Pair Of Sensors Research Articles

Smart AI Powered Auto Bill generation using Deep Learning and IOT

[1]. Smart shopping cart: In [1] this paper the author designed system for shopping mall. The system is placed in the trolleys. It consists of RFID reader and each product has RFID tag. The billing is done in smart trolley itself. Product name and its price display on LCD screen. At the cash counter the total bill relocated to Personal Computer by wireless Radio Frequency module. The disadvantage of this scheme is after completion of shopping, a key is pressed indicating the final promoting amount of the entire item, and we can’t add or remove the products. [2]. Innovative shopping cart:In [2] this paper the author designed system for shopping mall. In this paper they developed a smart way for shopping. In every item has RFID tag in its place of barcode scanner. The smart trolley involves of RFID reader LCD screen and ZigBee transmitter. The sum total cost of all the products will be added to the final bill, which will be kept in the microcontroller memory. Drawback of the system is in ZigBee there is distance barrier is occurred. [3]. Smart trolley using Arduino: In [3], this paper the author invented such a system it consists of RFID, ARM7, Display, Power supply, Switch, IR sensor pair, Barcode reader Visual Basics. The system works as the inventor use the barcode reader as well as RFID. And our shopping is done through the serial communication we transferred the total bill to the counter for printing the hardcopy of the product bill. There is also facility for the payment is credit card or any other. The drawback of the system is their uses both the reader RFID and Barcode Reader because of that the system becomes more multifaceted. [4]. RFID based advanced shopping trolley for super market: In this paper the author found such a system it consists of GSM, RFID, Automatic Billing, OTP, ZIGBEE, and PIC. In this scheme, the item can be read by the reader and the total of the Item is showed on the LED screen they categorized this paper because they added some additional features in the existing system like in this system the product weight and the name shown on display if item weight is less than stored weight then buzzer will beep.

Evaluating different geogrid products for modulus improvement with bender element sensor technology

Geogrids extend pavement lifespans by stiffening the unbound aggregate base/subbase through geogrid-aggregate interlocking and restraining of the lateral movements of aggregates. Different geogrids may provide different mechanisms of stabilization based on aggregate size and shape distributions, geogrid properties, and the stabilization mechanism. This study proposed the experimental results of evaluating five different geogrid products: three extruded, one woven, and one welded, with different physical properties tested using a dense graded crushed limestone base course aggregate material. The direct and quantitative stiffness measurements were performed using shear wave transducers (bender elements (BEs)). Small strain shear modulus was measured using three BE sensor pairs instrumented on 300 mm height and 150 mm diameter cylindrical specimens with geogrids installed at specimen midheight. The aggregate material was used based on its as-received gradation. Geogrid-stabilized and unstabilized control tests were conducted in repeated triaxial loading at the 15 stress states following the AASHTO T 307 resilient modulus test protocol. Although the resilient modulus tests could not differentiate the effects of geogrid stabilized specimens, local measurements of small strain shear modulus using the BE sensor pairs demonstrated certain increases in shear moduli owing to the incorporation geogrids. Thus, BE sensor technology was demonstrated as an effective tool for quantifying stiffening/stabilization of different geogrids through increased small strain shear modulus.

Seasonal Variation of VS at Shallow Depth and Nonlinear Behavior of Soil Based on the ARGONET Vertical Array Data

Ground acceleration time histories from earthquakes recorded by the ARGONET vertical array in Cephalonia, Greece, in the period from July 2016 to April 2022, are analyzed seeking evidence for nonlinear behavior of the shallow soil layers. Shear waves velocity, VS, between the shallowest sensor pair of the vertical array, i.e., within the top 5.6 m of the soil column, is derived for each earthquake record using the method of interferometry by deconvolution. The temporal variation of VS values is compared with indicators related to soil moisture and this leads to the identification of a clear difference in the level of the measured values between periods of intense rainfall and the dry summer months. Lower velocities, even lower than the level of VS during rainy periods (7-13% of the annual average), are obtained based on the strongest records of the analyzed sample. These low velocities are considered as indicators of nonlinear soil behavior, the threshold onset of which is difficult to determine without prior correction for the seasonal variation. The applied method can provide a detailed description of VS changes even during a single earthquake. Examples are provided on how nonlinear soil behavior may be manifested by a sudden VS value drop at the arrival of the strongest seismic phases.

