bluuebunny/crossref_metadata_2025 huggingface dataset:
Research and Development of Machine Vision Algorithm Performance Evaluation System in Complex Scenes
Authors: Zhao Runjing , Yang Lei
Published: August 2023

In recent years, deep learning technology has gained widespread attention and has been successfully applied in various applications, including machine vision for automatic driving. However, one of the major challenges in this field is the ability of AI models to perform well in complex scenes with varying weather conditions and lighting conditions. The proposed scheme uses VR technology to simulate different weather conditions and lighting conditions and to evaluate the performance of AI products in such complex scenes. To further enhance the generalization capabilities of AI models, the Software Development Kit has been updated with complex scene data with configurable parameters, and training data with typical characteristics according to machine vision tasks. This approach has the potential to significantly improve the accuracy and reliability of machine vision systems for automatic driving, ultimately enhancing their safety and effectiveness.
Improved TransT Target Tracking Algorithm Based on Hybrid Attention
Authors: Li Feng
Published: August 2023

The TransT method pioneered the introduction of the attention mechanism into the target tracking field, but there are still shortcomings in stability. To improve the stability of the algorithm, we propose an improved feature fusion algorithm for visual image target tracking, and we adopt the LKA attention mechanism to strengthen the local attention to the target to ensure the tracker achieves both global and local attention. The tracking of visual image targets is achieved. The experimental results show that the proposed method has a short execution time and high tracking accuracy.
Printing roller image salient object detection based on multi-scale feature fusion
Authors: Bao Chenyang , Cao Shaozhong , Zhu Weijun , Jian Tengfei
Published: August 2023

In this study, aiming at the problem that the surface defect image of the printing roller has different sizes and a large number of defects, a salient object detection method based on stepwise multi-scale feature fusion is proposed, which consists of a stepwise multi-scale feature guidance module, a multi-resolution information fusion module, and a deeply supervised side output. Specifically, the stepwise multi-scale feature guidance module contains two paths, top-down and bottom-up, which strengthen the transmission of multi-scale features in the network, and fuse multi-level features through the multi-resolution information fusion module to suppress the interference of background noise and redundant features. In addition, deeply supervised side outputs are used to eliminate differences caused by background and foreground imbalance. The experimental results on the roller surface defect dataset show that the method can achieve Fmax, MAE of 0.7959, 0.0144, and the detection rate can reach 24.98fps, which has better detection performance than other algorithms.
Cross-modal multi-scale feature fusion-based RGB-T saliency object detection method
Authors: Zhang Guangyu , Niu Lianqiang
Published: August 2023

To cope with the challenge of significant target detection in complex scenes, this study proposes an RGB-T significant target detection method called CMFF. The method utilizes the complete potential of RGB and thermal infrared modal images and employs a codec structure and cross-modal multiscale feature fusion techniques. In the coding stage, two VGG16 backbone networks are used for multi-level feature extraction and CBAM attention module feature enhancement, and the enhanced features are fused using a stepwise fusion approach. Meanwhile, the weights of the two modalities are assigned using the L1-parametric fusion strategy to enhance the complementarity between them. In the decoding stage, global features are extracted from the high-level fused features by introducing the pyramid pooling module (PPM), and the low-level fused features are fused with multi-scale features in the up-sampling and encoding stages to enrich the global and local information of the feature map. Finally, this study conducted comparison experiments on the publicly available VT5000 dataset, and the method achieved an F-measure value of 0.863 and a mean absolute error (MAE) of 0.062, which significantly improved the overall detection performance relative to the six existing methods.
Research on New Hotel Intelligent Non-inductive Power Supply System
Authors: Duan Hongyi , Liu Niucong
Published: August 2023

The mainstream hotel intelligent sensorless power acquisition system in the market is mainly composed of infrared sensors and wireless timing switches, among which the infrared sensor is the core device. The widely used infrared sensors in the market generally detect the temperature changes that occur when a person moves in a room to determine whether there is someone in the room. This means that when a person stays stationary for a long time, the machine may not be able to recognize the temperature changes, resulting in problems such as power outages in the room. Therefore, the existing products have some shortcomings and still need to be optimized. Therefore, in response to the problem that infrared sensors cannot respond to a stationary human body for a long time, a device capable of periodically shielding the probe of an internal pyroelectric sensor is studied in the product structure to enhance the sensor’s sensitivity to a stationary human body, thereby solving the problem of not responding to a stationary human body for a long time.
Temperature Control of Methanol Distillation with High Gain Sliding Mode Variable Structure Based on SVM Multi-Classification
Authors: Dong Chao , Wang Chengzhi , Bo Cuimei , Li Jun
Published: August 2023

