Test Scenarios Research Articles

Multi‐focal channel attention for medical image segmentation

AbstractMedical image segmentation through the use of deep learning is becoming a trend targeting to automate disease detection and provide effective treatment. Traditionally huge manual efforts are associated with medical image processing by medical staff. The wide use of neural networks driven by the progressive advancement in computing power and the availability of training data provides instrumental means to automate medical image processing. A polyp refers to an abnormal growth that can occur in various parts of the body. While the majority of polyps are noncancerous or benign, there are instances where certain types can develop into cancer. Detecting and segmenting polyps is highly valuable for identifying early signs and potentially enabling more effective treatment of colon cancer. In this paper, we introduce a novel method for segmenting polyp images. The proposed method utilizes convolutional neural networks (CNNs) and employs an enhanced attention mechanism. To cater to a more granular attention view, this paper enhances the Convolutional Block Attention Module by introducing the multi‐focal channel attention (MFCA) concept, which we call MFCA. In the proposed MFCA channel attention, focal attention spots allow us to consider scattered areas of interest more effectively. To evaluate the effectiveness of the proposed method, multiple experiments are conducted on five well‐known benchmark datasets for polyp image segmentation: Kvasir, CVC‐Clinic DB, CVC‐Colon DB, CVC‐T, and ETIS‐Larib. Various testing scenarios are examined, including the impact of two focal attention spots and four focal attention spots. The experimental results demonstrated that the proposed method achieved superior performance compared to previous state‐of‐the‐art techniques on two of the benchmark datasets, and ranked second on a third dataset.

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

Design and comparative analysis of alternative mooring systems for offshore floating photovoltaics arrays in ultra-shallow water with significant tidal range

Large-scale floating photovoltaics (FPV) are garnering increased attention as a sustainable solution for renewable energy production and efficient utilization of ocean spaces. These FPV arrays are predominantly deployed in shallow water environments, which present significant challenges in mooring system design due to substantial tidal fluctuations. In such settings, mooring systems may become slack and lose functionality at low tide or risk damage from excessive stiffness at high tide. The primary aim of this study is to develop a mooring system tailored to FPV arrays, capable of withstanding extensive tidal variations without causing damage to the floating modules due to high mooring line tension. Employing HydroD, a hydrodynamic analysis tool based on potential flow theory, alongside OrcaFlex, a time-domain coupled analysis tool predicated on the lumped mass method, we conducted a comparative assessment of six distinct mooring configurations. These configurations, varying in mooring line materials and components, were evaluated from static, dynamic, and fatigue analysis standpoints across a range of tidal levels. The results indicate that taut mooring systems integrated with buoys or clumps consistently exhibit superior performance in all test scenarios. Accordingly, these systems are highly recommended for securing FPV arrays in shallow water environments characterized by severe tidal variations, offering optimal stability and resilience.

Use of a Blood Biomarker Test Improves Economic Utility in the Evaluation of Older Patients Presenting with Cognitive Impairment.

More than 16 million Americans living with cognitive impairment warrant a diagnostic evaluation to determine the cause of this disorder. The recent availability of disease-modifying therapies for Alzheimer's disease (AD) is expected to significantly drive demand for such diagnostic testing. Accurate, accessible, and affordable methods are needed. Blood biomarkers (BBMs) offer advantages over usual care amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers in these regards. This study used a budget impact model to assess the economic utility of the PrecivityAD® blood test, a clinically validated BBM test for the evaluation of brain amyloid, a pathological hallmark of AD. The model compared 2 scenarios: (1) baseline testing involving usual care practice, and (2) early use of a BBM test before usual care CSF and PET biomarker use. At a modest 40% adoption rate, the BBM test scenario had comparable sensitivity and specificity to the usual care scenario and showed net savings in the diagnostic work-up of $3.57 million or $0.30 per member per month in a 1 million member population, translating to over $1B when extrapolated to the US population as a whole and representing a 11.4% cost reduction. Savings were driven by reductions in the frequency and need for CSF and PET testing. Additionally, BBM testing was associated with a cost savings of $643 per AD case identified. Use of the PrecivityAD blood test in the clinical care pathway may prevent unnecessary testing, provide cost savings, and reduce the burden on both patients and health plans.

