w) and all pass for T(w).
 
 
 Appendix A) Signal operators [JOS1 7.2]
diff --git a/applications/lofar2/model/pfb_os/quadrature_mirror_filter.ipynb b/applications/lofar2/model/pfb_os/quadrature_mirror_filter.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b2a9d7694a0c3f08b585d5eb3ee2b465ff798cd1
--- /dev/null
+++ b/applications/lofar2/model/pfb_os/quadrature_mirror_filter.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c69a2eb8",
+   "metadata": {},
+   "source": [
+    "# Quadrature Mirror Filter (QMF)\n",
+    "\n",
+    "Author: Eric Kooistra, Jul 2024\n",
+    "\n",
+    "Purpose:\n",
+    "* Practise DSP [1].\n",
+    "* Implement a QMF \n",
+    "\n",
+    "Results:\n",
+    "* For Ncoefs = Q = 2 the QMF yields perfect reconstruction [1], typically then choose hPrototype = [1, 1]\n",
+    "* For Ncoefs is even the QMF has high SNR, so magnitude reponse is near all pass\n",
+    "* For Ncoefs is odd the QMF response goes to 0 at fNyquist, as expected [1]\n",
+    "\n",
+    "References:\n",
+    "1. dsp_study_erko, summary of DSP books"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "02689e50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "65235f50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Auto reload module when it is changed\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "# Add rtdsp module path to $PYTHONPATH\n",
+    "import os\n",
+    "import sys\n",
+    "module_path = os.path.abspath(os.path.join('../'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.insert(0, module_path)\n",
+    "\n",
+    "# Import rtdsp\n",
+    "from rtdsp.firfilter import design_fir_low_pass_filter_adjust, nof_taps_kaiser_window, nof_taps_remez\n",
+    "from rtdsp.fourier import dtft, estimate_gain_at_frequency, estimate_frequency_at_gain\n",
+    "from rtdsp.multirate import downsample, upsample\n",
+    "from rtdsp.plotting import plot_power_spectrum, plot_magnitude_spectrum, plot_spectra\n",
+    "from rtdsp.utilities import pow_db"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c5c90a6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Samples\n",
+    "fs = 1.0  # sample rate\n",
+    "ts = 1 / fs  # sample period\n",
+    "fNyquist = fs / 2\n",
+    "\n",
+    "# Time\n",
+    "Nsim = 10000  # number of samples, choose >> QMF group delay = Ncoefs\n",
+    "t = np.arange(0, Nsim) * ts  # len(t) == NSim\n",
+    "\n",
+    "# QMF specifications\n",
+    "Q = 2\n",
+    "\n",
+    "firType = 'twotap'\n",
+    "firType = 'ntap'\n",
+    "\n",
+    "if firType == 'ntap':\n",
+    "    method = 'firls'\n",
+    "    method = 'remez'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2114358e",
+   "metadata": {},
+   "source": [
+    "# 1. Waveform Generator (WG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "43622c4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wgType = 'sinus'\n",
+    "wgType = 'noise'\n",
+    "\n",
+    "if wgType == 'sinus':\n",
+    "    freq = 0.8 * fNyquist\n",
+    "    phase = np.pi / 3\n",
+    "    wgData = np.sin(2 * np.pi * freq * t + phase) \n",
+    "if wgType == 'noise':\n",
+    "    rng = np.random.default_rng()\n",
+    "    mu = 0.0\n",
+    "    sigma = 1.0\n",
+    "    wgData = rng.normal(mu, sigma, Nsim)\n",
+    "    \n",
+    "plt.plot(t, wgData, 'b')\n",
+    "plt.title(wgType)\n",
+    "plt.xlabel('t [ts = ' + str(ts) + ']')\n",
+    "plt.ylabel('voltage')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15bf6804",
+   "metadata": {},
+   "source": [
+    "# 2. QMF filters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b00dac9",
+   "metadata": {},
+   "source": [
+    "## 2.1 Prototype LPF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bd587f6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> design_fir_low_pass_filter():\n",
+      ". Method              = remez\n",
+      ". Q                   = 1.000000\n",
+      ". fpass               = 0.215991\n",
+      ". fstop               = 0.300000\n",
+      ". lpBW                = 0.515991\n",
+      ". transistionBW       = 0.084009\n",
+      ". fNyquist            = 0.500000\n",
+      ". fs                  = 1.000000\n",
