diff --git a/src/scgenerator/const.py b/src/scgenerator/const.py
index c85f84a..6c6f15a 100644
--- a/src/scgenerator/const.py
+++ b/src/scgenerator/const.py
@@ -45,12 +45,8 @@ MANDATORY_PARAMETERS = {
     "length",
     "adapt_step_size",
     "tolerated_error",
-    "repeat",
     "linear_operator",
     "nonlinear_operator",
-    "soliton_length",
-    "nonlinear_length",
-    "dispersion_length",
 }
 
 ROOT_PARAMETERS = [
diff --git a/src/scgenerator/evaluator.py b/src/scgenerator/evaluator.py
index 88aa2e0..c455101 100644
--- a/src/scgenerator/evaluator.py
+++ b/src/scgenerator/evaluator.py
@@ -569,7 +569,11 @@ envelope_rules = default_rules + [
     Rule("gamma_op", operators.constant_quantity, ["gamma_arr"], priorities=1),
     Rule("gamma_op", lambda w_num, gamma: operators.constant_quantity(np.ones(w_num) * gamma)),
     Rule("gamma_op", lambda: operators.constant_quantity(0.0), priorities=-1),
-    Rule("ss_op", lambda w_c, w0: operators.constant_quantity(w_c / w0)),
+    Rule(
+        "ss_op",
+        lambda w_c, w0: operators.constant_quantity(w_c / w0),
+        conditions=dict(self_steepening=True),
+    ),
     Rule("ss_op", lambda: operators.constant_quantity(0), priorities=-1),
     Rule("spm_op", operators.envelope_spm, conditions=dict(spm=True)),
     Rule("spm_op", operators.no_op_freq, priorities=-1),
@@ -579,6 +583,7 @@ envelope_rules = default_rules + [
     Rule("dispersion_op", operators.constant_polynomial_dispersion),
     Rule("dispersion_op", operators.constant_direct_dispersion),
     Rule("dispersion_op", operators.direct_dispersion),
+    Rule("dispersion_op", lambda: operators.constant_quantity(0.0), priorities=-1),
     Rule("linear_operator", operators.envelope_linear_operator),
     Rule("conserved_quantity", operators.conserved_quantity),
 ]
diff --git a/src/scgenerator/noise.py b/src/scgenerator/noise.py
index 0878254..071d83a 100644
--- a/src/scgenerator/noise.py
+++ b/src/scgenerator/noise.py
@@ -232,7 +232,7 @@ class NoiseMeasurement:
             sample on a log-log scale rather than on a linear scale, by default False
         """
         nf = nf or len(self.freq)
-        if rng is None or isinstance(rng, int):
+        if rng is None or isinstance(rng, (int, np.integer)):
             rng = np.random.default_rng(rng)
 
         freq, amp = self.sample_spectrum(nf, dt, log_mode, left=0)