Cognitive Warfare is the deliberate and malicious
manipulation of human perception, belief, and
decision-making ability through weaponized information.
Artificial intelligence has industrialized influence
operations, social media algorithms structurally reward
emotionally charged content, legacy mass media and decades
of psychological operations research and practice have
refined the targeting to the highest precision. The result
is a threat that operates at a scale no previous era of
propaganda could match, yet cognitive defense against it
remains qualitatively and methodologically inconsistent and
difficult to compare across organizations or time. The gap
is a measurement problem and here metrology provides the
missing formal structure based on the International
Vocabulary of Metrology JCGM 200, the Guide to the
Expression of Uncertainty in Measurement JCGM 100, ISO
17025, ISO 17043 and ISO 42001. Without defined measurands,
calibrated instruments and traceable reference standards,
cognitive defense can’t answer the questions that any
credible threat assessment requires: “How much has belief
actually shifted?”, “Does that shift exceed normal
background variation?”, “How confident are we?”. These are
not rhetorical questions but metrological ones and they
have well-developed answers in metrological domain. Ten key
principles were developed and mapped each to the practical
problem of detecting and suppressing cognitive warfare
attacks. Measurement uncertainty, in both Type A
(statistical) and Type B (structural and adversarial)
forms, is placed at the operational center of the resulting
Cognitive Warfare Information Metrology Framework.

Urban Drainage Systems are part of the critical urban
infrastructure, needed to enable safe, healthy and
comfortable urban living areas. Their task is to mitigate
the pluvial flooding risks, or to facilitate the collection
of the surface runoff into the underground network of
pipes, peak runoff detention/retention and safe release
into the receiving water body. Urban Drainage System (UDS)
operational control and management rely strongly on the
monitored values of relevant hydraulic parameters within
the network at key, characteristic locations. From the
technical perspective, by increasing the number of
measurement locations and reliability of the measurements
used in the control process, better operational decisions
can be made. Lately, the research spotlight is placed on
the possibility of the application of the Digital Twins of
the Urban Drainage Systems, for improved system control. To
enable real-time representativeness of the Digital Twins it
must be supplied with a large amount of reliable measured
data. Often, a number of new measuring locations must be
established. However, due to the nature of the Urban
Drainage Systems, measurements of the hydraulic parameters
are complicated, expensive and often attributed to
unacceptably high measurement uncertainties. An attempt to
address these issues by employing the so called “low-cost”
sensors is investigated intensively, and mixed results are
achieved. Here, the results of the measurement uncertainty
laboratory benchmarks of the selected low-cost and
industrial (conventional) meters are presented, obtained
within the scope of DIGIDRAIN project. Practical
implications are discussed and future field tests are
presented.

This paper presents the design and implementation of an
Arduino–Python platform for near real-time multi-channel
signal acquisition systems, demonstrated through a
three-phase electrical measurement application. The
proposed platform integrates an analog signal conditioning
board, an Arduino MEGA board for analog-to-digital
conversion (ADC), and a PC-based Python application for
continuous serial data acquisition and processing,
visualization, and storage. Communication between the board
and the host computer is established via USB serial
interface, enabling continuous acquisition and monitoring
of six channels (three voltages and three currents). The
capabilities of the platform are evaluated experimentally
in a laboratory setup involving a three-phase induction
motor. A cohesive data processing pipeline that facilitates
the seamless transition from hardware acquisition to
Python-based analysis is proposed. The results demonstrate
the applicability of the developed platform for
laboratory-scale three-phase monitoring and signal analysis.

This paper analyzes common communication problems in Modbus
RTU networks based on the RS-485 physical layer in
industrial systems within the context of Industry 4.0. In
such environments, reliable field-level communication is
essential for enabling real-time data acquisition, system
interoperability, and data-driven industrial processes. The
focus is on installation-related issues, including improper
topology, incorrect termination, excessive bus length, a
high number of connected devices, and electromagnetic
interference. Communication quality is evaluated using CRC
and timeout errors as indirect indicators of transmission
reliability and signal integrity. The physical causes of
these problems and their typical manifestations in real
systems are discussed. In addition, practical
troubleshooting guidelines and corrective measures are
proposed. The results indicate that most communication
issues originate from improper implementation of the RS-485
physical layer rather than from the Modbus protocol itself.