+      ". Ncoefs              = 60\n",
+      ". DC sum              = 1.000000\n",
+      ". Symmetrical coefs   = True\n",
+      "\n",
+      "> design_fir_low_pass_filter_adjust():\n",
+      ". nofIterations        = 20\n",
+      ". FP / fpass           = 1.079956\n",
+      ". FS / fstop           = 1.000000\n",
+      ". fcutoff              = 0.250000\n",
+      ". fGain                = 0.707066\n",
+      ". fGain**2             = 0.499942\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Half power at center frequency of transition band\n",
+    "fcutoff = fs / 4\n",
+    "# Gain at fcutoff center frequency of the transition band\n",
+    "cutoffGain = 0.5\n",
+    "cutoffGain = np.sqrt(0.5)  # two in series yield 0.5\n",
+    "\n",
+    "if firType == 'twotap':\n",
+    "    Ncoefs = Q\n",
+    "    hPrototype = np.array([1.0, 1.0])\n",
+    "    #hPrototype = np.array([1.0, 0.5])\n",
+    "    hPrototype /= np.sum(hPrototype)\n",
+    "    \n",
+    "if firType == 'ntap':\n",
+    "    Ncoefs = 60\n",
+    "    cutoffBeta = 0.2\n",
+    "    fpass = (1 - cutoffBeta) * fcutoff  # pass band frequency\n",
+    "    fstop = (1 + cutoffBeta) * fcutoff  # stop band frequency\n",
+    "    # weight pass band ripple versus stop band ripple\n",
+    "    rippleWeights = [1, 1]\n",
+    "\n",
+    "    hPrototype = design_fir_low_pass_filter_adjust(method,\n",
+    "                 Ncoefs, fpass, fstop, fcutoff, cutoffGain, rippleWeights=[1, 1],\n",
+    "                 kaiserBeta=0, fs=fs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "59c4072a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nof_taps_kaiser_window = 51\n",
+      "nof_taps_remez         = 36\n"
+     ]
+    }
+   ],
+   "source": [
+    "atten_db = 80\n",
+    "print('nof_taps_kaiser_window = %d' % nof_taps_kaiser_window(fs, fpass, fstop, atten_db))\n",
+    "print('nof_taps_remez         = %d' % nof_taps_remez(fs, fpass, fstop, atten_db))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0a69b385",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LPF: estimate_gain_at_frequency fcutoff = 2.500000e-01 [Hz]:\n",
+      ". fIndex = 768\n",
+      ". fValue = 2.500000e-01 [Hz]\n",
+      ". fGain = 7.070661e-01\n",
+      "LPF: estimate_frequency_at_gain cutoffGain = 7.071068e-01:\n",
+      ". fIndex = 768\n",
+      ". fValue = 2.500000e-01 [Hz]\n",
+      ". fGain = 7.070661e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot LPF and HPF\n",
+    "hLpf = hPrototype\n",
+    "hHpf = hPrototype * np.cos(np.pi * np.arange(Ncoefs))  # hLpf * (-1)^n\n",
+    "\n",
+    "# Plot impulse response\n",
+    "plt.figure(1)\n",
+    "plt.plot(hPrototype, 'g')\n",
+    "plt.title('Impulse response')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Plot transfer function\n",
+    "_, fn, HFlpf = dtft(hLpf)\n",
+    "_, _, HFhpf = dtft(hHpf)\n",
+    "f = fn * fs\n",
+    "plt.figure(2)\n",
+    "fLim = (-3, 3)\n",
+    "fLim = None\n",
+    "dbLim = (-140, 5)\n",
+    "#dbLim = (-10, 5)\n",
+    "plot_power_spectrum(fn, HFlpf, 'r', fs, fLim, dbLim)\n",
+    "plot_power_spectrum(fn, HFhpf, 'g', fs, fLim, dbLim)\n",
+    "plt.legend(['LPF', 'HPF'])\n",
+    "plt.xlabel('Frequency [Hz]')\n",
+    "\n",
+    "plt.figure(3)\n",
+    "fLim = (0, 1)  # Zoom in at cutoffGain\n",
+    "fLim = None\n",
+    "voltLim = (0, 1.1)\n",
+    "plot_magnitude_spectrum(fn, HFlpf, 'r', fs, fLim, voltLim)\n",
+    "plot_magnitude_spectrum(fn, HFhpf, 'g', fs, fLim, voltLim)\n",
+    "plt.legend(['LPF', 'HPF'])\n",
+    "plt.xlabel('Frequency [Hz]')\n",
+    "\n",
+    "# Log abs(HF) at fcutoff\n",
+    "fIndex, fValue, fGain = estimate_gain_at_frequency(f, HFlpf, fcutoff)\n",
+    "print('LPF: estimate_gain_at_frequency fcutoff = %e [Hz]:' % fcutoff)\n",
+    "print('. fIndex = %d' % fIndex)\n",
+    "print('. fValue = %e [Hz]' % fValue)\n",
+    "print('. fGain = %e' % fGain)\n",
+    "fIndex, fValue, fGain = estimate_frequency_at_gain(f, HFlpf, cutoffGain)\n",
+    "print('LPF: estimate_frequency_at_gain cutoffGain = %e:' % cutoffGain)\n",
+    "print('. fIndex = %d' % fIndex)\n",
