Autoscale observed value being doubled during scale in evaluation

Describe the bug
I've created autoscaling for an application and defined rules (attached file named azure-autoscale-config.txt which has the azure cli commands run to create the autoscale profiles and rules) to scale in and out using PodCpuUsage and PodMemoryUsage metrics. When the current instance count is 2, the auto scale rules will not scale in the instance count to 1 because it is doubling the observed value used in the auto scale rule evaluation. I've attached the AutoscaleEvaluationsLog query output as an Excel file (named instashare-poc-autoscalelogs.xlsx) to show the details. If you look at correlationId 4292e174-c00c-4595-88c3-59c2213d8d0d you'll see I highlighted a few rows. You can see for the PodMemoryUsage observed value for the scale out rule is 52.79545455. However, the same metric values used for the scale in rule is 105.5909091 and for that evaluation, the Projection column has a value of 2. The scale in Projection column evaluation was blank. I'm not sure where that Projection column value is coming from and why it's different for the scale in and out evaluations. Perhaps the Projection column has nothing to do with the doubled observed value but it stood out to me as a possibility. The same thing is also occurring for the PodCpuUsage rule evaluations.

To Reproduce
Steps to reproduce the behavior:

1. Create a Spring App with autoscaling using a similar configuration as I've provided
2. Get 2 instances of the application running
3. Have the application memory and CPU usage low enough that the application scale in rules should be applied
4. Observe the application will not scale in

Expected behavior
When the scale in rules are met, the application scale in to reduce the instance count by 1 should occur

Can we contact you for additional details? Y/N
Y

azure-autoscale-config.txt
instashare-poc-autoscalelogs.xlsx

Comienza con azure-autoscale-config.txt y los datos de AutoscaleEvaluationsLog en instashare-poc-autoscalelogs.xlsx, centrándote en correlationId 4292e174-c00c-4595-88c3-59c2213d8d0d y en la columna Projection. Reprodúcelo con una aplicación Spring con dos instancias y un uso bajo de CPU y memoria; estará terminado cuando scale-in reduzca el número a una y los valores observados evaluados ya no estén duplicados.