Test Scenarios Research Articles

Motor test data quality evaluation model based on critic-fluctuant weight assignment method

Abstract The article addresses the limitations of traditional weight analysis methods in handling ground ignition experiment data for solid rocket motors, particularly the anti-interference ability to noise factors. To address this issue, the specialized Critic-Fluctuant weight analysis method is proposed. This method calculates objective weight values based on fluctuation values and integrates noise standard scores into data quality assessment, enabling researchers to effectively and intuitively identify different levels of data quality. After comparing and analyzing the objective weights and data quality scores of 100 ground ignition test data sets, it is verified that Critic-Fluctuant has higher adaptability and accuracy than traditional CRITIC methods in ground ignition test scenarios. The article provides new insights and tools for researchers to deal with similar data characteristics.

Objective Evaluation of Automatic Ambulance Performance and Weight Determination Through Analytic Hierarchy Process and Gray Correlation Theory

This paper proposes a comprehensive framework for testing and evaluating automatic ambulances, crucial for ensuring their reliability and safety in real-world scenarios. The framework includes designing test scenarios with varying complexity, covering environmental factors like road conditions, weather, and obstacles. An evaluation index system is introduced, comprising driving security, ride comfort, intelligence, and efficiency. Methodologies for calculating indicator weights, using the CRITIC and AHP methods, are presented to ensure fair evaluation. Additionally, evaluation methods including qualitative and quantitative techniques, such as grey correlation theory, are discussed. The test results show that the assessment results of the traditional fuzzy comprehensive evaluation method and the grey correlation theory evaluation method are highly consistent. The change in vehicle speed has less of an effect on accuracy during the real-time assessment process when the time interval is set to 0.1s, and the evaluation time of 0.098s can satisfy the requirement that the planning time of autonomous driving vehicles be shorter than 200 ms.

Adaptive Classroom Berbasis IoT (Internet of Things), Manajemen Penggunaan Air Conditioner (AC) secara otomatis

Temperature and humidity are one of the comfort factors. According to the SNI standard, the comfortable temperature range is at 20.5°C-27.1°C. If the temperature is above 27.1°C, an Air Conditioner (AC) is needed to get the effective thermal comfort. AC installed in each PSTI FT UNRAM classroom is operated manually. This research implements Internet of Things (IoT) for automatic AC usage management. This IoT device is made using PIR sensor and DHT22 sensor, the MQTT protocol for data communication between the IoT device and the web system. Based on the testing scenario for the classroom containing students and the empty classroom that has been done, the IoT device is able to manage the AC usage automatically, so that the classroom can be called adaptive classroom. From the MOS testing that has been done, a value of 4.57 from a scale of 5, the system is feasible to use.

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

Enhancing Photovoltaic-Powered DC Shunt Motor Performance for Water Pumping through Fuzzy Logic Optimization

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module’s operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios.

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks.

The critical micelle concentration (CMC) of surfactant molecules is an essential property for surfactant applications in the industry. Recently, classical quantitative structure-property relationship (QSPR) and graph neural networks (GNNs), a deep learning technique, have been successfully applied to predict the CMC of surfactants at room temperature. However, these models have not yet considered the temperature dependence of the CMC, which is highly relevant to practical applications. We herein develop a GNN model for the temperature-dependent CMC prediction of surfactants. We collected about 1400 data points from public sources for all surfactant classes, i.e., ionic, nonionic, and zwitterionic, at multiple temperatures. We test the predictive quality of the model for the following scenarios: (i) when CMC data for surfactants are present in the training of the model in at least one different temperature and (ii) CMC data for surfactants are not present in the training, i.e., generalizing to unseen surfactants. In both test scenarios, our model exhibits a high predictive performance of R2 ≥ 0.95 on test data. We also find that the model performance varies with the surfactant class. Finally, we evaluate the model for sugar-based surfactants with complex molecular structures, as these represent a more sustainable alternative to synthetic surfactants and are therefore of great interest for future applications in the personal and home care industries.

Опыт проведения киберучений с использованием разработанного сценария

The threat of cyber attacks has become a serious problem for organizations, the solution of which also lies in the plane of modeling scenarios of possible attacks using digital twin technologies. A cyber polygon based on the AMPIRE software suite is used to study the impact of cyber threats. The attacker's scenario includes the use of various tactics and vulnerabilities, such as active scanning, provisioning, vulnerabilities in public applications, malicious links and files, and compromising domain user accounts. The vector of the malicious attack is related to the tactics, techniques and procedures of the Mitre ATT&CK matrix. Vulnerabilities include the wpDiscuz plugin, Zerologon, and the consequences of using Wordpress Shell and gaining unauthorized access. The ongoing intercollegiate cyber exercises have revealed strengths and weaknesses that need to be taken into account when conducting the following events. Scenario testing with the participation of universities showed the effectiveness of the complex in assessing the time required to close vulnerabilities and eliminate consequences. Implementation of different scenarios with specialized functionality at the cyber polygon makes it possible to form practical skills to prevent attacks on computer networks, and also allows you to analyze the situation, interact with other participating specialists.