+    "print('. fValue = %e [Hz]' % fValue)\n",
+    "print('. fGain = %e' % fGain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b72c836",
+   "metadata": {},
+   "source": [
+    "## 2.2 Amplitude response\n",
+    "\n",
+    "For perfect reconstruction the amplitude response of the QMF should be all pass, so QMF magnitude reponse T(w) = 1 [1].\n",
+    "\n",
+    "T(w) = H^2(w) - H^2(w - pi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "91a2e7d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SNR_TF_db = 48.97 [dB]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TF = HFlpf**2 - HFhpf**2\n",
+    "TFpowers = np.abs(TF)\n",
+    "TFmean = np.mean(TFpowers)\n",
+    "\n",
+    "allPassPowers = TFmean * np.ones(len(TF))  # Expected all pass power\n",
+    "diffPowers = np.abs(allPassPowers - TFpowers)\n",
+    "SNR_TF_db = 10 * np.log10(np.sum(allPassPowers / diffPowers) / len(TF))\n",
+    "print('SNR_TF_db = %.2f [dB]' % SNR_TF_db)\n",
+    "\n",
+    "fLim = None\n",
+    "dbLim = None\n",
+    "plot_spectra(fn, TF, fs, fLim, dbLim)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9358e628",
+   "metadata": {},
+   "source": [
+    "# 3. QMF analysis and synthesis structure\n",
+    "\n",
+    "```\n",
+    "         |-- h0[n] --> Down Q --> x0[m] --> Up Q --> f0[n] --> y0 --|\n",
+    "  x[n] --|                                                          +--> y[n] = x^[n]\n",
+    "         |-- h1[n] --> Down Q --> x1[m] --> Up Q --> f1[n] --> y1 --|\n",
+    "\n",
+    "Downsample and upsample factor:\n",
+    "  Q = 2\n",
+    "\n",
+    "Filters:\n",
+    "  h0[n] = h[n]           <==>  H0(w) = H(w), prototype LPF\n",
+    "  h1[n] = (-1)^n h[n]    <==>  H1(w) = H(w - pi), mirror image HPF\n",
+    "  f0[n] = Q h[n]         <==>  F0(w) = Q H(w)\n",
+    "  f1[n] = -Q (-1)^n h[n] <==>  F1(w) = -Q H(w - pi), to eliminate aliasing\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "516501cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process QMF\n",
+    "# . Define filters\n",
+    "h0 = hLpf\n",
+    "h1 = hHpf\n",
+    "f0 = Q * hLpf\n",
+    "f1 = -Q * hHpf\n",
+    "# . Analysis section\n",
+    "x0 = downsample(wgData, Q, h0, verbosity=0)\n",
+    "x1 = downsample(wgData, Q, h1, verbosity=0)\n",
+    "# . Synthesis section\n",
+    "y0 = upsample(x0, Q, f0, verbosity=0)\n",
+    "y1 = upsample(x1, Q, f1, verbosity=0)\n",
+    "qmfData = y0 + y1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6d0cb603",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean(y / x) = 0.9987642668\n",
+      "QMF gain    = 1.0000000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total QMF group delay is (Ncoefs - 1) / 2 + (Ncoefs - 1) / 2\n",
+    "groupDelay = Ncoefs - 1\n",
+    "\n",
+    "# Strip group delay\n",
+    "x = wgData[0:-groupDelay]\n",
+    "y = qmfData[groupDelay:]\n",
+    "if firType == 'twotap':\n",
+    "    gain = np.mean(y / x)\n",
+    "else:\n",
+    "    gain = 1.0\n",
+    "diff = x - y / gain\n",
+    "tt = t[groupDelay:]\n",
+    "print('mean(y / x) = %.10f' % np.mean(y / x))\n",
+    "print('QMF gain    = %.10f' % gain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "abf4d9ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SNRdb = 68.83 [dB]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compare reconstructed output with input\n",
+    "SNRdb = pow_db(np.std(x) / np.std(diff))\n",
+    "print('SNRdb = %.2f [dB]' % SNRdb)\n",
+    "\n",
+    "plt.plot(tt, x, 'r')\n",
+    "plt.plot(tt, y, 'b')\n",
+    "plt.plot(tt, diff, 'm')\n",
+    "plt.title('Reconstructed')\n",
+    "plt.xlabel('t [ts = ' + str(ts) + ']')\n",
+    "plt.ylabel('voltage')\n",
+    "plt.xlim((200, 300))\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "628925af",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/applications/lofar2/model/rtdsp/firfilter.py b/applications/lofar2/model/rtdsp/firfilter.py
index 55ef871462e0299fbdc84e076c132f7bf21803a1..8ae1a6c419c7b24b005a570ab4d8c6de0fd0d819 100644
--- a/applications/lofar2/model/rtdsp/firfilter.py
+++ b/applications/lofar2/model/rtdsp/firfilter.py
@@ -297,7 +297,7 @@ def design_fir_low_pass_filter(method,
 