An investigation of ADAS testing scenarios based on vehicle-to-powered two-wheeler accidents occurring in a county-level district in Hunan province

Abstract To meet the requirements for high-risk testing scenarios in the safety assessment of advanced driver assistance systems (ADAS), this study selected 239 vehicle-to-powered two-wheeler accidents occurring in Xiangtan County from the China In-depth Mobility Safety Study—Traffic Accident (CIMSS-TA) database. Seven scenario elements were selected from the levels of static road elements, dynamic traffic participants and environment elements. The accident characteristics were obtained through statistical analysis. The results showed 75.73% of accidents occurred at T-junctions/intersections, and 34.73% of accidents were caused by the visual obstructions. Six typical vehicle-to-powered two-wheeler crash scenarios were established by cluster analysis, including two straight road scenarios (scenario 3 and 6), two T-junction scenarios (scenario 1 and 5), and two intersection scenarios (scenario 2 and 4). Furthermore, six vehicle-to-powered two-wheeler testing scenarios, which represent the traffic environment characteristics of Chinese county-level districts, were constructed by analyzing the vehicle/PTW speed, the first collision point of vehicle, obstacle type and location, etc. The results showed the vehicle speed in scenario 4 was higher than that in scenario 2, with the average values of 34.32 km/h and 21.99 km/h, respectively. The results of this study provided testing scenarios for the safety assessment of ADAS, and contributed to improving the vehicle active safety technology.

Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm

This article presents the utilization of a shunt active power filter (SHAPF) in combination with an Energy Storage System (ESS) and a Solar Energy System (SES). Voltage source converters (VSC) are connected in parallel to a direct current (DC) bus. The membership function (MSF) of fuzzy logic controller (FLC) for the shunt control system is optimally adjusted using the golden balloptimization algorithm (GBOA). The present effort aims to achieve the following primary objectives: 1) Quick implementation to stabilize the voltage of the DC Link capacitor (DCLCV); 2) Mitigation of harmonics and improvement of power factor (PF); 3) Satisfactory performance under load as well as solar power varying conditions. The effectiveness of the optimally designed controller is evaluated by studying four test scenarios with grid and standalone conditions. The results are then compared to the existing sliding mode (SMC) and fuzzy logic controllers (FLC).

A New Computational Algorithm for Assessing Overdispersion and Zero-Inflation in Machine Learning Count Models with Python

This article provides an overview of count data and count models, explores zero inflation, introduces likelihood ratio tests, and explains how the Vuong test can be used as a model selection criterion for assessing overdispersion. The motivation of this work was to create a Vuong test implementation from scratch using the Python programming language. This implementation supports our objective of enhancing the accessibility and applicability of the Vuong test in real-world scenarios, providing a valuable contribution to the academic community, since Python did not have an implementation of this statistical test.

Selective ion permeation enables reduced combined gypsum-organic fouling of loose nanofiltration membranes

During the long-term operation of NF membranes, the occurrence of combined gypsum-organic fouling is a significant challenge. While numerous studies have focused on enhancing the fouling resistance of NF through various methods (e.g., antiscalants, pretreatment), there has been limited exploration into the correlation between its structural looseness and fouling resistance. Herein, we conducted an experimental study to evaluate the correlation by employing three NF membranes with different looseness degrees: a custom-developed loosely structured NF membrane (CLNF), the moderately loose NF270, and the tightly structured NF90. Our findings indicated that NF membranes with loose polyamide structure exhibited superior fouling resistance to gypsum scaling and combined gypsum-organic fouling, induced by model organic matters like bovine serum albumin (BSA), humic acid (HA), and sodium alginate (SA). After applying SA-coating to calibrate the surface properties of the membranes to exclude the influence of surface roughness, hydrophilicity, and functional groups, our study attributed the difference in fouling resistance to variations in ionic selectivity stemming from membrane looseness. Specifically, the primary advantage of looser polyamide NF membranes lies in enhanced Ca2+ permeation, which not only reduces the “egg-box” effect and surface foulant accumulation but also diminishes cake-enhanced concentration polarization, effectively mitigating gypsum-organic fouling. In real water testing scenarios, the enhanced fouling resistance of loose NF membranes was further affirmed. This research underscored the unexpected benefit of loose NF membranes in terms of fouling resistance, providing a novel insight for future application.

