Abstract: Apparatuses, systems, and methods are provided for scalable networking systems. An example system includes a plurality of core switches and a first stage patch panel associated with operation of a first set of network ports. In an operational configuration in which the first stage patch panel is coupled with the plurality of core switches, the first stage patch panel is configured to operatively couple the first set of network ports and a first portion of the plurality of core switches such that signals may pass therebetween. Furthermore, the first stage patch panel may preclude communication to a remaining portion of the plurality of core switches. The system may include a second stage patch panel associated with a second set of network ports that is operatively coupled with the plurality of core switches in the absence of the first stage patch panel so as to scale the networking system.

Abstract: Systems and methods for resilience in network communications are provided. An example system includes a first network port pair including a first input network port and a first output network port. The system further includes an intermediate switch configured to communicably connect the first input network port and the first output network port and a first redundant network port communicably connected with the intermediate switch. The intermediate switch establishes communication between the first input network port and the first redundant network port in an instance in which the intermediate switch receives an indication of a malfunction associated with the first output network port or establishes communication between the first output network port and the first redundant network port in an instance in which the intermediate switch receives an indication of a malfunction associated with the first input network port.

Abstract: Systems, computer program products, and methods are described herein for machine learning (ML) based system for network resilience and steering. An example system monitors data movement across one or more network ports; extracts network performance indicators associated with the data movement; determines, via a machine learning (ML) subsystem, that a status of a first network port is indicative of operational failure based on at least the network performance indicators; determines that the first network port is associated with a first network port cluster; determines a redundant network port and an intermediate network switch associated with the first network port cluster; and triggers the intermediate network switch to reroute a portion of network traffic from the first network port to the redundant network port in response to the status of the first network port.

Abstract: Systems, apparatuses, and methods are provided for resilience in network communications. An example system includes at least one first network port including a first plurality of subports and at least one second network port including a second plurality of subports. The system also includes an intermediate switch communicably connected to the at least one first network port and the at least one second network port. At least one of the first plurality of subports includes at least one first offline subport that is inoperable in an instance in which each of the remaining first plurality of subports are operable. The intermediate switch is configured to route communication from one of the second plurality of subports to the at least one first offline subport in an instance in which the intermediate switch receives an indication of a malfunction associated with the first plurality of subports.

Abstract: Systems, computer program products, and methods are described herein for machine learning (ML) based network resilience and steering. An example system monitors data traffic across one or more network ports and determines a first data traffic pattern from the data traffic. The system further determines, via a ML subsystem, that the first data traffic pattern is indicative of a security threat to a first network port. In response to determining that the first data traffic pattern is indicative of the security threat to the first network port, the system further isolates the first network port from the one or more network ports.

Abstract: A beverage preparation machine (1) comprising a thermal conditioning device (71), such as a heater or cooler, said thermal conditioning device (71) comprising a control unit (4) for controlling a start-up phase of the thermal conditioning device (71) from a temperature of inactivity to an operative temperature, the control unit (4) comprising: a controller with a start-up profile for starting-up the thermal conditioning device (71), wherein the start-up profile has at least one parameter and the controller has a self-learning mode for adjusting the at least one parameter; and a temperature sensor connected to the controller for measuring a temperature of the thermal conditioning device (71); wherein the self-learning mode causes the controller during a start-up phase to: calculate a ramp value representative of a rate of change in temperature during the start-up phase of the thermal conditioning device; adjust the at least one parameter as a function of the adjusted ramp value; use the adjusted at least one p

Abstract: A beverage preparation machine (1) comprising a thermal conditioning device (71), such as a heater or cooler, said thermal conditioning device (71) comprising a control unit (4) for controlling a start-up phase of the thermal conditioning device (71) from a temperature of inactivity to an operative temperature, the control unit (4) comprising: a controller with a start-up profile for starting-up the thermal conditioning device (71), wherein the start-up profile has at least one parameter and the controller has a self-learning mode for adjusting the at least one parameter; and a temperature sensor connected to the controller for measuring a temperature of the thermal conditioning device (71); wherein the self-learning mode causes the controller during a start-up phase to: calculate a ramp value representative of a rate of change in temperature during the start-up phase of the thermal conditioning device; adjust the at least one parameter as a function of the adjusted ramp value; use the adjusted at least one p