 def design_fir_low_pass_filter_adjust(method,
                                       Ncoefs, fpass, fstop, fcutoff, cutoffGain=0.5, rippleWeights=[1, 1],
-                                      kaiserBeta=0, fs=1.0, resolution=1e-3, verbosity=1):
+                                      kaiserBeta=0, fs=1.0, resolution=1e-4, verbosity=1):
     """Derive FIR coefficients for low pass filter for exact fcutoff
 
     Uses design_fir_low_pass_filter() but achieves cutoffGain at fcutoff by adjusting fpass or fstop, and
diff --git a/applications/lofar2/model/rtdsp/multirate.py b/applications/lofar2/model/rtdsp/multirate.py
index 06d1cbbf3de9fe9889dff3191287ae6cd3583338..590977700280ba5e5210a89697214b7eedad61cd 100644
--- a/applications/lofar2/model/rtdsp/multirate.py
+++ b/applications/lofar2/model/rtdsp/multirate.py
@@ -134,7 +134,7 @@ class PolyPhaseFirFilterStructure:
         return outData
 
 
-def upsample(x, Nup, coefs, verify=False):  # interpolate
+def upsample(x, Nup, coefs, verify=False, verbosity=1):  # interpolate
     """Upsample x by factor I = Nup
 
     Input:
@@ -143,6 +143,7 @@ def upsample(x, Nup, coefs, verify=False):  # interpolate
     . coefs: FIR filter coefficients for antialiasing LPF
     . verify: when True then verify that output y is the same when calculated directly or when calculated using the
               polyphase implementation.
+    - verbosity: when > 0 print() status, else no print()
     Return:
     . y: Upsampled output signal y[m]
 
@@ -242,15 +243,16 @@ def upsample(x, Nup, coefs, verify=False):  # interpolate
         else:
             print('ERROR: wrong upsample result')
 
-    print('> upsample():')
-    print('. Nup = ' + str(Nup))
-    print('. Nx = ' + str(Nx))
-    print('. len(y) = ' + str(len(y)))
-    print('')
+    if verbosity:
+        print('> upsample():')
+        print('. Nup = ' + str(Nup))
+        print('. Nx = ' + str(Nx))
+        print('. len(y) = ' + str(len(y)))
+        print('')
     return y
 
 
-def downsample(x, Ndown, coefs, verify=False):  # decimate
+def downsample(x, Ndown, coefs, verify=False, verbosity=1):  # decimate
     """Downsample x by factor D = Ndown up
 
     Input:
@@ -259,6 +261,7 @@ def downsample(x, Ndown, coefs, verify=False):  # decimate
     . coefs: FIR filter coefficients for antialiasing LPF
     . verify: when True then verify that output y is the same when calculated directly or when calculated using the
               polyphase implementation.
+    - verbosity: when > 0 print() status, else no print()
     Return:
     . y: Downsampled output signal y[m]
 
@@ -368,17 +371,18 @@ def downsample(x, Ndown, coefs, verify=False):  # decimate
         else:
             print('ERROR: wrong downsample result')
 
-    print('> downsample():')
-    print('. len(x) = ' + str(len(x)))
-    print('. Ndown = ' + str(Ndown))
-    print('. Nx = ' + str(Nx))
-    print('. Nxp = ' + str(Nxp))
-    print('. len(y) = ' + str(len(y)))  # = Nxp
-    print('')
+    if verbosity:
+        print('> downsample():')
+        print('. len(x) = ' + str(len(x)))
+        print('. Ndown = ' + str(Ndown))
+        print('. Nx = ' + str(Nx))
+        print('. Nxp = ' + str(Nxp))
+        print('. len(y) = ' + str(len(y)))  # = Nxp
+        print('')
     return y
 
 
-def resample(x, Nup, Ndown, coefs, verify=False):  # interpolate and decimate by Nup / Ndown
+def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate and decimate by Nup / Ndown
     """Resample x by factor I / D = Nup / Ndown
 
     x[n] --> Nup --> v[m] --> LPF --> w[m] --> Ndown --> y[k]
@@ -408,6 +412,7 @@ def resample(x, Nup, Ndown, coefs, verify=False):  # interpolate and decimate by
     . coefs: FIR filter coefficients for antialiasing LPF
     . verify: when True then verify that output y is the same when calculated directly or when calculated using the
               polyphase implementation.
+    - verbosity: when > 0 print() status, else no print()
     Return:
     . y: Resampled output signal y[m]
 
@@ -458,12 +463,13 @@ def resample(x, Nup, Ndown, coefs, verify=False):  # interpolate and decimate by
         else:
             print('ERROR: wrong resample result')
 
-    print('> resample():')
-    print('. len(x) = ' + str(len(x)))
-    print('. Nx = ' + str(Nx))
-    print('. len(v) = ' + str(len(v)))
-    print('. Ny = ' + str(Ny))
-    print('. Nyp = ' + str(Nyp))
-    print('. len(y) = ' + str(len(y)))
-    print('')
+    if verbosity:
+        print('> resample():')
+        print('. len(x) = ' + str(len(x)))
+        print('. Nx = ' + str(Nx))
+        print('. len(v) = ' + str(len(v)))
+        print('. Ny = ' + str(Ny))
+        print('. Nyp = ' + str(Nyp))
+        print('. len(y) = ' + str(len(y)))
+        print('')
     return y
diff --git a/doc/erko_howto_tools.txt b/doc/erko_howto_tools.txt
index bb5c591ca8ac113917df9111313370d587c48315..07fe7059f695ca790b2fcf570be16c9c4f5782b4 100755
--- a/doc/erko_howto_tools.txt
+++ b/doc/erko_howto_tools.txt
@@ -1,4 +1,5 @@
 * RadioHDL with GIT (LOFAR2.0)
+* Network card MTU and IP address
 * Flash and reboot unb2
 * Flash and reboot on L2TS using tunnel to SDPTR
 * HDL images to keep on dop349
@@ -229,6 +230,29 @@ Synthesis neemt onegeveer 4GB voor Disturb image en 3GB voor SDP image, dus
 ongeveer 14GB / weekend. De regtest machine heeft nog 500GB vrij, dus we
 kunnen nog ongeveer 35x opslaan, voordat we moeten deleten.
 
