diff --git a/echopype/convert/parse_base.py b/echopype/convert/parse_base.py
index 8bd585503..23a08d52f 100644
--- a/echopype/convert/parse_base.py
+++ b/echopype/convert/parse_base.py
@@ -600,8 +600,14 @@ def pad_shorter_ping(data_list) -> np.ndarray:
         lens = np.array([len(item) for item in data_list])
         if np.unique(lens).size != 1:  # if some pings have different lengths along range
             if data_list[0].ndim == 2:
-                # Angle data have an extra dimension for alongship and athwartship samples
-                mask = lens[:, None, None] > np.array([np.arange(lens.max())] * 2).T
+                # Data may have an extra dimension:
+                #  - Angle data have an extra dimension for alongship and athwartship samples
+                #  - Complex data have an extra dimension for different transducer sectors
+                mask = (
+                    lens[:, None, None]
+                    > np.array([np.arange(lens.max())] * data_list[0].shape[1]).T
+                )
+
             else:
                 mask = lens[:, None] > np.arange(lens.max())
 
diff --git a/echopype/tests/calibrate/test_calibrate_ek80.py b/echopype/tests/calibrate/test_calibrate_ek80.py
index 8e544b5f5..36fe4ca3a 100644
--- a/echopype/tests/calibrate/test_calibrate_ek80.py
+++ b/echopype/tests/calibrate/test_calibrate_ek80.py
@@ -251,4 +251,21 @@ def test_ek80_BB_power_echoview(ek80_path):
     ev_vals = df_real.values[:, :]
     ep_vals = pc_mean.values.real[:, :]
     assert np.allclose(ev_vals[:, 69:8284], ep_vals[:, 69:], atol=1e-4)
-    assert np.allclose(ev_vals[:, 90:8284], ep_vals[:, 90:], atol=1e-5)
\ No newline at end of file
+    assert np.allclose(ev_vals[:, 90:8284], ep_vals[:, 90:], atol=1e-5)
+
+
+def test_ek80_CW_complex_Sv_receiver_sampling_freq(ek80_path):
+    ek80_raw_path = str(ek80_path.joinpath("D20230804-T083032.raw"))
+    ed = ep.open_raw(ek80_raw_path, sonar_model="EK80")
+    # Parsed receiver_sampling_frequency is 0
+    assert ed["Vendor_specific"]["receiver_sampling_frequency"] == 0
+    # Calibration object
+    waveform_mode = "CW"
+    encode_mode = "complex"
+    ds_Sv = ep.calibrate.compute_Sv(
+        ed, waveform_mode=waveform_mode, encode_mode=encode_mode
+    )
+
+    # receiver_sampling_frequency substituted with default value in compute_Sv
+    assert ds_Sv["receiver_sampling_frequency"] is not None
+    assert np.allclose(ds_Sv["receiver_sampling_frequency"].data, 1500000)