In the methanol distillation process, the production conditions are complex and changeable, and the temperature control requirements tend to be diversified. Traditional temperature control process requires manual adjustment of control parameters to adapt to different working conditions. In this paper, a sliding mode variable structure control algorithm based on support vector machine (SVM) multi-classification is proposed. By using the voting strategy of SVM multi-classification, the membership degree between the parameters η in the sliding mode control rate and the indexes of different working conditions is established, and the sliding mode control with a high gain observer is designed to control and adjust the actual temperature. The simulation of the whole temperature control system is realized by using the Simulink platform in Matlab, and the experimental results verify the effectiveness and correctness of the proposed control scheme.
Design and Implementation of Temperature-sensitive Particle Temperature Measuring Structure in Microchannel
Authors: Zhang Jianxin , Qie Jiating , Wang Lin , Liu Haoze
Published: August 2023

Any physical change or chemical reaction is closely related to temperature. Especially in the context of microscale continuous flow, the uniform temperature distribution affects the whole process of the reaction, so it is very important to detect the temperature in the microsystem. Temperature-sensitive particle temperature measurement is a kind of micro-scale non-contact temperature measurement. At present, the temperature-sensitive particle temperature measurement technology is mainly based on the direct design of experiments based on non-simulation. Starting from the microscopic point of view, this paper firstly analyzes the principle of fluorescence temperature measurement, establishes the consistency between the temperature-luminescence intensity relationship and the force-luminescence intensity relationship, and then uses the simulation software to build a microchannel temperature measurement model to analyze the temperature measurement effect of the selected temperature-sensitive particles. By comparing the Computational Fluid Dynamics (CFD) simulation results, the design of a non-contact particle temperature measuring structure inside the micro-channel is realized, which provides a feasible method in simulation.
Monocular 3D Object Detection Using Depth Fusion
Authors: Qiao Dewen , Niu Hongxia
Published: August 2023

It is an important task to estimate a 3D bounding box from monocular images for autonomous driving. However, the monocular pictures do not have distance information, so it is difficult to acquire accurate results. For the sake of solving the trouble of low accuracy of the monocular image in 3D target detection because of lacking distance information, an improved monocular three-dimensional target detection algorithm based on GUPNet and neural network was proposed to promote the precision of target detection. First, based on the geometric method proposed by GUPNet, the depth, and uncertainty are obtained by direct regression using a neural network. According to the difference in the accuracy of the two methods, a parameter α was introduced, and their depth scores are obtained from the uncertainty. According to the depth score and parameter α, the depth obtained by the two methods is fused to get the final depth. Test results prove that the proposed algorithm promotes average detection precision of KITTI data set in simple, medium, and difficult cases.
Material Consumption Smoothing for Mixed-model Assembly Lines Using an Improved Target Tracking Method
Authors: Zhang Yongyang , Mai Hanhua , Luo Junhao , Qiao Huarui , Liu Pai
Published: August 2023

To improve the material consumption smoothing for mixed-model assembly lines, this paper proposes a new optimized approach based on Euclidean distance and an improved target tracking algorithm. With this new method, the average demands for materials for each product are calculated, and the distances between the actual usage and average demand are computed with the Euclidean distance formula, which is the consumption rate of materials. Then, the material consumption rates of each product are sorted in order, and the minimum value is listed as the optimal scheduling. Therefore, by moving in circles, the optimal scheduling of all products can be achieved. In addition, the production simulation model is established using the FlexSim software to decrease the processing bottleneck. Through running the simulation model, the cycle time is reduced, and the line balance is increased.
Inverse Reconstruction of Rearview Mirror Shell and Mold Flow Analysis
Authors: Huang Xi-feng , Zhang Wu , Wang Yun-ming
Published: August 2023

The three-dimensional model of the automobile rearview mirror is made in three steps. Firstly, a non-contact 3D laser scanner is used to obtain the point cloud of the automobile rearview mirror, and then Image ware is used to denoise the point cloud, construct lines from the points, and obtain the grid model of the lines. Finally, the grid model is poured into the 3D software for surface reconstruction, and the product model is redesigned. After obtaining the three-dimensional model of the automobile rearview mirror, Moldflow is further used to simulate the injection molding process. The runner system of the first mock examination with two cavities is established. The filling parameters of the side gate runner system and the ejector rod latent gate runner system are compared. Combined with the CAE analysis results, it is concluded that the side gate runner system has better filling process parameters.
Research on 3D Particle Field Reconstruction Method Based on Deep Learning
Authors: Wang Shuai , You Fucheng , Wang Xuewei
Published: August 2023

Reconstructing 3D particle fields from single holograms is an important topic in the computer vision field. To increase network accuracy, we incorporate a channel attention mechanism based on the U-Net architecture in this research; meanwhile, we use the LeakyReLU activation function to accelerate the network convergence. Deep learning is used to extract information from a single hologram that can recreate the 3D particle field. The network receives the digital hologram as input, and the radius and 3D locations of the particles are converted into 2D grayscale images as real labels. Simulation and experimental results show that 2D grayscale images with clear edge textures can be rapidly encoded using the neural network, and the average SSIM and PSNR of the network output results with the real target on the test data set can reach 0.989 and 32.56.
Research and Design of Intelligent Anti-epidemic Disinfection Robot Based on ROS
Authors: Wang Siyu
Published: August 2023