AN NEGATIVE PRESSURE WAVE METHOD OPTIMIZATION FOR GAS PIPELINE LEAK LOCATION

The article is devoted to the optimization of the negative pressure wave method, designed to determine the location of leaks from a gas pipeline.The proposed negative pressure wave method provides a more accurate determination of the leak location, because a group of readable equally spaced sensors is used. To determine the location of a leak, the sign of the appearance of a minimum of a certain functional is used, which contains, as an integrand, the product of two exponential functions that cha racterize the weakening of a negative pressure wave propagating from the location of the leak to the position of the selected left and right sensors.Calculation formulas are given for calculating the optimal distance between a displaced pair of readable sensors, and the form of the optimal functional relationship between the distances from the place of leakage to the position of the sensors is established.

Novel baseline-free ultrasonic Lamb wave defect location method based on path amplitude matching

Ultrasonic Lamb wave detection technology constitutes a non-destructive evaluation approach extensively employed for the identification of flaws within plate-like structures. The conventional method for detecting and localizing defects in isotropic plate-like structures using ultrasonic Lamb waves relies on baseline signal data. However, the reliability of baseline data as a reference value is diminished due to varying working conditions of the structure. Therefore, to overcome the influence of mismatched baseline data, this paper proposes a novel non-baseline Lamb wave defect detection and localization method. Through simulation and experiment studies, it is discovered that defects at different positions have varied impacts on the amplitude of direct wave-packets under the same propagation path. By eliminating differences in the piezoelectric excitation characteristics of the sensing array (normalized through boundary reflect wave), the direct wave amplitude of multiple sensor pairs in the circular array can be compared and ranked. The paths closest to the location of the damage can be identified, enabling to obtain the defect location information. In this paper, the feasibility and effectiveness of this method has been verified by simulation and practical experiments. The experimental data and imaging results obtained over a four-month period demonstrate that, compared to the traditional baseline localization method, the baseline-free method proposed in this study exhibits a greater ability to resist interference caused by changes in environmental temperature. By increasing the number of sensors from 16 to 32, the positioning accuracy can be significantly improved, reducing the positioning deviation from 13 mm to 0.42 mm. This new non-baseline method based on path amplitude matching demonstrates enhanced practicality within the realm of engineering. Notably, this method holds the potential to be synergistically incorporated and applied in conjunction with various other measurement techniques.

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. This paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

In Vivo Detection of Metabolic Fluctuations in Real Time Using the NanoBiT Technology Based on PII Signalling Protein Interactions.

New protein-fragment complementation assays (PCA) have successfully been developed to characterize protein-protein interactions in vitro and in vivo. Notably, the NanoBiT technology, employing fragment complementation of NanoLuc luciferase, stands out for its high sensitivity, wide dynamic range, and straightforward read out. Previously, we explored the in vitro protein interaction dynamics of the PII signalling protein using NanoBiT, revealing significant modulation of luminescence signals generated by the interaction between PII and its receptor protein NAGK by 2-oxoglutarate levels. In the current work, we investigated this technology in vivo, to find out whether recombinantly expressed NanoBiT constructs using the NanoLuc large fragment fused to PII and PII-interaction partners NAGK or PipX-fused to the NanoLuc Small BiT are capable of detecting the metabolic fluctuations in Escherichia coli. Therefore, we devised an assay capable of capturing the metabolic responses of E. coli cells, demonstrating real-time metabolic perturbation upon nitrogen upshift or depletion treatments. In particular, the PII-NAGK NanoBitT sensor pair reported these changes in a highly sensitive manner.

Measurement of the water-to-liquid ratio of oil–water two-phase flow for low flow rates and high water content

Oil–water two-phase flow is widely present in the petroleum industry, and the vast majority of oilfields have entered the period of exploitation under high water content. Accurate measurement of water-to-liquid ratio (WLR) is crucial to oil production. Currently, there are fewer works related to the WLR measurement at high water content and low flow rates. In order to get a complete picture of WLR measurement in this context by electrical resistance tomography (ERT) sensors, dynamic experiments with a total flow rate not exceeding 4m3h−1 were conducted. These dynamic experiments considered the influence of the direction of the test pipelines, the inner diameter of the ERT sensor and the conductivity of the water. According to the relative positions of the measurement and excitation electrode pairs of the ERT sensor, different strategies for WLR calculation were established. This paper presents all results of the experiments of the WLR measurement. Especially, image reconstruction at different flow rates was conducted, as a way to qualitatively reflect the mixing of oil and water, thus helping to understand the WLR measurements. The whole work reveals the regularities of the WLR measurement by ERT, and provides a reference for subsequent work to realize the WLR accurate measurement in oil-water two-phase flow at high water content and low flow rates.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

Measurement Errors When Measuring Temperature in the Sun.