Experimental and numerical investigations on tensile failure mechanism and load-bearing capacity of expansion anchors under pull-through failure mode in HPSFRC

Pull-through(PT) is a unique failure mode of torque-controlled expansion(TCE) anchor after being pulled out from concrete. No universal design formula is available for predicting tensile bearing capacity of anchor of PT mode. In normal concrete(NC), PT failure usually occurs at large embedment depths(Hef), which is uncommon in the field and received less attention than the concrete breakout failure. However, due to the improved mechanical performance of the substrate, the PT mode dominates for anchors in high-performance steel fiber reinforced concrete(HPSFRC) substrate (even at small Hef). In this study, to study the PT mode in HPSFRC, the results of 34 PT failures from previous tests, including testing scenarios with different anchor diameters(D), Hef, and fiber contents(vf), are analyzed. The cracking morphology and load-bearing capacity distribution of PT mode are elaborated. Further, finite element models(FEM) of anchor pullout behavior in HPSFRC is established, which accurately identified different failure modes. The calculated Nu by FEM agree well with the tested Nu. The difference in damage domain between different failure modes is mainly reflected in the unloading stage. Parametric analyses were conducted on pre-tightening torque(Tini), Hef, D, sleeve length(Lsleeve), anchor cone width, anchor cone length(Lcone), and vf to study their influences on Nu of PT mode. Except for Lsleeve and Lcone, which do not affect Nu, changes in other factors can cause linear or nonlinear changes in Nu. The non-linear relationship between Nu and experimental parameters is formulated. Accordingly, a new prediction model that can accurately predict Nu of PT mode for anchors in HPSFRC is developed, of which calibration factors are derived using probability functions.

Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs.

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model's performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50's precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing-detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics.

Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework

Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model’s training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.

Automated traffic sign change detection using low-cost LiDAR scans and unsupervised machine learning

ABSTRACT Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115” per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area.

Dual-stage deep learning for sangac optical fiber sensing multi-event detection and localization

This paper introduces a dual-stage deep learning structure for multi-event detection and localization (DDMDL) for a loop-based Sagnac interferometer optical fiber sensing system to overcome the difficulties of detecting and localizing multiple events. The DDMDL approach comprises of a combination of (i) a detection stage made of a multi-class classification model that quantifies the events, and (ii) a localization stage made of separate deep-learning regression models tasked with precisely localizing the events. By addressing the multi-event localization as a combined multi-class classification and multi-regression problem, our approach enables more accurate prediction of event locations. We evaluate our DDMDL model in a broad range of test scenarios across three signal-to-noise ratio (SNR) levels: low, medium, and high, all outside the model’s training dataset. The model had high detection (i.e., classification) accuracy in simulation, with rates of 90%–99% for single-event scenarios, 94%–98% for two-event scenarios, and 82%–99% for three-event scenarios at the three SNR levels. The precision of localization (i.e., regression) was evaluated by Mean Absolute Error (MAE). The results showed that single-event scenarios could be localized within a range of 35–50 m, two-event scenarios within a range of 82–106 m, and three-event scenarios within a range of 131–151 m. These findings were consistent across different SNR levels, ranging from low to high, over a 50 km sensing fiber length. Experimental validations confirmed the DDMDL model’s practical applicability, with detection accuracy of 80% in single-event scenarios and localization accuracy with MAEs ranging between 32 and 65 m. For two-event scenarios, the model achieved an 87% success rate, with MAEs ranging between 122 m and 204 m, emphasizing the model’s potential effectiveness in different applications.

Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.

The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients' head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function. Four different single-sequence MR images and one multi-sequence MR image were used to evaluate the accuracy of sCT. During the model testing phase, five testing scenarios were employed to further assess the mean absolute error, peak signal-to-noise ratio, structural similarity index, and root mean square error between the actual CT images and the sCT images generated by different models. T1 sequence-based sCT achieved better results in single-sequence MR-based sCT. Multi-sequence MR-based sCT achieved better results with T1 sequence-based sCT in terms of evaluation metrics. For metrological evaluation, the global gamma passage rate of sCT based on sequence MR was greater than 95% at 3%/3mm, except for sCT based on T2 sequence MR. We developed a CycleGAN method to synthesize CT using different MR sequences, this method shows encouraging potential for dosimetric evaluation.