In the context of the global epidemic of COVID-19, in order to solve the problems of heavy disinfection and epidemic prevention tasks in densely populated indoor public places and a low degree of automation and intelligence, a smart anti-epidemic disinfection robot solution that integrates multiple disinfection methods is proposed. The solution is designed and developed based on the open-source robot operating system (ROS) with digital intelligent remote disinfection function, micron-level atomization disinfection function, and multi-angle ultraviolet light disinfection function, providing a full range of high-efficiency disinfection and disinfection for the air environment bacteria protection. It has the functions of autonomous positioning and navigation, intelligent path planning, and real-time perception of environmental parameters. It is also equipped with a remote monitoring platform to monitor the status of the disinfection robot itself and task execution in real time to ensure stable operation and to issue disinfection tasks at regular intervals or in certain areas. The practice has proved that this solution can effectively solve the problem of the high labor cost of manual disinfecting so that one person can quickly complete the whole-area disinfecting work, greatly improving work efficiency, and it has good application value and is worth promoting.
The Grass Square Latch Paving Agency Based on the Bionic Spinning Knife
Authors: Li Jia-lin , Li Chao , Wu Yong-tao , Cao Shao-peng , Wang Meng-hao
Published: August 2023

To better solve the problem of land desertification, this paper designed a portable grass grid-laying robot to lay grass squares to prevent wind and sand fixation. Among them, the ploughing module was proposed to use a bionic rotary tillage knife to improve soil cutting efficiency. By analyzing the characteristics of the forefoot claw contour curve of the groundhog, the point cloud data of the claw toe profile curve was obtained by reverse engineering technique, and then the Gaussian equation of the contour curve was fitted by MATLAB and the toe tip profile fitting curve was obtained. The BT model of the obtained fitting curve was applied to the national standard rotary tillage knife to design a preliminary bionic rotary tillage knife. When W0 was 50 mm and θ was 35°, the resistance of the rotary tillage knife was the least and the cutting performance was the best, and the final bionic rotary tillage knife was designed. In the field test, the data pointed out that compared with the traditional national standard rotary tillage knife, the maximum stress of the bionic rotary tillage knife was only 32.21 MPA, the average torque decreased by 10.16% compared with the national standard rotary tillage knife, the average power decreased by 7.68% to 71.4 KW, the tool quality was reduced by 8.26%, and the crushing rate was greatly improved. This study can provide some reference for the design of grass grid laying machines and rotary tillage knives to reduce resistance and consumption.
Research and Design of Speed Profiles for Stacker Cranes
Authors: Zhang Yuzhe , Li Minghui , Fang Yanchao
Published: August 2023

Given the industrial and commercial pursuit of efficiency, it has become urgent to enhance the overall operational efficiency of automated three-dimensional warehouses. This paper focuses on improving the operational efficiency of the stacker in such warehouses. To this end, we deduce the deflection formula and simulate the swing of the stacker to identify the factors that affect its operational stability. Based on these findings, we propose a new speed curve optimization control scheme that properly controls the maximum acceleration and selects an appropriate acceleration change form. This scheme can effectively restrain column swing and improve the stacker’s operational stability, thereby boosting work and production efficiency.
Design of an IoT Air Disinfection Machine Control System
Authors: Hu Yuanhang , Sun Shaodong , XiaJie  , Jiang Yongcheng
Published: August 2023

In this paper, a control system for an IoT air disinfection machine is designed based on the ESP32 microcontroller. The system utilizes the ESP32 as the main control chip and adopts a control scheme based on IoT technology. Remote control and monitoring of the air disinfection machine are achieved through a cloud server. The sensor module detects parameters such as particles, carbon dioxide, temperature, and humidity in the air to realize real-time monitoring of air quality. The control module programs the opening and closing of actuators such as relays and motors to achieve the automatic control of the air disinfection machine. The communication module is connected to the cloud server to realize remote control and monitor of the air disinfection machine. The power management module monitors and controls parameters such as current and voltage to achieve energy consumption monitoring and control of the air disinfection machine. After testing, the system fully meets the design requirements.
Research on Simulation Technology of Intelligent Manufacturing Scene Operation Mechanism Based on Evolutionary Game Theory
Authors: Men Feng , Wang Mian , Dong Fangqia
Published: August 2023

Intelligent manufacturing is a continuously evolving and iteratively improving system engineering that requires long-term adherence and step-by-step implementation. The continuous process changes during scenario iteration have stimulated the demand for process simulation tools in intelligent manufacturing management processes. However, currently, existing tools mostly simulate process coherence and rarely simulate and verify the stability of the process. Accordingly, the article proposes a construction idea for an intelligent manufacturing scenario iteration management platform and combines the analysis of scenario innovation entities and strategies to innovate the simulation method of scenario innovation operation mechanism based on evolutionary game models, improve the comprehensiveness of simulation, and support the continuous iteration of intelligent manufacturing scenarios.
Control Method of Wire Repairing Robot Based on Semantic Segmentation and Current Detection
Authors: Zhang Lingrui , Wang Xuhong , Fan Shaosheng
Published: August 2023