Integration of BESS in grid connected networks for reducing the power losses and CO[formula omitted] emissions: A parallel master-stage methodology based on PDVSA and PSO

This work addressed the problem of integration and operation of Ion-lithium batteries of different technical characteristics in grid-connected networks (GCN) for reducing the energy losses and CO2 emissions. To formulate the mathematical problem, nonlinear mathematical modeling was used, taking into account the previously mentioned objective functions and all the constraints that represent the operation of the grid in a scenario of variable photovoltaic generation and power demand. This paper proposed a master–slave methodology, which uses a parallel discrete version of the vortex search algorithm (PDVSA) for solving the problem of location and selection of the kind of battery in the GNC. The slave stage is entrusted to find the operation scheme of the batteries proposed for the master stage. This task is carried out by a particle swarm optimization algorithm (PSO) that defines the power to be supplied or stored for each battery. Using a matrix hourly power flow for calculating the effect in the objective function and constraints that represent the problem. As a test scenario, it was used a GCN of 33 buses and 32 lines that considered the photovoltaic generation and demand of Medellín-Colombia, and all technical and environmental indexes of the local grid operator. Six comparison methodologies were used from the literature and developed in this work to evaluate the effectiveness of the proposed methodology. Being all programming in MATLAB software, executing each one 1000 times with the aim to evaluate the performance in terms of solution, repeatability, and processing times. The results obtained demonstrating that the proposed PDVSA/PSO methodology achieved the best results in terms of solution, with reductions of 154.9094 kWh (6.23%) in energy losses and reductions of 0.0252 tons of CO2 (0.2548%) in pollutant gas emissions compared to the base case. Regarding repeatability, the PDVSA/PSO exhibits standard deviation values of 0.1383% for energy losses and 0.0034% for CO2 emissions.

Моделирование транспортных потоков методом системной динамики

The article discusses the application of the system dynamics methodology for simulation of controlled traffic flows. In the classical formulation, system dynamics models represent a system of finite-difference equations, which is an approximation of the Cauchy problem by the Euler method. The implementation of finite-difference equations in system dynamics by J. Forrester is carried out by the DYNAMO specialized graphical tools. It is proposed that the Cauchy problem be implemented using structural diagrams in which a one-to-one correspondence of specific structural links to system dynamics paradigms is established. As a result of the research, it was shown that an aperiodic link with a typical nonlinear “saturation” function identifies with a sufficient accuracy the basic paradigms of system dynamics, such as rates, levels and delays. It is shown that the resulting system of ordinary differential equations, when imposing control conditions, has discontinuities of the first kind in derivatives and requires integration methods with a variable step and variable order for effective solution. The proposed approach to modeling traffic flows, in contrast to the classical one, allows the use of modern modeling tools with a graphical language of structural diagrams and a library of methods for numerical analysis of complex dynamic processes. The implementation of a specific test scenario for modeling traffic flows was carried out in the SimInTech software package. The efficiency metrics of integration methods are obtained. A comparative analysis of the effectiveness of integration methods in solving this problem was carried out. It is shown that the most effective in solving problems of the considered class are the explicit adaptive integration methods ARK21, AM61 with a variable step from the SimInTech library, which are much more effective than the explicit Euler method in classical system dynamics.

Blindness of intentions and metacognitive deficits during moral judgements in schizophrenia.

Previous research has shown that moral judgments are affected by social cognitive abilities, such as theory of mind (ToM). This study examines how information about an actor's beliefs and the consequences of their actions affect the moral evaluation of the character's behavior in social events. Our research builds upon previous studies, which have shown that these factors contribute differently to moral judgments made by both adults and young children. This study aimed to explore how participants with schizophrenia and healthy controls read stories about social situations in the context of moral judgments. The study used the research procedure that included 4 variants of 16 scenarios describing social situations, and thus comprising 64 stories. After each story, participants evaluated their confidence level on a 4-point scale. To assess delusional beliefs, the Polish adaptation of the Peters Delusion Inventory (PDI) questionnaire and the Paranoia Checklist (PCh) were used. Respondents completed these questionnaires after completing the scenario test procedure. In social situations, patients with paranoid schizophrenia were found to evaluate actions of protagonists who attempted to harm another person more leniently than when it was an accident. Conversely, healthy individuals judged those actors who expressed intentions to hurt another person significantly more harshly than in an accident situation. Metacognition measures show that paranoid schizophrenia patients make moral judgments with high confidence, despite being based on an incorrect reading of the other person's intentions. The study indicates that ToM has a significant impact on the moral judgment of others. Decreased moral cognition can result from both positive and negative symptoms. Deficits related to metacognition can also sustain such cognitive distortions.