This paper presents initial developments of the digital
twin for the Garrett solenoid developed within the
framework of 24RPT02 MetroMag project. The project aims to
strengthen capabilities of European National Metrology
Institutes (NMIs) in performing traceable measurements in
the low magnetic field range. Garret solenoid was
identified as the most suitable to be used as a traveling
transfer standard between NMIs to enable easier and faster
intercomparison. The development of the digital twin of the
Garrett solenoid will aid in documenting transfer standard,
associated experimental setups and can aid in estimation of
the uncertainty components in magnetic field metrology.

Truck scales are essential measurement systems in
industrial logistics, where vehicle mass directly affects
financial transactions, safety, and process control.
Measurement accuracy depends not only on the metrological
characteristics of load cells but also on correct vehicle
positioning on the weighing platform. Improper positioning
introduces systematic errors due to partial support outside
the platform, uneven load distribution, and dynamic
instability.

This paper presents an optical system for verifying vehicle
position on a truck scale in an LPG storage facility. The
system is based on photoelectric sensors, a PLC controller,
and video surveillance, enabling automatic validation of
measurement conditions. The proposed solution prevents
weighing when metrological conditions are not satisfied,
thereby improving measurement accuracy, repeatability, and
reliability.

Experimental analysis shows that measurement error can be
reduced from several percent (up to 5%) to below 0.1%,
corresponding to a reduction of absolute error from
approximately 2000 kg to less than 40 kg. The system also
enables improved traceability and integration with
higher-level control and enterprise systems in accordance
with Industry 4.0 principles.

This paper presents a conceptual design of a wearable
system for detecting and interrupting sleep paralysis in
order to help patients with this disorder. The proposed
solution, envisioned as a smart headband, integrates an
accelerometer, electrooculography (EOG), and
electroencephalography (EEG) for precise monitoring of the
user's condition. The detection methodology is based on a
hierarchical logic: first, the absence of head movement is
analyzed, then the REM phase is identified through the
analysis of the EOG signal, while the final confirmation is
performed by analyzing the spectral analysis of the EEG
signal (FFT and PSD). In a practical implementation, the
system is expected to store recorded data and detected
events for later analysis.

Governments often tax fuel products to generate revenues to
support and stimulate their economies. They also subsidize
the cost of essential fuel products. Fuel taxation and
subsidization practices are both subject to fraud. Oil
marketing
companies also suffer from fuel fraud with loss of
legitimate sales and additional quality and liability
issues. The use of
an advanced marking system to identify and control fraud
has been shown to be effective in controlling illegal
activity. In order to avoid incorrect detection of the
presence of markers, marking systems must enable accuracy,
repeatability, and reliability during measurement. In case
it turns out that the concentration of markers in the fuel
tank at the gas station or in fuel truck is lower or higher
than prescribed, the tank or fuel truck would have to be
put out of use in order to carry out additional control by
authorized state authorities - which causes negative
effects on the volume of fuel sales, i.e. leads to
financial losses for oil company. Consequently, the
selection of a suitable marker measurement system
represents a critical aspect of the overall process

Mechanical vibrations are among the most
significant indicators of mechanical degradation and
improper
operation in electric motor–driven systems. Conventional
motor
control strategies rely on static Pulse Width Modulation
(PWM)
parameters, which do not adapt to changing mechanical
conditions
during operation. This paper presents a real–time
analog–digital
system for vibration detection and adaptive PWM regulation
aimed at reducing mechanical oscillations in electric
motors. The
proposed system integrates a MEMS accelerometer, real–time
signal acquisition and digital processing, and adaptive PWM
control implemented on an STM32 Cortex–M microcontroller
platform. The key contribution of this work is the direct
inclusion
of vibration feedback into the motor control loop, enabling
real
time adaptation of PWM parameters without complex control
algorithms or computationally intensive motor models. The
solution is designed to be low–cost, resource–efficient,
and suitable
for embedded and industrial systems, offering improved motor
stability, reduced vibration levels, and extended
operational
lifetime.

This paper presents the design and partial
implementation of a smart recycling system aimed at
automatic
classification and sorting of plastic waste. The system is
based on
an STM32 microcontroller and combines a proximity sensor for
object detection with a multispectral sensor for material
analysis.
A functional prototype was developed using Proximity Click
and
Spectral 3 Click modules, where reflected light
measurements are
processed in real time using a FreeRTOS based software
architecture. A simple threshold-based classification
algorithm is
applied to distinguish between clean plastic and plastic
with labels.
Experimental testing on a set of plastic samples
demonstrated that
the system can identify differences in spectral response and
perform basic sorting. However, limitations related to
environmental conditions and classification accuracy were
observed. The proposed solution represents a low-cost and
modular approach suitable for embedded applications and
serves
as a basis for further improvement using advanced
classification
methods.