In the validation of microclimate simulation software, the comparison of simulation results with on-site measurements is a common practice. To ensure reliable validation, it is crucial to utilize high-quality temperature sensors with a deviation smaller than the average absolute error of the simulation software. However, previous validation campaigns have identified significant absolute errors, particularly during periods of high solar radiation, possibly attributed to the use of non-ventilated radiation shields. This study addresses the issue by introducing a ventilated radiation shield created through 3D printing, aiming to enhance the accuracy of measurements on cloudless summer days with intense solar radiation. The investigation employs two pairs of sensors, each comprising one sensor with a ventilated and one with a non-ventilated radiation shield, placed on a south-oriented facade with two distinct albedos. Results from the measurement campaign indicate that the air temperature measured by the non-ventilated sensor is elevated by up to 2.8 °C at high albedo and up to 1.9 °C at a low albedo facade, compared to measurements with the ventilated radiation shield. An in-depth analysis of means, standard deviations, and 95% fractiles highlights the strong dependency of the non-ventilated sensor error on wind velocity. This research underscores the importance of employing ventilated radiation shields for accurate microclimate measurements, particularly in scenarios involving high solar radiation, contributing valuable insights for researchers and practitioners engaged in microclimate simulation validation processes.

Effects of UAS Rotor Wash on Air Quality Measurements.

Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85 and for both gas sensor pairs exceeded R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values.

Image based automatic wind velocity profiler for adaptive optics

Abstract Adaptive optics (AO) systems correct optical phase aberrations of the incoming light generated by the atmosphere. To do so simultaneous estimators of the atmospheric turbulence parameters are required. For the family of wide-field AO systems (WFAO), this information must be stratified in altitude. Among these vectorized estimations, wind profiling in altitude is needed for the reduction of temporal errors in AO loops or for the estimation of turbulence coherence time. This paper proposes a turbulence wind profiler called image processing based peak tracking algorithm (IPTA). IPTA is an image-processing based approach that automatically and reliably estimates wind velocity for several turbulent layers along the line of sight. The estimation of each wind layer is achieved by tracking peaks produced in cross-correlation maps from pairs of wavefront sensors (WFSs) slopes using the technique known as SLODAR (slope detection and ranging). Results for simulated and on-sky WFS datasets demonstrate that IPTA outperforms one of the state of the art wind profiler methods (the profiler covariance parametrization of wind velocity (CAW)) in terms of accuracy and speed. Results also show that, in terms of execution time, our method scales better when the number of WFS lenslets is increased. Being an open source and reliable tool, we believe IPTA can be a useful wind profiler for the adaptive optics community.

Improved Damage Mapping with Hyperbola Approach for Guided Waves Based Structural Health Monitoring Using Fiber Bragg Grating sensors

Ultrasonic guided waves (GW) are commonly used in aerospace, civil, and mechanical industries for inspecting the health of a structure non-destructively. Traditionally the piezoelectric based actuators and sensors have been used for the actuation and sensing of GW. But the use of fiber Bragg grating sensors (FBG) is on the increase due to the unique opportunities offered by these sensors. There are several techniques that are used for damage mapping. The most commonly used in reflection based assessment domain are the ellipse based and hyperbola based approaches. Hyperbola approach is based on the time difference of arrival (TDOA). The time difference in the arrival time for two sensors with a common actuator is used for obtaining the hyperbola. The superposition of these hyperbolas for all possible actuator and sensor pairs may be used for damage localization. In this paper a new improved hyperbola approach for damage mapping is proposed by combining it with the ellipse based approach. In order to limit the number of computations a binary variable is introduced to check if the pixel fulfils the hyperbola and ellipse conditions. So only the regions of the structure which fulfil both conditions are mapped. This limits the number of hyperbolas which are identified as possible damage and improves the damage localization. The methodology is employed on a simple aluminium plate with acoustically FBG sensors. The performance of the new approach is compared with existing methods. The results indicate that indeed the improved hyperbola approach improves the localization. Also the computational load is reduced allowing more real time damage mapping.

Optimizing Sensor Placement for Temperature Mapping during Ablation Procedures.