Enhancing DC distribution network efficiency through optimal power coordination in lithium-ion batteries: A sparse nonlinear optimization approach

This work deals with the problem regarding the optimal operation of lithium-ion batteries to improve the economic, technical, and environmental indices of standalone and grid-connected direct current (DC) distribution networks.To this effect, a general nonlinear programming model has been developed, which considers three objective functions: (i) the daily energy purchasing costs at the terminals of the substation, considering the maintenance costs of renewable generation (photovoltaic) and energy storage technologies (batteries); (ii) the daily energy losses associated with energy transport; and (iii) the CO2 emissions per day of operation, associated with conventional generators. This is done by integrating all the operations and technical constraints of the DC network and the devices it comprises. The sparse nonlinear optimization (SNOPT) solution method is employed, comparing its results against those of three recently reported metaheuristic optimization methodologies: the continuous genetic algorithm and the parallel versions of the particle swarm optimizer and the vortex search algorithm. The performance of the proposed methodology is validated on standalone and grid-connected networks, considering the technical and economic conditions of two regions of Colombia (rural and urban territories) and evaluating its effectiveness in terms of solution quality, standard deviation, and processing times. The results show that the SNOPT tool of the General Algebraic Modeling System (GAMS) achieves the global optimum in all test scenarios, exhibiting a standard deviation of zero and requiring less than three seconds on average to generate a solution for an entire day of operation.

Investigation of reciprocal rank method for automatic generation control in two-area interconnected power system

In this article, the design and performance analysis of Jaya-assisted reciprocal rank-based proportional–integral–derivative controller with derivative filter (PIDn) is proposed for the automatic generation control (AGC) problem of two-area interconnected power systems. The filter with a derivative gain is used to nullify the impact of noise in the input signal. The integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviations, and area-control errors is the foundation of the goal function developed for tuning controller parameters (ACEs). A single overall objective function is created by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas are weighted and summarized to form the overall goal function. Each sub-relative goal’s importance in the control design is evaluated using the weights in the overall objective function. The weights in this article are determined using the reciprocal rank approach in a methodical manner, in contrast to earlier techniques where weights were either assumed equally by ignoring the relative relevance of sub-objectives or chosen at random. The resultant total objective function is minimized using the Jaya method. Six different test cases involving various load disturbances in the interconnected areas are used to evaluate the performance of the suggested Jaya optimizer-assisted reciprocal rank technique-based controller. Additionally, the performance of various controllers tuned with the sine cosine algorithm (SCA), salp swarm algorithm (SSA), symbiotic organisms search (SOS), Nelder–Mead simplex (NMS), and Luus–Jaakkola (LJ) algorithms is compared with that of the Jaya-tuned controller. The time domain specifications are provided for each of the six test scenarios. To display the differences in frequencies and tie-line power, the results are also shown. Statistical analysis is also provided to assess the suggested controller’s overall efficacy.

Prototipe Aplikasi Komunikasi Menggunakan Jaringan Delay Tolerant Network Pada Perangkat Seluler Android

In the current global context, the exchange of information through Internet networks has become increasingly vital, necessitating the effectiveness of communication technology. This is particularly true in areas with weak or unstable signal coverage such as intermittent areas. To address challenges such as frequent connection drops in these areas, Delay Tolerant Networking (DTN) architecture is offered as an alternative solution to overcome these problems. The conducted research aims to design and test a data communication application prototype using DTN architecture implemented on the Android operating system. The approach taken involves using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. In detail, the application is implemented on the Android operating system and integrates various routing protocols such as Epidemic, Spray and Wait, MaxProp, and ProphetV2 to optimize data transmission and reception under varying network conditions. Data collection techniques include measuring latency and data transfer speed in predetermined testing scenarios, while data analysis techniques involve evaluating the measurement results to determine the effectiveness of the routing protocols used. Evaluation is conducted and the results showing an average latency between 800-1400ms and speeds of 100-400kbps, depending on the distance and varying network conditions. This study validates the effectiveness of DTN in maintaining consistent communication in challenging network environments. The impact of this research on the advancement of informatics is to pave the way for further development and adaptation of DTN technology in various mobile applications and data networks.