The traditional repair of conductors in power systems, using manual work at height, carries a high level of risk. To ensure personnel safety and improve operational efficiency. Based on the development of a conductor repair robot using pre-stranded wires, the focus is on a semantic segmentation-based adaptive conductor arc sag control method, which enables the robot to adapt to conductors with different arc sags by segmentation identification of the conductor, offset calculation, and proportional-integral controller design to realize the robot hanging on the conductor. Subsequently, the winding gap is monitored according to the change of motor current, and a fuzzy controller is used to realize the robot winding operation. The robot prototype tests show that the robot is capable of adapting to the wire pitch and completing soft repair work.
Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning
Authors: Zhong Defeng , Zhang Guobao , Zhang Yongchun , Liang Guoan , Huang Yongming
Published: August 2023

Accurate predictions of energy efficiency facilitate the energy-saving renovation of industrial boilers. To improve prediction accuracy, this paper proposes an industrial boiler energy efficiency prediction model based on the stacking ensemble learning method. The base models, including LR, RF, GBDT, XGBoost, and ANN, are established in this study, and then the stacking ensemble learning method is employed to integrate these base models into a strong prediction model. Experimental results indicate that the stacking model outperforms the base models in terms of accuracy and stability.
Study on the Propagation Law of Shock Wave Pressure in the Tunnel
Authors: Chen Jiahui , Xu Chundong
Published: August 2023

As a typical underground fortification, tunnels are one of the key targets of an explosive warhead. The study of the propagation law of shock wave pressure in the tunnel is of great significance to accurately evaluate the explosive power of ammunition. The mathematical model of the propagation law of shock wave pressure in the tunnel is established by carrying out the explosion test of the warhead under different test conditions based on dimensional analysis and combined with the experimental data. And the effectiveness of the mathematical model is verified by making the explosion test of the warhead in the tunnel, and the result shows that the maximum relative deviation between the measured value and the calculated value of the shock wave overpressure peak is 10.61%. Therefore, it is proved that the mathematical model can be used to predict the shock wave overpressure peak in the tunnel and provide some reference for the power evaluation of warhead explosion in the tunnel.
Dynamic Multi-objective Optimization for Coagulating Process of Carbon Fiber Precursor Based on Lifelong Learning
Authors: Liu Menglong , Jin Liang , Zhang Xiaomei , Fang Yilin
Published: August 2023

Process parameter optimization is an essential link in the coagulating process of carbon fiber. In this paper, considering the dynamic factors of the coagulating bath environment, a dynamic multi-objective optimization problem (DMOP) model for the coagulating process is constructed with process parameters as decision variables and performance indicators as optimization objectives. We combine lifelong learning (LLL) and multi-objective optimization to solve this model and propose a lifelong learning-based dynamic multi-objective evolutionary algorithm (LLL-DMOEA). In LLL-DMOEA, the lifelong learning method is used to learn common knowledge in historical environments. After the arrival of the new environment, we use the common knowledge to generate a high-quality initial population and speed up the process of the multi-objective optimization algorithm to find the Pareto-optimal set (POS) in the new environment. At the same time, common knowledge only requires information in historical environments. The response time to environmental changes can be sped up by learning before the new environment arrives. Experimental results show that the proposed algorithm can effectively solve the optimization problem of process parameters in the coagulating process.
Comparative Study of Furuta Pendulum Based on LQR and PID Control
Authors: Guo Hongpei , Wu Jun , Yin Ziqiang , Zhang Weidong
Published: August 2023

Furuta pendulum is an underdriven and unstable controlled object with one input and two outputs, and has an open-loop zero point at the origin of the complex plane. In this paper, a nonlinear dynamic model of the Furuta pendulum is established by the Eulerian-Lagrangian method, and a series-connected PID control strategy is designed by using the root locus method and the frequency domain analysis method based on the local linearization model at the steady-state operating point. Besides, an LQR control strategy is designed based on the discrete state-space model. In addition, a comparative analysis of the PID control strategy and the LQR control strategy is conducted, and it is found that the LQR control strategy can achieve stable control of the outer loop in the case of unstable inner loop. Finally, the effectiveness of the two control schemes is verified by real-time control experiments.
Design and Implementation of Handwritten Digit Recognition Accelerator Based on Systolic Array
Authors: Yue Zhifei , Ma Jun
Published: August 2023