+*******************************************************************************
+* Network card MTU and IP address
+*******************************************************************************
+
+# MTU wijzigen (when statistics packets get lost, show as Rx error in ifconfig)
+sudo ifconfig enp67s0f1 10.99.0.249 netmask 255.255.0.0 mtu 9000 up
+
+# When dop386 enp5s0 1GbE interface with sdp-arts has no ip address do:
+sudo ifconfig enp5s0 10.99.0.254
+sudo ifconfig enp5s0 netmask 255.255.0.0
+
+# When dop386 ens2 10GbE interface looses its IP or mtu settings (e.g. due
+# to PC reboot or cable reconnect) then to recover do:
+sudo ifconfig ens2 192.168.0.1 netmask 255.255.0.0
+sudo ip link set dev ens2 mtu 9000
+sudo ifconfig ens2 down
+sudo ifconfig ens2 up
+
+Last SDPFW version used in 2022:
+  kooistra@dop386:~/git/sdptr$ sdp_rw.py --host 10.99.0.250 --port 4842 -r firmware_version
+  read firmware_version:
+  node 64:  2022-11-06T02.45.36_e6769e2e3_lofar2_unb2b_sdp_station_full_wg
+
 *******************************************************************************
 * Flash and reboot unb2
 *******************************************************************************
@@ -281,19 +305,6 @@ sdp_rw.py --host localhost --port 4840 -r firmware_version
 sdp_firmware.py --host localhost --port 4840 -n 0 --reboot --image USER
 sdp_rw.py --host localhost --port 4840 -r firmware_version
 
-
-# MTU wijzigen (when statistics packets get lost, show as Rx error in ifconfig)
-sudo ifconfig enp67s0f1 10.99.0.249 netmask 255.255.0.0 mtu 9000 up
-
-# When dop386 with sdp-arts has no ip address do:
-sudo ifconfig enp5s0 10.99.0.254
-sudo ifconfig enp5s0 netmask 255.255.0.0
-
-Last SDPFW version used in 2022:
-  kooistra@dop386:~/git/sdptr$ sdp_rw.py --host 10.99.0.250 --port 4842 -r firmware_version
-  read firmware_version:
-  node 64:  2022-11-06T02.45.36_e6769e2e3_lofar2_unb2b_sdp_station_full_wg
-
 *******************************************************************************
 * Flash and reboot on L2TS using tunnel to SDPTR
 *******************************************************************************
diff --git a/libraries/base/common/hdllib.cfg b/libraries/base/common/hdllib.cfg
index 2958ab8001bc0c84c824da12b52acf598b0be7f1..345514a40e0830d2635e3636ead8befc57f14e40 100644
--- a/libraries/base/common/hdllib.cfg
+++ b/libraries/base/common/hdllib.cfg
@@ -1,8 +1,8 @@
 hdl_lib_name = common
 hdl_library_clause_name = common_lib
 hdl_lib_uses_synth = technology tech_memory tech_fifo tech_iobuf tst
-hdl_lib_uses_sim = 
-hdl_lib_technology = 
+hdl_lib_uses_sim =
+hdl_lib_technology =
 