Accurately mapping the temperature during ablation is crucial for improving clinical outcomes. While various sensor configurations have been suggested in the literature, depending on the sensors' type, number, and size, a comprehensive understanding of optimizing these parameters for precise temperature reconstruction is still lacking. This study addresses this gap by introducing a tool based on a theoretical model to optimize the placement of fiber Bragg grating sensors (FBG) within the organ undergoing ablation. The theoretical model serves as a general framework, allowing for adaptation to various situations. In practical application, the model provides a foundational structure, with the flexibility to tailor specific optimal solutions by adjusting problem-specific data. We propose a nonlinear and nonconvex (and, thus, only solvable in an approximated manner) optimization formulation to determine the optimal distribution and three-dimensional placement of FBG arrays. The optimization aims to find a trade-off among two objectives: maximizing the variance of the expected temperatures measured by the sensors, which can be obtained from a predictive simulation that considers both the type of applicator used and the specific organ involved, and maximizing the squared sum of the distances between the sensor pairs. The proposed approach provides a trade-off between collecting diverse temperatures and not having all the sensors concentrated in a single area. We address the optimization problem through the utilization of approximation schemes in programming. We then substantiate the efficacy of this approach through simulations. This study tackles optimizing the FBGs' sensor placement for precise temperature monitoring during tumor ablation. Optimizing the FBG placement enhances temperature mapping, aiding in tumor cell eradication while minimizing damage to surrounding tissues.

Safety Hat for Blind People

People who are visually handicapped or blind find it challenging to travel alone, mostly because they are less aware of their surroundings and unable to feel potential risks. These might endanger themselves. Thus, this work proposed a Safety Hat for Blind People to assist them in performing their daily routines such as walking the dog and buying groceries, only to name a few. The safety hat is made up of three major systems: an obstacle avoidance system, a health monitoring system, and a rain monitoring system, all of which are connected to an Arduino Mega 2560. The obstacle avoidance system, which makes use of four pairs of ultrasonic sensors, shall be activated to alert the user of approaching obstacles from the front, back, left, or right side. Based on experiments, the obstacle avoidance system may detect an obstruction within 100 cm. The user's pulse rate and amount of oxygen (SpO2) can be assessed by the health monitoring system, which employs a pulse oximeter and heart rate sensor, and the results are displayed on the LCD display attached to the safety hat. A buzzer that signals a high pulse rate shall be activated if the measured pulse rate exceeds 100 bpm. The rain monitoring system, on the other hand, uses a rain sensor module to detect the presence of water and uses a buzzer to alert the user. Based on the outcomes of the experiments, this Safety Hat for Blind People shall provide a secure environment for them to lead their daily life normally without any sense of danger.

Using Pearson correlation coefficient as a performance indicator in the compensation algorithm of asynchronous temperature-humidity sensor pair

Artificial Intelligence (AI) based control algorithms for heating, ventilation, and air conditioning (HVAC) equipment have been gradually applied to improve building energy efficiency. Nevertheless, a reliable dataset is certainly a cornerstone for any meaningful AI training. Unfortunately, significant errors exist on humidity records due to asynchronous humidity and temperature sensor time constants, which need to be better compensated. This study aims to verify the general applicability of the previously proposed compensation algorithm and discover a new method to determine essential parameters for the algorithm without lab testing, which makes it possible to apply the compensation algorithm to on-duty sensor pairs. Experiment results from newly tested sensor pairs are found comparable to the previous study's outcome, which confirms the algorithm's general applicability. Meanwhile, the newly proposed performance indicator – the Pearson correlation coefficient (PCC) of humidity ratio and temperature – results in a 64–97 % error reduction on the tested sensor pairs. Despite not being as steady as the original lab method, the PCC proved a possible alternative method worth further investigation due to its accessibility.

Laboratory Experiments Qualify Bit Influence on High-Frequency Torsional Oscillations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212566, “Qualifying Bit Influence on High-Frequency Torsional Oscillations Based on Full-Scale Laboratory Experiments,” by Armin Kueck, Eliah Everhard, and Xu Huang, Baker Hughes, et al. The paper has not been peer reviewed. _ High-frequency torsional oscillations (HFTOs) generate dynamic loads that can damage drilling tools, resulting in cracks, twistoffs, or broken electronics. Recently, a full-scale drilling test rig was proven to generate verified HFTO behavior under laboratory conditions. This rig allows for a comprehensive study of the influences of bit characteristics on HFTO. The complete paper presents methods to qualify bit features to suppress HFTO. Laboratory Rig Setup Test-Rig Design. The full-scale laboratory test rig drills rocks in a pressurized rock chamber. Rate of penetration (ROP), weight on bit (WOB), rotational speed, pressure, bit type, and rock type can be varied. The simulator consists of an open-loop mud-circulation system with capacity for 200 bbl of fluid, two 1,000-hp triplex pumps capable of up to 500 gal/min, a hoisting mechanism for raising and loading the drillstring up to 10,000 lbf, a 1,000-hp rotary drive capable of up to 10,000 ft-lbf, and a pressure vessel rated to 10,000 psi. High-frequency measurement instrumentation, including in-bit vibration sensors, records the tangential accelerations and dynamic torque at various positions in the BHA. The mounted device can be seen in the top left photo in Fig. 1. Measurement. To initially characterize the frequency response of the system, an impact test was performed on the system in a suspended nonoperating state with roving accelerometers (sensors) positioned as indicated by the blue lines in Fig. 1. Also indicated are the free-end and fixed-end boundary conditions at the bit (left) and drive (right), respectively, as well as the point of reference impact. The data from each pair of sensors are then processed to separate the torsional motions from lateral. Because HFTO is a self-excited vibration, it must be validated that this type of vibration is actually excited in the laboratory setup. To demonstrate that the excitation mechanism is representative for the field, one of the test results was used as a reference. A laboratory investigation showed that the measured vibration matches characteristics of self-excited vibrations. Investigations in the laboratory will, therefore, lead to valid solutions in the field.