TraModeAVTest: Modeling Scenario and Violation Testing for Autonomous Driving Systems Based on Traffic Regulations

Current testing methods for autonomous driving systems primarily focus on simple traffic scenarios, generating test cases based on traffic accidents, while research on generating edge test cases for complex driving environments by traffic regulations is not adequately comprehensive. Therefore, we propose a method for scenario modeling and violation testing using an autonomous driving system based on traffic regulations named TraModeAVTest. Initially, TraModeAVTest constructs a Petri net model for complex scenarios based on the combination relationships of basic traffic regulation scenarios and verifies the consistency of the model’s design with traffic regulation requirements using formal methods, to provide a representation of traffic regulation scenario models for the violation testing of autonomous driving systems. Subsequently, based on the coverage criteria of the Petri net model, it utilizes a search strategy to generate model paths that represent traffic regulations, and employs a parameter combination method to generate test cases that cover the model paths, to test the violation behaviors of autonomous driving systems. Finally, simulation experiment results on the Baidu Apollo demonstrate that the test cases representing traffic regulations generated by TraModeAVTest can effectively identify the behaviors of autonomous vehicles violating traffic regulations, and TraModeAVTest can effectively improve the efficiency of generating different types of violation scenarios.

Effects of feature reduction on emotion recognition using EEG signals and machine learning

AbstractElectroencephalography is a core technology of brain computer interfaces. Even a few number of electrodes can produce complex signals that are difficult to interpret. This is particularly true when trying to detect complex mental states, such as the identification of human emotions. This work analyzes the impact of feature reduction using the SJTU Emotion EEG data set, considering inter‐subject and inter‐session EEG classification. It is well established that EEG data tends to be subject dependent, and that the brain patterns produced by a person in response to a specific stimuli will tend to change over their lifetime, we can say that the above classification task is one of the most challenging test scenarios. We extract 1068 time and frequency domain features, single and multiple channels. Feature reduction was analyzed using both filter and wrapper based approaches. In the first group we use principal component analysis, Kernel PCA and Locally Linear Embedding, and in the second three meta‐heuristic algorithms are employed. For classification, we apply multi‐layer perceptron, quadratic discriminant analysis, random forest and support vector machines. Results show up to 100% accuracy on the median test error for some splits in the 10‐fold cross‐validation process. Best results were achieved by filter‐based selection, with the best average accuracy reached by Random Forest (93.20%) and Multi‐Layer Perceptron (92.71%), using components found with Kernel PCA. To the best of our knowledge, these classification results are the best ever achieved for the SEED data set on the subject‐independent and session‐independent problem. Comparisons using a Leave‐One‐Subject‐Out cross‐validation setting shows that the proposed methodology achieves state‐of‐the‐art results using only 12 electrodes. These off‐the‐shelf ML techniques, coupled with filter based feature reduction, achieve the best results, outperforming complex classification models and wrapper based meta‐heuristics.

The influence of magneto-hydrodynamics and vortex generators on nanofluids heat transfer

This paper investigates the effects of a vortex generator (VG) and magneto-hydrodynamics (MHD) on the thermal properties of nanofluids in a microchannel. A variety of test scenarios within the laminar flow regimes are numerically examined: nanofluids in a hot-walled simple channel (SC); nanofluids in a heated-walled channel where a circular cylinder is embedded within the channel and functions as a vortex generator (VG); nanofluids in a heated-walled channel that incorporates both a vortex generator and magneto-hydrodynamics (VG-MHD). The channel has a circular cylinder with a diameter of D that is fixed inside the channel with various gap ratios (GR = G/D) ranging from 0.5 to 4.0. Numerical investigations for laminar flow with Re ≤ 1,000 are carried out. The nanoparticle volume fractions range from 0 % to 7 %, and the magnetic field is set at 0.05 T. The best heat transmission conditions with the lowest skin friction coefficient are evaluated for the optimal GR. It is found that heat transfer is enhanced by approximately 20 % when MHD and VG effects are employed, in contrast to a traditional channel that does not utilize the MHD effect.