 synth_files =
     $HDL_WORK/libraries/base/common/src/vhdl/common_pkg.vhd
@@ -15,19 +15,20 @@ synth_files =
     src/vhdl/common_network_layers_pkg.vhd
     src/vhdl/common_network_total_header_pkg.vhd
     src/vhdl/common_components_pkg.vhd
-    
+
     #src/ip/MegaWizard/iobuf_in.vhd
-    
+
+    src/vhdl/common_pipeline.vhd
+    src/vhdl/common_pipeline_sl.vhd
+    src/vhdl/common_pipeline_integer.vhd
+    src/vhdl/common_pipeline_natural.vhd
+
     src/vhdl/common_async.vhd
     src/vhdl/common_async_slv.vhd
     src/vhdl/common_areset.vhd
     src/vhdl/common_acapture.vhd
     src/vhdl/common_acapture_slv.vhd
-    src/vhdl/common_pipeline.vhd
-    src/vhdl/common_pipeline_sl.vhd
-    src/vhdl/common_pipeline_integer.vhd
-    src/vhdl/common_pipeline_natural.vhd
-    
+
     src/vhdl/common_ram_crw_crw_ratio.vhd
     src/vhdl/common_ram_cr_cw_ratio.vhd
     src/vhdl/common_ram_crw_crw.vhd
@@ -37,14 +38,14 @@ synth_files =
     src/vhdl/common_ram_rw_rw.vhd
     src/vhdl/common_ram_r_w.vhd
     src/vhdl/common_rom.vhd
-    
+
     src/vhdl/common_fifo_sc.vhd
     src/vhdl/common_fifo_dc.vhd
     src/vhdl/common_fifo_dc_mixed_widths.vhd
-    
+
     src/vhdl/common_ddio_in.vhd
     src/vhdl/common_ddio_out.vhd
-    
+
     src/vhdl/common_create_strobes_from_valid.vhd
     src/vhdl/common_wideband_data_scope.vhd
     src/vhdl/common_iobuf_in.vhd
@@ -92,7 +93,7 @@ synth_files =
     src/vhdl/common_transpose_symbol.vhd
     src/vhdl/common_transpose.vhd
     src/vhdl/common_peak.vhd
-    
+
     src/vhdl/common_complex_round.vhd
     src/vhdl/common_add_sub.vhd
     src/vhdl/common_complex_add_sub.vhd
@@ -104,7 +105,7 @@ synth_files =
     src/vhdl/common_adder_tree_a_str.vhd
     src/vhdl/common_operation.vhd
     src/vhdl/common_operation_tree.vhd
-    
+
     src/vhdl/common_rl_decrease.vhd
     src/vhdl/common_rl_increase.vhd
     src/vhdl/common_rl_register.vhd
@@ -131,25 +132,25 @@ synth_files =
     src/vhdl/common_bit_delay.vhd
     src/vhdl/common_delay.vhd
     src/vhdl/common_shiftram.vhd
-    
+
     src/vhdl/mms_common_reg.vhd
-    
+
     src/vhdl/common_variable_delay.vhd
     src/vhdl/mms_common_variable_delay.vhd
     src/vhdl/mms_common_stable_monitor.vhd
     src/vhdl/common_pulse_delay_reg.vhd
     src/vhdl/mms_common_pulse_delay.vhd
-        
+
     src/vhdl/avs_common_mm.vhd
     src/vhdl/avs_common_mm_irq.vhd
     src/vhdl/avs_common_mm_readlatency0.vhd
     src/vhdl/avs_common_mm_readlatency2.vhd
     src/vhdl/avs_common_reg_r_w.vhd
-    
+
     tb/vhdl/tb_common_pkg.vhd
     tb/vhdl/tb_common_mem_pkg.vhd
-    
-test_bench_files = 
+
+test_bench_files =
     tb/vhdl/tb_common_log.vhd
     tb/vhdl/tb_common_acapture.vhd
     tb/vhdl/tb_common_add_sub.vhd
@@ -219,7 +220,7 @@ test_bench_files =
     tb/vhdl/tb_tb_common_transpose.vhd
     tb/vhdl/tb_tb_common_create_strobes_from_valid.vhd
 
-regression_test_vhdl = 
+regression_test_vhdl =
     tb/vhdl/tb_common_fifo_rd.vhd
     tb/vhdl/tb_common_mem_mux.vhd
     tb/vhdl/tb_common_paged_ram_crw_crw.vhd
@@ -244,7 +245,7 @@ regression_test_vhdl =
     tb/vhdl/tb_tb_common_fanout_tree.vhd
     tb/vhdl/tb_tb_common_multiplexer.vhd
     tb/vhdl/tb_tb_common_operation_tree.vhd
-    tb/vhdl/tb_tb_common_paged_ram_ww_rr.vhd 
+    tb/vhdl/tb_tb_common_paged_ram_ww_rr.vhd
     tb/vhdl/tb_tb_common_reorder_symbol.vhd
     tb/vhdl/tb_tb_common_rl.vhd
     tb/vhdl/tb_tb_common_rl_register.vhd
diff --git a/libraries/base/common/src/vhdl/common_areset.vhd b/libraries/base/common/src/vhdl/common_areset.vhd
index 13c3a2454c660456b76f0b0f3bfc1d6da2f559bb..57cc27cb308c6a760b2a8dde1b7d37d4f908d468 100644
--- a/libraries/base/common/src/vhdl/common_areset.vhd
+++ b/libraries/base/common/src/vhdl/common_areset.vhd
@@ -23,7 +23,8 @@
 -- Purpose: Immediately apply reset and synchronously release it at rising clk
 -- Description:
 --   When in_rst gets asserted, then the out_rst gets asserted immediately (= asynchronous reset apply).
---   When in_rst gets de-assered, then out_rst gets de-asserted after g_delay_len cycles (= synchronous reset release).
+--   When in_rst gets de-assered, then out_rst gets de-asserted after g_delay_len cycles (= synchronous
+--   reset release) + g_tree_len cycles (synchronous reset tree).
 --
 --   The in_rst assert level is set by g_in_rst_level.
 --   The out_rst assert level is set by c_out_rst_level = g_rst_level.
@@ -40,7 +41,8 @@ entity common_areset is
     g_in_rst_level : std_logic := '1';  -- = in_rst level
     g_rst_level    : std_logic := '1';  -- = out_rst level (keep original generic
                                         --   name for backward compatibility)
-    g_delay_len    : natural   := c_meta_delay_len
+    g_delay_len    : natural := c_meta_delay_len;
+    g_tree_len     : natural := c_tree_delay_len
   );
   port (
     in_rst    : in  std_logic;
@@ -50,13 +52,18 @@ entity common_areset is
 end;
 
