Fixes for documentation (#560)

Author: jan-janssen

README.md

@@ -87,7 +87,7 @@ with SlurmClusterExecutor() as exe:
 ```
 In this case the [Python simple queuing system adapter (pysqa)](https://pysqa.readthedocs.io) is used to submit the 
 `calc()` function to the [SLURM](https://slurm.schedmd.com) job scheduler and request an allocation with two CPU cores 
-for the execution of the function - [HPC Cluster Executor](https://executorlib.readthedocs.io/en/latest/2-hpc-submission.html). In the background the [sbatch](https://slurm.schedmd.com/sbatch.html) 
+for the execution of the function - [HPC Cluster Executor](https://executorlib.readthedocs.io/en/latest/2-hpc-cluster.html). In the background the [sbatch](https://slurm.schedmd.com/sbatch.html) 
 command is used to request the allocation to execute the Python function. 
 
 Within a given [SLURM](https://slurm.schedmd.com) job executorlib can also be used to assign a subset of the 

docs/installation.md

@@ -68,8 +68,8 @@ documentation covers the [installation of pysqa](https://pysqa.readthedocs.io/en
 detail.
 
 ## HPC Job Executor
-For optimal performance in [HPC Allocation Mode](https://executorlib.readthedocs.io/en/latest/3-hpc-job.html) the
-[flux framework](https://flux-framework.org) is recommended as job scheduler. Even when the [Simple Linux Utility for Resource Management (SLURM)](https://slurm.schedmd.com) 
+For optimal performance the [HPC Job Executor](https://executorlib.readthedocs.io/en/latest/3-hpc-job.html) leverages the
+[flux framework](https://flux-framework.org) as its recommended job scheduler. Even when the [Simple Linux Utility for Resource Management (SLURM)](https://slurm.schedmd.com) 
 or any other job scheduler is already installed on the HPC cluster. [flux framework](https://flux-framework.org) can be
 installed as a secondary job scheduler to leverage [flux framework](https://flux-framework.org) for the distribution of
 resources within a given allocation of the primary scheduler. 

docs/trouble_shooting.md

@@ -20,9 +20,10 @@ dependency. The installation of this and other optional dependencies is covered
 
 ## Missing Dependencies
 The default installation of executorlib only comes with a limited number of dependencies, especially the [zero message queue](https://zeromq.org)
-and [cloudpickle](https://github.com/cloudpipe/cloudpickle). Additional features like [caching](https://executorlib.readthedocs.io/en/latest/installation.html#caching), [HPC submission mode](https://executorlib.readthedocs.io/en/latest/installation.html#hpc-cluster-executor) 
-and [HPC allocation mode](https://executorlib.readthedocs.io/en/latest/installation.html#hpc-job-executor) require additional dependencies. The dependencies are explained in more detail in the 
-[installation section](https://executorlib.readthedocs.io/en/latest/installation.html#).
+and [cloudpickle](https://github.com/cloudpipe/cloudpickle). Additional features like [caching](https://executorlib.readthedocs.io/en/latest/installation.html#caching), the [HPC Cluster Executors](https://executorlib.readthedocs.io/en/latest/installation.html#hpc-cluster-executor) 
+and the [HPC Job Executors](https://executorlib.readthedocs.io/en/latest/installation.html#hpc-job-executor) require 
+additional dependencies. The dependencies are explained in more detail in the 
+[installation section](https://executorlib.readthedocs.io/en/latest/installation.html).
 
 ## Python Version 
 Executorlib supports all current Python version ranging from 3.9 to 3.13. Still some of the dependencies and especially 

notebooks/3-hpc-job.ipynb

@@ -124,7 +124,7 @@
    "metadata": {},
    "source": [
     "### Block Allocation\n",
-    "The block allocation for the HPC allocation mode follows the same implementation as the [block allocation for the local mode](https://executorlib.readthedocs.io/en/latest/1-local.html#block-allocation). It starts by defining the initialization function `init_function()` which returns a dictionary which is internally used to look up input parameters for Python functions submitted to the `FluxJobExecutor` class. Commonly this functionality is used to store large data objects inside the Python process created for the block allocation, rather than reloading these Python objects for each submitted function."
+    "The block allocation for the HPC allocation mode follows the same implementation as the [block allocation for the Single Node Executor](https://executorlib.readthedocs.io/en/latest/1-single-node.html#block-allocation). It starts by defining the initialization function `init_function()` which returns a dictionary which is internally used to look up input parameters for Python functions submitted to the `FluxJobExecutor` class. Commonly this functionality is used to store large data objects inside the Python process created for the block allocation, rather than reloading these Python objects for each submitted function."
    ]
   },
   {