Toward Optimal Irrigation Management at the Plot Level: Evaluation of Commercial Water Potential Sensors.

Proper irrigation practice consists of applying the optimum amount of water to the soil at the right time. The porous characteristics of the soil determine the capacity of the soil to absorb, infiltrate, and store water. In irrigation, it is not sufficient to only determine the water content of the soil; it is also necessary to determine the availability of water for plants: water potential. In this paper, a comprehensive laboratory evaluation-accuracy and variability-of the world's leading commercial water potential sensors is carried out. No such comprehensive and exhaustive comparative evaluation of these devices has been carried out to date. Ten pairs of representative commercial sensors from four different families were selected according to their principle of operation (tensiometers, capacitive sensors, heat dissipation sensors, and resistance blocks). The accuracy of the readings (0 kPa-200 kPa) was determined in two soils of contrasting textures. The variability in the recordings-repeatability and reproducibility-was carried out in a homogeneous and inert material (sand) in the same suction range. The response in terms of accuracy and value dispersion of the different sensor families was different according to the suction range considered. In the suction range of agronomic interest (0-100 kPa), the heat dissipation sensor and the capacitive sensors were the most accurate. In both families, registrations could be extended up to 150-200 kPa. The scatter in the readings across the different sensors was due to approximately 80% of the repeatability or intrinsic variability in the sensor unit and 20% of the reproducibility. Some sensors would significantly improve their performance with ad hoc calibrations.

Solar tracking system with fuzzy reasoning applied to crisp sets

In this paper, a low cost and an easy to implement solar tracker system is presented. The design of the system is based on the fuzzy reasoning applied to crisp sets. In this case, it can be easily implemented on general purpose microprocessor systems. Four light sensitive devices, such as LDR, photodiodes or phototransistors are mounted on the solar panel and placed in an enclosure. The four light detectors are screened from each other by opaque surfaces. Each pair of the light sensors is used to inform the controller on the orientation of the solar panel vertically and horizontally respectively.

Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK).

Clinical documentation is an essential component of the provision of medical care, enabling continuity of information across provider and site handoffs. This is particularly important in the combat casualty care setting when a single casualty may be treated by four or more or five completely disparate teams across the roles of care. The Battlefield Assisted Trauma Distributed Observation Kit (BATDOK) is a digital battlefield clinical documentation system developed by the Air Force Research Laboratory to address this need. To support the deployment of this tool, we integrated BATDOK into a commercially available virtual reality (VR) medical simulation platform used by the U.S. Air Force and Defense Health Agency personnel in order to provide an immersive simulation training experience which included battlefield documentation. A multidisciplinary team consisting of medical educators, VR simulation engineers, emergency physicians and pararescuemen, and BATDOK developers first developed a specification for a virtual BATDOK capability, including a detailed listing of learning objectives, critical interfaces and task plans, and sensor integrations. These specifications were then implemented into the commercially available Virtual Advancement of Learning for Operational Readiness VR Medical Simulation System and underwent developmental testing and evaluation during pararescueman training exercises at the Air Force Special Operations Command Special Operations Center for Medical Integration and Development. The BATDOK capability was successfully implemented within the VR Medical Simulation System. The capability consisted of a virtual tablet with replicated interfaces and capabilities based on the developed specifications. These capabilities included integrated point-of-care ultrasound capability, multi-patient management, vitals sign monitoring with sensor pairing and continuous monitoring, mechanism of injury documentation (including injury pattern documentation), intervention logging (including tourniquets, dressing, airways, lines, tubes and drains, splints, fluids, and medications), and event logging. The capability was found to be operational and in alignment with learning objectives and user acceptance goals.