Due to the rapid development in artificial intelligence, the classic Von Neumann architecture is no longer able to meet the demands of high-computing, high-storage, and high-bandwidth artificial intelligence applications. To address this issue, this paper proposes a handwritten digit recognition accelerator based on a systolic array. First, a 5 convolutional layer handwritten digit recognition network is built. Besides, The FPGA-based convolution accelerator employs im2col technology to convert convolution calculations into matrix multiplications and uses a systolic array to efficiently perform these multiplications. Furthermore, the four convolution processes are pipelined to improve the throughput. Finally, a handwritten digit recognition system is implemented on the PYNQ-Z2. Compared with the software implementation on Arm, the speedup of the accelerator is 598x, the delay is 1.24 s, and the power consumption is 2.51 w.
Comparative Analysis of Ultrasonic Phased Array Detection and CIVA Simulation
Authors: Wang Zuyue , Chen Le
Published: August 2023

The selection of an ultrasonic phased array inspection process has a great impact on the inspection results. For specific workpieces and processes, CIVA simulation can be used to quickly analyze the imaging effect, which is of great significance for process optimization. In this paper, CIVA software is used to carry out phased array detection simulation of defect holes in specific areas of phased array B-type test block, the same process is used to carry out actual phased array detection, and the positioning and quantitative accuracy of each defect hole is compared and analyzed. The results show that the difference between CIVA simulation and actual detection results is small, and the actual detection accuracy is slightly better than CIVA simulation.
Spectral-domain Spatial-temporal Convolution Graph Neural Network for Industrial Fault Diagnosis
Authors: Ru Jiapei , Zeng Wei
Published: August 2023

In modern industrial production, once a fault occurs in the production process, it will affect the whole production and bring huge economic losses. There are a large number of device sensors in modern production, and there are complex interactions between them. But the existing methods can not efficiently and accurately mine the depth of information from mass production data. We proposed the Spectral-domain Spatial-Temporal Convolution Graph Neural Network model, which comprehensively considers the information of spatial and temporal dimensions in production data. The nodes and edges of the heterogeneous graph represent the sensor signals and their interactions. Each sensor data is converted to the spectral domain to extract the time domain features, and graph convolution is used to carry out the spatial relationship between input sequences. Finally, the proposed model is verified using the Three-Phase Flow Facility (TFF) dataset, and the overall accuracy rate reached 96.28%, showing better performance than other baseline models.
The Influence of Photovoltaic Light and Heat Environment of Ceramic Studio on Making Technology and Human Health
Authors: Xu Peijian , Xu Zhanpeng , Peng Dan
Published: August 2023

To study the influence of the Photovoltaic light and heat environment of the ceramic studio on human health and ceramic production technology, we take the ceramic studio of a university in Wuhan as the research object. And we use data processing and fitting algorithm to build the model. This paper provides a basis for improving the photothermal environment of ceramic studios in Wuhan. The results show that the indoor illumination of a one-sided window studio in the daytime is significantly lower than that of a two-sided window studio. The studio with one side window had about 80 Lx (unit of illuminance) less daytime interior illumination than the studio with two sides. In terms of illumination, the illumination of the studio with single-side Windows is about 100 Lx in the daytime, and that of the studio with double-side Windows is about 180 Lx.
Nutrients and sea surface temperature drive harmful algal blooms in China’s coastal waters over the past decades
Authors: Wei Wenyu , Han Yue , Zhou Yuntao
Published: August 2023

Eutrophication under climate change is well known to affect the community, productivity, and distribution of phytoplankton. However, the specific drivers of harmful algal blooms (HABs) in coastal China are not fully understood. Using HAB observed data since 1981, we quantified the distribution changes in HABs and estimated key environmental drivers (e.g. nutrients, sea surface temperature (SST), and precipitation) of HABs in China’s near seas. After 1981, the geographic range of HABs significantly expanded; moreover, the annual impacting period (AIP) of four of China’s near seas increased from 259% to 1090%. We found that rising total nitrogen (TN) or SST dominated the increase in the AIP in each near sea. Compared to the major contribution of TN to AIP in the other three near seas, TN has relatively weaker impacts than SST on AIP in the South China Sea (SCS). The peak of AIP in the SCS is highly correlated with extremely high SST. The significant contributions of climate change to HABs underscore the growing urgency to strictly control watershed nutrient input to mitigate marine eutrophication.
Observed and simulated local climate responses to tropical deforestation
Authors: Smith Callum , Robertson Eddy , Chadwick Robin , Kelley Douglas I , Argles Arthur P K , Coelho Caio A S , de Souza Dayana C , Kubota Paulo Y , Talamoni Isabela L , Spracklen Dominick V , Baker Jessica C A
Published: August 2023