 architecture str of common_areset is
+  constant c_out_rst_value   : natural   := to_int(g_rst_level);
   constant c_out_rst_level   : std_logic := g_rst_level;
   constant c_out_rst_level_n : std_logic := not g_rst_level;
 
   signal i_rst               : std_logic;
+  signal o_rst               : std_logic;
 begin
   i_rst <= in_rst when g_in_rst_level = '1' else not in_rst;
 
+  -- 2009
+  -- Capture asynchronous reset assertion, to also support i_rst when there is
+  -- no clk.
   u_async : entity work.common_async
   generic map (
     g_rst_level => c_out_rst_level,
@@ -66,6 +73,24 @@ begin
     rst  => i_rst,
     clk  => clk,
     din  => c_out_rst_level_n,
-    dout => out_rst
+    dout => o_rst
+  );
+
+  -- 2024
+  -- Pass on synchronized reset with sufficient g_tree_len to ease timing
+  -- closure by FF duplication in out_rst tree. Keep rst = '0' to break
+  -- combinatorial path with in_rst to ease timing closure in the reset tree
+  -- network. Use g_tree_len = 0 for wire out_rst <= o_rst, so no reset tree
+  -- as in 2009.
+  u_pipe : entity work.common_pipeline_sl
+  generic map (
+    g_pipeline    => g_tree_len,
+    g_reset_value => c_out_rst_value
+  )
+  port map (
+    rst     => '0',
+    clk     => clk,
+    in_dat  => o_rst,
+    out_dat => out_rst
   );
 end str;
diff --git a/libraries/base/common/src/vhdl/common_pkg.vhd b/libraries/base/common/src/vhdl/common_pkg.vhd
index c072c8ea51468002ad4fab43e828f2d586cc1ae9..c40db3363dec211ada0560a108e162a63f6046d8 100644
--- a/libraries/base/common/src/vhdl/common_pkg.vhd
+++ b/libraries/base/common/src/vhdl/common_pkg.vhd
@@ -86,6 +86,7 @@ package common_pkg is
   constant c_eps                  : real := 1.0e-20;  -- add small epsilon value to avoid 1/0 and log(0), 1e-20 < 1/2**64
 
   -- FF, block RAM, FIFO
+  constant c_tree_delay_len       : natural := 10;  -- reset clock tree pipelining to facilitate FF duplication by synthesis tool
   constant c_meta_delay_len       : natural := 3;  -- default nof flipflops (FF) in meta stability recovery delay line (e.g. for clock domain crossing)
   constant c_meta_fifo_depth      : natural := 16;  -- default use 16 word deep FIFO to cross clock domain, typically > 2*c_meta_delay_len or >~ 8 is enough
 
@@ -214,7 +215,9 @@ package common_pkg is
   function slv(n: in std_logic)        return std_logic_vector;  -- standard logic to 1 element standard logic vector
   function sl( n: in std_logic_vector) return std_logic;  -- 1 element standard logic vector to standard logic
 
-  function to_sl(  n: in boolean)   return std_logic;  -- if TRUE       then return '1'   else '0'
+  function to_sl(  n: in boolean)   return std_logic;  -- if TRUE     then return '1'   else '0'
+  function to_sl(  n: in integer)   return std_logic;  -- if 0        then return '0'   else '1'
+  function to_int( n: in std_logic) return integer;  -- if '1' or 'H' then return '1'   else '0'
   function to_bool(n: in std_logic) return boolean;  -- if '1' or 'H' then return TRUE  else FALSE
   function to_bool(n: in integer)   return boolean;  -- if  0         then return FALSE else TRUE
 
@@ -506,8 +509,12 @@ package common_pkg is
   function COMPLEX_IM(ampl, phase : real)                       return real;  -- phase in degrees
   function COMPLEX_IM(ampl, phase : integer)                    return real;  -- phase in degrees
 
-  function SHIFT_UVEC(vec : std_logic_vector; shift : integer) return std_logic_vector;  -- < 0 shift left, > 0 shift right
-  function SHIFT_SVEC(vec : std_logic_vector; shift : integer) return std_logic_vector;  -- < 0 shift left, > 0 shift right
+  -- shift < 0 : shift left, shift > 0 : shift right, so divide by 2**shift
+  -- use shift to ensure that synthesis tool recognizes power of 2 multiply or divide as a shift
+  function SHIFT_UVEC(vec : std_logic_vector; shift : integer) return std_logic_vector;
+  function SHIFT_SVEC(vec : std_logic_vector; shift : integer) return std_logic_vector;
+  function SHIFT_UINT(uint : natural; shift : integer) return natural;
+  function SHIFT_SINT(sint : integer; shift : integer) return integer;
 
   function ROTATE_UVEC(vec : std_logic_vector; shift : integer) return std_logic_vector;  -- < 0 rotate left, > 0 rotate right
 