Transient stability investigation of a grid integrating solar and wind energies: a case study of southern Algeria

The primary goal of electricity producers is to ensure the efficient operation of the electrical grid and the reliable delivery of electricity to individual households. Strengthening the electricity network with the integration of micro-power plants such as wind farms and photovoltaic power plants, as in the case of Algeria, contributes to improving the quality of electricity supplied to consumers. This work presents a simulation study of the electrical network of the Adrar region using ETAP program, to show the influence of the insertion of photovoltaic and wind power plants on the electrical network. The results showed good tolerance of the grid to the integration of wind energy, while the integration of photovoltaic energy needs more requirements and technical challenges to overcome, because, as shown by the results of simulations, the integration of photovoltaic energy makes the network more vulnerable to disturbances. The primary reason for this phenomenon is the lack of mechanical inertia, enabling the system to minimize perturbations. Where wind turbines are unique in their ability to withstand load variations and respond quickly to disruptions. They return to their initial value in 20 seconds, while solar power systems signal return to their initial value after 45 seconds due to their lack of mechanical inertia. Owing to the limitations of the electrical network, the majority of test scenarios demonstrate that wind farm structures can sustain a steady voltage and frequency of 50 Hz. On the other hand, there are momentary fluctuations in voltage and frequency in structures that have photovoltaic systems installed.

A new adaptive droop control strategy for improved power sharing accuracy and voltage restoration in a DC microgrid

In a DC Microgrid, accurate power sharing and voltage restoration are two primary control goals to guarantee power quality and reliable operation. Inaccurate power sharing is a significant concern due to discrepancies in feeder line resistance, faulty equipment, lack of monitoring and control, and nonlinear load. Moreover, inaccurate power sharing may lead to overloading of converters and cause a cascade of failures throughout the entire system. This study proposes a new adaptive droop control strategy to address these challenges. To enhance accurate power sharing, error current sharing is formulated by considering bus current and total rated current. This is regulated by the adaptive controller to adjust droop resistance. Additionally, the impact of droop voltage because of feeder line resistance is considered in the proposed strategy. The primary control loop regulates the output voltage of converter utilizing the proposed observer-based optimum sliding mode control (OOSMC). The controller gain of the OOSMC is optimized using a gradient-based method to enhance transient and steady state response of the converter output. In the secondary loop, the twin-delayed deep deterministic policy gradient (TD3) for voltage restoration is employed with a new reward function to optimally tune the proportional-integral (PI) controller. Finally, the superiority of the proposed strategy is evaluated through rigorous testing scenarios, including the use of constant power load (CPL) and sudden failure in the converters. Moreover, the proposed strategy is validated by simulations and laboratory-based experiments. The results show that the OOSMC outperforms GA and PSO while the TD3 has better performance than traditional PI. Furthermore, when the equivalent power-rated converters are implemented in the system, the proposed strategy can transfer identical power sharing of 4 W to the load and provide accurate current sharing of 0.167 A compared to conventional droop control while the DC bus voltage is maintained at the 24 V reference value. In the case of different power-rated converters, the proposed strategy can achieve power sharing accuracy. The first, second and third converters supply 2 W, 4 W, and 6 W, respectively, to the load and provide accurate current sharing. Meanwhile the DC bus voltage can be kept at the reference voltage of 24 V. In the scenario of various kinds of disturbances, the proposed strategy can achieve accurate power and current sharing when the system is tested under load and input voltage variations. Meanwhile the DC bus voltage always returns to the voltage reference. Moreover, the proposed strategy can share power and current accurately when the converter occurs a sudden failure.

Enhancing unmanned ground vehicle performance in SAR operations: integrated gesture-control and deep learning framework for optimised victim detection.

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Data on state-of-the-art battery electric vehicles are crucial to academia; however, these data are not published due to non-disclosure policies in the industry. As a result, simulation models and their analyses are based on assumptions or insider information. To fill this information gap, we present a comprehensive analysis of the electric powertrain of a Tesla Model 3 Standard Range Plus (SR+) from 2020 with lithium iron phosphate (LFP) cells, focusing on the overall range. On the vehicle level, we observe the resulting range in multiple test scenarios, tracing the energy path from source to sink by conducting different test series on the vehicle dynamometer and through alternating current (AC) and direct current (DC) charging measurements. In addition to absolute electric range tests in different operating scenarios and electric and thermal operation strategies on the vehicle level, we analyze the energy density and the power unit’s efficiency on the component level. These tests are performed through procedures on the chassis dynamometer as well as efficiency analysis and electric characterization tests in charge/discharge scenarios. This study includes over 1 GB of attached measurement data on the battery pack and vehicle level from the lab to the real-world environment available as open-source data.