Tropical deforestation has local and regional effects on climate, but the sign and magnitude of these effects are still poorly constrained. Here we used satellite observations to evaluate the local land surface temperature and precipitation response to tropical deforestation in historical simulations from 24 CMIP6 models. We found tropical forest loss leads to an observed local dry season warming and reduced wet and dry season precipitation across the range of scales (0.25°-2°) analysed. At the largest scale analysed (2°), we observed a warming of 0.018 ± 0.001 °C per percentage point of forest loss (°C %−1), broadly captured in the multi-model mean response of 0.017 ± 0.005 °C %−1. The multi-model mean correctly simulates reduced precipitation due to forest loss in the dry season but simulates increased precipitation due to forest loss in the wet season, opposite to the observed response. We found that the simulated dry season surface temperature and precipitation changes due to forest loss depend on the simulated surface albedo change, with less warming and less drying in models with greater increases in surface albedo due to forest loss. Increased recognition of the local and regional climate benefits of tropical forests is needed to support sustainable land use policy.
First detection probability in quantum resetting via random projective measurements
Authors: Kulkarni Manas , Majumdar Satya N
Published: August 2023

We provide a general framework to compute the probability distribution

F
r

(
t
)

of the first detection time of a ‘state of interest’ in a closed quantum system subjected to random projective measurements. In our ‘quantum resetting’ protocol, resetting of a state is not implemented by an additional classical stochastic move, but rather by the random projective measurement. We then apply this general framework to Poissonian measurement protocol with a constant rate r and demonstrate that exact results for

F
r

(
t
)

can be obtained for a generic two level system. Interestingly, the result depends crucially on the detection schemes involved and we have studied two complementary schemes, where the state of interest either coincides or differs from the initial state. We show that

F
r

(
t
)

at short times vanishes universally as

F
r

(
t
)
∼

t
2

as t → 0 in the first scheme, while it approaches a constant as t → 0 in the second scheme. The mean first detection time, as a function of the measurement rate r, also shows rather different behaviors in the two schemes. In the former, the mean detection time is a non-monotonic function of r with a single minimum at an optimal value

r
∗

, while in the later, it is a monotonically decreasing function of r, signaling the absence of a finite optimal value. These general predictions for arbitrary two level systems are then verified via explicit computation in the Jaynes–Cummings model of light–matter interaction. We also generalize our results to non-Poissonian measurement protocols with a renewal structure where the intervals between successive independent measurements are distributed via a general distribution

p
(
τ
)

and show that the short time behavior of

F
r

(
t
)
∼
p
(
0
)

t
2

is universal as long as

p
(
0
)
≠
0

. This universal t
2 law emerges from purely quantum dynamics that dominates at early times.
Compliant mechanisms for dust mitigation in Lunar hardware development: technology and material considerations
Authors: Budzyn D H , Zare-Behtash H , Cowley A , Cammarano A
Published: August 2023

During the Apollo missions, astronauts observed negative impact of Lunar dust on the surface hardware. The characteristics of Lunar environment and the regolith properties accelerate the contamination and promote the abrasion and clogging of different components in the equipment. To protect the hardware from damages in the future Lunar missions, several mitigation technologies must be adapted. In this work, we propose to consider application of solutions that are naturally dust resilient. Such solutions, called implicit dust mitigation technologies, include usage of compliant mechanisms. Compliant mechanisms use elastic deformation to achieve motion and can replace rigid-body mechanisms that suffer increased friction and jamming due to dust accumulation in the inter-element gaps. Material selection for compliant mechanisms needs to be considered very early in the design process, and as demonstrated in our work, it is crucial to the final mechanism performance.
A Simple Method for Determining Shallow Charge Distributions in Dielectrics via Pulsed Electroacoustic Measurements
Authors: Gibson Zachary , Dennison J R
Published: August 2023

The understanding of charge dynamics in dielectric materials is paramount in mitigating electrostatic discharge events for spacecraft. The most critical spacecraft charging events are found to result from incident electrons in the energy range of 10 keV to 50 keV. The charge embedded in dielectric materials in this energy range are deposited a distance into the material on the order of a few to tens of microns. One way to measure and understand the deposited charge is via pulsed electroacoustic measurements (PEA). However, the typical PEA spatial resolution of ~ 10 μm is not sufficient to resolve or discern charge deposited at the lower end of this incident electron energy range, where deposited charge distributions are obscured by the superposition of the signal originating from induced mirror charge on the electrode of the pulsed electroacoustic system. A simple method is proposed and demonstrated in which reference measurements from a pristine sample are used to separate the effect of the induced mirror charge from the measured embedded charge to obtain a more accurate determination of the deposited charge distribution.
Contamination Level Prediction: Progress in Species Separation by TGA/MS
Authors: Lansade David , Roussel Jean-François , Faye Delphine , Rioland Guillaume , Suliga Agnieszka , Ergincan Orcun
Published: August 2023