@@ -773,6 +780,24 @@ package body common_pkg is
     end if;
   end;
 
+  function to_sl(n: in integer) return std_logic is
+  begin
+    if n = 0 then
+      return '0';
+    else
+      return '1';
+    end if;
+  end;
+
+  function to_int(n: in std_logic) return integer is
+  begin
+    if n = '1' or n = 'H' then
+      return 1;
+    else
+      return 0;
+    end if;
+  end;
+
   function to_bool(n: in std_logic) return boolean is
   begin
     return n = '1' or n = 'H';
@@ -2439,6 +2464,16 @@ package body common_pkg is
     end if;
   end;
 
+  function SHIFT_UINT(uint : natural; shift : integer) return natural is
+  begin
+    return TO_UINT(SHIFT_UVEC(TO_UVEC(uint, c_word_w), shift));
+  end;
+
+  function SHIFT_SINT(sint : integer; shift : integer) return integer is
+  begin
+    return TO_SINT(SHIFT_SVEC(TO_SVEC(sint, c_word_w), shift));
+  end;
+
   function ROTATE_UVEC(vec : std_logic_vector; shift : integer) return std_logic_vector is
   begin
     if shift < 0 then
diff --git a/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler.vhd b/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler.vhd
index 4cab59e25a3a5fb7940625da40cb2af9a33d673f..c7ae7c2f78b5913c6ab2482265207978c3538be0 100644
--- a/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler.vhd
+++ b/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler.vhd
@@ -76,6 +76,7 @@ entity mmp_dp_bsn_sync_scheduler is
     -- MM control
     reg_mosi        : in  t_mem_mosi := c_mem_mosi_rst;
     reg_miso        : out t_mem_miso;
+    reg_ctrl_interval_size : out natural;
 
     -- Streaming
     in_sosi            : in t_dp_sosi;
@@ -123,6 +124,8 @@ begin
   rd_input_bsn_at_sync_64 <= RESIZE_UVEC(mon_input_bsn_at_sync, 2 * c_word_w);
   rd_output_sync_bsn_64   <= RESIZE_UVEC(mon_output_sync_bsn,   2 * c_word_w);
 
+  reg_ctrl_interval_size <= ctrl_interval_size when rising_edge(dp_clk);
+
   -- Register mapping
   -- . Write
   wr_ctrl_enable                                  <=         reg_wr(                              0);
diff --git a/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler_arr.vhd b/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler_arr.vhd
index 85a154fa94cf94049225eab31c7d7e19d21e33c3..76f3599c7b1f025083e3bdd459b0138a5342866e 100644
--- a/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler_arr.vhd
+++ b/libraries/base/dp/src/vhdl/mmp_dp_bsn_sync_scheduler_arr.vhd
@@ -47,6 +47,7 @@ entity mmp_dp_bsn_sync_scheduler_arr is
     -- MM control
     reg_mosi        : in  t_mem_mosi := c_mem_mosi_rst;
     reg_miso        : out t_mem_miso;
+    reg_ctrl_interval_size : out natural;
 
     -- Streaming
     in_sosi_arr        : in  t_dp_sosi_arr(g_nof_streams - 1 downto 0);
@@ -79,6 +80,7 @@ begin
 
     reg_mosi => reg_mosi,
     reg_miso => reg_miso,
+    reg_ctrl_interval_size => reg_ctrl_interval_size,
 
     in_sosi  => in_sosi_arr(0),
     out_sosi => single_src_out,
diff --git a/libraries/base/dp/tb/vhdl/tb_dp_fifo_dc_mixed_widths.vhd b/libraries/base/dp/tb/vhdl/tb_dp_fifo_dc_mixed_widths.vhd
index 8e32f03f95313b0b8fce82847a51d9009710a1d4..5a553284e6af96f1ca212c1253fb16f700971e6a 100644
--- a/libraries/base/dp/tb/vhdl/tb_dp_fifo_dc_mixed_widths.vhd
+++ b/libraries/base/dp/tb/vhdl/tb_dp_fifo_dc_mixed_widths.vhd
@@ -140,8 +140,9 @@ begin
     test_fifo_afull <= '0';
     verify_done <= '0';
     wait until arst = '0';
-    proc_common_wait_some_cycles(wide_clk, 10);
-    proc_common_wait_some_cycles(narrow_clk, 10);  -- ensure that n2w and w2n FIFOs are out of internal reset, and align to narrow_clk
+    -- ensure that n2w and w2n FIFOs are out of internal reset, and align to narrow_clk
+    proc_common_wait_some_cycles(wide_clk, c_tree_delay_len + 10);
+    proc_common_wait_some_cycles(narrow_clk, c_tree_delay_len + 10);
 
     -- Frame data with incrementing data over all frames, so the data can also be used as unframed stimuli
     v_init := 0; v_len := 0;