Unsupervised Training Sequence Design: Efficient and Generalizable Agent Training

To train generalizable Reinforcement Learning (RL) agents, researchers recently proposed the Unsupervised Environment Design (UED) framework, in which a teacher agent creates a very large number of training environments and a student agent trains on the experiences in these environments to be robust against unseen testing scenarios. For example, to train a student to master the “stepping over stumps” task, the teacher will create numerous training environments with varying stump heights and shapes. In this paper, we argue that UED neglects training efficiency and its need for very large number of environments (henceforth referred to as infinite horizon training) makes it less suitable to training robots and non-expert humans. In real-world applications where either creating new training scenarios is expensive or training efficiency is of critical importance, we want to maximize both the learning efficiency and learning outcome of the student. To achieve efficient finite horizon training, we propose a novel Markov Decision Process (MDP) formulation for the teacher agent, referred to as Unsupervised Training Sequence Design (UTSD). Specifically, we encode salient information from the student policy (e.g., behaviors and learning progress) into the teacher's state space, enabling the teacher to closely track the student's learning progress and consequently discover the optimal training sequences with finite lengths. Additionally, we explore the teacher's efficient adaptation to unseen students at test time by employing the context-based meta-learning approach, which leverages the teacher's past experiences with various students. Finally, we empirically demonstrate our teacher's capability to design efficient and effective training sequences for students with varying capabilities.

Protect Your Score: Contact-Tracing with Differential Privacy Guarantees

The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet, it is precisely the communication and release of this score to a user that an adversary can leverage to gauge the private health status of an individual. We pinpoint a realistic attack scenario and propose a contact tracing algorithm with differential privacy guarantees against this attack. The algorithm is tested on the two most widely used agent-based COVID19 simulators and demonstrates superior performance in a wide range of settings. Especially for realistic test scenarios and while releasing each risk score with epsilon=1 differential privacy, we achieve a two to ten-fold reduction in the infection rate of the virus. To the best of our knowledge, this presents the first contact tracing algorithm with differential privacy guarantees when revealing risk scores for COVID19.

Implementation of auto failover on SD-WAN technology with BGP routing method on Fortigate routers at XYZ company

The current technological development is SD-WAN (Software-Defined Wide Area Network) which provides highperformance access for users located far from the head office so as to allow faster network connections and has been facilitated automation techniques for branch offices. This research solves the problem of XYZ company because it is known that the company requires network connectivity with a high SLA (Service Level Agreement) and no downtime in the information exchange process. This research hypothesis assumes that using SD-WAN would be ideal and the problems with XYZ company were resolved. The purpose of this research is the implementation of a WAN network using SD-WAN technology against two ISPs on the FortiGate router, as well as testing QoS (Quality of Service) that has been configured using the BGP (Border Gateway Protocol) routing method. This research plan consists of ISP-A using IP-VPN (Internet Protocol-Virtual Private Network) and ISP-B using broadband Internet. The test scenario was carried out using 3 methods, namely Full Service Scenario, Fail Over Scenario-1 when the IP VPN service is down and Fail Over Scenario-2 when the broadband Internet service is down. The final results of the research have obtained "Satisfactory" results for both services, including the average index on ISP-A and ISP-B of 3.7.

Design and Implementation of a Micro Controller for Robot Sensing and Driving

According to the control requirements of robot, the micro controller was designed, including the interface for the wireless transmission module NRF24L01, the sampling circuit for collecting the pressure sensing value, the DC motor drive function module, and the auxiliary circuit for ensuring the normal operation of the controller. After passing the hardware testing, the design of the PTZ control system is completed. The virtual reality environment is constructed using unity and VS software, Oculus is used as the head-mounted display, and then the pose sensor of Oculus is used to analyze and extract the pose sensing data for the control of the PTZ. The industrial camera is used to send back video, based on HSV algorithm for target recognition, and displaying video images in the virtual environment. And the above data was sent to the gimbal control system to finalize the virtual reality environment. Finally, the debugging and verification of the hardware architecture based on sensing and driving and the virtual reality environment were completed. And the scenario tests were conducted for the gimbal following and force feedback virtual interaction ball respectively, which verified the use of virtual reality technology to assist in controlling the robot and obtain the sense of immersive control experience.

Technological convergence for ethernet networks in railways process control systems

The tests carried out in the proposed implementation of an Ethernet network for railway signalling indicate that the topology in sections, with rings interconnected by optical fibre, offers an efficient solution in terms of throughput, latency and availability. Although a challenge was identified in the convergence time in Layer 3, exceeding one second, this is considered a calculated risk due to its occurrence being restricted to specific points in the network and the robustness of the equipment with high MTBF. The positive results in the bandwidth, latency and no frame loss tests in different scenarios reinforce the viability of the proposal to meet the demands of railway signalling. Careful implementation of the suggested topology, combined with redundancy strategies and proactive maintenance, emerges as a promising solution to meet the emerging challenges in railway traffic control automation, providing safety and operational efficiency. Continuous monitoring and preventive maintenance are key to ensuring the system's lasting and reliable performance.