Molecular contamination can be detrimental to spacecraft life expectancy as it may induce changes to the properties of the surfaces on which it deposits. In order to control these potential issues, manufacturers need to assess in-flight contamination levels in the quite early design phases, possibly leading to design changes or extra material bake-out to fulfil requirements. The accuracy of model assessments may thus have direct impacts on missions and costs. Prediction of outgassing and deposition at mission timescale is done by extrapolating short-term on-ground experimental results, by fitting the latter with some empirical or physical laws. Common methods are the power law interpolation (in isothermal conditions) or the residence time approach (with thermally activated time constants), which leads to exponential decays in isothermal conditions. Although they allow fitting the measured outgassed flux over few-day-long experiments, their extrapolation to ten or fifteen-year-long missions is more challenging. Validating empirical laws might involve long-term empirical comparisons, while obviously the validation of physical models should start with checking their physics. In spite of many years trying, a basic difficulty was yet found to block significant progress on physical validation. As long as measurements are limited to the total mass on QCMs it looks possible to fit data as a sum of almost any law, such as residence time or diffusion models e.g. for outgassing. Following this observation, a conclusion was drawn that the only way to discriminate between two different physics was to characterize contamination at chemical species level. Such characterizations are a prerequisite to unravel the physics at play during several phenomena: outgassing, deposit films dynamics, photochemistry, environment/ATOX interactions, etc. This innovative approach, presented herein, aims at identifying outgassed contaminants by means of coupled thermogravimetric analysis and mass spectrometry. For this purpose, this paper reports the successful species separation and identification of contaminants outgassed from a material used in space applications: the epoxy adhesive Scotchweld EC-9323-2.
A cryogenic method for testing thermal radiative properties of surfaces for space probes
Authors: Frolec J , Kralik T , Bras B , Portaluppi M
Published: August 2023

We present a cryogenic method for testing of thermal radiative properties of materials commonly used in ground-based systems as well as for space probes. It is possible to measure total hemispherical emissivity or absorptivity of metallic or dielectric surfaces (0.1% - 100%) in the range of temperatures from 10 K up to 320 K of the source of thermal radiation. Emissivity of thermal control coatings for the space probe JUICE are presented together with other materials used for space exploration or cryogenics.
Euro Material Ageing – Ground-based testing and selection of the ESA flight candidate materials
Authors: Suliga Agnieszka , Wessing Johanna , Hand Aurélie , Tighe Adrian , Vincent-Bonnieu Sebastien , Rampini Riccardo
Published: August 2023

One of the newest European additions to the International Space Station (ISS) is the Airbus Bartolomeo payload hosting platform, attached externally to the European Columbus Module. This will provide an unobstructed access to the external space environment offering research opportunities for a wide range of space science applications. One of the Bartolomeo missions under development is Euro Material Ageing (EMA) – a joint collaboration between the European Space Agency (ESA) and the French Space Agency (CNES). ESA is using the Bartolomeo platform as a testing opportunity to perform materials science research and calibrate its ground-based testing facilities against the real space environment. Additionally, it offers a unique opportunity to small space players of the European market for direct in-flight technology validation.
Euclid: Ice Contamination Testing
Authors: Szmolka S , Bras B , Portaluppi M , Paterson K , Schirmer M , Venancio L
Published: August 2023

Euclid is a medium-size ESA mission which will map the geometry of the Universe for a better understanding of dark matter and dark energy. Due to the mission profile and stringent performance requirements, the in-orbit contamination environment is a key aspect to control. Cryogenic operational temperatures root a particular concern over water/ice contamination on optical elements, since increased light scattering could potentially cause a significant impact on the performance of the payload. This challenging work under representative conditions closes the link between performance and ice contamination on specular surfaces (e.g. Mirror M1). The publication covers in detail the design and execution of the test campaign performed in ESA’s TEC-QEE Yuta vacuum facility, resulting in a data-set of spectral information at cryogenic temperatures. The optimised translation/rotation mechanisms incorporating fiber optics allowed scanning in different angular configurations: both the specular reflection as well as scattered light at different levels of surface contamination. The test results show that the effects of ice contamination are most noticeable in specular configuration. A summary is also given about ice-kinetics measured on the black paint used on the external baffle. The work supports Euclid’s calibration team in predicting expected performance and tuning the frequency and duration of in-flight decontamination processes.
Materials inspection of the ISS Columbus Trunnion scuff plate MLI
Authors: Mooney C , Dias N , Butenko Y , Schmeitzky O , van Papendrecht G
Published: August 2023

As part of the preparation for the Bartolomeo platform, various locations of the trunnion scuff plate were required. Scuff plate MLI blankets were removed and recovered from Columbus ISS in August 2019 as part of these EVA activities. These blankets having seen more than 11 years in orbit are extremely valuable from a materials-science perspective where direct analysis of the impact of the space environment can be seen and evaluated to see its effect on materials, contamination and functionality of the blankets.
Space polymers erosion in simulated LEO and VLEO environments
Authors: Iskanderova Z , Tagawa M , Kleiman J , Nishioka S , Horimoto S
Published: August 2023

The VLEO satellites, that are orbiting at an altitude of 200-300 km and that provide advantages for high-resolution Earth observation and atmospheric science, are becoming important not only for science, but also for agriculture, forestry, and oceans observations, for military, national security and the future of satellite businesses. We evaluated, for the first time, the performance and defined the erosion yields (Ey) for a selected number of main space hydrocarbon, fluorinated, and perfluorinated polymers in the ground-based simulator facility at conditions, imitating the LEO and VLEO environments. We also compared the results with the data for polymers erosion in LEO and with the previously established predictive model, developed for polymers and composites in LEO environment. The obtained data indicated that the predictive model can be extended and used, with some corrections for the predictions of space polymers erosion in the VLEO environment.
A drain current formula for two-dimensional field-effect transistors with one-shot convergence algorithm
Authors: Yan Zhao-Yi , Hou Zhan , Wu Fan , Zhao Ruiting , Yan Jianlan , Yan Anzhi , Wang Zhenze , Xue Kan-Hao , Liu Houfang , Tian He , Yang Yi , Ren Tian-Ling
Published: August 2023

Two-dimensional material-based field-effect transistors (2DM-FETs) exhibit both ambipolar and unipolar carrier transports. To physically and compactly cover both cases, a quasi-Fermi-level phase space (QFLPS) approach was proposed, but it still involves complicated integration operations. This article aims at improving the numerical efficiency of the QFLPS model by several orders of magnitude so that it can readily be implemented in a standard circuit simulator. We first rigorously derive the integral-free formula for the drain-source current to achieve this goal. Besides computationally benign, it explicitly gives the correlation terms between the electron and hole components. Secondly, to work out the boundary values required by the new expressions, we develop an algorithm for the channel electrostatic potential based on the zero-temperature limit property of the 2DM-FET system. By calibrating the model with the realistic device data of black phosphorus and monolayer molybdenum disulfide FETs, the algorithm is tested against practical cases. Two orders of magnitude improvement in time consumption can be achieved compared with the integral-form QFLPS approach, and it is even four orders of magnitude faster than the traditional continuity-equation based approach.
Cr silicate as a prototype for engineering magnetic phases in air-stable two-dimensional transition-metal silicates
Authors: Doudin Nassar , Saritas Kayahan , Zheng Jin-Cheng , Boscoboinik J Anibal , Sadowski Jerzy T , Shafer Padraic , N’Diaye Alpha T , Li Min , Ismail-Beigi Sohrab , Altman Eric I
Published: August 2023

Identifying environmentally inert, ferromagnetic two-dimensional (2D) materials with high Curie temperatures (T
c) down to the single layer limit has been an obstacle to fundamental studies of 2D magnetism and application of 2D heterostructures to spin-polarized devices. To address this challenge, the growth, structure and magnetic properties of a 2D Cr-silicate single layer on Pt(111) was investigated experimentally and theoretically. The layer was grown by sequentially depositing SiO and Cr followed by annealing in O2. Scanning tunneling microscopy (STM), low-energy electron diffraction (LEED), and low energy electron microscopy all indicated a well-ordered layer that uniformly covered the surface, with STM and LEED indicating that the silicate relaxed to its favored lattice constant. Further experimental characterizations demonstrated that the Cr was nominally 3+ but with a lower electron density than typical trivalent Cr compounds. Comparison with theory identified a Cr2Si2O9 structure that resembles a single layer of a dehydrogenated dioctahedral silicate. Magnetic circular dichroism in x-ray absorption spectroscopy revealed a ferromagnetically ordered state up to at least 80 K. Theoretical analysis revealed that the Cr in a dehydrogenated Cr-silicate/Pt(111) is more oxidized than Cr in freestanding Cr2Si2O9H4 layers. This greater oxidation was found to enhance ferromagnetic coupling and suggests that the magnetism may be tuned by doping. The 2D Cr-silicate is the first member of a broad series of possible layered first-row transition metal silicates with magnetic order; thus, this paper introduces a new platform for investigating 2D ferromagnetism and the development of magnetoelectronic and spintronic devices by stacking 2D atomic layers.
Simulation study of the tunnel junction position effect on the parameters of the InxGa1-xN dual junction solar cell
Authors: Aoulmit Salim , Bekhouche Khaled , Kaghouche Bessem , Guentri Hocine
Published: August 2023

The present work presents a SILVACO-Atlas numerical simulation to investigate the effect of the tunnel junction position on the performance of InxGa1−xN double-junction solar cells under AM1.5 solar illumination. The proposed cell is composed of two PN sub-cells, an upper sub-cell in In0.1Ga0.9N and a lower sub-cell in In0.4Ga0.6N for the p-type and In0.2Ga0.8N for the n-type, connected by a tunnel junction in In0.4Ga0.6N. The cell offers a remarkable open-circuit voltage value of about 3.9 V and a good fill shape value of about 93. A cell with a small overall thickness can offer better transfer efficiency than a cell with a large thickness if the tunnel junction position is carefully chosen. The proposed cell can achieve a transfer efficiency of around 18% with an overall thickness of 0.652 μm.